ARC AGI Example
In this example, we will see GEPA evolve the whole DSPy program/agent (not just the instruction), including modifying the structure/dataflow of the agent. We will use GEPA to tune a simple dspy.ChainOfThought module for ARC-AGI tasks into a full DSPy Program.
Notably, GEPA optimizes Gemini-2.5-Pro's performance from 44% to 49.5% on ARC-AGI evolving an elaborate 5-step schema to solve the problems:
- Ask LLM to hypothesize a natural language rule given training examples
- Ask LLM to generate a python program that executes the natural language rule
- Run the generated python program on all training examples, gathering feedback on how/when they fail to run, or identifying if it succeeds in all training examples.
- If
- succeed in all training examples: then proceed as-is
- otherwise, ask LLM to improve the program with gathered feedback
- Finally execute the improved program on all test-inputs, and return outputs.
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gemini_api_key = input("GEMINI_API_KEY: ")
gemini_api_key = input("GEMINI_API_KEY: ")
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from datasets import load_dataset
ds = load_dataset("dataartist/arc-agi")
from datasets import load_dataset
ds = load_dataset("dataartist/arc-agi")
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import dspy
trainset = [
dspy.Example(
training_examples=ex["train"],
test_inputs=[x["input"] for x in ex["test"]],
test_outputs=[x["output"] for x in ex["test"]],
).with_inputs("training_examples", "test_inputs")
for ex in ds["training"]
]
testset = [
dspy.Example(
training_examples=ex["train"],
test_inputs=[x["input"] for x in ex["test"]],
test_outputs=[x["output"] for x in ex["test"]],
).with_inputs("training_examples", "test_inputs")
for ex in ds["evaluation"]
]
import random
random.Random(0).shuffle(trainset)
test_set = testset
val_set = trainset[-200:]
train_set = [ex for ex in trainset[:-200]]
import dspy
trainset = [
dspy.Example(
training_examples=ex["train"],
test_inputs=[x["input"] for x in ex["test"]],
test_outputs=[x["output"] for x in ex["test"]],
).with_inputs("training_examples", "test_inputs")
for ex in ds["training"]
]
testset = [
dspy.Example(
training_examples=ex["train"],
test_inputs=[x["input"] for x in ex["test"]],
test_outputs=[x["output"] for x in ex["test"]],
).with_inputs("training_examples", "test_inputs")
for ex in ds["evaluation"]
]
import random
random.Random(0).shuffle(trainset)
test_set = testset
val_set = trainset[-200:]
train_set = [ex for ex in trainset[:-200]]
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len(train_set), len(val_set), len(test_set)
len(train_set), len(val_set), len(test_set)
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(200, 200, 400)
Defining a simple ChainOfThought program¶
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program_src = """import dspy
from typing import List
import pydantic
MATRIX = List[List[int]]
class TrainingExample(pydantic.BaseModel):
input: MATRIX
output: MATRIX
class SolveTaskSignature(dspy.Signature):
training_examples: List[TrainingExample] = dspy.InputField(description="Input and output examples demonstrating the task to be performed.")
test_inputs: List[MATRIX] = dspy.InputField(description="Input matrices to be solved following the task described in the training examples.")
test_outputs: List[MATRIX] = dspy.OutputField(description="Output matrices corresponding to the test inputs.")
program = dspy.ChainOfThought(SolveTaskSignature)"""
program_src = """import dspy
from typing import List
import pydantic
MATRIX = List[List[int]]
class TrainingExample(pydantic.BaseModel):
input: MATRIX
output: MATRIX
class SolveTaskSignature(dspy.Signature):
training_examples: List[TrainingExample] = dspy.InputField(description="Input and output examples demonstrating the task to be performed.")
test_inputs: List[MATRIX] = dspy.InputField(description="Input matrices to be solved following the task described in the training examples.")
test_outputs: List[MATRIX] = dspy.OutputField(description="Output matrices corresponding to the test inputs.")
program = dspy.ChainOfThought(SolveTaskSignature)"""
Defining the evaluation metric, which doubles as GEPA's optimization feedback¶
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def is_valid_matrix(matrix, gold_matrix):
if not isinstance(matrix, list):
return False, f"The matrix must be a List[List[int]]. The correct matrix is {gold_matrix}."
n = len(matrix)
if n == 0:
return False, f"The matrix must have at least one row. The correct matrix is {gold_matrix}."
m = len(matrix[0])
if m == 0:
return False, f"The matrix must have at least one column. The correct matrix is {gold_matrix}."
for i in range(n):
if not isinstance(matrix[i], list):
return False, f"The {i}-th row must be a List[int]. The correct matrix is {gold_matrix}."
if len(matrix[i]) != m:
return (
False,
f"The matrix is staggered. Row 0 has {m} columns, but row {i} has {len(matrix[i])} columns. The correct matrix is {gold_matrix}.",
)
for j in range(m):
if not isinstance(matrix[i][j], int):
return (
False,
f"The {i}-th row, {j}-th column must be an int, found {type(matrix[i][j])}. The correct matrix is {gold_matrix}.",
)
# Check consistency with gold matrix
gold_n = len(gold_matrix)
gold_m = len(gold_matrix[0])
if (n, m) != (gold_n, gold_m):
return (
False,
f"The matrix has dimensions {n}x{m}, but the gold matrix has dimensions {gold_n}x{gold_m}. The correct matrix is {gold_matrix}.",
)
same = True
wrong_indices = []
for i in range(n):
for j in range(m):
if matrix[i][j] != gold_matrix[i][j]:
same = False
wrong_indices.append((i, j))
if same:
return True, f"Your response is correct. The correct matrix is {gold_matrix}."
else:
if len(wrong_indices) < 10:
return (
False,
f"The matrix is incorrect. The following indices are incorrect: {wrong_indices}. The correct matrix is {gold_matrix}.",
)
else:
return False, f"The matrix is incorrect. The correct matrix is {gold_matrix}."
def metric_fn(example, pred, trace=None):
task_inputs = example.test_inputs
gold_task_outputs = example.test_outputs
pred_task_outputs = pred.test_outputs
if not isinstance(pred_task_outputs, list):
return dspy.Prediction(
score=0,
feedback=f"The response must be a List[List[List[int]]]. The correct response is {gold_task_outputs}.",
)
valids = []
feedbacks = []
feedback = ""
if len(task_inputs) != len(pred_task_outputs):
feedback = f"The number of output matrices ({len(pred_task_outputs)}) must match the number of input matrices ({len(task_inputs)}). The correct response is {gold_task_outputs}."
return dspy.Prediction(score=0, feedback=feedback)
for i, (input, gold_output, pred_output) in enumerate(
zip(task_inputs, gold_task_outputs, pred_task_outputs, strict=False)
):
is_valid, feedback = is_valid_matrix(pred_output, gold_output)
valids.append(is_valid)
feedbacks.append(f"Feedback on test input {i}: {feedback}")
score = sum(valids) / len(valids)
feedback_text = "\n".join(feedbacks)
return dspy.Prediction(score=score, feedback=feedback_text)
def is_valid_matrix(matrix, gold_matrix):
if not isinstance(matrix, list):
return False, f"The matrix must be a List[List[int]]. The correct matrix is {gold_matrix}."
n = len(matrix)
if n == 0:
return False, f"The matrix must have at least one row. The correct matrix is {gold_matrix}."
m = len(matrix[0])
if m == 0:
return False, f"The matrix must have at least one column. The correct matrix is {gold_matrix}."
for i in range(n):
if not isinstance(matrix[i], list):
return False, f"The {i}-th row must be a List[int]. The correct matrix is {gold_matrix}."
if len(matrix[i]) != m:
return (
False,
f"The matrix is staggered. Row 0 has {m} columns, but row {i} has {len(matrix[i])} columns. The correct matrix is {gold_matrix}.",
)
for j in range(m):
if not isinstance(matrix[i][j], int):
return (
False,
f"The {i}-th row, {j}-th column must be an int, found {type(matrix[i][j])}. The correct matrix is {gold_matrix}.",
)
# Check consistency with gold matrix
gold_n = len(gold_matrix)
gold_m = len(gold_matrix[0])
if (n, m) != (gold_n, gold_m):
return (
False,
f"The matrix has dimensions {n}x{m}, but the gold matrix has dimensions {gold_n}x{gold_m}. The correct matrix is {gold_matrix}.",
)
same = True
wrong_indices = []
for i in range(n):
for j in range(m):
if matrix[i][j] != gold_matrix[i][j]:
same = False
wrong_indices.append((i, j))
if same:
return True, f"Your response is correct. The correct matrix is {gold_matrix}."
else:
if len(wrong_indices) < 10:
return (
False,
f"The matrix is incorrect. The following indices are incorrect: {wrong_indices}. The correct matrix is {gold_matrix}.",
)
else:
return False, f"The matrix is incorrect. The correct matrix is {gold_matrix}."
def metric_fn(example, pred, trace=None):
task_inputs = example.test_inputs
gold_task_outputs = example.test_outputs
pred_task_outputs = pred.test_outputs
if not isinstance(pred_task_outputs, list):
return dspy.Prediction(
score=0,
feedback=f"The response must be a List[List[List[int]]]. The correct response is {gold_task_outputs}.",
)
valids = []
feedbacks = []
feedback = ""
if len(task_inputs) != len(pred_task_outputs):
feedback = f"The number of output matrices ({len(pred_task_outputs)}) must match the number of input matrices ({len(task_inputs)}). The correct response is {gold_task_outputs}."
return dspy.Prediction(score=0, feedback=feedback)
for i, (input, gold_output, pred_output) in enumerate(
zip(task_inputs, gold_task_outputs, pred_task_outputs, strict=False)
):
is_valid, feedback = is_valid_matrix(pred_output, gold_output)
valids.append(is_valid)
feedbacks.append(f"Feedback on test input {i}: {feedback}")
score = sum(valids) / len(valids)
feedback_text = "\n".join(feedbacks)
return dspy.Prediction(score=score, feedback=feedback_text)
Setting up the GEPA DSPy Adapter (which provides the evaluation harness)¶
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from gepa.adapters.dspy_full_program_adapter.full_program_adapter import DspyAdapter
reflection_lm = dspy.LM(model="gemini/gemini-2.5-pro", max_tokens=32000, api_key=gemini_api_key)
adapter = DspyAdapter(
task_lm=dspy.LM(model="gemini/gemini-2.5-pro", max_tokens=32000, api_key=gemini_api_key),
metric_fn=metric_fn,
num_threads=80,
reflection_lm=lambda x: reflection_lm(x)[0],
)
from gepa.adapters.dspy_full_program_adapter.full_program_adapter import DspyAdapter
reflection_lm = dspy.LM(model="gemini/gemini-2.5-pro", max_tokens=32000, api_key=gemini_api_key)
adapter = DspyAdapter(
task_lm=dspy.LM(model="gemini/gemini-2.5-pro", max_tokens=32000, api_key=gemini_api_key),
metric_fn=metric_fn,
num_threads=80,
reflection_lm=lambda x: reflection_lm(x)[0],
)
Evaluating the seed program¶
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o_base = adapter.evaluate(test_set, {"program": program_src})
o_base = adapter.evaluate(test_set, {"program": program_src})
2025/08/30 04:55:07 INFO dspy.evaluate.evaluate: Average Metric: 176.0 / 400 (44.0%)
The base program obtains a score of 44.0%
GEPA Optimization¶
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from gepa import optimize
o = optimize(
seed_candidate={"program": program_src},
trainset=train_set,
valset=val_set,
adapter=adapter,
reflection_lm=lambda x: reflection_lm(x)[0],
max_metric_calls=4000,
display_progress_bar=True,
)
from gepa import optimize
o = optimize(
seed_candidate={"program": program_src},
trainset=train_set,
valset=val_set,
adapter=adapter,
reflection_lm=lambda x: reflection_lm(x)[0],
max_metric_calls=4000,
display_progress_bar=True,
)
GEPA Optimization: 0%| | 0/4000 [00:00<?, ?rollouts/s]2025/08/28 21:33:56 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/28 21:33:56 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/28 21:33:56 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/28 21:33:56 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/28 21:33:56 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/28 21:33:56 INFO dspy.evaluate.evaluate: Average Metric: 134.0 / 200 (67.0%)
GEPA Optimization: 5%|█████▊ | 200/4000 [00:00<00:11, 318.27rollouts/s]Iteration 0: Base program full valset score: 0.67
Iteration 1: Selected program 0 score: 0.67
Average Metric: 3.00 / 3 (100.0%): 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 312.80it/s]2025/08/28 21:33:56 INFO dspy.evaluate.evaluate: Average Metric: 3.0 / 3 (100.0%)
Iteration 1: All subsample scores perfect. Skipping.
Iteration 1: Reflective mutation did not propose a new candidate
Iteration 2: Selected program 0 score: 0.67
Average Metric: 1.00 / 1 (100.0%): 0%| | 0/3 [00:00<?, ?it/s]2025/08/28 21:33:56 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
Average Metric: 1.00 / 3 (33.3%): 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 365.23it/s]2025/08/28 21:33:56 INFO dspy.evaluate.evaluate: Average Metric: 1.0 / 3 (33.3%)
2025/08/28 21:33:57 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 2, 2, 0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 2, 0, 1, 1, 1, 0, 0, 0, 0, 0], [0, 0, 0, 2, 2, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 4], [0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 1], [0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 1], [0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 1], [0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 1], [0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 4]], 'output': [[4, 0, 0, 0, 0, 0, 0, 4], [2, 2, 2, 0, 1, 0, 0, 1], [2, 0, 2, 0, 1, 1, 1, 1], [2, 0, 2, 2, 1, 0, 0, 1], [2, 0, 0, 2, 0, 0, 0, 1], [4, 0, 0, 0, 0, 0, 0, 4]]}, {'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 3, 0, 8, 0, 8, 0, 0, 0], [0, 0, 0, 0, 3, 3, 3, 8, 8, 8, 0, 0, 0], [0, 0, 0, 0, 0, 3, 0, 8, 0, 8, 0, 0, 0], [0, 0, 0, 0, 0, 3, 3, 8, 8, 8, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 8, 0, 8, 0, 0, 0], [0, 4, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0], [0, 8, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0], [0, 8, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0], [0, 8, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0], [0, 8, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0], [0, 8, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0], [0, 4, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0]], 'output': [[4, 0, 0, 0, 0, 0, 0, 4], [8, 8, 0, 8, 0, 3, 0, 3], [8, 8, 8, 8, 3, 3, 3, 3], [8, 8, 0, 8, 0, 3, 0, 3], [8, 8, 8, 8, 3, 3, 0, 3], [8, 8, 0, 8, 0, 0, 0, 3], [4, 0, 0, 0, 0, 0, 0, 4]]}, {'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 4, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0], [0, 0, 2, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 2, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 4, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 1, 2, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[4, 0, 0, 0, 0, 4], [2, 0, 2, 1, 1, 1], [2, 2, 2, 1, 0, 1], [4, 0, 0, 0, 0, 4]]}, {'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 4, 0, 0], [0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 3, 0, 0], [0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 3, 0, 0], [0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 3, 0, 0], [0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 4, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 7, 7, 0, 3, 0, 0, 0, 0], [0, 0, 0, 0, 0, 7, 7, 3, 3, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 7, 0, 3, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[4, 0, 0, 0, 0, 4], [7, 7, 7, 0, 3, 3], [7, 7, 7, 3, 3, 3], [7, 0, 7, 0, 3, 3], [4, 0, 0, 0, 0, 4]]}], 'test_inputs': [[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 4, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0], [0, 2, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0], [0, 2, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0], [0, 2, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0], [0, 2, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0], [0, 4, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 8, 0, 8, 2, 0, 0, 0, 0, 0, 0, 0, 0], [0, 8, 8, 8, 2, 2, 2, 0, 0, 0, 0, 0, 0], [0, 8, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0], [0, 8, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0]]], 'test_outputs': [[[4, 0, 0, 0, 0, 0, 0, 4], [2, 0, 0, 2, 8, 0, 8, 8], [2, 2, 2, 2, 8, 8, 8, 8], [2, 0, 2, 0, 0, 0, 8, 8], [2, 2, 2, 0, 0, 0, 8, 8], [4, 0, 0, 0, 0, 0, 0, 4]]]}) (input_keys={'test_inputs', 'training_examples'}): 'dict' object has no attribute 'model_dump'
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 49, in forward
File "<string>", line 49, in <listcomp>
AttributeError: 'dict' object has no attribute 'model_dump'
2025/08/28 21:33:57 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[2, 0, 0, 0, 0, 2, 0, 0, 2], [0, 4, 4, 4, 0, 0, 0, 0, 0], [0, 4, 2, 4, 0, 0, 2, 0, 0], [0, 4, 4, 4, 0, 0, 0, 2, 0], [2, 0, 0, 0, 0, 2, 0, 0, 0]], 'output': [[2]]}, {'input': [[8, 0, 8, 0, 0, 0, 0, 0, 8], [0, 0, 0, 0, 8, 0, 0, 0, 0], [0, 0, 8, 0, 0, 3, 3, 3, 0], [8, 0, 0, 3, 0, 3, 8, 3, 0], [0, 0, 0, 0, 0, 3, 3, 3, 0], [0, 0, 8, 0, 0, 0, 0, 0, 0], [3, 0, 0, 8, 0, 0, 0, 8, 0]], 'output': [[8]]}, {'input': [[1, 2, 0, 0, 0, 2, 0, 0, 0], [0, 0, 2, 0, 0, 0, 0, 0, 0], [2, 0, 1, 2, 0, 2, 0, 1, 1], [0, 1, 0, 0, 2, 0, 0, 0, 2], [0, 0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 2, 2, 2, 0, 0, 0, 0, 0], [1, 2, 1, 2, 0, 0, 0, 2, 0], [0, 2, 2, 2, 0, 0, 0, 0, 2], [0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 2, 0, 0, 0, 0, 0]], 'output': [[1]]}, {'input': [[0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 3, 8], [3, 0, 0, 0, 0, 0, 0, 8, 0, 3, 0, 0], [0, 3, 3, 8, 0, 0, 0, 0, 0, 0, 0, 8], [0, 0, 0, 3, 8, 0, 0, 0, 0, 0, 0, 0], [3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0], [0, 0, 0, 3, 8, 0, 0, 0, 0, 0, 0, 0], [0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 3, 3, 3, 0, 0, 8, 0, 3, 0], [0, 0, 3, 3, 8, 3, 0, 0, 0, 0, 0, 0], [0, 0, 0, 3, 3, 3, 0, 0, 0, 0, 0, 0], [0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[8]]}], 'test_inputs': [[[0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0], [1, 0, 0, 0, 0, 0, 4, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 4, 0, 0, 4, 0, 0, 0], [0, 4, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 4, 4, 0, 0, 1], [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 4, 0, 0, 0], [0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 4], [4, 0, 0, 0, 1, 4, 1, 1, 0, 0, 0, 0], [0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 4], [0, 0, 4, 4, 0, 0, 0, 1, 0, 0, 0, 0]]], 'test_outputs': [[[4]]]}) (input_keys={'test_inputs', 'training_examples'}): 'dict' object has no attribute 'model_dump'
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 49, in forward
File "<string>", line 49, in <listcomp>
AttributeError: 'dict' object has no attribute 'model_dump'
2025/08/28 21:33:57 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[0, 3, 0, 0], [2, 2, 2, 2], [0, 3, 0, 0], [0, 3, 0, 0]], 'output': [[4, 4, 4, 0], [4, 2, 4, 2], [4, 4, 4, 0], [0, 3, 0, 0]]}, {'input': [[0, 0, 0, 0, 6, 0, 0, 0], [0, 0, 0, 0, 6, 0, 0, 0], [0, 0, 0, 0, 6, 0, 0, 0], [0, 0, 0, 0, 6, 0, 0, 0], [8, 8, 8, 8, 6, 8, 8, 8], [0, 0, 0, 0, 6, 0, 0, 0], [0, 0, 0, 0, 6, 0, 0, 0], [0, 0, 0, 0, 6, 0, 0, 0]], 'output': [[0, 0, 0, 0, 6, 0, 0, 0], [0, 0, 0, 0, 6, 0, 0, 0], [0, 0, 0, 0, 6, 0, 0, 0], [0, 0, 0, 4, 4, 4, 0, 0], [8, 8, 8, 4, 6, 4, 8, 8], [0, 0, 0, 4, 4, 4, 0, 0], [0, 0, 0, 0, 6, 0, 0, 0], [0, 0, 0, 0, 6, 0, 0, 0]]}, {'input': [[0, 0, 1, 0, 0, 0], [0, 0, 1, 0, 0, 0], [9, 9, 1, 9, 9, 9], [0, 0, 1, 0, 0, 0], [0, 0, 1, 0, 0, 0], [0, 0, 1, 0, 0, 0]], 'output': [[0, 0, 1, 0, 0, 0], [0, 4, 4, 4, 0, 0], [9, 4, 1, 4, 9, 9], [0, 4, 4, 4, 0, 0], [0, 0, 1, 0, 0, 0], [0, 0, 1, 0, 0, 0]]}], 'test_inputs': [[[0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0], [3, 3, 3, 3, 3, 3, 3, 3, 5, 3, 3, 3], [0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0]]], 'test_outputs': [[[0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 4, 4, 4, 0, 0], [3, 3, 3, 3, 3, 3, 3, 4, 5, 4, 3, 3], [0, 0, 0, 0, 0, 0, 0, 4, 4, 4, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0]]]}) (input_keys={'test_inputs', 'training_examples'}): 'dict' object has no attribute 'model_dump'
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 49, in forward
File "<string>", line 49, in <listcomp>
AttributeError: 'dict' object has no attribute 'model_dump'
2025/08/28 21:33:57 INFO dspy.evaluate.evaluate: Average Metric: 0.0 / 3 (0.0%)
Iteration 2: Proposed new text for program: import dspy
from typing import List
import pydantic
import json
import traceback
import copy
# Define the type for a matrix, which is a list of lists of integers.
MATRIX = List[List[int]]
# Define a Pydantic model for a single training example, containing an input and output matrix.
class TrainingExample(pydantic.BaseModel):
input: MATRIX
output: MATRIX
# This signature defines the code generation sub-task. It's highly detailed to guide the LM.
class GenerateTransformerCode(dspy.Signature):
"""
Analyze pairs of input/output matrices to deduce the transformation logic.
Then, generate a self-contained Python function that implements this logic.
Successful strategies:
- Carefully observe the differences between input and output grids. Look for patterns related to shapes, colors (numbers), positions, counts, and spatial relationships.
- Common transformations include: moving objects, changing colors, filling areas, finding objects with unique properties (e.g., most isolated, largest area), and applying geometric rules (e.g., symmetry, rotation).
- Formulate a clear, step-by-step hypothesis for the transformation rule before writing the code.
Pitfalls to avoid:
- Do not hardcode solutions for the specific examples. The function must be general enough to solve new test cases.
- The generated code MUST define a single function named `transform_matrix`.
- This function must take exactly one argument: `matrix` (a list of lists of integers).
- It must return the transformed matrix (a list of lists of integers).
- Do not include any code outside the function definition. Do not call the function.
- Do not use external libraries like numpy or pandas. Standard Python libraries like 'copy' are acceptable.
"""
examples_json: str = dspy.InputField(desc="A JSON string representing the list of training examples.")
test_input_shapes: str = dspy.InputField(desc="A string describing the shapes of the test input matrices, e.g., '12x12, 10x10'.")
python_code: str = dspy.OutputField(desc="A string containing a single Python function `transform_matrix(matrix)` that implements the task logic.")
# This custom module implements the strategy of generating and then executing code.
class CodeGeneratingSolver(dspy.Module):
def __init__(self):
super().__init__()
# Use ChainOfThought to encourage the LM to reason about the rule before generating code.
self.code_generator = dspy.ChainOfThought(GenerateTransformerCode)
def forward(self, training_examples: List[TrainingExample], test_inputs: List[MATRIX]):
# 1. Prepare inputs for the code generator module.
# Convert Pydantic objects to a JSON string for cleaner LM input.
examples_dict = [ex.model_dump() for ex in training_examples]
examples_json_str = json.dumps(examples_dict, indent=2)
# Provide the shapes of test inputs to help the LM generate dimension-aware code.
shapes_str = ", ".join([f"{len(m)}x{len(m[0]) if m else 0}" for m in test_inputs])
# 2. Call the LM to generate the transformation code.
prediction = self.code_generator(
examples_json=examples_json_str,
test_input_shapes=shapes_str
)
generated_code = prediction.python_code
# Add the generated code to the trace for easier debugging.
dspy.Suggest(True, f"Generated Python code:\n---\n{generated_code}\n---")
# 3. Execute the generated code and apply it to test inputs.
results = []
try:
# Clean the generated code, as LMs often wrap it in markdown fences.
if "```python" in generated_code:
generated_code = generated_code.split("```python")[1].split("```")[0].strip()
# Execute the code in a dedicated local scope to define the function.
local_scope = {}
exec(generated_code, globals(), local_scope)
transform_func = local_scope['transform_matrix']
# Apply the generated function to each test input.
for test_matrix in test_inputs:
# Use a deep copy to prevent modifying the original input data.
matrix_copy = copy.deepcopy(test_matrix)
result = transform_func(matrix_copy)
results.append(result)
dspy.Suggest(True, "Code execution successful.")
except Exception as e:
# Fallback strategy: If code generation or execution fails, log the error
# and return a list of Nones to indicate failure without crashing.
error_message = f"Failed to execute generated code. Error: {e}\n{traceback.format_exc()}"
dspy.Suggest(False, error_message)
results = [None] * len(test_inputs)
# 4. Return the final prediction object.
return dspy.Prediction(test_outputs=results)
# The final program is an instance of our robust, code-generating module.
program = CodeGeneratingSolver()
Iteration 2: New subsample score is not better, skipping
Iteration 3: Selected program 0 score: 0.67
Average Metric: 3.00 / 3 (100.0%): 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 377.77it/s]2025/08/28 21:33:57 INFO dspy.evaluate.evaluate: Average Metric: 3.0 / 3 (100.0%)
Iteration 3: All subsample scores perfect. Skipping.
Iteration 3: Reflective mutation did not propose a new candidate
Iteration 4: Selected program 0 score: 0.67
Average Metric: 2.00 / 3 (66.7%): 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 193.52it/s]2025/08/28 21:33:57 INFO dspy.evaluate.evaluate: Average Metric: 2.0 / 3 (66.7%)
Iteration 4: Proposed new text for program: import dspy
from typing import List
import pydantic
import traceback
# Define the data structures for matrices and training examples.
# Using pydantic and type hints helps DSPy with structured data handling.
MATRIX = List[List[int]]
class TrainingExample(pydantic.BaseModel):
input: MATRIX
output: MATRIX
# This signature instructs the LM to act as a programmer, analyzing examples
# and generating a Python function that encapsulates the transformation rule.
class GeneratePythonRule(dspy.Signature):
"""You are an expert programmer. Your task is to analyze a set of input-output matrix examples that demonstrate a visual transformation rule.
Based on your analysis, write a single, self-contained Python function named `transform` that takes one argument, `matrix` (a list of lists of integers), and returns the transformed matrix.
The function must not rely on any external libraries (e.g., numpy, pandas). Standard Python built-in functions and modules like 'math' or 'copy' are allowed.
Your output must be only the raw Python code for the function. Do not include any explanations, comments, or markdown formatting like ```python ... ```.
"""
training_examples: str = dspy.InputField(desc="A string representation of a list of input-output pairs demonstrating the transformation.")
python_code_rule: str = dspy.OutputField(desc="A string containing only the Python code for the 'transform' function.")
# This is a fallback signature, similar to the original approach, but designed
# to solve for a single test case if the code generation method fails.
class SolveSingleTask(dspy.Signature):
"""Given the training examples demonstrating a rule, apply the same rule to the test input and provide the corresponding output matrix."""
training_examples: str = dspy.InputField(description="Input and output examples demonstrating the task to be performed.")
test_input: str = dspy.InputField(description="A single input matrix to be solved.")
test_output: MATRIX = dspy.OutputField(description="The output matrix corresponding to the test input.")
# The main custom module that orchestrates the two-step process:
# 1. Generate a Python function representing the rule.
# 2. Execute the function for each test case.
class RuleBasedSolver(dspy.Module):
def __init__(self):
super().__init__()
# Use ChainOfThought to encourage the LM to reason before writing code.
self.rule_generator = dspy.ChainOfThought(GeneratePythonRule)
# A simpler Predict module for the fallback strategy.
self.fallback_solver = dspy.Predict(SolveSingleTask)
def forward(self, training_examples: List[TrainingExample], test_inputs: List[MATRIX]) -> dspy.Prediction:
# Convert examples to a string format for the LM prompt.
training_examples_str = str([ex.model_dump() for ex in training_examples])
transform_func = None
try:
# Step 1: Generate the Python rule.
rule_prediction = self.rule_generator(training_examples=training_examples_str)
python_code = rule_prediction.python_code_rule
# Execute the generated code in a restricted scope to define the function.
scope = {}
exec(python_code, scope)
transform_func = scope.get('transform')
except Exception as e:
print(f"Failed to generate or execute Python rule: {e}")
print(traceback.format_exc())
transform_func = None
# Step 2: Apply the rule to each test input.
final_outputs = []
for test_matrix in test_inputs:
output_matrix = None
if callable(transform_func):
try:
# Use a deep copy to prevent the function from modifying the original input
import copy
output_matrix = transform_func(copy.deepcopy(test_matrix))
except Exception as e:
print(f"Execution of generated function failed for a test case: {e}")
print(traceback.format_exc())
output_matrix = None # Ensure fallback is triggered
# If code generation or execution failed, use the fallback solver.
if output_matrix is None:
print("Using fallback solver for a test case.")
fallback_pred = self.fallback_solver(
training_examples=training_examples_str,
test_input=str(test_matrix)
)
output_matrix = fallback_pred.test_output
final_outputs.append(output_matrix)
return dspy.Prediction(test_outputs=final_outputs)
# The final program object is an instance of our new, more robust module.
program = RuleBasedSolver()
2025/08/28 21:33:57 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 5, 5], [5, 5, 0, 0, 0, 5, 5, 5, 5, 0, 0, 0, 0, 5, 0, 0, 0, 5, 0], [0, 5, 5, 5, 5, 5, 0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0], [5, 5, 0, 0, 0, 5, 0, 0, 5, 0, 0, 0, 0, 5, 0, 0, 0, 5, 0], [0, 0, 0, 0, 0, 5, 0, 0, 5, 0, 0, 0, 0, 5, 0, 0, 0, 5, 5], [0, 0, 0, 0, 0, 5, 0, 0, 5, 0, 0, 5, 5, 5, 5, 0, 0, 0, 0], [0, 0, 0, 0, 0, 5, 0, 0, 5, 0, 0, 5, 0, 0, 5, 0, 0, 0, 0], [0, 0, 0, 0, 0, 5, 5, 5, 5, 0, 0, 5, 0, 0, 5, 5, 5, 5, 5], [0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 5, 5, 5, 5, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 5, 5], [5, 5, 0, 0, 0, 5, 5, 5, 5, 0, 0, 0, 0, 5, 0, 0, 0, 5, 4], [4, 5, 5, 5, 5, 5, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 4], [5, 5, 0, 0, 0, 5, 4, 4, 5, 0, 0, 0, 0, 5, 0, 0, 0, 5, 4], [0, 0, 0, 0, 0, 5, 4, 4, 5, 0, 0, 0, 0, 5, 0, 0, 0, 5, 5], [0, 0, 0, 0, 0, 5, 4, 4, 5, 0, 0, 5, 5, 5, 5, 0, 0, 0, 0], [0, 0, 0, 0, 0, 5, 4, 4, 5, 0, 0, 5, 4, 4, 5, 0, 0, 0, 0], [0, 0, 0, 0, 0, 5, 5, 5, 5, 0, 0, 5, 4, 4, 5, 5, 5, 5, 5], [0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 5, 5, 5, 5, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0]]}, {'input': [[0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0], [0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0], [0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0], [0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0], [0, 0, 0, 5, 5, 5, 5, 5, 0, 0, 0, 5, 0, 0, 5, 5], [0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 5, 0], [0, 0, 0, 5, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 0], [0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 5, 0], [0, 0, 0, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 5, 5], [0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0], [0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0], [0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0], [0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0], [0, 0, 0, 5, 5, 5, 5, 5, 0, 0, 0, 5, 0, 0, 5, 5], [0, 0, 0, 5, 4, 4, 4, 5, 0, 0, 0, 5, 0, 0, 5, 4], [0, 0, 0, 5, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 4], [0, 0, 0, 5, 4, 4, 4, 5, 0, 0, 0, 0, 0, 0, 5, 4], [0, 0, 0, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 5, 5], [0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}, {'input': [[0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 0], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 5, 0], [0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 5, 5], [0, 0, 0, 5, 5, 5, 5, 0, 0, 0, 0, 5, 0, 0, 0, 5, 0], [5, 5, 5, 5, 0, 0, 5, 0, 0, 0, 0, 5, 5, 5, 5, 5, 0], [0, 0, 0, 5, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0], [0, 0, 0, 5, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0], [0, 0, 0, 5, 5, 5, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0], [0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0], [0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 5, 5, 5, 5, 0, 0, 0], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 5, 0, 0, 0], [0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 5, 0, 0, 5, 0, 0, 0], [0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 5, 5, 5, 5, 0, 0, 0], [0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 0], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 4, 4, 4, 5, 0], [0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 4, 4, 4, 5, 5], [0, 0, 0, 5, 5, 5, 5, 0, 0, 0, 0, 5, 4, 4, 4, 5, 0], [5, 5, 5, 5, 4, 4, 5, 0, 0, 0, 0, 5, 5, 5, 5, 5, 0], [0, 0, 0, 5, 4, 4, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0], [0, 0, 0, 5, 4, 4, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0], [0, 0, 0, 5, 5, 5, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0], [0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0], [0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 5, 5, 5, 5, 0, 0, 0], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 4, 4, 5, 0, 0, 0], [0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 5, 4, 4, 5, 0, 0, 0], [0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 5, 5, 5, 5, 0, 0, 0], [0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0]]}], 'test_inputs': [[[0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5, 5, 5, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [5, 5, 5, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 5, 5, 5, 0], [0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 0, 0, 0, 5, 0, 0, 5, 0], [5, 5, 5, 5, 5, 5, 0, 0, 0, 5, 5, 5, 5, 5, 0, 0, 5, 5], [0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 5, 0], [0, 5, 0, 0, 0, 5, 5, 5, 5, 5, 0, 0, 0, 5, 5, 5, 5, 0], [0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5, 5, 5, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5, 0, 5, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5, 5, 5, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]], 'test_outputs': [[[0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5, 5, 5, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [5, 5, 5, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 5, 5, 5, 0], [0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 0, 0, 0, 5, 4, 4, 5, 0], [5, 5, 5, 5, 5, 5, 4, 4, 4, 5, 5, 5, 5, 5, 4, 4, 5, 5], [0, 5, 0, 0, 0, 5, 4, 4, 4, 5, 0, 0, 0, 5, 4, 4, 5, 0], [0, 5, 0, 0, 0, 5, 5, 5, 5, 5, 0, 0, 0, 5, 5, 5, 5, 0], [0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5, 5, 5, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5, 4, 5, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5, 5, 5, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]]}) (input_keys={'test_inputs', 'training_examples'}): 'dict' object has no attribute 'model_dump'
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 50, in forward
File "<string>", line 50, in <listcomp>
AttributeError: 'dict' object has no attribute 'model_dump'
2025/08/28 21:33:57 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 5, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 5, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 5, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 1, 1, 1, 0], [0, 0, 0, 0, 0, 1, 5, 1, 0], [0, 0, 0, 0, 0, 1, 1, 1, 0], [0, 0, 1, 1, 1, 0, 0, 0, 0], [0, 0, 1, 5, 1, 0, 0, 0, 0], [0, 0, 1, 1, 1, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0, 0, 0], [1, 5, 1, 0, 0, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0, 0, 0]]}, {'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 5, 0], [0, 0, 0, 5, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 5, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 5, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 1, 1, 1], [0, 0, 1, 1, 1, 0, 1, 5, 1], [0, 0, 1, 5, 1, 0, 1, 1, 1], [0, 0, 1, 1, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 1, 1], [0, 0, 0, 0, 0, 0, 1, 5, 1], [0, 0, 1, 1, 1, 0, 1, 1, 1], [0, 0, 1, 5, 1, 0, 0, 0, 0], [0, 0, 1, 1, 1, 0, 0, 0, 0]]}], 'test_inputs': [[[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 5, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 5, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 5, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 5, 0, 0, 0, 5, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]]], 'test_outputs': [[[1, 1, 1, 0, 0, 0, 0, 0, 0], [1, 5, 1, 0, 0, 0, 1, 1, 1], [1, 1, 1, 0, 0, 0, 1, 5, 1], [0, 0, 1, 1, 1, 0, 1, 1, 1], [0, 0, 1, 5, 1, 0, 0, 0, 0], [0, 0, 1, 1, 1, 0, 0, 0, 0], [1, 1, 1, 0, 1, 1, 1, 0, 0], [1, 5, 1, 0, 1, 5, 1, 0, 0], [1, 1, 1, 0, 1, 1, 1, 0, 0]]]}) (input_keys={'test_inputs', 'training_examples'}): 'dict' object has no attribute 'model_dump'
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 50, in forward
File "<string>", line 50, in <listcomp>
AttributeError: 'dict' object has no attribute 'model_dump'
2025/08/28 21:33:57 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 2, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 2, 5, 2, 0, 2, 5, 2, 0, 2], [0, 2, 0, 2, 0, 2, 0, 2, 0, 2], [0, 2, 0, 2, 0, 2, 0, 2, 0, 2], [0, 2, 0, 2, 0, 2, 0, 2, 0, 2], [0, 2, 0, 2, 0, 2, 0, 2, 0, 2], [0, 2, 0, 2, 0, 2, 0, 2, 0, 2], [0, 2, 0, 2, 0, 2, 0, 2, 0, 2], [0, 2, 0, 2, 0, 2, 0, 2, 0, 2], [0, 2, 0, 2, 0, 2, 0, 2, 0, 2], [0, 2, 0, 2, 5, 2, 0, 2, 5, 2]]}, {'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 3, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 3, 5, 3, 0, 3], [0, 0, 0, 0, 0, 3, 0, 3, 0, 3], [0, 0, 0, 0, 0, 3, 0, 3, 0, 3], [0, 0, 0, 0, 0, 3, 0, 3, 0, 3], [0, 0, 0, 0, 0, 3, 0, 3, 0, 3], [0, 0, 0, 0, 0, 3, 0, 3, 0, 3], [0, 0, 0, 0, 0, 3, 0, 3, 0, 3], [0, 0, 0, 0, 0, 3, 0, 3, 0, 3], [0, 0, 0, 0, 0, 3, 0, 3, 0, 3], [0, 0, 0, 0, 0, 3, 0, 3, 5, 3]]}, {'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 4, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 4, 5, 4, 0, 4, 5], [0, 0, 0, 0, 4, 0, 4, 0, 4, 0], [0, 0, 0, 0, 4, 0, 4, 0, 4, 0], [0, 0, 0, 0, 4, 0, 4, 0, 4, 0], [0, 0, 0, 0, 4, 0, 4, 0, 4, 0], [0, 0, 0, 0, 4, 0, 4, 0, 4, 0], [0, 0, 0, 0, 4, 0, 4, 0, 4, 0], [0, 0, 0, 0, 4, 0, 4, 0, 4, 0], [0, 0, 0, 0, 4, 0, 4, 0, 4, 0], [0, 0, 0, 0, 4, 0, 4, 5, 4, 0]]}], 'test_inputs': [[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0, 0]]], 'test_outputs': [[[0, 0, 1, 5, 1, 0, 1, 5, 1, 0], [0, 0, 1, 0, 1, 0, 1, 0, 1, 0], [0, 0, 1, 0, 1, 0, 1, 0, 1, 0], [0, 0, 1, 0, 1, 0, 1, 0, 1, 0], [0, 0, 1, 0, 1, 0, 1, 0, 1, 0], [0, 0, 1, 0, 1, 0, 1, 0, 1, 0], [0, 0, 1, 0, 1, 0, 1, 0, 1, 0], [0, 0, 1, 0, 1, 0, 1, 0, 1, 0], [0, 0, 1, 0, 1, 0, 1, 0, 1, 0], [0, 0, 1, 0, 1, 5, 1, 0, 1, 5]]]}) (input_keys={'test_inputs', 'training_examples'}): 'dict' object has no attribute 'model_dump'
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 50, in forward
File "<string>", line 50, in <listcomp>
AttributeError: 'dict' object has no attribute 'model_dump'
2025/08/28 21:33:57 INFO dspy.evaluate.evaluate: Average Metric: 0.0 / 3 (0.0%)
Iteration 4: New subsample score is not better, skipping
Iteration 5: Selected program 0 score: 0.67
Average Metric: 1.00 / 1 (100.0%): 0%| | 0/3 [00:00<?, ?it/s]2025/08/28 21:33:57 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
Average Metric: 2.00 / 3 (66.7%): 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 206.00it/s]2025/08/28 21:33:57 INFO dspy.evaluate.evaluate: Average Metric: 2.0 / 3 (66.7%)
2025/08/28 21:33:57 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[2, 0, 4, 0, 8, 0], [0, 3, 0, 8, 8, 8], [0, 0, 0, 0, 8, 0]], 'output': [[0, 2, 0, 0, 0, 0, 0, 4, 0], [2, 2, 2, 0, 0, 0, 4, 4, 4], [0, 2, 0, 0, 0, 0, 0, 4, 0], [0, 0, 0, 0, 3, 0, 0, 0, 0], [0, 0, 0, 3, 3, 3, 0, 0, 0], [0, 0, 0, 0, 3, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]]}, {'input': [[0, 0, 8, 0, 4, 0], [8, 0, 0, 1, 2, 4], [8, 8, 0, 0, 1, 0]], 'output': [[0, 0, 0, 0, 0, 4, 0, 0, 0], [0, 0, 0, 4, 0, 0, 0, 0, 0], [0, 0, 0, 4, 4, 0, 0, 0, 0], [0, 0, 1, 0, 0, 2, 0, 0, 4], [1, 0, 0, 2, 0, 0, 4, 0, 0], [1, 1, 0, 2, 2, 0, 4, 4, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 1, 1, 0, 0, 0, 0]]}, {'input': [[2, 0, 0, 4, 0, 0, 8, 0], [0, 2, 4, 0, 8, 8, 8, 8], [0, 4, 2, 0, 0, 0, 8, 0], [4, 0, 0, 2, 0, 0, 8, 0]], 'output': [[0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0], [2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4, 4], [0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0], [0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0], [0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 4, 0, 0, 0, 0, 0], [0, 0, 0, 0, 2, 2, 2, 2, 4, 4, 4, 4, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 4, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 4, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 2, 0, 0, 0, 0, 0], [0, 0, 0, 0, 4, 4, 4, 4, 2, 2, 2, 2, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 2, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 2, 0, 0, 0, 0, 0], [0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0], [4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2], [0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0], [0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0]]}], 'test_inputs': [[[3, 0, 0, 1], [0, 2, 2, 0], [0, 2, 2, 0], [3, 0, 0, 3], [0, 8, 8, 0], [8, 8, 8, 8], [8, 0, 0, 8], [8, 8, 8, 8]]], 'test_outputs': [[[0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0], [3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1], [3, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1], [3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1], [0, 0, 0, 0, 0, 2, 2, 0, 0, 2, 2, 0, 0, 0, 0, 0], [0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0], [0, 0, 0, 0, 2, 0, 0, 2, 2, 0, 0, 2, 0, 0, 0, 0], [0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0], [0, 0, 0, 0, 0, 2, 2, 0, 0, 2, 2, 0, 0, 0, 0, 0], [0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0], [0, 0, 0, 0, 2, 0, 0, 2, 2, 0, 0, 2, 0, 0, 0, 0], [0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0], [0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 0], [3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3], [3, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 3], [3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3]]]}) (input_keys={'test_inputs', 'training_examples'}): 'dict' object has no attribute 'model_dump'
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 45, in forward
File "<string>", line 45, in <listcomp>
AttributeError: 'dict' object has no attribute 'model_dump'
2025/08/28 21:33:57 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[0, 0, 6], [0, 4, 0], [3, 0, 0]], 'output': [[0, 0, 6], [0, 4, 6], [3, 4, 6]]}, {'input': [[0, 2, 0], [7, 0, 8], [0, 0, 0]], 'output': [[0, 2, 0], [7, 2, 8], [7, 2, 8]]}, {'input': [[4, 0, 0], [0, 2, 0], [0, 0, 0]], 'output': [[4, 0, 0], [4, 2, 0], [4, 2, 0]]}], 'test_inputs': [[[4, 0, 8], [0, 0, 0], [0, 7, 0]]], 'test_outputs': [[[4, 0, 8], [4, 0, 8], [4, 7, 8]]]}) (input_keys={'test_inputs', 'training_examples'}): 'dict' object has no attribute 'model_dump'
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 45, in forward
File "<string>", line 45, in <listcomp>
AttributeError: 'dict' object has no attribute 'model_dump'
2025/08/28 21:33:57 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0], [0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 4, 4, 4, 0, 0, 0, 0, 0], [6, 6, 6, 0, 4, 8, 4, 0, 0, 0, 0, 0], [6, 3, 6, 0, 4, 4, 4, 1, 1, 1, 0, 0], [6, 6, 6, 0, 0, 0, 0, 1, 2, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}, {'input': [[0, 0, 0, 0, 0, 0], [0, 0, 0, 3, 0, 0], [0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 6, 6, 6, 0], [0, 0, 6, 3, 6, 0], [0, 0, 6, 6, 6, 0], [0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0]]}, {'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 6, 6, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 3, 6, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 6, 6, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 2, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}, {'input': [[0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], [0, 0, 8, 0, 0, 0], [0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0], [0, 4, 4, 4, 0, 0], [0, 4, 8, 4, 0, 0], [0, 4, 4, 4, 0, 0], [0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0]]}], 'test_inputs': [[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]], 'test_outputs': [[[6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [6, 3, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 4, 8, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]]}) (input_keys={'test_inputs', 'training_examples'}): 'dict' object has no attribute 'model_dump'
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 45, in forward
File "<string>", line 45, in <listcomp>
AttributeError: 'dict' object has no attribute 'model_dump'
2025/08/28 21:33:57 INFO dspy.evaluate.evaluate: Average Metric: 0.0 / 3 (0.0%)
Iteration 5: Proposed new text for program: import dspy
from typing import List
import pydantic
import json
# Define the type for a matrix
MATRIX = List[List[int]]
# Define the Pydantic model for a single training example
class TrainingExample(pydantic.BaseModel):
input: MATRIX
output: MATRIX
class GeneratePythonFunctionSignature(dspy.Signature):
"""
Analyze the provided training examples, which consist of input and output matrices.
Deduce the transformation rule that converts the input matrix to the output matrix.
Then, write a single, self-contained Python function named 'transform_matrix' that implements this rule.
**Successful Strategies:**
1. **Analyze Dimensions:** First, check if the output dimensions are different from the input dimensions. Describe this relationship.
2. **Identify Patterns:** Look for patterns related to colors, shapes, object counting, repetition, or spatial rearrangement.
3. **Formulate the Rule:** Clearly state the step-by-step logic for the transformation.
4. **Write the Function:** Implement the logic in a Python function `transform_matrix(matrix)`. This function must take one argument (the input matrix as a list of lists) and return the transformed matrix (a new list of lists).
5. **Code Style:** The function should be pure and not rely on any external libraries or global state. Add comments to your code to explain complex parts.
**Pitfalls to Avoid:**
- Do not write a script that only solves the provided test case. The function must be general enough to work for any input following the same pattern.
- Ensure the function returns a valid, non-jagged list of lists of integers.
- Do not include any code outside of the function definition.
"""
training_examples: str = dspy.InputField(desc="A string representation of the list of training examples, each with an 'input' and 'output' matrix.")
test_input_example: str = dspy.InputField(desc="A string representation of a single test input matrix to understand the target format.")
python_function: str = dspy.OutputField(desc="A string containing a single Python function `transform_matrix(matrix)` that solves the task.")
class ARC_Solver(dspy.Module):
"""A module that solves ARC-like tasks by generating and executing Python code."""
def __init__(self):
super().__init__()
self.code_generator = dspy.ChainOfThought(GeneratePythonFunctionSignature)
def forward(self, training_examples: List[TrainingExample], test_inputs: List[MATRIX]):
# Serialize the Pydantic objects and lists to JSON strings for the LM
# Using indentation makes it more readable for the LM
training_examples_str = json.dumps([ex.model_dump() for ex in training_examples], indent=2)
test_input_example_str = json.dumps(test_inputs[0], indent=2)
# Generate the Python function that solves the task
prediction = self.code_generator(
training_examples=training_examples_str,
test_input_example=test_input_example_str
)
function_code = prediction.python_function
# Prepare a safe execution environment
local_namespace = {}
all_test_outputs = []
try:
# Extract the function code, which might be wrapped in backticks
if "```python" in function_code:
function_code = function_code.split("```python")[1].split("```")[0]
# Execute the generated code to define the function
exec(function_code, globals(), local_namespace)
transform_matrix_func = local_namespace['transform_matrix']
# Apply the function to each test input
for test_input in test_inputs:
# The function needs a deep copy to avoid modifying the original list
input_copy = [row[:] for row in test_input]
output_matrix = transform_matrix_func(input_copy)
all_test_outputs.append(output_matrix)
except Exception as e:
print(f"Failed to execute generated code: {e}")
# Fallback strategy: return a list of empty matrices with the same
# number of items as the test inputs to match the required output format.
all_test_outputs = [[] for _ in test_inputs]
return dspy.Prediction(test_outputs=all_test_outputs)
# The final program is an instance of our custom solver module.
program = ARC_Solver()
Iteration 5: New subsample score is not better, skipping
Iteration 6: Selected program 0 score: 0.67
Average Metric: 2.00 / 3 (66.7%): 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 269.57it/s]2025/08/28 21:33:57 INFO dspy.evaluate.evaluate: Average Metric: 2.0 / 3 (66.7%)
Iteration 6: Proposed new text for program: import dspy
from typing import List
import pydantic
# Define a clear type alias for a matrix
MATRIX = List[List[int]]
class TrainingExample(pydantic.BaseModel):
"""A Pydantic model for a single training example."""
input: MATRIX
output: MATRIX
class DescribeAlgorithm(dspy.Signature):
"""
Analyzes training examples to describe the transformation rule.
The user will provide several examples, each consisting of an 'input' matrix and an 'output' matrix.
Your task is to carefully analyze these pairs to understand the underlying transformation rule.
Describe this rule in clear, step-by-step English. Be precise and detailed.
**Successful Strategies:**
- Look for patterns related to colors, shapes, and object counts.
- Consider transformations like rotation, reflection, scaling, or movement.
- Pay attention to how the grid is partitioned or structured (e.g., by solid lines of a specific color).
- Identify if the rule is about finding a unique object or pattern among several repeated ones.
**Pitfalls to Avoid:**
- Do not just describe one example. Find the general rule that applies to all of them.
- Avoid vague descriptions. Be specific about how to find key features and how to construct the output.
"""
training_examples: List[TrainingExample] = dspy.InputField(desc="A list of input/output pairs demonstrating the task.")
algorithm_description: str = dspy.OutputField(desc="A step-by-step English description of the transformation rule.")
class GeneratePythonCode(dspy.Signature):
"""
Generates a Python function from an algorithm description to solve the task.
You are an expert Python programmer. Based on the provided algorithm description, write a single Python function named `transform_matrix`.
This function must take one argument: `matrix` (the input matrix as a list of lists of integers).
It must return the transformed matrix (a list of lists of integers).
**Important Constraints:**
- The function must be named exactly `transform_matrix`.
- The function must be self-contained. Do not use any external libraries that are not standard in Python (like numpy).
- Your output should be only the Python code for the function definition. Do not include any import statements, example usage, or explanations.
"""
algorithm_description: str = dspy.InputField(desc="The English description of the algorithm.")
test_input_example: MATRIX = dspy.InputField(desc="A sample test input to help contextualize the code generation.")
python_code: str = dspy.OutputField(desc="A string containing the Python code for the `transform_matrix` function.")
class SolveWithGeneratedCode(dspy.Module):
"""A module that solves matrix tasks by generating and executing Python code."""
def __init__(self):
super().__init__()
self.describe_algorithm = dspy.ChainOfThought(DescribeAlgorithm)
self.generate_code = dspy.Predict(GeneratePythonCode)
def forward(self, training_examples: List[TrainingExample], test_inputs: List[MATRIX]):
# Step 1: Use the LM to analyze examples and describe the algorithm.
prediction = self.describe_algorithm(training_examples=training_examples)
algorithm_description = prediction.algorithm_description
# Step 2: Use the LM to generate a Python function based on the description.
# We provide the first test input as an example to help the LM generate correct code.
code_gen_prediction = self.generate_code(
algorithm_description=algorithm_description,
test_input_example=test_inputs[0]
)
python_code = code_gen_prediction.python_code
# Step 3: Execute the generated code for each test input.
all_test_outputs = []
# Prepare a namespace for safe execution of the generated code.
local_namespace = {}
try:
# Execute the generated Python code string to define the function in our namespace.
exec(python_code, globals(), local_namespace)
transform_func = local_namespace.get('transform_matrix')
if transform_func:
# If the function was defined successfully, apply it to all test inputs.
for test_input in test_inputs:
try:
# It's good practice to have a try-except here as well,
# in case the function fails on a specific input.
result = transform_func(test_input)
all_test_outputs.append(result)
except Exception:
# Fallback for a single failing test case: append an empty matrix.
all_test_outputs.append([[]])
else:
# Fallback if the function wasn't defined correctly.
# Mark all outputs as failed.
all_test_outputs = [[[]] for _ in test_inputs]
except Exception:
# Fallback if the generated code has a syntax error or fails to execute.
# Mark all outputs as failed.
all_test_outputs = [[[]] for _ in test_inputs]
return dspy.Prediction(test_outputs=all_test_outputs)
# The final program object that will be used to solve the task.
program = SolveWithGeneratedCode()
2025/08/28 21:33:57 INFO dspy.evaluate.evaluate: Average Metric: 2.0 / 3 (66.7%)
Iteration 6: New subsample score is not better, skipping
Iteration 7: Selected program 0 score: 0.67
Average Metric: 2.00 / 3 (66.7%): 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 497.74it/s]2025/08/28 21:33:57 INFO dspy.evaluate.evaluate: Average Metric: 2.0 / 3 (66.7%)
2025/08/28 21:33:57 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[4, 5, 4], [5, 5, 5], [4, 5, 4]], 'output': [[0, 4, 0], [4, 4, 4], [0, 4, 0]]}, {'input': [[5, 5, 6, 6, 6], [6, 5, 5, 6, 6], [6, 6, 5, 5, 6], [6, 6, 6, 5, 5], [5, 6, 6, 6, 5]], 'output': [[6, 6, 0, 0, 0], [0, 6, 6, 0, 0], [0, 0, 6, 6, 0], [0, 0, 0, 6, 6], [6, 0, 0, 0, 6]]}, {'input': [[9, 5, 9, 9, 9], [9, 9, 5, 5, 9], [9, 5, 9, 9, 9], [9, 9, 5, 9, 9], [9, 9, 9, 5, 5]], 'output': [[0, 9, 0, 0, 0], [0, 0, 9, 9, 0], [0, 9, 0, 0, 0], [0, 0, 9, 0, 0], [0, 0, 0, 9, 9]]}], 'test_inputs': [[[3, 3, 3, 5, 3], [3, 5, 3, 3, 3], [3, 5, 5, 3, 5], [3, 3, 3, 5, 3], [5, 5, 5, 3, 3]]], 'test_outputs': [[[0, 0, 0, 3, 0], [0, 3, 0, 0, 0], [0, 3, 3, 0, 3], [0, 0, 0, 3, 0], [3, 3, 3, 0, 0]]]}) (input_keys={'test_inputs', 'training_examples'}): 'dict' object has no attribute 'model_dump'
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 69, in forward
File "<string>", line 69, in <listcomp>
AttributeError: 'dict' object has no attribute 'model_dump'
2025/08/28 21:33:57 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[0, 0, 8, 0], [0, 8, 0, 8], [0, 0, 8, 0]], 'output': [[0, 0, 8, 0, 0, 8, 0, 0], [0, 8, 0, 8, 8, 0, 8, 0], [0, 0, 8, 0, 0, 8, 0, 0], [0, 0, 8, 0, 0, 8, 0, 0], [0, 8, 0, 8, 8, 0, 8, 0], [0, 0, 8, 0, 0, 8, 0, 0]]}, {'input': [[0, 0, 3, 3], [0, 3, 0, 3], [3, 3, 3, 0]], 'output': [[0, 0, 3, 3, 3, 3, 0, 0], [0, 3, 0, 3, 3, 0, 3, 0], [3, 3, 3, 0, 0, 3, 3, 3], [3, 3, 3, 0, 0, 3, 3, 3], [0, 3, 0, 3, 3, 0, 3, 0], [0, 0, 3, 3, 3, 3, 0, 0]]}, {'input': [[3, 3, 3, 3], [3, 0, 0, 0], [3, 0, 0, 0]], 'output': [[3, 3, 3, 3, 3, 3, 3, 3], [3, 0, 0, 0, 0, 0, 0, 3], [3, 0, 0, 0, 0, 0, 0, 3], [3, 0, 0, 0, 0, 0, 0, 3], [3, 0, 0, 0, 0, 0, 0, 3], [3, 3, 3, 3, 3, 3, 3, 3]]}], 'test_inputs': [[[4, 0, 0, 0], [0, 0, 0, 4], [4, 4, 0, 0]]], 'test_outputs': [[[4, 0, 0, 0, 0, 0, 0, 4], [0, 0, 0, 4, 4, 0, 0, 0], [4, 4, 0, 0, 0, 0, 4, 4], [4, 4, 0, 0, 0, 0, 4, 4], [0, 0, 0, 4, 4, 0, 0, 0], [4, 0, 0, 0, 0, 0, 0, 4]]]}) (input_keys={'test_inputs', 'training_examples'}): 'dict' object has no attribute 'model_dump'
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 69, in forward
File "<string>", line 69, in <listcomp>
AttributeError: 'dict' object has no attribute 'model_dump'
2025/08/28 21:33:57 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[0, 0, 1, 1, 1, 1, 1, 0, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0, 0], [0, 0, 1, 1, 0, 1, 1, 0, 0, 0], [0, 0, 1, 1, 0, 1, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 2, 0, 0, 0, 0, 0], [0, 0, 0, 0, 2, 0, 0, 0, 0, 0], [0, 0, 0, 0, 2, 0, 0, 0, 0, 0]], 'output': [[0, 0, 1, 1, 1, 1, 1, 0, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0, 0], [0, 0, 1, 1, 2, 1, 1, 0, 0, 0], [0, 0, 1, 1, 2, 1, 1, 0, 0, 0], [0, 0, 0, 0, 2, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}, {'input': [[0, 0, 1, 1, 1, 1, 1, 1, 1, 0], [0, 0, 1, 1, 0, 1, 1, 1, 1, 0], [0, 0, 1, 1, 0, 1, 0, 1, 1, 0], [0, 0, 1, 1, 0, 1, 0, 1, 1, 0], [0, 0, 0, 0, 0, 0, 0, 1, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 2, 0, 0, 0], [0, 0, 0, 0, 0, 0, 2, 0, 0, 0], [0, 0, 0, 0, 0, 0, 2, 0, 0, 0], [0, 0, 0, 0, 2, 0, 2, 0, 0, 0]], 'output': [[0, 0, 1, 1, 1, 1, 1, 1, 1, 0], [0, 0, 1, 1, 2, 1, 1, 1, 1, 0], [0, 0, 1, 1, 0, 1, 2, 1, 1, 0], [0, 0, 1, 1, 0, 1, 2, 1, 1, 0], [0, 0, 0, 0, 0, 0, 2, 1, 1, 0], [0, 0, 0, 0, 0, 0, 2, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}, {'input': [[0, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 1, 1, 0, 1, 1, 1, 1, 1, 1], [0, 1, 1, 0, 1, 1, 1, 1, 0, 1], [0, 1, 1, 0, 0, 1, 0, 1, 0, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 2, 0, 0, 0], [0, 0, 0, 2, 0, 0, 2, 0, 2, 0], [0, 0, 0, 2, 2, 0, 2, 0, 2, 0], [0, 0, 0, 2, 2, 0, 2, 0, 2, 0]], 'output': [[0, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 1, 1, 2, 1, 1, 1, 1, 1, 1], [0, 1, 1, 2, 1, 1, 1, 1, 2, 1], [0, 1, 1, 2, 2, 1, 2, 1, 2, 1], [0, 0, 0, 0, 2, 0, 2, 0, 2, 0], [0, 0, 0, 0, 0, 0, 2, 0, 0, 0], [0, 0, 0, 0, 0, 0, 2, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}], 'test_inputs': [[[0, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 1, 0, 1, 1, 1, 1, 1, 0, 1], [0, 1, 0, 1, 0, 1, 0, 1, 0, 1], [0, 1, 0, 1, 0, 0, 0, 1, 0, 1], [0, 0, 0, 1, 0, 0, 0, 0, 2, 1], [0, 0, 0, 0, 0, 0, 0, 0, 2, 0], [0, 0, 0, 0, 0, 2, 0, 0, 2, 0], [0, 0, 2, 0, 0, 2, 0, 0, 2, 0], [0, 0, 2, 0, 2, 2, 0, 0, 2, 0], [0, 0, 2, 0, 2, 2, 2, 0, 2, 0]]], 'test_outputs': [[[0, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 1, 2, 1, 1, 1, 1, 1, 2, 1], [0, 1, 2, 1, 2, 1, 2, 1, 2, 1], [0, 1, 2, 1, 2, 2, 0, 1, 2, 1], [0, 0, 0, 1, 0, 2, 0, 0, 2, 1], [0, 0, 0, 0, 0, 2, 0, 0, 2, 0], [0, 0, 0, 0, 0, 2, 0, 0, 2, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]]}) (input_keys={'test_inputs', 'training_examples'}): 'dict' object has no attribute 'model_dump'
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 69, in forward
File "<string>", line 69, in <listcomp>
AttributeError: 'dict' object has no attribute 'model_dump'
2025/08/28 21:33:57 INFO dspy.evaluate.evaluate: Average Metric: 0.0 / 3 (0.0%)
GEPA Optimization: 6%|██████▊ | 236/4000 [00:01<00:18, 202.82rollouts/s]
Iteration 7: Proposed new text for program: import dspy
from typing import List
import pydantic
import traceback
import json
# --- Type Definitions and Pydantic Models ---
# Define a type hint for a matrix for clarity.
MATRIX = List[List[int]]
# Pydantic model to structure the training examples, ensuring data integrity.
class TrainingExample(pydantic.BaseModel):
input: MATRIX
output: MATRIX
# --- Signature for Step 1: Deducing the Transformation Rule ---
class RuleDeductionSignature(dspy.Signature):
"""
You are an expert in abstract reasoning puzzles. Your task is to analyze a series of input/output matrix pairs and deduce the transformation rule.
Analyze the provided training examples, which show input matrices and their corresponding output matrices. Identify the underlying pattern or algorithm that transforms each input into its output.
Describe the rule in clear, step-by-step natural language. Be precise and unambiguous. Your description should be detailed enough for a programmer to implement it without needing to see the original examples.
Consider various transformation types:
- Pixel-wise operations (e.g., color swapping, conditional changes).
- Row or column-wise operations (e.g., gravity, sorting, shifting).
- Global properties (e.g., counting objects of a certain color, finding the most frequent color).
- Geometric transformations (e.g., rotation, reflection, scaling, tiling).
- Combinations of the above.
"""
training_examples: str = dspy.InputField(desc="A JSON string of input-output pairs demonstrating the task.")
rule_description: str = dspy.OutputField(desc="A clear, step-by-step natural language description of the transformation rule.")
# --- Signature for Step 2: Generating Python Code from the Rule ---
class CodeGenerationSignature(dspy.Signature):
"""
You are an expert Python programmer. Your task is to write a Python function that implements a given transformation rule for a matrix (list of lists of integers).
The function must be named `transform_matrix` and accept one argument: `matrix`.
It must return the transformed matrix as a list of lists.
- The input `matrix` will be a list of lists of integers.
- The function should be self-contained and not rely on any external libraries (e.g., numpy). Standard Python libraries like 'copy' are allowed.
- Do not include any code outside the function definition.
- Do not include example usage or print statements.
- Ensure the logic correctly handles matrix dimensions and edge cases implied by the rule.
"""
rule_description: str = dspy.InputField(desc="The natural language description of the transformation rule.")
test_input_example: str = dspy.InputField(desc="An example of a test input matrix to help understand data structure.")
python_code: str = dspy.OutputField(desc="A Python function `transform_matrix(matrix)` that implements the rule.")
# --- Custom Module to Orchestrate the Two-Step Process ---
class RuleBasedMatrixSolver(dspy.Module):
"""A custom module that first deduces a rule, then generates and executes code to solve the task."""
def __init__(self):
super().__init__()
# Module for deducing the rule from examples. ChainOfThought is used for better reasoning.
self.deduce_rule = dspy.ChainOfThought(RuleDeductionSignature)
# Module for generating code based on the rule. Predict is sufficient here.
self.generate_code = dspy.Predict(CodeGenerationSignature)
def forward(self, training_examples: List[TrainingExample], test_inputs: List[MATRIX]):
# Convert Pydantic objects to a JSON string for the LM prompt.
training_examples_str = json.dumps([ex.model_dump() for ex in training_examples])
# Step 1: Deduce the transformation rule from the training examples.
rule_prediction = self.deduce_rule(training_examples=training_examples_str)
rule_description = rule_prediction.rule_description
# Step 2: Generate and execute code for each test input.
final_outputs = []
for test_matrix in test_inputs:
try:
# Generate Python code that implements the deduced rule.
code_prediction = self.generate_code(
rule_description=rule_description,
test_input_example=str(test_matrix)
)
python_code = code_prediction.python_code
# Clean the generated code, removing markdown fences.
clean_code = python_code.strip().replace("```python", "").replace("```", "").strip()
# Prepare a local scope to execute the code in.
local_scope = {}
exec(clean_code, globals(), local_scope)
transform_func = local_scope['transform_matrix']
# Execute the generated function on the test matrix.
result_matrix = transform_func(test_matrix)
final_outputs.append(result_matrix)
except Exception as e:
print(f"Error executing generated code for a test input: {e}")
print(f"Generated code snippet:\n{python_code}")
print(f"Traceback: {traceback.format_exc()}")
# Fallback strategy: If code execution fails, return the original input.
final_outputs.append(test_matrix)
return dspy.Prediction(test_outputs=final_outputs)
# The final 'program' object is an instance of our improved custom module.
program = RuleBasedMatrixSolver()
Iteration 7: New subsample score is not better, skipping
Iteration 8: Selected program 0 score: 0.67
Average Metric: 2.00 / 3 (66.7%): 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 232.49it/s]2025/08/28 21:33:57 INFO dspy.evaluate.evaluate: Average Metric: 2.0 / 3 (66.7%)
2025/08/28 21:34:34 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0], [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0], [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0], [4, 0, 4, 0, 4, 0, 4, 0, 4, 0, 4, 0, 4, 0, 4, 0, 4, 0, 4, 0, 4, 0, 4, 0, 4, 0, 4, 0, 4, 0], [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0]]}, {'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 0, 0], [0, 0, 0, 0, 0, 0, 0, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 0, 0], [0, 0, 0, 0, 0, 0, 0, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 0, 0], [0, 0, 0, 0, 0, 0, 0, 7, 0, 7, 0, 7, 0, 7, 0, 7, 0, 7, 0, 0], [0, 0, 0, 0, 0, 0, 0, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 0, 0]]}], 'test_inputs': [[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0], [0, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0], [0, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8]]], 'test_outputs': [[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 5, 0, 5, 0, 5, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0], [0, 4, 0, 4, 0, 4, 0, 4, 0, 4, 0, 4, 0, 4, 0, 0, 0, 0, 0, 0], [0, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 8, 0, 8], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8]]]}) (input_keys={'test_inputs', 'training_examples'}): 'dict' object has no attribute 'dict'
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 68, in forward
File "<string>", line 68, in <listcomp>
AttributeError: 'dict' object has no attribute 'dict'
2025/08/28 21:34:34 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [8, 8, 8, 8, 8, 8, 8, 8, 6, 6, 6, 6, 8, 8, 8], [8, 8, 8, 8, 8, 8, 8, 8, 6, 8, 8, 6, 8, 8, 8], [8, 8, 8, 6, 6, 8, 8, 8, 6, 8, 8, 6, 8, 8, 8], [8, 8, 8, 6, 6, 8, 8, 8, 6, 8, 8, 6, 8, 8, 8], [8, 8, 8, 8, 8, 8, 8, 8, 6, 6, 6, 6, 8, 8, 8], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [8, 8, 8, 8, 8, 6, 6, 6, 6, 8, 8, 8, 8, 8, 8], [8, 8, 8, 8, 8, 6, 6, 6, 6, 8, 8, 8, 8, 8, 8], [8, 8, 8, 8, 8, 6, 6, 6, 6, 8, 8, 8, 8, 8, 8], [8, 8, 8, 8, 8, 6, 6, 6, 6, 8, 8, 8, 8, 8, 8], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8]], 'output': [[8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [8, 8, 8, 8, 8, 8, 8, 3, 3, 3, 3, 3, 3, 8, 8], [8, 8, 8, 8, 8, 8, 8, 3, 6, 6, 6, 6, 3, 8, 8], [8, 8, 3, 3, 3, 3, 8, 3, 6, 4, 4, 6, 3, 8, 8], [8, 8, 3, 6, 6, 3, 8, 3, 6, 4, 4, 6, 3, 8, 8], [8, 8, 3, 6, 6, 3, 8, 3, 6, 4, 4, 6, 3, 8, 8], [8, 8, 3, 3, 3, 3, 8, 3, 6, 6, 6, 6, 3, 8, 8], [8, 8, 8, 8, 8, 8, 8, 3, 3, 3, 3, 3, 3, 8, 8], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [8, 8, 8, 8, 3, 3, 3, 3, 3, 3, 8, 8, 8, 8, 8], [8, 8, 8, 8, 3, 6, 6, 6, 6, 3, 8, 8, 8, 8, 8], [8, 8, 8, 8, 3, 6, 6, 6, 6, 3, 8, 8, 8, 8, 8], [8, 8, 8, 8, 3, 6, 6, 6, 6, 3, 8, 8, 8, 8, 8], [8, 8, 8, 8, 3, 6, 6, 6, 6, 3, 8, 8, 8, 8, 8], [8, 8, 8, 8, 3, 3, 3, 3, 3, 3, 8, 8, 8, 8, 8]]}, {'input': [[8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [8, 8, 8, 8, 8, 8, 8, 8, 6, 6, 6, 8, 8, 8, 8], [8, 8, 8, 8, 8, 8, 8, 8, 6, 6, 6, 8, 8, 8, 8], [8, 8, 6, 6, 6, 6, 8, 8, 6, 6, 6, 8, 8, 8, 8], [8, 8, 6, 8, 6, 6, 8, 8, 8, 8, 8, 8, 8, 8, 8], [8, 8, 6, 8, 6, 6, 8, 8, 8, 8, 8, 8, 8, 8, 8], [8, 8, 6, 6, 6, 6, 8, 8, 8, 8, 8, 8, 8, 8, 8], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [8, 8, 8, 8, 8, 8, 8, 8, 6, 6, 6, 6, 6, 6, 8], [8, 8, 8, 8, 8, 8, 8, 8, 6, 8, 8, 8, 8, 6, 8], [8, 8, 8, 8, 8, 8, 8, 8, 6, 8, 8, 8, 8, 6, 8], [8, 8, 8, 8, 8, 8, 8, 8, 6, 8, 8, 8, 8, 6, 8], [8, 8, 8, 8, 8, 8, 8, 8, 6, 8, 8, 8, 8, 6, 8], [8, 8, 8, 8, 8, 8, 8, 8, 6, 6, 6, 6, 6, 6, 8], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8]], 'output': [[8, 8, 8, 8, 8, 8, 8, 3, 3, 3, 3, 3, 8, 8, 8], [8, 8, 8, 8, 8, 8, 8, 3, 6, 6, 6, 3, 8, 8, 8], [8, 3, 3, 3, 3, 3, 3, 3, 6, 6, 6, 3, 8, 8, 8], [8, 3, 6, 6, 6, 6, 3, 3, 6, 6, 6, 3, 8, 8, 8], [8, 3, 6, 4, 6, 6, 3, 3, 3, 3, 3, 3, 8, 8, 8], [8, 3, 6, 4, 6, 6, 3, 8, 8, 8, 8, 8, 8, 8, 8], [8, 3, 6, 6, 6, 6, 3, 8, 8, 8, 8, 8, 8, 8, 8], [8, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3], [8, 8, 8, 8, 8, 8, 8, 3, 6, 6, 6, 6, 6, 6, 3], [8, 8, 8, 8, 8, 8, 8, 3, 6, 4, 4, 4, 4, 6, 3], [8, 8, 8, 8, 8, 8, 8, 3, 6, 4, 4, 4, 4, 6, 3], [8, 8, 8, 8, 8, 8, 8, 3, 6, 4, 4, 4, 4, 6, 3], [8, 8, 8, 8, 8, 8, 8, 3, 6, 4, 4, 4, 4, 6, 3], [8, 8, 8, 8, 8, 8, 8, 3, 6, 6, 6, 6, 6, 6, 3], [8, 8, 8, 8, 8, 8, 8, 3, 3, 3, 3, 3, 3, 3, 3]]}], 'test_inputs': [[[8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [8, 8, 8, 8, 8, 8, 8, 8, 8, 6, 6, 6, 8, 8, 8], [8, 8, 6, 6, 6, 6, 8, 8, 8, 6, 6, 6, 8, 8, 8], [8, 8, 6, 8, 8, 6, 8, 8, 8, 6, 8, 6, 8, 8, 8], [8, 8, 6, 8, 8, 6, 8, 8, 8, 6, 8, 6, 8, 8, 8], [8, 8, 6, 6, 6, 6, 8, 8, 8, 6, 8, 6, 8, 8, 8], [8, 8, 8, 8, 8, 8, 8, 8, 8, 6, 6, 6, 8, 8, 8], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [8, 8, 8, 8, 6, 6, 6, 6, 6, 6, 6, 8, 8, 8, 8], [8, 8, 8, 8, 6, 6, 8, 8, 6, 6, 6, 8, 8, 8, 8], [8, 8, 8, 8, 6, 6, 6, 6, 6, 6, 6, 8, 8, 8, 8], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8]]], 'test_outputs': [[[8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [8, 8, 8, 8, 8, 8, 8, 8, 3, 3, 3, 3, 3, 8, 8], [8, 3, 3, 3, 3, 3, 3, 8, 3, 6, 6, 6, 3, 8, 8], [8, 3, 6, 6, 6, 6, 3, 8, 3, 6, 6, 6, 3, 8, 8], [8, 3, 6, 4, 4, 6, 3, 8, 3, 6, 4, 6, 3, 8, 8], [8, 3, 6, 4, 4, 6, 3, 8, 3, 6, 4, 6, 3, 8, 8], [8, 3, 6, 6, 6, 6, 3, 8, 3, 6, 4, 6, 3, 8, 8], [8, 3, 3, 3, 3, 3, 3, 8, 3, 6, 6, 6, 3, 8, 8], [8, 8, 8, 8, 8, 8, 8, 8, 3, 3, 3, 3, 3, 8, 8], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [8, 8, 8, 3, 3, 3, 3, 3, 3, 3, 3, 3, 8, 8, 8], [8, 8, 8, 3, 6, 6, 6, 6, 6, 6, 6, 3, 8, 8, 8], [8, 8, 8, 3, 6, 6, 4, 4, 6, 6, 6, 3, 8, 8, 8], [8, 8, 8, 3, 6, 6, 6, 6, 6, 6, 6, 3, 8, 8, 8], [8, 8, 8, 3, 3, 3, 3, 3, 3, 3, 3, 3, 8, 8, 8]]]}) (input_keys={'test_inputs', 'training_examples'}): 'dict' object has no attribute 'dict'
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 68, in forward
File "<string>", line 68, in <listcomp>
AttributeError: 'dict' object has no attribute 'dict'
2025/08/28 21:34:34 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[0, 0, 0, 1, 0, 0, 0, 5, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [2, 0, 0, 0, 0, 2, 0, 0, 0, 1], [0, 0, 1, 0, 0, 0, 0, 0, 0, 5], [0, 0, 8, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 4, 0, 0, 0, 0, 0, 0, 0, 0], [0, 5, 1, 0, 1, 0, 0, 0, 0, 0], [0, 8, 1, 0, 0, 0, 1, 0, 3, 0], [0, 0, 0, 0, 0, 0, 0, 3, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [2, 2, 2, 0, 0, 0, 0, 0, 0, 0], [2, 4, 2, 0, 0, 0, 0, 0, 0, 0], [2, 2, 2, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}, {'input': [[2, 7, 7, 1, 0, 3, 0, 0, 0, 3], [0, 0, 0, 9, 0, 0, 0, 0, 3, 7], [0, 0, 0, 1, 0, 0, 0, 6, 0, 9], [0, 0, 0, 0, 0, 0, 0, 1, 0, 0], [9, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 2, 0, 0, 0, 3, 0], [0, 5, 0, 7, 3, 0, 0, 0, 1, 0], [4, 4, 0, 0, 0, 1, 0, 0, 0, 5], [0, 0, 0, 0, 0, 0, 0, 5, 3, 0], [0, 0, 0, 0, 4, 5, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 2, 2, 2, 0], [0, 0, 0, 0, 0, 0, 2, 6, 2, 0], [0, 0, 0, 0, 0, 0, 2, 2, 2, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}, {'input': [[6, 0, 0, 0, 8, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 2, 8], [0, 7, 0, 0, 2, 0, 5, 0, 2, 0], [0, 9, 0, 1, 0, 0, 0, 0, 0, 0], [0, 9, 0, 0, 0, 0, 0, 0, 0, 1], [0, 0, 0, 0, 0, 6, 0, 0, 0, 0], [0, 1, 0, 7, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 3, 0, 0, 0], [0, 0, 5, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 2, 2, 2, 0, 0], [0, 0, 0, 0, 0, 2, 3, 2, 0, 0], [0, 0, 0, 0, 0, 2, 2, 2, 0, 0]]}], 'test_inputs': [[[0, 0, 0, 0, 2, 5, 7, 0, 0, 0], [0, 0, 0, 5, 6, 0, 2, 0, 0, 0], [0, 0, 0, 0, 0, 0, 3, 0, 0, 0], [0, 0, 8, 0, 3, 0, 0, 0, 0, 8], [7, 4, 7, 7, 4, 0, 0, 0, 0, 4], [0, 0, 0, 8, 0, 0, 7, 0, 0, 0], [0, 0, 0, 0, 0, 9, 0, 4, 0, 0], [5, 5, 0, 3, 0, 0, 6, 7, 0, 7], [0, 0, 3, 0, 0, 0, 0, 0, 0, 2], [1, 0, 1, 0, 0, 0, 0, 0, 6, 7]]], 'test_outputs': [[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 2, 2, 2, 0, 0, 0], [0, 0, 0, 0, 2, 9, 2, 0, 0, 0], [0, 0, 0, 0, 2, 2, 2, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]]}) (input_keys={'test_inputs', 'training_examples'}): 'dict' object has no attribute 'dict'
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 68, in forward
File "<string>", line 68, in <listcomp>
AttributeError: 'dict' object has no attribute 'dict'
2025/08/28 21:34:34 INFO dspy.evaluate.evaluate: Average Metric: 0.0 / 3 (0.0%)
Iteration 8: Proposed new text for program: import dspy
import pydantic
from typing import List
import json
import copy
# Define the type for a matrix for clarity
MATRIX = List[List[int]]
# Pydantic model for structuring training examples
class TrainingExample(pydantic.BaseModel):
input: MATRIX
output: MATRIX
# --- Signature for Step 1: Discovering the Transformation Rule ---
class RuleSignature(dspy.Signature):
"""
Analyze the provided input-output pairs of grids to determine the transformation rule.
Describe the rule in clear, unambiguous, step-by-step instructions.
**Successful Strategies:**
- Identify the role of different numbers (e.g., background color, foreground color).
- Look for geometric or spatial operations: framing, filling enclosed areas, moving objects, scaling, rotating.
- Look for logical or numerical operations: finding objects with unique properties (e.g., color frequency), applying patterns based on position (e.g., dashed lines in middle rows), conditional changes.
- Break down the transformation into a sequence of simple, independent steps.
**Pitfalls to Avoid:**
- Do not just describe the examples. Generalize the rule so it applies to new, unseen test cases.
- Avoid ambiguous language. Be precise about conditions, colors, and locations.
"""
training_examples: str = dspy.InputField(desc="A JSON string representing a list of input-output grid pairs.")
rule_description: str = dspy.OutputField(desc="A step-by-step description of the transformation rule.")
# --- Signature for Step 2: Generating Python Code from the Rule ---
class CodeGenerationSignature(dspy.Signature):
"""
Given a rule description and a sample input matrix, write a Python function to perform the transformation.
**Instructions:**
- Write a single Python function named `transform_matrix` that accepts one argument: `matrix` (a list of lists of integers).
- The function must return the transformed matrix (a new list of lists of integers).
- Use only standard Python libraries. `copy.deepcopy` is recommended for safely modifying the input.
- Do not include any code outside the function definition.
- Ensure the function is self-contained and implements the logic described in the rule.
"""
rule_description: str = dspy.InputField(desc="The step-by-step rule to implement.")
sample_input_matrix: str = dspy.InputField(desc="A sample input matrix to guide the implementation.")
python_code: str = dspy.OutputField(desc="A string containing only the Python function `transform_matrix`.")
# --- The Original Signature (used for Fallback) ---
class SolveTaskSignature(dspy.Signature):
"""Given a list of training examples (input/output pairs) and a list of test inputs, solve the task for the test inputs."""
training_examples: List[TrainingExample] = dspy.InputField(description="Input and output examples demonstrating the task to be performed.")
test_inputs: List[MATRIX] = dspy.InputField(description="Input matrices to be solved following the task described in the training examples.")
test_outputs: List[MATRIX] = dspy.OutputField(description="Output matrices corresponding to the test inputs.")
# --- The Improved Custom Module ---
class SolveWithCodeGeneration(dspy.Module):
"""A module that solves tasks by first inferring a rule, then generating and executing code."""
def __init__(self):
super().__init__()
self.get_rule = dspy.ChainOfThought(RuleSignature)
self.generate_code = dspy.Predict(CodeGenerationSignature, n=1)
self.fallback = dspy.ChainOfThought(SolveTaskSignature)
def forward(self, training_examples: List[TrainingExample], test_inputs: List[MATRIX]):
# Convert Pydantic objects to JSON strings for the LM
training_examples_str = json.dumps([ex.dict() for ex in training_examples])
try:
# Step 1: Infer the rule from the examples
rule = self.get_rule(training_examples=training_examples_str).rule_description
# Step 2: Generate Python code based on the rule
sample_input_str = json.dumps(test_inputs[0])
code_generation_result = self.generate_code(rule_description=rule, sample_input_matrix=sample_input_str)
python_code = code_generation_result.completions[0].python_code
# Clean up the generated code block
if python_code.startswith("```python"):
python_code = python_code[len("```python"):].strip()
if python_code.endswith("```"):
python_code = python_code[:-len("```")].strip()
# Step 3: Execute the generated code
local_scope = {}
exec(python_code, globals(), local_scope)
transform_func = local_scope.get('transform_matrix')
if not callable(transform_func):
raise ValueError("Generated code did not define a callable function 'transform_matrix'.")
# Apply the function to each test input
solved_outputs = []
for test_input in test_inputs:
# Use deepcopy to avoid modifying the original input
input_copy = copy.deepcopy(test_input)
solved_output = transform_func(input_copy)
solved_outputs.append(solved_output)
return dspy.Prediction(test_outputs=solved_outputs)
except Exception as e:
# Fallback to the original ChainOfThought method if any step fails
print(f"Code generation/execution failed: {e}. Using fallback.")
return self.fallback(training_examples=training_examples, test_inputs=test_inputs)
# Assign the improved module to the 'program' variable
program = SolveWithCodeGeneration()
Iteration 8: New subsample score is not better, skipping
Iteration 9: Selected program 0 score: 0.67
Average Metric: 1.00 / 3 (33.3%): 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 188.47it/s]2025/08/28 21:34:34 INFO dspy.evaluate.evaluate: Average Metric: 1.0 / 3 (33.3%)
Iteration 9: Proposed new text for program: import dspy
from typing import List
import pydantic
# Define the type for a matrix for clarity
MATRIX = List[List[int]]
class TrainingExample(pydantic.BaseModel):
"""A Pydantic model for a single training example, containing an input and output matrix."""
input: MATRIX
output: MATRIX
class InferRuleSignature(dspy.Signature):
"""
Analyzes pairs of input/output matrices to deduce the transformation rule.
You are an expert analyst of abstract visual reasoning puzzles. Your task is to carefully examine the provided training examples. Each example consists of an 'input' matrix and a corresponding 'output' matrix.
Your goal is to deduce the single, consistent transformation rule that converts each input to its output. Describe this rule in clear, unambiguous, step-by-step natural language. The description must be precise enough for another AI to follow it to solve a new, unseen test input.
Consider various possible transformation patterns, including but not limited to:
- Geometric operations: rotation, reflection, scaling, repetition, shifting.
- Color/value transformations: changing specific numbers to others, conditional changes.
- Object-based logic: identifying shapes or objects and applying rules based on their properties (size, color, position).
- Fill/completion patterns: such as flood fills or completing a grid based on object boundaries.
- Bounding box or frame logic: creating structures based on the extents of objects.
"""
training_examples: List[TrainingExample] = dspy.InputField(description="A list of input and output examples demonstrating the task.")
transformation_rule: str = dspy.OutputField(description="A step-by-step natural language description of the transformation rule.")
class ApplyRuleSignature(dspy.Signature):
"""
Applies a given transformation rule to a single test input matrix.
You are an expert in meticulously following instructions to transform matrices. You will be given a specific, detailed transformation rule and a single test input matrix.
Your task is to:
1. Carefully read and understand the provided rule.
2. Apply the rule step-by-step to the test input matrix.
3. Produce the final output matrix.
**Crucially, your output must be ONLY the resulting matrix, formatted as a valid JSON list of lists of integers. Do not include any extra text, explanations, or markdown formatting.**
"""
transformation_rule: str = dspy.InputField(description="The natural language rule to apply.")
test_input: MATRIX = dspy.InputField(description="The input matrix to be transformed.")
test_output: MATRIX = dspy.OutputField(description="The resulting matrix after applying the rule, as a list of lists of integers.")
class ARCProgram(dspy.Module):
"""A program that first infers a rule and then applies it to all test inputs."""
def __init__(self):
super().__init__()
# Use ChainOfThought for the complex reasoning task of rule inference.
self.rule_inferrer = dspy.ChainOfThought(InferRuleSignature)
# Use a simple Predict for the more direct task of applying the inferred rule.
self.rule_applier = dspy.Predict(ApplyRuleSignature)
def forward(self, training_examples: List[TrainingExample], test_inputs: List[MATRIX]) -> dspy.Prediction:
"""
Infers a rule from training examples and applies it to each test input.
Args:
training_examples: A list of TrainingExample objects.
test_inputs: A list of input matrices to be solved.
Returns:
A dspy.Prediction object with the 'test_outputs' field populated.
"""
# 1. Infer the transformation rule once based on all training examples.
inferred = self.rule_inferrer(training_examples=training_examples)
rule = inferred.transformation_rule
all_test_outputs = []
# 2. Iterate through each test input and apply the inferred rule.
for test_matrix in test_inputs:
try:
# Apply the rule to the current test matrix.
result = self.rule_applier(transformation_rule=rule, test_input=test_matrix)
all_test_outputs.append(result.test_output)
except Exception:
# Fallback strategy: if parsing or generation fails, append an empty matrix
# to maintain the correct number of outputs.
if test_matrix:
all_test_outputs.append([([0] * len(test_matrix[0])) for _ in range(len(test_matrix))])
else:
all_test_outputs.append([])
# 3. Return the collected outputs in a single Prediction object.
return dspy.Prediction(test_outputs=all_test_outputs)
# The final 'program' object is an instance of our robust, multi-step module.
program = ARCProgram()
2025/08/28 21:39:12 INFO dspy.evaluate.evaluate: Average Metric: 2.0 / 3 (66.7%)
2025/08/28 21:43:02 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/28 21:43:08 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/28 21:43:14 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/28 21:43:16 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/28 21:44:15 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/28 21:44:54 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/28 21:45:50 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/28 21:46:42 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/28 21:46:42 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/28 21:48:42 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/28 21:50:01 INFO dspy.evaluate.evaluate: Average Metric: 126.0 / 200 (63.0%)
GEPA Optimization: 11%|████████████▊ | 448/4000 [16:05<2:50:27, 2.88s/rollouts]Iteration 9: Full valset score for new program: 0.63
Iteration 9: Full train_val score for new program: 0.63
Iteration 9: Individual valset scores for new program: [True, True, False, False, True, False, True, True, True, False, True, True, True, False, True, True, False, True, True, False, True, False, True, True, False, True, False, False, True, True, True, False, False, True, True, True, False, True, False, False, False, True, True, True, False, True, False, False, True, True, True, True, True, True, True, False, True, True, False, True, True, True, False, True, False, False, True, True, False, False, True, True, True, False, True, True, False, False, True, False, False, True, True, True, True, True, True, True, False, False, True, True, True, True, True, True, True, False, False, True, True, True, False, False, True, True, True, True, True, True, False, False, True, False, True, False, True, False, True, True, False, True, True, False, False, True, False, True, True, True, False, True, True, True, True, False, True, False, True, True, True, True, True, False, True, False, False, False, True, True, False, False, True, True, True, True, False, True, True, False, True, True, False, False, False, True, True, True, True, False, False, True, False, True, True, True, False, True, False, True, False, True, True, False, False, True, True, True, True, False, True, True, True, True, True, False, True, False, False, False]
Iteration 9: New valset pareto front scores: [True, True, True, True, True, True, True, True, True, True, True, True, True, False, True, True, False, True, True, True, True, True, True, True, True, True, False, True, True, True, True, False, False, True, True, True, False, True, False, True, False, True, True, True, False, True, False, False, True, True, True, True, True, True, True, 0, True, True, False, True, True, True, False, True, False, False, True, True, 0, False, True, True, True, False, True, True, True, False, True, True, True, True, True, True, True, True, True, True, True, False, True, True, True, True, True, True, True, False, True, True, True, True, True, False, True, True, True, True, True, True, False, True, True, False, True, False, True, False, True, True, False, True, True, False, False, True, False, True, True, True, False, True, True, True, True, False, True, False, True, True, True, True, True, False, True, False, True, False, True, True, False, False, True, True, True, True, True, True, True, False, True, True, True, True, False, True, True, True, True, False, True, True, False, True, True, True, False, True, False, True, True, True, True, False, False, True, True, True, True, True, True, True, True, True, True, False, True, True, False, True]
Iteration 9: Full valset pareto front score: 0.755
Iteration 9: Updated valset pareto front programs: [{1}, {0, 1}, {0}, {0}, {0, 1}, {0}, {0, 1}, {0, 1}, {0, 1}, {0}, {0, 1}, {0, 1}, {0, 1}, {0, 1}, {0, 1}, {0, 1}, {0, 1}, {1}, {0, 1}, {0}, {0, 1}, {0}, {0, 1}, {1}, {0}, {0, 1}, {0, 1}, {0}, {0, 1}, {0, 1}, {1}, {0, 1}, {0, 1}, {0, 1}, {0, 1}, {0, 1}, {0, 1}, {0, 1}, {0, 1}, {0}, {0, 1}, {0, 1}, {0, 1}, {1}, {0, 1}, {0, 1}, {0, 1}, {0, 1}, {0, 1}, {0, 1}, {0, 1}, {0, 1}, {0, 1}, {0, 1}, {0, 1}, {0, 1}, {0, 1}, {0, 1}, {0, 1}, {0, 1}, {0, 1}, {0, 1}, {0, 1}, {0, 1}, {0, 1}, {0, 1}, {1}, {0, 1}, {0, 1}, {0, 1}, {0, 1}, {0, 1}, {0, 1}, {0, 1}, {0, 1}, {1}, {0}, {0, 1}, {0, 1}, {0}, {0}, {1}, {0, 1}, {0, 1}, {0, 1}, {0, 1}, {0, 1}, {0, 1}, {0}, {0, 1}, {0, 1}, {0, 1}, {0, 1}, {0, 1}, {0, 1}, {0, 1}, {1}, {0, 1}, {0}, {0, 1}, {0, 1}, {0, 1}, {0}, {0, 1}, {0, 1}, {0, 1}, {1}, {0, 1}, {1}, {0, 1}, {0, 1}, {0}, {1}, {0, 1}, {0, 1}, {0, 1}, {0, 1}, {0, 1}, {0, 1}, {0, 1}, {0, 1}, {0, 1}, {0, 1}, {0, 1}, {0, 1}, {0, 1}, {0, 1}, {0, 1}, {0, 1}, {0, 1}, {0, 1}, {0, 1}, {0, 1}, {0, 1}, {0, 1}, {0, 1}, {0, 1}, {0, 1}, {1}, {1}, {0, 1}, {0, 1}, {0, 1}, {0, 1}, {0, 1}, {0, 1}, {0}, {0, 1}, {0, 1}, {0, 1}, {0, 1}, {0, 1}, {0, 1}, {0, 1}, {0, 1}, {0, 1}, {0}, {0, 1}, {0, 1}, {0, 1}, {1}, {0, 1}, {0}, {0}, {0, 1}, {0, 1}, {0, 1}, {0, 1}, {0, 1}, {0, 1}, {0}, {0, 1}, {0, 1}, {0, 1}, {0, 1}, {1}, {0, 1}, {0, 1}, {0, 1}, {0, 1}, {0}, {0, 1}, {0, 1}, {0, 1}, {0, 1}, {0, 1}, {0, 1}, {0, 1}, {0, 1}, {0}, {0, 1}, {0, 1}, {0, 1}, {1}, {0, 1}, {0, 1}, {0, 1}, {0}, {0, 1}, {0}]
Iteration 9: Best valset aggregate score so far: 0.67
Iteration 9: Best program as per aggregate score on train_val: 0
Iteration 9: Best program as per aggregate score on valset: 0
Iteration 9: Best score on valset: 0.67
Iteration 9: Best score on train_val: 0.67
Iteration 9: Linear pareto front program index: 0
Iteration 9: New program candidate index: 1
Iteration 10: Selected program 0 score: 0.67
Average Metric: 1.00 / 3 (33.3%): 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 47.65it/s]2025/08/28 21:50:02 INFO dspy.evaluate.evaluate: Average Metric: 1.0 / 3 (33.3%)
2025/08/28 21:50:48 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[5, 0, 0, 5, 0, 0, 5, 5, 5, 5, 5, 5, 0, 0, 5, 5, 5, 5, 0, 0], [5, 0, 5, 5, 5, 5, 5, 5, 0, 0, 5, 5, 5, 5, 0, 5, 5, 0, 5, 5], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 5, 5], [0, 5, 5, 5, 5, 5, 0, 0, 0, 5, 0, 5, 5, 0, 5, 5, 0, 0, 0, 5], [5, 5, 5, 5, 5, 5, 0, 5, 0, 0, 5, 5, 5, 0, 0, 0, 5, 5, 0, 5], [0, 5, 0, 5, 0, 5, 0, 0, 5, 0, 5, 0, 5, 0, 5, 0, 5, 5, 5, 5], [5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 5, 0, 5, 5, 5, 0, 0, 0, 5], [0, 0, 0, 5, 5, 5, 0, 0, 0, 5, 5, 5, 0, 5, 0, 5, 0, 0, 0, 5], [5, 5, 0, 0, 5, 0, 0, 5, 5, 5, 5, 0, 0, 5, 0, 5, 0, 0, 0, 5], [0, 0, 5, 0, 0, 0, 5, 5, 0, 5, 5, 5, 5, 0, 5, 5, 5, 0, 5, 5], [5, 5, 5, 0, 5, 5, 5, 5, 5, 0, 0, 5, 0, 0, 5, 5, 5, 5, 5, 5], [5, 0, 5, 5, 5, 5, 5, 0, 5, 5, 5, 5, 0, 5, 0, 5, 5, 5, 0, 5], [5, 0, 0, 5, 5, 5, 5, 0, 0, 5, 5, 5, 0, 5, 5, 5, 5, 5, 5, 5], [5, 5, 0, 5, 5, 5, 5, 5, 5, 5, 5, 0, 5, 5, 5, 0, 5, 5, 0, 5], [0, 0, 5, 5, 5, 5, 0, 5, 5, 0, 5, 5, 5, 5, 0, 5, 5, 5, 0, 5], [5, 0, 0, 5, 0, 5, 0, 0, 0, 5, 5, 5, 0, 5, 0, 5, 5, 0, 5, 0], [0, 5, 0, 5, 0, 5, 5, 0, 0, 5, 0, 0, 5, 0, 5, 0, 0, 0, 5, 0], [5, 5, 5, 5, 5, 0, 5, 5, 5, 5, 5, 0, 0, 0, 5, 0, 5, 5, 0, 5], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 5, 5, 0, 5, 5], [5, 5, 5, 0, 0, 5, 5, 5, 5, 0, 5, 5, 0, 5, 0, 5, 0, 0, 0, 5]], 'output': [[5, 0, 0, 5, 0, 0, 5, 5, 5, 5, 5, 5, 0, 0, 5, 5, 5, 5, 0, 0], [5, 0, 5, 5, 5, 5, 5, 5, 0, 0, 5, 5, 5, 5, 0, 5, 5, 0, 5, 5], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 5, 5], [0, 5, 5, 5, 5, 5, 0, 0, 0, 5, 0, 5, 5, 0, 5, 5, 0, 0, 0, 5], [5, 5, 5, 5, 5, 5, 0, 5, 0, 0, 5, 5, 5, 0, 0, 0, 5, 5, 0, 5], [0, 5, 0, 5, 0, 5, 0, 0, 5, 0, 5, 0, 5, 0, 5, 0, 5, 5, 5, 5], [5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 5, 0, 5, 5, 5, 1, 1, 1, 5], [0, 0, 0, 5, 5, 5, 0, 0, 0, 5, 5, 5, 0, 5, 0, 5, 1, 1, 1, 5], [5, 5, 0, 0, 5, 0, 0, 5, 5, 5, 5, 0, 0, 5, 0, 5, 1, 1, 1, 5], [0, 0, 5, 0, 0, 0, 5, 5, 0, 5, 5, 5, 5, 0, 5, 5, 5, 0, 5, 5], [5, 5, 5, 0, 5, 5, 5, 5, 5, 0, 0, 5, 0, 0, 5, 5, 5, 5, 5, 5], [5, 0, 5, 5, 5, 5, 5, 0, 5, 5, 5, 5, 0, 5, 0, 5, 5, 5, 0, 5], [5, 0, 0, 5, 5, 5, 5, 0, 0, 5, 5, 5, 0, 5, 5, 5, 5, 5, 5, 5], [5, 5, 0, 5, 5, 5, 5, 5, 5, 5, 5, 0, 5, 5, 5, 0, 5, 5, 0, 5], [0, 0, 5, 5, 5, 5, 0, 5, 5, 0, 5, 5, 5, 5, 0, 5, 5, 5, 0, 5], [5, 0, 0, 5, 0, 5, 0, 0, 0, 5, 5, 5, 0, 5, 0, 5, 5, 0, 5, 0], [0, 5, 0, 5, 0, 5, 5, 0, 0, 5, 0, 0, 5, 0, 5, 0, 0, 0, 5, 0], [5, 5, 5, 5, 5, 0, 5, 5, 5, 5, 5, 0, 0, 0, 5, 0, 5, 5, 0, 5], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 5, 5, 0, 5, 5], [5, 5, 5, 0, 0, 5, 5, 5, 5, 0, 5, 5, 0, 5, 0, 5, 0, 0, 0, 5]]}, {'input': [[3, 3, 3, 3, 0, 3, 0, 3, 0, 3, 3, 0, 0, 3, 3, 3, 0, 3, 0, 0], [0, 0, 3, 3, 0, 0, 3, 0, 3, 3, 0, 3, 0, 3, 3, 0, 0, 3, 3, 0], [3, 3, 3, 3, 3, 0, 0, 3, 0, 0, 0, 3, 0, 3, 3, 0, 3, 3, 3, 3], [3, 0, 3, 3, 0, 0, 0, 0, 3, 0, 3, 3, 0, 3, 3, 3, 0, 3, 3, 0], [0, 0, 0, 3, 0, 3, 0, 3, 3, 3, 0, 3, 3, 3, 0, 3, 3, 3, 0, 0], [3, 3, 0, 0, 3, 3, 0, 3, 3, 3, 3, 0, 0, 3, 0, 3, 3, 3, 3, 0], [0, 3, 0, 0, 0, 0, 3, 3, 0, 3, 0, 0, 3, 0, 0, 0, 3, 0, 3, 0], [3, 0, 3, 0, 0, 0, 0, 0, 0, 3, 3, 3, 0, 3, 3, 3, 3, 3, 3, 3], [0, 3, 3, 0, 0, 0, 0, 3, 0, 3, 3, 0, 3, 3, 0, 0, 3, 3, 3, 3], [0, 0, 0, 3, 3, 0, 0, 3, 3, 3, 3, 3, 0, 3, 0, 3, 0, 3, 3, 3], [3, 0, 3, 3, 0, 3, 3, 3, 0, 0, 3, 0, 3, 0, 0, 0, 3, 3, 0, 3], [3, 0, 0, 3, 0, 0, 0, 3, 3, 3, 3, 0, 0, 3, 0, 3, 0, 3, 3, 3], [0, 3, 3, 0, 0, 0, 3, 3, 0, 3, 3, 3, 3, 0, 0, 3, 0, 0, 3, 3], [0, 0, 3, 0, 3, 3, 3, 3, 0, 0, 0, 3, 3, 3, 0, 0, 3, 0, 3, 0], [3, 0, 3, 3, 3, 0, 3, 3, 0, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 3], [0, 0, 3, 0, 3, 3, 0, 0, 3, 0, 3, 0, 3, 3, 0, 3, 3, 3, 0, 0], [3, 3, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 3, 0, 3, 0, 0, 0, 3, 3], [0, 3, 0, 3, 0, 0, 3, 3, 3, 0, 3, 3, 3, 0, 0, 3, 3, 0, 0, 0], [3, 0, 0, 3, 0, 3, 3, 0, 3, 0, 0, 3, 0, 0, 3, 3, 3, 3, 3, 3], [3, 0, 3, 3, 0, 3, 3, 3, 0, 0, 0, 3, 0, 3, 0, 3, 3, 3, 0, 3]], 'output': [[3, 3, 3, 3, 0, 3, 0, 3, 0, 3, 3, 0, 0, 3, 3, 3, 0, 3, 0, 0], [0, 0, 3, 3, 0, 0, 3, 0, 3, 3, 0, 3, 0, 3, 3, 0, 0, 3, 3, 0], [3, 3, 3, 3, 3, 0, 0, 3, 0, 0, 0, 3, 0, 3, 3, 0, 3, 3, 3, 3], [3, 0, 3, 3, 0, 0, 0, 0, 3, 0, 3, 3, 0, 3, 3, 3, 0, 3, 3, 0], [0, 0, 0, 3, 0, 3, 0, 3, 3, 3, 0, 3, 3, 3, 0, 3, 3, 3, 0, 0], [3, 3, 0, 0, 3, 3, 0, 3, 3, 3, 3, 0, 0, 3, 0, 3, 3, 3, 3, 0], [0, 3, 0, 1, 1, 1, 3, 3, 0, 3, 0, 0, 3, 0, 0, 0, 3, 0, 3, 0], [3, 0, 3, 1, 1, 1, 0, 0, 0, 3, 3, 3, 0, 3, 3, 3, 3, 3, 3, 3], [0, 3, 3, 1, 1, 1, 0, 3, 0, 3, 3, 0, 3, 3, 0, 0, 3, 3, 3, 3], [0, 0, 0, 3, 3, 0, 0, 3, 3, 3, 3, 3, 0, 3, 0, 3, 0, 3, 3, 3], [3, 0, 3, 3, 0, 3, 3, 3, 0, 0, 3, 0, 3, 0, 0, 0, 3, 3, 0, 3], [3, 0, 0, 3, 0, 0, 0, 3, 3, 3, 3, 0, 0, 3, 0, 3, 0, 3, 3, 3], [0, 3, 3, 0, 0, 0, 3, 3, 0, 3, 3, 3, 3, 0, 0, 3, 0, 0, 3, 3], [0, 0, 3, 0, 3, 3, 3, 3, 0, 0, 0, 3, 3, 3, 0, 0, 3, 0, 3, 0], [3, 0, 3, 3, 3, 0, 3, 3, 0, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 3], [0, 0, 3, 0, 3, 3, 0, 0, 3, 0, 3, 0, 3, 3, 0, 3, 3, 3, 0, 0], [3, 3, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 3, 0, 3, 0, 0, 0, 3, 3], [0, 3, 0, 3, 0, 0, 3, 3, 3, 0, 3, 3, 3, 0, 0, 3, 3, 0, 0, 0], [3, 0, 0, 3, 0, 3, 3, 0, 3, 0, 0, 3, 0, 0, 3, 3, 3, 3, 3, 3], [3, 0, 3, 3, 0, 3, 3, 3, 0, 0, 0, 3, 0, 3, 0, 3, 3, 3, 0, 3]]}, {'input': [[7, 0, 7, 7, 7, 7, 0, 7, 7, 0, 0, 7, 7, 0, 0, 7, 0, 7, 7, 7], [0, 0, 7, 0, 7, 0, 7, 0, 7, 7, 7, 0, 0, 0, 0, 7, 7, 0, 0, 7], [0, 0, 0, 0, 0, 7, 0, 0, 7, 7, 7, 7, 0, 7, 0, 0, 0, 0, 7, 0], [7, 0, 7, 0, 7, 0, 7, 7, 0, 0, 0, 7, 7, 0, 0, 7, 7, 0, 7, 0], [0, 0, 7, 0, 0, 7, 0, 0, 7, 0, 7, 7, 7, 7, 0, 0, 7, 0, 0, 7], [7, 7, 7, 7, 7, 7, 7, 7, 0, 7, 7, 0, 7, 7, 0, 0, 0, 7, 0, 7], [0, 0, 0, 7, 0, 7, 0, 0, 7, 7, 0, 7, 0, 7, 0, 0, 0, 0, 7, 7], [0, 7, 7, 7, 7, 0, 7, 0, 7, 0, 0, 7, 7, 7, 0, 0, 0, 0, 0, 7], [0, 0, 0, 7, 0, 0, 0, 0, 7, 7, 7, 0, 0, 7, 7, 0, 0, 0, 7, 7], [7, 7, 0, 7, 7, 7, 0, 7, 0, 0, 7, 0, 7, 7, 0, 7, 7, 0, 7, 0], [7, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 7, 0, 0, 0, 0, 7, 7, 0], [7, 7, 0, 0, 7, 7, 7, 0, 7, 7, 7, 7, 0, 7, 0, 0, 7, 7, 7, 7], [0, 7, 0, 7, 7, 7, 0, 0, 0, 7, 7, 0, 7, 7, 0, 7, 0, 0, 7, 7], [0, 0, 7, 7, 0, 7, 7, 7, 7, 7, 0, 7, 7, 0, 7, 7, 7, 0, 7, 7], [0, 0, 7, 7, 7, 0, 7, 0, 7, 7, 0, 7, 0, 7, 7, 7, 0, 7, 7, 7], [7, 0, 7, 7, 7, 0, 7, 0, 7, 7, 7, 7, 7, 0, 0, 7, 7, 7, 0, 0], [7, 7, 7, 0, 0, 0, 7, 7, 7, 0, 7, 7, 0, 7, 0, 7, 0, 0, 0, 0], [7, 7, 7, 0, 0, 0, 7, 0, 7, 7, 0, 7, 0, 0, 7, 0, 0, 0, 0, 0], [7, 0, 0, 0, 0, 0, 7, 7, 0, 7, 0, 0, 0, 7, 0, 7, 7, 7, 0, 7], [0, 7, 7, 0, 7, 7, 0, 7, 0, 0, 7, 7, 7, 7, 0, 0, 7, 0, 7, 7]], 'output': [[7, 0, 7, 7, 7, 7, 0, 7, 7, 0, 0, 7, 7, 0, 0, 7, 0, 7, 7, 7], [0, 0, 7, 0, 7, 0, 7, 0, 7, 7, 7, 0, 0, 0, 0, 7, 7, 0, 0, 7], [0, 0, 0, 0, 0, 7, 0, 0, 7, 7, 7, 7, 0, 7, 0, 0, 0, 0, 7, 0], [7, 0, 7, 0, 7, 0, 7, 7, 0, 0, 0, 7, 7, 0, 0, 7, 7, 0, 7, 0], [0, 0, 7, 0, 0, 7, 0, 0, 7, 0, 7, 7, 7, 7, 0, 0, 7, 0, 0, 7], [7, 7, 7, 7, 7, 7, 7, 7, 0, 7, 7, 0, 7, 7, 1, 1, 1, 7, 0, 7], [0, 0, 0, 7, 0, 7, 0, 0, 7, 7, 0, 7, 0, 7, 1, 1, 1, 0, 7, 7], [0, 7, 7, 7, 7, 0, 7, 0, 7, 0, 0, 7, 7, 7, 1, 1, 1, 0, 0, 7], [0, 0, 0, 7, 0, 0, 0, 0, 7, 7, 7, 0, 0, 7, 7, 0, 0, 0, 7, 7], [7, 7, 0, 7, 7, 7, 0, 7, 0, 0, 7, 0, 7, 7, 0, 7, 7, 0, 7, 0], [7, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 7, 0, 0, 0, 0, 7, 7, 0], [7, 7, 0, 0, 7, 7, 7, 0, 7, 7, 7, 7, 0, 7, 0, 0, 7, 7, 7, 7], [0, 7, 0, 7, 7, 7, 0, 0, 0, 7, 7, 0, 7, 7, 0, 7, 0, 0, 7, 7], [0, 0, 7, 7, 0, 7, 7, 7, 7, 7, 0, 7, 7, 0, 7, 7, 7, 0, 7, 7], [0, 0, 7, 7, 7, 0, 7, 0, 7, 7, 0, 7, 0, 7, 7, 7, 0, 7, 7, 7], [7, 0, 7, 7, 7, 0, 7, 0, 7, 7, 7, 7, 7, 0, 0, 7, 7, 7, 0, 0], [7, 7, 7, 1, 1, 1, 7, 7, 7, 0, 7, 7, 0, 7, 0, 7, 0, 0, 0, 0], [7, 7, 7, 1, 1, 1, 7, 0, 7, 7, 0, 7, 0, 0, 7, 0, 0, 0, 0, 0], [7, 0, 0, 1, 1, 1, 7, 7, 0, 7, 0, 0, 0, 7, 0, 7, 7, 7, 0, 7], [0, 7, 7, 0, 7, 7, 0, 7, 0, 0, 7, 7, 7, 7, 0, 0, 7, 0, 7, 7]]}], 'test_inputs': [[[0, 4, 0, 4, 4, 0, 4, 4, 4, 0, 0, 0, 4, 0, 4, 4, 4, 4, 4, 0], [0, 0, 4, 4, 0, 0, 4, 0, 4, 4, 0, 0, 0, 0, 4, 4, 4, 4, 4, 0], [4, 4, 4, 0, 0, 4, 0, 4, 0, 4, 0, 4, 4, 4, 4, 4, 4, 0, 4, 0], [4, 4, 0, 4, 0, 0, 4, 0, 0, 0, 0, 0, 0, 4, 4, 4, 0, 4, 0, 0], [4, 0, 0, 4, 4, 0, 4, 4, 4, 4, 4, 4, 4, 0, 4, 4, 0, 4, 0, 4], [4, 4, 0, 0, 4, 0, 0, 4, 4, 4, 4, 4, 4, 0, 0, 4, 4, 0, 4, 0], [0, 0, 0, 4, 0, 0, 0, 0, 4, 4, 4, 4, 4, 0, 4, 0, 4, 4, 0, 4], [4, 0, 4, 4, 0, 0, 0, 4, 4, 0, 0, 0, 0, 4, 4, 0, 0, 0, 0, 0], [0, 4, 4, 4, 0, 0, 0, 4, 4, 4, 0, 0, 4, 0, 4, 4, 4, 0, 0, 0], [4, 0, 0, 0, 4, 4, 0, 0, 4, 0, 0, 4, 0, 4, 4, 4, 0, 4, 0, 4], [0, 0, 0, 4, 0, 4, 0, 4, 4, 4, 0, 0, 4, 0, 4, 4, 4, 0, 4, 4], [0, 4, 4, 0, 0, 4, 4, 4, 4, 0, 0, 0, 4, 4, 4, 4, 4, 0, 4, 0], [0, 0, 4, 0, 0, 4, 0, 0, 4, 0, 0, 4, 0, 4, 4, 0, 0, 0, 4, 4], [4, 0, 4, 4, 0, 4, 0, 0, 4, 4, 4, 0, 0, 0, 0, 4, 4, 4, 0, 0], [0, 4, 4, 4, 4, 0, 0, 4, 0, 4, 0, 0, 4, 4, 0, 4, 4, 4, 4, 4], [4, 0, 0, 4, 4, 0, 4, 0, 4, 0, 0, 4, 0, 4, 0, 4, 0, 4, 0, 0], [4, 4, 0, 4, 0, 4, 0, 4, 4, 0, 0, 4, 4, 4, 0, 0, 0, 0, 4, 4], [4, 0, 0, 0, 0, 4, 4, 0, 4, 4, 0, 4, 0, 4, 0, 0, 0, 4, 4, 4], [0, 0, 0, 0, 0, 4, 4, 4, 4, 0, 4, 0, 0, 4, 0, 0, 0, 0, 0, 0], [4, 4, 0, 0, 0, 0, 0, 4, 4, 0, 0, 0, 4, 0, 4, 0, 4, 0, 4, 4]]], 'test_outputs': [[[0, 4, 0, 4, 4, 0, 4, 4, 4, 0, 0, 0, 4, 0, 4, 4, 4, 4, 4, 0], [0, 0, 4, 4, 0, 0, 4, 0, 4, 4, 0, 0, 0, 0, 4, 4, 4, 4, 4, 0], [4, 4, 4, 0, 0, 4, 0, 4, 0, 4, 0, 4, 4, 4, 4, 4, 4, 0, 4, 0], [4, 4, 0, 4, 0, 0, 4, 0, 0, 0, 0, 0, 0, 4, 4, 4, 0, 4, 0, 0], [4, 0, 0, 4, 4, 0, 4, 4, 4, 4, 4, 4, 4, 0, 4, 4, 0, 4, 0, 4], [4, 4, 0, 0, 4, 0, 0, 4, 4, 4, 4, 4, 4, 0, 0, 4, 4, 0, 4, 0], [0, 0, 0, 4, 1, 1, 1, 0, 4, 4, 4, 4, 4, 0, 4, 0, 4, 4, 0, 4], [4, 0, 4, 4, 1, 1, 1, 4, 4, 0, 0, 0, 0, 4, 4, 0, 0, 0, 0, 0], [0, 4, 4, 4, 1, 1, 1, 4, 4, 4, 0, 0, 4, 0, 4, 4, 4, 0, 0, 0], [4, 0, 0, 0, 4, 4, 0, 0, 4, 0, 0, 4, 0, 4, 4, 4, 0, 4, 0, 4], [0, 0, 0, 4, 0, 4, 0, 4, 4, 4, 0, 0, 4, 0, 4, 4, 4, 0, 4, 4], [0, 4, 4, 0, 0, 4, 4, 4, 4, 0, 0, 0, 4, 4, 4, 4, 4, 0, 4, 0], [0, 0, 4, 0, 0, 4, 0, 0, 4, 0, 0, 4, 0, 4, 4, 0, 0, 0, 4, 4], [4, 0, 4, 4, 0, 4, 0, 0, 4, 4, 4, 0, 0, 0, 0, 4, 4, 4, 0, 0], [0, 4, 4, 4, 4, 0, 0, 4, 0, 4, 0, 0, 4, 4, 0, 4, 4, 4, 4, 4], [4, 0, 0, 4, 4, 0, 4, 0, 4, 0, 0, 4, 0, 4, 0, 4, 0, 4, 0, 0], [4, 4, 0, 4, 0, 4, 0, 4, 4, 0, 0, 4, 4, 4, 1, 1, 1, 0, 4, 4], [4, 0, 1, 1, 1, 4, 4, 0, 4, 4, 0, 4, 0, 4, 1, 1, 1, 4, 4, 4], [0, 0, 1, 1, 1, 4, 4, 4, 4, 0, 4, 0, 0, 4, 1, 1, 1, 0, 0, 0], [4, 4, 1, 1, 1, 0, 0, 4, 4, 0, 0, 0, 4, 0, 4, 0, 4, 0, 4, 4]]]}) (input_keys={'test_inputs', 'training_examples'}): 'dict' object has no attribute 'model_dump'
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 56, in forward
File "<string>", line 56, in <listcomp>
AttributeError: 'dict' object has no attribute 'model_dump'
2025/08/28 21:50:48 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[0, 0, 0, 0, 1, 1, 4, 0, 2, 0, 0, 0, 0, 2, 0, 5], [0, 0, 0, 3, 5, 0, 0, 0, 9, 9, 8, 0, 4, 0, 5, 8], [1, 0, 8, 2, 8, 0, 0, 6, 0, 8, 5, 0, 0, 0, 8, 0], [0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0], [0, 0, 1, 2, 2, 2, 0, 0, 1, 9, 5, 0, 0, 2, 0, 4], [0, 4, 0, 2, 2, 2, 0, 2, 0, 0, 7, 0, 0, 0, 0, 0], [3, 0, 6, 2, 2, 2, 0, 0, 0, 3, 5, 0, 7, 0, 0, 0], [7, 0, 4, 6, 0, 0, 4, 7, 7, 3, 0, 2, 0, 0, 7, 1], [0, 7, 0, 0, 0, 0, 0, 9, 7, 7, 0, 0, 0, 8, 5, 2], [1, 5, 6, 4, 9, 3, 0, 3, 0, 0, 0, 0, 0, 9, 4, 6], [0, 2, 4, 0, 0, 0, 0, 0, 0, 0, 2, 0, 1, 6, 0, 0], [0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 4], [0, 0, 6, 0, 0, 0, 0, 0, 6, 0, 0, 2, 0, 0, 0, 0], [0, 3, 0, 0, 7, 0, 2, 0, 7, 9, 0, 0, 0, 0, 0, 0], [0, 0, 5, 0, 7, 0, 0, 0, 0, 0, 0, 0, 6, 5, 3, 0], [1, 0, 0, 9, 0, 0, 0, 2, 0, 0, 0, 1, 0, 0, 9, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}, {'input': [[0, 0, 7, 0, 0, 6, 0, 6, 0, 0, 0, 7, 3, 0, 0, 0], [0, 0, 3, 0, 0, 1, 0, 0, 8, 0, 0, 2, 0, 0, 0, 0], [0, 0, 0, 0, 3, 9, 0, 0, 0, 0, 0, 0, 0, 8, 0, 8], [2, 2, 0, 2, 9, 0, 0, 0, 0, 1, 0, 2, 0, 0, 0, 0], [0, 5, 2, 0, 0, 7, 0, 6, 0, 0, 0, 3, 0, 0, 1, 0], [4, 4, 0, 3, 9, 0, 0, 0, 0, 7, 0, 2, 0, 0, 0, 0], [8, 0, 0, 0, 0, 6, 0, 0, 0, 8, 0, 0, 3, 0, 0, 0], [0, 9, 0, 0, 0, 4, 8, 0, 0, 0, 7, 0, 0, 0, 0, 0], [0, 0, 9, 5, 0, 0, 0, 0, 4, 6, 0, 1, 4, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 3, 1, 0, 8, 0, 5, 9, 4], [0, 9, 3, 9, 0, 3, 0, 0, 5, 6, 7, 0, 5, 0, 0, 0], [0, 0, 6, 6, 6, 6, 6, 6, 6, 0, 0, 0, 0, 7, 0, 0], [0, 4, 6, 6, 6, 6, 6, 6, 6, 0, 0, 4, 4, 6, 0, 2], [0, 5, 0, 0, 0, 0, 4, 5, 3, 0, 8, 0, 0, 0, 6, 9], [0, 0, 9, 7, 5, 0, 0, 0, 0, 0, 0, 0, 1, 0, 7, 1], [0, 8, 0, 0, 0, 0, 0, 1, 0, 3, 0, 0, 3, 8, 7, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 6, 6, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0], [0, 0, 6, 6, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}, {'input': [[3, 0, 0, 0, 0, 0, 6, 2, 0, 0, 0, 5, 0, 0, 0, 3], [0, 7, 0, 0, 0, 0, 9, 0, 0, 0, 0, 0, 0, 0, 5, 0], [0, 0, 0, 0, 0, 8, 8, 0, 7, 7, 7, 0, 0, 0, 0, 4], [0, 2, 0, 0, 0, 0, 0, 0, 7, 7, 7, 0, 2, 0, 5, 0], [0, 8, 0, 0, 9, 6, 1, 7, 7, 7, 7, 0, 0, 0, 0, 0], [5, 0, 0, 0, 0, 3, 6, 0, 6, 0, 0, 3, 3, 0, 0, 0], [0, 4, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0], [9, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 8, 0, 0, 0, 0], [0, 0, 3, 0, 0, 0, 0, 6, 0, 9, 0, 0, 0, 0, 0, 0], [9, 0, 0, 0, 1, 0, 0, 3, 0, 8, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 0, 0, 7, 0], [0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 5, 0, 0], [4, 0, 0, 1, 7, 0, 3, 0, 0, 7, 5, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 7, 2, 0, 0, 5, 0, 0, 1, 0, 4], [0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 2, 0, 0, 0, 0, 0], [0, 2, 0, 0, 0, 7, 9, 0, 0, 0, 5, 0, 2, 0, 3, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 7, 7, 7, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 7, 7, 7, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 7, 7, 7, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}], 'test_inputs': [[[0, 0, 1, 7, 3, 0, 0, 0, 0, 0, 1, 2, 0, 4, 7, 0], [0, 0, 0, 3, 0, 0, 6, 8, 0, 0, 0, 0, 0, 0, 0, 0], [6, 0, 0, 8, 0, 1, 0, 0, 1, 0, 0, 0, 7, 0, 4, 8], [0, 3, 8, 0, 0, 0, 3, 0, 8, 0, 0, 0, 0, 0, 0, 0], [5, 0, 0, 0, 1, 0, 0, 8, 0, 0, 3, 8, 0, 0, 5, 0], [0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 3, 7, 0, 0, 0, 0], [0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 5, 0, 7], [0, 0, 0, 0, 0, 0, 0, 9, 0, 0, 2, 7, 0, 7, 0, 0], [9, 4, 0, 2, 1, 0, 0, 0, 0, 0, 7, 0, 0, 0, 9, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 5], [0, 8, 9, 4, 0, 5, 5, 5, 5, 5, 5, 3, 0, 0, 0, 0], [0, 0, 3, 0, 6, 5, 5, 5, 5, 5, 5, 0, 1, 4, 0, 0], [9, 5, 2, 0, 0, 5, 1, 3, 0, 0, 6, 2, 0, 0, 1, 5], [0, 7, 0, 0, 0, 0, 1, 6, 0, 7, 0, 3, 0, 6, 0, 0], [0, 0, 9, 0, 0, 3, 7, 7, 0, 6, 0, 0, 8, 0, 0, 0], [5, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 9]]], 'test_outputs': [[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]]}) (input_keys={'test_inputs', 'training_examples'}): 'dict' object has no attribute 'model_dump'
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 56, in forward
File "<string>", line 56, in <listcomp>
AttributeError: 'dict' object has no attribute 'model_dump'
2025/08/28 21:50:48 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[1, 4, 0, 1, 1, 0, 1, 4], [1, 1, 0, 1, 1, 0, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 0, 1, 1, 0, 1, 1], [2, 1, 0, 1, 1, 0, 2, 1], [0, 0, 0, 0, 0, 0, 0, 0], [1, 4, 0, 1, 1, 0, 1, 1], [1, 1, 0, 1, 1, 0, 1, 1]], 'output': [[1, 4, 0, 1, 4, 0, 1, 4], [1, 1, 0, 1, 1, 0, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0], [1, 4, 0, 1, 1, 0, 1, 1], [2, 1, 0, 2, 1, 0, 2, 1], [0, 0, 0, 0, 0, 0, 0, 0], [1, 4, 0, 1, 1, 0, 1, 1], [1, 1, 0, 1, 1, 0, 1, 1]]}, {'input': [[1, 1, 0, 7, 1, 0, 1, 1], [1, 3, 0, 1, 1, 0, 1, 3], [0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 0, 1, 1, 0, 1, 1], [1, 1, 0, 1, 1, 0, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 0, 7, 1, 0, 1, 1], [1, 1, 0, 1, 1, 0, 1, 3]], 'output': [[1, 1, 0, 7, 1, 0, 1, 1], [1, 3, 0, 1, 3, 0, 1, 3], [0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 0, 7, 1, 0, 1, 1], [1, 1, 0, 1, 1, 0, 1, 3], [0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 0, 7, 1, 0, 1, 1], [1, 1, 0, 1, 1, 0, 1, 3]]}, {'input': [[1, 1, 0, 1, 1, 0, 1, 1], [1, 1, 0, 1, 1, 0, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 0, 1, 1, 0, 1, 1], [3, 1, 0, 1, 1, 0, 3, 1], [0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 0, 1, 1, 0, 1, 1], [1, 1, 0, 1, 1, 0, 1, 1]], 'output': [[1, 1, 0, 1, 1, 0, 1, 1], [1, 1, 0, 1, 1, 0, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 0, 1, 1, 0, 1, 1], [3, 1, 0, 3, 1, 0, 3, 1], [0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 0, 1, 1, 0, 1, 1], [1, 1, 0, 1, 1, 0, 1, 1]]}], 'test_inputs': [[[1, 1, 0, 1, 1, 0, 1, 1], [1, 8, 0, 1, 1, 0, 1, 8], [0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 0, 1, 1, 0, 1, 1], [1, 1, 0, 1, 1, 0, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0], [6, 1, 0, 1, 1, 0, 6, 1], [1, 1, 0, 1, 1, 0, 1, 8]]], 'test_outputs': [[[1, 1, 0, 1, 1, 0, 1, 1], [1, 8, 0, 1, 8, 0, 1, 8], [0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 0, 1, 1, 0, 1, 1], [1, 1, 0, 1, 1, 0, 1, 8], [0, 0, 0, 0, 0, 0, 0, 0], [6, 1, 0, 6, 1, 0, 6, 1], [1, 1, 0, 1, 1, 0, 1, 8]]]}) (input_keys={'test_inputs', 'training_examples'}): 'dict' object has no attribute 'model_dump'
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 56, in forward
File "<string>", line 56, in <listcomp>
AttributeError: 'dict' object has no attribute 'model_dump'
2025/08/28 21:50:48 INFO dspy.evaluate.evaluate: Average Metric: 0.0 / 3 (0.0%)
GEPA Optimization: 11%|████████████▉ | 454/4000 [16:52<2:57:23, 3.00s/rollouts]Iteration 10: Proposed new text for program: import dspy
from typing import List
import pydantic
import json
import copy
# Define the type for a matrix, which is a list of lists of integers.
MATRIX = List[List[int]]
# Define a Pydantic model for a single training example to ensure data consistency.
class TrainingExample(pydantic.BaseModel):
input: MATRIX
output: MATRIX
# This is the overall signature for the task, defining the final inputs and outputs.
# While not used directly by the custom module's internal steps, it represents the contract of the final 'program'.
class SolveTaskSignature(dspy.Signature):
"""Given training examples of a matrix transformation, apply the same transformation to the test inputs."""
training_examples: List[TrainingExample] = dspy.InputField(description="Input and output examples demonstrating the task to be performed.")
test_inputs: List[MATRIX] = dspy.InputField(description="Input matrices to be solved following the task described in the training examples.")
test_outputs: List[MATRIX] = dspy.OutputField(description="Output matrices corresponding to the test inputs.")
# A specialized signature for the first step: deducing the rule and generating code.
class DeduceRuleAndGenerateCodeSignature(dspy.Signature):
"""
Analyze the provided training examples, each consisting of an input and an output matrix, to deduce the transformation rule.
First, in the 'rule_description' field, describe the transformation rule in clear, step-by-step natural language.
Second, in the 'python_code' field, implement this rule as a single, self-contained Python function named 'transform_matrix'.
This function MUST:
- Be named exactly 'transform_matrix'.
- Accept one argument: 'matrix' (a list of lists of integers).
- Return the transformed matrix (a list of lists of integers).
- Not rely on any external libraries like numpy or pandas. Standard Python libraries are allowed.
**Successful Strategies to Consider:**
- Look for patterns related to colors, shapes, positions, counts, symmetry, and object manipulation (e.g., moving, copying, deleting objects).
- The rule must be general and consistently explain the transformation for ALL provided training examples.
- Ensure the Python code is robust and correctly handles matrix dimensions and edge cases.
"""
training_examples: str = dspy.InputField(desc="A JSON string representing a list of input-output matrix pairs.")
rule_description: str = dspy.OutputField(desc="A natural language description of the transformation rule.")
python_code: str = dspy.OutputField(desc="A self-contained Python function 'transform_matrix' that implements the rule.")
# Custom DSPy module that orchestrates the two-step process.
class SolveByGeneratingCode(dspy.Module):
"""A module that solves tasks by first generating Python code for the rule, then executing it."""
def __init__(self):
super().__init__()
# Use ChainOfThought for the complex reasoning task of rule deduction and code generation.
self.rule_deducer = dspy.ChainOfThought(DeduceRuleAndGenerateCodeSignature)
def forward(self, training_examples: List[TrainingExample], test_inputs: List[MATRIX]):
# Convert the structured training examples into a JSON string for the LM.
training_examples_dict = [ex.model_dump() for ex in training_examples]
training_examples_str = json.dumps(training_examples_dict, indent=2)
# Step 1: Call the LM to deduce the rule and generate the Python function.
prediction = self.rule_deducer(training_examples=training_examples_str)
python_code = prediction.python_code
# Prepare a namespace for safely executing the generated code.
local_namespace = {}
# Step 2: Execute the generated Python code to define the 'transform_matrix' function.
try:
# LMs often wrap code in markdown backticks; this removes them.
if python_code.strip().startswith("```python"):
python_code = python_code.strip()[len("```python"):].strip()
if python_code.strip().endswith("```"):
python_code = python_code.strip()[:-len("```")].strip()
exec(python_code, globals(), local_namespace)
transform_function = local_namespace['transform_matrix']
except Exception as e:
print(f"Error executing generated code: {e}. Falling back to returning original inputs.")
# Fallback strategy: If code generation or execution fails, return the original test inputs.
return dspy.Prediction(test_outputs=test_inputs)
# Step 3: Apply the successfully defined function to each test input.
final_outputs = []
for test_matrix in test_inputs:
try:
# Use a deep copy to prevent the function from modifying the original input list.
input_copy = copy.deepcopy(test_matrix)
transformed_matrix = transform_function(input_copy)
final_outputs.append(transformed_matrix)
except Exception as e:
print(f"Error applying 'transform_matrix' to a test case: {e}. Falling back for this case.")
# Fallback for a single failing test case: return the original input for that case.
final_outputs.append(test_matrix)
return dspy.Prediction(test_outputs=final_outputs)
# Assign the improved custom module to the 'program' variable.
program = SolveByGeneratingCode()
Iteration 10: New subsample score is not better, skipping
Iteration 11: Selected program 1 score: 0.63
Average Metric: 1.00 / 3 (33.3%): : 4it [05:18, 79.68s/it] 2025/08/28 21:56:07 INFO dspy.evaluate.evaluate: Average Metric: 1.0 / 3 (33.3%)
2025/08/28 21:56:28 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
Iteration 11: Proposed new text for program: import dspy
from typing import List
import pydantic
import copy
import traceback
# Define the type for a matrix for clarity
MATRIX = List[List[int]]
class TrainingExample(pydantic.BaseModel):
"""A Pydantic model for a single training example, containing an input and output matrix."""
input: MATRIX
output: MATRIX
class InferCodeSignature(dspy.Signature):
"""
Analyzes pairs of input/output matrices to deduce the transformation logic and write a Python function to implement it.
You are an expert programmer and analyst of abstract visual reasoning puzzles. Your task is to carefully examine the provided training examples. Each example consists of an 'input' matrix and a corresponding 'output' matrix.
Your goal is to write a single, standalone Python function named `transform_matrix` that takes one argument, `matrix` (a 2D list of integers), and returns the transformed matrix. This function must implement the logic that correctly converts each input example to its corresponding output example.
**Function Requirements:**
- The function signature MUST be `def transform_matrix(matrix):`.
- It must return a new 2D list of integers.
- Do NOT modify the input `matrix` in place. Use `copy.deepcopy` if necessary.
- The function should not rely on any external libraries other than `copy`.
**Your output MUST be ONLY the Python code for the function. Do not include any explanations, example usage, or markdown formatting like ```python ... ```.**
**Analysis Strategies:**
- Consider geometric operations: rotation, reflection, scaling, repetition, shifting.
- Look for color/value transformations: changing specific numbers, conditional changes based on neighbors.
- Identify object-based logic: find shapes or objects and apply rules based on their properties (size, color, position).
- Think about fill/completion patterns: such as flood fills or propagating colors downwards or sideways.
- Consider rules based on the grid's properties, like its dimensions.
"""
training_examples: List[TrainingExample] = dspy.InputField(description="A list of input and output examples demonstrating the task.")
python_code: str = dspy.OutputField(description="A string containing ONLY the Python code for the `transform_matrix` function.")
class ARCProgram(dspy.Module):
"""A program that infers a transformation function in Python and then executes it."""
def __init__(self):
super().__init__()
# Use ChainOfThought for the complex reasoning task of code inference.
self.code_inferrer = dspy.ChainOfThought(InferCodeSignature)
def forward(self, training_examples: List[TrainingExample], test_inputs: List[MATRIX]) -> dspy.Prediction:
"""
Infers a Python function from training examples and applies it to each test input.
Args:
training_examples: A list of TrainingExample objects.
test_inputs: A list of input matrices to be solved.
Returns:
A dspy.Prediction object with the 'test_outputs' field populated.
"""
# 1. Infer the transformation logic as a Python function.
inferred = self.code_inferrer(training_examples=training_examples)
python_code = inferred.python_code
# Prepare a safe execution environment for the generated code.
# The generated function will be defined in this dictionary.
execution_scope = {"copy": copy}
transform_func = None
try:
# 2. Execute the generated code string to define the function.
exec(python_code, execution_scope)
transform_func = execution_scope.get('transform_matrix')
except Exception:
print("Failed to execute or define 'transform_matrix' function.")
traceback.print_exc()
all_test_outputs = []
# 3. Iterate through each test input and apply the function.
for test_matrix in test_inputs:
# Fallback strategy: if function is invalid or fails, return a correctly sized empty grid.
if not callable(transform_func):
if test_matrix and test_matrix[0]:
all_test_outputs.append([([0] * len(test_matrix[0])) for _ in range(len(test_matrix))])
else:
all_test_outputs.append([])
continue
try:
# Apply the inferred function to the current test matrix.
# We pass a deepcopy to ensure the original test input is not modified.
output_matrix = transform_func(copy.deepcopy(test_matrix))
all_test_outputs.append(output_matrix)
except Exception:
# Fallback for runtime errors within the generated function.
print(f"Function 'transform_matrix' failed on a test input.")
traceback.print_exc()
if test_matrix and test_matrix[0]:
all_test_outputs.append([([0] * len(test_matrix[0])) for _ in range(len(test_matrix))])
else:
all_test_outputs.append([])
# 4. Return the collected outputs in a single Prediction object.
return dspy.Prediction(test_outputs=all_test_outputs)
# The final 'program' object is an instance of our robust, code-generating module.
program = ARCProgram()
Traceback (most recent call last):
File "<string>", line 70, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
2025/08/28 22:02:09 INFO dspy.evaluate.evaluate: Average Metric: 1.0 / 3 (33.3%)
GEPA Optimization: 12%|█████████████ | 460/4000 [28:13<6:41:25, 6.80s/rollouts]Failed to execute or define 'transform_matrix' function.
Iteration 11: New subsample score is not better, skipping
Iteration 12: Selected program 1 score: 0.63
Average Metric: 3.00 / 3 (100.0%): 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [01:07<00:00, 22.46s/it]2025/08/28 22:03:17 INFO dspy.evaluate.evaluate: Average Metric: 3.0 / 3 (100.0%)
GEPA Optimization: 12%|█████████████▏ | 463/4000 [29:20<7:03:13, 7.18s/rollouts]
Iteration 12: All subsample scores perfect. Skipping.
Iteration 12: Reflective mutation did not propose a new candidate
Iteration 13: Selected program 0 score: 0.67
0%| | 0/3 [00:00<?, ?it/s]2025/08/28 22:03:17 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
Average Metric: 1.00 / 3 (33.3%): 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 58.84it/s]2025/08/28 22:03:17 INFO dspy.evaluate.evaluate: Average Metric: 1.0 / 3 (33.3%)
2025/08/28 22:03:56 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 1, 1], [0, 0, 0, 5, 0, 0, 0, 1, 1, 0], [0, 0, 0, 1, 0, 0, 0, 0, 1, 0], [0, 0, 1, 1, 1, 0, 0, 0, 0, 0], [0, 0, 0, 1, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 1, 0, 0], [0, 0, 0, 0, 0, 1, 1, 1, 0, 0], [0, 0, 0, 0, 0, 0, 1, 1, 0, 0]], 'output': [[0, 1, 0], [1, 1, 1], [0, 1, 1]]}, {'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 5, 0, 0], [0, 0, 0, 0, 0, 0, 4, 4, 0, 0], [0, 0, 4, 0, 0, 0, 0, 0, 4, 0], [0, 4, 0, 4, 0, 0, 0, 4, 0, 0], [0, 0, 4, 4, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[4, 4, 0], [0, 0, 4], [0, 4, 0]]}, {'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 2, 2, 0, 0, 0, 0, 0, 0], [0, 2, 0, 2, 0, 0, 0, 0, 0, 0], [0, 0, 2, 0, 0, 0, 0, 5, 0, 0], [0, 0, 0, 0, 0, 0, 0, 2, 2, 0], [0, 0, 0, 0, 0, 0, 2, 2, 0, 0], [0, 0, 0, 0, 0, 0, 0, 2, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 2, 2], [2, 2, 0], [0, 2, 0]]}], 'test_inputs': [[[0, 0, 0, 0, 0, 0, 5, 0, 0, 0], [0, 0, 0, 0, 0, 0, 3, 0, 0, 0], [0, 0, 0, 0, 0, 3, 3, 0, 0, 0], [0, 0, 0, 0, 0, 0, 3, 3, 0, 0], [0, 0, 3, 0, 0, 0, 0, 0, 0, 0], [0, 3, 3, 0, 0, 0, 0, 0, 0, 0], [0, 0, 3, 0, 0, 0, 3, 0, 0, 0], [0, 0, 0, 0, 0, 3, 3, 3, 0, 0], [0, 0, 0, 0, 0, 0, 3, 3, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]], 'test_outputs': [[[0, 3, 0], [3, 3, 0], [0, 3, 3]]]}) (input_keys={'test_inputs', 'training_examples'}): 'dict' object has no attribute 'dict'
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 63, in forward
File "<string>", line 63, in <listcomp>
AttributeError: 'dict' object has no attribute 'dict'
2025/08/28 22:03:56 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [8, 8, 8, 8, 8, 8, 0, 0, 0, 0], [0, 0, 0, 0, 0, 8, 0, 0, 0, 0], [8, 8, 8, 8, 0, 8, 0, 0, 0, 0], [0, 0, 0, 8, 0, 8, 0, 0, 0, 0], [8, 8, 0, 8, 0, 8, 0, 0, 0, 0], [0, 8, 0, 8, 0, 8, 0, 0, 0, 0]], 'output': [[8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [5, 5, 5, 5, 5, 5, 5, 5, 5, 8], [8, 8, 8, 8, 8, 8, 8, 8, 5, 8], [5, 5, 5, 5, 5, 5, 5, 8, 5, 8], [8, 8, 8, 8, 8, 8, 5, 8, 5, 8], [5, 5, 5, 5, 5, 8, 5, 8, 5, 8], [8, 8, 8, 8, 5, 8, 5, 8, 5, 8], [5, 5, 5, 8, 5, 8, 5, 8, 5, 8], [8, 8, 5, 8, 5, 8, 5, 8, 5, 8], [5, 8, 5, 8, 5, 8, 5, 8, 5, 8]]}, {'input': [[0, 0, 0, 0, 1, 0, 0, 1, 0, 0], [0, 0, 0, 0, 1, 0, 0, 1, 0, 0], [0, 0, 0, 0, 1, 0, 0, 1, 1, 1], [0, 0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[5, 1, 5, 5, 1, 5, 5, 1, 5, 5], [5, 1, 5, 5, 1, 5, 5, 1, 5, 5], [5, 1, 5, 5, 1, 5, 5, 1, 1, 1], [5, 1, 5, 5, 1, 5, 5, 5, 5, 5], [5, 1, 5, 5, 1, 5, 5, 5, 5, 5], [5, 1, 5, 5, 1, 1, 1, 1, 1, 1], [5, 1, 5, 5, 5, 5, 5, 5, 5, 5], [5, 1, 5, 5, 5, 5, 5, 5, 5, 5], [5, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5]]}, {'input': [[0, 2, 0, 2, 0, 2, 0, 2, 0, 0], [0, 2, 0, 2, 2, 2, 0, 2, 0, 0], [0, 2, 0, 0, 0, 0, 0, 2, 0, 0], [0, 2, 2, 2, 2, 2, 2, 2, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[5, 2, 5, 2, 5, 2, 5, 2, 5, 2], [5, 2, 5, 2, 2, 2, 5, 2, 5, 2], [5, 2, 5, 5, 5, 5, 5, 2, 5, 2], [5, 2, 2, 2, 2, 2, 2, 2, 5, 2], [5, 5, 5, 5, 5, 5, 5, 5, 5, 2], [2, 2, 2, 2, 2, 2, 2, 2, 2, 2], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [2, 2, 2, 2, 2, 2, 2, 2, 2, 2], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [2, 2, 2, 2, 2, 2, 2, 2, 2, 2]]}], 'test_inputs': [[[4, 4, 4, 4, 4, 4, 0, 0, 0, 0], [0, 0, 0, 0, 0, 4, 0, 0, 0, 0], [0, 0, 0, 0, 0, 4, 0, 0, 0, 0], [4, 4, 4, 0, 0, 4, 0, 0, 0, 0], [0, 0, 4, 0, 0, 4, 0, 0, 0, 0], [0, 0, 4, 0, 0, 4, 0, 0, 0, 0], [4, 4, 4, 0, 0, 4, 0, 0, 0, 0], [0, 0, 0, 0, 0, 4, 0, 0, 0, 0], [0, 0, 0, 0, 0, 4, 0, 0, 0, 0], [4, 4, 4, 4, 4, 4, 0, 0, 0, 0]]], 'test_outputs': [[[4, 4, 4, 4, 4, 4, 5, 5, 4, 5], [5, 5, 5, 5, 5, 4, 5, 5, 4, 5], [5, 5, 5, 5, 5, 4, 5, 5, 4, 5], [4, 4, 4, 5, 5, 4, 5, 5, 4, 5], [5, 5, 4, 5, 5, 4, 5, 5, 4, 5], [5, 5, 4, 5, 5, 4, 5, 5, 4, 5], [4, 4, 4, 5, 5, 4, 5, 5, 4, 5], [5, 5, 5, 5, 5, 4, 5, 5, 4, 5], [5, 5, 5, 5, 5, 4, 5, 5, 4, 5], [4, 4, 4, 4, 4, 4, 5, 5, 4, 5]]]}) (input_keys={'test_inputs', 'training_examples'}): 'dict' object has no attribute 'dict'
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 63, in forward
File "<string>", line 63, in <listcomp>
AttributeError: 'dict' object has no attribute 'dict'
2025/08/28 22:03:56 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[0, 4, 0, 9], [0, 0, 0, 0], [0, 4, 6, 0], [1, 0, 0, 0]], 'output': [[0, 0, 0, 0], [0, 0, 0, 0], [0, 4, 0, 0], [1, 4, 6, 9]]}, {'input': [[0, 0, 0, 0, 0, 9], [0, 0, 0, 8, 0, 0], [0, 0, 0, 0, 0, 0], [4, 0, 0, 0, 0, 0], [4, 0, 7, 8, 0, 0], [4, 0, 7, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], [4, 0, 0, 0, 0, 0], [4, 0, 7, 8, 0, 0], [4, 0, 7, 8, 0, 9]]}, {'input': [[0, 0, 0, 1, 0], [0, 3, 0, 0, 0], [0, 3, 0, 1, 2], [6, 0, 0, 0, 0], [0, 3, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 3, 0, 0, 0], [0, 3, 0, 1, 0], [6, 3, 0, 1, 2]]}], 'test_inputs': [[[0, 2, 0, 4, 3], [5, 0, 0, 0, 0], [0, 0, 6, 0, 0], [5, 2, 0, 4, 0], [5, 0, 0, 0, 0]]], 'test_outputs': [[[0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [5, 0, 0, 0, 0], [5, 2, 0, 4, 0], [5, 2, 6, 4, 3]]]}) (input_keys={'test_inputs', 'training_examples'}): 'dict' object has no attribute 'dict'
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 63, in forward
File "<string>", line 63, in <listcomp>
AttributeError: 'dict' object has no attribute 'dict'
2025/08/28 22:03:56 INFO dspy.evaluate.evaluate: Average Metric: 0.0 / 3 (0.0%)
GEPA Optimization: 12%|█████████████▎ | 469/4000 [30:00<7:00:08, 7.14s/rollouts]Iteration 13: Proposed new text for program: import dspy
from typing import List
import pydantic
import json
# Define the type for a matrix, which is a list of lists of integers.
MATRIX = List[List[int]]
class TrainingExample(pydantic.BaseModel):
"""A Pydantic model to structure a single training example, containing an input and output matrix."""
input: MATRIX
output: MATRIX
class SolveTaskSignature(dspy.Signature):
"""
This is the primary signature for the overall task. Given training examples and test inputs,
it should produce the corresponding test outputs.
"""
training_examples: List[TrainingExample] = dspy.InputField(
description="A list of input and output matrix pairs that demonstrate the transformation rule."
)
test_inputs: List[MATRIX] = dspy.InputField(
description="A list of input matrices to be transformed according to the rule."
)
test_outputs: List[MATRIX] = dspy.OutputField(
description="The corresponding output matrices for the test inputs."
)
class GeneratePythonFunctionSignature(dspy.Signature):
"""
This signature guides an LM to act as a programmer. It analyzes examples and writes a Python function to solve the task.
**Instructions for the LM:**
1. **Analyze Examples:** Carefully study the provided `training_examples` to deduce the underlying transformation rule. The rule could involve patterns like gravity (moving non-zero elements down columns), object extraction based on markers (e.g., the number 5), pattern filling (like checkerboards), or geometric transformations.
2. **Write a Python Function:** Implement the discovered rule in a single, self-contained Python function named `transform_matrix`.
3. **Function Signature:** The function must accept one argument, `matrix` (a list of lists of integers), and return the transformed matrix (also a list of lists of integers).
4. **Self-Contained Code:** The function must not rely on any external libraries like numpy. Standard Python 3 features are allowed.
5. **Output Format:** Your output must be ONLY the Python code for the `transform_matrix` function. Do not include any explanations, comments outside the function, or example usage. Start your response with `def transform_matrix(matrix):` and end with the last line of the function's code.
"""
training_examples: str = dspy.InputField(
description="A string representation of the training examples, each showing an input and its corresponding output matrix."
)
python_function: str = dspy.OutputField(
description="A string containing only the Python code for the 'transform_matrix' function."
)
class SolveWithCodeGeneration(dspy.Module):
"""
A custom DSPy module that solves matrix tasks by generating and executing Python code.
"""
def __init__(self):
super().__init__()
# The primary module for generating the transformation logic as Python code.
self.code_generator = dspy.Predict(GeneratePythonFunctionSignature)
# A fallback module in case the code generation or execution pipeline fails.
self.fallback_solver = dspy.ChainOfThought(SolveTaskSignature)
def forward(self, training_examples: List[TrainingExample], test_inputs: List[MATRIX]) -> dspy.Prediction:
"""
The forward method implements the "reason-then-execute" logic.
"""
# Serialize the structured training examples into a clear, readable string for the prompt.
examples_str = json.dumps([ex.dict() for ex in training_examples], indent=2)
try:
# Step 1: Generate the Python function using the LM.
prediction = self.code_generator(training_examples=examples_str)
python_code = prediction.python_function
# Prepare a local scope to safely execute the generated code.
local_scope = {}
# Step 2: Execute the generated code string to define the function within the local scope.
exec(python_code, {}, local_scope)
transform_func = local_scope.get('transform_matrix')
if not callable(transform_func):
raise ValueError("Generated code did not define a callable function named 'transform_matrix'.")
# Step 3: Apply the successfully defined function to all test inputs.
final_outputs = []
for test_matrix in test_inputs:
# Pass a deep copy to prevent the function from modifying the original input list.
matrix_copy = [row[:] for row in test_matrix]
result = transform_func(matrix_copy)
final_outputs.append(result)
return dspy.Prediction(test_outputs=final_outputs)
except Exception as e:
# Step 4: If any part of the try block fails, use the more direct fallback solver.
print(f"Code generation/execution failed: {e}. Using fallback ChainOfThought solver.")
return self.fallback_solver(training_examples=training_examples, test_inputs=test_inputs)
# The final 'program' object is an instance of our improved custom module.
program = SolveWithCodeGeneration()
Iteration 13: New subsample score is not better, skipping
Iteration 14: Selected program 0 score: 0.67
Average Metric: 1.00 / 3 (33.3%): 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 181.81it/s]2025/08/28 22:03:56 INFO dspy.evaluate.evaluate: Average Metric: 1.0 / 3 (33.3%)
2025/08/28 22:04:42 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[1, 1, 1, 1, 0, 0, 0, 0, 0, 0], [1, 0, 0, 1, 0, 0, 0, 0, 0, 0], [1, 0, 0, 1, 0, 0, 1, 1, 1, 0], [1, 1, 1, 1, 0, 0, 1, 0, 1, 0], [0, 0, 0, 0, 0, 0, 1, 1, 1, 0], [1, 1, 1, 1, 1, 0, 0, 0, 0, 0], [1, 0, 0, 0, 1, 0, 0, 0, 0, 0], [1, 0, 0, 0, 1, 0, 0, 0, 0, 0], [1, 0, 0, 0, 1, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 0, 0, 0, 0, 0]], 'output': [[1, 1, 1, 1, 0, 0, 0, 0, 0, 0], [1, 2, 2, 1, 0, 0, 0, 0, 0, 0], [1, 2, 2, 1, 0, 0, 1, 1, 1, 0], [1, 1, 1, 1, 0, 0, 1, 7, 1, 0], [0, 0, 0, 0, 0, 0, 1, 1, 1, 0], [1, 1, 1, 1, 1, 0, 0, 0, 0, 0], [1, 7, 7, 7, 1, 0, 0, 0, 0, 0], [1, 7, 7, 7, 1, 0, 0, 0, 0, 0], [1, 7, 7, 7, 1, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 0, 0, 0, 0, 0]]}, {'input': [[1, 1, 1, 0, 1, 1, 1, 1, 1, 1], [1, 0, 1, 0, 1, 0, 0, 0, 0, 1], [1, 1, 1, 0, 1, 0, 0, 0, 0, 1], [0, 0, 0, 0, 1, 0, 0, 0, 0, 1], [0, 0, 0, 0, 1, 0, 0, 0, 0, 1], [0, 0, 0, 0, 1, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[1, 1, 1, 0, 1, 1, 1, 1, 1, 1], [1, 7, 1, 0, 1, 2, 2, 2, 2, 1], [1, 1, 1, 0, 1, 2, 2, 2, 2, 1], [0, 0, 0, 0, 1, 2, 2, 2, 2, 1], [0, 0, 0, 0, 1, 2, 2, 2, 2, 1], [0, 0, 0, 0, 1, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}, {'input': [[1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0], [1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1], [0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1], [0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1], [0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1]], 'output': [[1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 7, 7, 7, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 7, 7, 7, 1, 0, 1, 2, 2, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 7, 7, 7, 1, 0, 1, 2, 2, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0], [1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 2, 2, 2, 2, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 2, 2, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 2, 2, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 2, 2, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 7, 7, 7, 7, 7, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 7, 7, 7, 7, 7, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 7, 7, 7, 7, 7, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1], [0, 0, 1, 7, 7, 7, 7, 7, 1, 0, 0, 0, 1, 2, 2, 2, 2, 2, 2, 1], [0, 0, 1, 7, 7, 7, 7, 7, 1, 0, 0, 0, 1, 2, 2, 2, 2, 2, 2, 1], [0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 2, 2, 2, 2, 2, 2, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 2, 2, 2, 2, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 2, 2, 2, 2, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 2, 2, 2, 2, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1]]}, {'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0], [0, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0], [1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0], [0, 1, 7, 1, 0, 1, 1, 1, 1, 0, 0], [0, 1, 1, 1, 0, 1, 2, 2, 1, 0, 0], [0, 0, 0, 0, 0, 1, 2, 2, 1, 0, 0], [0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0], [1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0], [1, 7, 7, 7, 1, 0, 0, 0, 0, 0, 0], [1, 7, 7, 7, 1, 0, 0, 0, 0, 0, 0], [1, 7, 7, 7, 1, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0]]}, {'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0], [0, 1, 7, 7, 7, 7, 7, 1, 0, 0, 0, 0, 0, 0, 0], [0, 1, 7, 7, 7, 7, 7, 1, 0, 0, 0, 0, 0, 0, 0], [0, 1, 7, 7, 7, 7, 7, 1, 0, 0, 0, 0, 0, 0, 0], [0, 1, 7, 7, 7, 7, 7, 1, 0, 0, 0, 0, 0, 0, 0], [0, 1, 7, 7, 7, 7, 7, 1, 0, 0, 0, 0, 0, 0, 0], [0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 2, 2, 2, 2, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 2, 2, 2, 2, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 2, 2, 2, 2, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 2, 2, 2, 2, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0]]}], 'test_inputs': [[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0], [0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1], [0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1], [0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1], [0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1], [0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1], [0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1], [0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1], [0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]], 'test_outputs': [[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 7, 7, 7, 1, 0, 0, 0], [0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 7, 7, 7, 1, 0, 0, 0], [0, 1, 2, 2, 2, 2, 2, 2, 1, 0, 0, 0, 1, 7, 7, 7, 1, 0, 0, 0], [0, 1, 2, 2, 2, 2, 2, 2, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0], [0, 1, 2, 2, 2, 2, 2, 2, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 2, 2, 2, 2, 2, 2, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 2, 2, 2, 2, 2, 2, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 1, 2, 2, 2, 2, 2, 2, 1, 0, 1, 2, 2, 2, 2, 2, 2, 2, 2, 1], [0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 2, 2, 2, 2, 2, 2, 2, 2, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 2, 2, 2, 2, 2, 2, 1], [0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 2, 2, 2, 2, 2, 2, 2, 2, 1], [0, 1, 7, 7, 7, 7, 7, 1, 0, 0, 1, 2, 2, 2, 2, 2, 2, 2, 2, 1], [0, 1, 7, 7, 7, 7, 7, 1, 0, 0, 1, 2, 2, 2, 2, 2, 2, 2, 2, 1], [0, 1, 7, 7, 7, 7, 7, 1, 0, 0, 1, 2, 2, 2, 2, 2, 2, 2, 2, 1], [0, 1, 7, 7, 7, 7, 7, 1, 0, 0, 1, 2, 2, 2, 2, 2, 2, 2, 2, 1], [0, 1, 7, 7, 7, 7, 7, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]]}) (input_keys={'test_inputs', 'training_examples'}): 'dict' object has no attribute 'model_dump_json'
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 58, in forward
File "<string>", line 58, in <listcomp>
AttributeError: 'dict' object has no attribute 'model_dump_json'
2025/08/28 22:04:42 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[5, 4, 5, 6, 1, 2, 5, 4, 5, 6, 1, 2, 5, 4, 5, 6, 1, 2, 5, 4, 5, 6, 1, 2, 5, 4, 5, 6, 1], [4, 5, 2, 1, 2, 3, 4, 5, 2, 1, 2, 3, 4, 5, 2, 1, 2, 3, 4, 5, 2, 1, 2, 3, 4, 5, 2, 1, 2], [5, 6, 1, 2, 5, 4, 5, 6, 1, 2, 5, 4, 5, 6, 1, 2, 5, 4, 5, 6, 1, 2, 5, 4, 5, 6, 1, 2, 5], [2, 1, 2, 3, 4, 5, 0, 0, 2, 3, 4, 5, 2, 1, 2, 3, 4, 5, 2, 1, 2, 3, 4, 5, 2, 1, 2, 3, 4], [1, 2, 5, 4, 5, 6, 0, 0, 5, 4, 5, 6, 1, 2, 5, 4, 5, 6, 1, 2, 5, 4, 5, 6, 1, 2, 5, 4, 5], [2, 0, 0, 0, 0, 1, 0, 0, 4, 5, 2, 1, 2, 3, 4, 5, 2, 1, 2, 3, 4, 5, 2, 1, 2, 3, 4, 5, 2], [5, 0, 0, 0, 0, 2, 0, 0, 5, 6, 1, 2, 5, 4, 5, 6, 1, 2, 5, 4, 5, 6, 1, 2, 0, 0, 0, 6, 1], [4, 0, 0, 0, 0, 0, 0, 0, 2, 1, 2, 3, 4, 5, 2, 1, 2, 3, 4, 5, 2, 1, 2, 3, 0, 0, 0, 1, 2], [5, 6, 1, 2, 0, 0, 0, 0, 1, 2, 5, 4, 5, 6, 1, 2, 5, 4, 5, 6, 1, 2, 5, 4, 0, 0, 0, 2, 5], [2, 1, 2, 3, 0, 0, 2, 1, 2, 3, 4, 5, 2, 1, 2, 3, 4, 5, 2, 1, 2, 3, 4, 5, 0, 0, 0, 3, 4], [1, 2, 5, 4, 5, 6, 1, 2, 5, 4, 5, 6, 1, 2, 5, 4, 5, 6, 1, 2, 5, 4, 5, 6, 0, 0, 0, 4, 5], [2, 3, 4, 5, 2, 1, 2, 3, 4, 5, 2, 1, 2, 3, 4, 5, 2, 1, 2, 3, 4, 5, 2, 1, 0, 0, 0, 5, 2], [5, 4, 5, 6, 1, 2, 5, 4, 5, 6, 1, 2, 5, 4, 5, 6, 1, 2, 5, 4, 5, 6, 1, 2, 5, 4, 5, 6, 1], [4, 5, 2, 1, 2, 3, 4, 5, 2, 1, 2, 3, 4, 5, 2, 1, 2, 3, 4, 5, 2, 1, 2, 3, 4, 5, 2, 1, 2], [5, 6, 1, 2, 5, 4, 5, 6, 1, 2, 5, 4, 5, 6, 1, 2, 5, 4, 5, 6, 1, 2, 5, 4, 5, 6, 1, 2, 5], [2, 1, 2, 3, 4, 5, 2, 1, 2, 3, 4, 5, 2, 1, 2, 3, 4, 5, 2, 1, 2, 3, 4, 5, 2, 1, 2, 3, 4], [1, 2, 5, 4, 5, 6, 1, 2, 5, 4, 5, 6, 1, 2, 5, 4, 5, 6, 1, 2, 5, 4, 5, 6, 1, 2, 5, 4, 5], [2, 3, 4, 5, 2, 1, 2, 3, 4, 5, 2, 1, 2, 3, 4, 5, 2, 1, 2, 3, 4, 5, 2, 1, 2, 3, 4, 5, 2], [5, 4, 5, 6, 1, 2, 5, 4, 5, 6, 1, 2, 5, 4, 5, 6, 1, 2, 5, 4, 5, 6, 1, 2, 5, 4, 5, 6, 1], [4, 5, 2, 1, 2, 3, 4, 5, 2, 1, 2, 3, 4, 5, 2, 1, 2, 3, 4, 5, 2, 1, 2, 3, 4, 5, 2, 1, 2], [5, 6, 1, 2, 5, 4, 5, 6, 1, 2, 5, 4, 5, 6, 1, 2, 5, 4, 5, 6, 1, 2, 5, 4, 5, 6, 1, 2, 5], [2, 1, 2, 3, 4, 5, 2, 1, 2, 3, 4, 5, 2, 0, 0, 0, 0, 5, 2, 1, 2, 3, 4, 5, 2, 1, 2, 3, 4], [1, 2, 5, 4, 5, 6, 1, 2, 5, 4, 5, 6, 1, 0, 0, 0, 0, 6, 1, 2, 5, 4, 5, 6, 1, 2, 5, 4, 5], [2, 3, 4, 5, 2, 1, 2, 3, 4, 5, 2, 1, 2, 0, 0, 0, 0, 1, 2, 3, 4, 5, 2, 1, 2, 3, 4, 5, 2], [5, 4, 5, 6, 1, 2, 5, 4, 5, 6, 1, 2, 5, 4, 5, 6, 1, 2, 5, 4, 5, 6, 1, 2, 5, 4, 5, 6, 1], [4, 5, 2, 1, 2, 3, 4, 5, 2, 1, 2, 3, 4, 5, 2, 1, 2, 3, 4, 5, 2, 1, 2, 3, 4, 5, 2, 1, 2], [5, 6, 1, 2, 5, 4, 5, 6, 1, 2, 5, 4, 5, 6, 1, 2, 5, 4, 5, 6, 1, 2, 5, 4, 5, 6, 1, 2, 5], [2, 1, 2, 3, 4, 5, 2, 1, 2, 3, 4, 5, 2, 1, 2, 3, 4, 5, 2, 1, 2, 3, 4, 5, 2, 1, 2, 3, 4], [1, 2, 5, 4, 5, 6, 1, 2, 5, 4, 5, 6, 1, 2, 5, 4, 5, 6, 1, 2, 5, 4, 5, 6, 1, 2, 5, 4, 5]], 'output': [[5, 4, 5, 6, 1, 2, 5, 4, 5, 6, 1, 2, 5, 4, 5, 6, 1, 2, 5, 4, 5, 6, 1, 2, 5, 4, 5, 6, 1], [4, 5, 2, 1, 2, 3, 4, 5, 2, 1, 2, 3, 4, 5, 2, 1, 2, 3, 4, 5, 2, 1, 2, 3, 4, 5, 2, 1, 2], [5, 6, 1, 2, 5, 4, 5, 6, 1, 2, 5, 4, 5, 6, 1, 2, 5, 4, 5, 6, 1, 2, 5, 4, 5, 6, 1, 2, 5], [2, 1, 2, 3, 4, 5, 2, 1, 2, 3, 4, 5, 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2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 0, 0, 0, 0, 1, 2], [1, 4, 1, 6, 1, 8, 1, 2, 1, 4, 1, 6, 1, 8, 1, 2, 1, 4, 1, 6, 1, 8, 1, 0, 0, 0, 0, 6, 1], [2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 0, 0, 0, 0, 0, 0, 0, 2], [1, 6, 1, 8, 1, 2, 1, 4, 1, 6, 1, 8, 1, 2, 1, 4, 1, 6, 1, 8, 0, 0, 0, 0, 0, 0, 0, 0, 1], [2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 0, 0, 0, 0, 0, 0, 0, 0, 2], [1, 8, 1, 2, 1, 4, 1, 6, 1, 8, 1, 2, 1, 4, 1, 6, 1, 8, 1, 2, 1, 0, 0, 0, 0, 0, 0, 0, 1], [2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 0, 0, 0, 0, 0, 0, 0, 2], [1, 2, 1, 4, 1, 6, 1, 8, 1, 2, 1, 4, 1, 6, 1, 8, 1, 2, 1, 4, 1, 6, 1, 8, 0, 0, 0, 0, 1], [2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 0, 0, 0, 0, 2], [1, 4, 1, 6, 1, 8, 1, 2, 1, 4, 1, 6, 1, 8, 1, 2, 1, 4, 1, 6, 1, 8, 1, 2, 1, 4, 1, 6, 1], [2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2], [1, 6, 1, 8, 1, 2, 1, 4, 1, 6, 1, 8, 1, 2, 1, 4, 1, 6, 1, 8, 1, 2, 1, 4, 1, 6, 1, 8, 1]], 'output': [[1, 2, 1, 4, 1, 6, 1, 8, 1, 2, 1, 4, 1, 6, 1, 8, 1, 2, 1, 4, 1, 6, 1, 8, 1, 2, 1, 4, 1], [2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2], [1, 4, 1, 6, 1, 8, 1, 2, 1, 4, 1, 6, 1, 8, 1, 2, 1, 4, 1, 6, 1, 8, 1, 2, 1, 4, 1, 6, 1], [2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2], [1, 6, 1, 8, 1, 2, 1, 4, 1, 6, 1, 8, 1, 2, 1, 4, 1, 6, 1, 8, 1, 2, 1, 4, 1, 6, 1, 8, 1], [2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2], [1, 8, 1, 2, 1, 4, 1, 6, 1, 8, 1, 2, 1, 4, 1, 6, 1, 8, 1, 2, 1, 4, 1, 6, 1, 8, 1, 2, 1], [2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2], [1, 2, 1, 4, 1, 6, 1, 8, 1, 2, 1, 4, 1, 6, 1, 8, 1, 2, 1, 4, 1, 6, 1, 8, 1, 2, 1, 4, 1], [2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2], [1, 4, 1, 6, 1, 8, 1, 2, 1, 4, 1, 6, 1, 8, 1, 2, 1, 4, 1, 6, 1, 8, 1, 2, 1, 4, 1, 6, 1], [2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2], [1, 6, 1, 8, 1, 2, 1, 4, 1, 6, 1, 8, 1, 2, 1, 4, 1, 6, 1, 8, 1, 2, 1, 4, 1, 6, 1, 8, 1], [2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2], [1, 8, 1, 2, 1, 4, 1, 6, 1, 8, 1, 2, 1, 4, 1, 6, 1, 8, 1, 2, 1, 4, 1, 6, 1, 8, 1, 2, 1], [2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2], [1, 2, 1, 4, 1, 6, 1, 8, 1, 2, 1, 4, 1, 6, 1, 8, 1, 2, 1, 4, 1, 6, 1, 8, 1, 2, 1, 4, 1], [2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2], [1, 4, 1, 6, 1, 8, 1, 2, 1, 4, 1, 6, 1, 8, 1, 2, 1, 4, 1, 6, 1, 8, 1, 2, 1, 4, 1, 6, 1], [2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2], [1, 6, 1, 8, 1, 2, 1, 4, 1, 6, 1, 8, 1, 2, 1, 4, 1, 6, 1, 8, 1, 2, 1, 4, 1, 6, 1, 8, 1], [2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2], [1, 8, 1, 2, 1, 4, 1, 6, 1, 8, 1, 2, 1, 4, 1, 6, 1, 8, 1, 2, 1, 4, 1, 6, 1, 8, 1, 2, 1], [2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2], [1, 2, 1, 4, 1, 6, 1, 8, 1, 2, 1, 4, 1, 6, 1, 8, 1, 2, 1, 4, 1, 6, 1, 8, 1, 2, 1, 4, 1], [2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2], [1, 4, 1, 6, 1, 8, 1, 2, 1, 4, 1, 6, 1, 8, 1, 2, 1, 4, 1, 6, 1, 8, 1, 2, 1, 4, 1, 6, 1], [2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2], [1, 6, 1, 8, 1, 2, 1, 4, 1, 6, 1, 8, 1, 2, 1, 4, 1, 6, 1, 8, 1, 2, 1, 4, 1, 6, 1, 8, 1]]}], 'test_inputs': [[[8, 1, 2, 6, 1, 2, 0, 0, 0, 0, 1, 2, 5, 1, 2, 9, 1, 2, 8, 1, 2, 6, 1, 2, 2, 1, 2, 3, 1], [1, 8, 2, 1, 5, 9, 0, 0, 0, 0, 8, 9, 1, 5, 0, 0, 0, 9, 1, 8, 2, 1, 5, 9, 1, 2, 2, 1, 8], [5, 3, 1, 8, 2, 1, 0, 0, 0, 0, 8, 1, 8, 9, 0, 0, 0, 1, 5, 3, 1, 8, 2, 1, 2, 6, 1, 5, 8], [5, 1, 2, 9, 1, 2, 0, 0, 0, 0, 1, 2, 2, 1, 0, 0, 0, 2, 5, 1, 2, 9, 1, 2, 8, 1, 2, 6, 1], [1, 5, 2, 1, 2, 9, 0, 0, 0, 0, 5, 9, 1, 2, 0, 0, 0, 9, 1, 5, 2, 1, 2, 9, 1, 8, 2, 1, 5], [8, 9, 1, 2, 5, 1, 5, 3, 1, 8, 2, 1, 2, 6, 0, 0, 0, 1, 8, 9, 1, 2, 5, 1, 5, 3, 1, 8, 2], [2, 1, 2, 3, 1, 2, 5, 1, 2, 9, 1, 2, 8, 1, 0, 0, 0, 2, 2, 1, 2, 3, 1, 2, 5, 1, 2, 9, 1], [1, 2, 2, 1, 8, 9, 1, 5, 2, 1, 2, 9, 1, 8, 0, 0, 0, 9, 1, 2, 2, 1, 8, 9, 1, 5, 2, 1, 2], [2, 6, 1, 5, 8, 1, 8, 9, 1, 2, 5, 1, 5, 3, 1, 8, 2, 1, 2, 6, 1, 5, 8, 1, 8, 9, 1, 2, 5], [8, 1, 2, 6, 1, 2, 2, 1, 2, 3, 1, 2, 5, 1, 2, 9, 1, 2, 8, 1, 2, 6, 1, 2, 2, 1, 2, 3, 1], [1, 8, 2, 1, 5, 9, 1, 2, 2, 1, 8, 9, 1, 5, 2, 1, 2, 9, 1, 8, 2, 1, 5, 9, 1, 2, 2, 1, 8], [5, 3, 1, 8, 2, 1, 2, 6, 1, 5, 8, 1, 8, 9, 1, 2, 5, 1, 5, 3, 1, 8, 2, 1, 2, 6, 1, 5, 8], [5, 1, 0, 0, 1, 2, 8, 1, 2, 6, 1, 2, 2, 0, 0, 0, 1, 2, 5, 1, 2, 9, 1, 2, 8, 1, 2, 6, 1], [1, 5, 0, 0, 2, 9, 1, 8, 2, 1, 5, 9, 1, 0, 0, 0, 8, 9, 1, 5, 2, 1, 2, 9, 1, 8, 2, 1, 5], [8, 9, 0, 0, 5, 1, 5, 3, 1, 8, 2, 1, 2, 6, 1, 5, 8, 1, 8, 9, 1, 2, 5, 1, 5, 3, 1, 8, 2], [2, 1, 0, 0, 0, 2, 5, 1, 2, 9, 1, 2, 8, 1, 2, 6, 1, 2, 2, 1, 2, 3, 1, 2, 5, 1, 2, 9, 1], [1, 2, 0, 0, 0, 9, 1, 5, 2, 1, 2, 9, 1, 8, 2, 1, 5, 9, 1, 2, 2, 1, 8, 9, 1, 5, 2, 1, 2], [2, 6, 1, 0, 0, 1, 8, 9, 1, 2, 5, 1, 5, 3, 1, 8, 2, 1, 2, 6, 1, 5, 8, 1, 8, 9, 1, 2, 5], [8, 1, 2, 0, 0, 2, 2, 1, 2, 3, 1, 2, 5, 1, 2, 9, 1, 2, 8, 1, 2, 6, 1, 2, 2, 1, 2, 3, 1], [1, 8, 2, 1, 5, 9, 1, 2, 2, 1, 8, 9, 1, 5, 2, 1, 2, 9, 1, 8, 2, 1, 5, 9, 1, 2, 2, 1, 8], [5, 3, 1, 8, 2, 1, 2, 6, 1, 5, 8, 1, 8, 9, 1, 2, 5, 1, 5, 3, 1, 8, 2, 1, 2, 6, 1, 5, 8], [5, 1, 2, 9, 1, 2, 8, 1, 2, 6, 1, 2, 2, 1, 2, 3, 1, 2, 5, 1, 2, 9, 1, 2, 8, 1, 2, 6, 1], [1, 5, 2, 1, 2, 9, 1, 8, 2, 1, 5, 9, 1, 2, 2, 1, 8, 9, 1, 5, 2, 1, 2, 9, 1, 8, 2, 1, 5], [8, 9, 1, 2, 5, 1, 5, 3, 1, 8, 2, 1, 2, 6, 1, 5, 8, 1, 8, 9, 1, 2, 5, 1, 5, 3, 1, 8, 2], [2, 1, 2, 3, 1, 2, 5, 1, 2, 9, 1, 2, 8, 1, 2, 6, 1, 2, 2, 1, 2, 3, 1, 2, 5, 1, 2, 9, 1], [1, 2, 2, 1, 8, 9, 1, 5, 2, 1, 2, 9, 1, 8, 2, 1, 5, 9, 1, 2, 2, 1, 8, 9, 1, 5, 2, 1, 2], [2, 6, 1, 5, 8, 1, 8, 9, 1, 2, 5, 1, 5, 3, 1, 8, 2, 1, 2, 6, 1, 5, 8, 1, 8, 9, 1, 2, 5], [8, 1, 2, 6, 1, 2, 2, 1, 2, 3, 1, 2, 5, 1, 2, 9, 1, 2, 8, 1, 2, 6, 1, 2, 2, 1, 2, 3, 1], [1, 8, 2, 1, 5, 9, 1, 2, 2, 1, 8, 9, 1, 5, 2, 1, 2, 9, 1, 8, 2, 1, 5, 9, 1, 2, 2, 1, 8]]], 'test_outputs': [[[8, 1, 2, 6, 1, 2, 2, 1, 2, 3, 1, 2, 5, 1, 2, 9, 1, 2, 8, 1, 2, 6, 1, 2, 2, 1, 2, 3, 1], [1, 8, 2, 1, 5, 9, 1, 2, 2, 1, 8, 9, 1, 5, 2, 1, 2, 9, 1, 8, 2, 1, 5, 9, 1, 2, 2, 1, 8], [5, 3, 1, 8, 2, 1, 2, 6, 1, 5, 8, 1, 8, 9, 1, 2, 5, 1, 5, 3, 1, 8, 2, 1, 2, 6, 1, 5, 8], [5, 1, 2, 9, 1, 2, 8, 1, 2, 6, 1, 2, 2, 1, 2, 3, 1, 2, 5, 1, 2, 9, 1, 2, 8, 1, 2, 6, 1], [1, 5, 2, 1, 2, 9, 1, 8, 2, 1, 5, 9, 1, 2, 2, 1, 8, 9, 1, 5, 2, 1, 2, 9, 1, 8, 2, 1, 5], [8, 9, 1, 2, 5, 1, 5, 3, 1, 8, 2, 1, 2, 6, 1, 5, 8, 1, 8, 9, 1, 2, 5, 1, 5, 3, 1, 8, 2], [2, 1, 2, 3, 1, 2, 5, 1, 2, 9, 1, 2, 8, 1, 2, 6, 1, 2, 2, 1, 2, 3, 1, 2, 5, 1, 2, 9, 1], [1, 2, 2, 1, 8, 9, 1, 5, 2, 1, 2, 9, 1, 8, 2, 1, 5, 9, 1, 2, 2, 1, 8, 9, 1, 5, 2, 1, 2], [2, 6, 1, 5, 8, 1, 8, 9, 1, 2, 5, 1, 5, 3, 1, 8, 2, 1, 2, 6, 1, 5, 8, 1, 8, 9, 1, 2, 5], [8, 1, 2, 6, 1, 2, 2, 1, 2, 3, 1, 2, 5, 1, 2, 9, 1, 2, 8, 1, 2, 6, 1, 2, 2, 1, 2, 3, 1], [1, 8, 2, 1, 5, 9, 1, 2, 2, 1, 8, 9, 1, 5, 2, 1, 2, 9, 1, 8, 2, 1, 5, 9, 1, 2, 2, 1, 8], [5, 3, 1, 8, 2, 1, 2, 6, 1, 5, 8, 1, 8, 9, 1, 2, 5, 1, 5, 3, 1, 8, 2, 1, 2, 6, 1, 5, 8], [5, 1, 2, 9, 1, 2, 8, 1, 2, 6, 1, 2, 2, 1, 2, 3, 1, 2, 5, 1, 2, 9, 1, 2, 8, 1, 2, 6, 1], [1, 5, 2, 1, 2, 9, 1, 8, 2, 1, 5, 9, 1, 2, 2, 1, 8, 9, 1, 5, 2, 1, 2, 9, 1, 8, 2, 1, 5], [8, 9, 1, 2, 5, 1, 5, 3, 1, 8, 2, 1, 2, 6, 1, 5, 8, 1, 8, 9, 1, 2, 5, 1, 5, 3, 1, 8, 2], [2, 1, 2, 3, 1, 2, 5, 1, 2, 9, 1, 2, 8, 1, 2, 6, 1, 2, 2, 1, 2, 3, 1, 2, 5, 1, 2, 9, 1], [1, 2, 2, 1, 8, 9, 1, 5, 2, 1, 2, 9, 1, 8, 2, 1, 5, 9, 1, 2, 2, 1, 8, 9, 1, 5, 2, 1, 2], [2, 6, 1, 5, 8, 1, 8, 9, 1, 2, 5, 1, 5, 3, 1, 8, 2, 1, 2, 6, 1, 5, 8, 1, 8, 9, 1, 2, 5], [8, 1, 2, 6, 1, 2, 2, 1, 2, 3, 1, 2, 5, 1, 2, 9, 1, 2, 8, 1, 2, 6, 1, 2, 2, 1, 2, 3, 1], [1, 8, 2, 1, 5, 9, 1, 2, 2, 1, 8, 9, 1, 5, 2, 1, 2, 9, 1, 8, 2, 1, 5, 9, 1, 2, 2, 1, 8], [5, 3, 1, 8, 2, 1, 2, 6, 1, 5, 8, 1, 8, 9, 1, 2, 5, 1, 5, 3, 1, 8, 2, 1, 2, 6, 1, 5, 8], [5, 1, 2, 9, 1, 2, 8, 1, 2, 6, 1, 2, 2, 1, 2, 3, 1, 2, 5, 1, 2, 9, 1, 2, 8, 1, 2, 6, 1], [1, 5, 2, 1, 2, 9, 1, 8, 2, 1, 5, 9, 1, 2, 2, 1, 8, 9, 1, 5, 2, 1, 2, 9, 1, 8, 2, 1, 5], [8, 9, 1, 2, 5, 1, 5, 3, 1, 8, 2, 1, 2, 6, 1, 5, 8, 1, 8, 9, 1, 2, 5, 1, 5, 3, 1, 8, 2], [2, 1, 2, 3, 1, 2, 5, 1, 2, 9, 1, 2, 8, 1, 2, 6, 1, 2, 2, 1, 2, 3, 1, 2, 5, 1, 2, 9, 1], [1, 2, 2, 1, 8, 9, 1, 5, 2, 1, 2, 9, 1, 8, 2, 1, 5, 9, 1, 2, 2, 1, 8, 9, 1, 5, 2, 1, 2], [2, 6, 1, 5, 8, 1, 8, 9, 1, 2, 5, 1, 5, 3, 1, 8, 2, 1, 2, 6, 1, 5, 8, 1, 8, 9, 1, 2, 5], [8, 1, 2, 6, 1, 2, 2, 1, 2, 3, 1, 2, 5, 1, 2, 9, 1, 2, 8, 1, 2, 6, 1, 2, 2, 1, 2, 3, 1], [1, 8, 2, 1, 5, 9, 1, 2, 2, 1, 8, 9, 1, 5, 2, 1, 2, 9, 1, 8, 2, 1, 5, 9, 1, 2, 2, 1, 8]]]}) (input_keys={'test_inputs', 'training_examples'}): 'dict' object has no attribute 'model_dump_json'
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 58, in forward
File "<string>", line 58, in <listcomp>
AttributeError: 'dict' object has no attribute 'model_dump_json'
2025/08/28 22:04:42 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0], [5, 0, 0, 2, 0, 0, 5, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0], [5, 0, 2, 2, 2, 0, 5, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0], [5, 0, 0, 2, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 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3, 3, 3, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 5, 0, 3, 3, 3, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0], [0, 5, 5, 5, 5, 5, 5, 5, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 3, 3, 0, 0], [0, 0, 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0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}], 'test_inputs': [[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 2, 2, 2, 0, 0], [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 5, 0, 0, 2, 2, 2, 0, 0], [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 5], [0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 0], [0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0], [0, 0, 2, 2, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 2, 0, 0, 5, 0], [0, 2, 2, 2, 2, 0, 5, 0, 0, 0, 0, 0, 5, 0, 2, 2, 2, 0, 5, 0], [0, 2, 2, 2, 2, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 2, 0, 0, 5, 0], [0, 0, 2, 2, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0], [0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 0]]], 'test_outputs': [[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 2, 2, 2, 0, 0], [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 5, 0, 0, 2, 2, 2, 0, 0], [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 5], [0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 2, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0], [0, 2, 2, 2, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0], [0, 0, 2, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 0], [0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0], [0, 0, 2, 2, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 2, 0, 0, 5, 0], [0, 2, 2, 2, 2, 0, 5, 0, 0, 0, 0, 0, 5, 0, 2, 2, 2, 0, 5, 0], [0, 2, 2, 2, 2, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 2, 0, 0, 5, 0], [0, 0, 2, 2, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0], [0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 0]]]}) (input_keys={'test_inputs', 'training_examples'}): 'dict' object has no attribute 'model_dump_json'
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 58, in forward
File "<string>", line 58, in <listcomp>
AttributeError: 'dict' object has no attribute 'model_dump_json'
2025/08/28 22:04:42 INFO dspy.evaluate.evaluate: Average Metric: 0.0 / 3 (0.0%)
GEPA Optimization: 12%|█████████████▌ | 475/4000 [30:46<7:02:14, 7.19s/rollouts]Iteration 14: Proposed new text for program: import dspy
from typing import List
import pydantic
import copy
import traceback
# Define the data structures for the inputs and outputs.
MATRIX = List[List[int]]
class TrainingExample(pydantic.BaseModel):
input: MATRIX
output: MATRIX
# Signature for the internal module that generates a Python solver function.
class GenerateRuleSignature(dspy.Signature):
"""
You are an expert programmer specializing in algorithmic puzzles.
Your task is to analyze a set of input-output grid pairs and write a single, self-contained Python function named `solve` that implements the transformation rule.
**Function Signature:**
The function must have the following signature: `def solve(input_matrix: list[list[int]]) -> list[list[int]]:`
**Requirements:**
1. The function must take one argument: `input_matrix`, which is a 2D list of integers.
2. The function must return a new 2D list of integers representing the transformed grid.
3. **Crucially, do not modify the input matrix in place.** Create a deep copy before making changes. The `copy` library is available.
4. The function must be entirely self-contained. You can define helper functions inside `solve` if needed, but not outside.
5. Do not use any external libraries like numpy, pandas, etc. Only standard Python libraries are allowed.
**Common Patterns to Look For:**
- **Object Manipulation:** Finding all objects of a certain color and applying a transformation (e.g., recoloring, moving, resizing) based on their properties (size, shape, neighbors, bounding box).
- **Pattern Completion:** Identifying a repeating background pattern (e.g., a tiled or repeating set of rows/columns) and using it to fill in corrupted or missing areas (often marked with 0s).
- **Rule Inference from Templates:** Finding a "template" or "key" object in the grid that defines a rule to be applied to other objects. For example, a shape of color C1 enclosed by a shape of color C2 might indicate that all shapes with the same properties as the C1 shape should be changed to color C2.
- **Flood Fill / Enclosed Area Operations:** Identifying enclosed areas and filling them with a new color. The fill color might depend on properties of the enclosing border.
Now, analyze the provided training examples and write the `solve` function.
"""
training_examples: str = dspy.InputField(desc="String representation of the input-output training pairs.")
python_function: str = dspy.OutputField(desc="A self-contained Python function `solve(input_matrix)` that implements the transformation.")
# The main signature for our solver module, which remains the same.
class SolveTaskSignature(dspy.Signature):
"""Given training examples and test inputs, solve the task for the test inputs."""
training_examples: List[TrainingExample] = dspy.InputField(description="Input and output examples demonstrating the task to be performed.")
test_inputs: List[MATRIX] = dspy.InputField(description="Input matrices to be solved following the task described in the training examples.")
test_outputs: List[MATRIX] = dspy.OutputField(description="Output matrices corresponding to the test inputs.")
# Custom module to orchestrate the two-step process: generate code, then execute it.
class ARC_Solver(dspy.Module):
def __init__(self):
super().__init__()
# Use ChainOfThought to encourage the LM to reason before writing the code.
self.rule_generator = dspy.ChainOfThought(GenerateRuleSignature)
def forward(self, training_examples: List[TrainingExample], test_inputs: List[MATRIX]):
# 1. Generate the Python solver function from the training examples.
# Convert Pydantic models to a string representation for the LM prompt.
training_examples_str = [ex.model_dump_json() for ex in training_examples]
generated_code = self.rule_generator(training_examples=str(training_examples_str)).python_function
# Extract the function code, handling potential markdown fences.
if "```python" in generated_code:
generated_code = generated_code.split("```python")[1].split("```")[0]
elif "```" in generated_code:
generated_code = generated_code.split("```")[1].split("```")[0]
outputs = []
try:
# 2. Execute the generated code to define the `solve` function.
local_namespace = {}
# Provide the 'copy' module to the execution environment.
exec_globals = {'copy': copy}
exec(generated_code, exec_globals, local_namespace)
solve_func = local_namespace['solve']
# 3. Apply the solver function to each test input.
for test_input in test_inputs:
# The generated function might fail on a specific input.
try:
output = solve_func(test_input)
outputs.append(output)
except Exception as e:
print(f"Generated function failed on a test input: {e}")
# Fallback for a single failing test case: return the input itself.
outputs.append(test_input)
except Exception as e:
print(f"Failed to generate or execute valid Python code. Error: {traceback.format_exc()}")
# Fallback strategy: If code generation or execution fails entirely,
# return the original test inputs as outputs.
outputs = test_inputs
return dspy.Prediction(test_outputs=outputs)
# Assign the improved custom module to the 'program' variable.
program = ARC_Solver()
Iteration 14: New subsample score is not better, skipping
Iteration 15: Selected program 1 score: 0.63
Average Metric: 3.00 / 3 (100.0%): 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [02:02<00:00, 40.85s/it]2025/08/28 22:06:45 INFO dspy.evaluate.evaluate: Average Metric: 3.0 / 3 (100.0%)
GEPA Optimization: 12%|█████████████▌ | 478/4000 [32:49<8:53:38, 9.09s/rollouts]
Iteration 15: All subsample scores perfect. Skipping.
Iteration 15: Reflective mutation did not propose a new candidate
Iteration 16: Selected program 0 score: 0.67
Average Metric: 2.00 / 3 (66.7%): 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 155.32it/s]2025/08/28 22:06:45 INFO dspy.evaluate.evaluate: Average Metric: 2.0 / 3 (66.7%)
2025/08/28 22:07:34 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 8, 8, 8], [0, 0, 4, 0, 0, 0, 0, 0, 0, 8], [0, 0, 4, 0, 0, 6, 6, 0, 0, 8], [0, 0, 4, 4, 0, 0, 6, 0, 0, 0], [0, 0, 4, 0, 0, 6, 6, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 3, 3, 0, 0, 0, 0, 0, 0, 0], [3, 3, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[4, 6, 8], [4, 6, 8], [4, 6, 8], [4, 6, 8], [4, 6, 8]]}, {'input': [[0, 0, 0, 0, 0, 0, 0, 4, 4, 4], [0, 0, 0, 0, 0, 0, 0, 4, 0, 4], [0, 0, 0, 0, 0, 0, 0, 0, 0, 4], [0, 9, 0, 0, 0, 6, 0, 0, 4, 4], [0, 9, 9, 0, 0, 6, 0, 0, 0, 4], [9, 9, 0, 0, 6, 6, 6, 0, 0, 0], [0, 9, 0, 0, 0, 0, 6, 0, 0, 0], [0, 9, 9, 0, 0, 0, 0, 0, 0, 0], [0, 9, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[9, 4], [9, 4], [9, 4], [9, 4], [9, 4], [9, 4], [9, 4], [9, 4], [9, 4]]}, {'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 1], [7, 7, 7, 0, 0, 2, 2, 0, 0, 1], [0, 0, 7, 0, 0, 0, 2, 2, 0, 1], [0, 0, 0, 0, 0, 0, 2, 0, 0, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 3, 0, 0, 0, 0, 0, 0], [0, 0, 0, 3, 3, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[2], [2], [2], [2], [2]]}, {'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 8, 0, 0, 0, 0, 0, 0, 0], [0, 0, 8, 0, 0, 0, 6, 0, 0, 0], [0, 0, 8, 0, 0, 0, 6, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 4, 4, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[8], [8], [8]]}, {'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 3, 3, 0, 0, 0], [0, 0, 0, 0, 0, 0, 3, 0, 0, 0], [0, 2, 0, 0, 0, 0, 0, 0, 0, 0], [0, 2, 2, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[2, 3], [2, 3], [2, 3]]}, {'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 8, 8, 8], [0, 1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 4, 4, 0, 0, 0, 0, 0], [0, 0, 0, 4, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[1, 4, 8], [1, 4, 8], [1, 4, 8]]}], 'test_inputs': [[[0, 0, 0, 0, 0, 0, 0, 0, 1, 0], [0, 0, 5, 0, 0, 0, 0, 1, 1, 1], [0, 5, 5, 0, 0, 0, 0, 0, 0, 0], [0, 0, 5, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 9, 9, 0, 0, 0], [8, 0, 0, 0, 0, 0, 0, 0, 0, 0], [8, 0, 0, 2, 2, 0, 0, 0, 0, 0], [8, 0, 0, 2, 0, 0, 0, 0, 0, 0], [0, 0, 0, 2, 0, 0, 0, 0, 0, 0]]], 'test_outputs': [[[5, 2, 1], [5, 2, 1], [5, 2, 1], [5, 2, 1]]]}) (input_keys={'test_inputs', 'training_examples'}): 'dict' object has no attribute 'input'
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 57, in forward
File "<string>", line 57, in <listcomp>
AttributeError: 'dict' object has no attribute 'input'
2025/08/28 22:07:34 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[6, 6, 0], [0, 6, 6], [0, 0, 6]], 'output': [[6, 6, 0, 6, 6, 0, 6, 6, 0, 6, 6, 0], [0, 6, 6, 0, 6, 6, 0, 6, 6, 0, 6, 6], [0, 0, 6, 0, 0, 6, 0, 0, 6, 0, 0, 6], [6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}, {'input': [[0, 4, 0], [0, 4, 4], [4, 0, 0]], 'output': [[0, 4, 0, 0, 4, 0, 0, 4, 0, 0, 4, 0, 0, 0, 0], [0, 4, 4, 0, 4, 4, 0, 4, 4, 0, 4, 4, 0, 0, 0], [4, 0, 0, 4, 0, 0, 4, 0, 0, 4, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}, {'input': [[3, 0, 3], [3, 0, 3], [0, 3, 3]], 'output': [[3, 0, 3, 3, 0, 3, 3, 0, 3], [3, 0, 3, 3, 0, 3, 3, 0, 3], [0, 3, 3, 0, 3, 3, 0, 3, 3], [3, 0, 3, 3, 0, 3, 3, 0, 3], [3, 0, 3, 3, 0, 3, 3, 0, 3], [0, 3, 3, 0, 3, 3, 0, 3, 3], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]]}, {'input': [[2, 0, 2], [0, 2, 0], [0, 0, 0]], 'output': [[2, 0, 2, 2, 0, 2, 2, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 2, 0, 0, 2, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}], 'test_inputs': [[[0, 0, 8], [0, 8, 0], [0, 0, 0]]], 'test_outputs': [[[0, 0, 8, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 8, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]]}) (input_keys={'test_inputs', 'training_examples'}): 'dict' object has no attribute 'input'
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 57, in forward
File "<string>", line 57, in <listcomp>
AttributeError: 'dict' object has no attribute 'input'
2025/08/28 22:07:34 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[1, 0, 7, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 1], [0, 0, 0, 0, 0, 0, 0, 0, 1, 0], [0, 0, 0, 0, 0, 0, 0, 1, 0, 7], [0, 0, 0, 0, 0, 0, 1, 0, 7, 0], [0, 0, 0, 0, 0, 1, 0, 7, 0, 0], [0, 0, 0, 0, 1, 0, 7, 0, 0, 0], [0, 0, 0, 1, 0, 7, 0, 0, 0, 0], [0, 0, 1, 0, 7, 0, 0, 0, 0, 0], [0, 1, 0, 7, 0, 0, 0, 0, 0, 0], [1, 0, 7, 0, 0, 0, 0, 0, 0, 0]]}, {'input': [[0, 0, 2, 0, 0]], 'output': [[0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 2], [0, 0, 0, 2, 0], [0, 0, 2, 0, 0]]}, {'input': [[4, 0, 6, 0, 8]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 6], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 6, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 6, 0, 8], [0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 6, 0, 8, 0], [0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 6, 0, 8, 0, 0], [0, 0, 0, 0, 0, 0, 0, 4, 0, 6, 0, 8, 0, 0, 0], [0, 0, 0, 0, 0, 0, 4, 0, 6, 0, 8, 0, 0, 0, 0], [0, 0, 0, 0, 0, 4, 0, 6, 0, 8, 0, 0, 0, 0, 0], [0, 0, 0, 0, 4, 0, 6, 0, 8, 0, 0, 0, 0, 0, 0], [0, 0, 0, 4, 0, 6, 0, 8, 0, 0, 0, 0, 0, 0, 0], [0, 0, 4, 0, 6, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0], [0, 4, 0, 6, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0], [4, 0, 6, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}, {'input': [[0, 9, 0, 8, 4]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9, 0, 8], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9, 0, 8, 4], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9, 0, 8, 4, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 9, 0, 8, 4, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 9, 0, 8, 4, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 9, 0, 8, 4, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 9, 0, 8, 4, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 9, 0, 8, 4, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 9, 0, 8, 4, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 9, 0, 8, 4, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 9, 0, 8, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 9, 0, 8, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}, {'input': [[0, 4, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0], [0, 0, 0, 0, 4], [0, 0, 0, 4, 0], [0, 0, 4, 0, 0], [0, 4, 0, 0, 0]]}], 'test_inputs': [[[0, 6, 7, 8, 9]]], 'test_outputs': [[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 7], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 7, 8], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 7, 8, 9], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 7, 8, 9, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 7, 8, 9, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 7, 8, 9, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 7, 8, 9, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 7, 8, 9, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 7, 8, 9, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 7, 8, 9, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 6, 7, 8, 9, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 6, 7, 8, 9, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 6, 7, 8, 9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 6, 7, 8, 9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 6, 7, 8, 9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 6, 7, 8, 9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 6, 7, 8, 9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 6, 7, 8, 9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]]}) (input_keys={'test_inputs', 'training_examples'}): 'dict' object has no attribute 'input'
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 57, in forward
File "<string>", line 57, in <listcomp>
AttributeError: 'dict' object has no attribute 'input'
2025/08/28 22:07:34 INFO dspy.evaluate.evaluate: Average Metric: 0.0 / 3 (0.0%)
GEPA Optimization: 12%|█████████████▊ | 484/4000 [33:38<8:45:33, 8.97s/rollouts]Iteration 16: Proposed new text for program: import dspy
from typing import List
import pydantic
import traceback
# Define the data structures using Pydantic and type hints for clarity and validation.
MATRIX = List[List[int]]
class TrainingExample(pydantic.BaseModel):
input: MATRIX
output: MATRIX
# This is the signature for the code-generating sub-module. It's highly detailed
# to guide the LM effectively.
class GeneratePythonSolverSignature(dspy.Signature):
"""
Analyze the provided training examples to understand the underlying transformation rule that maps each input matrix to its corresponding output matrix. Based on this inferred rule, write a single, self-contained Python function named 'solve'.
**Function Requirements:**
- The function must be named `solve`.
- It must accept one argument: `test_inputs`, which is a list of matrices (list of lists of integers).
- It must return a list of the transformed matrices.
- The function should not use any external libraries (like numpy or pandas). Standard Python libraries are allowed.
- The function should be robust and correctly implement the logic observed in the examples.
**Common Transformation Patterns to Consider:**
- **Tiling/Repetition:** The output might be a larger grid formed by repeating the input pattern. The scaling factor might depend on properties of the input grid like the count or color of non-zero cells.
- **Object/Blob Analysis:** The logic might involve identifying connected components (blobs) of the same color.
- **Filtering and Sorting:** The transformation may require filtering these blobs based on properties like size, and then sorting them based on properties like position (e.g., left-most column).
- **Pattern Drawing:** The output could be a new pattern constructed based on the colors, shapes, or positions of objects in the input. For example, creating diagonal lines from input rows.
Carefully deduce the complete algorithm from the examples and implement it in the `solve` function. Your output must be only the Python code for the function.
"""
training_examples: str = dspy.InputField(
description="A string representation of the input-output examples."
)
python_code: str = dspy.OutputField(
description="A string containing a single Python function `solve(test_inputs)` that implements the transformation."
)
class ARCProgram(dspy.Module):
"""
A program that solves Abstract Reasoning Corpus (ARC) tasks by
decomposing the problem into two steps:
1. Generating a Python function that encapsulates the task's logic.
2. Executing that function to solve for the test inputs.
"""
def __init__(self):
super().__init__()
# A ChainOfThought module is well-suited for the complex reasoning required
# to analyze examples and generate a correct Python function.
self.code_generator = dspy.ChainOfThought(GeneratePythonSolverSignature)
def forward(self, training_examples: List[TrainingExample], test_inputs: List[MATRIX]):
# Convert the structured training examples into a clean string format,
# which is easier for the LM to process.
examples_str = "\n\n".join([f"Input:\n{ex.input}\nOutput:\n{ex.output}" for ex in training_examples])
# Step 1: Generate the Python solver function.
prediction = self.code_generator(training_examples=examples_str)
python_code = prediction.python_code
# Step 2: Execute the generated code to solve the test inputs.
try:
# Clean up the generated code, as LMs often wrap it in markdown fences.
if python_code.startswith("```python"):
python_code = python_code[len("```python"):].strip()
if python_code.endswith("```"):
python_code = python_code[:-len("```")].strip()
# Prepare a local scope to safely execute the generated code.
local_scope = {}
exec(python_code, globals(), local_scope)
solve_function = local_scope.get('solve')
if solve_function:
# Call the dynamically created function.
test_outputs = solve_function(test_inputs)
else:
# The generated code did not define the 'solve' function.
raise ValueError("Generated code did not define the 'solve' function.")
except Exception as e:
print(f"Error executing generated code: {e}")
print(f"Traceback: {traceback.format_exc()}")
print(f"Generated code that failed:\n{python_code}")
# Fallback strategy: If code generation or execution fails,
# return the original test inputs to avoid crashing. This ensures
# the program always returns a validly typed object.
test_outputs = test_inputs
return dspy.Prediction(test_outputs=test_outputs)
# The final 'program' object is an instance of our new, more robust custom module.
# This serves as a drop-in replacement for the original program.
program = ARCProgram()
Iteration 16: New subsample score is not better, skipping
Iteration 17: Selected program 1 score: 0.63
Average Metric: 3.00 / 3 (100.0%): 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [03:16<00:00, 65.36s/it]2025/08/28 22:10:50 INFO dspy.evaluate.evaluate: Average Metric: 3.0 / 3 (100.0%)
GEPA Optimization: 12%|█████████████▊ | 487/4000 [36:54<13:43:32, 14.07s/rollouts]
Iteration 17: All subsample scores perfect. Skipping.
Iteration 17: Reflective mutation did not propose a new candidate
Iteration 18: Selected program 1 score: 0.63
Average Metric: 2.00 / 3 (66.7%): : 4it [05:04, 76.00s/it] 2025/08/28 22:15:54 INFO dspy.evaluate.evaluate: Average Metric: 2.0 / 3 (66.7%)
Iteration 18: Proposed new text for program: import dspy
from typing import List
import pydantic
# Define the type for a matrix for clarity
MATRIX = List[List[int]]
class TrainingExample(pydantic.BaseModel):
"""A Pydantic model for a single training example, containing an input and output matrix."""
input: MATRIX
output: MATRIX
class InferRuleSignature(dspy.Signature):
"""
Analyzes pairs of input/output matrices to deduce the transformation rule.
You are an expert analyst of abstract visual reasoning puzzles. Your task is to carefully examine the provided training examples. Each example consists of an 'input' matrix and a corresponding 'output' matrix.
Your goal is to deduce the single, consistent transformation rule that converts each input to its output. Describe this rule in clear, unambiguous, step-by-step natural language, as if you were explaining an algorithm to a programmer. The description must be precise enough for another AI to follow it to solve a new, unseen test input.
Consider various possible transformation patterns, including but not limited to:
- Geometric operations: rotation, reflection, scaling, repetition, shifting.
- Color/value transformations: changing specific numbers to others, conditional changes.
- Object-based logic: identifying shapes or objects and applying rules based on their properties (size, color, position).
- Fill/completion patterns: such as flood fills or completing a grid based on object boundaries.
- Bounding box or frame logic: creating structures based on the extents of objects.
"""
training_examples: List[TrainingExample] = dspy.InputField(description="A list of input and output examples demonstrating the task.")
transformation_rule: str = dspy.OutputField(description="A step-by-step natural language description of the transformation algorithm.")
class CodeGenerationSignature(dspy.Signature):
"""
Given a natural language transformation rule and an example input matrix, write a self-contained Python function to implement it.
You are an expert programmer. Your task is to write a Python function that precisely implements the given transformation rule.
**Function Requirements:**
- The function must be named `transform_matrix`.
- It must accept a single argument: `matrix` (a list of lists of integers).
- It must return the transformed matrix (a list of lists of integers).
- The function must be entirely self-contained. Do not use any external libraries like numpy or pandas. Standard Python functions and modules like 'copy' are acceptable.
- The output must be ONLY the Python code for the function. Do not include any explanations, comments outside the function, or markdown formatting like ```python.
"""
transformation_rule: str = dspy.InputField(description="The natural language rule to implement.")
test_input: MATRIX = dspy.InputField(description="An example input matrix to help understand the context and required dimensions.")
python_code: str = dspy.OutputField(description="A string containing the complete, self-contained Python function `transform_matrix`.")
class ARCProgram(dspy.Module):
"""A program that first infers a rule, generates code for it, and then executes the code."""
def __init__(self):
super().__init__()
# Use ChainOfThought for the complex reasoning task of rule inference.
self.rule_inferrer = dspy.ChainOfThought(InferRuleSignature)
# Use a simple Predict for the structured task of generating code from the rule.
self.code_generator = dspy.Predict(CodeGenerationSignature)
def forward(self, training_examples: List[TrainingExample], test_inputs: List[MATRIX]) -> dspy.Prediction:
"""
Infers a rule, generates a Python function to apply it, and executes it for each test input.
Args:
training_examples: A list of TrainingExample objects.
test_inputs: A list of input matrices to be solved.
Returns:
A dspy.Prediction object with the 'test_outputs' field populated.
"""
# 1. Infer the transformation rule once based on all training examples.
inferred = self.rule_inferrer(training_examples=training_examples)
rule = inferred.transformation_rule
all_test_outputs = []
# 2. Iterate through each test input.
for test_matrix in test_inputs:
try:
# 3. Generate Python code that implements the rule.
# Providing the test_input as context helps the LM generate correct code.
generated = self.code_generator(transformation_rule=rule, test_input=test_matrix)
code_string = generated.python_code
# 4. Execute the generated code in a restricted scope.
local_scope = {}
exec(code_string, {}, local_scope)
transform_func = local_scope['transform_matrix']
# 5. Apply the generated function to the test matrix.
output_matrix = transform_func(test_matrix)
all_test_outputs.append(output_matrix)
except Exception as e:
# Fallback strategy: if code generation, execution, or the function itself fails,
# append a correctly sized zero matrix.
print(f"An error occurred during code generation or execution: {e}")
if test_matrix and test_matrix[0]:
all_test_outputs.append([([0] * len(test_matrix[0])) for _ in range(len(test_matrix))])
else:
all_test_outputs.append([])
# 6. Return the collected outputs in a single Prediction object.
return dspy.Prediction(test_outputs=all_test_outputs)
# The final 'program' object is an instance of our robust, multi-step module.
program = ARCProgram()
2025/08/28 22:22:15 INFO dspy.evaluate.evaluate: Average Metric: 2.0 / 3 (66.7%)
GEPA Optimization: 12%|█████████████▉ | 493/4000 [48:19<33:43:06, 34.61s/rollouts]Iteration 18: New subsample score is not better, skipping
Iteration 19: Selected program 1 score: 0.63
Average Metric: 2.00 / 3 (66.7%): : 4it [03:15, 48.99s/it] 2025/08/28 22:25:31 INFO dspy.evaluate.evaluate: Average Metric: 2.0 / 3 (66.7%)
Iteration 19: Proposed new text for program: import dspy
from typing import List
import pydantic
import traceback
import copy
# Define the type for a matrix for clarity
MATRIX = List[List[int]]
class TrainingExample(pydantic.BaseModel):
"""A Pydantic model for a single training example, containing an input and output matrix."""
input: MATRIX
output: MATRIX
class InferRuleSignature(dspy.Signature):
"""
Analyzes pairs of input/output matrices to deduce the transformation rule.
You are an expert analyst of abstract visual reasoning puzzles. Your task is to carefully examine the provided training examples. Each example consists of an 'input' matrix and a corresponding 'output' matrix.
Your goal is to deduce the single, consistent transformation rule that converts each input to its output. Describe this rule in clear, unambiguous, step-by-step natural language, as if you were writing a specification for a programmer. The description must be precise enough for another AI to follow it to generate code that solves a new, unseen test input.
Consider various possible transformation patterns, including but not limited to:
- Geometric operations: rotation, reflection, scaling, repetition, shifting.
- Color/value transformations: changing specific numbers to others, conditional changes.
- Object-based logic: identifying shapes or objects and applying rules based on their properties (size, color, position).
- Fill/completion patterns: such as flood fills or completing a grid based on object boundaries.
- Bounding box or frame logic: creating structures based on the extents of objects.
"""
training_examples: List[TrainingExample] = dspy.InputField(description="A list of input and output examples demonstrating the task.")
transformation_rule: str = dspy.OutputField(description="A step-by-step natural language description of the transformation rule, written like a programming specification.")
class GeneratePythonCodeSignature(dspy.Signature):
"""
Writes a Python function to apply a transformation rule to a matrix.
You are an expert Python programmer. Your task is to write a single, self-contained Python function named `transform_matrix`. This function will implement the provided transformation rule.
The function must:
1. Be named `transform_matrix`.
2. Accept one argument: `matrix`, which is a list of lists of integers.
3. Return the transformed matrix as a list of lists of integers.
**IMPORTANT CONSTRAINTS:**
- Your output must be ONLY the Python code for the function.
- Do NOT include any import statements unless absolutely necessary (e.g., `import copy`).
- Do not include example usage, explanations, or markdown formatting like ```python ... ```.
- The function should not rely on any external libraries like numpy or pandas.
- Ensure the function handles edge cases gracefully (e.g., empty input matrix).
"""
transformation_rule: str = dspy.InputField(description="The natural language rule to implement.")
test_input: MATRIX = dspy.InputField(description="A sample input matrix to help understand the data structure and context.")
python_code: str = dspy.OutputField(description="The complete Python code for the `transform_matrix` function.")
class ARCProgram(dspy.Module):
"""A program that first infers a rule, generates code to implement it, and then executes the code."""
def __init__(self):
super().__init__()
# Use ChainOfThought for the complex reasoning task of rule inference.
self.rule_inferrer = dspy.ChainOfThought(InferRuleSignature)
# Use a simple Predict for the more direct task of generating code from the rule.
self.code_generator = dspy.Predict(GeneratePythonCodeSignature)
def execute_generated_code(self, code: str, test_matrix: MATRIX) -> MATRIX:
"""Safely executes the generated Python code."""
# Create a copy to avoid modifying the original input
matrix_copy = copy.deepcopy(test_matrix)
# Prepare the execution scope
local_scope = {}
# The generated code should define the 'transform_matrix' function.
# We execute it to make the function available in the local_scope.
exec(code, globals(), local_scope)
# Get the function from the scope
transform_func = local_scope.get('transform_matrix')
if not callable(transform_func):
raise ValueError("Generated code did not define a callable function named 'transform_matrix'.")
# Call the function with the input matrix
result = transform_func(matrix_copy)
return result
def forward(self, training_examples: List[TrainingExample], test_inputs: List[MATRIX]) -> dspy.Prediction:
"""
Infers a rule, generates Python code, and applies it to each test input.
Args:
training_examples: A list of TrainingExample objects.
test_inputs: A list of input matrices to be solved.
Returns:
A dspy.Prediction object with the 'test_outputs' field populated.
"""
# 1. Infer the transformation rule once based on all training examples.
inferred = self.rule_inferrer(training_examples=training_examples)
rule = inferred.transformation_rule
all_test_outputs = []
# 2. Iterate through each test input.
for test_matrix in test_inputs:
try:
# 2a. Generate Python code to implement the rule for the current test case.
# Providing the test_input gives the LM context about the matrix dimensions.
code_generation_result = self.code_generator(transformation_rule=rule, test_input=test_matrix)
generated_code = code_generation_result.python_code
# 2b. Execute the generated code to get the output matrix.
output_matrix = self.execute_generated_code(generated_code, test_matrix)
all_test_outputs.append(output_matrix)
except Exception as e:
# Fallback strategy: if code generation or execution fails, append a correctly sized empty matrix.
print(f"An error occurred during code generation or execution: {e}")
traceback.print_exc()
if test_matrix and isinstance(test_matrix, list) and len(test_matrix) > 0 and isinstance(test_matrix[0], list):
all_test_outputs.append([([0] * len(test_matrix[0])) for _ in range(len(test_matrix))])
else:
all_test_outputs.append([]) # Handle empty or malformed input
# 3. Return the collected outputs in a single Prediction object.
return dspy.Prediction(test_outputs=all_test_outputs)
# The final 'program' object is an instance of our robust, multi-step module.
program = ARCProgram()
Traceback (most recent call last):
File "<string>", line 110, in forward
File "<string>", line 82, in execute_generated_code
File "<string>", line 127, in transform_matrix
File "<string>", line 91, in classify_objects
TypeError: unhashable type: 'dict'
2025/08/28 22:31:46 INFO dspy.evaluate.evaluate: Average Metric: 1.0 / 3 (33.3%)
GEPA Optimization: 12%|██████████████ | 499/4000 [57:49<46:58:49, 48.31s/rollouts]An error occurred during code generation or execution: unhashable type: 'dict'
Iteration 19: New subsample score is not better, skipping
Iteration 20: Selected program 0 score: 0.67
Average Metric: 2.00 / 3 (66.7%): 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 188.54it/s]2025/08/28 22:31:46 INFO dspy.evaluate.evaluate: Average Metric: 2.0 / 3 (66.7%)
2025/08/28 22:32:39 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0], [0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0], [0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0], [0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0], [0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0], [0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8]]}, {'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0], [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2], [2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0], [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2], [2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0], [2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0], [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2], [2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0], [2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0], [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2], [2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0], [2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0]]}], 'test_inputs': [[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0], [1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1], [1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]], 'test_outputs': [[[0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0], [1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1], [1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1], [0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0], [1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1], [1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1], [0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0], [1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1], [1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1], [0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0], [1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1], [1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1], [0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0], [1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1]]]}) (input_keys={'test_inputs', 'training_examples'}): 'dict' object has no attribute 'input'
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 52, in forward
AttributeError: 'dict' object has no attribute 'input'
2025/08/28 22:32:39 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[3, 7, 3, 3, 3, 3, 3, 3, 3, 3, 7, 3, 3, 7, 3], [3, 7, 3, 3, 3, 3, 3, 3, 3, 3, 7, 3, 3, 7, 3], [7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7], [3, 7, 3, 3, 3, 3, 3, 3, 3, 3, 7, 3, 3, 7, 3], [3, 7, 3, 3, 3, 3, 3, 3, 3, 3, 7, 3, 3, 7, 3], [3, 7, 3, 3, 3, 3, 3, 3, 3, 3, 7, 3, 3, 7, 3], [3, 7, 3, 3, 3, 3, 3, 3, 3, 3, 7, 3, 3, 7, 3], [3, 7, 3, 3, 3, 3, 3, 3, 3, 3, 7, 3, 3, 7, 3], [3, 7, 3, 3, 3, 3, 3, 3, 3, 3, 7, 3, 3, 7, 3], [3, 7, 3, 3, 3, 3, 3, 3, 3, 3, 7, 3, 3, 7, 3], [3, 7, 3, 3, 3, 3, 3, 3, 3, 3, 7, 3, 3, 7, 3], [3, 7, 3, 3, 3, 3, 3, 3, 3, 3, 7, 3, 3, 7, 3], [3, 7, 3, 3, 3, 3, 3, 3, 3, 3, 7, 3, 3, 7, 3], [3, 7, 3, 3, 3, 3, 3, 3, 3, 3, 7, 3, 3, 7, 3], [3, 7, 3, 3, 3, 3, 3, 3, 3, 3, 7, 3, 3, 7, 3]], 'output': [[3, 3, 3, 3], [3, 3, 3, 3]]}, {'input': [[1, 1, 1, 1, 8, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 8, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 8, 1, 1, 1, 1, 1, 1], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [1, 1, 1, 1, 8, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 8, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 8, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 8, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 8, 1, 1, 1, 1, 1, 1], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [1, 1, 1, 1, 8, 1, 1, 1, 1, 1, 1]], 'output': [[1, 1], [1, 1], [1, 1]]}, {'input': [[3, 3, 3, 3, 3, 3, 1, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 1, 3, 1, 3, 1, 3], [3, 3, 3, 3, 3, 3, 1, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 1, 3, 1, 3, 1, 3], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [3, 3, 3, 3, 3, 3, 1, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 1, 3, 1, 3, 1, 3], [3, 3, 3, 3, 3, 3, 1, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 1, 3, 1, 3, 1, 3], [3, 3, 3, 3, 3, 3, 1, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 1, 3, 1, 3, 1, 3], [3, 3, 3, 3, 3, 3, 1, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 1, 3, 1, 3, 1, 3], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [3, 3, 3, 3, 3, 3, 1, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 1, 3, 1, 3, 1, 3], [3, 3, 3, 3, 3, 3, 1, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 1, 3, 1, 3, 1, 3], [3, 3, 3, 3, 3, 3, 1, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 1, 3, 1, 3, 1, 3], [3, 3, 3, 3, 3, 3, 1, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 1, 3, 1, 3, 1, 3], [3, 3, 3, 3, 3, 3, 1, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 1, 3, 1, 3, 1, 3], [3, 3, 3, 3, 3, 3, 1, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 1, 3, 1, 3, 1, 3], [3, 3, 3, 3, 3, 3, 1, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 1, 3, 1, 3, 1, 3], [3, 3, 3, 3, 3, 3, 1, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 1, 3, 1, 3, 1, 3], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [3, 3, 3, 3, 3, 3, 1, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 1, 3, 1, 3, 1, 3], [3, 3, 3, 3, 3, 3, 1, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 1, 3, 1, 3, 1, 3], [3, 3, 3, 3, 3, 3, 1, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 1, 3, 1, 3, 1, 3], [3, 3, 3, 3, 3, 3, 1, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 1, 3, 1, 3, 1, 3], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [3, 3, 3, 3, 3, 3, 1, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 1, 3, 1, 3, 1, 3], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [3, 3, 3, 3, 3, 3, 1, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 1, 3, 1, 3, 1, 3], [3, 3, 3, 3, 3, 3, 1, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 1, 3, 1, 3, 1, 3], [3, 3, 3, 3, 3, 3, 1, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 1, 3, 1, 3, 1, 3]], 'output': [[3, 3, 3, 3, 3], [3, 3, 3, 3, 3], [3, 3, 3, 3, 3], [3, 3, 3, 3, 3], [3, 3, 3, 3, 3], [3, 3, 3, 3, 3]]}], 'test_inputs': [[[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 5, 1, 1, 1, 1, 5, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 5, 1, 1, 1, 1, 5, 1], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 5, 1, 1, 1, 1, 5, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 5, 1, 1, 1, 1, 5, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 5, 1, 1, 1, 1, 5, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 5, 1, 1, 1, 1, 5, 1], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 5, 1, 1, 1, 1, 5, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 5, 1, 1, 1, 1, 5, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 5, 1, 1, 1, 1, 5, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 5, 1, 1, 1, 1, 5, 1], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 5, 1, 1, 1, 1, 5, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 5, 1, 1, 1, 1, 5, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 5, 1, 1, 1, 1, 5, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 5, 1, 1, 1, 1, 5, 1], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 5, 1, 1, 1, 1, 5, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 5, 1, 1, 1, 1, 5, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 5, 1, 1, 1, 1, 5, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 5, 1, 1, 1, 1, 5, 1]]], 'test_outputs': [[[1, 1, 1], [1, 1, 1], [1, 1, 1], [1, 1, 1], [1, 1, 1]]]}) (input_keys={'test_inputs', 'training_examples'}): 'dict' object has no attribute 'input'
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 52, in forward
AttributeError: 'dict' object has no attribute 'input'
2025/08/28 22:32:39 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[0, 0, 0, 8, 0, 8, 0, 0, 0], [0, 0, 0, 0, 8, 8, 0, 0, 0], [0, 0, 0, 0, 0, 8, 0, 0, 0], [0, 0, 0, 4, 0, 0, 0, 0, 0], [0, 0, 0, 4, 4, 4, 0, 0, 0], [0, 0, 0, 0, 4, 0, 0, 0, 0]], 'output': [[8, 0, 8, 8, 0, 8, 0, 0, 0], [8, 8, 0, 0, 8, 8, 0, 0, 0], [8, 0, 0, 0, 0, 8, 0, 0, 0], [0, 0, 0, 4, 0, 0, 0, 0, 0], [0, 0, 0, 4, 4, 4, 0, 0, 0], [0, 0, 0, 0, 4, 0, 0, 0, 0]]}, {'input': [[0, 0, 0, 8, 0, 8, 0, 0, 0], [0, 0, 0, 8, 8, 8, 0, 0, 0], [0, 0, 0, 8, 8, 0, 0, 0, 0], [0, 0, 0, 0, 0, 4, 0, 0, 0], [0, 0, 0, 4, 4, 4, 0, 0, 0], [0, 0, 0, 0, 4, 0, 0, 0, 0]], 'output': [[0, 0, 0, 8, 0, 8, 8, 0, 8], [0, 0, 0, 8, 8, 8, 8, 8, 8], [0, 0, 0, 8, 8, 0, 0, 8, 8], [0, 0, 0, 0, 0, 4, 0, 0, 0], [0, 0, 0, 4, 4, 4, 0, 0, 0], [0, 0, 0, 0, 4, 0, 0, 0, 0]]}, {'input': [[0, 0, 0, 8, 0, 0, 0, 0, 0], [0, 0, 0, 0, 8, 8, 0, 0, 0], [0, 0, 0, 8, 0, 0, 0, 0, 0], [0, 0, 0, 4, 0, 0, 0, 0, 0], [0, 0, 0, 4, 4, 4, 0, 0, 0], [0, 0, 0, 0, 4, 0, 0, 0, 0]], 'output': [[0, 0, 8, 8, 0, 0, 0, 0, 0], [8, 8, 0, 0, 8, 8, 0, 0, 0], [0, 0, 8, 8, 0, 0, 0, 0, 0], [0, 0, 0, 4, 0, 0, 0, 0, 0], [0, 0, 0, 4, 4, 4, 0, 0, 0], [0, 0, 0, 0, 4, 0, 0, 0, 0]]}], 'test_inputs': [[[0, 0, 0, 8, 0, 8, 0, 0, 0], [0, 0, 0, 0, 8, 8, 0, 0, 0], [0, 0, 0, 8, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 4, 0, 0, 0], [0, 0, 0, 4, 4, 4, 0, 0, 0], [0, 0, 0, 0, 4, 0, 0, 0, 0]]], 'test_outputs': [[[0, 0, 0, 8, 0, 8, 8, 0, 8], [0, 0, 0, 0, 8, 8, 8, 8, 0], [0, 0, 0, 8, 0, 0, 0, 0, 8], [0, 0, 0, 0, 0, 4, 0, 0, 0], [0, 0, 0, 4, 4, 4, 0, 0, 0], [0, 0, 0, 0, 4, 0, 0, 0, 0]]]}) (input_keys={'test_inputs', 'training_examples'}): 'dict' object has no attribute 'input'
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 52, in forward
AttributeError: 'dict' object has no attribute 'input'
2025/08/28 22:32:39 INFO dspy.evaluate.evaluate: Average Metric: 0.0 / 3 (0.0%)
GEPA Optimization: 13%|██████████████▎ | 505/4000 [58:42<37:31:24, 38.65s/rollouts]Iteration 20: Proposed new text for program: import dspy
from typing import List
import pydantic
import copy
# Define the type for a matrix for clarity
MATRIX = List[List[int]]
# Pydantic model for structuring training examples, ensuring type safety
class TrainingExample(pydantic.BaseModel):
input: MATRIX
output: MATRIX
class GenerateTransformer(dspy.Signature):
"""
You are an expert programmer specializing in solving visual reasoning puzzles.
Your task is to analyze a series of input/output matrix examples and generate a single, self-contained Python function named `transform_matrix` that implements the transformation rule.
**Function Signature:**
The function you generate MUST have the following signature:
`def transform_matrix(matrix: list[list[int]]) -> list[list[int]]:`
**Analysis Strategy:**
1. **Holistic View:** First, look at the overall change between inputs and outputs. Are dimensions changing? Are colors/numbers changing?
2. **Identify Patterns:** Look for common visual patterns and transformations:
* **Geometric:** Tiling, repetition, reflection (horizontal/vertical), rotation, scaling, cropping, padding.
* **Object-based:** Identify distinct objects/shapes based on color. Do they move, change color, get duplicated, or removed?
* **Grid/Line Operations:** Does the input have separator lines that define regions? The task might be to count these regions and create an output of that size.
* **Conditional Logic:** Is the transformation different based on a property of the input? (e.g., number of certain colored cells, presence of a specific shape, size of the grid).
* **Sorting/Ordering:** Are rows or columns being reordered based on some criteria (e.g., position of the first non-zero element, sum of elements)?
3. **Code Generation:**
* Write clean, correct Python code for the `transform_matrix` function.
* You can use standard libraries like `copy` if needed (e.g., `import copy; new_matrix = copy.deepcopy(matrix)`).
* Do NOT include any code outside the function definition.
* The function should be robust and handle the general case, not just the provided examples.
"""
training_examples: str = dspy.InputField(desc="A string representation of the input-output pairs demonstrating the task.")
transformer_code: str = dspy.OutputField(desc="A Python function string: `def transform_matrix(matrix): ...`")
class CodeGeneratorSolver(dspy.Module):
"""A module that solves matrix tasks by generating and executing Python code."""
def __init__(self):
super().__init__()
# Use dspy.Predict to generate the Python function based on the enhanced signature.
self.code_generator = dspy.Predict(GenerateTransformer)
def forward(self, training_examples: List[TrainingExample], test_inputs: List[MATRIX]) -> dspy.Prediction:
# Convert the structured training examples into a single string for the LM prompt.
examples_str = ""
for i, ex in enumerate(training_examples):
# Using a simple string representation for the matrices.
input_str = '\n'.join([str(row) for row in ex.input])
output_str = '\n'.join([str(row) for row in ex.output])
examples_str += f"--- Example {i+1} ---\n"
examples_str += f"Input:\n{input_str}\n"
examples_str += f"Output:\n{output_str}\n\n"
# Generate the Python function code.
prediction = self.code_generator(training_examples=examples_str)
transformer_code = prediction.transformer_code
# Prepare a safe, isolated scope for executing the generated code.
local_scope = {}
outputs = []
try:
# Execute the generated string to define the function in our scope.
exec(transformer_code, globals(), local_scope)
transform_func = local_scope.get('transform_matrix')
if callable(transform_func):
# If the function was defined successfully, apply it to each test input.
for test_matrix in test_inputs:
try:
# Use deepcopy to prevent the function from modifying the original input list.
input_copy = copy.deepcopy(test_matrix)
output_matrix = transform_func(input_copy)
outputs.append(output_matrix)
except Exception:
# Fallback for a single failing test case during execution.
outputs.append([])
else:
# Fallback if the LM failed to generate a callable function.
outputs = [[] for _ in test_inputs]
except Exception:
# Fallback if the generated code has a syntax error or other definition-time issue.
outputs = [[] for _ in test_inputs]
return dspy.Prediction(test_outputs=outputs)
# The final 'program' is an instance of our new, more robust module.
program = CodeGeneratorSolver()
Iteration 20: New subsample score is not better, skipping
Iteration 21: Selected program 1 score: 0.63
Average Metric: 3.00 / 3 (100.0%): 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:54<00:00, 18.21s/it]2025/08/28 22:33:33 INFO dspy.evaluate.evaluate: Average Metric: 3.0 / 3 (100.0%)
GEPA Optimization: 13%|██████████████▎ | 508/4000 [59:37<34:32:34, 35.61s/rollouts]
Iteration 21: All subsample scores perfect. Skipping.
Iteration 21: Reflective mutation did not propose a new candidate
Iteration 22: Selected program 1 score: 0.63
Average Metric: 3.00 / 3 (100.0%): 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [05:16<00:00, 105.42s/it]2025/08/28 22:38:50 INFO dspy.evaluate.evaluate: Average Metric: 3.0 / 3 (100.0%)
GEPA Optimization: 13%|██████████████▏ | 511/4000 [1:04:53<46:22:18, 47.85s/rollouts]
Iteration 22: All subsample scores perfect. Skipping.
Iteration 22: Reflective mutation did not propose a new candidate
Iteration 23: Selected program 1 score: 0.63
Average Metric: 1.00 / 1 (100.0%): 33%|█████████████████████████████████████ | 1/3 [00:26<00:52, 26.02s/it]2025/08/28 22:39:33 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
Average Metric: 3.00 / 3 (100.0%): 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [01:30<00:00, 30.20s/it]2025/08/28 22:40:20 INFO dspy.evaluate.evaluate: Average Metric: 3.0 / 3 (100.0%)
GEPA Optimization: 13%|██████████████▎ | 514/4000 [1:06:24<42:54:41, 44.31s/rollouts]
Iteration 23: All subsample scores perfect. Skipping.
Iteration 23: Reflective mutation did not propose a new candidate
Iteration 24: Selected program 0 score: 0.67
Average Metric: 2.00 / 2 (100.0%): 67%|██████████████████████████████████████████████████████████████████████████ | 2/3 [00:50<00:22, 22.93s/it]2025/08/28 22:41:11 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
Average Metric: 2.00 / 3 (66.7%): 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [04:15<00:00, 85.28s/it]2025/08/28 22:44:36 INFO dspy.evaluate.evaluate: Average Metric: 2.0 / 3 (66.7%)
2025/08/28 22:45:16 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 3, 0, 0, 0, 0, 0], [0, 1, 0, 3, 3, 3, 0, 1, 0, 0], [0, 0, 0, 0, 3, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}, {'input': [[0, 0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 3, 0, 0, 0, 0, 0, 0], [0, 0, 3, 3, 3, 0, 0, 0, 0, 0], [0, 0, 0, 3, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}], 'test_inputs': [[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], [[0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0]]], 'test_outputs': [[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 3, 3, 3, 0, 0, 0, 1], [0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], [[0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0], [0, 0, 0, 3, 0, 0], [0, 0, 3, 3, 3, 0], [0, 0, 0, 3, 0, 0], [0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0]]]}) (input_keys={'test_inputs', 'training_examples'}): 'dict' object has no attribute 'dict'
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 60, in forward
File "<string>", line 60, in <listcomp>
AttributeError: 'dict' object has no attribute 'dict'
2025/08/28 22:45:16 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 3, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 3, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 3, 0, 0], [0, 0, 0, 0, 0, 0, 3, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 8, 0, 0, 0, 0, 0], [0, 0, 3, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 3, 0, 0, 0, 0, 0, 0], [0, 8, 0, 0, 0, 8, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 3, 0, 0], [0, 0, 0, 0, 0, 0, 3, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 8, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}, {'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 3, 3, 0, 0, 0, 0, 0], [0, 0, 0, 3, 3, 0, 0, 0, 0, 0], [0, 3, 3, 0, 0, 0, 0, 0, 0, 0], [0, 3, 3, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [8, 0, 0, 0, 0, 0, 0, 0, 0, 0], [8, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 3, 3, 0, 0, 0, 0, 0], [0, 0, 0, 3, 3, 0, 0, 0, 0, 0], [0, 3, 3, 0, 0, 0, 0, 0, 0, 0], [0, 3, 3, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 8, 8, 0, 0, 0], [0, 0, 0, 0, 0, 8, 8, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}, {'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 3, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 3, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 8, 0, 0, 0, 0], [0, 0, 0, 3, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 3, 0, 0, 0, 0, 0], [0, 0, 8, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}], 'test_inputs': [[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 3, 3, 3, 0], [0, 0, 0, 0, 0, 0, 3, 3, 3, 0], [0, 0, 0, 0, 0, 0, 3, 3, 3, 0], [0, 0, 0, 3, 3, 3, 0, 0, 0, 0], [0, 0, 0, 3, 3, 3, 0, 0, 0, 0], [0, 0, 0, 3, 3, 3, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]], 'test_outputs': [[[8, 8, 8, 0, 0, 0, 0, 0, 0, 0], [8, 8, 8, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 3, 3, 3, 0], [0, 0, 0, 0, 0, 0, 3, 3, 3, 0], [0, 0, 0, 0, 0, 0, 3, 3, 3, 0], [0, 0, 0, 3, 3, 3, 0, 0, 0, 0], [0, 0, 0, 3, 3, 3, 0, 0, 0, 0], [0, 0, 0, 3, 3, 3, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 8], [0, 0, 0, 0, 0, 0, 0, 0, 0, 8]]]}) (input_keys={'test_inputs', 'training_examples'}): 'dict' object has no attribute 'dict'
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 60, in forward
File "<string>", line 60, in <listcomp>
AttributeError: 'dict' object has no attribute 'dict'
2025/08/28 22:45:16 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 4, 4, 0, 0, 0, 0, 0], [0, 4, 4, 4, 4, 0, 0, 0, 0], [0, 0, 4, 4, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 4, 4, 4, 4, 0, 0], [0, 0, 4, 4, 4, 4, 0, 0], [4, 4, 4, 4, 4, 4, 4, 4], [4, 4, 4, 4, 4, 4, 4, 4], [0, 0, 4, 4, 4, 4, 0, 0], [0, 0, 4, 4, 4, 4, 0, 0]]}, {'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 4, 0, 0, 0, 0], [0, 0, 0, 4, 4, 0, 0, 0, 0], [0, 0, 0, 0, 0, 4, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 4, 4, 0, 0], [0, 0, 4, 4, 0, 0], [4, 4, 4, 4, 0, 0], [4, 4, 4, 4, 0, 0], [0, 0, 0, 0, 4, 4], [0, 0, 0, 0, 4, 4]]}, {'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 4, 0, 0, 0, 0, 0, 0], [0, 4, 4, 0, 0, 0, 0, 0, 0], [0, 0, 4, 0, 0, 0, 0, 0, 0], [0, 0, 4, 4, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 4, 4, 0, 0], [0, 0, 4, 4, 0, 0], [4, 4, 4, 4, 0, 0], [4, 4, 4, 4, 0, 0], [0, 0, 4, 4, 0, 0], [0, 0, 4, 4, 0, 0], [0, 0, 4, 4, 4, 4], [0, 0, 4, 4, 4, 4]]}], 'test_inputs': [[[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 4, 0, 4, 0, 0], [0, 0, 0, 4, 0, 4, 0, 4, 0], [0, 0, 0, 0, 4, 0, 4, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]]], 'test_outputs': [[[0, 0, 4, 4, 0, 0, 4, 4, 0, 0], [0, 0, 4, 4, 0, 0, 4, 4, 0, 0], [4, 4, 0, 0, 4, 4, 0, 0, 4, 4], [4, 4, 0, 0, 4, 4, 0, 0, 4, 4], [0, 0, 4, 4, 0, 0, 4, 4, 0, 0], [0, 0, 4, 4, 0, 0, 4, 4, 0, 0]]]}) (input_keys={'test_inputs', 'training_examples'}): 'dict' object has no attribute 'dict'
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 60, in forward
File "<string>", line 60, in <listcomp>
AttributeError: 'dict' object has no attribute 'dict'
2025/08/28 22:45:16 INFO dspy.evaluate.evaluate: Average Metric: 0.0 / 3 (0.0%)
GEPA Optimization: 13%|██████████████▍ | 520/4000 [1:11:20<44:37:06, 46.16s/rollouts]Iteration 24: Proposed new text for program: import dspy
from typing import List
import pydantic
import json
import re
import copy
# Define the data structures for matrices and training examples using Pydantic.
# This ensures type safety and clear data representation.
MATRIX = List[List[int]]
class TrainingExample(pydantic.BaseModel):
input: MATRIX
output: MATRIX
# This is the original signature, which we will replace with a more robust module.
# We keep the Pydantic models as they are useful for structuring data.
class SolveTaskSignature(dspy.Signature):
training_examples: List[TrainingExample] = dspy.InputField(description="Input and output examples demonstrating the task to be performed.")
test_inputs: List[MATRIX] = dspy.InputField(description="Input matrices to be solved following the task described in the training examples.")
test_outputs: List[MATRIX] = dspy.OutputField(description="Output matrices corresponding to the test inputs.")
# New signature to guide the LM to generate a Python function.
class GeneratePythonFunction(dspy.Signature):
"""
You are an expert programmer specializing in abstract reasoning. Your task is to analyze pairs of input and output grids (matrices of integers) to deduce the transformation rule.
Based on your analysis, you must write a single, self-contained Python function named `transform_matrix`. This function will serve as the solution to the task.
Function requirements:
1. The function must be named exactly `transform_matrix`.
2. It must accept one argument: `matrix`, which is a list of lists of integers.
3. It must return the transformed matrix, also as a list of lists of integers.
4. You can use common Python libraries like `numpy` if needed, but you must include the necessary import statements *inside* the function's code string.
5. Your entire output must be a single Python code block containing only the function definition. Do not include any explanatory text or example usage outside the function.
Example of a valid function format:
def transform_matrix(matrix):
# import numpy as np # if needed
# Your logic to find the pattern and transform the matrix goes here.
# For example, to double every cell's value:
new_matrix = [[cell * 2 for cell in row] for row in matrix]
return new_matrix
"""
training_examples: str = dspy.InputField(description="A string representation of the training examples, each showing an input and its corresponding output matrix.")
test_input_shapes: str = dspy.InputField(description="A string showing the shapes of the test input matrices to provide context on the expected dimensions.")
python_function: str = dspy.OutputField(description="A string containing the complete Python code for the `transform_matrix` function.")
# Custom module to orchestrate the code generation and execution process.
class ARCProgram(dspy.Module):
def __init__(self):
super().__init__()
# Use ChainOfThought to encourage the LM to reason before generating the code.
self.generate_function = dspy.ChainOfThought(GeneratePythonFunction)
def forward(self, training_examples: List[TrainingExample], test_inputs: List[MATRIX]):
# 1. Format the inputs for the language model.
# Pydantic models are converted to JSON strings for clear, structured input.
examples_str = json.dumps([ex.dict() for ex in training_examples], indent=2)
shapes_str = json.dumps([f"{len(m)}x{len(m[0]) if m else 0}" for m in test_inputs])
# 2. Call the LM to generate the Python function.
prediction = self.generate_function(
training_examples=examples_str,
test_input_shapes=shapes_str
)
python_code = prediction.python_function
# 3. Execute the generated code safely.
final_outputs = []
try:
# Clean the generated code block from markdown fences.
cleaned_code = re.sub(r'^```python\n|```$', '', python_code, flags=re.MULTILINE).strip()
# Execute the function definition in a restricted local scope.
local_scope = {}
exec(cleaned_code, globals(), local_scope)
transform_func = local_scope.get('transform_matrix')
if not callable(transform_func):
raise ValueError("`transform_matrix` function not found or is not callable.")
# Apply the generated function to each test input.
for test_matrix in test_inputs:
# Use deepcopy to prevent the function from modifying the original input list.
input_copy = copy.deepcopy(test_matrix)
result = transform_func(input_copy)
final_outputs.append(result)
except Exception as e:
print(f"Failed to execute generated code: {e}")
# Fallback strategy: If code generation or execution fails,
# return a list of empty matrices to satisfy the output signature.
final_outputs = [[] for _ in test_inputs]
# 4. Return the final prediction object.
return dspy.Prediction(test_outputs=final_outputs)
# The final 'program' object is an instance of our robust, custom module.
program = ARCProgram()
Iteration 24: New subsample score is not better, skipping
Iteration 25: Selected program 0 score: 0.67
Average Metric: 1.00 / 3 (33.3%): 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [04:05<00:00, 81.83s/it]2025/08/28 22:49:22 INFO dspy.evaluate.evaluate: Average Metric: 1.0 / 3 (33.3%)
2025/08/28 22:50:03 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0], [0, 4, 4, 4, 4, 4, 0, 0, 0, 0, 4, 0, 0], [0, 3, 0, 3, 0, 3, 0, 0, 0, 0, 4, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 2, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0], [0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0], [0, 4, 4, 4, 4, 4, 0, 0, 0, 3, 4, 0, 0], [0, 3, 0, 3, 0, 3, 0, 0, 0, 0, 4, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 2, 0], [0, 0, 0, 3, 0, 3, 0, 3, 0, 0, 0, 0, 0], [0, 0, 0, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0], [0, 0, 0, 2, 1, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}, {'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 4, 4, 4, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 3, 3, 4, 3, 0, 0, 0, 2, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 4, 4, 4, 2, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 4, 4, 4, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 3, 3, 4, 3, 0, 0, 0, 2, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 1, 4, 4, 4, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 3, 3, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 3, 0, 4, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 3, 0, 4, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 4, 4, 4, 2, 0, 0, 0, 0, 0, 0], [0, 0, 0, 3, 0, 1, 0, 0, 0, 0, 0, 0, 0]]}, {'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 4, 2, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 2, 4, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 4, 4, 4, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 2, 4, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 4, 2, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 2, 4, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 4, 4, 4, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 4, 3, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 4, 3, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 4, 4, 4, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 2, 4, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}], 'test_inputs': [[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 4, 4, 2, 0, 0, 0, 0, 0, 0, 4, 4, 0, 0], [0, 0, 4, 0, 4, 3, 0, 0, 0, 0, 4, 0, 4, 0, 0], [0, 0, 0, 4, 4, 1, 0, 0, 0, 0, 4, 4, 2, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 4, 4, 0, 0, 0, 0, 0, 2, 4, 4, 0, 0, 0, 0], [0, 4, 0, 4, 0, 0, 0, 0, 4, 0, 4, 0, 0, 0, 0], [0, 2, 4, 4, 0, 0, 0, 0, 4, 4, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]], 'test_outputs': [[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 4, 4, 2, 0, 0, 0, 0, 0, 0, 4, 4, 1, 0], [0, 0, 4, 0, 4, 3, 0, 0, 0, 0, 4, 0, 4, 3, 0], [0, 0, 0, 4, 4, 1, 0, 0, 0, 0, 4, 4, 2, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 3, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 1, 0, 0, 0, 0], [1, 4, 4, 0, 0, 0, 0, 0, 2, 4, 4, 0, 0, 0, 0], [3, 4, 0, 4, 0, 0, 0, 3, 4, 0, 4, 0, 0, 0, 0], [0, 2, 4, 4, 0, 0, 0, 1, 4, 4, 0, 0, 0, 0, 0], [0, 0, 3, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]]}) (input_keys={'test_inputs', 'training_examples'}): 'dict' object has no attribute 'model_dump'
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 58, in forward
File "<string>", line 58, in <listcomp>
AttributeError: 'dict' object has no attribute 'model_dump'
2025/08/28 22:50:03 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[5, 5, 0], [5, 0, 5], [0, 5, 0]], 'output': [[1]]}, {'input': [[8, 0, 8], [0, 8, 0], [8, 0, 8]], 'output': [[2]]}, {'input': [[5, 0, 5], [0, 5, 0], [5, 0, 5]], 'output': [[2]]}, {'input': [[0, 1, 1], [0, 1, 1], [1, 0, 0]], 'output': [[3]]}, {'input': [[0, 8, 8], [0, 8, 8], [8, 0, 0]], 'output': [[3]]}, {'input': [[4, 4, 0], [4, 0, 4], [0, 4, 0]], 'output': [[1]]}, {'input': [[0, 5, 0], [5, 5, 5], [0, 5, 0]], 'output': [[6]]}], 'test_inputs': [[[0, 8, 0], [8, 8, 8], [0, 8, 0]], [[7, 7, 0], [7, 0, 7], [0, 7, 0]], [[2, 0, 2], [0, 2, 0], [2, 0, 2]]], 'test_outputs': [[[6]], [[1]], [[2]]]}) (input_keys={'test_inputs', 'training_examples'}): 'dict' object has no attribute 'model_dump'
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 58, in forward
File "<string>", line 58, in <listcomp>
AttributeError: 'dict' object has no attribute 'model_dump'
2025/08/28 22:50:03 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 5, 5, 5, 5, 5, 5, 0, 0, 5, 0, 0, 0], [0, 5, 0, 0, 5, 5, 5, 5, 0, 0, 5, 0, 5, 0, 5, 0, 0, 5, 0, 0, 5, 5], [5, 0, 5, 5, 0, 5, 5, 5, 0, 0, 5, 5, 2, 0, 0, 0, 0, 0, 0, 0, 5, 0], [5, 0, 0, 5, 5, 0, 0, 0, 5, 0, 0, 0, 2, 5, 5, 5, 0, 5, 5, 5, 0, 0], [0, 0, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 2, 5, 5, 0, 0, 5, 0, 5, 5, 0], [0, 5, 0, 0, 5, 0, 0, 0, 5, 2, 5, 2, 5, 5, 5, 2, 5, 0, 5, 0, 0, 0], [0, 5, 5, 0, 5, 0, 0, 0, 0, 0, 5, 0, 2, 5, 0, 0, 5, 0, 0, 5, 5, 5], [0, 0, 0, 0, 0, 5, 0, 0, 0, 5, 5, 0, 2, 5, 0, 5, 5, 0, 5, 0, 0, 0], [5, 0, 0, 0, 0, 0, 5, 0, 5, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 5, 0, 5], [5, 0, 0, 5, 0, 0, 0, 0, 0, 5, 5, 0, 5, 5, 0, 0, 0, 0, 0, 5, 0, 0], [0, 5, 0, 5, 0, 5, 5, 5, 5, 5, 0, 0, 0, 0, 5, 0, 5, 5, 5, 0, 5, 5], [0, 5, 5, 0, 0, 5, 0, 0, 5, 0, 5, 5, 0, 5, 5, 0, 5, 5, 0, 0, 5, 5], [0, 0, 5, 5, 0, 2, 5, 5, 5, 0, 0, 5, 0, 0, 0, 0, 0, 5, 5, 0, 0, 0], [5, 0, 5, 0, 0, 5, 5, 5, 0, 0, 0, 0, 0, 5, 0, 0, 5, 5, 0, 0, 0, 5], [0, 0, 2, 5, 5, 2, 2, 2, 2, 0, 0, 0, 5, 5, 0, 5, 0, 0, 5, 0, 5, 0], [0, 5, 5, 0, 0, 5, 5, 0, 5, 0, 0, 5, 0, 0, 5, 5, 5, 0, 0, 0, 0, 0], [5, 0, 0, 0, 5, 2, 0, 5, 5, 0, 5, 0, 0, 5, 0, 0, 5, 5, 5, 0, 0, 0], [0, 0, 5, 5, 0, 2, 5, 0, 0, 0, 5, 0, 0, 0, 5, 5, 0, 0, 0, 5, 5, 5], [0, 5, 5, 5, 0, 0, 0, 5, 5, 5, 5, 0, 0, 5, 5, 0, 5, 0, 0, 0, 5, 5], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 5, 0, 5, 0, 0, 0, 5]], 'output': [[0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 5, 5, 5, 5, 5, 5, 0, 0, 5, 0, 0, 0], [0, 5, 0, 0, 5, 5, 5, 5, 0, 0, 5, 0, 5, 0, 5, 0, 0, 5, 0, 0, 5, 5], [5, 0, 5, 5, 0, 5, 5, 5, 0, 0, 5, 5, 2, 0, 0, 0, 0, 0, 0, 0, 5, 0], [5, 0, 0, 5, 5, 0, 0, 0, 5, 0, 0, 0, 2, 5, 5, 5, 0, 5, 5, 5, 0, 0], [0, 0, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 2, 5, 5, 0, 0, 5, 0, 5, 5, 0], [0, 5, 0, 0, 5, 0, 0, 0, 5, 2, 8, 2, 8, 8, 8, 2, 5, 0, 5, 0, 0, 0], [0, 5, 5, 0, 5, 0, 0, 0, 0, 0, 5, 0, 2, 5, 0, 0, 5, 0, 0, 5, 5, 5], [0, 0, 0, 0, 0, 5, 0, 0, 0, 5, 5, 0, 2, 5, 0, 5, 5, 0, 5, 0, 0, 0], [5, 0, 0, 0, 0, 0, 5, 0, 5, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 5, 0, 5], [5, 0, 0, 5, 0, 0, 0, 0, 0, 5, 5, 0, 5, 5, 0, 0, 0, 0, 0, 5, 0, 0], [0, 5, 0, 5, 0, 5, 5, 5, 5, 5, 0, 0, 0, 0, 5, 0, 5, 5, 5, 0, 5, 5], [0, 5, 5, 0, 0, 8, 0, 0, 5, 0, 5, 5, 0, 5, 5, 0, 5, 5, 0, 0, 5, 5], [0, 0, 5, 5, 0, 2, 5, 5, 5, 0, 0, 5, 0, 0, 0, 0, 0, 5, 5, 0, 0, 0], [5, 0, 5, 0, 0, 8, 5, 5, 0, 0, 0, 0, 0, 5, 0, 0, 5, 5, 0, 0, 0, 5], [0, 0, 2, 8, 8, 2, 2, 2, 2, 0, 0, 0, 5, 5, 0, 5, 0, 0, 5, 0, 5, 0], [0, 5, 5, 0, 0, 8, 5, 0, 5, 0, 0, 5, 0, 0, 5, 5, 5, 0, 0, 0, 0, 0], [5, 0, 0, 0, 5, 2, 0, 5, 5, 0, 5, 0, 0, 5, 0, 0, 5, 5, 5, 0, 0, 0], [0, 0, 5, 5, 0, 2, 5, 0, 0, 0, 5, 0, 0, 0, 5, 5, 0, 0, 0, 5, 5, 5], [0, 5, 5, 5, 0, 0, 0, 5, 5, 5, 5, 0, 0, 5, 5, 0, 5, 0, 0, 0, 5, 5], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 5, 0, 5, 0, 0, 0, 5]]}, {'input': [[0, 5, 0, 5, 0, 0, 0, 5, 5, 0, 5, 5, 0, 0, 0, 5, 5, 0, 5, 5], [5, 5, 0, 5, 5, 5, 0, 5, 5, 0, 5, 0, 0, 5, 0, 0, 0, 5, 5, 0], [0, 5, 0, 5, 5, 0, 5, 5, 0, 5, 0, 0, 5, 0, 0, 5, 0, 0, 5, 5], [5, 0, 0, 5, 5, 0, 2, 5, 0, 5, 0, 5, 0, 0, 0, 5, 5, 5, 5, 5], [0, 5, 0, 5, 2, 5, 2, 2, 2, 0, 5, 5, 0, 5, 0, 5, 5, 0, 0, 0], [5, 5, 0, 0, 5, 5, 2, 5, 5, 5, 0, 5, 0, 0, 5, 5, 0, 0, 0, 0], [0, 0, 5, 5, 0, 0, 5, 5, 0, 0, 5, 5, 0, 0, 5, 0, 0, 5, 0, 5], [0, 0, 0, 5, 0, 5, 0, 5, 5, 5, 0, 5, 5, 5, 0, 0, 5, 5, 0, 5], [5, 0, 0, 0, 5, 0, 0, 5, 5, 5, 5, 0, 5, 5, 5, 0, 0, 5, 0, 5], [5, 0, 0, 5, 0, 5, 5, 5, 0, 5, 5, 0, 5, 0, 5, 5, 5, 5, 5, 5], [5, 0, 5, 5, 0, 5, 5, 5, 5, 5, 0, 5, 2, 5, 2, 2, 2, 0, 0, 5], [0, 0, 5, 0, 0, 0, 0, 0, 0, 5, 5, 5, 0, 0, 5, 0, 0, 5, 0, 5], [0, 0, 5, 0, 0, 5, 0, 5, 5, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0], [5, 5, 0, 0, 5, 5, 0, 5, 0, 0, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0], [5, 5, 0, 0, 0, 5, 5, 5, 0, 5, 5, 0, 5, 5, 5, 5, 0, 0, 5, 5], [0, 0, 5, 0, 5, 5, 5, 2, 2, 5, 5, 0, 0, 5, 0, 0, 5, 5, 0, 0], [0, 5, 5, 0, 0, 5, 5, 2, 5, 0, 5, 5, 0, 0, 5, 0, 5, 5, 0, 0], [0, 0, 5, 0, 5, 0, 5, 5, 0, 5, 5, 5, 0, 0, 5, 0, 0, 0, 5, 0], [0, 0, 5, 0, 5, 5, 0, 5, 5, 5, 0, 5, 5, 5, 0, 5, 0, 0, 5, 5], [5, 5, 5, 0, 5, 0, 5, 0, 5, 5, 0, 0, 5, 5, 0, 0, 0, 0, 0, 5]], 'output': [[0, 5, 0, 5, 0, 0, 0, 5, 5, 0, 5, 5, 0, 0, 0, 5, 5, 0, 5, 5], [5, 5, 0, 5, 5, 5, 0, 5, 5, 0, 5, 0, 0, 5, 0, 0, 0, 5, 5, 0], [0, 5, 0, 5, 5, 0, 8, 5, 0, 5, 0, 0, 5, 0, 0, 5, 0, 0, 5, 5], [5, 0, 0, 5, 5, 0, 2, 5, 0, 5, 0, 5, 0, 0, 0, 5, 5, 5, 5, 5], [0, 5, 0, 5, 2, 8, 2, 2, 2, 0, 5, 5, 0, 5, 0, 5, 5, 0, 0, 0], [5, 5, 0, 0, 5, 5, 2, 5, 5, 5, 0, 5, 0, 0, 5, 5, 0, 0, 0, 0], [0, 0, 5, 5, 0, 0, 8, 5, 0, 0, 5, 5, 0, 0, 5, 0, 0, 5, 0, 5], [0, 0, 0, 5, 0, 5, 0, 5, 5, 5, 0, 5, 5, 5, 0, 0, 5, 5, 0, 5], [5, 0, 0, 0, 5, 0, 0, 5, 5, 5, 5, 0, 5, 5, 8, 0, 0, 5, 0, 5], [5, 0, 0, 5, 0, 5, 5, 5, 0, 5, 5, 0, 5, 0, 8, 5, 5, 5, 5, 5], [5, 0, 5, 5, 0, 5, 5, 5, 5, 5, 0, 5, 2, 8, 2, 2, 2, 0, 0, 5], [0, 0, 5, 0, 0, 0, 0, 0, 0, 5, 5, 5, 0, 0, 8, 0, 0, 5, 0, 5], [0, 0, 5, 0, 0, 5, 0, 5, 5, 0, 5, 5, 5, 5, 8, 5, 5, 5, 5, 0], [5, 5, 0, 0, 5, 5, 0, 8, 0, 0, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0], [5, 5, 0, 0, 0, 5, 5, 8, 0, 5, 5, 0, 5, 5, 5, 5, 0, 0, 5, 5], [0, 0, 5, 0, 5, 8, 8, 2, 2, 8, 5, 0, 0, 5, 0, 0, 5, 5, 0, 0], [0, 5, 5, 0, 0, 5, 5, 2, 5, 0, 5, 5, 0, 0, 5, 0, 5, 5, 0, 0], [0, 0, 5, 0, 5, 0, 5, 8, 0, 5, 5, 5, 0, 0, 5, 0, 0, 0, 5, 0], [0, 0, 5, 0, 5, 5, 0, 5, 5, 5, 0, 5, 5, 5, 0, 5, 0, 0, 5, 5], [5, 5, 5, 0, 5, 0, 5, 0, 5, 5, 0, 0, 5, 5, 0, 0, 0, 0, 0, 5]]}, {'input': [[0, 0, 5, 0, 5, 0, 5, 5, 5, 5, 0, 5, 5, 0, 0, 0, 5, 5, 0], [0, 0, 5, 5, 5, 0, 5, 5, 5, 5, 0, 0, 5, 5, 5, 5, 5, 0, 5], [0, 5, 5, 5, 0, 5, 0, 5, 5, 0, 0, 0, 5, 5, 5, 0, 5, 0, 0], [5, 5, 5, 5, 5, 0, 0, 0, 5, 5, 5, 5, 5, 5, 0, 0, 5, 0, 0], [5, 5, 0, 0, 0, 5, 5, 5, 0, 5, 5, 5, 5, 0, 0, 0, 5, 0, 0], [5, 0, 0, 0, 0, 0, 5, 0, 5, 0, 5, 2, 5, 0, 0, 5, 0, 5, 5], [5, 0, 5, 0, 0, 5, 5, 0, 5, 2, 2, 5, 2, 2, 5, 5, 0, 5, 0], [0, 5, 0, 5, 5, 5, 5, 5, 0, 5, 0, 5, 5, 5, 5, 0, 5, 5, 5], [5, 5, 5, 0, 5, 5, 5, 5, 0, 0, 5, 2, 5, 5, 5, 0, 0, 0, 0], [5, 2, 2, 5, 0, 0, 5, 0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0], [5, 2, 5, 5, 5, 0, 0, 5, 5, 5, 5, 0, 0, 5, 5, 5, 5, 0, 5], [0, 2, 5, 0, 5, 5, 0, 0, 5, 5, 5, 5, 5, 0, 5, 5, 5, 5, 0], [0, 5, 0, 0, 5, 5, 0, 0, 5, 5, 0, 0, 5, 0, 0, 5, 0, 0, 0], [5, 0, 0, 5, 5, 5, 5, 5, 0, 0, 5, 5, 5, 0, 5, 5, 5, 0, 5], [0, 5, 5, 5, 0, 0, 5, 0, 0, 0, 5, 0, 5, 5, 5, 5, 0, 0, 0], [5, 5, 0, 0, 5, 5, 5, 5, 0, 5, 5, 0, 5, 0, 5, 0, 0, 0, 0], [5, 0, 5, 0, 5, 0, 0, 0, 0, 0, 0, 5, 0, 0, 5, 0, 5, 0, 5], [0, 5, 5, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 5, 0, 5, 5, 5, 5]], 'output': [[0, 0, 5, 0, 5, 0, 5, 5, 5, 5, 0, 5, 5, 0, 0, 0, 5, 5, 0], [0, 0, 5, 5, 5, 0, 5, 5, 5, 5, 0, 0, 5, 5, 5, 5, 5, 0, 5], [0, 5, 5, 5, 0, 5, 0, 5, 5, 0, 0, 0, 5, 5, 5, 0, 5, 0, 0], [5, 5, 5, 5, 5, 0, 0, 0, 5, 5, 5, 5, 5, 5, 0, 0, 5, 0, 0], [5, 5, 0, 0, 0, 5, 5, 5, 0, 5, 5, 8, 5, 0, 0, 0, 5, 0, 0], [5, 0, 0, 0, 0, 0, 5, 0, 5, 0, 5, 2, 5, 0, 0, 5, 0, 5, 5], [5, 0, 5, 0, 0, 5, 5, 0, 5, 2, 2, 8, 2, 2, 5, 5, 0, 5, 0], [0, 8, 0, 5, 5, 5, 5, 5, 0, 5, 0, 8, 5, 5, 5, 0, 5, 5, 5], [5, 8, 5, 0, 5, 5, 5, 5, 0, 0, 5, 2, 5, 5, 5, 0, 0, 0, 0], [8, 2, 2, 8, 0, 0, 5, 0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0], [5, 2, 5, 5, 5, 0, 0, 5, 5, 5, 5, 0, 0, 5, 5, 5, 5, 0, 5], [0, 2, 5, 0, 5, 5, 0, 0, 5, 5, 5, 5, 5, 0, 5, 5, 5, 5, 0], [0, 5, 0, 0, 5, 5, 0, 0, 5, 5, 0, 0, 5, 0, 0, 5, 0, 0, 0], [5, 0, 0, 5, 5, 5, 5, 5, 0, 0, 5, 5, 5, 0, 5, 5, 5, 0, 5], [0, 5, 5, 5, 0, 0, 5, 0, 0, 0, 5, 0, 5, 5, 5, 5, 0, 0, 0], [5, 5, 0, 0, 5, 5, 5, 5, 0, 5, 5, 0, 5, 0, 5, 0, 0, 0, 0], [5, 0, 5, 0, 5, 0, 0, 0, 0, 0, 0, 5, 0, 0, 5, 0, 5, 0, 5], [0, 5, 5, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 5, 0, 5, 5, 5, 5]]}, {'input': [[0, 5, 0, 0, 0, 0, 5, 0, 0, 0, 0, 5], [5, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 5], [5, 0, 5, 0, 0, 5, 5, 0, 2, 0, 5, 0], [5, 5, 0, 0, 5, 0, 5, 0, 2, 5, 0, 5], [5, 0, 0, 5, 5, 5, 2, 5, 2, 2, 2, 0], [5, 5, 5, 0, 5, 5, 0, 5, 2, 0, 0, 5], [5, 5, 5, 0, 5, 0, 0, 5, 5, 0, 0, 0], [5, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0], [0, 5, 5, 0, 5, 0, 0, 0, 0, 5, 0, 0], [5, 0, 0, 0, 5, 5, 5, 5, 5, 0, 0, 0], [5, 0, 0, 0, 0, 5, 0, 0, 5, 5, 5, 5]], 'output': [[0, 5, 0, 0, 0, 0, 5, 0, 0, 0, 0, 5], [5, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 5], [5, 0, 5, 0, 0, 5, 5, 0, 2, 0, 5, 0], [5, 5, 0, 0, 5, 0, 5, 0, 2, 5, 0, 5], [5, 0, 0, 5, 5, 5, 2, 8, 2, 2, 2, 0], [5, 5, 5, 0, 5, 5, 0, 5, 2, 0, 0, 5], [5, 5, 5, 0, 5, 0, 0, 5, 8, 0, 0, 0], [5, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0], [0, 5, 5, 0, 5, 0, 0, 0, 0, 5, 0, 0], [5, 0, 0, 0, 5, 5, 5, 5, 5, 0, 0, 0], [5, 0, 0, 0, 0, 5, 0, 0, 5, 5, 5, 5]]}], 'test_inputs': [[[0, 5, 0, 5, 0, 0, 5, 5, 0, 5, 0, 0, 0, 5, 0, 5, 0, 0, 0, 5, 5, 0], [0, 5, 0, 5, 5, 0, 0, 0, 5, 5, 0, 0, 5, 5, 0, 0, 0, 0, 0, 5, 5, 5], [0, 0, 0, 0, 5, 5, 5, 0, 0, 0, 0, 5, 5, 0, 0, 5, 5, 0, 0, 5, 5, 5], [0, 0, 5, 5, 0, 5, 5, 5, 0, 5, 0, 5, 0, 5, 0, 5, 5, 0, 5, 5, 5, 0], [0, 5, 0, 5, 2, 2, 5, 2, 2, 5, 0, 0, 5, 0, 5, 5, 5, 0, 0, 5, 5, 0], [0, 0, 0, 5, 0, 5, 2, 5, 5, 5, 0, 5, 0, 0, 0, 0, 5, 5, 5, 5, 0, 0], [5, 5, 0, 0, 5, 5, 2, 0, 5, 5, 0, 0, 0, 5, 0, 0, 0, 5, 5, 5, 5, 5], [5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 0, 5, 0, 0, 5, 0, 5, 0], [5, 5, 5, 5, 5, 0, 0, 5, 5, 0, 5, 2, 5, 2, 5, 5, 0, 0, 5, 5, 5, 0], [0, 0, 0, 5, 5, 5, 0, 0, 5, 0, 0, 0, 5, 5, 0, 5, 5, 5, 0, 0, 0, 0], [0, 0, 0, 5, 5, 5, 0, 5, 0, 5, 0, 5, 5, 2, 5, 0, 5, 0, 0, 5, 5, 0], [0, 5, 5, 5, 0, 0, 0, 5, 5, 5, 5, 0, 0, 5, 0, 5, 5, 0, 0, 0, 5, 5], [5, 5, 0, 0, 5, 5, 5, 0, 0, 5, 5, 0, 5, 0, 5, 5, 0, 0, 5, 5, 0, 5], [0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 5, 5, 5, 5, 5, 0, 0, 5, 0, 5], [5, 5, 0, 5, 5, 2, 2, 2, 5, 5, 5, 0, 5, 5, 5, 0, 5, 0, 0, 5, 5, 0], [5, 0, 0, 0, 5, 2, 5, 0, 5, 0, 5, 0, 5, 5, 5, 5, 0, 0, 0, 0, 5, 5], [5, 5, 5, 0, 0, 2, 0, 5, 5, 0, 0, 2, 2, 2, 2, 2, 5, 0, 5, 0, 5, 5], [5, 0, 5, 0, 0, 5, 0, 5, 0, 0, 0, 0, 0, 5, 0, 5, 5, 5, 0, 5, 5, 0], [5, 5, 5, 5, 5, 0, 5, 0, 5, 5, 5, 5, 0, 5, 0, 0, 5, 5, 0, 5, 0, 5]]], 'test_outputs': [[[0, 5, 0, 5, 0, 0, 5, 5, 0, 5, 0, 0, 0, 5, 0, 5, 0, 0, 0, 5, 5, 0], [0, 5, 0, 5, 5, 0, 0, 0, 5, 5, 0, 0, 5, 5, 0, 0, 0, 0, 0, 5, 5, 5], [0, 0, 0, 0, 5, 5, 8, 0, 0, 0, 0, 5, 5, 0, 0, 5, 5, 0, 0, 5, 5, 5], [0, 0, 5, 5, 0, 5, 8, 5, 0, 5, 0, 5, 0, 5, 0, 5, 5, 0, 5, 5, 5, 0], [0, 5, 0, 5, 2, 2, 8, 2, 2, 5, 0, 0, 5, 0, 5, 5, 5, 0, 0, 5, 5, 0], [0, 0, 0, 5, 0, 5, 2, 5, 5, 5, 0, 5, 0, 0, 0, 0, 5, 5, 5, 5, 0, 0], [5, 5, 0, 0, 5, 5, 2, 0, 5, 5, 0, 0, 0, 8, 0, 0, 0, 5, 5, 5, 5, 5], [5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 8, 0, 5, 0, 0, 5, 0, 5, 0], [5, 5, 5, 5, 5, 0, 0, 5, 5, 0, 5, 2, 8, 2, 8, 8, 0, 0, 5, 5, 5, 0], [0, 0, 0, 5, 5, 5, 0, 0, 5, 0, 0, 0, 5, 8, 0, 5, 5, 5, 0, 0, 0, 0], [0, 0, 0, 5, 5, 5, 0, 5, 0, 5, 0, 5, 5, 2, 5, 0, 5, 0, 0, 5, 5, 0], [0, 5, 5, 5, 0, 0, 0, 5, 5, 5, 5, 0, 0, 5, 0, 5, 5, 0, 0, 0, 5, 5], [5, 5, 0, 0, 5, 8, 5, 0, 0, 5, 5, 0, 5, 0, 5, 5, 0, 0, 5, 5, 0, 5], [0, 0, 5, 5, 5, 8, 5, 5, 5, 5, 0, 0, 5, 5, 5, 5, 5, 0, 0, 5, 0, 5], [5, 5, 0, 8, 8, 2, 2, 2, 5, 5, 5, 0, 5, 8, 5, 0, 5, 0, 0, 5, 5, 0], [5, 0, 0, 0, 5, 2, 5, 0, 5, 0, 5, 0, 5, 8, 5, 5, 0, 0, 0, 0, 5, 5], [5, 5, 5, 0, 0, 2, 0, 5, 5, 0, 0, 2, 2, 2, 2, 2, 5, 0, 5, 0, 5, 5], [5, 0, 5, 0, 0, 5, 0, 5, 0, 0, 0, 0, 0, 8, 0, 5, 5, 5, 0, 5, 5, 0], [5, 5, 5, 5, 5, 0, 5, 0, 5, 5, 5, 5, 0, 8, 0, 0, 5, 5, 0, 5, 0, 5]]]}) (input_keys={'test_inputs', 'training_examples'}): 'dict' object has no attribute 'model_dump'
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 58, in forward
File "<string>", line 58, in <listcomp>
AttributeError: 'dict' object has no attribute 'model_dump'
2025/08/28 22:50:03 INFO dspy.evaluate.evaluate: Average Metric: 0.0 / 3 (0.0%)
GEPA Optimization: 13%|██████████████▌ | 526/4000 [1:16:07<45:04:04, 46.70s/rollouts]Iteration 25: Proposed new text for program: import dspy
from typing import List, Any
import pydantic
import traceback
# Define the type for a matrix for clarity.
MATRIX = List[List[int]]
class TrainingExample(pydantic.BaseModel):
"""A Pydantic model for a single training example, containing an input and output matrix."""
input: MATRIX
output: MATRIX
class RuleToCodeSignature(dspy.Signature):
"""
Analyzes training examples of matrix transformations to deduce the underlying rule.
Based on the rule, it generates a complete, self-contained Python function to perform the transformation.
**Task Description:**
You will be given a series of input-output matrix pairs that demonstrate a specific transformation rule. Your task is to understand this rule and then write a single Python function named `transform_matrix` that implements it. This function should take one argument, an input matrix (as a list of lists of integers), and return the transformed matrix.
**General Strategies to Consider:**
- **Pattern Recognition:** Look for simple patterns first. Does the rule depend on the value of a cell, its neighbors, or its position (row/column)?
- **Binary Abstraction:** Sometimes, the specific non-zero numbers don't matter, only their position. Consider converting the grid to a binary format (0 for zero, 1 for non-zero) to simplify the problem.
- **Geometric & Spatial Rules:** Analyze geometric properties. This could include:
- Filling bounded areas (e.g., changing values between two specific numbers like '2').
- Symmetry (rotational, reflectional).
- Adding elements based on an object's position (e.g., adding 'braces' to a column, with the side depending on whether the column is in the left or right half of the grid).
- **Sub-Grid Analysis:** The rule might operate on smaller sub-grids (e.g., 2x2 or 3x3 windows) within the larger matrix.
- **Algorithmic Logic:** The transformation could be an algorithm applied row-by-row, column-by-column, or to the grid as a whole.
**Output Requirements:**
- You MUST provide a complete, executable Python function definition.
- The function MUST be named `transform_matrix`.
- The function must be self-contained and should not require any external libraries (like numpy). Standard Python libraries are acceptable.
- Do not just write a description of the rule. The final output must be the code itself.
"""
training_examples: str = dspy.InputField(desc="A string representation of the list of training examples, each with an 'input' and 'output' matrix.")
python_code: str = dspy.OutputField(desc="A string containing a single Python function `transform_matrix(matrix)`.")
class SolveTaskSignature(dspy.Signature):
"""Given training examples and test inputs, deduce the rule and apply it to produce the test outputs."""
training_examples: List[TrainingExample] = dspy.InputField(description="Input and output examples demonstrating the task to be performed.")
test_inputs: List[MATRIX] = dspy.InputField(description="Input matrices to be solved following the task described in the training examples.")
test_outputs: List[MATRIX] = dspy.OutputField(description="Output matrices corresponding to the test inputs.")
class SolveWithCode(dspy.Module):
"""A module that generates and executes code to solve matrix transformation tasks."""
def __init__(self):
super().__init__()
# Module to generate Python code from examples.
self.rule_generator = dspy.ChainOfThought(RuleToCodeSignature)
# Fallback module if code generation or execution fails.
self.fallback_solver = dspy.ChainOfThought(SolveTaskSignature)
def forward(self, training_examples: List[TrainingExample], test_inputs: List[MATRIX]) -> dspy.Prediction:
# Convert Pydantic objects to a string representation for the LM prompt.
examples_str = str([ex.model_dump() for ex in training_examples])
try:
# Step 1: Generate Python code based on the examples.
code_generation_result = self.rule_generator(training_examples=examples_str)
python_code = code_generation_result.python_code
# Prepare a local scope for executing the generated code.
local_scope = {}
exec(python_code, globals(), local_scope)
transform_matrix_func = local_scope.get('transform_matrix')
if not callable(transform_matrix_func):
raise ValueError("`transform_matrix` function not found or is not callable in the generated code.")
# Step 2: Execute the generated function on each test input.
outputs = []
for test_matrix in test_inputs:
# Create a deep copy to avoid modifying the original input
matrix_copy = [row[:] for row in test_matrix]
result = transform_matrix_func(matrix_copy)
outputs.append(result)
return dspy.Prediction(test_outputs=outputs)
except Exception as e:
print(f"Code generation or execution failed. Error: {e}")
print(f"Traceback: {traceback.format_exc()}")
print("Falling back to direct ChainOfThought solver.")
# Fallback: Use the original, direct approach.
fallback_prediction = self.fallback_solver(
training_examples=training_examples,
test_inputs=test_inputs
)
return fallback_prediction
# The final program object that implements the improved strategy.
program = SolveWithCode()
Iteration 25: New subsample score is not better, skipping
Iteration 26: Selected program 0 score: 0.67
Average Metric: 3.00 / 3 (100.0%): 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [01:07<00:00, 22.35s/it]2025/08/28 22:51:10 INFO dspy.evaluate.evaluate: Average Metric: 3.0 / 3 (100.0%)
GEPA Optimization: 13%|██████████████▋ | 529/4000 [1:17:14<40:25:13, 41.92s/rollouts]
Iteration 26: All subsample scores perfect. Skipping.
Iteration 26: Reflective mutation did not propose a new candidate
Iteration 27: Selected program 1 score: 0.63
Average Metric: 0.00 / 3 (0.0%): 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [05:26<00:00, 108.73s/it]2025/08/28 22:56:36 INFO dspy.evaluate.evaluate: Average Metric: 0.0 / 3 (0.0%)
GEPA Optimization: 13%|██████████████▊ | 535/4000 [1:23:23<47:06:46, 48.95s/rollouts]Iteration 27: Proposed new text for program: import dspy
from typing import List
import pantic
import re
# Define the type for a matrix for clarity
MATRIX = List[List[int]]
class TrainingExample(pantic.BaseModel):
"""A Pydantic model for a single training example, containing an input and output matrix."""
input: MATRIX
output: MATRIX
class GenerateTransformationFunctionSignature(dspy.Signature):
"""
Analyzes pairs of input/output matrices to deduce the transformation rule and express it as a Python function.
You are an expert programmer and analyst of abstract visual reasoning puzzles. Your task is to write a single Python function named `transform_matrix` that implements the transformation rule observed in the provided training examples.
**Function Requirements:**
1. The function must be named `transform_matrix`.
2. It must accept one argument: `matrix` (a list of lists of integers).
3. It must return a new `matrix` (a list of lists of integers) representing the transformed output.
4. The function must be self-contained and should not rely on any external libraries like numpy or pandas. Standard Python libraries are permitted.
5. Your output must be ONLY the Python code for the function, enclosed in a single markdown code block (```python ... ```). Do not include any other text, explanations, or example usage.
**Analysis Strategies to Consider:**
- **Object-based logic:** Identify shapes/objects and apply rules based on their properties (size, color, position, neighbors).
- **Geometric operations:** Look for patterns of rotation, reflection, scaling, repetition, or shifting.
- **Fill/completion patterns:** Consider rules like flood fills, completing shapes, or drawing bounding boxes.
- **Priority-based overlays:** In cases where the grid is partitioned (e.g., into quadrants), the output might be a composite where colors are chosen based on a priority order.
- **Density or neighborhood logic:** Rules might depend on the number of certain colored cells in a region or around a specific cell.
"""
training_examples: List[TrainingExample] = dspy.InputField(description="A list of input and output examples demonstrating the task.")
python_function: str = dspy.OutputField(description="A self-contained Python function `transform_matrix(matrix)` that implements the transformation.")
class ARCProgram(dspy.Module):
"""A program that first generates a transformation function in Python and then executes it to solve all test inputs."""
def __init__(self):
super().__init__()
# Use ChainOfThought for the complex reasoning task of generating a Python function.
self.code_generator = dspy.ChainOfThought(GenerateTransformationFunctionSignature)
def _parse_python_function(self, generated_code: str) -> str:
"""Extracts the Python code from a markdown block."""
match = re.search(r"```python\n(.*?)\n```", generated_code, re.DOTALL)
if match:
return match.group(1)
# Fallback for code that might not be in a markdown block
return generated_code.strip()
def _create_fallback_output(self, matrix: MATRIX) -> MATRIX:
"""Creates a fallback output (a grid of zeros) of the same dimensions as the input."""
if not matrix or not matrix[0]:
return []
return [[0] * len(matrix[0]) for _ in range(len(matrix))]
def forward(self, training_examples: List[TrainingExample], test_inputs: List[MATRIX]) -> dspy.Prediction:
"""
Generates a transformation function from training examples and applies it to each test input.
Args:
training_examples: A list of TrainingExample objects.
test_inputs: A list of input matrices to be solved.
Returns:
A dspy.Prediction object with the 'test_outputs' field populated.
"""
# 1. Generate the transformation function as a Python code string.
prediction = self.code_generator(training_examples=training_examples)
func_code_str = self._parse_python_function(prediction.python_function)
transform_func = None
try:
# 2. Define the function dynamically in a restricted scope.
local_scope = {}
exec(func_code_str, globals(), local_scope)
transform_func = local_scope.get('transform_matrix')
except Exception:
# If code generation or exec fails, transform_func will be None.
pass
all_test_outputs = []
# 3. Iterate through each test input and apply the generated function.
for test_matrix in test_inputs:
if transform_func:
try:
# Apply the dynamically created function.
result_matrix = transform_func(test_matrix)
all_test_outputs.append(result_matrix)
except Exception:
# Fallback if the function fails on a specific input.
all_test_outputs.append(self._create_fallback_output(test_matrix))
else:
# Fallback if the function could not be created at all.
all_test_outputs.append(self._create_fallback_output(test_matrix))
# 4. Return the collected outputs in a single Prediction object.
return dspy.Prediction(test_outputs=all_test_outputs)
# The final 'program' object is an instance of our robust, code-generating module.
program = ARCProgram()
Iteration 27: New subsample score is not better, skipping
Iteration 28: Selected program 0 score: 0.67
Average Metric: 3.00 / 3 (100.0%): 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [02:28<00:00, 49.40s/it]2025/08/28 22:59:47 INFO dspy.evaluate.evaluate: Average Metric: 3.0 / 3 (100.0%)
GEPA Optimization: 13%|██████████████▉ | 538/4000 [1:25:51<47:09:40, 49.04s/rollouts]
Iteration 28: All subsample scores perfect. Skipping.
Iteration 28: Reflective mutation did not propose a new candidate
Iteration 29: Selected program 0 score: 0.67
Average Metric: 2.00 / 3 (66.7%): 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [04:20<00:00, 86.88s/it]2025/08/28 23:04:08 INFO dspy.evaluate.evaluate: Average Metric: 2.0 / 3 (66.7%)
Iteration 29: Proposed new text for program: import dspy
from typing import List
import pydantic
# Define the basic types for matrices using Pydantic and type hints.
# This ensures data is structured correctly and improves clarity.
MATRIX = List[List[int]]
class TrainingExample(pydantic.BaseModel):
input: MATRIX
output: MATRIX
# --- Step 1: Define Signatures for a Decomposed, Code-Generating Approach ---
class RuleExtractionSignature(dspy.Signature):
"""
Analyzes pairs of input and output matrices to deduce the transformation rule.
The rule should be described in clear, step-by-step natural language.
This description will serve as a blueprint for generating Python code to solve the task.
Common patterns observed in these tasks include:
- Geometric constructions: Drawing shapes or paths (e.g., L-shaped lines) between points.
- Propagation/Diffusion: A "defect" or special cell's color spreads along specific paths (e.g., diagonals).
- Downsampling/Summarization: A large grid of blocks is summarized into a smaller grid representing the blocks.
- Object manipulation: Identifying, moving, rotating, or modifying colored objects within the grid.
Focus on creating a generalizable algorithm from the examples.
"""
training_examples: List[TrainingExample] = dspy.InputField(
desc="A list of input-output pairs demonstrating the transformation."
)
rule_description: str = dspy.OutputField(
desc="A step-by-step description of the transformation rule in English."
)
class CodeGenerationSignature(dspy.Signature):
"""
Given a rule description and a test input matrix, write a self-contained Python function
to perform the described transformation.
The function MUST:
1. Be named `transform_matrix`.
2. Accept a single argument: `matrix` (which will be a list of lists of integers).
3. Return the transformed matrix (a list of lists of integers).
4. NOT use any external libraries (like numpy or pandas). Standard Python libraries are allowed.
5. Contain all logic within the function body. Do not define helper functions outside its scope unless they are nested inside.
"""
rule_description: str = dspy.InputField(
desc="The natural language description of the transformation rule."
)
test_input_example: MATRIX = dspy.InputField(
desc="An example of a test input matrix to guide the code generation."
)
python_code: str = dspy.OutputField(
desc="A string containing the complete Python function `transform_matrix`."
)
# The final signature remains the same as the original problem statement.
# Our custom module will implement this signature, making it a drop-in replacement.
class SolveTaskSignature(dspy.Signature):
"""Given training examples demonstrating a task and a list of test inputs, solve the task for each test input."""
training_examples: List[TrainingExample] = dspy.InputField(description="Input and output examples demonstrating the task to be performed.")
test_inputs: List[MATRIX] = dspy.InputField(description="Input matrices to be solved following the task described in the training examples.")
test_outputs: List[MATRIX] = dspy.OutputField(description="Output matrices corresponding to the test inputs.")
# --- Step 2: Create a Custom Module to Orchestrate the Solution ---
class CodeGeneratingSolver(dspy.Module):
"""
A DSPy module that solves matrix transformation tasks by:
1. Deducing the transformation rule from examples.
2. Generating Python code to implement the rule.
3. Executing the generated code to solve the test inputs.
"""
def __init__(self):
super().__init__()
# Use ChainOfThought for rule extraction to encourage deeper reasoning.
self.rule_extractor = dspy.ChainOfThought(RuleExtractionSignature)
# Use a simple Predict for code generation as the logic is already laid out.
self.code_generator = dspy.Predict(CodeGenerationSignature)
# A fallback predictor in case code generation or execution fails.
self.fallback_predictor = dspy.Predict(SolveTaskSignature)
def execute_generated_code(self, code_str: str, input_matrix: MATRIX) -> MATRIX:
"""
Safely executes the generated Python code string.
"""
# The LM sometimes wraps the code in markdown backticks; this removes them.
if code_str.strip().startswith("```python"):
code_str = code_str.strip()[9:].strip()
if code_str.strip().endswith("```"):
code_str = code_str.strip()[:-3].strip()
# Prepare a local scope for exec to run in.
local_scope = {}
# Execute the code, which should define the `transform_matrix` function.
exec(code_str, globals(), local_scope)
# Retrieve the function from the local scope.
transform_function = local_scope.get('transform_matrix')
if not callable(transform_function):
raise ValueError("Generated code did not define a callable function named 'transform_matrix'.")
# Call the function with the test input.
return transform_function(input_matrix)
def forward(self, training_examples: List[TrainingExample], test_inputs: List[MATRIX]) -> dspy.Prediction:
# Step 1: Extract the rule from the training examples. This is done once.
rule_prediction = self.rule_extractor(training_examples=training_examples)
rule_description = rule_prediction.rule_description
# Step 2: Generate Python code based on the rule. This is also done once.
# We provide one test input as an example for the generator's context.
code_prediction = self.code_generator(
rule_description=rule_description,
test_input_example=test_inputs[0]
)
python_code = code_prediction.python_code
# Step 3: Execute the single generated code for each test input.
final_outputs = []
for test_matrix in test_inputs:
try:
# Attempt to execute the generated code.
output_matrix = self.execute_generated_code(python_code, test_matrix)
final_outputs.append(output_matrix)
except Exception as e:
# If anything goes wrong, use the fallback for this specific input.
print(f"Code execution failed: {e}. Using fallback predictor.")
# The fallback takes a list of test inputs, so we wrap the current one.
fallback_prediction = self.fallback_predictor(
training_examples=training_examples,
test_inputs=[test_matrix]
)
# The fallback output is also a list, so we take the first element.
if fallback_prediction.test_outputs:
final_outputs.append(fallback_prediction.test_outputs[0])
else: # Handle case where fallback might fail to produce output
final_outputs.append([]) # Append an empty matrix as a failure signal
return dspy.Prediction(test_outputs=final_outputs)
# --- Step 3: Assign the improved module to the 'program' variable ---
program = CodeGeneratingSolver()
2025/08/28 23:10:52 INFO dspy.evaluate.evaluate: Average Metric: 1.0 / 3 (33.3%)
GEPA Optimization: 14%|███████████████ | 544/4000 [1:36:56<68:57:42, 71.84s/rollouts]Iteration 29: New subsample score is not better, skipping
Iteration 30: Selected program 0 score: 0.67
Average Metric: 2.00 / 3 (66.7%): 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [01:21<00:00, 27.10s/it]2025/08/28 23:12:14 INFO dspy.evaluate.evaluate: Average Metric: 2.0 / 3 (66.7%)
2025/08/28 23:13:10 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 2, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 3, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 2, 0, 0, 0, 0, 0, 0], [0, 0, 2, 0, 0, 0, 0, 0, 0], [0, 0, 2, 0, 0, 0, 0, 0, 0], [0, 0, 2, 0, 0, 0, 0, 0, 0], [3, 3, 3, 3, 3, 3, 3, 3, 3], [0, 0, 2, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 0, 2, 0, 0, 0, 0, 0, 0], [0, 0, 2, 0, 0, 0, 0, 0, 0]]}, {'input': [[0, 0, 0, 0, 0, 0, 0, 0], [0, 3, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 3, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 2, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 2, 0, 0], [3, 3, 3, 3, 3, 3, 3, 3], [0, 0, 0, 0, 0, 2, 0, 0], [0, 0, 0, 0, 0, 2, 0, 0], [3, 3, 3, 3, 3, 3, 3, 3], [0, 0, 0, 0, 0, 2, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 2, 0, 0], [0, 0, 0, 0, 0, 2, 0, 0], [0, 0, 0, 0, 0, 2, 0, 0]]}, {'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0]], 'output': [[0, 0, 0, 2, 0, 0, 0, 0, 0, 2, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 0, 0, 2, 0, 0, 0, 0, 0, 2, 0], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3], [0, 0, 0, 2, 0, 0, 0, 0, 0, 2, 0], [0, 0, 0, 2, 0, 0, 0, 0, 0, 2, 0], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3], [0, 0, 0, 2, 0, 0, 0, 0, 0, 2, 0], [0, 0, 0, 2, 0, 0, 0, 0, 0, 2, 0], [0, 0, 0, 2, 0, 0, 0, 0, 0, 2, 0]]}], 'test_inputs': [[[0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]], 'test_outputs': [[[3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3], [0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0], [0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3], [0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0], [0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0], [0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0], [0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0]]]}) (input_keys={'test_inputs', 'training_examples'}): 'dict' object has no attribute 'model_dump'
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 85, in forward
File "<string>", line 85, in <listcomp>
AttributeError: 'dict' object has no attribute 'model_dump'
2025/08/28 23:13:10 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0], [0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0], [0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[8, 8, 2, 2], [8, 0, 0, 2], [3, 0, 0, 1], [3, 3, 1, 1]]}, {'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0], [0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 4, 4, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[1, 1, 8, 8], [1, 0, 0, 8], [4, 0, 0, 2], [4, 4, 2, 2]]}], 'test_inputs': [[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 8, 8, 0], [0, 0, 6, 6, 0, 0, 0, 0, 0, 0, 0, 8, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]], 'test_outputs': [[[3, 3, 8, 8], [3, 0, 0, 8], [1, 0, 0, 6], [1, 1, 6, 6]]]}) (input_keys={'test_inputs', 'training_examples'}): 'dict' object has no attribute 'model_dump'
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 85, in forward
File "<string>", line 85, in <listcomp>
AttributeError: 'dict' object has no attribute 'model_dump'
2025/08/28 23:13:10 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[2, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0], [0, 2, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 2, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 2, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 2, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0], [2, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2]]}, {'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 7], [0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 7, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 7, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 7, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 7, 0], [0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 7], [0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}, {'input': [[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 8, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 8, 0], [8, 0, 0, 0, 8, 0, 0], [0, 8, 0, 8, 0, 0, 0], [0, 0, 8, 0, 0, 0, 0], [0, 8, 0, 8, 0, 0, 0], [8, 0, 0, 0, 8, 0, 0], [0, 0, 0, 0, 0, 8, 0]]}], 'test_inputs': [[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]], 'test_outputs': [[[0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 6], [0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 6, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 6, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 6, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 6, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 6, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 6, 0], [0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 6], [0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]]}) (input_keys={'test_inputs', 'training_examples'}): 'dict' object has no attribute 'model_dump'
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 85, in forward
File "<string>", line 85, in <listcomp>
AttributeError: 'dict' object has no attribute 'model_dump'
2025/08/28 23:13:10 INFO dspy.evaluate.evaluate: Average Metric: 0.0 / 3 (0.0%)
GEPA Optimization: 14%|███████████████▎ | 550/4000 [1:39:13<52:40:07, 54.96s/rollouts]Iteration 30: Proposed new text for program: import dspy
from typing import List
import pydantic
import json
# Define the structured types for matrices and training examples.
MATRIX = List[List[int]]
class TrainingExample(pydantic.BaseModel):
input: MATRIX
output: MATRIX
# Signature for the overall task, defining the final inputs and outputs.
class SolveTaskSignature(dspy.Signature):
"""
Given a set of training examples that demonstrate a hidden transformation rule,
apply that same rule to a new set of test inputs to produce the correct test outputs.
The transformation can involve geometric patterns, color logic, propagation, or assembly of shapes.
"""
training_examples: List[TrainingExample] = dspy.InputField(
description="Input and output examples demonstrating the task to be performed."
)
test_inputs: List[MATRIX] = dspy.InputField(
description="Input matrices to be solved following the task described in the training examples."
)
test_outputs: List[MATRIX] = dspy.OutputField(
description="Output matrices corresponding to the test inputs."
)
# Internal signature for the first step: Deducing the rule.
class RuleDiscoverySignature(dspy.Signature):
"""
Analyze the provided input-output pairs of grids to discover the underlying transformation rule.
The rule should be a general algorithm that explains how to get from any input to its corresponding output.
Consider various types of transformations:
- Geometric: Rotation, reflection, scaling, symmetry, drawing shapes (e.g., lines, diagonals).
- Propagation: Colors filling rows, columns, or areas based on certain triggers. Note any precedence rules (e.g., horizontal fill overwrites vertical).
- Object-based: Identifying shapes/objects, moving them, combining them, or altering them.
- Conditional Logic: Rules that change based on properties of the input, such as the sum of colors, number of objects, etc.
Describe the discovered rule clearly and step-by-step in natural language.
"""
training_examples: str = dspy.InputField(
desc="A JSON string representing the list of training examples, each with an 'input' and 'output' matrix."
)
rule_description: str = dspy.OutputField(
desc="A step-by-step, algorithmic description of the transformation rule in natural language."
)
# Internal signature for the second step: Implementing the rule in code.
class RuleToCodeSignature(dspy.Signature):
"""
You are an expert Python programmer. Given a natural language description of a grid transformation rule,
write a single, self-contained Python function named `solve` that implements this rule.
The function must have the signature `solve(matrix: List[List[int]]) -> List[List[int]]`.
- It takes one argument: the input matrix (a list of lists of integers).
- It must return the transformed output matrix (a list of lists of integers).
Do NOT use any external libraries like numpy. Stick to standard Python lists, loops, and conditionals.
Ensure the code correctly handles grid dimensions and boundary conditions.
"""
rule_description: str = dspy.InputField(
desc="The natural language description of the transformation rule."
)
test_input_example: str = dspy.InputField(
desc="A sample test input matrix to help understand dimensions and structure."
)
python_code: str = dspy.OutputField(
desc="A string containing the complete Python code for the `solve` function."
)
# Custom module to orchestrate the two-step process.
class ARCProgram(dspy.Module):
def __init__(self):
super().__init__()
# Use ChainOfThought for the complex reasoning task of finding the rule.
self.rule_discoverer = dspy.ChainOfThought(RuleDiscoverySignature)
# Use a simple Predict for the more constrained task of translating the rule to code.
self.code_generator = dspy.Predict(RuleToCodeSignature)
def forward(self, training_examples: List[TrainingExample], test_inputs: List[MATRIX]):
# Convert Pydantic objects to a JSON string for the LM prompt.
training_examples_str = json.dumps([ex.model_dump() for ex in training_examples])
# 1. Discover the rule from the training examples.
rule_pred = self.rule_discoverer(training_examples=training_examples_str)
rule_description = rule_pred.rule_description
all_test_outputs = []
for test_input in test_inputs:
# 2. Generate a Python function that implements the rule.
code_pred = self.code_generator(
rule_description=rule_description,
test_input_example=json.dumps(test_input)
)
python_code = code_pred.python_code
# 3. Execute the generated code to get the output.
try:
# Prepare a local scope for safe execution.
local_scope = {}
# Execute the generated function definition.
exec(python_code, globals(), local_scope)
solve_func = local_scope['solve']
# Apply the function to the actual test input matrix.
output_matrix = solve_func(test_input)
all_test_outputs.append(output_matrix)
except Exception as e:
print(f"Error executing generated code: {e}")
# Fallback: If code execution fails, append an empty list as a failure signal.
all_test_outputs.append([])
return dspy.Prediction(test_outputs=all_test_outputs)
# The final program is an instance of our custom module.
program = ARCProgram()
Iteration 30: New subsample score is not better, skipping
Iteration 31: Selected program 0 score: 0.67
Average Metric: 2.00 / 3 (66.7%): 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [03:55<00:00, 78.36s/it]2025/08/28 23:17:05 INFO dspy.evaluate.evaluate: Average Metric: 2.0 / 3 (66.7%)
Iteration 31: Proposed new text for program: import dspy
from typing import List
import pydantic
import traceback
# Define the structured data types for inputs and outputs.
# Using Pydantic ensures data validation and clarity.
MATRIX = List[List[int]]
class TrainingExample(pydantic.BaseModel):
input: MATRIX
output: MATRIX
# This is the original signature, now used for the fallback mechanism.
class SolveTaskSignature(dspy.Signature):
"""Given training examples of input and output matrices, solve the test inputs."""
training_examples: List[TrainingExample] = dspy.InputField(description="Input and output examples demonstrating the task to be performed.")
test_inputs: List[MATRIX] = dspy.InputField(description="Input matrices to be solved following the task described in the training examples.")
test_outputs: List[MATRIX] = dspy.OutputField(description="Output matrices corresponding to the test inputs.")
# A new, more powerful signature that instructs the LM to generate a Python function.
# This leverages the LM's reasoning and code-generation capabilities.
class GeneratePythonSolverSignature(dspy.Signature):
"""
You are an expert programmer and a specialist in abstract reasoning puzzles.
Given a set of training examples that demonstrate a transformation from an input matrix to an output matrix, your task is to deduce the transformation rule and write a self-contained Python function to solve it.
**Instructions:**
1. **Analyze the Examples:** Carefully examine the `training_examples` to understand the underlying logic. The transformations can be complex and varied.
2. **Identify the Pattern:** Common patterns include:
- Mathematical operations based on cell values or their row/column indices (e.g., `value = (row + col) % max_val + 1`).
- Procedural manipulations triggered by specific numbers (e.g., a '5' causing a 'push' or 'pull' of adjacent data).
- Regional filling or coloring based on structural boundaries within the matrix (e.g., filling areas between lines of '8's).
3. **Write a Solver Function:** Implement the logic in a single Python function named `solve`.
- The function signature must be: `def solve(test_input_matrix: list[list[int]]) -> list[list[int]]:`
- The function must be entirely self-contained and should not rely on any external libraries beyond standard Python 3.
- Do not include any code outside of this function definition.
4. **Provide Analysis:** In the `analysis` field, briefly explain the logic you discovered and how your `solve` function implements it.
"""
training_examples: List[TrainingExample] = dspy.InputField(
desc="A list of input/output pairs demonstrating the transformation rule."
)
test_inputs: List[MATRIX] = dspy.InputField(
desc="A list of input matrices to be solved by the generated function."
)
analysis: str = dspy.OutputField(
desc="A brief explanation of the discovered transformation logic."
)
solver_code: str = dspy.OutputField(
desc="A self-contained Python function `solve(test_input_matrix)` that implements the transformation."
)
# A custom module that orchestrates the code generation and execution.
class CodeGeneratingSolver(dspy.Module):
def __init__(self):
super().__init__()
# The primary module for generating the solver code.
self.generate_solver = dspy.Predict(GeneratePythonSolverSignature)
# A fallback module, similar to the original program, for robustness.
self.fallback_solver = dspy.ChainOfThought(SolveTaskSignature)
def forward(self, training_examples: List[TrainingExample], test_inputs: List[MATRIX]):
# Step 1: Generate the Python solver function and the analysis.
prediction = self.generate_solver(
training_examples=training_examples,
test_inputs=test_inputs
)
solver_code = prediction.solver_code
try:
# Step 2: Execute the generated code in a controlled environment.
local_scope = {}
# `exec` will define the `solve` function within `local_scope`.
exec(solver_code, globals(), local_scope)
solve_func = local_scope['solve']
# Step 3: Apply the generated function to each test input.
solved_outputs = [solve_func(test_matrix) for test_matrix in test_inputs]
# Return the successfully computed outputs.
return dspy.Prediction(test_outputs=solved_outputs)
except Exception as e:
# Step 4: If code generation or execution fails, use the fallback.
print(f"Code execution failed: {e}")
print(f"Generated code that failed:\n{solver_code}")
print("---")
traceback.print_exc()
print("---")
print("Falling back to direct ChainOfThought solving.")
fallback_prediction = self.fallback_solver(
training_examples=training_examples,
test_inputs=test_inputs
)
return dspy.Prediction(test_outputs=fallback_prediction.test_outputs)
# Instantiate the new, more robust module as the final program.
program = CodeGeneratingSolver()
Code execution failed: invalid syntax (<string>, line 1)
Generated code that failed:
```python
def solve(test_input_matrix: list[list[int]]) -> list[list[int]]:
"""
Solves the puzzle by regenerating the grid based on a diagonal, cyclic pattern.
"""
height = len(test_input_matrix)
if height == 0:
return []
width = len(test_input_matrix[0])
if width == 0:
return [[] for _ in range(height)]
# 1. Determine the cycle length by finding the maximum value in the input grid.
max_val = 0
for row in test_input_matrix:
for cell in row:
if cell > max_val:
max_val = cell
# If the grid were all zeros, the pattern is undefined. Based on the examples,
# we can assume max_val will be greater than 0.
if max_val == 0:
return [row[:] for row in test_input_matrix]
# 2. Get the pattern's starting value from the top-left corner.
start_val = test_input_matrix[0][0]
# 3. Regenerate the entire grid using the derived formula.
output_matrix = [[0] * width for _ in range(height)]
for r in range(height):
for c in range(width):
# The formula for the pattern:
# ((start_val - 1) + r + c) creates a 0-indexed diagonal sum.
# % max_val makes the pattern cyclic.
# + 1 converts the result back to a 1-based number.
value = ((start_val - 1) + r + c) % max_val + 1
output_matrix[r][c] = value
return output_matrix
```
---
---
Falling back to direct ChainOfThought solving.
Traceback (most recent call last):
File "<string>", line 76, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Code execution failed: invalid syntax (<string>, line 1)
Generated code that failed:
```python
def solve(test_input_matrix: list[list[int]]) -> list[list[int]]:
"""
Fills regions defined by a grid of 8s based on a fixed color map.
"""
output_matrix = [row[:] for row in test_input_matrix]
num_rows = len(output_matrix)
num_cols = len(output_matrix[0])
# Find horizontal lines of 8s
h_lines = []
for r in range(num_rows):
if all(val == 8 for val in output_matrix[r]):
h_lines.append(r)
# Find vertical lines of 8s
v_lines = []
for c in range(num_cols):
if all(output_matrix[r][c] == 8 for r in range(num_rows)):
v_lines.append(c)
# Define region boundaries, including the matrix edges
h_bounds = [-1] + h_lines + [num_rows]
v_bounds = [-1] + v_lines + [num_cols]
# This fixed color map is derived from the training examples.
# It maps a region's (row_index, col_index) to a color.
color_map = [
[0, 2, 0],
[4, 6, 3],
[0, 1, 0]
]
# Iterate through the grid of regions defined by the boundaries.
# i is the region's row index, j is the region's column index.
for i in range(len(h_bounds) - 1):
for j in range(len(v_bounds) - 1):
color = color_map[i][j]
if color > 0:
# Get the pixel coordinates for the current region
r_start = h_bounds[i] + 1
r_end = h_bounds[i+1]
c_start = v_bounds[j] + 1
c_end = v_bounds[j+1]
# Fill the region with the determined color
for r in range(r_start, r_end):
for c in range(c_start, c_end):
output_matrix[r][c] = color
return output_matrix
```
---
---
Falling back to direct ChainOfThought solving.
Traceback (most recent call last):
File "<string>", line 76, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Code execution failed: invalid syntax (<string>, line 1)
Generated code that failed:
```python
import math
def solve(test_input_matrix: list[list[int]]) -> list[list[int]]:
"""
Solves the puzzle by first filling in the '5' values based on the nearest
non-zero/non-5 "source" number, and then pruning columns based on a
specific pattern in the original grid.
"""
height = len(test_input_matrix)
width = len(test_input_matrix[0])
# Step 1: Identify all "source" numbers (colors) and "target" cells (5s).
sources = []
targets = []
for r in range(height):
for c in range(width):
val = test_input_matrix[r][c]
if val != 0 and val != 5:
sources.append({'r': r, 'c': c, 'val': val})
elif val == 5:
targets.append({'r': r, 'c': c})
# Step 2: Create a new grid by filling the target '5's.
filled_grid = [row[:] for row in test_input_matrix]
for target in targets:
tr, tc = target['r'], target['c']
min_dist_sq = float('inf')
best_source = None
# Find the nearest source for the current target.
for source in sources:
sr, sc = source['r'], source['c']
# Using squared Euclidean distance to avoid floating point issues.
dist_sq = (tr - sr)**2 + (tc - sc)**2
if dist_sq < min_dist_sq:
min_dist_sq = dist_sq
best_source = source
elif dist_sq == min_dist_sq:
# Tie-breaking rule: choose the source that appears first
# in row-major order (top-to-bottom, left-to-right).
if source['r'] < best_source['r'] or \
(source['r'] == best_source['r'] and source['c'] < best_source['c']):
best_source = source
if best_source:
filled_grid[tr][tc] = best_source['val']
# Step 3: Identify columns to prune from the *original* grid.
# A column is pruned if it contains a source number horizontally adjacent to a '5'.
cols_to_prune = set()
for r in range(height):
for c in range(width):
val = test_input_matrix[r][c]
if val != 0 and val != 5: # It's a source number
# Check left neighbor
if c > 0 and test_input_matrix[r][c-1] == 5:
cols_to_prune.add(c)
# Check right neighbor
if c < width - 1 and test_input_matrix[r][c+1] == 5:
cols_to_prune.add(c)
# Step 4: Construct the final output by removing the pruned columns from the filled grid.
output_grid = []
for r in range(height):
new_row = []
for c in range(width):
if c not in cols_to_prune:
new_row.append(filled_grid[r][c])
output_grid.append(new_row)
return output_grid
```
---
---
Falling back to direct ChainOfThought solving.
Traceback (most recent call last):
File "<string>", line 76, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Code execution failed: invalid syntax (<string>, line 1)
Generated code that failed:
```python
import math
def solve(test_input_matrix: list[list[int]]) -> list[list[int]]:
"""
Solves the puzzle by first filling in the '5' values based on the nearest
non-zero/non-5 "source" number, and then pruning columns based on a
specific pattern in the original grid.
"""
height = len(test_input_matrix)
width = len(test_input_matrix[0])
# Step 1: Identify all "source" numbers (colors) and "target" cells (5s).
sources = []
targets = []
for r in range(height):
for c in range(width):
val = test_input_matrix[r][c]
if val != 0 and val != 5:
sources.append({'r': r, 'c': c, 'val': val})
elif val == 5:
targets.append({'r': r, 'c': c})
# Step 2: Create a new grid by filling the target '5's.
filled_grid = [row[:] for row in test_input_matrix]
for target in targets:
tr, tc = target['r'], target['c']
min_dist_sq = float('inf')
best_source = None
# Find the nearest source for the current target.
for source in sources:
sr, sc = source['r'], source['c']
# Using squared Euclidean distance to avoid floating point issues.
dist_sq = (tr - sr)**2 + (tc - sc)**2
if dist_sq < min_dist_sq:
min_dist_sq = dist_sq
best_source = source
elif dist_sq == min_dist_sq:
# Tie-breaking rule: choose the source that appears first
# in row-major order (top-to-bottom, left-to-right).
if source['r'] < best_source['r'] or \
(source['r'] == best_source['r'] and source['c'] < best_source['c']):
best_source = source
if best_source:
filled_grid[tr][tc] = best_source['val']
# Step 3: Identify columns to prune from the *original* grid.
# A column is pruned if it contains a source number horizontally adjacent to a '5'.
cols_to_prune = set()
for r in range(height):
for c in range(width):
val = test_input_matrix[r][c]
if val != 0 and val != 5: # It's a source number
# Check left neighbor
if c > 0 and test_input_matrix[r][c-1] == 5:
cols_to_prune.add(c)
# Check right neighbor
if c < width - 1 and test_input_matrix[r][c+1] == 5:
cols_to_prune.add(c)
# Step 4: Construct the final output by removing the pruned columns from the filled grid.
output_grid = []
for r in range(height):
new_row = []
for c in range(width):
if c not in cols_to_prune:
new_row.append(filled_grid[r][c])
output_grid.append(new_row)
return output_grid
```
---
---
Falling back to direct ChainOfThought solving.
Traceback (most recent call last):
File "<string>", line 76, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
2025/08/28 23:24:12 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/28 23:25:31 INFO dspy.evaluate.evaluate: Average Metric: 2.0 / 3 (66.7%)
GEPA Optimization: 14%|███████████████▍ | 556/4000 [1:51:35<74:15:15, 77.62s/rollouts]Iteration 31: New subsample score is not better, skipping
Iteration 32: Selected program 0 score: 0.67
Average Metric: 1.00 / 1 (100.0%): 33%|█████████████████████████████████████ | 1/3 [00:25<00:51, 25.83s/it]2025/08/28 23:26:13 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
Average Metric: 3.00 / 3 (100.0%): 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [02:11<00:00, 43.71s/it]2025/08/28 23:27:43 INFO dspy.evaluate.evaluate: Average Metric: 3.0 / 3 (100.0%)
GEPA Optimization: 14%|███████████████▌ | 559/4000 [1:53:46<68:00:25, 71.15s/rollouts]
Iteration 32: All subsample scores perfect. Skipping.
Iteration 32: Reflective mutation did not propose a new candidate
Iteration 33: Selected program 1 score: 0.63
Average Metric: 2.00 / 2 (100.0%): 67%|██████████████████████████████████████████████████████████████████████████ | 2/3 [01:10<00:30, 30.29s/it]Code execution failed: name 'collections' is not defined
Generated code that failed:
import collections
def solve(test_input_matrix: list[list[int]]) -> list[list[int]]:
rows = len(test_input_matrix)
if rows == 0:
return []
cols = len(test_input_matrix[0])
if cols == 0:
return [[] for _ in range(rows)]
# 1. Identify active 5s, their values, and columns to skip
active_fives = []
cols_to_skip = set()
for r in range(rows):
for c in range(cols - 1):
if test_input_matrix[r][c] == 5 and test_input_matrix[r][c + 1] != 0:
value = test_input_matrix[r][c + 1]
active_fives.append({'r': r, 'c': c, 'val': value})
cols_to_skip.add(c + 1)
# 2. Create the mapping for columns to the new grid dimensions
col_map = []
new_cols = 0
for c in range(cols):
if c not in cols_to_skip:
col_map.append(new_cols)
new_cols += 1
else:
col_map.append(-1) # Indicates a skipped column
if new_cols == 0:
return [[] for _ in range(rows)]
# 3. Prepare for BFS
# -1: empty, -2: barrier, >0: filled by a seed
bfs_grid = [[-1] * new_cols for _ in range(rows)]
output_grid = [[0] * new_cols for _ in range(rows)]
queue = collections.deque()
# Place barriers (original non-5, non-0 numbers)
for r in range(rows):
for c in range(cols):
if col_map[c] != -1:
new_c = col_map[c]
val = test_input_matrix[r][c]
if val != 0 and val != 5:
bfs_grid[r][new_c] = -2
output_grid[r][new_c] = val
# Place seeds, sorted for deterministic tie-breaking (higher value wins)
seeds = []
for five in active_fives:
r, c, val = five['r'], five['c'], five['val']
new_c = col_map[c]
seeds.append({'r': r, 'c': new_c, 'val': val})
seeds.sort(key=lambda s: (-s['val'], s['r'], s['c']))
for seed in seeds:
r, c, val = seed['r'], seed['c'], seed['val']
if bfs_grid[r][c] == -1:
bfs_grid[r][c] = val
queue.append((r, c))
# 4. Run the multi-source BFS
while queue:
r, c = queue.popleft()
val = bfs_grid[r][c]
for dr, dc in [(0, 1), (0, -1), (1, 0), (-1, 0)]:
nr, nc = r + dr, c + dc
if 0 <= nr < rows and 0 <= nc < new_cols and bfs_grid[nr][nc] == -1:
bfs_grid[nr][nc] = val
queue.append((nr, nc))
# 5. Populate the final output grid from the BFS result
for r in range(rows):
for c in range(new_cols):
if output_grid[r][c] == 0: # If not already a barrier
if bfs_grid[r][c] > 0:
output_grid[r][c] = bfs_grid[r][c]
return output_grid
---
---
Falling back to direct ChainOfThought solving.
Traceback (most recent call last):
File "<string>", line 80, in forward
File "<string>", line 80, in <listcomp>
File "<string>", line 38, in solve
NameError: name 'collections' is not defined
Average Metric: 3.00 / 3 (100.0%): 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [02:24<00:00, 48.14s/it]2025/08/28 23:30:07 INFO dspy.evaluate.evaluate: Average Metric: 3.0 / 3 (100.0%)
GEPA Optimization: 14%|███████████████▌ | 562/4000 [1:56:11<63:14:31, 66.22s/rollouts]
Iteration 33: All subsample scores perfect. Skipping.
Iteration 33: Reflective mutation did not propose a new candidate
Iteration 34: Selected program 1 score: 0.63
Average Metric: 1.00 / 3 (33.3%): : 4it [05:07, 76.95s/it] 2025/08/28 23:35:15 INFO dspy.evaluate.evaluate: Average Metric: 1.0 / 3 (33.3%)
Iteration 34: Proposed new text for program: import dspy
from typing import List
import pydantic
import re
# Define the type for a matrix for clarity
MATRIX = List[List[int]]
class TrainingExample(pydantic.BaseModel):
"""A Pydantic model for a single training example, containing an input and output matrix."""
input: MATRIX
output: MATRIX
class InferRuleSignature(dspy.Signature):
"""
Analyzes pairs of input/output matrices to deduce the transformation rule.
You are an expert analyst of abstract visual reasoning puzzles. Your task is to carefully examine the provided training examples. Each example consists of an 'input' matrix and a corresponding 'output' matrix.
Your goal is to deduce the single, consistent transformation rule that converts each input to its output. Describe this rule in clear, unambiguous, step-by-step natural language. The description must be precise enough for another AI to follow it to solve a new, unseen test input.
Consider various possible transformation patterns, including but not limited to:
- Geometric operations: rotation, reflection, scaling, repetition, shifting.
- Color/value transformations: changing specific numbers to others, conditional changes.
- Object-based logic: identifying shapes or objects and applying rules based on their properties (size, color, position).
- Fill/completion patterns: such as flood fills or completing a grid based on object boundaries.
- Bounding box or frame logic: creating structures based on the extents of objects.
"""
training_examples: List[TrainingExample] = dspy.InputField(description="A list of input and output examples demonstrating the task.")
transformation_rule: str = dspy.OutputField(description="A step-by-step natural language description of the transformation rule.")
class GenerateCodeSignature(dspy.Signature):
"""
Given a natural language transformation rule and a set of training examples, write a Python function that implements this rule.
You are an expert programmer specializing in matrix transformations and algorithmic logic. Your task is to write a single, self-contained Python function named `transform_matrix` that takes one argument, `matrix` (a list of lists of integers), and returns the transformed matrix.
**Instructions:**
1. Carefully analyze the provided `transformation_rule`.
2. Examine the `training_examples` to understand how the rule is applied in practice. Your function MUST correctly reproduce the 'output' from the 'input' for all provided examples.
3. Write the `transform_matrix` function. It should not rely on any external libraries (like numpy). Standard Python 3 features are allowed.
4. Your output must be ONLY the Python code for the function, enclosed in a single markdown code block (```python...```). Do not include any other text, explanations, or import statements.
"""
transformation_rule: str = dspy.InputField(description="The natural language rule to implement.")
training_examples: List[TrainingExample] = dspy.InputField(description="A list of input/output examples to verify the function's logic.")
python_code: str = dspy.OutputField(description="A string containing the complete Python function `transform_matrix`.")
class ARCProgram(dspy.Module):
"""A program that first infers a rule, generates code to implement it, and then executes the code."""
def __init__(self):
super().__init__()
self.rule_inferrer = dspy.ChainOfThought(InferRuleSignature)
self.code_generator = dspy.Predict(GenerateCodeSignature)
def forward(self, training_examples: List[TrainingExample], test_inputs: List[MATRIX]) -> dspy.Prediction:
"""
Infers a rule, generates a Python function, and applies it to each test input.
Args:
training_examples: A list of TrainingExample objects.
test_inputs: A list of input matrices to be solved.
Returns:
A dspy.Prediction object with the 'test_outputs' field populated.
"""
# 1. Infer the transformation rule from the training examples.
inferred = self.rule_inferrer(training_examples=training_examples)
rule = inferred.transformation_rule
# 2. Generate a Python function that implements the rule.
# Pass the original examples to help the LM verify its code.
generated = self.code_generator(transformation_rule=rule, training_examples=training_examples)
code_str = generated.python_code
# Clean up the generated code string, removing markdown fences.
code_match = re.search(r"```python\n(.*?)\n```", code_str, re.DOTALL)
if code_match:
code_str = code_match.group(1)
all_test_outputs = []
transform_func = None
# 3. Prepare a safe execution environment and execute the generated code.
try:
# Create a dictionary to serve as the local namespace for exec.
local_namespace = {}
# Execute the code string, which should define the `transform_matrix` function.
exec(code_str, globals(), local_namespace)
# Retrieve the function from the namespace.
transform_func = local_namespace.get('transform_matrix')
except Exception as e:
print(f"Failed to define function from generated code: {e}")
transform_func = None # Ensure function is None if exec fails
# 4. Apply the function to each test input.
for test_matrix in test_inputs:
if transform_func:
try:
# Apply the successfully defined function.
result_matrix = transform_func(test_matrix)
all_test_outputs.append(result_matrix)
except Exception as e:
# Fallback if the function runs but fails on a specific test case.
print(f"Generated function failed during execution: {e}")
if test_matrix:
all_test_outputs.append([([0] * len(test_matrix[0])) for _ in range(len(test_matrix))])
else:
all_test_outputs.append([])
else:
# Fallback if the function was never defined correctly.
if test_matrix:
all_test_outputs.append([([0] * len(test_matrix[0])) for _ in range(len(test_matrix))])
else:
all_test_outputs.append([])
return dspy.Prediction(test_outputs=all_test_outputs)
# The final 'program' object is an instance of our robust, multi-step module.
program = ARCProgram()
2025/08/28 23:42:12 INFO dspy.evaluate.evaluate: Average Metric: 1.0 / 3 (33.3%)
GEPA Optimization: 14%|███████████████▊ | 568/4000 [2:08:16<82:53:42, 86.95s/rollouts]Generated function failed during execution: name 'collections' is not defined
Iteration 34: New subsample score is not better, skipping
Iteration 35: Selected program 0 score: 0.67
Average Metric: 2.00 / 3 (66.7%): 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:50<00:00, 16.76s/it]2025/08/28 23:43:02 INFO dspy.evaluate.evaluate: Average Metric: 2.0 / 3 (66.7%)
Generated function failed during execution: name 'collections' is not defined
2025/08/28 23:43:49 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 4, 4, 4, 4, 0, 0, 0, 0, 0], [0, 4, 4, 4, 4, 0, 0, 0, 0, 0], [0, 4, 4, 4, 4, 0, 0, 0, 0, 0], [0, 4, 4, 4, 4, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 4, 4, 4, 4, 4, 4, 0], [0, 0, 0, 4, 4, 4, 4, 4, 4, 0], [0, 0, 0, 4, 4, 4, 4, 4, 4, 0], [0, 0, 0, 4, 4, 4, 4, 4, 4, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 4, 4, 4, 4, 0, 0, 0, 0, 0], [0, 4, 1, 1, 4, 0, 0, 0, 0, 0], [0, 4, 1, 1, 4, 0, 0, 0, 0, 0], [0, 4, 4, 4, 4, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 4, 4, 4, 4, 4, 4, 0], [0, 0, 0, 4, 2, 2, 2, 2, 4, 0], [0, 0, 0, 4, 2, 2, 2, 2, 4, 0], [0, 0, 0, 4, 4, 4, 4, 4, 4, 0]]}, {'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 4, 4, 4, 4, 4, 0, 0, 0, 0], [0, 4, 4, 4, 4, 4, 0, 0, 0, 0], [0, 4, 4, 4, 4, 4, 0, 0, 0, 0], [0, 4, 4, 4, 4, 4, 0, 0, 0, 0], [0, 4, 4, 4, 4, 4, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 4, 4, 4, 4, 0], [0, 0, 0, 0, 0, 4, 4, 4, 4, 0], [0, 0, 0, 0, 0, 4, 4, 4, 4, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 4, 4, 4, 4, 4, 0, 0, 0, 0], [0, 4, 2, 2, 2, 4, 0, 0, 0, 0], [0, 4, 2, 2, 2, 4, 0, 0, 0, 0], [0, 4, 2, 2, 2, 4, 0, 0, 0, 0], [0, 4, 4, 4, 4, 4, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 4, 4, 4, 4, 0], [0, 0, 0, 0, 0, 4, 1, 1, 4, 0], [0, 0, 0, 0, 0, 4, 4, 4, 4, 0]]}], 'test_inputs': [[[4, 4, 4, 4, 4, 4, 0, 0, 0, 0], [4, 4, 4, 4, 4, 4, 0, 0, 0, 0], [4, 4, 4, 4, 4, 4, 0, 0, 0, 0], [4, 4, 4, 4, 4, 4, 0, 0, 0, 0], [4, 4, 4, 4, 4, 4, 0, 0, 0, 0], [4, 4, 4, 4, 4, 4, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 4, 4, 4, 4, 4, 4], [0, 0, 0, 0, 4, 4, 4, 4, 4, 4], [0, 0, 0, 0, 4, 4, 4, 4, 4, 4]]], 'test_outputs': [[[4, 4, 4, 4, 4, 4, 0, 0, 0, 0], [4, 2, 2, 2, 2, 4, 0, 0, 0, 0], [4, 2, 2, 2, 2, 4, 0, 0, 0, 0], [4, 2, 2, 2, 2, 4, 0, 0, 0, 0], [4, 2, 2, 2, 2, 4, 0, 0, 0, 0], [4, 4, 4, 4, 4, 4, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 4, 4, 4, 4, 4, 4], [0, 0, 0, 0, 4, 1, 1, 1, 1, 4], [0, 0, 0, 0, 4, 4, 4, 4, 4, 4]]]}) (input_keys={'test_inputs', 'training_examples'}): 'dict' object has no attribute 'input'
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 44, in forward
AttributeError: 'dict' object has no attribute 'input'
2025/08/28 23:43:49 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[6, 0, 6], [0, 6, 6], [6, 0, 6], [4, 0, 4], [0, 4, 4], [4, 0, 4], [8, 8, 8], [8, 0, 8], [8, 8, 8]], 'output': [[8, 8, 8], [8, 0, 8], [8, 8, 8]]}, {'input': [[2, 0, 0, 3, 0, 0, 7, 0, 7, 1, 0, 0], [2, 0, 0, 3, 0, 0, 0, 7, 0, 1, 0, 0], [0, 2, 2, 0, 3, 3, 7, 0, 7, 0, 1, 1]], 'output': [[7, 0, 7], [0, 7, 0], [7, 0, 7]]}, {'input': [[3, 0, 0, 4, 0, 4, 2, 0, 0, 8, 0, 0, 1, 0, 0], [0, 3, 3, 4, 4, 4, 0, 2, 2, 0, 8, 8, 0, 1, 1], [0, 3, 0, 4, 0, 4, 0, 2, 0, 0, 8, 0, 0, 1, 0]], 'output': [[4, 0, 4], [4, 4, 4], [4, 0, 4]]}, {'input': [[0, 7, 7], [7, 7, 0], [7, 0, 7], [3, 0, 0], [0, 3, 3], [3, 0, 0], [2, 0, 0], [0, 2, 2], [2, 0, 0], [8, 0, 0], [0, 8, 8], [8, 0, 0]], 'output': [[0, 7, 7], [7, 7, 0], [7, 0, 7]]}], 'test_inputs': [[[0, 5, 0], [5, 0, 5], [0, 5, 0], [0, 3, 0], [3, 0, 3], [0, 3, 0], [6, 0, 6], [6, 6, 0], [6, 0, 6], [0, 4, 0], [4, 0, 4], [0, 4, 0], [0, 8, 0], [8, 0, 8], [0, 8, 0]]], 'test_outputs': [[[6, 0, 6], [6, 6, 0], [6, 0, 6]]]}) (input_keys={'test_inputs', 'training_examples'}): 'dict' object has no attribute 'input'
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 44, in forward
AttributeError: 'dict' object has no attribute 'input'
2025/08/28 23:43:49 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [9, 0, 0, 0, 0, 0, 0, 0, 0, 6], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [8, 0, 0, 0, 0, 0, 0, 0, 0, 9], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [4, 0, 0, 0, 0, 0, 0, 0, 0, 4], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [6, 0, 0, 0, 0, 0, 0, 0, 0, 8], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [9, 0, 0, 0, 0, 0, 0, 0, 0, 6], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [8, 0, 0, 0, 0, 0, 0, 0, 0, 9], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [4, 4, 4, 4, 4, 4, 4, 4, 4, 4], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [6, 0, 0, 0, 0, 0, 0, 0, 0, 8], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}, {'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [8, 0, 0, 0, 0, 0, 0, 0, 0, 8], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [4, 0, 0, 0, 0, 0, 0, 0, 0, 2], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [3, 0, 0, 0, 0, 0, 0, 0, 0, 4], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0, 0, 0, 0, 0, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [2, 0, 0, 0, 0, 0, 0, 0, 0, 3]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [4, 0, 0, 0, 0, 0, 0, 0, 0, 2], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [3, 0, 0, 0, 0, 0, 0, 0, 0, 4], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [2, 0, 0, 0, 0, 0, 0, 0, 0, 3]]}, {'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [2, 0, 0, 0, 0, 0, 0, 0, 0, 8], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [3, 0, 0, 0, 0, 0, 0, 0, 0, 4], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5, 0, 0, 0, 0, 0, 0, 0, 0, 3], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [8, 0, 0, 0, 0, 0, 0, 0, 0, 2], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [2, 0, 0, 0, 0, 0, 0, 0, 0, 8], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [3, 0, 0, 0, 0, 0, 0, 0, 0, 4], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5, 0, 0, 0, 0, 0, 0, 0, 0, 3], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [8, 0, 0, 0, 0, 0, 0, 0, 0, 2], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}], 'test_inputs': [[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [4, 0, 0, 0, 0, 0, 0, 0, 0, 2], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [3, 0, 0, 0, 0, 0, 0, 0, 0, 3], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [2, 0, 0, 0, 0, 0, 0, 0, 0, 9], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [6, 0, 0, 0, 0, 0, 0, 0, 0, 6], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [9, 0, 0, 0, 0, 0, 0, 0, 0, 4]]], 'test_outputs': [[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [4, 0, 0, 0, 0, 0, 0, 0, 0, 2], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [2, 0, 0, 0, 0, 0, 0, 0, 0, 9], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [6, 6, 6, 6, 6, 6, 6, 6, 6, 6], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [9, 0, 0, 0, 0, 0, 0, 0, 0, 4]]]}) (input_keys={'test_inputs', 'training_examples'}): 'dict' object has no attribute 'input'
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 44, in forward
AttributeError: 'dict' object has no attribute 'input'
2025/08/28 23:43:49 INFO dspy.evaluate.evaluate: Average Metric: 0.0 / 3 (0.0%)
GEPA Optimization: 14%|███████████████▉ | 574/4000 [2:09:53<59:04:18, 62.07s/rollouts]Iteration 35: Proposed new text for program: import dspy
from typing import List
import pydantic
import json
# Define the type for a matrix for clarity
MATRIX = List[List[int]]
class TrainingExample(pydantic.BaseModel):
"""A Pydantic model for a single training example, containing an input and output matrix."""
input: MATRIX
output: MATRIX
class GenerateRuleSignature(dspy.Signature):
"""
Analyzes pairs of input and output matrices to deduce the transformation rule.
Based on this rule, generates a self-contained Python function to perform the transformation.
**Instructions:**
1. Carefully examine the `training_examples`. Each example shows a 'before' (input) and 'after' (output) grid.
2. Identify the underlying pattern or algorithm that transforms the input grid into the output grid. This could involve colors, shapes, counts, positions, or geometric operations.
3. Think step-by-step to deconstruct the logic.
4. Write a single, self-contained Python function named `transform_matrix` that implements this logic.
5. The function must accept one argument: `matrix` (a list of lists of integers).
6. The function must return the transformed matrix (a list of lists of integers).
7. Do NOT include any code outside of the function definition. The output should be a string containing only the `def transform_matrix(...): ...` block.
8. The function should not rely on any external libraries beyond Python's standard library.
"""
training_examples: str = dspy.InputField(desc="A string representation of input-output matrix pairs demonstrating the task.")
python_function: str = dspy.OutputField(desc="A string containing a single Python function `transform_matrix(matrix)` that implements the deduced transformation rule.")
class SolveWithGeneratedCode(dspy.Module):
"""A module that solves matrix tasks by first generating a Python function and then executing it."""
def __init__(self):
super().__init__()
# Module to generate the transformation logic as a Python function
self.rule_generator = dspy.Predict(GenerateRuleSignature)
def forward(self, training_examples: List[TrainingExample], test_inputs: List[MATRIX]) -> dspy.Prediction:
# Format the training examples into a readable string for the LM
examples_str = []
for i, example in enumerate(training_examples):
# Using json.dumps for a compact and unambiguous representation
input_str = json.dumps(example.input)
output_str = json.dumps(example.output)
examples_str.append(f"Example {i+1}:\nInput:\n{input_str}\nOutput:\n{output_str}\n")
formatted_examples = "\n".join(examples_str)
# Generate the Python function string
prediction = self.rule_generator(training_examples=formatted_examples)
python_function_str = prediction.python_function
# Prepare a local scope for executing the generated function
local_scope = {}
# Execute the generated Python code to define the function
# A try-except block handles potential syntax errors in the generated code
try:
# The generated string might be wrapped in markdown code fences
if python_function_str.startswith("```python"):
python_function_str = python_function_str[len("```python"):].strip()
if python_function_str.endswith("```"):
python_function_str = python_function_str[:-len("```")].strip()
exec(python_function_str, globals(), local_scope)
transform_func = local_scope['transform_matrix']
# Apply the function to each test input
final_outputs = [transform_func(matrix) for matrix in test_inputs]
except Exception as e:
print(f"Error executing generated code: {e}")
# Fallback: return empty matrices of the same size if code fails
final_outputs = [[([0] * len(row)) for row in matrix] for matrix in test_inputs]
return dspy.Prediction(test_outputs=final_outputs)
# The final program object to be used for solving the task.
program = SolveWithGeneratedCode()
Iteration 35: New subsample score is not better, skipping
Iteration 36: Selected program 0 score: 0.67
Average Metric: 1.00 / 3 (33.3%): : 4it [07:19, 109.80s/it] 2025/08/28 23:51:08 INFO dspy.evaluate.evaluate: Average Metric: 1.0 / 3 (33.3%)
2025/08/28 23:51:54 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[0, 1, 0], [1, 1, 0], [0, 1, 0], [0, 1, 1], [0, 1, 0], [1, 1, 0]], 'output': [[0, 2, 0], [2, 2, 0], [0, 2, 0], [0, 2, 2], [0, 2, 0], [2, 2, 0], [0, 2, 0], [0, 2, 2], [0, 2, 0]]}, {'input': [[0, 1, 0], [1, 0, 1], [0, 1, 0], [1, 0, 1], [0, 1, 0], [1, 0, 1]], 'output': [[0, 2, 0], [2, 0, 2], [0, 2, 0], [2, 0, 2], [0, 2, 0], [2, 0, 2], [0, 2, 0], [2, 0, 2], [0, 2, 0]]}, {'input': [[0, 1, 0], [1, 1, 0], [0, 1, 0], [0, 1, 0], [1, 1, 0], [0, 1, 0]], 'output': [[0, 2, 0], [2, 2, 0], [0, 2, 0], [0, 2, 0], [2, 2, 0], [0, 2, 0], [0, 2, 0], [2, 2, 0], [0, 2, 0]]}], 'test_inputs': [[[1, 1, 1], [0, 1, 0], [0, 1, 0], [1, 1, 1], [0, 1, 0], [0, 1, 0]]], 'test_outputs': [[[2, 2, 2], [0, 2, 0], [0, 2, 0], [2, 2, 2], [0, 2, 0], [0, 2, 0], [2, 2, 2], [0, 2, 0], [0, 2, 0]]]}) (input_keys={'test_inputs', 'training_examples'}): 'dict' object has no attribute 'dict'
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 57, in forward
File "<string>", line 57, in <listcomp>
AttributeError: 'dict' object has no attribute 'dict'
2025/08/28 23:51:54 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[0, 0, 0, 0, 3], [0, 8, 8, 0, 3], [0, 8, 8, 0, 3], [0, 0, 0, 0, 3], [3, 3, 3, 3, 3]], 'output': [[2, 0, 0, 0, 0, 0, 0, 2, 3, 3], [0, 2, 0, 0, 0, 0, 2, 0, 3, 3], [0, 0, 8, 8, 8, 8, 0, 0, 3, 3], [0, 0, 8, 8, 8, 8, 0, 0, 3, 3], [0, 0, 8, 8, 8, 8, 0, 0, 3, 3], [0, 0, 8, 8, 8, 8, 0, 0, 3, 3], [0, 2, 0, 0, 0, 0, 2, 0, 3, 3], [2, 0, 0, 0, 0, 0, 0, 2, 3, 3], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3]]}, {'input': [[0, 0, 0, 0, 7], [4, 4, 0, 0, 7], [4, 4, 0, 0, 6], [0, 0, 0, 0, 6], [7, 7, 6, 6, 6]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 7, 7, 7], [0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 7, 7, 7], [0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 7, 7, 7], [4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 7, 7, 7], [4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 7, 7, 7], [4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 7, 7, 7], [4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 6, 6, 6], [4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 6, 6, 6], [4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 6, 6, 6], [0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 6, 6, 6], [0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 6, 6, 6], [0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 6, 6, 6], [7, 7, 7, 7, 7, 7, 6, 6, 6, 6, 6, 6, 6, 6, 6], [7, 7, 7, 7, 7, 7, 6, 6, 6, 6, 6, 6, 6, 6, 6], [7, 7, 7, 7, 7, 7, 6, 6, 6, 6, 6, 6, 6, 6, 6]]}, {'input': [[0, 0, 0, 0, 9], [0, 1, 1, 0, 9], [0, 1, 1, 0, 3], [0, 0, 0, 0, 3], [9, 9, 3, 3, 4]], 'output': [[2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 9, 9, 9, 9], [0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 9, 9, 9, 9], [0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 9, 9, 9, 9], [0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 9, 9, 9, 9], [0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 9, 9, 9, 9], [0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 9, 9, 9, 9], [0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 9, 9, 9, 9], [0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 9, 9, 9, 9], [0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 3, 3, 3, 3], [0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 3, 3, 3, 3], [0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 3, 3, 3, 3], [0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 3, 3, 3, 3], [0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 3, 3, 3, 3], [0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 3, 3, 3, 3], [0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 3, 3, 3, 3], [2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 3, 3, 3, 3], [9, 9, 9, 9, 9, 9, 9, 9, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4], [9, 9, 9, 9, 9, 9, 9, 9, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4], [9, 9, 9, 9, 9, 9, 9, 9, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4], [9, 9, 9, 9, 9, 9, 9, 9, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4]]}], 'test_inputs': [[[0, 6, 6, 0, 8], [0, 6, 6, 0, 8], [0, 0, 0, 0, 1], [0, 0, 0, 0, 7], [8, 8, 1, 7, 9]]], 'test_outputs': [[[0, 0, 0, 0, 0, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8], [0, 0, 0, 0, 0, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8], [0, 0, 0, 0, 0, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8], [0, 0, 0, 0, 0, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8], [0, 0, 0, 0, 0, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8], [0, 0, 0, 0, 0, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8], [0, 0, 0, 0, 0, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8], [0, 0, 0, 0, 0, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8], [0, 0, 0, 0, 0, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8], [0, 0, 0, 0, 0, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8], [0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 1, 1, 1, 1, 1], [0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 1, 1, 1, 1, 1], [0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 1, 1, 1, 1, 1], [0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 1, 1, 1, 1, 1], [2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 7, 7, 7, 7], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 7, 7, 7, 7], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 7, 7, 7, 7], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 7, 7, 7, 7], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 7, 7, 7, 7], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 1, 1, 1, 1, 1, 7, 7, 7, 7, 7, 9, 9, 9, 9, 9], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 1, 1, 1, 1, 1, 7, 7, 7, 7, 7, 9, 9, 9, 9, 9], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 1, 1, 1, 1, 1, 7, 7, 7, 7, 7, 9, 9, 9, 9, 9], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 1, 1, 1, 1, 1, 7, 7, 7, 7, 7, 9, 9, 9, 9, 9], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 1, 1, 1, 1, 1, 7, 7, 7, 7, 7, 9, 9, 9, 9, 9]]]}) (input_keys={'test_inputs', 'training_examples'}): 'dict' object has no attribute 'dict'
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 57, in forward
File "<string>", line 57, in <listcomp>
AttributeError: 'dict' object has no attribute 'dict'
2025/08/28 23:51:54 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 2, 0, 2, 2, 2, 2, 2, 0, 2, 0, 0, 1, 2, 1, 0, 0, 0], [0, 0, 2, 0, 0, 2, 2, 2, 2, 0, 2, 0, 0, 2, 2, 2, 0, 0, 0], [0, 0, 2, 2, 2, 2, 2, 2, 2, 0, 2, 0, 0, 1, 2, 1, 0, 0, 0], [0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 2, 2, 2, 2, 0, 2, 2, 2, 2, 0, 0, 0, 3, 3, 3, 0, 0], [0, 0, 2, 2, 2, 0, 0, 0, 2, 2, 2, 0, 0, 0, 2, 2, 2, 0, 0], [0, 0, 2, 2, 2, 2, 0, 2, 2, 2, 2, 0, 0, 0, 3, 3, 3, 0, 0], [0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 2, 0, 0, 2, 2, 2, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 2, 2, 0, 2, 2, 2, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 2, 0, 0, 2, 2, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 8, 8, 8, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 8, 2, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0], [0, 0, 0, 4, 4, 4, 0, 0, 5, 5, 2, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 4, 2, 4, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 2, 2, 4, 0, 0, 5, 5, 2, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[2, 2, 2, 2, 2, 2, 2, 2, 2], [2, 2, 4, 4, 2, 2, 3, 2, 3], [2, 2, 2, 4, 2, 2, 3, 2, 3], [2, 4, 4, 4, 2, 2, 3, 2, 3], [2, 2, 2, 2, 2, 2, 2, 2, 2], [2, 2, 2, 2, 2, 2, 2, 2, 2], [2, 2, 2, 1, 2, 1, 2, 2, 2], [2, 2, 2, 2, 2, 2, 2, 2, 2], [2, 2, 2, 1, 2, 1, 2, 2, 2], [2, 2, 2, 2, 2, 2, 2, 2, 2], [2, 2, 2, 2, 2, 2, 2, 2, 2], [2, 2, 2, 2, 2, 2, 2, 2, 2], [2, 2, 2, 2, 2, 2, 2, 2, 2], [8, 2, 2, 2, 2, 5, 2, 5, 2], [8, 8, 2, 2, 2, 5, 2, 5, 2], [8, 2, 2, 2, 2, 2, 2, 2, 2], [2, 2, 2, 2, 2, 2, 2, 2, 2]]}, {'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 2, 2, 2, 2, 2, 2, 2, 2, 0], [0, 2, 2, 0, 2, 2, 2, 2, 2, 0], [0, 2, 0, 0, 0, 2, 2, 2, 2, 0], [0, 2, 2, 2, 2, 2, 2, 2, 2, 0], [0, 2, 2, 2, 2, 0, 0, 2, 2, 0], [0, 2, 2, 2, 2, 0, 2, 0, 2, 0], [0, 2, 2, 2, 2, 2, 0, 0, 2, 0], [0, 2, 2, 2, 2, 2, 2, 2, 2, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 4, 2, 4, 0, 0, 0, 0, 0, 0], [0, 2, 2, 4, 0, 0, 0, 0, 0, 0], [0, 4, 2, 4, 0, 2, 2, 3, 0, 0], [0, 0, 0, 0, 0, 2, 3, 2, 0, 0], [0, 0, 0, 0, 0, 3, 2, 2, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[2, 2, 2, 2, 2, 2, 2, 2], [2, 4, 2, 4, 2, 2, 2, 2], [2, 2, 2, 2, 2, 2, 2, 2], [2, 4, 4, 4, 2, 2, 2, 2], [2, 2, 2, 2, 2, 2, 3, 2], [2, 2, 2, 2, 2, 3, 2, 2], [2, 2, 2, 2, 3, 2, 2, 2], [2, 2, 2, 2, 2, 2, 2, 2]]}, {'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 8, 2, 8, 0, 0, 0, 0], [0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0], [0, 0, 0, 0, 0, 8, 2, 8, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0], [0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0], [0, 2, 2, 2, 0, 2, 2, 2, 2, 2, 0, 0], [0, 2, 2, 0, 0, 0, 2, 2, 2, 2, 0, 0], [0, 2, 2, 2, 0, 2, 2, 2, 2, 2, 0, 0], [0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0], [0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0], [0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0], [0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[2, 2, 2, 2, 2, 2, 2, 2, 2], [2, 2, 2, 2, 2, 2, 2, 2, 2], [2, 2, 8, 2, 8, 2, 2, 2, 2], [2, 2, 2, 2, 2, 2, 2, 2, 2], [2, 2, 8, 2, 8, 2, 2, 2, 2], [2, 2, 2, 2, 2, 2, 2, 2, 2], [2, 2, 2, 2, 2, 2, 2, 2, 2], [2, 2, 2, 2, 2, 2, 2, 2, 2], [2, 2, 2, 2, 2, 2, 2, 2, 2]]}], 'test_inputs': [[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0], [0, 0, 2, 0, 2, 2, 2, 2, 0, 2, 2, 2, 0, 2, 0], [0, 0, 2, 0, 0, 2, 2, 0, 0, 2, 2, 2, 2, 2, 0], [0, 0, 2, 2, 0, 0, 2, 2, 0, 2, 2, 2, 2, 2, 0], [0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0], [0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0], [0, 0, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 2, 2, 0], [0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 1, 1, 0, 0, 0, 0, 2, 3, 3, 0, 0, 0, 0], [0, 1, 2, 1, 0, 0, 0, 0, 2, 2, 3, 0, 0, 0, 0], [0, 1, 1, 1, 0, 0, 0, 0, 2, 3, 3, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 2, 0], [0, 0, 0, 4, 2, 4, 0, 0, 0, 0, 0, 8, 2, 2, 0], [0, 0, 0, 4, 2, 4, 0, 0, 0, 0, 0, 2, 2, 8, 0], [0, 0, 0, 4, 2, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0]]], 'test_outputs': [[[2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1], [2, 2, 8, 8, 3, 3, 2, 2, 2, 1, 2, 1], [2, 2, 2, 8, 3, 2, 2, 2, 2, 1, 1, 1], [2, 8, 2, 2, 3, 3, 2, 2, 2, 2, 2, 2], [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2], [2, 2, 2, 2, 2, 2, 2, 4, 4, 4, 2, 2], [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2], [2, 2, 2, 2, 2, 2, 2, 4, 4, 4, 2, 2]]]}) (input_keys={'test_inputs', 'training_examples'}): 'dict' object has no attribute 'dict'
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 57, in forward
File "<string>", line 57, in <listcomp>
AttributeError: 'dict' object has no attribute 'dict'
2025/08/28 23:51:54 INFO dspy.evaluate.evaluate: Average Metric: 0.0 / 3 (0.0%)
GEPA Optimization: 14%|████████████████ | 580/4000 [2:17:58<64:56:34, 68.36s/rollouts]Iteration 36: Proposed new text for program: import dspy
from typing import List
import pydantic
# Define the basic data structures for matrices and training examples.
MATRIX = List[List[int]]
class TrainingExample(pydantic.BaseModel):
input: MATRIX
output: MATRIX
# The original signature, now used for the fallback mechanism.
class SolveTaskSignature(dspy.Signature):
"""
Given a list of training examples demonstrating a task, and a list of test inputs,
solve the task for each test input.
"""
training_examples: List[TrainingExample] = dspy.InputField(description="Input and output examples demonstrating the task to be performed.")
test_inputs: List[MATRIX] = dspy.InputField(description="Input matrices to be solved following the task described in the training examples.")
test_outputs: List[MATRIX] = dspy.OutputField(description="Output matrices corresponding to the test inputs.")
# A new, highly detailed signature for the rule induction step.
class InduceRuleSignature(dspy.Signature):
"""Analyze pairs of input/output matrices to deduce the transformation rule. Then, write a self-contained Python function named 'transform' that implements this rule.
**Task Description:**
You are given several examples, each with an 'input' matrix and a corresponding 'output' matrix. Your task is to understand the transformation logic that converts the input to the output and encapsulate this logic into a single Python function.
**Successful Strategies to Consider:**
- **Scaling:** Does the output grid's size relate to the input grid's size? Is each input cell expanded into a k x k block in the output? How is 'k' determined (e.g., by the number of unique colors, a fixed value)?
- **Pattern Repetition:** Are there repeating patterns (e.g., rows, subgrids) in the input that are used to construct the output?
- **Neighborhood Analysis:** Does the value of an output cell depend on the 3x3 neighborhood of the corresponding input cell? This is common in tasks involving Conway's Game of Life-like rules or image processing filters.
- **Object-Based Transformation:** Does the input contain distinct "objects" or "shapes"? Are these objects moved, rotated, colored, or combined to form the output?
- **Algorithmic Construction:** The rule might be a multi-step algorithm. For example: 1. Find the largest shape. 2. Find a "key" or "dictionary" of patterns. 3. Use the key to "decode" the shape.
**Output Requirements (Crucial):**
- The function MUST be named `transform`.
- It MUST accept exactly one argument: `matrix`, which is the input grid (a list of lists of integers).
- It MUST return the transformed grid (a list of lists of integers).
- The function must be entirely self-contained. If you need libraries like `numpy`, import them *inside* the function.
- Your output should ONLY be the Python code for the function, starting with `def transform(matrix):` and nothing else. Do not include example usage or explanations outside the function's docstring.
"""
training_examples: str = dspy.InputField(desc="A string representation of the list of training examples, each with an 'input' and 'output' matrix.")
python_function: str = dspy.OutputField(desc="A single, self-contained Python function string that implements the transformation rule. It must be named 'transform'.")
# The custom module that orchestrates rule induction and execution.
class VisualReasoningSolver(dspy.Module):
def __init__(self):
super().__init__()
# The primary strategy: induce a rule and generate code.
self.induce_rule = dspy.ChainOfThought(InduceRuleSignature)
# The fallback strategy: use a single LM call to solve directly.
self.fallback = dspy.ChainOfThought(SolveTaskSignature)
def forward(self, training_examples: List[TrainingExample], test_inputs: List[MATRIX]):
# Convert training examples to a string for the prompt.
training_examples_str = str([ex.dict() for ex in training_examples])
try:
# 1. Induce the transformation rule as a Python function.
prediction = self.induce_rule(training_examples=training_examples_str)
code_string = prediction.python_function
# Clean up the code string in case the LM wraps it in markdown.
if "```python" in code_string:
code_string = code_string.split("```python")[1].strip()
if code_string.endswith("```"):
code_string = code_string[:-3].strip()
# 2. Execute the generated code to define the function in a local scope.
local_scope = {}
exec(code_string, {}, local_scope)
transform_func = local_scope['transform']
# 3. Apply the generated function to all test inputs.
test_outputs = [transform_func(test_input) for test_input in test_inputs]
return dspy.Prediction(test_outputs=test_outputs)
except Exception as e:
# 4. If code generation or execution fails, use the fallback.
print(f"Code generation or execution failed: {e}. Using fallback.")
return self.fallback(training_examples=training_examples, test_inputs=test_inputs)
# The final program is an instance of our new, more sophisticated module.
program = VisualReasoningSolver()
Iteration 36: New subsample score is not better, skipping
Iteration 37: Selected program 0 score: 0.67
Average Metric: 1.00 / 3 (33.3%): 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [01:55<00:00, 38.66s/it]2025/08/28 23:53:50 INFO dspy.evaluate.evaluate: Average Metric: 1.0 / 3 (33.3%)
Iteration 37: Proposed new text for program: import dspy
from typing import List
import pydantic
import copy
# Define type aliases and pydantic models for clarity and structure.
MATRIX = List[List[int]]
class TrainingExample(pydantic.BaseModel):
input: MATRIX
output: MATRIX
class GeneratePythonFunction(dspy.Signature):
"""
You are an expert programmer. Your task is to analyze a series of input and output matrix pairs from the Abstraction and Reasoning Corpus (ARC). Based on these examples, you must deduce the underlying transformation rule and write a single, self-contained Python function that implements this rule.
**Function Requirements:**
- The function must be named `transform_matrix`.
- It must accept one argument: `matrix`, which is a list of lists of integers (representing the input grid).
- It must return a list of lists of integers (representing the transformed output grid).
- The function should not use any external libraries except for `copy` if needed (e.g., `import copy; new_matrix = copy.deepcopy(matrix)`).
- Your output must be ONLY the Python code for the function. Do not include any explanations, comments outside the function, or markdown formatting like ```python.
**Successful Strategies to Consider:**
- **Object Permanence:** Most transformations preserve the grid dimensions.
- **Color Transformations:** Look for rules that change colors based on their value or their neighbors.
- **Spatial Reasoning:** Analyze shapes, positions, and relationships. Common patterns include filling enclosed areas, drawing lines, moving objects, or detecting symmetry.
- **Iterative Processes:** Some rules are applied repeatedly until the grid no longer changes. Consider using a `while` loop that continues as long as modifications are being made in a pass.
- **Neighborhoods:** Often, a cell's new value depends on its immediate neighbors (e.g., von Neumann or Moore neighborhoods).
**Example of a Correctly Formatted Output:**
def transform_matrix(matrix: list[list[int]]) -> list[list[int]]:
# Your implementation here
# For example, find the most frequent color and fill the grid
from collections import Counter
import itertools
if not matrix or not matrix[0]:
return []
counts = Counter(itertools.chain.from_iterable(matrix))
if counts:
# Handle ties by picking the smaller number value
most_common_color = sorted(counts.items(), key=lambda item: (-item[1], item[0]))[0][0]
else:
return []
height = len(matrix)
width = len(matrix[0])
return [[most_common_color for _ in range(width)] for _ in range(height)]
"""
training_examples: List[TrainingExample] = dspy.InputField(desc="A list of input/output pairs demonstrating the transformation rule.")
python_function: str = dspy.OutputField(desc="A string containing a single Python function `transform_matrix` that implements the rule.")
class ARCSolver(dspy.Module):
"""A module that solves ARC tasks by generating and executing Python code."""
def __init__(self):
super().__init__()
self.code_generator = dspy.Predict(GeneratePythonFunction)
def forward(self, training_examples: List[TrainingExample], test_inputs: List[MATRIX]):
# Step 1: Generate the Python function as a string from the examples.
prediction = self.code_generator(training_examples=training_examples)
python_code = prediction.python_function
# Prepare a dictionary to hold the executed function.
local_scope = {}
# Create fallback outputs in case of any failure.
fallback_outputs = [copy.deepcopy(matrix) for matrix in test_inputs]
try:
# Step 2: Execute the generated code string to define the function.
# This safely executes the code in a controlled scope.
exec(python_code, globals(), local_scope)
transform_func = local_scope.get('transform_matrix')
if not callable(transform_func):
# If function definition failed or the name is wrong, use the fallback.
return dspy.Prediction(test_outputs=fallback_outputs)
# Step 3: Apply the generated function to each test input.
solved_outputs = []
for test_matrix in test_inputs:
try:
# Use a deepcopy to prevent the function from modifying the original input list.
input_copy = copy.deepcopy(test_matrix)
result = transform_func(input_copy)
solved_outputs.append(result)
except Exception:
# If the function fails on a specific test case, append the original
# matrix as a fallback for that case and continue.
solved_outputs.append(copy.deepcopy(test_matrix))
return dspy.Prediction(test_outputs=solved_outputs)
except Exception:
# If code generation or `exec` fails entirely, return the original inputs.
return dspy.Prediction(test_outputs=fallback_outputs)
# The original SolveTaskSignature is no longer directly used by the program module,
# but it's good practice to define the overall task signature.
class SolveTaskSignature(dspy.Signature):
"""
Given a set of training examples demonstrating a matrix transformation,
apply the same transformation to a new set of test matrices.
"""
training_examples: List[TrainingExample] = dspy.InputField(description="Input and output examples demonstrating the task to be performed.")
test_inputs: List[MATRIX] = dspy.InputField(description="Input matrices to be solved following the task described in the training examples.")
test_outputs: List[MATRIX] = dspy.OutputField(description="Output matrices corresponding to the test inputs.")
# The final program is an instance of our new, more robust module.
program = ARCSolver()
2025/08/28 23:58:49 INFO dspy.evaluate.evaluate: Average Metric: 3.0 / 3 (100.0%)
2025/08/29 00:02:38 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 00:02:41 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 00:02:54 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 00:03:00 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 00:03:18 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 00:03:43 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 00:06:35 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 00:09:31 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 00:12:50 INFO dspy.evaluate.evaluate: Average Metric: 121.0 / 200 (60.5%)
GEPA Optimization: 20%|██████████████████████ | 786/4000 [2:38:54<8:38:08, 9.67s/rollouts]Iteration 37: Full valset score for new program: 0.605
Iteration 37: Full train_val score for new program: 0.605
Iteration 37: Individual valset scores for new program: [False, True, False, True, True, False, True, False, True, True, True, True, True, False, True, True, False, False, True, True, True, True, True, False, False, False, False, False, True, True, False, False, True, True, True, True, False, True, True, True, False, True, True, False, False, True, False, False, True, True, True, True, False, True, True, False, False, True, False, True, True, False, False, True, False, False, True, False, False, False, True, True, True, True, True, True, False, False, True, True, True, True, True, True, True, True, True, True, False, False, True, True, True, True, True, True, False, False, False, True, True, True, False, False, True, True, True, True, True, False, False, True, False, True, True, False, True, True, True, True, True, False, True, False, False, True, False, True, True, True, True, False, True, False, True, False, False, False, True, True, True, True, True, False, True, False, True, False, True, True, False, False, True, False, False, True, False, True, True, False, False, True, False, False, False, True, True, True, True, False, False, True, False, False, True, True, False, True, False, False, True, True, True, False, False, True, True, True, True, False, True, True, True, True, True, False, True, False, False, True]
Iteration 37: New valset pareto front scores: [True, True, True, True, True, True, True, True, True, True, True, True, True, False, True, True, False, True, True, True, True, True, True, True, True, True, False, True, True, True, True, False, True, True, True, True, False, True, True, True, False, True, True, True, False, True, False, False, True, True, True, True, True, True, True, 0, True, True, False, True, True, True, False, True, False, False, True, True, 0, False, True, True, True, True, True, True, True, False, True, True, True, True, True, True, True, True, True, True, True, False, True, True, True, True, True, True, True, False, True, True, True, True, True, False, True, True, True, True, True, True, False, True, True, True, True, False, True, True, True, True, True, True, True, False, False, True, False, True, True, True, True, True, True, True, True, False, True, False, True, True, True, True, True, False, True, False, True, False, True, True, False, False, True, True, True, True, True, True, True, False, True, True, True, True, False, True, True, True, True, False, True, True, False, True, True, True, False, True, False, True, True, True, True, False, False, True, True, True, True, True, True, True, True, True, True, False, True, True, False, True]
Iteration 37: Full valset pareto front score: 0.79
Iteration 37: Updated valset pareto front programs: [{1}, {0, 1, 2}, {0}, {0, 2}, {0, 1, 2}, {0}, {0, 1, 2}, {0, 1}, {0, 1, 2}, {0, 2}, {0, 1, 2}, {0, 1, 2}, {0, 1, 2}, {0, 1, 2}, {0, 1, 2}, {0, 1, 2}, {0, 1, 2}, {1}, {0, 1, 2}, {0, 2}, {0, 1, 2}, {0, 2}, {0, 1, 2}, {1}, {0}, {0, 1}, {0, 1, 2}, {0}, {0, 1, 2}, {0, 1, 2}, {1}, {0, 1, 2}, {2}, {0, 1, 2}, {0, 1, 2}, {0, 1, 2}, {0, 1, 2}, {0, 1, 2}, {2}, {0, 2}, {0, 1, 2}, {0, 1, 2}, {0, 1, 2}, {1}, {0, 1, 2}, {0, 1, 2}, {0, 1, 2}, {0, 1, 2}, {0, 1, 2}, {0, 1, 2}, {0, 1, 2}, {0, 1, 2}, {0, 1}, {0, 1, 2}, {0, 1, 2}, {0, 1, 2}, {0, 1}, {0, 1, 2}, {0, 1, 2}, {0, 1, 2}, {0, 1, 2}, {0, 1}, {0, 1, 2}, {0, 1, 2}, {0, 1, 2}, {0, 1, 2}, {1, 2}, {0, 1}, {0, 1, 2}, {0, 1, 2}, {0, 1, 2}, {0, 1, 2}, {0, 1, 2}, {2}, {0, 1, 2}, {1, 2}, {0}, {0, 1, 2}, {0, 1, 2}, {0, 2}, {0, 2}, {1, 2}, {0, 1, 2}, {0, 1, 2}, {0, 1, 2}, {0, 1, 2}, {0, 1, 2}, {0, 1, 2}, {0}, {0, 1, 2}, {0, 1, 2}, {0, 1, 2}, {0, 1, 2}, {0, 1, 2}, {0, 1, 2}, {0, 1, 2}, {1}, {0, 1, 2}, {0}, {0, 1, 2}, {0, 1, 2}, {0, 1, 2}, {0}, {0, 1, 2}, {0, 1, 2}, {0, 1, 2}, {1, 2}, {0, 1, 2}, {1, 2}, {0, 1}, {0, 1, 2}, {0, 2}, {1}, {2}, {0, 1, 2}, {0, 1, 2}, {0, 1, 2}, {2}, {0, 1, 2}, {0, 1, 2}, {2}, {0, 1}, {0, 1, 2}, {0, 1, 2}, {0, 1, 2}, {0, 1, 2}, {0, 1, 2}, {0, 1, 2}, {0, 1, 2}, {0, 1, 2}, {2}, {0, 1}, {0, 1, 2}, {0, 1}, {0, 1, 2}, {0, 1, 2}, {0, 1}, {0, 1, 2}, {1, 2}, {1, 2}, {0, 1, 2}, {0, 1, 2}, {0, 1, 2}, {0, 1, 2}, {0, 1, 2}, {0, 1, 2}, {0, 2}, {0, 1, 2}, {0, 1, 2}, {0, 1, 2}, {0, 1, 2}, {0, 1, 2}, {0, 1, 2}, {0, 1}, {0, 1}, {0, 1, 2}, {0}, {0, 1, 2}, {0, 1, 2}, {0, 1, 2}, {1}, {0, 1, 2}, {0}, {0}, {0, 1, 2}, {0, 1, 2}, {0, 1, 2}, {0, 1, 2}, {0, 1, 2}, {0, 1, 2}, {0}, {0, 1, 2}, {0, 1, 2}, {0, 1}, {0, 1, 2}, {1, 2}, {0, 1, 2}, {0, 1, 2}, {0, 1, 2}, {0, 1}, {0, 2}, {0, 1, 2}, {0, 1, 2}, {0, 1, 2}, {0, 1, 2}, {0, 1, 2}, {0, 1, 2}, {0, 1, 2}, {0, 1, 2}, {0}, {0, 1, 2}, {0, 1, 2}, {0, 1, 2}, {1, 2}, {0, 1, 2}, {0, 1, 2}, {0, 1, 2}, {0}, {0, 1, 2}, {0, 2}]
Iteration 37: Best valset aggregate score so far: 0.67
Iteration 37: Best program as per aggregate score on train_val: 0
Iteration 37: Best program as per aggregate score on valset: 0
Iteration 37: Best score on valset: 0.67
Iteration 37: Best score on train_val: 0.67
Iteration 37: Linear pareto front program index: 0
Iteration 37: New program candidate index: 2
Iteration 38: Selected program 1 score: 0.63
Average Metric: 3.00 / 3 (100.0%): : 4it [02:39, 39.84s/it] 2025/08/29 00:15:29 INFO dspy.evaluate.evaluate: Average Metric: 3.0 / 3 (100.0%)
GEPA Optimization: 20%|██████████████████████ | 789/4000 [2:41:33<9:22:22, 10.51s/rollouts]
Iteration 38: All subsample scores perfect. Skipping.
Iteration 38: Reflective mutation did not propose a new candidate
Iteration 39: Selected program 1 score: 0.63
0%| | 0/3 [00:00<?, ?it/s]2025/08/29 00:15:40 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
Average Metric: 3.00 / 3 (100.0%): 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [01:19<00:00, 26.36s/it]2025/08/29 00:16:48 INFO dspy.evaluate.evaluate: Average Metric: 3.0 / 3 (100.0%)
GEPA Optimization: 20%|██████████████████████▏ | 792/4000 [2:42:52<9:44:31, 10.93s/rollouts]
Iteration 39: All subsample scores perfect. Skipping.
Iteration 39: Reflective mutation did not propose a new candidate
Iteration 40: Selected program 0 score: 0.67
Average Metric: 2.00 / 3 (66.7%): 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [01:09<00:00, 23.13s/it]2025/08/29 00:17:58 INFO dspy.evaluate.evaluate: Average Metric: 2.0 / 3 (66.7%)
2025/08/29 00:18:59 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0], [0, 5, 0, 0, 0, 1, 0, 1, 0, 0, 0, 5, 0], [0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0], [0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0], [0, 5, 1, 0, 0, 1, 0, 1, 0, 0, 1, 5, 0], [0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0], [0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0]]}, {'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 5, 0, 0, 3, 3, 3, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 3, 0, 3, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 3, 3, 3, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0], [0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0], [0, 0, 3, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0], [0, 0, 3, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0], [0, 5, 3, 0, 3, 3, 3, 0, 0, 3, 0, 0, 0], [0, 0, 3, 0, 3, 0, 3, 0, 0, 3, 0, 0, 0], [0, 0, 3, 0, 3, 3, 3, 0, 0, 3, 0, 0, 0], [0, 0, 3, 0, 0, 0, 0, 0, 0, 3, 5, 0, 0], [0, 0, 3, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0], [0, 0, 3, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0], [0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0], [0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}, {'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 4, 0, 0, 4, 0, 0, 0, 0, 5, 0], [0, 0, 0, 0, 4, 0, 0, 4, 0, 0, 0, 0, 0, 0], [0, 5, 0, 0, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0], [0, 0, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0], [0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0], [0, 0, 4, 0, 4, 4, 4, 4, 0, 0, 0, 4, 0, 0], [0, 0, 4, 0, 4, 0, 0, 4, 0, 0, 0, 4, 5, 0], [0, 0, 4, 0, 4, 0, 0, 4, 0, 0, 0, 4, 0, 0], [0, 5, 4, 0, 4, 4, 4, 4, 0, 0, 0, 4, 0, 0], [0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0], [0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0], [0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0], [0, 0, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0], [0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}], 'test_inputs': [[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0], [0, 0, 5, 0, 0, 8, 0, 0, 8, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]], 'test_outputs': [[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0], [0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0], [0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0], [0, 0, 0, 8, 0, 8, 8, 8, 8, 0, 0, 8, 0, 0, 0], [0, 0, 5, 8, 0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 0], [0, 0, 0, 8, 0, 8, 8, 8, 8, 0, 0, 8, 0, 0, 0], [0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0], [0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 8, 5, 0, 0], [0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0], [0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0], [0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0], [0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]]}) (input_keys={'test_inputs', 'training_examples'}): 'dict' object has no attribute 'input'
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 69, in forward
File "<string>", line 64, in _format_examples
AttributeError: 'dict' object has no attribute 'input'
2025/08/29 00:18:59 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 7, 7, 0, 0, 0, 0], [0, 0, 0, 6, 8, 8, 6, 0, 0, 0], [0, 0, 7, 8, 4, 4, 8, 7, 0, 0], [0, 0, 7, 8, 4, 4, 8, 7, 0, 0], [0, 0, 0, 6, 8, 8, 6, 0, 0, 0], [0, 0, 0, 0, 7, 7, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 7], [0, 6, 8], [7, 8, 4]]}, {'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 1, 0, 0, 0], [0, 0, 3, 6, 5, 3, 0, 0, 0, 0], [0, 0, 5, 2, 2, 6, 0, 0, 0, 0], [0, 0, 6, 2, 2, 5, 0, 0, 0, 0], [0, 0, 3, 5, 6, 3, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[1, 0, 0], [0, 3, 6], [0, 5, 2]]}], 'test_inputs': [[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 8, 0, 0, 0, 0], [0, 0, 0, 4, 4, 8, 4, 0, 0, 0], [0, 0, 8, 8, 3, 3, 4, 0, 0, 0], [0, 0, 0, 4, 3, 3, 8, 8, 0, 0], [0, 0, 0, 4, 8, 4, 4, 0, 0, 0], [0, 0, 0, 0, 8, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]], 'test_outputs': [[[0, 0, 0], [0, 4, 4], [8, 8, 3]]]}) (input_keys={'test_inputs', 'training_examples'}): 'dict' object has no attribute 'input'
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 69, in forward
File "<string>", line 64, in _format_examples
AttributeError: 'dict' object has no attribute 'input'
2025/08/29 00:18:59 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[0, 0, 0, 0, 0, 5, 0, 0, 0], [0, 5, 0, 0, 0, 5, 0, 0, 0], [0, 5, 0, 0, 0, 5, 0, 0, 0], [0, 5, 0, 5, 0, 5, 0, 0, 0], [0, 5, 0, 5, 0, 5, 0, 0, 0], [0, 5, 0, 5, 0, 5, 0, 0, 0], [0, 5, 0, 5, 0, 5, 0, 5, 0], [0, 5, 0, 5, 0, 5, 0, 5, 0], [0, 5, 0, 5, 0, 5, 0, 5, 0]], 'output': [[0, 0, 0, 0, 0, 1, 0, 0, 0], [0, 2, 0, 0, 0, 1, 0, 0, 0], [0, 2, 0, 0, 0, 1, 0, 0, 0], [0, 2, 0, 3, 0, 1, 0, 0, 0], [0, 2, 0, 3, 0, 1, 0, 0, 0], [0, 2, 0, 3, 0, 1, 0, 0, 0], [0, 2, 0, 3, 0, 1, 0, 4, 0], [0, 2, 0, 3, 0, 1, 0, 4, 0], [0, 2, 0, 3, 0, 1, 0, 4, 0]]}, {'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 5, 0], [0, 0, 0, 0, 0, 0, 0, 5, 0], [0, 0, 0, 0, 0, 0, 0, 5, 0], [0, 0, 0, 5, 0, 0, 0, 5, 0], [0, 0, 0, 5, 0, 5, 0, 5, 0], [0, 0, 0, 5, 0, 5, 0, 5, 0], [0, 5, 0, 5, 0, 5, 0, 5, 0], [0, 5, 0, 5, 0, 5, 0, 5, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 1, 0], [0, 0, 0, 0, 0, 0, 0, 1, 0], [0, 0, 0, 0, 0, 0, 0, 1, 0], [0, 0, 0, 2, 0, 0, 0, 1, 0], [0, 0, 0, 2, 0, 3, 0, 1, 0], [0, 0, 0, 2, 0, 3, 0, 1, 0], [0, 4, 0, 2, 0, 3, 0, 1, 0], [0, 4, 0, 2, 0, 3, 0, 1, 0]]}], 'test_inputs': [[[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 5, 0, 0, 0, 0, 0, 0, 0], [0, 5, 0, 0, 0, 5, 0, 0, 0], [0, 5, 0, 0, 0, 5, 0, 0, 0], [0, 5, 0, 0, 0, 5, 0, 5, 0], [0, 5, 0, 0, 0, 5, 0, 5, 0], [0, 5, 0, 5, 0, 5, 0, 5, 0], [0, 5, 0, 5, 0, 5, 0, 5, 0], [0, 5, 0, 5, 0, 5, 0, 5, 0]]], 'test_outputs': [[[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 2, 0, 0, 0], [0, 1, 0, 0, 0, 2, 0, 0, 0], [0, 1, 0, 0, 0, 2, 0, 3, 0], [0, 1, 0, 0, 0, 2, 0, 3, 0], [0, 1, 0, 4, 0, 2, 0, 3, 0], [0, 1, 0, 4, 0, 2, 0, 3, 0], [0, 1, 0, 4, 0, 2, 0, 3, 0]]]}) (input_keys={'test_inputs', 'training_examples'}): 'dict' object has no attribute 'input'
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 69, in forward
File "<string>", line 64, in _format_examples
AttributeError: 'dict' object has no attribute 'input'
2025/08/29 00:18:59 INFO dspy.evaluate.evaluate: Average Metric: 0.0 / 3 (0.0%)
GEPA Optimization: 20%|██████████████████████▏ | 798/4000 [2:45:03<10:24:43, 11.71s/rollouts]Iteration 40: Proposed new text for program: import dspy
from typing import List
import pydantic
import traceback
# Define the data structures using Pydantic, as in the original code.
MATRIX = List[List[int]]
class TrainingExample(pydantic.BaseModel):
input: MATRIX
output: MATRIX
# The main signature for the overall program remains the same, defining the I/O for our custom module.
class SolveTaskSignature(dspy.Signature):
"""Given a list of training examples (input/output matrix pairs) that demonstrate a task, and a list of test input matrices, solve the task for each test input."""
training_examples: List[TrainingExample] = dspy.InputField(description="Input and output examples demonstrating the task to be performed.")
test_inputs: List[MATRIX] = dspy.InputField(description="Input matrices to be solved following the task described in the training examples.")
test_outputs: List[MATRIX] = dspy.OutputField(description="Output matrices corresponding to the test inputs.")
# --- Internal Signatures for the Decomposed Steps ---
class InferRuleSignature(dspy.Signature):
"""
Analyze the provided training examples, each consisting of an input and an output matrix.
Infer the transformation rule that converts the input matrix to the output matrix.
Describe this rule in clear, step-by-step natural language.
Successful strategies to consider:
- Bounding Boxes: Are operations performed within the bounding box of all non-zero cells, or a specific color's cells? Does the bounding box include all non-zero elements, or just a subset?
- Color Mapping & Counting: Are colors changed? Is the new color based on a property like the count of cells in a row/column, their size, or their order?
- Shape Operations: Are shapes extracted, copied, moved, resized, combined, or drawn? Is a new shape (like a hollow rectangle) drawn based on the properties of existing shapes?
- Element Preservation: Are some elements (e.g., 'marker' colors) always preserved in their original positions while other elements are transformed or replaced?
- Multiple Steps: The transformation might involve multiple sequential steps. For example: 1) Find the bounding box of ALL non-zero elements. 2) Preserve certain 'marker' elements. 3) Draw a new shape based on the bounding box. 4) Also preserve the original shape. Describe the full sequence.
"""
training_examples: str = dspy.InputField(desc="A string representation of the list of input/output training pairs.")
rule_description: str = dspy.OutputField(desc="A step-by-step natural language description of the transformation rule.")
class GeneratePythonCodeSignature(dspy.Signature):
"""
Given a rule description and a single test input matrix, write a Python function named 'solve' that implements the rule.
The function must take one argument, 'input_matrix' (a list of lists of integers), and return the transformed matrix (a list of lists of integers).
The function should be self-contained and not rely on any external libraries.
Ensure the function correctly handles all details from the rule, such as preserving original shapes or markers when adding new ones.
Your output must be only the Python code for the function, enclosed in ```python ... ```.
"""
rule_description: str = dspy.InputField(desc="The natural language description of the transformation rule.")
test_input: str = dspy.InputField(desc="A string representation of a single input matrix to be solved, for context.")
python_code: str = dspy.OutputField(desc="A string containing a single Python function 'solve(input_matrix)' that implements the rule.")
# --- Custom Module for the Decomposed Solution ---
class ARCSolver(dspy.Module):
def __init__(self):
super().__init__()
# Decompose the problem into two steps: inferring the rule and generating code to apply it.
self.rule_inferrer = dspy.ChainOfThought(InferRuleSignature)
self.code_generator = dspy.ChainOfThought(GeneratePythonCodeSignature)
def _format_examples(self, examples: List[TrainingExample]) -> str:
"""Helper to format examples into a readable string for the LM."""
formatted_str = ""
for i, ex in enumerate(examples):
formatted_str += f"Example {i+1}:\n"
formatted_str += f"Input:\n{ex.input}\n"
formatted_str += f"Output:\n{ex.output}\n\n"
return formatted_str
def forward(self, training_examples: List[TrainingExample], test_inputs: List[MATRIX]) -> dspy.Prediction:
training_examples_str = self._format_examples(training_examples)
# 1. Infer the rule from the examples.
rule_pred = self.rule_inferrer(training_examples=training_examples_str)
rule_description = rule_pred.rule_description
solved_outputs = []
for test_input_matrix in test_inputs:
# 2. Generate Python code to apply the rule to a single test input.
code_pred = self.code_generator(
rule_description=rule_description,
test_input=str(test_input_matrix)
)
python_code = code_pred.python_code
# 3. Execute the generated code safely.
try:
# Clean up the code string to remove markdown fences and leading/trailing whitespace.
if "```python" in python_code:
python_code = python_code.split("```python")[1]
if "```" in python_code:
python_code = python_code.split("```")[0]
python_code = python_code.strip()
# Prepare a local scope for exec to capture the 'solve' function.
local_scope = {}
exec(python_code, globals(), local_scope)
solve_func = local_scope['solve']
# Call the generated function to get the output.
output_matrix = solve_func(test_input_matrix)
solved_outputs.append(output_matrix)
except Exception:
# Fallback strategy: If code generation or execution fails,
# return an empty list for this specific test case.
# This prevents a crash and signals a failure for this item.
print(f"Failed to execute generated code for a test input.\nCode:\n{python_code}\nError:\n{traceback.format_exc()}")
solved_outputs.append([]) # Append an empty list as a failure indicator.
return dspy.Prediction(test_outputs=solved_outputs)
# Final program assignment
program = ARCSolver()
Iteration 40: New subsample score is not better, skipping
Iteration 41: Selected program 1 score: 0.63
Average Metric: 2.00 / 3 (66.7%): 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [03:53<00:00, 77.88s/it]2025/08/29 00:22:53 INFO dspy.evaluate.evaluate: Average Metric: 2.0 / 3 (66.7%)
Iteration 41: Proposed new text for program: import dspy
from typing import List
import pydantic
import copy
import traceback
# Define the type for a matrix for clarity
MATRIX = List[List[int]]
class TrainingExample(pydantic.BaseModel):
"""A Pydantic model for a single training example, containing an input and output matrix."""
input: MATRIX
output: MATRIX
class GeneratePythonFunctionSignature(dspy.Signature):
"""
Analyzes pairs of input/output matrices to deduce the transformation rule and express it as a Python function.
You are an expert programmer and analyst of abstract visual reasoning puzzles. Your task is to carefully examine the provided training examples. Each example consists of an 'input' matrix and a corresponding 'output' matrix.
Your goal is to deduce the single, consistent transformation rule that converts each input to its output. Then, you must write a single, self-contained Python function named `transform_matrix` that implements this rule.
**Function Requirements:**
- The function name must be exactly `transform_matrix`.
- It must accept one argument: `matrix: List[List[int]]`.
- It must return the transformed matrix as a `List[List[int]]`.
- The function should not rely on any external libraries (standard libraries like `copy` are allowed).
- It's good practice to work on a deep copy of the input matrix to avoid side effects.
**Example Function Structure:**
```python
import copy
def transform_matrix(matrix: list[list[int]]) -> list[list[int]]:
# Good practice: work on a copy.
output_matrix = copy.deepcopy(matrix)
if not output_matrix:
return []
height = len(output_matrix)
width = len(output_matrix[0])
# ... your transformation logic here ...
return output_matrix
```
**Analysis Guidance:**
Consider various possible transformation patterns, including but not limited to:
- Geometric operations: rotation, reflection, scaling, repetition, shifting.
- Color/value transformations: changing specific numbers to others, conditional changes.
- Object-based logic: identifying shapes or objects and applying rules based on their properties (size, color, position).
- Fill/completion patterns: such as flood fills or completing a grid based on object boundaries.
- Bounding box or frame logic: creating structures based on the extents of objects.
"""
training_examples: List[TrainingExample] = dspy.InputField(description="A list of input and output examples demonstrating the task.")
python_function: str = dspy.OutputField(description="A self-contained Python function `transform_matrix` that implements the transformation rule.")
class ARCProgram(dspy.Module):
"""A program that first infers a rule as Python code and then executes it to solve test inputs."""
def __init__(self):
super().__init__()
# Use ChainOfThought for the complex reasoning and coding task.
self.rule_generator = dspy.ChainOfThought(GeneratePythonFunctionSignature)
def forward(self, training_examples: List[TrainingExample], test_inputs: List[MATRIX]) -> dspy.Prediction:
"""
Infers a rule as a Python function from training examples and applies it to each test input.
Args:
training_examples: A list of TrainingExample objects.
test_inputs: A list of input matrices to be solved.
Returns:
A dspy.Prediction object with the 'test_outputs' field populated.
"""
# 1. Generate a Python function that encapsulates the transformation rule.
prediction = self.rule_generator(training_examples=training_examples)
# The LM sometimes wraps the code in markdown, so we clean it.
python_code = prediction.python_function.strip().replace("```python", "").replace("```", "").strip()
# Prepare a scope for executing the generated code safely.
execution_scope = {'copy': copy}
transform_func = None
try:
# Execute the generated Python code to define the function in a controlled scope.
exec(python_code, execution_scope)
transform_func = execution_scope.get('transform_matrix')
except Exception as e:
print(f"Failed to define the generated function: {e}")
print(f"Generated code:\n{python_code}")
traceback.print_exc()
all_test_outputs = []
# 2. Iterate through each test input and apply the generated function.
for test_matrix in test_inputs:
# Default fallback output in case of any failure
if test_matrix and test_matrix[0]:
fallback_output = [[0] * len(test_matrix[0]) for _ in range(len(test_matrix))]
else:
fallback_output = []
if callable(transform_func):
try:
# Create a deep copy to ensure the original test input is not modified.
input_copy = copy.deepcopy(test_matrix)
# Apply the generated function to the current test matrix.
result = transform_func(input_copy)
all_test_outputs.append(result)
except Exception as e:
print(f"Execution of generated function failed for a test case: {e}")
traceback.print_exc()
# Fallback strategy: if execution fails, append a default matrix.
all_test_outputs.append(fallback_output)
else:
# Fallback strategy: if the function wasn't defined correctly.
print("`transform_matrix` function not found or is not callable.")
all_test_outputs.append(fallback_output)
# 3. Return the collected outputs in a single Prediction object.
return dspy.Prediction(test_outputs=all_test_outputs)
# The final 'program' object is an instance of our robust, code-generating module.
program = ARCProgram()
2025/08/29 00:25:47 INFO dspy.evaluate.evaluate: Average Metric: 1.0 / 3 (33.3%)
GEPA Optimization: 20%|██████████████████████▎ | 804/4000 [2:51:51<14:59:14, 16.88s/rollouts]Iteration 41: New subsample score is not better, skipping
Iteration 42: Selected program 2 score: 0.605
Average Metric: 3.00 / 3 (100.0%): : 5it [04:10, 50.02s/it] 2025/08/29 00:29:57 INFO dspy.evaluate.evaluate: Average Metric: 3.0 / 3 (100.0%)
GEPA Optimization: 20%|██████████████████████▍ | 807/4000 [2:56:01<18:36:44, 20.98s/rollouts]
Iteration 42: All subsample scores perfect. Skipping.
Iteration 42: Reflective mutation did not propose a new candidate
Iteration 43: Selected program 0 score: 0.67
Average Metric: 2.00 / 3 (66.7%): 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [02:46<00:00, 55.66s/it]2025/08/29 00:32:44 INFO dspy.evaluate.evaluate: Average Metric: 2.0 / 3 (66.7%)
2025/08/29 00:33:39 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[0, 0, 0, 0, 0, 0, 0, 0], [2, 0, 0, 0, 0, 0, 0, 0], [2, 0, 0, 0, 0, 0, 0, 0], [2, 0, 0, 0, 8, 0, 0, 0], [2, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 2], [0, 0, 0, 0, 0, 0, 0, 2], [0, 0, 0, 0, 0, 0, 0, 2], [0, 0, 0, 0, 0, 0, 0, 2], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0], [2, 0, 0, 0, 0, 0, 0, 0], [2, 0, 0, 0, 0, 0, 0, 0], [2, 8, 8, 8, 4, 0, 0, 0], [2, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 2], [0, 0, 0, 0, 0, 0, 0, 2], [8, 8, 8, 8, 8, 8, 8, 2], [0, 0, 0, 0, 0, 0, 0, 2], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]]}, {'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [2, 0, 0, 0, 0, 0, 0, 0, 0, 0], [2, 0, 0, 0, 0, 0, 0, 8, 0, 0], [2, 0, 0, 0, 0, 0, 0, 0, 0, 0], [2, 0, 0, 0, 0, 8, 0, 0, 0, 0], [2, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 2], [0, 0, 0, 0, 0, 0, 0, 0, 0, 2], [0, 0, 0, 0, 0, 0, 0, 0, 0, 2], [0, 0, 0, 0, 0, 0, 0, 0, 0, 2], [0, 0, 0, 0, 0, 0, 0, 0, 0, 2], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [2, 0, 0, 0, 0, 0, 0, 0, 0, 0], [2, 8, 8, 8, 8, 8, 8, 4, 0, 0], [2, 0, 0, 0, 0, 0, 0, 0, 0, 0], [2, 8, 8, 8, 8, 4, 0, 0, 0, 0], [2, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 2], [8, 8, 8, 8, 8, 8, 8, 8, 8, 2], [0, 0, 0, 0, 0, 0, 0, 0, 0, 2], [8, 8, 8, 8, 8, 8, 8, 8, 8, 2], [0, 0, 0, 0, 0, 0, 0, 0, 0, 2], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}, {'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 2], [0, 0, 0, 0, 0, 0, 8, 0, 0, 2], [0, 0, 8, 0, 0, 0, 0, 0, 0, 2], [0, 0, 0, 0, 0, 0, 0, 0, 0, 2], [0, 0, 0, 0, 8, 0, 0, 0, 0, 2], [0, 0, 0, 0, 0, 0, 0, 0, 0, 2], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [2, 0, 0, 0, 0, 0, 0, 0, 0, 0], [2, 0, 0, 0, 0, 0, 0, 0, 0, 0], [2, 0, 0, 0, 0, 0, 0, 0, 0, 0], [2, 0, 0, 0, 0, 0, 0, 0, 0, 0], [2, 0, 0, 0, 0, 0, 0, 0, 0, 0], [2, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 2], [0, 0, 0, 0, 0, 0, 4, 8, 8, 2], [0, 0, 4, 8, 8, 8, 8, 8, 8, 2], [0, 0, 0, 0, 0, 0, 0, 0, 0, 2], [0, 0, 0, 0, 4, 8, 8, 8, 8, 2], [0, 0, 0, 0, 0, 0, 0, 0, 0, 2], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [2, 0, 0, 0, 0, 0, 0, 0, 0, 0], [2, 8, 8, 8, 8, 8, 8, 8, 8, 8], [2, 8, 8, 8, 8, 8, 8, 8, 8, 8], [2, 0, 0, 0, 0, 0, 0, 0, 0, 0], [2, 8, 8, 8, 8, 8, 8, 8, 8, 8], [2, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}], 'test_inputs': [[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [2, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0], [2, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0], [2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [2, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0], [2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]], 'test_outputs': [[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [2, 8, 8, 8, 8, 8, 8, 8, 4, 0, 0, 0], [2, 8, 8, 8, 8, 8, 8, 4, 0, 0, 0, 0], [2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [2, 8, 8, 8, 4, 0, 0, 0, 0, 0, 0, 0], [2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 2], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 2], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 2], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]]}) (input_keys={'test_inputs', 'training_examples'}): 'dict' object has no attribute 'input'
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 68, in forward
File "<string>", line 68, in <listcomp>
AttributeError: 'dict' object has no attribute 'input'
2025/08/29 00:33:39 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0], [0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0], [8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8]], 'output': [[0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0], [0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0], [8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8], [0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0], [0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0], [0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0], [8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8], [0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0, 8, 0], [0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0]]}, {'input': [[0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0], [0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0], [0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0], [2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2]], 'output': [[0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0], [0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0], [0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0], [2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2], [0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0], [0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0], [0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0], [0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0], [0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0], [2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2], [0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0], [0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0], [0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0]]}], 'test_inputs': [[[0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0], [0, 3, 0, 3, 0, 3, 0, 3, 0, 3, 0, 3, 0, 3, 0, 3, 0], [0, 3, 0, 3, 0, 3, 0, 3, 0, 3, 0, 3, 0, 3, 0, 3, 0], [0, 3, 0, 3, 0, 3, 0, 3, 0, 3, 0, 3, 0, 3, 0, 3, 0], [3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3]]], 'test_outputs': [[[0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0], [0, 3, 0, 3, 0, 3, 0, 3, 0, 3, 0, 3, 0, 3, 0, 3, 0], [0, 3, 0, 3, 0, 3, 0, 3, 0, 3, 0, 3, 0, 3, 0, 3, 0], [0, 3, 0, 3, 0, 3, 0, 3, 0, 3, 0, 3, 0, 3, 0, 3, 0], [3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3], [0, 3, 0, 3, 0, 3, 0, 3, 0, 3, 0, 3, 0, 3, 0, 3, 0], [0, 3, 0, 3, 0, 3, 0, 3, 0, 3, 0, 3, 0, 3, 0, 3, 0], [0, 3, 0, 3, 0, 3, 0, 3, 0, 3, 0, 3, 0, 3, 0, 3, 0], [0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0], [0, 3, 0, 3, 0, 3, 0, 3, 0, 3, 0, 3, 0, 3, 0, 3, 0], [0, 3, 0, 3, 0, 3, 0, 3, 0, 3, 0, 3, 0, 3, 0, 3, 0], [0, 3, 0, 3, 0, 3, 0, 3, 0, 3, 0, 3, 0, 3, 0, 3, 0], [3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3], [0, 3, 0, 3, 0, 3, 0, 3, 0, 3, 0, 3, 0, 3, 0, 3, 0], [0, 3, 0, 3, 0, 3, 0, 3, 0, 3, 0, 3, 0, 3, 0, 3, 0], [0, 3, 0, 3, 0, 3, 0, 3, 0, 3, 0, 3, 0, 3, 0, 3, 0], [0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0]]]}) (input_keys={'test_inputs', 'training_examples'}): 'dict' object has no attribute 'input'
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 68, in forward
File "<string>", line 68, in <listcomp>
AttributeError: 'dict' object has no attribute 'input'
2025/08/29 00:33:39 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0], [0, 8, 8, 8, 8, 8, 8, 2, 8, 8, 8, 8, 0], [0, 8, 8, 2, 2, 2, 2, 2, 2, 2, 8, 8, 0], [0, 8, 8, 2, 2, 2, 2, 2, 2, 2, 8, 8, 0], [0, 8, 8, 2, 2, 2, 2, 2, 2, 2, 2, 8, 0], [0, 8, 8, 2, 2, 2, 2, 2, 2, 2, 8, 8, 0], [0, 8, 8, 2, 2, 2, 2, 2, 2, 2, 8, 8, 0], [0, 8, 8, 2, 2, 2, 2, 2, 2, 2, 8, 8, 0], [0, 8, 8, 2, 2, 2, 2, 2, 2, 2, 8, 8, 0], [0, 8, 8, 2, 2, 2, 2, 2, 2, 2, 8, 8, 0], [0, 8, 8, 2, 2, 2, 2, 2, 2, 2, 8, 8, 0], [0, 8, 8, 8, 2, 8, 8, 8, 8, 8, 8, 8, 0], [0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 2, 0, 0, 2, 0, 0, 0, 0, 0], [0, 0, 0, 0, 2, 0, 0, 2, 0, 0, 0, 0, 0], [0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0], [0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0], [0, 8, 8, 2, 8, 2, 2, 8, 2, 2, 8, 8, 0], [0, 8, 8, 2, 8, 2, 2, 8, 2, 2, 8, 8, 0], [2, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 2], [0, 8, 8, 2, 8, 2, 2, 8, 2, 2, 8, 8, 0], [0, 8, 8, 2, 8, 2, 2, 8, 2, 2, 8, 8, 0], [0, 8, 8, 2, 8, 2, 2, 8, 2, 2, 8, 8, 0], [0, 8, 8, 2, 8, 2, 2, 8, 2, 2, 8, 8, 0], [0, 8, 8, 2, 8, 2, 2, 8, 2, 2, 8, 8, 0], [0, 8, 8, 2, 8, 2, 2, 8, 2, 2, 8, 8, 0], [0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0], [0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0], [0, 0, 0, 0, 2, 0, 0, 2, 0, 0, 0, 0, 0], [0, 0, 0, 0, 2, 0, 0, 2, 0, 0, 0, 0, 0], [0, 0, 0, 0, 2, 0, 0, 2, 0, 0, 0, 0, 0], [0, 0, 0, 0, 2, 0, 0, 2, 0, 0, 0, 0, 0]]}, {'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0], [0, 0, 1, 1, 1, 1, 4, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0], [0, 0, 1, 1, 4, 4, 4, 4, 4, 4, 4, 4, 1, 1, 0, 0, 0], [0, 0, 1, 1, 4, 4, 4, 4, 4, 4, 4, 4, 4, 1, 0, 0, 0], [0, 0, 1, 1, 4, 4, 4, 4, 4, 4, 4, 4, 1, 1, 0, 0, 0], [0, 0, 1, 1, 4, 4, 4, 4, 4, 4, 4, 4, 1, 1, 0, 0, 0], [0, 0, 1, 1, 1, 1, 1, 1, 4, 1, 1, 1, 1, 1, 0, 0, 0], [0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 4, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 4, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 4, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 4, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0], [0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0], [0, 0, 1, 1, 4, 4, 1, 4, 1, 4, 4, 4, 1, 1, 0, 0, 0], [4, 4, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 4, 4, 4], [0, 0, 1, 1, 4, 4, 1, 4, 1, 4, 4, 4, 1, 1, 0, 0, 0], [0, 0, 1, 1, 4, 4, 1, 4, 1, 4, 4, 4, 1, 1, 0, 0, 0], [0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0], [0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 4, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 4, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 4, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0]]}, {'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0], [0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 3, 2, 2, 2, 2, 0, 0], [0, 0, 0, 0, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 2, 2, 0, 0], [0, 0, 0, 0, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 2, 2, 0, 0], [0, 0, 0, 0, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 2, 2, 0, 0], [0, 0, 0, 0, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 2, 0, 0], [0, 0, 0, 0, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 2, 2, 0, 0], [0, 0, 0, 0, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 2, 2, 0, 0], [0, 0, 0, 0, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 2, 2, 0, 0], [0, 0, 0, 0, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 2, 2, 0, 0], [0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0], [0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0], [0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0], [0, 0, 0, 0, 2, 2, 3, 3, 3, 3, 3, 2, 3, 3, 2, 2, 0, 0], [0, 0, 0, 0, 2, 2, 3, 3, 3, 3, 3, 2, 3, 3, 2, 2, 0, 0], [0, 0, 0, 0, 2, 2, 3, 3, 3, 3, 3, 2, 3, 3, 2, 2, 0, 0], [3, 3, 3, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3], [0, 0, 0, 0, 2, 2, 3, 3, 3, 3, 3, 2, 3, 3, 2, 2, 0, 0], [0, 0, 0, 0, 2, 2, 3, 3, 3, 3, 3, 2, 3, 3, 2, 2, 0, 0], [0, 0, 0, 0, 2, 2, 3, 3, 3, 3, 3, 2, 3, 3, 2, 2, 0, 0], [0, 0, 0, 0, 2, 2, 3, 3, 3, 3, 3, 2, 3, 3, 2, 2, 0, 0], [0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0], [0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0]]}], 'test_inputs': [[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0], [0, 1, 1, 1, 1, 1, 8, 1, 1, 1, 8, 1, 1, 1, 1, 0, 0, 0], [0, 1, 1, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 1, 1, 0, 0, 0], [0, 1, 1, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 1, 1, 0, 0, 0], [0, 1, 1, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 1, 1, 0, 0, 0], [0, 1, 1, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 1, 1, 0, 0, 0], [0, 1, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 1, 1, 0, 0, 0], [0, 1, 1, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 1, 1, 0, 0, 0], [0, 1, 1, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 1, 1, 0, 0, 0], [0, 1, 1, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 1, 1, 0, 0, 0], [0, 1, 1, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 1, 1, 0, 0, 0], [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0], [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]], 'test_outputs': [[[0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0], [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0], [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0], [0, 1, 1, 8, 8, 8, 1, 8, 8, 8, 1, 8, 8, 1, 1, 0, 0, 0], [0, 1, 1, 8, 8, 8, 1, 8, 8, 8, 1, 8, 8, 1, 1, 0, 0, 0], [0, 1, 1, 8, 8, 8, 1, 8, 8, 8, 1, 8, 8, 1, 1, 0, 0, 0], [0, 1, 1, 8, 8, 8, 1, 8, 8, 8, 1, 8, 8, 1, 1, 0, 0, 0], [8, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 8, 8, 8], [0, 1, 1, 8, 8, 8, 1, 8, 8, 8, 1, 8, 8, 1, 1, 0, 0, 0], [0, 1, 1, 8, 8, 8, 1, 8, 8, 8, 1, 8, 8, 1, 1, 0, 0, 0], [0, 1, 1, 8, 8, 8, 1, 8, 8, 8, 1, 8, 8, 1, 1, 0, 0, 0], [0, 1, 1, 8, 8, 8, 1, 8, 8, 8, 1, 8, 8, 1, 1, 0, 0, 0], [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0], [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0]]]}) (input_keys={'test_inputs', 'training_examples'}): 'dict' object has no attribute 'input'
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 68, in forward
File "<string>", line 68, in <listcomp>
AttributeError: 'dict' object has no attribute 'input'
2025/08/29 00:33:39 INFO dspy.evaluate.evaluate: Average Metric: 0.0 / 3 (0.0%)
GEPA Optimization: 20%|██████████████████████▌ | 813/4000 [2:59:42<20:41:31, 23.37s/rollouts]Iteration 43: Proposed new text for program: import dspy
from typing import List
import pydantic
import copy
# Define the data structures using Pydantic and type hints for clarity and validation.
MATRIX = List[List[int]]
class TrainingExample(pydantic.BaseModel):
input: MATRIX
output: MATRIX
# The main signature for the overall program, defining the top-level I/O.
class SolveTaskSignature(dspy.Signature):
"""Given a list of training examples demonstrating a task, and a list of test inputs, solve the task for each test input."""
training_examples: List[TrainingExample] = dspy.InputField(description="Input and output examples demonstrating the task to be performed.")
test_inputs: List[MATRIX] = dspy.InputField(description="Input matrices to be solved following the task described in the training examples.")
test_outputs: List[MATRIX] = dspy.OutputField(description="Output matrices corresponding to the test inputs.")
# A new, specialized signature for the first step: Deducing the rule from examples.
class DeduceRuleSignature(dspy.Signature):
"""
Analyzes pairs of input/output grids to deduce the transformation rule.
**Instructions:**
1. **Analyze Deeply:** Carefully examine the provided `training_examples`.
2. **Identify Core Concepts:** Look for patterns related to:
- Objects: Identify distinct shapes, their colors, sizes, and positions.
- Transformations: Note any changes like moving, rotating, scaling, or color-swapping objects.
- Patterns: Look for symmetry, repetition, tiling, or fractal-like growth.
- Logic: Determine if rules are conditional (e.g., "if a shape is blue, then...") or based on counts, neighbors, or positions.
- Global vs. Local: Decide if the transformation applies to the whole grid or to individual objects/regions.
3. **Describe the Rule:** Write a clear, step-by-step description of the transformation logic. This description will be used to write code, so be precise and unambiguous.
"""
training_examples: str = dspy.InputField(description="String representation of input-output grid pairs.")
rule_description: str = dspy.OutputField(description="A clear, step-by-step English description of the transformation rule.")
# A new, specialized signature for the second step: Generating Python code from the rule.
class GeneratePythonCodeSignature(dspy.Signature):
"""
Writes a Python function to implement a grid transformation rule.
**Instructions:**
1. **Goal:** Create a self-contained Python function named `transform_grid`.
2. **Function Signature:** The function must be `def transform_grid(grid):`.
3. **Input:** It accepts one argument, `grid`, which is a 2D list of integers (list of lists).
4. **Output:** It must return a new 2D list of integers representing the transformed grid.
5. **Important:**
- Do NOT modify the input `grid` in place. Create a deep copy if necessary.
- Use only standard Python libraries (no external imports like numpy).
- The entire output should be a single block of Python code containing just the function definition.
"""
rule_description: str = dspy.InputField(description="The English description of the rule to implement.")
test_input_example: str = dspy.InputField(description="An example of a test input grid to help with context and dimensions.")
python_code: str = dspy.OutputField(description="A self-contained Python function `transform_grid(grid)` that implements the rule.")
# A custom module to orchestrate the two-step "deduce-then-execute" process.
class RuleBasedGridSolver(dspy.Module):
def __init__(self):
super().__init__()
# Step 1: Deduce the rule using ChainOfThought for better reasoning.
self.rule_inducer = dspy.ChainOfThought(DeduceRuleSignature)
# Step 2: Generate code based on the rule. A simple Predict is sufficient here.
self.code_generator = dspy.Predict(GeneratePythonCodeSignature)
def forward(self, training_examples: List[TrainingExample], test_inputs: List[MATRIX]) -> dspy.Prediction:
# Convert Pydantic models to a string representation for the LM.
training_examples_str = "\n\n".join([f"Example:\nInput:\n{ex.input}\nOutput:\n{ex.output}" for ex in training_examples])
# Step 1: Deduce the transformation rule from the examples.
rule_prediction = self.rule_inducer(training_examples=training_examples_str)
rule_description = rule_prediction.rule_description
solved_outputs = []
for test_input in test_inputs:
try:
# Step 2: Generate Python code to apply the rule.
# Providing an example test input helps the LM generate correct code for the specific grid sizes.
code_prediction = self.code_generator(
rule_description=rule_description,
test_input_example=str(test_input)
)
python_code = code_prediction.python_code
# Prepare a local namespace to execute the generated code safely.
local_namespace = {}
# Clean up the code string in case the LM wraps it in markdown.
cleaned_code = python_code.strip().replace("```python", "").replace("```", "").strip()
exec(cleaned_code, globals(), local_namespace)
transform_function = local_namespace['transform_grid']
# Execute the function on a deep copy of the test input to avoid side effects.
input_copy = copy.deepcopy(test_input)
output_grid = transform_function(input_copy)
solved_outputs.append(output_grid)
except Exception as e:
# Fallback strategy: If code generation or execution fails,
# return the original test input. This ensures the program doesn't crash
# and returns a validly shaped output.
print(f"An error occurred during code execution: {e}. Using fallback.")
solved_outputs.append(test_input)
return dspy.Prediction(test_outputs=solved_outputs)
# The final 'program' object is an instance of our new, more robust custom module.
program = RuleBasedGridSolver()
Iteration 43: New subsample score is not better, skipping
Iteration 44: Selected program 1 score: 0.63
Average Metric: 3.00 / 3 (100.0%): 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [01:38<00:00, 32.82s/it]2025/08/29 00:35:17 INFO dspy.evaluate.evaluate: Average Metric: 3.0 / 3 (100.0%)
GEPA Optimization: 20%|██████████████████████▋ | 816/4000 [3:01:21<21:28:50, 24.29s/rollouts]
Iteration 44: All subsample scores perfect. Skipping.
Iteration 44: Reflective mutation did not propose a new candidate
Iteration 45: Selected program 1 score: 0.63
Average Metric: 3.00 / 3 (100.0%): 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [02:00<00:00, 40.07s/it]2025/08/29 00:37:17 INFO dspy.evaluate.evaluate: Average Metric: 3.0 / 3 (100.0%)
GEPA Optimization: 20%|██████████████████████▋ | 819/4000 [3:03:21<23:09:11, 26.20s/rollouts]
Iteration 45: All subsample scores perfect. Skipping.
Iteration 45: Reflective mutation did not propose a new candidate
Iteration 46: Selected program 1 score: 0.63
Average Metric: 3.00 / 3 (100.0%): 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:58<00:00, 19.64s/it]2025/08/29 00:38:16 INFO dspy.evaluate.evaluate: Average Metric: 3.0 / 3 (100.0%)
GEPA Optimization: 21%|██████████████████████▊ | 822/4000 [3:04:20<22:16:31, 25.23s/rollouts]
Iteration 46: All subsample scores perfect. Skipping.
Iteration 46: Reflective mutation did not propose a new candidate
Iteration 47: Selected program 0 score: 0.67
Average Metric: 2.00 / 2 (100.0%): 67%|██████████████████████████████████████████████████████████████████████████ | 2/3 [01:38<00:48, 48.72s/it]2025/08/29 00:42:41 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
Average Metric: 3.00 / 3 (100.0%): 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [04:45<00:00, 95.28s/it]2025/08/29 00:43:02 INFO dspy.evaluate.evaluate: Average Metric: 3.0 / 3 (100.0%)
GEPA Optimization: 21%|██████████████████████▉ | 825/4000 [3:09:06<33:01:24, 37.44s/rollouts]
Iteration 47: All subsample scores perfect. Skipping.
Iteration 47: Reflective mutation did not propose a new candidate
Iteration 48: Selected program 0 score: 0.67
Average Metric: 2.00 / 3 (66.7%): 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [03:12<00:00, 64.00s/it]2025/08/29 00:46:14 INFO dspy.evaluate.evaluate: Average Metric: 2.0 / 3 (66.7%)
Iteration 48: Proposed new text for program: import dspy
from typing import List
import pydantic
import traceback
# Define the type for a matrix for clarity
MATRIX = List[List[int]]
# Pydantic model for a single training example, ensuring structured input
class TrainingExample(pydantic.BaseModel):
input: MATRIX
output: MATRIX
class GeneratePythonSolverSignature(dspy.Signature):
"""
You are an expert programmer. Your task is to write a Python function that solves a matrix transformation puzzle based on a few examples.
Analyze the provided `training_examples` to understand the underlying transformation rule that maps each input matrix to its corresponding output matrix.
Then, write a single, self-contained Python function named `solve` that implements this transformation.
**Function Requirements:**
- The function must be named exactly `solve`.
- It must accept a single argument: `test_input_matrix` (a list of lists of integers).
- It must return the transformed matrix (a list of lists of integers).
- The function should not rely on any external libraries that are not standard in Python (e.g., you can use `copy`, but not `numpy` or `pandas` unless absolutely necessary, and if so, assume it's already imported as `np`).
- Do not write any code outside the function definition. Do not include example usage, calls to the function, or print statements.
**Successful Strategies to Consider:**
- **Geometric Operations:** Look for simple transformations like flipping, rotating, transposing, or concatenating rows/columns.
- **Pattern Completion/Repair:** For noisy or incomplete grids, try to identify the dimensions of a repeating pattern. Reconstruct a canonical version of the pattern (e.g., by majority vote of non-zero cells) and then use it to tile the output grid.
- **Object-Based Reasoning:** For more complex visual puzzles, identify objects or shapes based on color, contiguity, or other properties. Analyze their attributes (size, location, color) and determine the rules for how they are composed or transformed in the output.
"""
training_examples: List[TrainingExample] = dspy.InputField(desc="A list of input-output pairs demonstrating the task.")
test_inputs: List[MATRIX] = dspy.InputField(desc="A list of input matrices to be solved.")
python_code: str = dspy.OutputField(desc="A string containing a single Python function `def solve(test_input_matrix): ...`.")
class SolveWithCodeGeneration(dspy.Module):
"""A module that generates and executes Python code to solve matrix tasks."""
def __init__(self):
super().__init__()
# Module to generate the Python solver function
self.code_generator = dspy.Predict(GeneratePythonSolverSignature)
# Fallback module for simple cases or when code generation fails
self.fallback_solver = dspy.ChainOfThought(
"training_examples, test_inputs -> test_outputs"
)
def forward(self, training_examples: List[TrainingExample], test_inputs: List[MATRIX]):
try:
# Step 1: Generate the Python code for the solver function
prediction = self.code_generator(
training_examples=training_examples,
test_inputs=test_inputs
)
python_code = prediction.python_code
# Prepare a local scope for executing the generated code
local_scope = {}
exec(python_code, globals(), local_scope)
solve_func = local_scope.get('solve')
if not callable(solve_func):
raise ValueError("Generated code did not define a callable 'solve' function.")
# Step 2: Execute the generated function for each test input
all_outputs = []
for test_matrix in test_inputs:
# The generated function might need a deepcopy of the input
import copy
input_copy = copy.deepcopy(test_matrix)
result = solve_func(input_copy)
all_outputs.append(result)
return dspy.Prediction(test_outputs=all_outputs)
except Exception as e:
print(f"Code generation or execution failed: {e}")
print(traceback.format_exc())
# Step 3: Fallback to a simpler ChainOfThought model
print("Falling back to direct ChainOfThought solver.")
return self.fallback_solver(
training_examples=str(training_examples), # Convert to string for simpler models
test_inputs=str(test_inputs)
)
# The final program is an instance of our robust, code-generating module.
program = SolveWithCodeGeneration()
2025/08/29 00:48:58 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 00:52:42 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
Code generation or execution failed: exec() arg 1 must be a string, bytes or code object
Traceback (most recent call last):
File "<string>", line 61, in forward
TypeError: exec() arg 1 must be a string, bytes or code object
Falling back to direct ChainOfThought solver.
2025/08/29 00:53:13 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
Code generation or execution failed: exec() arg 1 must be a string, bytes or code object
Traceback (most recent call last):
File "<string>", line 61, in forward
TypeError: exec() arg 1 must be a string, bytes or code object
Falling back to direct ChainOfThought solver.
Code generation or execution failed: name 'collections' is not defined
Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 46, in solve
File "<string>", line 28, in get_background_color
NameError: name 'collections' is not defined
Falling back to direct ChainOfThought solver.
Code generation or execution failed: name 'collections' is not defined
Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 22, in solve
NameError: name 'collections' is not defined
Falling back to direct ChainOfThought solver.
2025/08/29 00:55:14 INFO dspy.evaluate.evaluate: Average Metric: 2.0 / 3 (66.7%)
GEPA Optimization: 21%|███████████████████████ | 831/4000 [3:21:18<57:42:34, 65.56s/rollouts]Iteration 48: New subsample score is not better, skipping
Iteration 49: Selected program 0 score: 0.67
Average Metric: 1.00 / 1 (100.0%): 33%|█████████████████████████████████████ | 1/3 [01:05<02:10, 65.21s/it]2025/08/29 00:56:33 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
Average Metric: 2.00 / 3 (66.7%): 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [02:37<00:00, 52.64s/it]2025/08/29 00:57:52 INFO dspy.evaluate.evaluate: Average Metric: 2.0 / 3 (66.7%)
2025/08/29 00:58:23 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 00:58:34 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 2, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 4, 0, 4, 0, 0, 0, 0, 0], [0, 0, 2, 0, 0, 0, 0, 0, 0], [0, 4, 0, 4, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 7, 0, 0], [0, 0, 0, 0, 0, 7, 1, 7, 0], [0, 0, 0, 0, 0, 0, 7, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]]}, {'input': [[0, 0, 0, 8, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 2, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 0, 0], [0, 2, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 8, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 4, 0, 4, 0], [0, 0, 7, 0, 0, 0, 2, 0, 0], [0, 7, 1, 7, 0, 4, 0, 4, 0], [0, 0, 7, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 7, 0, 0], [4, 0, 4, 0, 0, 7, 1, 7, 0], [0, 2, 0, 0, 0, 0, 7, 0, 0], [4, 0, 4, 0, 0, 0, 0, 0, 0]]}, {'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 2, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 6, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 4, 0, 4, 0, 0, 0, 0, 0], [0, 0, 2, 0, 0, 0, 0, 0, 0], [0, 4, 0, 4, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 6, 0, 0], [0, 0, 0, 7, 0, 0, 0, 0, 0], [0, 0, 7, 1, 7, 0, 0, 0, 0], [0, 0, 0, 7, 0, 0, 0, 0, 0]]}], 'test_inputs': [[[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 2, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 8, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 6, 0, 0, 0, 0, 0, 2, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]]], 'test_outputs': [[[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 7, 0, 0], [0, 4, 0, 4, 0, 7, 1, 7, 0], [0, 0, 2, 0, 0, 0, 7, 0, 0], [0, 4, 0, 4, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 8, 0, 0, 0], [0, 0, 0, 0, 0, 0, 4, 0, 4], [0, 6, 0, 0, 0, 0, 0, 2, 0], [0, 0, 0, 0, 0, 0, 4, 0, 4]]]}) (input_keys={'test_inputs', 'training_examples'}): 'dict' object has no attribute 'model_dump'
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 63, in forward
File "<string>", line 63, in <listcomp>
AttributeError: 'dict' object has no attribute 'model_dump'
2025/08/29 00:58:34 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[3, 3, 0], [0, 3, 0], [3, 0, 3]], 'output': [[8, 8, 0], [0, 8, 0], [3, 0, 3]]}, {'input': [[0, 3, 0, 0, 0, 3], [0, 3, 3, 3, 0, 0], [0, 0, 0, 0, 3, 0], [0, 3, 0, 0, 0, 0]], 'output': [[0, 8, 0, 0, 0, 3], [0, 8, 8, 8, 0, 0], [0, 0, 0, 0, 3, 0], [0, 3, 0, 0, 0, 0]]}, {'input': [[3, 3, 0, 3], [3, 3, 0, 0], [3, 0, 0, 3], [0, 0, 3, 3]], 'output': [[8, 8, 0, 3], [8, 8, 0, 0], [8, 0, 0, 8], [0, 0, 8, 8]]}, {'input': [[3, 3, 0, 0, 0, 0], [0, 3, 0, 0, 3, 0], [3, 0, 0, 0, 0, 0], [0, 3, 3, 0, 0, 0], [0, 3, 3, 0, 0, 3]], 'output': [[8, 8, 0, 0, 0, 0], [0, 8, 0, 0, 3, 0], [3, 0, 0, 0, 0, 0], [0, 8, 8, 0, 0, 0], [0, 8, 8, 0, 0, 3]]}], 'test_inputs': [[[3, 0, 3, 0, 3], [3, 3, 3, 0, 0], [0, 0, 0, 0, 3], [0, 3, 3, 0, 0], [0, 3, 3, 0, 0]]], 'test_outputs': [[[8, 0, 8, 0, 3], [8, 8, 8, 0, 0], [0, 0, 0, 0, 3], [0, 8, 8, 0, 0], [0, 8, 8, 0, 0]]]}) (input_keys={'test_inputs', 'training_examples'}): 'dict' object has no attribute 'model_dump'
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 63, in forward
File "<string>", line 63, in <listcomp>
AttributeError: 'dict' object has no attribute 'model_dump'
2025/08/29 00:58:34 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0], [0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0], [0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0], [0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 8, 8, 8, 8, 8, 8], [0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0], [0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0], [0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0], [8, 8, 8, 8, 8, 8, 8, 8, 0, 8, 8, 8, 8, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0], [0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0], [0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 8, 8, 0], [0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0], [0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0], [0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0], [0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0], [0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0], [0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0], [0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0]], 'output': [[3, 3, 3, 3, 8, 3, 3, 3, 3, 8, 3, 3, 3, 3, 8, 4, 4, 4, 4, 8, 3, 3, 3, 3], [3, 3, 3, 3, 8, 3, 3, 3, 3, 8, 3, 3, 3, 3, 8, 4, 4, 4, 4, 8, 3, 3, 3, 3], [3, 3, 3, 3, 8, 3, 3, 3, 3, 8, 3, 3, 3, 3, 8, 4, 4, 4, 4, 8, 3, 3, 3, 3], [3, 3, 3, 3, 8, 3, 3, 3, 3, 8, 3, 3, 3, 3, 8, 4, 4, 4, 4, 8, 3, 3, 3, 3], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 4, 8, 8, 8, 8, 8, 8], [4, 4, 4, 4, 8, 4, 4, 4, 4, 8, 4, 4, 4, 4, 8, 4, 4, 4, 4, 8, 4, 4, 4, 4], [4, 4, 4, 4, 8, 4, 4, 4, 4, 8, 4, 4, 4, 4, 8, 4, 4, 4, 4, 8, 4, 4, 4, 4], [4, 4, 4, 4, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 8, 4, 4, 4, 4, 8, 4, 4, 4, 4], [4, 4, 4, 4, 8, 4, 4, 4, 4, 8, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 4, 4, 4, 4], [8, 8, 8, 8, 8, 8, 8, 8, 4, 8, 8, 8, 8, 4, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [4, 4, 4, 4, 8, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4], [4, 4, 4, 4, 8, 4, 4, 4, 4, 8, 4, 4, 4, 4, 8, 4, 4, 4, 4, 8, 4, 4, 4, 4], [4, 4, 4, 4, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 8, 4, 4, 4, 4, 8, 4, 4, 4, 4], [4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 8, 4, 4, 4, 4], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 4, 8, 8, 4], [3, 3, 3, 3, 8, 4, 4, 4, 4, 8, 4, 4, 4, 4, 8, 3, 3, 3, 3, 8, 4, 4, 4, 4], [3, 3, 3, 3, 8, 4, 4, 4, 4, 8, 4, 4, 4, 4, 8, 3, 3, 3, 3, 8, 4, 4, 4, 4], [3, 3, 3, 3, 8, 4, 4, 4, 4, 4, 4, 4, 4, 4, 8, 3, 3, 3, 3, 8, 4, 4, 4, 4], [3, 3, 3, 3, 8, 4, 4, 4, 4, 8, 4, 4, 4, 4, 8, 3, 3, 3, 3, 8, 4, 4, 4, 4], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [3, 3, 3, 3, 8, 3, 3, 3, 3, 8, 3, 3, 3, 3, 8, 3, 3, 3, 3, 8, 3, 3, 3, 3], [3, 3, 3, 3, 8, 3, 3, 3, 3, 8, 3, 3, 3, 3, 8, 3, 3, 3, 3, 8, 3, 3, 3, 3], [3, 3, 3, 3, 8, 3, 3, 3, 3, 8, 3, 3, 3, 3, 8, 3, 3, 3, 3, 8, 3, 3, 3, 3], [3, 3, 3, 3, 8, 3, 3, 3, 3, 8, 3, 3, 3, 3, 8, 3, 3, 3, 3, 8, 3, 3, 3, 3]]}, {'input': [[0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1], [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1], [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0]], 'output': [[3, 3, 3, 3, 3, 1, 3, 3, 3, 3, 3, 1, 3, 3, 3, 3, 3, 1, 3, 3, 3, 3, 3, 1, 3, 3, 3, 3, 3], [3, 3, 3, 3, 3, 1, 3, 3, 3, 3, 3, 1, 3, 3, 3, 3, 3, 1, 3, 3, 3, 3, 3, 1, 3, 3, 3, 3, 3], [3, 3, 3, 3, 3, 1, 3, 3, 3, 3, 3, 1, 3, 3, 3, 3, 3, 1, 3, 3, 3, 3, 3, 1, 3, 3, 3, 3, 3], [3, 3, 3, 3, 3, 1, 3, 3, 3, 3, 3, 1, 3, 3, 3, 3, 3, 1, 3, 3, 3, 3, 3, 1, 3, 3, 3, 3, 3], [3, 3, 3, 3, 3, 1, 3, 3, 3, 3, 3, 1, 3, 3, 3, 3, 3, 1, 3, 3, 3, 3, 3, 1, 3, 3, 3, 3, 3], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [4, 4, 4, 4, 4, 1, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 1, 4, 4, 4, 4, 4, 1, 4, 4, 4, 4, 4], [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 1, 4, 4, 4, 4, 4, 1, 4, 4, 4, 4, 4, 1, 4, 4, 4, 4, 4], [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 1, 4, 4, 4, 4, 4, 1, 4, 4, 4, 4, 4, 1, 4, 4, 4, 4, 4], [4, 4, 4, 4, 4, 1, 4, 4, 4, 4, 4, 1, 4, 4, 4, 4, 4, 1, 4, 4, 4, 4, 4, 1, 4, 4, 4, 4, 4], [4, 4, 4, 4, 4, 1, 4, 4, 4, 4, 4, 1, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 1, 4, 4, 4, 4, 4], [1, 1, 1, 1, 1, 1, 1, 1, 4, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 4, 1, 1, 1, 1], [4, 4, 4, 4, 4, 1, 4, 4, 4, 4, 4, 1, 4, 4, 4, 4, 4, 1, 4, 4, 4, 4, 4, 1, 4, 4, 4, 4, 4], [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 1, 4, 4, 4, 4, 4, 1, 4, 4, 4, 4, 4], [4, 4, 4, 4, 4, 1, 4, 4, 4, 4, 4, 1, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 1, 4, 4, 4, 4, 4], [4, 4, 4, 4, 4, 1, 4, 4, 4, 4, 4, 1, 4, 4, 4, 4, 4, 1, 4, 4, 4, 4, 4, 1, 4, 4, 4, 4, 4], [4, 4, 4, 4, 4, 1, 4, 4, 4, 4, 4, 1, 4, 4, 4, 4, 4, 1, 4, 4, 4, 4, 4, 1, 4, 4, 4, 4, 4], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 4, 1, 1, 1, 1, 1, 4, 1, 1, 1, 1, 4, 1, 1, 1], [4, 4, 4, 4, 4, 1, 3, 3, 3, 3, 3, 1, 4, 4, 4, 4, 4, 1, 4, 4, 4, 4, 4, 1, 4, 4, 4, 4, 4], [4, 4, 4, 4, 4, 1, 3, 3, 3, 3, 3, 1, 4, 4, 4, 4, 4, 1, 4, 4, 4, 4, 4, 1, 4, 4, 4, 4, 4], [4, 4, 4, 4, 4, 1, 3, 3, 3, 3, 3, 1, 4, 4, 4, 4, 4, 1, 4, 4, 4, 4, 4, 1, 4, 4, 4, 4, 4], [4, 4, 4, 4, 4, 1, 3, 3, 3, 3, 3, 1, 4, 4, 4, 4, 4, 1, 4, 4, 4, 4, 4, 1, 4, 4, 4, 4, 4], [4, 4, 4, 4, 4, 1, 3, 3, 3, 3, 3, 1, 4, 4, 4, 4, 4, 1, 4, 4, 4, 4, 4, 1, 4, 4, 4, 4, 4], [4, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [4, 4, 4, 4, 4, 1, 3, 3, 3, 3, 3, 1, 3, 3, 3, 3, 3, 1, 3, 3, 3, 3, 3, 1, 3, 3, 3, 3, 3], [4, 4, 4, 4, 4, 1, 3, 3, 3, 3, 3, 1, 3, 3, 3, 3, 3, 1, 3, 3, 3, 3, 3, 1, 3, 3, 3, 3, 3], [4, 4, 4, 4, 4, 1, 3, 3, 3, 3, 3, 1, 3, 3, 3, 3, 3, 1, 3, 3, 3, 3, 3, 1, 3, 3, 3, 3, 3], [4, 4, 4, 4, 4, 1, 3, 3, 3, 3, 3, 1, 3, 3, 3, 3, 3, 1, 3, 3, 3, 3, 3, 1, 3, 3, 3, 3, 3], [4, 4, 4, 4, 4, 1, 3, 3, 3, 3, 3, 1, 3, 3, 3, 3, 3, 1, 3, 3, 3, 3, 3, 1, 3, 3, 3, 3, 3]]}, {'input': [[0, 0, 0, 0, 9, 0, 0, 0, 0, 9, 0, 0, 0, 0, 9, 0, 0, 0, 0], [0, 0, 0, 0, 9, 0, 0, 0, 0, 9, 0, 0, 0, 0, 9, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 9, 0, 0, 0, 0, 9, 0, 0, 0, 0], [9, 9, 9, 9, 9, 9, 9, 0, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9], [0, 0, 0, 0, 9, 0, 0, 0, 0, 9, 0, 0, 0, 0, 9, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 9, 0, 0, 0, 0, 9, 0, 0, 0, 0], [0, 0, 0, 0, 9, 0, 0, 0, 0, 9, 0, 0, 0, 0, 9, 0, 0, 0, 0], [0, 0, 0, 0, 9, 0, 0, 0, 0, 9, 0, 0, 0, 0, 9, 0, 0, 0, 0], [9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 0, 9, 9, 9, 9, 9, 9, 9, 9], [0, 0, 0, 0, 9, 0, 0, 0, 0, 9, 0, 0, 0, 0, 9, 0, 0, 0, 0], [0, 0, 0, 0, 9, 0, 0, 0, 0, 9, 0, 0, 0, 0, 9, 0, 0, 0, 0], [0, 0, 0, 0, 9, 0, 0, 0, 0, 9, 0, 0, 0, 0, 9, 0, 0, 0, 0], [0, 0, 0, 0, 9, 0, 0, 0, 0, 9, 0, 0, 0, 0, 9, 0, 0, 0, 0], [9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 0], [0, 0, 0, 0, 9, 0, 0, 0, 0, 9, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 9, 0, 0, 0, 0, 9, 0, 0, 0, 0, 9, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 9, 0, 0, 0, 0, 9, 0, 0, 0, 0], [0, 0, 0, 0, 9, 0, 0, 0, 0, 9, 0, 0, 0, 0, 9, 0, 0, 0, 0]], 'output': [[4, 4, 4, 4, 9, 4, 4, 4, 4, 9, 4, 4, 4, 4, 9, 3, 3, 3, 3], [4, 4, 4, 4, 9, 4, 4, 4, 4, 9, 4, 4, 4, 4, 9, 3, 3, 3, 3], [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 9, 3, 3, 3, 3], [4, 4, 4, 4, 4, 4, 4, 4, 4, 9, 4, 4, 4, 4, 9, 3, 3, 3, 3], [9, 9, 9, 9, 9, 9, 9, 4, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9], [4, 4, 4, 4, 9, 4, 4, 4, 4, 9, 4, 4, 4, 4, 9, 3, 3, 3, 3], [4, 4, 4, 4, 4, 4, 4, 4, 4, 9, 4, 4, 4, 4, 9, 3, 3, 3, 3], [4, 4, 4, 4, 9, 4, 4, 4, 4, 9, 4, 4, 4, 4, 9, 3, 3, 3, 3], [4, 4, 4, 4, 9, 4, 4, 4, 4, 9, 4, 4, 4, 4, 9, 3, 3, 3, 3], [9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 4, 9, 9, 9, 9, 9, 9, 9, 9], [3, 3, 3, 3, 9, 3, 3, 3, 3, 9, 4, 4, 4, 4, 9, 4, 4, 4, 4], [3, 3, 3, 3, 9, 3, 3, 3, 3, 9, 4, 4, 4, 4, 9, 4, 4, 4, 4], [3, 3, 3, 3, 9, 3, 3, 3, 3, 9, 4, 4, 4, 4, 9, 4, 4, 4, 4], [3, 3, 3, 3, 9, 3, 3, 3, 3, 9, 4, 4, 4, 4, 9, 4, 4, 4, 4], [9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 4], [4, 4, 4, 4, 9, 4, 4, 4, 4, 9, 4, 4, 4, 4, 4, 4, 4, 4, 4], [4, 4, 4, 4, 9, 4, 4, 4, 4, 9, 4, 4, 4, 4, 9, 4, 4, 4, 4], [4, 4, 4, 4, 4, 4, 4, 4, 4, 9, 4, 4, 4, 4, 9, 4, 4, 4, 4], [4, 4, 4, 4, 9, 4, 4, 4, 4, 9, 4, 4, 4, 4, 9, 4, 4, 4, 4]]}], 'test_inputs': [[[0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0], [0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0], [0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0], [5, 5, 5, 5, 5, 5, 0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0], [0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0], [0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0], [0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0], [0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0], [0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0], [5, 5, 5, 5, 5, 0, 0, 5, 5, 5, 0, 5, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0], [0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0], [5, 5, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 5, 5, 5, 5, 5], [0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0], [0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0], [0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 5, 5, 5, 5], [0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0], [0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0], [5, 5, 5, 5, 5, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 5, 5, 5, 5, 5, 5, 5, 5], [0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0], [0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0]]], 'test_outputs': [[[3, 3, 3, 5, 4, 4, 4, 5, 4, 4, 4, 5, 3, 3, 3, 5, 3, 3, 3, 5, 3, 3, 3, 5, 4, 4, 4], [3, 3, 3, 5, 4, 4, 4, 5, 4, 4, 4, 5, 3, 3, 3, 5, 3, 3, 3, 5, 3, 3, 3, 5, 4, 4, 4], [3, 3, 3, 5, 4, 4, 4, 4, 4, 4, 4, 5, 3, 3, 3, 5, 3, 3, 3, 5, 3, 3, 3, 5, 4, 4, 4], [5, 5, 5, 5, 5, 5, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 4], [4, 4, 4, 4, 4, 4, 4, 5, 4, 4, 4, 5, 4, 4, 4, 5, 4, 4, 4, 5, 3, 3, 3, 5, 4, 4, 4], [4, 4, 4, 5, 4, 4, 4, 5, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 3, 3, 3, 5, 4, 4, 4], [4, 4, 4, 5, 4, 4, 4, 5, 4, 4, 4, 5, 4, 4, 4, 5, 4, 4, 4, 5, 3, 3, 3, 5, 4, 4, 4], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [3, 3, 3, 5, 4, 4, 4, 5, 4, 4, 4, 5, 4, 4, 4, 5, 3, 3, 3, 5, 3, 3, 3, 5, 3, 3, 3], [3, 3, 3, 5, 4, 4, 4, 4, 4, 4, 4, 5, 4, 4, 4, 5, 3, 3, 3, 5, 3, 3, 3, 5, 3, 3, 3], [3, 3, 3, 5, 4, 4, 4, 5, 4, 4, 4, 5, 4, 4, 4, 5, 3, 3, 3, 5, 3, 3, 3, 5, 3, 3, 3], [5, 5, 5, 5, 5, 4, 4, 5, 5, 5, 4, 5, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [4, 4, 4, 4, 4, 4, 4, 5, 4, 4, 4, 4, 4, 4, 4, 5, 4, 4, 4, 4, 4, 4, 4, 5, 3, 3, 3], [4, 4, 4, 4, 4, 4, 4, 5, 4, 4, 4, 5, 4, 4, 4, 5, 4, 4, 4, 5, 4, 4, 4, 5, 3, 3, 3], [4, 4, 4, 5, 4, 4, 4, 5, 4, 4, 4, 5, 4, 4, 4, 5, 4, 4, 4, 5, 4, 4, 4, 5, 3, 3, 3], [5, 5, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 4, 5, 5, 5, 5, 5], [4, 4, 4, 5, 3, 3, 3, 5, 4, 4, 4, 5, 4, 4, 4, 5, 3, 3, 3, 5, 4, 4, 4, 5, 3, 3, 3], [4, 4, 4, 5, 3, 3, 3, 5, 4, 4, 4, 5, 4, 4, 4, 5, 3, 3, 3, 5, 4, 4, 4, 5, 3, 3, 3], [4, 4, 4, 5, 3, 3, 3, 5, 4, 4, 4, 4, 4, 4, 4, 5, 3, 3, 3, 5, 4, 4, 4, 5, 3, 3, 3], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 4, 5, 5, 5, 5], [3, 3, 3, 5, 4, 4, 4, 5, 4, 4, 4, 5, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 4, 4, 4], [3, 3, 3, 5, 4, 4, 4, 5, 4, 4, 4, 5, 4, 4, 4, 4, 4, 4, 4, 5, 4, 4, 4, 4, 4, 4, 4], [3, 3, 3, 5, 4, 4, 4, 5, 4, 4, 4, 4, 4, 4, 4, 5, 4, 4, 4, 5, 4, 4, 4, 4, 4, 4, 4], [5, 5, 5, 5, 5, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 4, 5, 5, 5, 5, 5, 5, 5, 5], [4, 4, 4, 5, 4, 4, 4, 5, 4, 4, 4, 4, 4, 4, 4, 5, 4, 4, 4, 5, 3, 3, 3, 5, 3, 3, 3], [4, 4, 4, 4, 4, 4, 4, 5, 4, 4, 4, 5, 4, 4, 4, 5, 4, 4, 4, 5, 3, 3, 3, 5, 3, 3, 3], [4, 4, 4, 5, 4, 4, 4, 4, 4, 4, 4, 5, 4, 4, 4, 4, 4, 4, 4, 5, 3, 3, 3, 5, 3, 3, 3]]]}) (input_keys={'test_inputs', 'training_examples'}): 'dict' object has no attribute 'model_dump'
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 63, in forward
File "<string>", line 63, in <listcomp>
AttributeError: 'dict' object has no attribute 'model_dump'
2025/08/29 00:58:34 INFO dspy.evaluate.evaluate: Average Metric: 0.0 / 3 (0.0%)
GEPA Optimization: 21%|███████████████████████▏ | 837/4000 [3:24:37<48:27:48, 55.16s/rollouts]Iteration 49: Proposed new text for program: import dspy
from typing import List
import pydantic
import re
import traceback
# Define the type for a matrix for clarity
MATRIX = List[List[int]]
class TrainingExample(pydantic.BaseModel):
"""A Pydantic model for a single training example, containing an input and output matrix."""
input: MATRIX
output: MATRIX
class SolveTaskSignature(dspy.Signature):
"""
You are an expert at solving Abstract Reasoning Corpus (ARC) puzzles.
Your goal is to infer a general transformation rule from a series of `training_examples`.
Each example shows an `input` grid and a corresponding `output` grid.
After inferring the rule, you must apply it to the `test_inputs` to generate the `test_outputs`.
Common patterns in these puzzles include:
- Geometric operations: rotation, reflection, scaling, cropping.
- Object-based logic: identifying objects, counting them, applying rules based on their properties (color, shape, size, position).
- Neighborhood analysis: changing a cell's color based on its neighbors (e.g., Conway's Game of Life, propagation).
- Connectivity and flood-fill: filling areas based on whether they are enclosed or connected to certain points.
- Pattern repetition and completion.
Think step-by-step. First, deeply analyze the examples to find the simplest, most general rule that explains all transformations. Then, describe how to apply this rule precisely to the test cases to produce the final output.
"""
training_examples: List[TrainingExample] = dspy.InputField(description="Input and output examples demonstrating the task to be performed.")
test_inputs: List[MATRIX] = dspy.InputField(description="Input matrices to be solved following the task described in the training examples.")
test_outputs: List[MATRIX] = dspy.OutputField(description="Output matrices corresponding to the test inputs.")
class RuleDeductionSignature(dspy.Signature):
"""Given a series of input-output grid examples, deduce the underlying transformation rule."""
training_examples: str = dspy.InputField(desc="String representation of input-output grid pairs.")
rule_description: str = dspy.OutputField(desc="A step-by-step, algorithmic description of the transformation rule in plain English.")
class CodeGenerationSignature(dspy.Signature):
"""
Generate a Python function `solve(test_inputs)` that implements the described rule.
The function must take one argument: `test_inputs`, which is a list of matrices (list of lists of ints).
The function must return a list of the solved matrices in the same format.
Make sure to handle all necessary imports inside the function if they are not standard Python libraries.
Do not include any example usage or calls to the function in your output.
Only output the Python code for the function, enclosed in a single markdown code block.
"""
rule_description: str = dspy.InputField(desc="The natural language description of the rule.")
test_inputs: str = dspy.InputField(desc="A string representation of the test inputs to guide function definition.")
python_code: str = dspy.OutputField(desc="A single Python code block containing a function `solve(test_inputs)` that returns the solved matrices.")
class ARCProgram(dspy.Module):
"""A program that solves ARC puzzles by deducing the rule, generating code, and executing it."""
def __init__(self):
super().__init__()
self.deduce_rule = dspy.ChainOfThought(RuleDeductionSignature)
self.generate_code = dspy.Predict(CodeGenerationSignature)
self.fallback = dspy.ChainOfThought(SolveTaskSignature)
def forward(self, training_examples: List[TrainingExample], test_inputs: List[MATRIX]):
# Convert Pydantic objects to dicts for string representation
training_examples_dict = [ex.model_dump() for ex in training_examples]
try:
# Step 1: Deduce the rule from the examples.
rule_pred = self.deduce_rule(training_examples=str(training_examples_dict))
rule_description = rule_pred.rule_description
# Step 2: Generate Python code based on the rule.
code_pred = self.generate_code(rule_description=rule_description, test_inputs=str(test_inputs))
# Extract Python code from the markdown block.
code_match = re.search(r'```python\n(.*?)\n```', code_pred.python_code, re.DOTALL)
if not code_match:
raise ValueError("Generated output is not a valid Python markdown block.")
python_code = code_match.group(1)
# Step 3: Execute the generated code.
local_scope = {}
exec(python_code, globals(), local_scope)
solve_func = local_scope.get('solve')
if not callable(solve_func):
raise ValueError("`solve` function not found or not callable in generated code.")
# The function takes the original typed inputs
predicted_outputs = solve_func(test_inputs)
return dspy.Prediction(test_outputs=predicted_outputs)
except Exception as e:
print(f"Code generation or execution failed: {e}")
print(traceback.format_exc())
print("Falling back to direct ChainOfThought prediction.")
# Fallback to the original, direct approach.
return self.fallback(training_examples=training_examples, test_inputs=test_inputs)
program = ARCProgram()
Iteration 49: New subsample score is not better, skipping
Iteration 50: Selected program 2 score: 0.605
Average Metric: 3.00 / 3 (100.0%): 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [03:01<00:00, 60.41s/it]2025/08/29 01:01:35 INFO dspy.evaluate.evaluate: Average Metric: 3.0 / 3 (100.0%)
GEPA Optimization: 21%|███████████████████████▎ | 840/4000 [3:27:39<49:16:47, 56.14s/rollouts]
Iteration 50: All subsample scores perfect. Skipping.
Iteration 50: Reflective mutation did not propose a new candidate
Iteration 51: Selected program 2 score: 0.605
Average Metric: 1.00 / 2 (50.0%): : 4it [05:09, 63.59s/it] 2025/08/29 01:09:17 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
Average Metric: 1.00 / 3 (33.3%): : 5it [07:42, 92.42s/it]2025/08/29 01:09:17 INFO dspy.evaluate.evaluate: Average Metric: 1.0 / 3 (33.3%)
Iteration 51: Proposed new text for program: import dspy
from typing import List
import pydantic
import copy
# Define type aliases and pydantic models for clarity and structure.
MATRIX = List[List[int]]
class TrainingExample(pydantic.BaseModel):
input: MATRIX
output: MATRIX
class GenerateHypothesis(dspy.Signature):
"""
Analyzes pairs of input/output matrices from the Abstraction and Reasoning Corpus (ARC) and deduces the transformation rule.
Your goal is to describe the transformation rule in clear, step-by-step natural language. This description will be used by a programmer to implement the solution.
**Common ARC Patterns to Look For:**
- **Object Manipulation:** Identifying distinct shapes/objects and moving, rotating, copying, or resizing them.
- **Filling/Completion:** Filling enclosed areas, completing patterns, or extending lines.
- **Color Mapping:** Changing colors based on a consistent rule (e.g., mapping color 2 to 4, or changing the color of the largest object).
- **Pattern Extraction & Reconstruction:** Identifying repeating patterns or features (like colored rows/columns) and using them to construct a new, often smaller, output grid.
- **Symmetry/Repetition:** Detecting and applying symmetrical operations or repeating a core pattern.
**Example Analysis:**
- **Input 1:** A 5x5 grid with a blue square.
- **Output 1:** A 5x5 grid with a blue square moved to the opposite corner.
- **Input 2:** A 6x6 grid with a red circle.
- **Output 2:** A 6x6 grid with a red circle moved to the opposite corner.
- **Correct Hypothesis:** The rule is to identify the single colored object in the input grid and move it to the corner diagonally opposite its current position.
"""
training_examples: List[TrainingExample] = dspy.InputField(desc="A list of input/output pairs demonstrating the transformation rule.")
hypothesis: str = dspy.OutputField(desc="A clear, step-by-step natural language description of the transformation rule.")
class GeneratePythonFunction(dspy.Signature):
"""
You are an expert programmer. Your task is to write a single, self-contained Python function based on a provided natural language hypothesis and a series of example matrix pairs from the Abstraction and Reasoning Corpus (ARC).
**Function Requirements:**
- The function must be named `transform_matrix`.
- It must accept one argument: `matrix`, which is a list of lists of integers.
- It must return a list of lists of integers (the transformed grid).
- The function should not use any external libraries except for `copy`.
- Your output must be ONLY the Python code for the function. Do not include explanations or markdown formatting.
**Successful Strategies to Consider:**
- **Hypothesis First:** Your primary goal is to accurately translate the given `hypothesis` into code. The examples are for context and clarification.
- **Object Identification:** Often, you'll need to first find distinct objects (connected components of non-zero colors). A breadth-first search (BFS) or depth-first search (DFS) is excellent for this.
- **Fill Enclosed Areas:** For tasks involving filling, a flood-fill algorithm (often implemented with BFS/DFS) is necessary to identify the interior points of a shape.
- **Pattern Extraction & Reconstruction:** For tasks where the output is a summary, identify key features (like colored rows/columns), store their properties (color, position), and then use this data to build the new output grid.
- **Iterative Processes:** Some rules are applied repeatedly. Consider a `while` loop that continues as long as the grid is changing.
"""
training_examples: List[TrainingExample] = dspy.InputField(desc="A list of input/output pairs for context.")
hypothesis: str = dspy.InputField(desc="The natural language hypothesis describing the transformation to implement.")
python_function: str = dspy.OutputField(desc="A string containing a single Python function `transform_matrix` that implements the rule.")
class ARCSolver(dspy.Module):
"""A module that solves ARC tasks by generating a hypothesis and then generating code."""
def __init__(self):
super().__init__()
# Decompose the problem: 1) Reason about the rule, 2) Write code for the rule.
self.generate_hypothesis = dspy.ChainOfThought(GenerateHypothesis)
self.generate_code = dspy.Predict(GeneratePythonFunction)
def forward(self, training_examples: List[TrainingExample], test_inputs: List[MATRIX]):
# Step 1: Generate a natural language hypothesis about the transformation rule.
prediction = self.generate_hypothesis(training_examples=training_examples)
hypothesis = prediction.hypothesis
# Step 2: Generate Python code based on the hypothesis and examples.
prediction = self.generate_code(training_examples=training_examples, hypothesis=hypothesis)
python_code = prediction.python_function
# Prepare a dictionary to hold the executed function.
local_scope = {}
# Create fallback outputs in case of any failure.
fallback_outputs = [copy.deepcopy(matrix) for matrix in test_inputs]
# Guard against the LM failing to generate any code.
if not python_code:
return dspy.Prediction(test_outputs=fallback_outputs)
try:
# Step 3: Execute the generated code string to define the function.
exec(python_code, globals(), local_scope)
transform_func = local_scope.get('transform_matrix')
if not callable(transform_func):
return dspy.Prediction(test_outputs=fallback_outputs)
# Step 4: Apply the generated function to each test input.
solved_outputs = []
for test_matrix in test_inputs:
try:
input_copy = copy.deepcopy(test_matrix)
result = transform_func(input_copy)
solved_outputs.append(result)
except Exception:
# If the function fails on a specific test case, append the original
# matrix as a fallback for that case.
solved_outputs.append(copy.deepcopy(test_matrix))
return dspy.Prediction(test_outputs=solved_outputs)
except Exception:
# If code generation or `exec` fails entirely, return the original inputs.
return dspy.Prediction(test_outputs=fallback_outputs)
# The overall task signature, useful for evaluation and context.
class SolveTaskSignature(dspy.Signature):
"""
Given a set of training examples demonstrating a matrix transformation,
apply the same transformation to a new set of test matrices.
"""
training_examples: List[TrainingExample] = dspy.InputField(description="Input and output examples demonstrating the task to be performed.")
test_inputs: List[MATRIX] = dspy.InputField(description="Input matrices to be solved following the task described in the training examples.")
test_outputs: List[MATRIX] = dspy.OutputField(description="Output matrices corresponding to the test inputs.")
# The final program is an instance of our new, more robust module.
program = ARCSolver()
2025/08/29 01:19:13 INFO dspy.evaluate.evaluate: Average Metric: 1.0 / 3 (33.3%)
GEPA Optimization: 21%|███████████████████████▍ | 846/4000 [3:45:17<85:52:32, 98.02s/rollouts]Iteration 51: New subsample score is not better, skipping
Iteration 52: Selected program 2 score: 0.605
Average Metric: 1.00 / 3 (33.3%): : 6it [07:05, 70.95s/it] 2025/08/29 01:26:19 INFO dspy.evaluate.evaluate: Average Metric: 1.0 / 3 (33.3%)
Iteration 52: Proposed new text for program: import dspy
from typing import List, Optional
import pydantic
import copy
import traceback
# Define type aliases and pydantic models for clarity and structure.
MATRIX = List[List[int]]
class TrainingExample(pydantic.BaseModel):
input: MATRIX
output: MATRIX
# New Signature with self-correction and Chain of Thought
class HypothesizeAndCode(dspy.Signature):
"""
You are an expert programmer and a master of abstract reasoning. Your task is to solve a task from the Abstraction and Reasoning Corpus (ARC). You will be given a series of input-output matrix pairs that demonstrate a transformation rule.
**Your Process:**
1. **Analyze and Hypothesize:** First, carefully analyze the training examples. In a step-by-step thought process, describe the transformation rule you have deduced. Consider shapes, colors, positions, symmetry, repetition, and object manipulation. This is your hypothesis.
2. **Implement in Python:** Second, based ONLY on your hypothesis, write a single, self-contained Python function that implements this rule.
**Function Requirements:**
- The function must be named `transform_matrix`.
- It must accept one argument: `matrix`, which is a list of lists of integers.
- It must return a list of lists of integers.
- The function should not use any external libraries except for `copy`.
- Your output must be ONLY the Python code for the function. Do not include any explanations, comments outside the function, or markdown formatting like ```python.
**Critical Constraints & Strategies:**
- **Dimension Preservation:** The output grid dimensions MUST match the input grid dimensions unless the examples explicitly and consistently show a change. This is a very common failure point. Double-check this.
- **Simplicity First:** Start with the simplest possible hypothesis that explains the examples. Don't overcomplicate.
- **Self-Correction:** You may be given feedback on previous, failed attempts. Analyze the feedback carefully to identify the flaw in your previous logic and generate a corrected hypothesis and function. Avoid repeating the same mistakes.
**Example of a Correctly Formatted Output:**
def transform_matrix(matrix: list[list[int]]) -> list[list[int]]:
# Your implementation here
# For example, find the most frequent color and fill the grid
import collections
import itertools
if not matrix or not matrix[0]:
return []
counts = collections.Counter(itertools.chain.from_iterable(matrix))
# Handle the case of an empty grid after flattening
if not counts:
return [[] for _ in matrix]
# Handle ties by picking the smaller number value
most_common_color = sorted(counts.items(), key=lambda item: (-item[1], item[0]))[0][0]
height = len(matrix)
width = len(matrix[0])
return [[most_common_color for _ in range(width)] for _ in range(height)]
"""
training_examples: List[TrainingExample] = dspy.InputField(desc="A list of input/output pairs demonstrating the transformation rule.")
feedback: Optional[str] = dspy.InputField(desc="Feedback on the previous failed attempt. Use this to correct your logic.", default=None)
past_attempts: Optional[List[str]] = dspy.InputField(desc="A list of previously generated functions that failed verification.", default=None)
reasoning: str = dspy.OutputField(desc="Your step-by-step thought process to deduce the transformation rule (your hypothesis).")
python_function: str = dspy.OutputField(desc="A string containing a single Python function `transform_matrix` that implements the rule.")
class ARCSolver(dspy.Module):
"""A module that solves ARC tasks by generating, verifying, and refining Python code."""
def __init__(self, max_attempts=3):
super().__init__()
self.max_attempts = max_attempts
self.code_generator = dspy.ChainOfThought(HypothesizeAndCode)
def forward(self, training_examples: List[TrainingExample], test_inputs: List[MATRIX]):
feedback = None
past_attempts = []
transform_func = None
# Create fallback outputs in case of any failure.
fallback_outputs = [copy.deepcopy(matrix) for matrix in test_inputs]
for attempt in range(self.max_attempts):
# Step 1: Generate a hypothesis and the corresponding Python function.
prediction = self.code_generator(
training_examples=training_examples,
feedback=feedback,
past_attempts=past_attempts
)
python_code = prediction.python_function
# Prepare for execution
local_scope = {}
is_verified = True
try:
# Step 2: Execute the generated code string to define the function.
exec(python_code, globals(), local_scope)
current_func = local_scope.get('transform_matrix')
if not callable(current_func):
feedback = "Syntax Error: The generated code did not define a callable function named 'transform_matrix'."
past_attempts.append(python_code)
continue
# Step 3: Verify the function against all training examples.
for example in training_examples:
input_copy = copy.deepcopy(example.input)
try:
result = current_func(input_copy)
if result != example.output:
is_verified = False
feedback = f"Verification Failed on a training example.\nInput:\n{example.input}\n\nYour function's output:\n{result}\n\nExpected output:\n{example.output}"
break # Stop checking other examples
except Exception:
is_verified = False
feedback = f"Runtime Error during verification on training example.\nInput:\n{example.input}\n\nError:\n{traceback.format_exc()}"
break
if is_verified:
transform_func = current_func
break # Success, exit the retry loop
else:
past_attempts.append(python_code) # Add failed code to history
except Exception:
feedback = f"Syntax or Execution Error: The generated code could not be executed.\nError:\n{traceback.format_exc()}"
past_attempts.append(python_code)
continue
# After the loop, check if we have a valid function
if transform_func:
# Step 4: Apply the validated function to each test input.
solved_outputs = []
for test_matrix in test_inputs:
try:
input_copy = copy.deepcopy(test_matrix)
result = transform_func(input_copy)
solved_outputs.append(result)
except Exception:
# If the function fails on a test case, fallback for that case.
solved_outputs.append(copy.deepcopy(test_matrix))
return dspy.Prediction(test_outputs=solved_outputs)
else:
# If all attempts fail, return the original inputs as a fallback.
return dspy.Prediction(test_outputs=fallback_outputs)
# The overall task signature remains the same for clarity.
class SolveTaskSignature(dspy.Signature):
"""
Given a set of training examples demonstrating a matrix transformation,
apply the same transformation to a new set of test matrices.
"""
training_examples: List[TrainingExample] = dspy.InputField(description="Input and output examples demonstrating the task to be performed.")
test_inputs: List[MATRIX] = dspy.InputField(description="Input matrices to be solved following the task described in the training examples.")
test_outputs: List[MATRIX] = dspy.OutputField(description="Output matrices corresponding to the test inputs.")
# The final program is an instance of our new, more robust module.
program = ARCSolver()
2025/08/29 01:38:29 INFO dspy.evaluate.evaluate: Average Metric: 0.0 / 3 (0.0%)
GEPA Optimization: 21%|███████████████████████▏ | 852/4000 [4:04:33<113:12:37, 129.47s/rollouts]Iteration 52: New subsample score is not better, skipping
Iteration 53: Selected program 0 score: 0.67
Average Metric: 1.00 / 2 (50.0%): 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [04:00<00:00, 77.27s/it]2025/08/29 01:43:19 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
Average Metric: 1.00 / 3 (33.3%): : 4it [04:49, 72.36s/it] 2025/08/29 01:43:19 INFO dspy.evaluate.evaluate: Average Metric: 1.0 / 3 (33.3%)
2025/08/29 01:44:13 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0], [0, 0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0], [0, 0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8, 8, 8, 8, 8, 8, 8, 0, 0, 8, 0, 0], [0, 0, 8, 0, 0, 8, 0, 0, 8, 0, 0, 8, 0, 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0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]], 'test_outputs': [[[4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0], [0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0], [0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0], [0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0], [0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0], [0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0], [0, 4, 0, 0, 0, 0, 0, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 4, 0, 0], [0, 4, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 4, 0, 0], [0, 4, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 4, 0, 0], [0, 4, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 4, 0, 0], [0, 4, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 4, 0, 0], [0, 4, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 4, 0, 0], [0, 4, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 4, 0, 0], [0, 4, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 4, 0, 0], [0, 4, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 4, 0, 0], [0, 4, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 4, 0, 0], [0, 4, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 4, 0, 0], [0, 4, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 4, 0, 0], [0, 4, 0, 0, 0, 0, 0, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 4, 0, 0], [0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0], [0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0], [0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0]]]}) (input_keys={'test_inputs', 'training_examples'}): 'dict' object has no attribute 'model_dump'
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 59, in forward
File "<string>", line 59, in <listcomp>
AttributeError: 'dict' object has no attribute 'model_dump'
2025/08/29 01:44:13 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[5, 1, 1, 1, 1, 1, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0], [0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1], [1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0]], 'output': [[5, 1, 1, 1, 1, 1, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0], [0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 6, 6, 6, 6, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1], [1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 6, 6, 6, 6, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0]]}, {'input': [[1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1], [1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1], [0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0], [0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1]], 'output': [[1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 6, 6, 6, 1, 0, 1], [1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 6, 6, 6, 1, 1, 1], [0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0], [0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1]]}, {'input': [[1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1], [0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0]], 'output': [[1, 1, 6, 6, 6, 6, 6, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1], [0, 1, 6, 6, 6, 6, 6, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0]]}, {'input': [[0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0], [1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0], [0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1]], 'output': [[0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 6, 6, 6], [1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 6, 6, 6], [1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 6, 6, 6], [0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1]]}], 'test_inputs': [[[0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0], [1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0], [0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1], [0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1]]], 'test_outputs': [[[0, 6, 6, 6, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0], [1, 6, 6, 6, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0], [0, 6, 6, 6, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1], [0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1]]]}) (input_keys={'test_inputs', 'training_examples'}): 'dict' object has no attribute 'model_dump'
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 59, in forward
File "<string>", line 59, in <listcomp>
AttributeError: 'dict' object has no attribute 'model_dump'
2025/08/29 01:44:13 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[1, 0, 0, 5, 0, 1, 0], [0, 1, 0, 5, 1, 1, 1], [1, 0, 0, 5, 0, 0, 0]], 'output': [[0, 0, 0], [0, 2, 0], [0, 0, 0]]}, {'input': [[1, 1, 0, 5, 0, 1, 0], [0, 0, 1, 5, 1, 1, 1], [1, 1, 0, 5, 0, 1, 0]], 'output': [[0, 2, 0], [0, 0, 2], [0, 2, 0]]}, {'input': [[0, 0, 1, 5, 0, 0, 0], [1, 1, 0, 5, 1, 0, 1], [0, 1, 1, 5, 1, 0, 1]], 'output': [[0, 0, 0], [2, 0, 0], [0, 0, 2]]}], 'test_inputs': [[[1, 0, 1, 5, 1, 0, 1], [0, 1, 0, 5, 1, 0, 1], [1, 0, 1, 5, 0, 1, 0]]], 'test_outputs': [[[2, 0, 2], [0, 0, 0], [0, 0, 0]]]}) (input_keys={'test_inputs', 'training_examples'}): 'dict' object has no attribute 'model_dump'
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 59, in forward
File "<string>", line 59, in <listcomp>
AttributeError: 'dict' object has no attribute 'model_dump'
2025/08/29 01:44:13 INFO dspy.evaluate.evaluate: Average Metric: 0.0 / 3 (0.0%)
GEPA Optimization: 21%|███████████████████████▌ | 858/4000 [4:10:17<92:43:50, 106.25s/rollouts]Iteration 53: Proposed new text for program: import dspy
from typing import List
import pydantic
import json
# Define the type for a matrix, which is a list of lists of integers.
MATRIX = List[List[int]]
# Pydantic models for structured input/output, as in the original code.
class TrainingExample(pydantic.BaseModel):
input: MATRIX
output: MATRIX
# The main signature for the overall task remains the same.
class SolveTaskSignature(dspy.Signature):
"""
Given a set of training examples that demonstrate a hidden transformation rule on 2D integer matrices,
and a list of new test input matrices, apply the inferred rule to each test input to generate the corresponding output matrix.
"""
training_examples: List[TrainingExample] = dspy.InputField(description="Input and output examples demonstrating the task to be performed.")
test_inputs: List[MATRIX] = dspy.InputField(description="Input matrices to be solved following the task described in the training examples.")
test_outputs: List[MATRIX] = dspy.OutputField(description="Output matrices corresponding to the test inputs.")
# A new signature specifically for the code generation step.
class GeneratePythonRule(dspy.Signature):
"""
You are an expert programmer solving abstract reasoning challenges. Your goal is to analyze a set of input-output examples of matrix transformations and write a single, self-contained Python function that implements the transformation rule.
**Analysis Strategies to Consider:**
- Analyze the relationship between input and output grids, looking for patterns in colors, shapes, object counts, symmetry, and geometric transformations.
- The rule might involve splitting the grid. For example, a column with a unique color could be a separator.
- The rule could be based on the coordinates of specific colored cells.
- The rule might involve filling areas based on boundary conditions, such as finding the largest rectangular block of a certain color between non-zero "walls".
**Function Requirements:**
- The function must be named `solve`.
- It must accept one argument: `input_matrix` (a list of lists of integers).
- It must return the transformed matrix (a list of lists of integers).
- The function must be self-contained and should not use any external libraries like numpy or pandas. Standard Python is all you have.
- Ensure the returned matrix is a valid list of lists, with all rows having the same number of columns.
- Include comments in your code to explain your reasoning and the steps of the algorithm.
"""
examples_json: str = dspy.InputField(desc="A JSON string representing the list of training examples.")
test_input_shapes: str = dspy.InputField(desc="A string describing the shapes of the test input matrices, e.g., '1 matrix of 4x24'.")
python_code: str = dspy.OutputField(desc="A self-contained Python function `solve(input_matrix)` that implements the transformation rule.")
# A custom module to orchestrate the two-step process: rule generation -> rule execution.
class ARCsolver(dspy.Module):
def __init__(self):
super().__init__()
# This module uses the LM to generate a Python function based on the examples.
self.code_generator = dspy.Predict(GeneratePythonRule)
def forward(self, training_examples: List[TrainingExample], test_inputs: List[MATRIX]):
# 1. Generate the Python code for the solution.
# Convert Pydantic objects to a JSON string for a cleaner and more reliable prompt format.
examples_dict_list = [ex.model_dump() for ex in training_examples]
examples_json_str = json.dumps(examples_dict_list, indent=2)
# Describe the test input shapes to give the LM context about the target dimensions.
shapes_desc = ", ".join([f"{len(m)}x{len(m[0]) if m and m[0] else 0}" for m in test_inputs])
test_input_shapes_str = f"{len(test_inputs)} matrix/matrices of shape(s): {shapes_desc}"
# Call the LM to generate the Python function as a string.
prediction = self.code_generator(
examples_json=examples_json_str,
test_input_shapes=test_input_shapes_str
)
python_code = prediction.python_code
# 2. Execute the generated code for each test input.
all_outputs = []
# Prepare a local scope for exec() to run in, capturing the defined `solve` function.
local_scope = {}
try:
# Execute the generated Python code string. This will define the `solve` function within local_scope.
exec(python_code, globals(), local_scope)
solve_func = local_scope.get('solve')
if solve_func and callable(solve_func):
# If the function was defined successfully, run it on each test input.
for test_matrix in test_inputs:
# Create a deep copy to avoid modifying the original input matrix.
input_copy = [row[:] for row in test_matrix]
try:
output_matrix = solve_func(input_copy)
all_outputs.append(output_matrix)
except Exception:
# The generated function failed on a specific test case.
# Fallback: return an empty matrix for this test case.
all_outputs.append([])
else:
# The generated code did not define a callable `solve` function.
all_outputs = [[] for _ in test_inputs]
except Exception:
# The generated code was syntactically incorrect or had other fatal errors.
# Fallback for all test cases.
all_outputs = [[] for _ in test_inputs]
return dspy.Prediction(test_outputs=all_outputs)
# The final program is an instance of our robust, code-generating module.
program = ARCsolver()
Iteration 53: New subsample score is not better, skipping
Iteration 54: Selected program 2 score: 0.605
Average Metric: 2.00 / 3 (66.7%): : 4it [04:04, 61.21s/it] 2025/08/29 01:48:18 INFO dspy.evaluate.evaluate: Average Metric: 2.0 / 3 (66.7%)
Iteration 54: Proposed new text for program: import dspy
from typing import List, Tuple, Optional, Callable
import pydantic
import copy
import traceback
# Define type aliases and pydantic models for clarity and structure.
MATRIX = List[List[int]]
class TrainingExample(pydantic.BaseModel):
input: MATRIX
output: MATRIX
class GenerateAndReason(dspy.Signature):
"""
You are an expert programmer. Your task is to analyze a series of input and output matrix pairs from the Abstraction and Reasoning Corpus (ARC). Based on these examples, you must first reason about the underlying transformation rule and then write a single, self-contained Python function that implements this rule.
**Function Requirements:**
- The function must be named `transform_matrix`.
- It must accept one argument: `matrix`, which is a list of lists of integers.
- It must return a list of lists of integers (the transformed grid).
- The function must be self-contained and not use any external libraries except for `copy`.
- Your output must be ONLY the Python code for the function. Do not include explanations or markdown formatting like ```python.
**Successful Strategies to Consider:**
- **Deduce the Core Principle:** First, describe the transformation in simple terms. Is it about colors, shapes, positions, symmetry, or counting?
- **Color Transformations:** Look for rules that change colors based on their value or their neighbors.
- **Spatial Reasoning:** Analyze shapes, positions, and relationships. Common patterns include filling enclosed areas, drawing lines, moving objects, or detecting symmetry.
- **Object-Based Logic:** Identify distinct objects in the grid and analyze how they are created, destroyed, or modified.
- **Iterative Processes:** Some rules are applied repeatedly. Consider a `while` loop that continues as long as modifications are being made.
"""
training_examples: List[TrainingExample] = dspy.InputField(desc="A list of input/output pairs demonstrating the transformation rule.")
reasoning: str = dspy.OutputField(desc="A step-by-step explanation of the transformation rule deduced from the examples.")
python_function: str = dspy.OutputField(desc="A string containing a single Python function `transform_matrix` that implements the rule.")
class RefineCode(dspy.Signature):
"""
You are an expert programmer. You are given a Python function that attempted to solve an ARC task but failed on a specific training example. Your task is to analyze the original examples, the faulty code, and the specific error feedback to produce a corrected version of the function.
The corrected function must adhere to the original requirements:
- Named `transform_matrix`.
- Accepts one argument: `matrix` (list of lists of integers).
- Returns a list of lists of integers.
- Self-contained, using only the `copy` library if needed.
- The output must be ONLY the Python code.
"""
training_examples: List[TrainingExample] = dspy.InputField(desc="The original list of input/output pairs.")
previous_code: str = dspy.InputField(desc="The Python code that failed validation.")
feedback: str = dspy.InputField(desc="Detailed feedback explaining which training example failed and showing the difference between the actual and expected output.")
python_function: str = dspy.OutputField(desc="The corrected, single Python function `transform_matrix`.")
class ARCSolver(dspy.Module):
"""A module that solves ARC tasks by generating, validating, and refining Python code."""
def __init__(self, max_attempts: int = 3):
super().__init__()
self.max_attempts = max_attempts
self.generate_and_reason = dspy.ChainOfThought(GenerateAndReason)
self.refine_code = dspy.Predict(RefineCode)
def _validate_code(self, code_str: str, examples: List[TrainingExample]) -> Tuple[Optional[Callable], Optional[str]]:
"""
Executes the code and validates it against all training examples.
Returns (function, None) on success, or (None, feedback_string) on failure.
"""
local_scope = {}
try:
exec(code_str, globals(), local_scope)
transform_func = local_scope.get('transform_matrix')
if not callable(transform_func):
return None, "Validation failed: `transform_matrix` function not found or not callable."
for i, example in enumerate(examples):
input_copy = copy.deepcopy(example.input)
try:
result = transform_func(input_copy)
if result != example.output:
feedback = (
f"Validation failed on training example {i}.\n"
f"Input:\n{example.input}\n"
f"Expected Output:\n{example.output}\n"
f"Actual Output:\n{result}"
)
return None, feedback
except Exception as e:
feedback = (
f"Validation failed on training example {i} with a runtime error.\n"
f"Input:\n{example.input}\n"
f"Error:\n{traceback.format_exc()}"
)
return None, feedback
return transform_func, None # Success
except Exception as e:
return None, f"Code execution failed entirely. Error:\n{traceback.format_exc()}"
def forward(self, training_examples: List[TrainingExample], test_inputs: List[MATRIX]):
fallback_outputs = [copy.deepcopy(matrix) for matrix in test_inputs]
code_to_refine = None
feedback = None
valid_func = None
for attempt in range(self.max_attempts):
if attempt == 0:
# First attempt: generate from scratch
prediction = self.generate_and_reason(training_examples=training_examples)
current_code = prediction.python_function
else:
# Subsequent attempts: refine previous code
prediction = self.refine_code(
training_examples=training_examples,
previous_code=code_to_refine,
feedback=feedback
)
current_code = prediction.python_function
# Validate the generated code
transform_func, feedback = self._validate_code(current_code, training_examples)
if transform_func:
valid_func = transform_func
break # Success, exit the loop
else:
# Prepare for the next refinement iteration
code_to_refine = current_code
# After the loop, use the validated function or fallback
if valid_func:
solved_outputs = []
for test_matrix in test_inputs:
try:
input_copy = copy.deepcopy(test_matrix)
result = valid_func(input_copy)
solved_outputs.append(result)
except Exception:
solved_outputs.append(copy.deepcopy(test_matrix))
return dspy.Prediction(test_outputs=solved_outputs)
else:
# All attempts failed
return dspy.Prediction(test_outputs=fallback_outputs)
# The final program is an instance of our new, more robust module.
program = ARCSolver()
2025/08/29 01:55:47 INFO dspy.evaluate.evaluate: Average Metric: 0.0 / 3 (0.0%)
GEPA Optimization: 22%|███████████████████████▊ | 864/4000 [4:21:51<95:07:45, 109.20s/rollouts]Iteration 54: New subsample score is not better, skipping
Iteration 55: Selected program 0 score: 0.67
Average Metric: 1.00 / 3 (33.3%): 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [06:13<00:00, 124.46s/it]2025/08/29 02:02:01 INFO dspy.evaluate.evaluate: Average Metric: 1.0 / 3 (33.3%)
2025/08/29 02:02:48 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 5, 0, 0, 0, 0, 0, 0, 0, 0], [0, 5, 0, 0, 5, 0, 0, 0, 0, 0], [0, 5, 0, 0, 5, 0, 0, 0, 0, 0], [0, 5, 0, 0, 5, 0, 0, 5, 0, 0], [0, 5, 0, 0, 5, 0, 0, 5, 0, 0], [0, 5, 0, 0, 5, 0, 0, 5, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 4, 0, 0, 0, 0, 0], [0, 1, 0, 0, 4, 0, 0, 0, 0, 0], [0, 1, 0, 0, 4, 0, 0, 2, 0, 0], [0, 1, 0, 0, 4, 0, 0, 2, 0, 0], [0, 1, 0, 0, 4, 0, 0, 2, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}, {'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 5, 0, 0, 0, 0, 0], [0, 0, 0, 0, 5, 0, 0, 0, 0, 0], [0, 5, 0, 0, 5, 0, 0, 0, 0, 0], [0, 5, 0, 0, 5, 0, 0, 0, 0, 0], [0, 5, 0, 0, 5, 0, 0, 5, 0, 0], [0, 5, 0, 0, 5, 0, 0, 5, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 4, 0, 0, 1, 0, 0, 0, 0, 0], [0, 4, 0, 0, 1, 0, 0, 0, 0, 0], [0, 4, 0, 0, 1, 0, 0, 2, 0, 0], [0, 4, 0, 0, 1, 0, 0, 2, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}, {'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5, 5, 5, 5, 5, 5, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 5, 0, 0], [0, 0, 0, 0, 0, 0, 0, 5, 0, 0], [0, 0, 0, 0, 0, 0, 0, 5, 0, 0], [0, 0, 0, 0, 0, 0, 0, 5, 0, 0], [0, 0, 5, 5, 5, 0, 0, 5, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 4, 0, 0], [0, 0, 0, 0, 0, 0, 0, 4, 0, 0], [0, 0, 0, 0, 0, 0, 0, 4, 0, 0], [0, 0, 0, 0, 0, 0, 0, 4, 0, 0], [0, 0, 2, 2, 2, 0, 0, 4, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}, {'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 5, 0, 0], [0, 5, 5, 5, 5, 0, 0, 5, 0, 0], [0, 0, 0, 0, 0, 0, 0, 5, 0, 0], [0, 0, 0, 0, 0, 0, 0, 5, 0, 0], [0, 5, 5, 5, 5, 5, 0, 5, 0, 0], [0, 0, 0, 0, 0, 0, 0, 5, 0, 0], [0, 0, 0, 0, 0, 0, 0, 5, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 1, 0, 0], [0, 2, 2, 2, 2, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 1, 0, 0], [0, 4, 4, 4, 4, 4, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}], 'test_inputs': [[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 5, 0, 0, 0, 0, 0, 0], [0, 0, 0, 5, 0, 0, 0, 0, 0, 0], [0, 0, 0, 5, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 5, 5, 5, 5, 5, 5], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5, 5, 5, 5, 5, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]], 'test_outputs': [[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 2, 0, 0, 0, 0, 0, 0], [0, 0, 0, 2, 0, 0, 0, 0, 0, 0], [0, 0, 0, 2, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [4, 4, 4, 4, 4, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]]}) (input_keys={'test_inputs', 'training_examples'}): 'dict' object has no attribute 'model_dump'
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 43, in forward
File "<string>", line 43, in <listcomp>
AttributeError: 'dict' object has no attribute 'model_dump'
2025/08/29 02:02:48 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0, 0], [0, 0, 0, 0, 1, 1, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 1, 0], [0, 0, 0, 0, 1, 1, 1, 1, 1, 0], [0, 0, 0, 0, 1, 1, 0, 1, 1, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 1, 1, 0, 0, 0, 0], [0, 0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0, 0], [0, 0, 0, 0, 1, 1, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 1, 0], [0, 2, 2, 0, 1, 1, 1, 1, 1, 0], [0, 2, 2, 2, 1, 1, 0, 1, 1, 0], [0, 2, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 1, 1, 0, 0, 0, 0], [0, 0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}, {'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 1, 1], [0, 0, 0, 0, 0, 1, 0, 0, 1, 1], [0, 0, 0, 0, 0, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 1, 0, 0, 1, 1], [0, 0, 0, 0, 0, 1, 0, 0, 1, 1], [0, 0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 0, 1, 1, 1, 1, 1, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 2, 2, 0, 0, 1, 0, 0, 1, 1], [0, 2, 2, 0, 0, 1, 0, 0, 1, 1], [0, 2, 2, 2, 2, 1, 1, 1, 1, 1], [0, 2, 2, 0, 0, 1, 0, 0, 1, 1], [0, 2, 2, 0, 0, 1, 0, 0, 1, 1], [0, 0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 0, 1, 1, 1, 1, 1, 0, 0]]}, {'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 1, 1, 1, 0, 0, 1], [0, 0, 0, 0, 0, 1, 0, 1, 0, 1], [0, 0, 0, 0, 0, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 1, 0, 1, 0, 1], [0, 0, 0, 0, 1, 1, 1, 0, 0, 1], [0, 0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 1, 1, 1, 1, 1, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 2, 0, 0, 1, 1, 1, 0, 0, 1], [0, 2, 0, 2, 0, 1, 0, 1, 0, 1], [0, 2, 2, 2, 2, 1, 1, 1, 1, 1], [0, 2, 0, 2, 0, 1, 0, 1, 0, 1], [0, 2, 0, 0, 1, 1, 1, 0, 0, 1], [0, 0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 1, 1, 1, 1, 1, 0, 0]]}], 'test_inputs': [[[0, 0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0, 0], [0, 0, 0, 0, 1, 1, 0, 0, 1, 0], [0, 0, 0, 0, 1, 0, 0, 0, 1, 0], [0, 0, 0, 0, 1, 1, 1, 1, 1, 1], [0, 0, 0, 0, 1, 1, 0, 1, 1, 0], [0, 0, 0, 0, 0, 1, 0, 0, 1, 0], [0, 0, 0, 0, 1, 1, 0, 0, 0, 0], [0, 0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0, 0]]], 'test_outputs': [[[0, 0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0, 0], [0, 0, 0, 0, 1, 1, 0, 0, 1, 0], [0, 2, 0, 0, 1, 0, 0, 0, 1, 0], [0, 2, 2, 0, 1, 1, 1, 1, 1, 1], [2, 2, 2, 2, 1, 1, 0, 1, 1, 0], [0, 2, 0, 0, 0, 1, 0, 0, 1, 0], [0, 2, 0, 0, 1, 1, 0, 0, 0, 0], [0, 0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0, 0]]]}) (input_keys={'test_inputs', 'training_examples'}): 'dict' object has no attribute 'model_dump'
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 43, in forward
File "<string>", line 43, in <listcomp>
AttributeError: 'dict' object has no attribute 'model_dump'
2025/08/29 02:02:48 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 5, 0, 0, 0, 0, 0, 0], [0, 0, 5, 0, 0, 0, 5, 0, 0], [5, 0, 5, 0, 0, 0, 5, 0, 0], [5, 0, 5, 0, 0, 0, 5, 0, 0], [5, 0, 5, 0, 5, 0, 5, 0, 0], [5, 0, 5, 0, 5, 0, 5, 0, 5], [5, 0, 5, 0, 5, 0, 5, 0, 5], [5, 0, 5, 0, 5, 0, 5, 0, 5]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 2], [0, 0, 1, 0, 0, 0, 0, 0, 2], [0, 0, 1, 0, 0, 0, 0, 0, 2]]}, {'input': [[0, 0, 0, 0, 5, 0, 0, 0, 0], [0, 0, 0, 0, 5, 0, 0, 0, 0], [5, 0, 0, 0, 5, 0, 0, 0, 0], [5, 0, 0, 0, 5, 0, 5, 0, 0], [5, 0, 0, 0, 5, 0, 5, 0, 0], [5, 0, 0, 0, 5, 0, 5, 0, 0], [5, 0, 0, 0, 5, 0, 5, 0, 0], [5, 0, 5, 0, 5, 0, 5, 0, 0], [5, 0, 5, 0, 5, 0, 5, 0, 0]], 'output': [[0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 2, 0, 1, 0, 0, 0, 0], [0, 0, 2, 0, 1, 0, 0, 0, 0]]}], 'test_inputs': [[[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 5, 0], [0, 0, 0, 5, 0, 0, 0, 5, 0], [0, 0, 0, 5, 0, 0, 0, 5, 0], [0, 0, 0, 5, 0, 5, 0, 5, 0], [0, 0, 0, 5, 0, 5, 0, 5, 0], [0, 0, 0, 5, 0, 5, 0, 5, 0], [0, 0, 0, 5, 0, 5, 0, 5, 0], [0, 5, 0, 5, 0, 5, 0, 5, 0]]], 'test_outputs': [[[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 1, 0], [0, 0, 0, 0, 0, 0, 0, 1, 0], [0, 0, 0, 0, 0, 0, 0, 1, 0], [0, 0, 0, 0, 0, 0, 0, 1, 0], [0, 0, 0, 0, 0, 0, 0, 1, 0], [0, 0, 0, 0, 0, 0, 0, 1, 0], [0, 0, 0, 0, 0, 0, 0, 1, 0], [0, 2, 0, 0, 0, 0, 0, 1, 0]]]}) (input_keys={'test_inputs', 'training_examples'}): 'dict' object has no attribute 'model_dump'
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 43, in forward
File "<string>", line 43, in <listcomp>
AttributeError: 'dict' object has no attribute 'model_dump'
2025/08/29 02:02:48 INFO dspy.evaluate.evaluate: Average Metric: 0.0 / 3 (0.0%)
GEPA Optimization: 22%|████████████████████████▏ | 870/4000 [4:28:51<84:23:44, 97.07s/rollouts]Iteration 55: Proposed new text for program: import dspy
from typing import List
import pydantic
import traceback
# Define the type for a matrix for clarity
MATRIX = List[List[int]]
class TrainingExample(pydantic.BaseModel):
"""A Pydantic model for a single training example."""
input: MATRIX
output: MATRIX
class GenerateTransformerSignature(dspy.Signature):
"""
Analyzes training examples to infer a transformation rule, then generates a Python function to implement it.
**Analysis and Implementation Guidelines:**
1. **Observe the Pattern:** Carefully examine the relationship between input and output grids. Consider patterns like:
* Operations on connected components of a specific color/number (e.g., finding shapes, their sizes, orientations).
* Rules based on sorting these components by position (e.g., top-most, then left-most).
* Column-wise or row-wise operations (e.g., counting elements in each column/row).
* Geometric transformations like filling concavities or "inlets" in shapes.
2. **Formulate a Step-by-Step Algorithm:** Before writing code, mentally outline the precise steps needed to perform the transformation. This plan should be directly translatable to code.
3. **Write the Python Function:** Implement the algorithm in a single, self-contained Python function named `transform_matrix`.
* The function must have the exact signature: `def transform_matrix(matrix: list[list[int]]) -> list[list[int]]:`
* It must return a new matrix (list of lists of integers) of the same dimensions as the input.
* **Crucially, do NOT use any external libraries like numpy or pandas.** Use only standard Python data structures and functions.
* Your response must ONLY contain the Python code for the function, starting with `def transform_matrix...` and nothing else.
"""
training_examples: str = dspy.InputField(description="A string representation of input-output examples demonstrating the task.")
transformer_code: str = dspy.OutputField(description="A Python function 'transform_matrix' that implements the inferred transformation rule.")
class CodeGeneratorSolver(dspy.Module):
"""A module that generates and executes code to solve matrix transformation tasks."""
def __init__(self):
super().__init__()
# The generator module infers the logic and writes a Python function.
self.generator = dspy.Predict(GenerateTransformerSignature)
def forward(self, training_examples: List[TrainingExample], test_inputs: List[MATRIX]) -> dspy.Prediction:
# Convert Pydantic objects to a string representation for the LM prompt.
examples_str = str([ex.model_dump() for ex in training_examples])
# Generate the Python code for the transformation function.
prediction = self.generator(training_examples=examples_str)
code = prediction.transformer_code
# Clean the generated code, removing markdown fences if present.
if "```python" in code:
code = code.split("```python")[1].strip()
if "```" in code:
code = code.split("```")[0].strip()
solved_outputs = []
try:
# Execute the generated code in a restricted scope to define the function.
local_scope = {}
exec(code, globals(), local_scope)
transform_func = local_scope.get('transform_matrix')
if transform_func:
# If the function was defined successfully, apply it to all test inputs.
for test_input in test_inputs:
# Create a deep copy to avoid modifying the original input
input_copy = [row[:] for row in test_input]
solved_outputs.append(transform_func(input_copy))
else:
# Fallback if the LM failed to generate a valid function.
print("Warning: 'transform_matrix' function not found in generated code. Using fallback.")
# As a fallback, return the original inputs.
solved_outputs = test_inputs
except Exception as e:
# Fallback if the generated code has an error during execution.
print(f"Error executing generated code: {e}")
print("Traceback:")
traceback.print_exc()
print("Generated code that caused the error:\n", code)
# As a fallback, return the original inputs.
solved_outputs = test_inputs
return dspy.Prediction(test_outputs=solved_outputs)
# The final program is an instance of our robust code-generating module.
program = CodeGeneratorSolver()
Iteration 55: New subsample score is not better, skipping
Iteration 56: Selected program 1 score: 0.63
Average Metric: 1.00 / 2 (50.0%): 67%|██████████████████████████████████████████████████████████████████████████▋ | 2/3 [02:21<01:02, 62.41s/it]2025/08/29 02:06:30 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
Average Metric: 1.00 / 2 (50.0%): 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [03:53<00:00, 76.31s/it]2025/08/29 02:06:49 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
Average Metric: 1.00 / 3 (33.3%): : 4it [04:03, 60.91s/it] 2025/08/29 02:06:51 INFO dspy.evaluate.evaluate: Average Metric: 1.0 / 3 (33.3%)
Iteration 56: Proposed new text for program: import dspy
from typing import List
import pydantic
import numpy as np
import traceback
# Define the type for a matrix for clarity
MATRIX = List[List[int]]
class TrainingExample(pydantic.BaseModel):
"""A Pydantic model for a single training example, containing an input and output matrix."""
input: MATRIX
output: MATRIX
class GeneratePythonRuleSignature(dspy.Signature):
"""
Analyzes pairs of input/output matrices to deduce the transformation rule and express it as a Python function.
You are an expert programmer and analyst of abstract visual reasoning puzzles. Your task is to carefully examine the provided training examples. Each example consists of an 'input' matrix and a corresponding 'output' matrix.
Your goal is to write a single, standalone Python function named `transform` that implements the transformation rule. This function must take one argument, `matrix` (a list of lists of integers), and return the transformed matrix (also a list of lists of integers).
**Python Environment:**
- You have access to the `numpy` library, which you can import as `np`. Using numpy is highly encouraged for efficient matrix operations.
- Do not use any other external libraries.
- The function should not rely on any global variables.
**Function Definition:**
Your output must be a single string containing only the Python code for the `transform` function. It must follow this exact structure:
```python
def transform(matrix: list[list[int]]) -> list[list[int]]:
# Your implementation here
# ...
return new_matrix
```
**Analysis Strategy:**
1. First, think step-by-step about the transformation logic. Consider patterns like geometric operations (rotation, reflection), color changes, object manipulation, and filling patterns.
2. Translate this logic into a clear, well-commented Python function.
3. Ensure your function correctly handles all the provided training examples.
"""
training_examples: List[TrainingExample] = dspy.InputField(description="A list of input and output examples demonstrating the task.")
python_code: str = dspy.OutputField(description="A string containing a single Python function `transform(matrix)` that implements the rule.")
class ARCProgram(dspy.Module):
"""A program that first infers a rule as Python code and then executes it to solve test inputs."""
def __init__(self):
super().__init__()
# Use ChainOfThought for the complex reasoning task of generating a Python function.
self.rule_coder = dspy.ChainOfThought(GeneratePythonRuleSignature)
def forward(self, training_examples: List[TrainingExample], test_inputs: List[MATRIX]) -> dspy.Prediction:
"""
Generates a Python function from training examples and applies it to each test input.
Args:
training_examples: A list of TrainingExample objects.
test_inputs: A list of input matrices to be solved.
Returns:
A dspy.Prediction object with the 'test_outputs' field populated.
"""
# 1. Generate the Python function that encapsulates the transformation rule.
prediction = self.rule_coder(training_examples=training_examples)
python_code = prediction.python_code
all_test_outputs = []
try:
# 2. Prepare a local scope for executing the generated code safely.
# Pass numpy to the execution scope.
execution_scope = {"np": np}
exec(python_code, execution_scope)
transform_func = execution_scope['transform']
# 3. Iterate through each test input and apply the generated function.
for test_matrix in test_inputs:
# The generated function might expect a numpy array or a list of lists.
# For robustness, we can convert to numpy and back if needed,
# but for now, we pass it as is, assuming the function handles it.
result_matrix = transform_func(test_matrix)
all_test_outputs.append(result_matrix)
except Exception as e:
# 4. Fallback strategy: If code generation, execution, or application fails,
# return correctly sized grids of zeros.
print(f"An error occurred during code execution: {e}")
print(f"Generated code was:\n{python_code}")
traceback.print_exc()
for test_matrix in test_inputs:
if test_matrix and len(test_matrix) > 0:
height = len(test_matrix)
width = len(test_matrix[0])
all_test_outputs.append([[0] * width for _ in range(height)])
else:
all_test_outputs.append([])
# 5. Return the collected outputs in a single Prediction object.
return dspy.Prediction(test_outputs=all_test_outputs)
# The final 'program' object is an instance of our robust, code-generating module.
program = ARCProgram()
An error occurred during code execution: invalid syntax (<string>, line 1)
Generated code was:
```python
import numpy as np
def transform(matrix: list[list[int]]) -> list[list[int]]:
"""
Moves points of color 5 towards a central shape.
The transformation rule is as follows:
1. Identify a static "attractor" shape (any non-zero color other than 5).
2. Identify mobile "mover" points (color 5).
3. Determine the bounding box of the attractor shape.
4. For each mover, calculate its new position by moving it towards the
bounding box until it is one cell away from the box's edge.
- If a mover is directly above/below the box's column span, it moves only vertically.
- If a mover is directly left/right of the box's row span, it moves only horizontally.
- Otherwise, it moves both vertically and horizontally.
5. The original positions of the movers are cleared, and the new positions are filled.
"""
# Convert the input matrix to a NumPy array for easier manipulation.
grid = np.array(matrix, dtype=int)
output_grid = grid.copy()
# Find the coordinates of the "attractor" shape.
# The attractor is any non-zero color that is not 5.
attractor_coords = np.where((grid != 0) & (grid != 5))
# If there's no attractor shape, no transformation is needed.
if attractor_coords[0].size == 0:
return matrix
# Calculate the bounding box of the attractor shape.
min_row = np.min(attractor_coords[0])
max_row = np.max(attractor_coords[0])
min_col = np.min(attractor_coords[1])
max_col = np.max(attractor_coords[1])
# Find the coordinates of the "mover" cells (color 5).
mover_rows, mover_cols = np.where(grid == 5)
# Iterate through each mover point.
for r, c in zip(mover_rows, mover_cols):
# Erase the mover from its original position in the output grid.
output_grid[r, c] = 0
# Initialize the new position to the original position.
new_r, new_c = r, c
# Determine the new vertical position.
if r < min_row:
new_r = min_row - 1
elif r > max_row:
new_r = max_row + 1
# Determine the new horizontal position.
if c < min_col:
new_c = min_col - 1
elif c > max_col:
new_c = max_col + 1
# Place the mover at its new calculated position.
output_grid[new_r, new_c] = 5
# Convert the final NumPy array back to a list of lists and return.
return output_grid.tolist()
```
Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
An error occurred during code execution: invalid syntax (<string>, line 1)
Generated code was:
```python
import numpy as np
def transform(matrix: list[list[int]]) -> list[list[int]]:
"""
Transforms the input matrix based on the following rule:
1. Identify connected components of cells with the value 2 (using 4-way adjacency).
2. For each component with more than one cell:
a. Find the component's bounding box.
b. Expand this bounding box by one cell in all four directions.
c. Fill this expanded rectangular area with the value 3, but only in cells that are currently 0.
"""
# Use numpy for efficient array manipulation
input_array = np.array(matrix, dtype=int)
output_array = np.copy(input_array)
height, width = input_array.shape
# A grid to keep track of visited cells to avoid processing the same component multiple times
visited = np.zeros_like(input_array, dtype=bool)
# Iterate through each cell of the grid
for r in range(height):
for c in range(width):
# If we find an unvisited '2', it's the start of a new component
if input_array[r, c] == 2 and not visited[r, c]:
component_cells = []
# Use a queue for Breadth-First Search (BFS) to find the component
queue = [(r, c)]
visited[r, c] = True
head = 0
while head < len(queue):
curr_r, curr_c = queue[head]
head += 1
component_cells.append((curr_r, curr_c))
# Explore 4-way adjacent neighbors
for dr, dc in [(0, 1), (0, -1), (1, 0), (-1, 0)]:
next_r, next_c = curr_r + dr, curr_c + dc
# Check if the neighbor is within bounds, is a '2', and hasn't been visited
if 0 <= next_r < height and 0 <= next_c < width and \
input_array[next_r, next_c] == 2 and not visited[next_r, next_c]:
visited[next_r, next_c] = True
queue.append((next_r, next_c))
# If the component has more than one cell, apply the transformation
if len(component_cells) > 1:
# Determine the bounding box of the component
rows = [p[0] for p in component_cells]
cols = [p[1] for p in component_cells]
min_r, max_r = min(rows), max(rows)
min_c, max_c = min(cols), max(cols)
# Expand the bounding box by 1 in each direction, clamping to grid boundaries
start_row = max(0, min_r - 1)
end_row = min(height - 1, max_r + 1)
start_col = max(0, min_c - 1)
end_col = min(width - 1, max_c + 1)
# Fill the expanded area: change 0s to 3s
for fill_r in range(start_row, end_row + 1):
for fill_c in range(start_col, end_col + 1):
if output_array[fill_r, fill_c] == 0:
output_array[fill_r, fill_c] = 3
# Convert the final numpy array back to a list of lists
return output_array.tolist()
```
Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
An error occurred during code execution: invalid syntax (<string>, line 1)
Generated code was:
```python
import numpy as np
def transform(matrix: list[list[int]]) -> list[list[int]]:
"""
Transforms the input matrix based on the following rule:
1. Identify connected components of cells with the value 2 (using 4-way adjacency).
2. For each component with more than one cell:
a. Find the component's bounding box.
b. Expand this bounding box by one cell in all four directions.
c. Fill this expanded rectangular area with the value 3, but only in cells that are currently 0.
"""
# Use numpy for efficient array manipulation
input_array = np.array(matrix, dtype=int)
output_array = np.copy(input_array)
height, width = input_array.shape
# A grid to keep track of visited cells to avoid processing the same component multiple times
visited = np.zeros_like(input_array, dtype=bool)
# Iterate through each cell of the grid
for r in range(height):
for c in range(width):
# If we find an unvisited '2', it's the start of a new component
if input_array[r, c] == 2 and not visited[r, c]:
component_cells = []
# Use a queue for Breadth-First Search (BFS) to find the component
queue = [(r, c)]
visited[r, c] = True
head = 0
while head < len(queue):
curr_r, curr_c = queue[head]
head += 1
component_cells.append((curr_r, curr_c))
# Explore 4-way adjacent neighbors
for dr, dc in [(0, 1), (0, -1), (1, 0), (-1, 0)]:
next_r, next_c = curr_r + dr, curr_c + dc
# Check if the neighbor is within bounds, is a '2', and hasn't been visited
if 0 <= next_r < height and 0 <= next_c < width and \
input_array[next_r, next_c] == 2 and not visited[next_r, next_c]:
visited[next_r, next_c] = True
queue.append((next_r, next_c))
# If the component has more than one cell, apply the transformation
if len(component_cells) > 1:
# Determine the bounding box of the component
rows = [p[0] for p in component_cells]
cols = [p[1] for p in component_cells]
min_r, max_r = min(rows), max(rows)
min_c, max_c = min(cols), max(cols)
# Expand the bounding box by 1 in each direction, clamping to grid boundaries
start_row = max(0, min_r - 1)
end_row = min(height - 1, max_r + 1)
start_col = max(0, min_c - 1)
end_col = min(width - 1, max_c + 1)
# Fill the expanded area: change 0s to 3s
for fill_r in range(start_row, end_row + 1):
for fill_c in range(start_col, end_col + 1):
if output_array[fill_r, fill_c] == 0:
output_array[fill_r, fill_c] = 3
# Convert the final numpy array back to a list of lists
return output_array.tolist()
```
Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
2025/08/29 02:12:11 INFO dspy.evaluate.evaluate: Average Metric: 1.0 / 3 (33.3%)
GEPA Optimization: 22%|████████████████████████▎ | 876/4000 [4:38:15<83:23:58, 96.11s/rollouts]Iteration 56: New subsample score is not better, skipping
Iteration 57: Selected program 0 score: 0.67
Average Metric: 1.00 / 1 (100.0%): 33%|█████████████████████████████████████ | 1/3 [00:34<01:09, 34.72s/it]An error occurred during code execution: invalid syntax (<string>, line 1)
Generated code was:
```python
import numpy as np
def transform(matrix: list[list[int]]) -> list[list[int]]:
"""
Fills cells with the value 3 if they are 'enclosed' by cells with the value 2.
A cell (r, c) is considered enclosed if there is at least one cell with value 2
in each of the four diagonal quadrants relative to (r, c):
1. Upper-Left: a 2 at (r', c') where r' < r and c' < c
2. Upper-Right: a 2 at (r', c') where r' < r and c' > c
3. Lower-Left: a 2 at (r', c') where r' > r and c' < c
4. Lower-Right: a 2 at (r', c') where r' > r and c' > c
"""
# Convert to numpy array for easier manipulation and vectorized operations
input_matrix = np.array(matrix, dtype=int)
output_matrix = np.copy(input_matrix)
height, width = input_matrix.shape
# Find the coordinates of all '2's, which act as boundary points
points = np.argwhere(input_matrix == 2)
# If there are not enough points to form an enclosure, return the original matrix
if len(points) < 4:
return matrix
# Iterate over each cell of the matrix to check if it should be filled
for r in range(height):
for c in range(width):
# We only consider transforming cells that are currently 0
if input_matrix[r, c] == 0:
# Check for the existence of a '2' in each of the four diagonal quadrants.
# This is done using vectorized boolean indexing on the 'points' array.
# Quadrant 1: Upper-Left (r' < r, c' < c)
has_ul = np.any((points[:, 0] < r) & (points[:, 1] < c))
# If the first quadrant is empty, no need to check others
if not has_ul:
continue
# Quadrant 2: Upper-Right (r' < r, c' > c)
has_ur = np.any((points[:, 0] < r) & (points[:, 1] > c))
if not has_ur:
continue
# Quadrant 3: Lower-Left (r' > r, c' < c)
has_ll = np.any((points[:, 0] > r) & (points[:, 1] < c))
if not has_ll:
continue
# Quadrant 4: Lower-Right (r' > r, c' > c)
has_lr = np.any((points[:, 0] > r) & (points[:, 1] > c))
# If a '2' exists in all four quadrants, the cell is 'enclosed'
# and should be filled with a '3'.
if has_lr: # All previous checks must have passed to reach here
output_matrix[r, c] = 3
return output_matrix.tolist()
```
Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Average Metric: 2.00 / 2 (100.0%): 67%|██████████████████████████████████████████████████████████████████████████ | 2/3 [00:58<00:28, 28.09s/it]2025/08/29 02:13:55 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
Average Metric: 2.00 / 3 (66.7%): 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [03:53<00:00, 77.67s/it]2025/08/29 02:16:04 INFO dspy.evaluate.evaluate: Average Metric: 2.0 / 3 (66.7%)
2025/08/29 02:16:54 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4], [4, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 4], [4, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2, 4], [4, 2, 1, 3, 3, 3, 3, 3, 3, 1, 2, 4], [4, 2, 1, 3, 5, 5, 5, 5, 3, 1, 2, 4], [4, 2, 1, 3, 5, 8, 8, 5, 3, 1, 2, 4], [4, 2, 1, 3, 5, 8, 8, 5, 3, 1, 2, 4], [4, 2, 1, 3, 5, 5, 5, 5, 3, 1, 2, 4], [4, 2, 1, 3, 3, 3, 3, 3, 3, 1, 2, 4], [4, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2, 4], [4, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 4], [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4]], 'output': [[8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [8, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 8], [8, 5, 3, 3, 3, 3, 3, 3, 3, 3, 5, 8], [8, 5, 3, 1, 1, 1, 1, 1, 1, 3, 5, 8], [8, 5, 3, 1, 2, 2, 2, 2, 1, 3, 5, 8], [8, 5, 3, 1, 2, 4, 4, 2, 1, 3, 5, 8], [8, 5, 3, 1, 2, 4, 4, 2, 1, 3, 5, 8], [8, 5, 3, 1, 2, 2, 2, 2, 1, 3, 5, 8], [8, 5, 3, 1, 1, 1, 1, 1, 1, 3, 5, 8], [8, 5, 3, 3, 3, 3, 3, 3, 3, 3, 5, 8], [8, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 8], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8]]}, {'input': [[2, 2, 2, 2, 2, 2], [2, 1, 1, 1, 1, 2], [2, 1, 6, 6, 1, 2], [2, 1, 6, 6, 1, 2], [2, 1, 1, 1, 1, 2], [2, 2, 2, 2, 2, 2]], 'output': [[6, 6, 6, 6, 6, 6], [6, 1, 1, 1, 1, 6], [6, 1, 2, 2, 1, 6], [6, 1, 2, 2, 1, 6], [6, 1, 1, 1, 1, 6], [6, 6, 6, 6, 6, 6]]}, {'input': [[8, 8, 8, 8, 8, 8, 8, 8], [8, 1, 1, 1, 1, 1, 1, 8], [8, 1, 2, 2, 2, 2, 1, 8], [8, 1, 2, 4, 4, 2, 1, 8], [8, 1, 2, 4, 4, 2, 1, 8], [8, 1, 2, 2, 2, 2, 1, 8], [8, 1, 1, 1, 1, 1, 1, 8], [8, 8, 8, 8, 8, 8, 8, 8]], 'output': [[4, 4, 4, 4, 4, 4, 4, 4], [4, 2, 2, 2, 2, 2, 2, 4], [4, 2, 1, 1, 1, 1, 2, 4], [4, 2, 1, 8, 8, 1, 2, 4], [4, 2, 1, 8, 8, 1, 2, 4], [4, 2, 1, 1, 1, 1, 2, 4], [4, 2, 2, 2, 2, 2, 2, 4], [4, 4, 4, 4, 4, 4, 4, 4]]}, {'input': [[7, 7, 7, 7, 7, 7, 7, 7, 7, 7], [7, 2, 2, 2, 2, 2, 2, 2, 2, 7], [7, 2, 4, 4, 4, 4, 4, 4, 2, 7], [7, 2, 4, 1, 1, 1, 1, 4, 2, 7], [7, 2, 4, 1, 3, 3, 1, 4, 2, 7], [7, 2, 4, 1, 3, 3, 1, 4, 2, 7], [7, 2, 4, 1, 1, 1, 1, 4, 2, 7], [7, 2, 4, 4, 4, 4, 4, 4, 2, 7], [7, 2, 2, 2, 2, 2, 2, 2, 2, 7], [7, 7, 7, 7, 7, 7, 7, 7, 7, 7]], 'output': [[3, 3, 3, 3, 3, 3, 3, 3, 3, 3], [3, 1, 1, 1, 1, 1, 1, 1, 1, 3], [3, 1, 4, 4, 4, 4, 4, 4, 1, 3], [3, 1, 4, 2, 2, 2, 2, 4, 1, 3], [3, 1, 4, 2, 7, 7, 2, 4, 1, 3], [3, 1, 4, 2, 7, 7, 2, 4, 1, 3], [3, 1, 4, 2, 2, 2, 2, 4, 1, 3], [3, 1, 4, 4, 4, 4, 4, 4, 1, 3], [3, 1, 1, 1, 1, 1, 1, 1, 1, 3], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3]]}], 'test_inputs': [[[8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [8, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 8], [8, 2, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 2, 8], [8, 2, 4, 3, 3, 3, 3, 3, 3, 3, 3, 4, 2, 8], [8, 2, 4, 3, 7, 7, 7, 7, 7, 7, 3, 4, 2, 8], [8, 2, 4, 3, 7, 6, 6, 6, 6, 7, 3, 4, 2, 8], [8, 2, 4, 3, 7, 6, 5, 5, 6, 7, 3, 4, 2, 8], [8, 2, 4, 3, 7, 6, 5, 5, 6, 7, 3, 4, 2, 8], [8, 2, 4, 3, 7, 6, 6, 6, 6, 7, 3, 4, 2, 8], [8, 2, 4, 3, 7, 7, 7, 7, 7, 7, 3, 4, 2, 8], [8, 2, 4, 3, 3, 3, 3, 3, 3, 3, 3, 4, 2, 8], [8, 2, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 2, 8], [8, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 8], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8]]], 'test_outputs': [[[5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 5], [5, 6, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 6, 5], [5, 6, 7, 3, 3, 3, 3, 3, 3, 3, 3, 7, 6, 5], [5, 6, 7, 3, 4, 4, 4, 4, 4, 4, 3, 7, 6, 5], [5, 6, 7, 3, 4, 2, 2, 2, 2, 4, 3, 7, 6, 5], [5, 6, 7, 3, 4, 2, 8, 8, 2, 4, 3, 7, 6, 5], [5, 6, 7, 3, 4, 2, 8, 8, 2, 4, 3, 7, 6, 5], [5, 6, 7, 3, 4, 2, 2, 2, 2, 4, 3, 7, 6, 5], [5, 6, 7, 3, 4, 4, 4, 4, 4, 4, 3, 7, 6, 5], [5, 6, 7, 3, 3, 3, 3, 3, 3, 3, 3, 7, 6, 5], [5, 6, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 6, 5], [5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 5], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5]]]}) (input_keys={'test_inputs', 'training_examples'}): 'dict' object has no attribute 'dict'
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 74, in forward
File "<string>", line 74, in <listcomp>
AttributeError: 'dict' object has no attribute 'dict'
2025/08/29 02:16:54 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [8, 8, 8, 8, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8], [8, 8, 8, 0, 8, 0, 8, 8, 8, 8, 8, 8, 8, 8], [8, 8, 8, 8, 0, 8, 2, 8, 8, 8, 8, 8, 8, 8], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8]], 'output': [[8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [8, 8, 8, 8, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8], [8, 8, 8, 0, 8, 0, 8, 8, 8, 8, 8, 8, 8, 8], [8, 8, 8, 8, 0, 8, 2, 8, 8, 8, 8, 8, 8, 8], [8, 8, 8, 8, 8, 2, 8, 2, 8, 8, 8, 8, 8, 8], [8, 8, 8, 8, 8, 8, 2, 8, 2, 8, 8, 8, 8, 8], [8, 8, 8, 8, 8, 8, 8, 2, 8, 2, 8, 8, 8, 8], [8, 8, 8, 8, 8, 8, 8, 8, 2, 8, 2, 8, 8, 8], [8, 8, 8, 8, 8, 8, 8, 8, 8, 2, 8, 2, 8, 8], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 2, 8, 2, 8]]}, {'input': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 3, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], 'output': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 3, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 3, 3, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 3, 1, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 3, 3, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [3, 1, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [3, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}, {'input': [[4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4], [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4], [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4], [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4], [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4], [4, 4, 4, 4, 4, 4, 0, 0, 4, 0, 0, 4, 4, 4, 4, 4], [4, 4, 4, 4, 4, 4, 0, 0, 4, 0, 0, 4, 4, 4, 4, 4], [4, 4, 4, 4, 4, 4, 4, 4, 0, 4, 4, 4, 4, 4, 4, 4], [4, 4, 4, 4, 4, 4, 0, 0, 4, 0, 0, 4, 4, 4, 4, 4], [4, 4, 4, 4, 4, 4, 0, 0, 4, 0, 0, 4, 4, 4, 4, 4], [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4], [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4]], 'output': [[4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 8, 8, 4, 8, 8], [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 8, 8, 4, 8, 8], [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 8, 4, 4], [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 8, 8, 4, 8, 8], [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 8, 8, 4, 8, 8], [4, 4, 4, 4, 4, 4, 0, 0, 4, 0, 0, 4, 4, 4, 4, 4], [4, 4, 4, 4, 4, 4, 0, 0, 4, 0, 0, 4, 4, 4, 4, 4], [4, 4, 4, 4, 4, 4, 4, 4, 0, 4, 4, 4, 4, 4, 4, 4], [4, 4, 4, 4, 4, 4, 0, 0, 4, 0, 0, 4, 4, 4, 4, 4], [4, 4, 4, 4, 4, 4, 0, 0, 4, 0, 0, 4, 4, 4, 4, 4], [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4], [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4]]}], 'test_inputs': [[[3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3], [3, 3, 3, 6, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3], [3, 3, 3, 3, 0, 3, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3], [3, 3, 3, 3, 3, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3], [3, 3, 3, 3, 0, 3, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3]]], 'test_outputs': [[[6, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3], [6, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3], [3, 6, 3, 6, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3], [3, 3, 6, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3], [3, 6, 3, 6, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3], [3, 3, 3, 3, 0, 3, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3], [3, 3, 3, 3, 3, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3], [3, 3, 3, 3, 0, 3, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3]]]}) (input_keys={'test_inputs', 'training_examples'}): 'dict' object has no attribute 'dict'
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 74, in forward
File "<string>", line 74, in <listcomp>
AttributeError: 'dict' object has no attribute 'dict'
2025/08/29 02:16:54 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 5, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 5, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 5, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[1, 1, 1, 0, 0, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 1, 1], [0, 0, 0, 0, 0, 0, 1, 1, 1], [0, 0, 0, 0, 0, 0, 1, 1, 1]]}, {'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 5, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 5, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 5, 0, 0, 5, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 0, 1, 1, 1, 1, 1, 1], [0, 0, 0, 1, 1, 1, 1, 1, 1], [0, 0, 0, 1, 1, 1, 1, 1, 1]]}], 'test_inputs': [[[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 5, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 5, 0, 0, 0, 0, 0, 5, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 5, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]]], 'test_outputs': [[[0, 0, 0, 0, 0, 0, 1, 1, 1], [0, 0, 0, 0, 0, 0, 1, 1, 1], [0, 0, 0, 0, 0, 0, 1, 1, 1], [1, 1, 1, 0, 0, 0, 1, 1, 1], [1, 1, 1, 0, 0, 0, 1, 1, 1], [1, 1, 1, 0, 0, 0, 1, 1, 1], [1, 1, 1, 0, 0, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0, 0, 0]]]}) (input_keys={'test_inputs', 'training_examples'}): 'dict' object has no attribute 'dict'
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 74, in forward
File "<string>", line 74, in <listcomp>
AttributeError: 'dict' object has no attribute 'dict'
2025/08/29 02:16:54 INFO dspy.evaluate.evaluate: Average Metric: 0.0 / 3 (0.0%)
GEPA Optimization: 22%|████████████████████████▍ | 882/4000 [4:42:58<70:18:13, 81.17s/rollouts]Iteration 57: Proposed new text for program: import dspy
from typing import List
import pydantic
import copy
import json
# Define the data structures using Pydantic for clarity and validation.
# This matches the original structure.
MATRIX = List[List[int]]
class TrainingExample(pydantic.BaseModel):
input: MATRIX
output: MATRIX
# --- Signature for the overall task ---
# This defines the final I/O of our custom module, ensuring it's a drop-in replacement.
class SolveTaskSignature(dspy.Signature):
"""Given a set of training examples demonstrating a task, and a list of test inputs, solve the task for each test input."""
training_examples: List[TrainingExample] = dspy.InputField(description="Input and output examples demonstrating the task to be performed.")
test_inputs: List[MATRIX] = dspy.InputField(description="Input matrices to be solved following the task described in the training examples.")
test_outputs: List[MATRIX] = dspy.OutputField(description="Output matrices corresponding to the test inputs.")
# --- Signature for Step 1: Deriving the rule in natural language ---
class DeriveRuleSignature(dspy.Signature):
"""
Analyze the provided training examples, each consisting of an input and an output grid.
Deduce the transformation rule that converts the input grid to the output grid.
Describe this rule in clear, step-by-step natural language. Be precise and unambiguous.
Focus on geometric, color-based, or logical patterns.
Successful strategies:
- Look for simple transformations first: rotation, reflection, color swaps, object duplication.
- Consider object-based rules: identify objects, their properties (color, shape, size), and how they are transformed or moved.
- Analyze pixel-level rules: how the color of a pixel is determined by its neighbors or position.
- For complex patterns, think algorithmically: describe a process or simulation (e.g., "a particle starts at X and moves Y, reflecting off Z").
Pitfalls to avoid:
- Do not just describe one example; find a rule that generalizes to all training examples.
- Avoid vague descriptions. Be specific about coordinates, directions, and conditions.
"""
training_examples: str = dspy.InputField(description="JSON string of input-output examples.")
rule_description: str = dspy.OutputField(description="A clear, step-by-step natural language description of the transformation rule.")
# --- Signature for Step 2: Generating Python code from the rule ---
class GenerateCodeSignature(dspy.Signature):
"""
Based on the provided rule description and training examples, write a self-contained Python function named 'solve' that implements the rule.
Function requirements:
- The function must be named `solve`.
- It must accept one argument: `grid` (a list of lists of integers).
- It must return the transformed grid (a list of lists of integers).
- The first line of the function must be `grid = copy.deepcopy(grid)` to avoid modifying the input grid in place.
- The function should not rely on any external libraries other than `copy`.
- The output should be only the Python code for the function, starting with `def solve(grid):` and containing no other text or explanations.
"""
rule_description: str = dspy.InputField(description="The natural language rule to implement.")
training_examples: str = dspy.InputField(description="JSON string of input-output examples for context and testing.")
python_code: str = dspy.OutputField(description="A self-contained Python function `solve(grid)` that implements the rule.")
# --- Custom Module to orchestrate the solution ---
class RuleToCodeSolver(dspy.Module):
"""A DSPy module that solves grid-based tasks by first deriving the rule, then generating and executing code."""
def __init__(self):
super().__init__()
# Module to derive the rule. ChainOfThought is good for this reasoning step.
self.rule_deriver = dspy.ChainOfThought(DeriveRuleSignature)
# Module to generate code. Predict is sufficient as the instructions are very specific.
self.code_generator = dspy.Predict(GenerateCodeSignature)
def forward(self, training_examples: List[TrainingExample], test_inputs: List[MATRIX]):
# Convert Pydantic objects to a JSON string for the LM prompt.
# LMs handle structured text like JSON well.
examples_str = json.dumps([ex.dict() for ex in training_examples])
# Step 1: Derive the rule in natural language.
rule_pred = self.rule_deriver(training_examples=examples_str)
rule_description = rule_pred.rule_description
# Step 2: Generate Python code from the rule.
code_pred = self.code_generator(rule_description=rule_description, training_examples=examples_str)
python_code = code_pred.python_code
# Step 3: Execute the generated code safely.
solved_outputs = []
try:
# Create a scope for exec to run in.
local_scope = {}
# The generated code should contain the 'solve' function.
# We pass 'copy' into the scope so the generated code can use it.
exec(python_code, {"copy": copy}, local_scope)
solve_func = local_scope['solve']
# Apply the generated function to each test input.
for test_grid in test_inputs:
result_grid = solve_func(test_grid)
solved_outputs.append(result_grid)
except Exception as e:
print(f"Code execution failed: {e}")
print(f"Generated code:\n{python_code}")
# Fallback strategy: If code execution fails, return the original test inputs.
# This ensures the program doesn't crash and returns a validly typed output.
solved_outputs = test_inputs
# Return the final prediction in the required format.
return dspy.Prediction(test_outputs=solved_outputs)
# Assign the improved program to the 'program' variable.
program = RuleToCodeSolver()
Iteration 57: New subsample score is not better, skipping
Iteration 58: Selected program 2 score: 0.605
Average Metric: 0.00 / 1 (0.0%): 33%|█████████████████████████████████████▎ | 1/3 [05:56<11:53, 356.52s/it]2025/08/29 02:23:21 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
Average Metric: 0.00 / 2 (0.0%): 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [06:28<00:00, 89.94s/it]2025/08/29 02:23:22 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
Average Metric: 0.00 / 2 (0.0%): : 5it [07:58, 67.36s/it] 2025/08/29 02:25:23 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
Average Metric: 0.00 / 3 (0.0%): : 6it [08:34, 85.72s/it]2025/08/29 02:25:29 INFO dspy.evaluate.evaluate: Average Metric: 0.0 / 3 (0.0%)
Iteration 58: Proposed new text for program: import dspy
from typing import List
import pydantic
import copy
# Define type aliases and pydantic models for clarity and structure.
MATRIX = List[List[int]]
class TrainingExample(pydantic.BaseModel):
input: MATRIX
output: MATRIX
class DeduceRuleSignature(dspy.Signature):
"""
Analyze a series of input/output matrix pairs from the ARC challenge.
Deduce the single, underlying transformation rule that applies to all pairs.
Describe this rule clearly, concisely, and unambiguously in natural language.
**Successful Strategies:**
- Focus on high-level concepts: Instead of pixel-by-pixel changes, think about objects, shapes, colors, symmetry, repetition, counting, or filling.
- Be precise: "Fill the area enclosed by the blue border with red" is better than "fill something". "Reflect the largest contiguous non-black object horizontally" is better than "move the object".
- Step-by-step: If the transformation is complex, break it down into a logical sequence of simple operations. For example: "1. Find all red squares. 2. For each red square, draw a blue line from it to the top edge of the grid."
"""
training_examples: List[TrainingExample] = dspy.InputField(desc="A list of input/output pairs demonstrating the transformation rule.")
rule_description: str = dspy.OutputField(desc="A clear, natural language description of the transformation rule.")
class GeneratePythonFunctionFromRuleSignature(dspy.Signature):
"""
You are an expert programmer. Your task is to write a single, self-contained Python function based on a provided rule description and example matrix pairs.
**Primary Instruction:**
Your main goal is to faithfully implement the logic described in the `rule_description`. The `training_examples` are for context and to understand the data format, but the `rule_description` is your primary source of truth for the algorithm. Do not invent new logic not present in the rule description.
**Function Requirements:**
- The function must be named `transform_matrix`.
- It must accept one argument: `matrix`, which is a list of lists of integers.
- It must return a list of lists of integers.
- It should not use any external libraries except for `copy` if needed (e.g., `import copy; new_matrix = copy.deepcopy(matrix)`).
- Your output must be ONLY the Python code for the function. Do not include any explanations, comments outside the function, or markdown formatting like ```python.
**Example of a Correctly Formatted Output:**
def transform_matrix(matrix: list[list[int]]) -> list[list[int]]:
# Implementation based on the rule_description
import copy
new_matrix = copy.deepcopy(matrix)
# ... rest of the logic ...
return new_matrix
"""
training_examples: List[TrainingExample] = dspy.InputField(desc="A list of input/output pairs for context.")
rule_description: str = dspy.InputField(desc="The natural language description of the rule to implement.")
python_function: str = dspy.OutputField(desc="A string containing a single Python function `transform_matrix` that implements the rule.")
class ARCSolver(dspy.Module):
"""A module that solves ARC tasks by first deducing a rule and then generating code to implement it."""
def __init__(self):
super().__init__()
# A ChainOfThought module is better for the complex reasoning task of rule deduction.
self.rule_deducer = dspy.ChainOfThought(DeduceRuleSignature)
# A simple Predict module is sufficient for the more constrained task of code generation from a rule.
self.code_generator = dspy.Predict(GeneratePythonFunctionFromRuleSignature)
def forward(self, training_examples: List[TrainingExample], test_inputs: List[MATRIX]):
# Step 1: Deduce the transformation rule in natural language from the examples.
deduction = self.rule_deducer(training_examples=training_examples)
rule_description = deduction.rule_description
# Step 2: Generate the Python function from the rule, using examples for context.
generation = self.code_generator(
training_examples=training_examples,
rule_description=rule_description
)
python_code = generation.python_function
# Prepare a dictionary to hold the executed function and fallback outputs.
local_scope = {}
fallback_outputs = [copy.deepcopy(matrix) for matrix in test_inputs]
try:
# Step 3: Execute the generated code string to define the function in a controlled scope.
exec(python_code, globals(), local_scope)
transform_func = local_scope.get('transform_matrix')
if not callable(transform_func):
# If function definition failed or the name is wrong, use the fallback.
return dspy.Prediction(test_outputs=fallback_outputs)
# Step 4: Apply the generated function to each test input with individual error handling.
solved_outputs = []
for test_matrix in test_inputs:
try:
# Use a deepcopy to prevent the function from modifying the original input list.
input_copy = copy.deepcopy(test_matrix)
result = transform_func(input_copy)
solved_outputs.append(result)
except Exception:
# If the function fails on a specific test case, append the original
# matrix as a fallback for that case and continue.
solved_outputs.append(copy.deepcopy(test_matrix))
return dspy.Prediction(test_outputs=solved_outputs)
except Exception:
# If code generation or `exec` fails entirely, return the original inputs.
return dspy.Prediction(test_outputs=fallback_outputs)
# The overall task signature defines the final input/output of the program.
class SolveTaskSignature(dspy.Signature):
"""
Given a set of training examples demonstrating a matrix transformation,
apply the same transformation to a new set of test matrices.
"""
training_examples: List[TrainingExample] = dspy.InputField(description="Input and output examples demonstrating the task to be performed.")
test_inputs: List[MATRIX] = dspy.InputField(description="Input matrices to be solved following the task described in the training examples.")
test_outputs: List[MATRIX] = dspy.OutputField(description="Output matrices corresponding to the test inputs.")
# The final program is an instance of our new, more robust module.
program = ARCSolver()
2025/08/29 02:35:40 INFO dspy.evaluate.evaluate: Average Metric: 1.0 / 3 (33.3%)
2025/08/29 02:39:28 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 02:39:44 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 02:42:56 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 02:43:29 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 02:43:49 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 02:44:30 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 02:45:59 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 02:48:43 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 02:49:10 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 02:50:16 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 02:52:05 INFO dspy.evaluate.evaluate: Average Metric: 129.0 / 200 (64.5%)
GEPA Optimization: 27%|█████████████████████████████▉ | 1088/4000 [5:18:09<11:52:59, 14.69s/rollouts]Iteration 58: Full valset score for new program: 0.645
Iteration 58: Full train_val score for new program: 0.645
Iteration 58: Individual valset scores for new program: [True, True, False, True, True, False, True, True, True, True, True, True, True, False, True, True, False, False, True, False, True, True, True, False, True, True, False, False, True, True, False, False, False, True, True, True, False, True, False, True, False, True, True, False, False, True, False, False, True, True, True, True, True, True, True, False, True, True, False, True, True, False, False, True, False, False, True, True, False, False, True, True, True, True, True, True, True, False, True, True, False, True, True, True, True, True, False, True, True, False, False, True, True, True, True, True, True, False, True, True, True, True, False, False, True, True, True, True, False, False, False, False, True, False, True, True, True, True, True, True, True, False, True, False, False, True, False, True, True, True, False, True, True, True, True, False, True, True, True, True, True, True, True, False, True, False, True, False, True, True, False, False, True, True, True, True, True, True, True, False, False, True, False, True, False, True, True, False, False, False, False, True, False, True, True, True, False, True, False, True, False, True, True, False, False, True, True, True, False, False, True, True, True, False, True, False, True, False, False, True]
Iteration 58: New valset pareto front scores: [True, True, True, True, True, True, True, True, True, True, True, True, True, False, True, True, False, True, True, True, True, True, True, True, True, True, False, True, True, True, True, False, True, True, True, True, False, True, True, True, False, True, True, True, False, True, False, False, True, True, True, True, True, True, True, 0, True, True, False, True, True, True, False, True, False, False, True, True, 0, False, True, True, True, True, True, True, True, False, True, True, True, True, True, True, True, True, True, True, True, False, True, True, True, True, True, True, True, False, True, True, True, True, True, False, True, True, True, True, True, True, False, True, True, True, True, True, True, True, True, True, True, True, True, False, False, True, False, True, True, True, True, True, True, True, True, False, True, True, True, True, True, True, True, False, True, False, True, False, True, True, False, False, True, True, True, True, True, True, True, False, True, True, True, True, False, True, True, True, True, False, True, True, False, True, True, True, False, True, False, True, True, True, True, False, False, True, True, True, True, True, True, True, True, True, True, False, True, True, False, True]
Iteration 58: Full valset pareto front score: 0.8
Iteration 58: Updated valset pareto front programs: [{1, 3}, {0, 1, 2, 3}, {0}, {0, 2, 3}, {0, 1, 2, 3}, {0}, {0, 1, 2, 3}, {0, 1, 3}, {0, 1, 2, 3}, {0, 2, 3}, {0, 1, 2, 3}, {0, 1, 2, 3}, {0, 1, 2, 3}, {0, 1, 2, 3}, {0, 1, 2, 3}, {0, 1, 2, 3}, {0, 1, 2, 3}, {1}, {0, 1, 2, 3}, {0, 2}, {0, 1, 2, 3}, {0, 2, 3}, {0, 1, 2, 3}, {1}, {0, 3}, {0, 1, 3}, {0, 1, 2, 3}, {0}, {0, 1, 2, 3}, {0, 1, 2, 3}, {1}, {0, 1, 2, 3}, {2}, {0, 1, 2, 3}, {0, 1, 2, 3}, {0, 1, 2, 3}, {0, 1, 2, 3}, {0, 1, 2, 3}, {2}, {0, 2, 3}, {0, 1, 2, 3}, {0, 1, 2, 3}, {0, 1, 2, 3}, {1}, {0, 1, 2, 3}, {0, 1, 2, 3}, {0, 1, 2, 3}, {0, 1, 2, 3}, {0, 1, 2, 3}, {0, 1, 2, 3}, {0, 1, 2, 3}, {0, 1, 2, 3}, {0, 1, 3}, {0, 1, 2, 3}, {0, 1, 2, 3}, {0, 1, 2, 3}, {0, 1, 3}, {0, 1, 2, 3}, {0, 1, 2, 3}, {0, 1, 2, 3}, {0, 1, 2, 3}, {0, 1}, {0, 1, 2, 3}, {0, 1, 2, 3}, {0, 1, 2, 3}, {0, 1, 2, 3}, {1, 2, 3}, {0, 1, 3}, {0, 1, 2, 3}, {0, 1, 2, 3}, {0, 1, 2, 3}, {0, 1, 2, 3}, {0, 1, 2, 3}, {2, 3}, {0, 1, 2, 3}, {1, 2, 3}, {0, 3}, {0, 1, 2, 3}, {0, 1, 2, 3}, {0, 2, 3}, {0, 2}, {1, 2, 3}, {0, 1, 2, 3}, {0, 1, 2, 3}, {0, 1, 2, 3}, {0, 1, 2, 3}, {0, 1, 2}, {0, 1, 2, 3}, {0, 3}, {0, 1, 2, 3}, {0, 1, 2}, {0, 1, 2, 3}, {0, 1, 2, 3}, {0, 1, 2, 3}, {0, 1, 2, 3}, {0, 1, 2, 3}, {1, 3}, {0, 1, 2, 3}, {0, 3}, {0, 1, 2, 3}, {0, 1, 2, 3}, {0, 1, 2, 3}, {0}, {0, 1, 2, 3}, {0, 1, 2, 3}, {0, 1, 2, 3}, {1, 2, 3}, {0, 1, 2, 3}, {1, 2}, {0, 1}, {0, 1, 2, 3}, {0, 2}, {1, 3}, {2}, {0, 1, 2, 3}, {3}, {0, 1, 2, 3}, {2, 3}, {0, 1, 2, 3}, {0, 1, 2, 3}, {2, 3}, {0, 1}, {0, 1, 2, 3}, {0, 1, 2, 3}, {0, 1, 2, 3}, {0, 1, 2, 3}, {0, 1, 2, 3}, {0, 1, 2, 3}, {0, 1, 2, 3}, {0, 1, 2, 3}, {2}, {0, 1, 3}, {0, 1, 2, 3}, {0, 1, 3}, {0, 1, 2, 3}, {0, 1, 2, 3}, {0, 1, 3}, {3}, {1, 2, 3}, {1, 2, 3}, {0, 1, 2, 3}, {0, 1, 2, 3}, {0, 1, 2, 3}, {0, 1, 2, 3}, {0, 1, 2, 3}, {0, 1, 2, 3}, {0, 2, 3}, {0, 1, 2, 3}, {0, 1, 2, 3}, {0, 1, 2, 3}, {0, 1, 2, 3}, {0, 1, 2, 3}, {0, 1, 2, 3}, {0, 1, 3}, {0, 1, 3}, {0, 1, 2, 3}, {0, 3}, {0, 1, 2, 3}, {0, 1, 2, 3}, {0, 1, 2, 3}, {1}, {0, 1, 2, 3}, {0}, {0, 3}, {0, 1, 2, 3}, {0, 1, 2, 3}, {0, 1, 2, 3}, {0, 1, 2}, {0, 1, 2}, {0, 1, 2, 3}, {0}, {0, 1, 2, 3}, {0, 1, 2, 3}, {0, 1, 3}, {0, 1, 2, 3}, {1, 2, 3}, {0, 1, 2, 3}, {0, 1, 2, 3}, {0, 1, 2, 3}, {0, 1, 3}, {0, 2}, {0, 1, 2, 3}, {0, 1, 2, 3}, {0, 1, 2, 3}, {0, 1, 2, 3}, {0, 1, 2, 3}, {0, 1, 2, 3}, {0, 1, 2, 3}, {0, 1, 2}, {0}, {0, 1, 2, 3}, {0, 1, 2, 3}, {0, 1, 2, 3}, {1, 2}, {0, 1, 2, 3}, {0, 1, 2, 3}, {0, 1, 2, 3}, {0}, {0, 1, 2, 3}, {0, 2, 3}]
Iteration 58: Best valset aggregate score so far: 0.67
Iteration 58: Best program as per aggregate score on train_val: 0
Iteration 58: Best program as per aggregate score on valset: 0
Iteration 58: Best score on valset: 0.67
Iteration 58: Best score on train_val: 0.67
Iteration 58: Linear pareto front program index: 0
Iteration 58: New program candidate index: 3
Iteration 59: Selected program 2 score: 0.605
0%| | 0/3 [00:00<?, ?it/s]2025/08/29 02:53:10 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
Average Metric: 2.00 / 3 (66.7%): : 4it [06:31, 97.87s/it] 2025/08/29 02:58:36 INFO dspy.evaluate.evaluate: Average Metric: 2.0 / 3 (66.7%)
Iteration 59: Proposed new text for program: import dspy
from typing import List, Optional
import pydantic
import copy
# Define type aliases and pydantic models for clarity and structure.
MATRIX = List[List[int]]
class TrainingExample(pydantic.BaseModel):
input: MATRIX
output: MATRIX
class DeduceTransformationRule(dspy.Signature):
"""
Analyzes pairs of input/output matrices from the Abstraction and Reasoning Corpus (ARC)
and deduces the underlying transformation rule. The output should be a clear, step-by-step
description of the logic that transforms any input matrix into its corresponding output matrix.
"""
training_examples: List[TrainingExample] = dspy.InputField(
desc="A list of input/output pairs demonstrating the transformation rule."
)
rule_hypothesis: str = dspy.OutputField(
desc="A step-by-step natural language description of the transformation rule."
)
class GeneratePythonFunctionWithHypothesis(dspy.Signature):
"""
You are an expert programmer. Your task is to write a single, self-contained Python function based on a natural language hypothesis that describes a transformation rule for matrices from the Abstraction and Reasoning Corpus (ARC).
**Function Requirements:**
- The function must be named `transform_matrix`.
- It must accept one argument: `matrix`, which is a list of lists of integers (representing the input grid).
- It must return a list of lists of integers (representing the transformed output grid).
- The function should not use any external libraries except for `copy` if needed (e.g., `import copy; new_matrix = copy.deepcopy(matrix)`).
- Your output must be ONLY the Python code for the function. Do not include any explanations, comments outside the function, or markdown formatting like ```python.
**Pitfalls to Avoid:**
- **Hardcoding Dimensions:** Do not assume the input matrix will have the same dimensions as the examples. The function must be general and work for matrices of any size. Use `len(matrix)` and `len(matrix[0])` to get dimensions dynamically.
- **Overfitting to Specific Colors:** Do not hardcode specific color values unless the hypothesis explicitly states they are constant. The rule might be about relationships between colors (e.g., "the most frequent color," "the color of the largest object") rather than specific integer values.
**Example of a Correctly Formatted Output:**
def transform_matrix(matrix: list[list[int]]) -> list[list[int]]:
# Your implementation here
# For example, find the most frequent color and fill the grid
from collections import Counter
import itertools
if not matrix or not matrix[0]:
return []
counts = Counter(itertools.chain.from_iterable(matrix))
if counts:
# Handle ties by picking the smaller number value
most_common_color = sorted(counts.items(), key=lambda item: (-item[1], item[0]))[0][0]
else:
return []
height = len(matrix)
width = len(matrix[0])
return [[most_common_color for _ in range(width)] for _ in range(height)]
"""
training_examples: List[TrainingExample] = dspy.InputField(desc="A list of input/output pairs for context.")
rule_hypothesis: str = dspy.InputField(desc="The natural language hypothesis to implement.")
python_function: str = dspy.OutputField(desc="A string containing a single Python function `transform_matrix` that implements the rule.")
class ARCSolver(dspy.Module):
"""A module that solves ARC tasks by hypothesizing a rule, generating code, and verifying it."""
def __init__(self, max_retries=2):
super().__init__()
self.rule_deducer = dspy.ChainOfThought(DeduceTransformationRule)
self.code_generator = dspy.Predict(GeneratePythonFunctionWithHypothesis)
self.max_retries = max_retries
def _execute_code(self, python_code: str) -> Optional[callable]:
"""Safely execute the generated Python code string in a restricted scope."""
local_scope = {}
try:
exec(python_code, globals(), local_scope)
return local_scope.get('transform_matrix')
except Exception:
return None
def _verify_and_apply(self, transform_func: callable, training_examples: List[TrainingExample], test_inputs: List[MATRIX]) -> Optional[dspy.Prediction]:
"""Verify the function against training examples and apply to test inputs if successful."""
# Step 1: Verify the function with all training examples.
for example in training_examples:
try:
input_copy = copy.deepcopy(example.input)
predicted_output = transform_func(input_copy)
if predicted_output != example.output:
return None # Verification failed: output mismatch.
except Exception:
return None # Verification failed: function crashed.
# Step 2: If verification passes, apply the function to test inputs.
solved_outputs = []
for test_matrix in test_inputs:
try:
input_copy = copy.deepcopy(test_matrix)
result = transform_func(input_copy)
solved_outputs.append(result)
except Exception:
# If the function fails on a test case, append the original as a fallback.
solved_outputs.append(copy.deepcopy(test_matrix))
return dspy.Prediction(test_outputs=solved_outputs)
def forward(self, training_examples: List[TrainingExample], test_inputs: List[MATRIX]):
# Prepare a fallback prediction in case all attempts fail.
fallback_outputs = [copy.deepcopy(matrix) for matrix in test_inputs]
# Step 1: Deduce the transformation rule in natural language.
prediction = self.rule_deducer(training_examples=training_examples)
hypothesis = prediction.rule_hypothesis
for attempt in range(self.max_retries):
# Step 2: Generate Python code based on the hypothesis.
code_pred = self.code_generator(
training_examples=training_examples,
rule_hypothesis=hypothesis
)
python_code = code_pred.python_function
# Step 3: Execute the generated code string to define the function.
transform_func = self._execute_code(python_code)
if callable(transform_func):
# Step 4: Verify the function. If it works, apply it and return.
result_prediction = self._verify_and_apply(transform_func, training_examples, test_inputs)
if result_prediction is not None:
return result_prediction
# If all retries fail to produce a verifiable function, return the fallback.
return dspy.Prediction(test_outputs=fallback_outputs)
# The overall task signature defines the final input/output of the program.
class SolveTaskSignature(dspy.Signature):
"""
Given a set of training examples demonstrating a matrix transformation,
apply the same transformation to a new set of test matrices.
"""
training_examples: List[TrainingExample] = dspy.InputField(description="Input and output examples demonstrating the task to be performed.")
test_inputs: List[MATRIX] = dspy.InputField(description="Input matrices to be solved following the task described in the training examples.")
test_outputs: List[MATRIX] = dspy.OutputField(description="Output matrices corresponding to the test inputs.")
# The final program is an instance of our new, more robust module.
program = ARCSolver()
2025/08/29 03:01:32 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 03:05:41 INFO dspy.evaluate.evaluate: Average Metric: 0.0 / 3 (0.0%)
GEPA Optimization: 27%|██████████████████████████████ | 1094/4000 [5:31:45<15:32:04, 19.24s/rollouts]Iteration 59: New subsample score is not better, skipping
Iteration 60: Selected program 0 score: 0.67
Average Metric: 0.00 / 2 (0.0%): 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [04:00<00:00, 76.00s/it]2025/08/29 03:09:50 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
Average Metric: 0.00 / 3 (0.0%): : 4it [04:09, 62.28s/it] 2025/08/29 03:09:50 INFO dspy.evaluate.evaluate: Average Metric: 0.0 / 3 (0.0%)
Iteration 60: Proposed new text for program: import dspy
from typing import List
import pydantic
import traceback
import copy
# Define the type for a matrix, which is a list of lists of integers.
MATRIX = List[List[int]]
# Define a Pydantic model for a single training example, containing an input and output matrix.
class TrainingExample(pydantic.BaseModel):
input: MATRIX
output: MATRIX
# This is the overall signature for the task, but it will be implemented by our custom module.
class SolveTaskSignature(dspy.Signature):
"""Given a set of training examples demonstrating a hidden rule for transforming matrices,
apply the same rule to a new set of test input matrices."""
training_examples: List[TrainingExample] = dspy.InputField(description="Input and output examples demonstrating the task to be performed.")
test_inputs: List[MATRIX] = dspy.InputField(description="Input matrices to be solved following the task described in the training examples.")
test_outputs: List[MATRIX] = dspy.OutputField(description="Output matrices corresponding to the test inputs.")
# Define a signature specifically for the rule-inference and code-generation step.
class GeneratePythonFunction(dspy.Signature):
"""
Analyze the provided training examples, which consist of input and output matrices.
Deduce the single, consistent transformation rule that maps every input to its corresponding output.
Then, write a standalone Python function named 'transform_matrix' that implements this rule.
Successful Strategies to Consider:
- Object-based analysis: Identify "objects" (contiguous groups of non-zero cells of the same color). Analyze their properties like color, cell count, bounding box area, and density (count / area). The rule might be to select an object based on a specific property (e.g., highest density, largest area) and then perform an action (e.g., crop to its bounding box).
- Geometric transformations: Look for patterns of duplication, scaling, rotation, flipping, or tiling of the input grid or parts of it.
- Color transformations: Rules might involve changing colors based on their value, position, or neighbors.
- Coordinate-based logic: The transformation might depend on the row or column index of a cell. For example, a 2x2 block's priority might be determined by `column_index - row_index`.
Function Requirements:
- The function must be named `transform_matrix`.
- It must accept one argument: `matrix: List[List[int]]`.
- It must return the transformed matrix as a `List[List[int]]`.
- The function should be self-contained and not rely on any external state or libraries beyond standard Python.
- Precede the function with a clear, multi-line comment explaining the discovered rule.
"""
training_examples: List[TrainingExample] = dspy.InputField(desc="A list of input-output pairs demonstrating the transformation rule.")
python_function: str = dspy.OutputField(desc="A string containing a Python function `transform_matrix(matrix)` that implements the transformation rule.")
# Create a custom module to generate and then execute the transformation logic.
class CodeGeneratingSolver(dspy.Module):
def __init__(self):
super().__init__()
# Use ChainOfThought to encourage the LM to reason about the rule before writing the code.
self.rule_generator = dspy.ChainOfThought(GeneratePythonFunction)
def forward(self, training_examples: List[TrainingExample], test_inputs: List[MATRIX]):
# Step 1: Generate the Python function string from the training examples.
prediction = self.rule_generator(training_examples=training_examples)
python_code = prediction.python_function
# Prepare a local scope for exec() to run in, preventing it from polluting the global scope.
local_scope = {}
# Step 2: Execute the generated code string to define the function within the local_scope.
try:
exec(python_code, globals(), local_scope)
transform_matrix_func = local_scope.get('transform_matrix')
if not callable(transform_matrix_func):
# Fallback if 'transform_matrix' wasn't defined correctly in the generated code.
print("Error: 'transform_matrix' function not found or not callable.")
return dspy.Prediction(test_outputs=[[] for _ in test_inputs])
except Exception as e:
print(f"Error executing generated code: {e}\nGenerated code:\n{python_code}")
traceback.print_exc()
# Return a list of empty matrices on failure to define the function.
return dspy.Prediction(test_outputs=[[] for _ in test_inputs])
# Step 3: Apply the now-defined function to all test inputs.
solved_outputs = []
for test_matrix in test_inputs:
try:
# Pass a deep copy of the input to the function to prevent accidental mutation.
matrix_copy = copy.deepcopy(test_matrix)
result = transform_matrix_func(matrix_copy)
solved_outputs.append(result)
except Exception as e:
print(f"Error applying 'transform_matrix' to a test input: {e}")
traceback.print_exc()
# Append an empty list for this specific failed test case and continue.
solved_outputs.append([])
return dspy.Prediction(test_outputs=solved_outputs)
# The final program object is an instance of our robust, code-generating module.
program = CodeGeneratingSolver()
Error executing generated code: invalid syntax (<string>, line 1)
Generated code:
```python
def transform_matrix(matrix):
"""
This function transforms the input matrix based on the following rule:
1. Identify all distinct contiguous objects of non-zero color in the input grid.
2. For each object, calculate its "density". Density is defined as the number of
cells belonging to the object divided by the area of its bounding box.
3. Select the object with the highest density. In case of a tie, the first
object found during a top-to-bottom, left-to-right scan is chosen.
4. The output is the sub-grid corresponding to the bounding box of the selected object.
"""
height = len(matrix)
if height == 0:
return []
width = len(matrix[0])
if width == 0:
return [[] for _ in range(height)]
visited = [[False for _ in range(width)] for _ in range(height)]
objects = []
for r in range(height):
for c in range(width):
if matrix[r][c] != 0 and not visited[r][c]:
# Found a new object, start a Breadth-First Search (BFS)
color = matrix[r][c]
q = [(r, c)]
visited[r][c] = True
object_cells = []
min_r, max_r = r, r
min_c, max_c = c, c
head = 0
while head < len(q):
curr_r, curr_c = q[head]
head += 1
object_cells.append((curr_r, curr_c))
min_r = min(min_r, curr_r)
max_r = max(max_r, curr_r)
min_c = min(min_c, curr_c)
max_c = max(max_c, curr_c)
# Check neighbors (up, down, left, right)
for dr, dc in [(0, 1), (0, -1), (1, 0), (-1, 0)]:
nr, nc = curr_r + dr, curr_c + dc
if 0 <= nr < height and 0 <= nc < width and \
not visited[nr][nc] and matrix[nr][nc] == color:
visited[nr][nc] = True
q.append((nr, nc))
# Calculate properties of the found object
cell_count = len(object_cells)
bbox_height = max_r - min_r + 1
bbox_width = max_c - min_c + 1
bbox_area = bbox_height * bbox_width
density = cell_count / bbox_area
objects.append({
'density': density,
'bbox': (min_r, max_r, min_c, max_c)
})
if not objects:
return []
# Find the object with the highest density
best_object = max(objects, key=lambda obj: obj['density'])
# Crop the matrix to the bounding box of the best object
min_r, max_r, min_c, max_c = best_object['bbox']
output_matrix = []
for i in range(min_r, max_r + 1):
output_matrix.append(matrix[i][min_c:max_c + 1])
return output_matrix
```
Traceback (most recent call last):
File "<string>", line 63, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
2025/08/29 03:14:44 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
Traceback (most recent call last):
File "<string>", line 63, in forward
TypeError: exec() arg 1 must be a string, bytes or code object
2025/08/29 03:14:45 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
Traceback (most recent call last):
File "<string>", line 63, in forward
TypeError: exec() arg 1 must be a string, bytes or code object
Error executing generated code: exec() arg 1 must be a string, bytes or code object
Generated code:
None
Error executing generated code: exec() arg 1 must be a string, bytes or code object
Generated code:
None
Traceback (most recent call last):
File "<string>", line 63, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
2025/08/29 03:14:48 INFO dspy.evaluate.evaluate: Average Metric: 0.0 / 3 (0.0%)
GEPA Optimization: 28%|██████████████████████████████▎ | 1100/4000 [5:40:52<18:27:06, 22.91s/rollouts]Error executing generated code: invalid syntax (<string>, line 1)
Generated code:
```python
def transform_matrix(matrix: list[list[int]]) -> list[list[int]]:
"""
Transforms the input matrix based on a local density rule.
The rule identifies "solid" and "sparse" regions of the input color (e.g., 5)
and recolors them to 8 and 2, respectively.
1. A cell is considered part of a "solid" region if it belongs to any 2x2 block
of the original non-zero color.
2. To implement this, a boolean grid `is_solid` of the same dimensions is created.
3. The function iterates through the input matrix, checking every possible 2x2 subgrid.
If a 2x2 subgrid consists entirely of the non-zero color, all four corresponding
cells in the `is_solid` grid are marked as True.
4. After identifying all solid cells, the output matrix is constructed.
5. For each cell in the input matrix:
- If the cell is 0 (background), it remains 0.
- If the cell is non-zero and is marked as "solid" (True in `is_solid`),
its color becomes 8.
- If the cell is non-zero and is not "solid" (False in `is_solid`),
its color becomes 2.
"""
if not matrix or not matrix[0]:
return []
height = len(matrix)
width = len(matrix[0])
# Find the single non-zero color used in the input.
input_color = 0
for r in range(height):
for c in range(width):
if matrix[r][c] != 0:
input_color = matrix[r][c]
break
if input_color != 0:
break
if input_color == 0: # Handle all-zero matrices
return [row[:] for row in matrix]
is_solid = [[False for _ in range(width)] for _ in range(height)]
# Identify all cells that are part of any 2x2 block of the input color
for r in range(height - 1):
for c in range(width - 1):
if (matrix[r][c] == input_color and
matrix[r + 1][c] == input_color and
matrix[r][c + 1] == input_color and
matrix[r + 1][c + 1] == input_color):
is_solid[r][c] = True
is_solid[r + 1][c] = True
is_solid[r][c + 1] = True
is_solid[r + 1][c + 1] = True
# Create the output matrix based on the is_solid grid
output_matrix = [[0 for _ in range(width)] for _ in range(height)]
for r in range(height):
for c in range(width):
if matrix[r][c] == input_color:
if is_solid[r][c]:
output_matrix[r][c] = 8
else:
output_matrix[r][c] = 2
# else, it remains 0 as initialized
return output_matrix
```
Iteration 60: New subsample score is not better, skipping
Iteration 61: Selected program 1 score: 0.63
0%| | 0/3 [00:00<?, ?it/s]Error executing generated code: invalid syntax (<string>, line 1)
Generated code:
```python
def transform_matrix(matrix: list[list[int]]) -> list[list[int]]:
"""
Transforms the input matrix based on the following rule:
1. Identify all cells that are part of a 2x2 square of the color 5.
2. In the output matrix, change the color of these identified cells to 8.
3. Change the color of all other cells that were originally 5 to 2.
4. All other cells (originally 0) remain 0.
The process involves two passes:
- The first pass identifies all cells belonging to a 2x2 block of 5s.
- The second pass constructs the output matrix by applying the color change rule.
"""
if not matrix or not matrix[0]:
return []
height = len(matrix)
width = len(matrix[0])
# Step 1: Create a boolean matrix to mark cells in a 2x2 block of 5s.
is_in_block = [[False for _ in range(width)] for _ in range(height)]
# Step 2: Iterate through the matrix to find all 2x2 blocks of 5s.
for r in range(height - 1):
for c in range(width - 1):
# Check if the 2x2 block starting at (r, c) is all 5s.
if (matrix[r][c] == 5 and
matrix[r+1][c] == 5 and
matrix[r][c+1] == 5 and
matrix[r+1][c+1] == 5):
# Mark all four cells as being part of a block.
is_in_block[r][c] = True
is_in_block[r+1][c] = True
is_in_block[r][c+1] = True
is_in_block[r+1][c+1] = True
# Step 3: Create the output matrix based on the rule.
output_matrix = [[0 for _ in range(width)] for _ in range(height)]
for r in range(height):
for c in range(width):
if matrix[r][c] == 5:
if is_in_block[r][c]:
output_matrix[r][c] = 8 # Part of a 2x2 block
else:
output_matrix[r][c] = 2 # Not part of a 2x2 block
# Cells that are 0 in the input remain 0 in the output.
return output_matrix
```
Traceback (most recent call last):
File "<string>", line 63, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Average Metric: 2.00 / 2 (100.0%): 67%|██████████████████████████████████████████████████████████████████████████ | 2/3 [00:53<00:25, 25.76s/it]2025/08/29 03:16:43 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
Traceback (most recent call last):
File "<string>", line 63, in forward
TypeError: exec() arg 1 must be a string, bytes or code object
Error executing generated code: exec() arg 1 must be a string, bytes or code object
Generated code:
None
Average Metric: 2.00 / 3 (66.7%): 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [01:57<00:00, 39.00s/it]2025/08/29 03:16:45 INFO dspy.evaluate.evaluate: Average Metric: 2.0 / 3 (66.7%)
Iteration 61: Proposed new text for program: import dspy
from typing import List
import pydantic
import traceback
import numpy as np
# Define the type for a matrix for clarity
MATRIX = List[List[int]]
class TrainingExample(pydantic.BaseModel):
"""A Pydantic model for a single training example, containing an input and output matrix."""
input: MATRIX
output: MATRIX
class InferRuleSignature(dspy.Signature):
"""
Analyzes pairs of input/output matrices to deduce the transformation rule.
You are an expert analyst of abstract visual reasoning puzzles. Your task is to carefully examine the provided training examples. Each example consists of an 'input' matrix and a corresponding 'output' matrix.
Your goal is to deduce the single, consistent transformation rule that converts each input to its output. Describe this rule in clear, unambiguous, step-by-step natural language. The description must be precise enough to be translated into a Python function by another AI.
Consider various possible transformation patterns, including but not limited to:
- Geometric operations: rotation, reflection, scaling, repetition, shifting.
- Color/value transformations: changing specific numbers to others, conditional changes.
- Object-based logic: identifying shapes or objects and applying rules based on their properties (size, color, position, shape).
- Fill/completion patterns: such as flood fills or completing a grid based on object boundaries.
- Bounding box or frame logic: creating structures based on the extents of objects.
- Grid-based logic: operations based on row, column, or diagonal patterns.
"""
training_examples: List[TrainingExample] = dspy.InputField(description="A list of input and output examples demonstrating the task.")
transformation_rule: str = dspy.OutputField(description="A step-by-step natural language description of the transformation rule.")
class GeneratePythonFunctionSignature(dspy.Signature):
"""
Translates a natural language transformation rule into a Python function.
You are an expert Python programmer. Your task is to write a single Python function named `transform_matrix` that implements the given natural language rule.
**Function Requirements:**
- The function must be named exactly `transform_matrix`.
- It must accept a single argument: `matrix`, which is a list of lists of integers.
- It must return a list of lists of integers representing the transformed matrix.
- You can use standard Python libraries. The `numpy` library is available as `np`.
**Output Format:**
- Your output must be ONLY the Python code for the function.
- Do NOT include any import statements, example usage, explanations, or markdown formatting like ```python ... ```.
- The function should be self-contained.
**Example of a valid output:**
def transform_matrix(matrix):
# Example: Invert non-zero colors 1 and 2
new_matrix = []
for r in range(len(matrix)):
new_row = []
for c in range(len(matrix[0])):
if matrix[r][c] == 1:
new_row.append(2)
elif matrix[r][c] == 2:
new_row.append(1)
else:
new_row.append(matrix[r][c])
new_matrix.append(new_row)
return new_matrix
"""
transformation_rule: str = dspy.InputField(description="The natural language rule to implement.")
python_function: str = dspy.OutputField(description="A string containing only the Python code for the 'transform_matrix' function.")
def execute_generated_code(code_string: str, test_matrix: MATRIX) -> MATRIX:
"""
Safely executes the generated Python function string on a given matrix.
"""
# Create a new local scope for execution
local_scope = {}
# Provide numpy in the scope for the generated code to use
global_scope = {"np": np}
# Execute the function definition within the specified scopes
exec(code_string, global_scope, local_scope)
# Retrieve the function from the local scope where it was defined
transform_func = local_scope.get('transform_matrix')
if not callable(transform_func):
raise ValueError("The generated code did not define a callable function named 'transform_matrix'.")
# Call the function with the test matrix and return the result
return transform_func(test_matrix)
class ARCProgram(dspy.Module):
"""A program that infers a rule, generates code for it, and then executes the code."""
def __init__(self):
super().__init__()
# Module for complex reasoning to infer the rule in natural language.
self.rule_inferrer = dspy.ChainOfThought(InferRuleSignature)
# Module to translate the natural language rule into executable Python code.
self.code_generator = dspy.Predict(GeneratePythonFunctionSignature)
def forward(self, training_examples: List[TrainingExample], test_inputs: List[MATRIX]) -> dspy.Prediction:
"""
Infers a rule, generates a Python function, and applies it to each test input.
Args:
training_examples: A list of TrainingExample objects.
test_inputs: A list of input matrices to be solved.
Returns:
A dspy.Prediction object with the 'test_outputs' field populated.
"""
# 1. Infer the transformation rule once from all training examples.
inferred = self.rule_inferrer(training_examples=training_examples)
rule = inferred.transformation_rule
# 2. Generate a Python function that implements the rule.
generated_code = self.code_generator(transformation_rule=rule)
python_code = generated_code.python_function
all_test_outputs = []
# 3. Execute the generated function for each test input.
for test_matrix in test_inputs:
try:
# Execute the code and get the result.
output_matrix = execute_generated_code(python_code, test_matrix)
all_test_outputs.append(output_matrix)
except Exception as e:
# Fallback strategy: if code generation or execution fails,
# append a default matrix to maintain output structure.
print(f"An error occurred during code execution: {e}")
print(f"Traceback: {traceback.format_exc()}")
print(f"Generated Code that failed:\n{python_code}")
# Append a default empty or zero-filled matrix.
if test_matrix and len(test_matrix) > 0 and len(test_matrix[0]) > 0:
all_test_outputs.append([([0] * len(test_matrix[0])) for _ in range(len(test_matrix))])
else:
all_test_outputs.append([])
# 4. Return the collected outputs.
return dspy.Prediction(test_outputs=all_test_outputs)
# The final 'program' object is an instance of our robust, multi-step module.
program = ARCProgram()
2025/08/29 03:18:51 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 03:20:48 INFO dspy.evaluate.evaluate: Average Metric: 2.0 / 3 (66.7%)
GEPA Optimization: 28%|██████████████████████████████▍ | 1106/4000 [5:46:52<20:26:04, 25.42s/rollouts]Iteration 61: New subsample score is not better, skipping
Iteration 62: Selected program 3 score: 0.645
Average Metric: 3.00 / 3 (100.0%): : 5it [06:39, 79.86s/it] 2025/08/29 03:27:27 INFO dspy.evaluate.evaluate: Average Metric: 3.0 / 3 (100.0%)
GEPA Optimization: 28%|██████████████████████████████▍ | 1109/4000 [5:53:31<24:24:25, 30.39s/rollouts]
Iteration 62: All subsample scores perfect. Skipping.
Iteration 62: Reflective mutation did not propose a new candidate
Iteration 63: Selected program 0 score: 0.67
Average Metric: 1.00 / 3 (33.3%): 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [01:59<00:00, 39.93s/it]2025/08/29 03:29:27 INFO dspy.evaluate.evaluate: Average Metric: 1.0 / 3 (33.3%)
Iteration 63: Proposed new text for program: import dspy
from typing import List
import pydantic
import traceback
# Define the data structures for matrices using Pydantic for validation and typing.
MATRIX = List[List[int]]
class TrainingExample(pydantic.BaseModel):
input: MATRIX
output: MATRIX
# --- Signature for the Code Generation Step ---
# This signature asks the LM to act as a programmer, converting the visual puzzle logic into a Python function.
class GeneratePythonFunctionSignature(dspy.Signature):
"""
You are an expert programmer and pattern recognition specialist. Your task is to analyze a set of input-output matrix examples that demonstrate a visual transformation rule.
Based on this analysis, write a single, self-contained Python function named `solve`.
- The function must accept one argument: `matrix` (a list of lists of integers).
- It must return a new matrix (a list of lists of integers) representing the transformed output.
- The function should be pure and not rely on any external state or libraries beyond standard Python.
- Think step-by-step to deconstruct the logic from the examples before writing the code.
- Common patterns include: finding shapes, repeating patterns, quadrant-based logic, color mapping, geometric transformations, etc.
Finally, output *only* the complete Python function code. Do not include any explanatory text before or after the function definition.
"""
training_examples: str = dspy.InputField(desc="A string representation of the training examples (input/output pairs).")
test_input_matrix: str = dspy.InputField(desc="A representative test input matrix to provide context for the required transformation.")
python_function: str = dspy.OutputField(desc="A string containing a single Python function `solve(matrix)`.")
# --- Main Signature for the Overall Task (used as a fallback) ---
# This signature is for the end-to-end task, providing high-level guidance.
class SolveTaskSignature(dspy.Signature):
"""
You are an expert in solving abstract reasoning challenges. You will be given 'training' examples demonstrating a hidden transformation rule between input and output grids.
Your task is to deduce this rule and apply it to the 'test' input grids to produce the correct output grids.
Think step-by-step:
1. **Analyze:** Carefully examine the training examples to identify the core patterns, transformations, and logic.
2. **Synthesize:** Formulate a clear, general rule that explains all the training examples.
3. **Apply:** Apply this rule meticulously to each of the test inputs to generate the final output matrices.
"""
training_examples: List[TrainingExample] = dspy.InputField(description="Input and output examples demonstrating the task.")
test_inputs: List[MATRIX] = dspy.InputField(description="Input matrices to be solved.")
test_outputs: List[MATRIX] = dspy.OutputField(description="Output matrices corresponding to the test inputs.")
# --- Custom Module Implementing the Code Generation and Execution Strategy ---
class SolveWithGeneratedCode(dspy.Module):
def __init__(self):
super().__init__()
# The primary strategy: generate and execute Python code.
self.generate_code = dspy.ChainOfThought(GeneratePythonFunctionSignature)
# The fallback strategy: use a direct ChainOfThought on the original task.
self.fallback = dspy.ChainOfThought(SolveTaskSignature)
def forward(self, training_examples: List[TrainingExample], test_inputs: List[MATRIX]):
try:
# 1. Attempt the code generation strategy.
# Convert complex objects to strings for the LM prompt.
training_examples_str = "\n".join([ex.model_dump_json(indent=2) for ex in training_examples])
test_input_matrix_str = str(test_inputs[0])
# Call the LM to generate the Python function.
generated_code = self.generate_code(
training_examples=training_examples_str,
test_input_matrix=test_input_matrix_str
).python_function
# Clean up the generated code, removing markdown fences if present.
if generated_code.startswith("```python"):
generated_code = generated_code[len("```python"):].strip()
if generated_code.endswith("```"):
generated_code = generated_code[:-len("```")].strip()
# 2. Execute the generated code in a restricted namespace.
local_namespace = {}
exec(generated_code, globals(), local_namespace)
solve_func = local_namespace.get('solve')
if not callable(solve_func):
raise ValueError("Generated code did not define a callable function named 'solve'.")
# 3. Apply the generated function to all test inputs.
outputs = [solve_func(matrix) for matrix in test_inputs]
return dspy.Prediction(test_outputs=outputs)
except Exception as e:
# 4. If any part of the primary strategy fails, revert to the fallback.
print(f"Code generation/execution failed: {e}")
print(f"Traceback: {traceback.format_exc()}")
print("Reverting to fallback strategy.")
return self.fallback(training_examples=training_examples, test_inputs=test_inputs)
# Instantiate the final program.
program = SolveWithGeneratedCode()
Code generation/execution failed: 'dict' object has no attribute 'model_dump_json'
Traceback: Traceback (most recent call last):
File "<string>", line 62, in forward
File "<string>", line 62, in <listcomp>
AttributeError: 'dict' object has no attribute 'model_dump_json'
Reverting to fallback strategy.
Code generation/execution failed: 'dict' object has no attribute 'model_dump_json'
Traceback: Traceback (most recent call last):
File "<string>", line 62, in forward
File "<string>", line 62, in <listcomp>
AttributeError: 'dict' object has no attribute 'model_dump_json'
Reverting to fallback strategy.
Code generation/execution failed: 'dict' object has no attribute 'model_dump_json'
Traceback: Traceback (most recent call last):
File "<string>", line 62, in forward
File "<string>", line 62, in <listcomp>
AttributeError: 'dict' object has no attribute 'model_dump_json'
Reverting to fallback strategy.
2025/08/29 03:31:39 INFO dspy.evaluate.evaluate: Average Metric: 1.0 / 3 (33.3%)
GEPA Optimization: 28%|██████████████████████████████▋ | 1115/4000 [5:57:43<25:26:24, 31.75s/rollouts]Iteration 63: New subsample score is not better, skipping
Iteration 64: Selected program 1 score: 0.63
Average Metric: 1.00 / 3 (33.3%): : 5it [05:32, 66.59s/it] 2025/08/29 03:37:12 INFO dspy.evaluate.evaluate: Average Metric: 1.0 / 3 (33.3%)
Iteration 64: Proposed new text for program: import dspy
from typing import List
import pydantic
import copy
import json
# Define the type for a matrix for clarity
MATRIX = List[List[int]]
class TrainingExample(pydantic.BaseModel):
"""A Pydantic model for a single training example, containing an input and output matrix."""
input: MATRIX
output: MATRIX
class PythonCode(pydantic.BaseModel):
"""A Pydantic model to structure the output of the code-generating LM."""
reasoning: str = pydantic.Field(description="A step-by-step explanation of the logic used to derive the transformation rule.")
code: str = pydantic.Field(description="A self-contained Python function named 'transform_matrix' that implements the transformation rule.")
class GeneratePythonicRuleSignature(dspy.Signature):
"""
Analyzes pairs of input/output matrices to deduce the transformation rule and express it as a Python function.
You are an expert programmer and analyst of abstract visual reasoning puzzles. Your task is to carefully examine the provided training examples. Each example consists of an 'input' matrix and a corresponding 'output' matrix.
Your goal is to deduce the single, consistent transformation rule that converts each input to its output. Then, you must implement this rule as a self-contained Python function.
**Reasoning Steps:**
1. Analyze the relationship between input and output grids across all examples.
2. Identify the core transformation patterns. Consider geometric operations (rotation, reflection, shifting), color/value changes, object-based logic (identifying shapes, properties, and interactions), fill/completion patterns, or logic based on grid properties (e.g., size, symmetry).
3. Formulate a precise, step-by-step algorithm in your reasoning.
**Python Function Implementation:**
1. Translate your algorithm into a single Python function with the exact signature: `def transform_matrix(matrix: list[list[int]]) -> list[list[int]]:`.
2. The function must be entirely self-contained. Do not call any functions defined outside of it.
3. You may use the `copy` library (e.g., `copy.deepcopy`) to avoid modifying the input matrix in place.
4. The function should correctly handle all examples provided.
5. Ensure the function returns the transformed matrix in the correct format (a list of lists of integers).
"""
training_examples: List[TrainingExample] = dspy.InputField(description="A list of input and output examples demonstrating the task.")
python_code: PythonCode = dspy.OutputField(description="A JSON object containing the reasoning and the Python function.")
class ARCProgram(dspy.Module):
"""A program that infers a rule as a Python function and then executes it to solve test inputs."""
def __init__(self):
super().__init__()
# Use ChainOfThought for the complex reasoning and code generation task.
self.rule_generator = dspy.ChainOfThought(GeneratePythonicRuleSignature)
def _create_fallback_matrix(self, matrix: MATRIX) -> MATRIX:
"""Creates a zero-filled matrix of the same dimensions as the input."""
if not matrix or not matrix[0]:
return []
return [[0] * len(matrix[0]) for _ in range(len(matrix))]
def forward(self, training_examples: List[TrainingExample], test_inputs: List[MATRIX]) -> dspy.Prediction:
"""
Generates a Python function from training examples and applies it to each test input.
Args:
training_examples: A list of TrainingExample objects.
test_inputs: A list of input matrices to be solved.
Returns:
A dspy.Prediction object with the 'test_outputs' field populated.
"""
# 1. Generate the Python function that solves the task.
prediction = self.rule_generator(training_examples=training_examples)
generated_code = prediction.python_code.code
all_test_outputs = []
try:
# 2. Prepare a safe execution environment for the generated code.
local_namespace = {}
# The generated code is instructed that it can use 'copy'.
exec_globals = {'copy': copy}
exec(generated_code, exec_globals, local_namespace)
# 3. Retrieve the generated function from the namespace.
transform_func = local_namespace['transform_matrix']
# 4. Apply the function to each test input.
for test_matrix in test_inputs:
# Pass a deepcopy to the generated function to prevent side effects.
input_copy = copy.deepcopy(test_matrix)
try:
output_matrix = transform_func(input_copy)
all_test_outputs.append(output_matrix)
except Exception:
# If the function fails on a specific matrix, fallback for that matrix.
all_test_outputs.append(self._create_fallback_matrix(test_matrix))
except Exception:
# 5. Fallback if code generation, exec, or function retrieval fails entirely.
for test_matrix in test_inputs:
all_test_outputs.append(self._create_fallback_matrix(test_matrix))
return dspy.Prediction(test_outputs=all_test_outputs)
# The final 'program' object is an instance of our robust, code-generating module.
program = ARCProgram()
2025/08/29 03:44:21 INFO dspy.evaluate.evaluate: Average Metric: 1.0 / 3 (33.3%)
GEPA Optimization: 28%|██████████████████████████████▊ | 1121/4000 [6:10:25<36:15:06, 45.33s/rollouts]Iteration 64: New subsample score is not better, skipping
Iteration 65: Selected program 3 score: 0.645
0%| | 0/3 [00:00<?, ?it/s]2025/08/29 03:48:27 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
Average Metric: 0.00 / 2 (0.0%): : 5it [07:16, 47.47s/it] 2025/08/29 03:55:08 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
Average Metric: 0.00 / 3 (0.0%): : 6it [10:47, 107.93s/it]2025/08/29 03:55:08 INFO dspy.evaluate.evaluate: Average Metric: 0.0 / 3 (0.0%)
Iteration 65: Proposed new text for program: import dspy
from typing import List, Optional
import pydantic
import copy
import json
# Define type aliases and pydantic models for clarity and structure.
MATRIX = List[List[int]]
class TrainingExample(pydantic.BaseModel):
input: MATRIX
output: MATRIX
# Pydantic models in DSPy signatures are serialized to JSON strings.
# This custom serializer provides a more compact representation for the LM prompt.
def __str__(self):
return json.dumps({'input': self.input, 'output': self.output})
class DeduceRuleSignature(dspy.Signature):
"""
Analyze a series of input/output matrix pairs from the ARC challenge.
Deduce the single, underlying transformation rule that applies to all pairs.
Describe this rule clearly, concisely, and unambiguously in natural language.
**Successful Strategies:**
- Focus on high-level concepts: Instead of pixel-by-pixel changes, think about objects, shapes, colors, symmetry, repetition, counting, or filling.
- Be precise: "Fill the area enclosed by the blue border with red" is better than "fill something".
- Step-by-step: If the transformation is complex, break it down into a logical sequence of simple operations.
**Common Task Types to Consider:**
- In-place pixel changes based on local neighbors.
- Object manipulation: moving, rotating, copying, or transforming specific shapes.
- Subgrid extraction or dimension changes: The output grid may be a different size than the input.
- Drawing lines or patterns based on the location of certain key pixels.
**Pitfalls to Avoid:**
- Do not over-generalize. If a rule seems to apply to some shapes of a certain color but not others, there must be an additional condition you are missing. Explicitly check for these counter-examples within the provided pairs.
"""
training_examples: List[TrainingExample] = dspy.InputField(desc="A list of input/output pairs demonstrating the transformation rule.")
rule_description: str = dspy.OutputField(desc="A clear, natural language description of the transformation rule.")
class GeneratePythonFunctionFromRuleSignature(dspy.Signature):
"""
You are an expert programmer. Your task is to write a single, self-contained Python function based on a provided rule description and example matrix pairs.
**Primary Instruction:**
Your main goal is to faithfully implement the logic described in the `rule_description`. The `training_examples` are for context and to understand the data format, but the `rule_description` is your primary source of truth for the algorithm. Do not invent new logic not present in the rule description.
**Function Requirements:**
- The function must be named `transform_matrix`.
- It must accept one argument: `matrix`, which is a list of lists of integers.
- It must return a list of lists of integers.
- It should not use any external libraries except for `copy`.
- The output matrix may have different dimensions than the input matrix. Your function should create a new matrix of the correct size if the rule implies it.
- Your output must be ONLY the Python code for the function. Do not include any explanations, comments outside the function, or markdown formatting like ```python.
"""
training_examples: List[TrainingExample] = dspy.InputField(desc="A list of input/output pairs for context.")
rule_description: str = dspy.InputField(desc="The natural language description of the rule to implement.")
python_function: str = dspy.OutputField(desc="A string containing a single Python function `transform_matrix` that implements the rule.")
class RefineRuleSignature(dspy.Signature):
"""
Analyze a transformation rule and feedback detailing how it fails on a specific example.
Your task is to produce a refined, corrected rule that addresses the failure.
**Successful Strategies:**
- Focus on the specific discrepancy. If the feedback says "row 2 should not have been changed", analyze the properties of row 2 in the input that differentiate it from rows that *were* changed correctly.
- Propose a specific, concrete change. Instead of "the rule is wrong", say "The rule should be modified to only apply when the row below is empty".
- If the original rule is completely wrong (e.g., it modifies pixels when the grid dimensions should change), discard it and propose a new high-level concept (e.g., "The rule is not about pixel modification, but about extracting the 5x5 subgrid that contains the most unique colors.").
"""
original_rule: str = dspy.InputField(desc="The initial, flawed rule description.")
feedback: str = dspy.InputField(desc="Detailed feedback on which training example failed and how the output was incorrect.")
refined_rule: str = dspy.OutputField(desc="A new, corrected version of the rule that accounts for the feedback.")
class IterativeARCSolver(dspy.Module):
"""A module that solves ARC tasks by iteratively deducing and refining a rule."""
def __init__(self, refinement_iterations: int = 3):
super().__init__()
self.refinement_iterations = refinement_iterations
self.rule_deducer = dspy.ChainOfThought(DeduceRuleSignature)
self.code_generator = dspy.Predict(GeneratePythonFunctionFromRuleSignature)
self.rule_refiner = dspy.ChainOfThought(RefineRuleSignature)
def _execute_code(self, python_code: str) -> Optional[callable]:
"""Safely execute the generated Python code and return the transform function."""
if not python_code or "def transform_matrix" not in python_code:
return None
local_scope = {}
try:
exec(python_code, globals(), local_scope)
transform_func = local_scope.get('transform_matrix')
return transform_func if callable(transform_func) else None
except Exception:
return None
def forward(self, training_examples: List[TrainingExample], test_inputs: List[MATRIX]):
# Step 1: Deduce the initial transformation rule.
deduction = self.rule_deducer(training_examples=training_examples)
current_rule = deduction.rule_description
transform_func = None
# Step 2: Iteratively refine the rule and generate/verify code.
for i in range(self.refinement_iterations):
# Generate code for the current rule.
generation = self.code_generator(
training_examples=training_examples,
rule_description=current_rule
)
python_code = generation.python_function
# Execute the code to get the function.
candidate_func = self._execute_code(python_code)
if not candidate_func:
# If code generation or execution fails, we can't proceed with this rule.
# We will rely on the last valid function or fallback.
break
transform_func = candidate_func # Store the latest valid function
# Verify the function against all training examples.
mismatch_found = False
for example in training_examples:
try:
predicted_output = transform_func(copy.deepcopy(example.input))
if predicted_output != example.output:
# Mismatch found, prepare feedback for the refiner.
feedback = (
f"The rule failed on a training example.\n"
f"Input Matrix:\n{example.input}\n\n"
f"Your Code's Output:\n{predicted_output}\n\n"
f"Correct Output:\n{example.output}\n\n"
f"Please analyze the discrepancy and provide a corrected rule."
)
refinement = self.rule_refiner(original_rule=current_rule, feedback=feedback)
current_rule = refinement.refined_rule
mismatch_found = True
break # Break from example loop to start next refinement iteration
except Exception as e:
# Function failed to run, provide this as feedback.
feedback = (
f"The code failed to execute on a training example with error: {e}\n"
f"Input Matrix:\n{example.input}\n\n"
f"Please refine the rule to prevent this error."
)
refinement = self.rule_refiner(original_rule=current_rule, feedback=feedback)
current_rule = refinement.refined_rule
mismatch_found = True
break
if not mismatch_found:
# If we looped through all examples with no mismatches, the rule is correct.
break
# Step 3: Apply the final, best-effort function to the test inputs.
fallback_outputs = [copy.deepcopy(matrix) for matrix in test_inputs]
if not transform_func:
return dspy.Prediction(test_outputs=fallback_outputs)
solved_outputs = []
for test_matrix in test_inputs:
try:
result = transform_func(copy.deepcopy(test_matrix))
solved_outputs.append(result)
except Exception:
# If the function fails on a test case, use the original matrix as a fallback.
solved_outputs.append(copy.deepcopy(test_matrix))
return dspy.Prediction(test_outputs=solved_outputs)
# The overall task signature defines the final input/output of the program.
class SolveTaskSignature(dspy.Signature):
"""
Given a set of training examples demonstrating a matrix transformation,
apply the same transformation to a new set of test matrices.
"""
training_examples: List[TrainingExample] = dspy.InputField(description="Input and output examples demonstrating the task to be performed.")
test_inputs: List[MATRIX] = dspy.InputField(description="Input matrices to be solved following the task described in the training examples.")
test_outputs: List[MATRIX] = dspy.OutputField(description="Output matrices corresponding to the test inputs.")
# The final program is an instance of our new, more robust iterative module.
program = IterativeARCSolver()
2025/08/29 04:00:36 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 04:02:57 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 8, 8, 8, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 8, 0, 8, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 8, 8, 8, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 8, 8, 8, 0, 3, 3, 3, 0, 3, 3, 3, 0, 3, 3, 3], [0, 0, 0, 0, 0, 0, 8, 0, 8, 0, 3, 0, 3, 0, 3, 0, 3, 0, 3, 0, 3], [0, 0, 0, 0, 0, 0, 8, 8, 8, 0, 3, 3, 3, 0, 3, 3, 3, 0, 3, 3, 3], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 2, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 2, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 2, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}, {'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 1, 1, 1, 0, 4, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 1, 0, 0, 0, 4, 0, 0, 0, 4], [2, 2, 0, 2, 2, 2, 0, 2, 2, 2, 0, 1, 1, 1, 0, 4, 4, 4, 0, 4, 4], [2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 1, 0, 0, 0, 4, 0, 0, 0, 4], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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0, 0, 0, 0, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 3, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]]}) (input_keys={'test_inputs', 'training_examples'}): 'dict' object has no attribute 'input'
Traceback (most recent call last):
File "<string>", line 126, in forward
AttributeError: 'dict' object has no attribute 'input'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 144, in forward
AttributeError: 'dict' object has no attribute 'input'
2025/08/29 04:03:19 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [8, 8, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 8, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 8, 8, 8, 8, 8, 8, 8], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [8, 8, 8, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 8, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [8, 8, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 8, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [8, 8, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 8, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 8, 8, 8, 8, 8, 8, 8], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [8, 8, 8, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 8, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [8, 8, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 8, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 1, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}, {'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [8, 8, 8, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [8, 8, 8, 8, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [8, 8, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [8, 8, 8, 1, 1, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [8, 8, 8, 8, 1, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [8, 8, 1, 1, 1, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}, {'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [8, 0, 8, 0, 8, 0, 8, 0, 8, 0], [0, 8, 0, 8, 0, 8, 0, 8, 0, 8], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [8, 0, 8, 0, 0, 0, 0, 0, 0, 0], [0, 8, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [8, 0, 8, 0, 8, 0, 0, 0, 0, 0], [0, 8, 0, 8, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [8, 0, 8, 0, 8, 0, 8, 0, 8, 0], [0, 8, 0, 8, 0, 8, 0, 8, 0, 8], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [8, 0, 8, 0, 1, 0, 1, 0, 1, 0], [0, 8, 0, 1, 0, 1, 0, 1, 0, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [8, 0, 8, 0, 8, 0, 1, 0, 1, 0], [0, 8, 0, 8, 0, 1, 0, 1, 0, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}], 'test_inputs': [[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 8, 8, 8, 8, 8, 8, 8, 8], [8, 8, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 8, 8, 8, 8, 8, 8, 8, 8], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 8, 8, 0, 0, 0, 0, 0], [8, 8, 8, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 8, 8, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]], 'test_outputs': [[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 8, 8, 8, 8, 8, 8, 8, 8], [8, 8, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 8, 8, 8, 8, 8, 8, 8, 8], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 8, 8, 1, 1, 1, 1, 1], [8, 8, 8, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 8, 8, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]]}) (input_keys={'test_inputs', 'training_examples'}): 'dict' object has no attribute 'input'
Traceback (most recent call last):
File "<string>", line 126, in forward
AttributeError: 'dict' object has no attribute 'input'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 144, in forward
AttributeError: 'dict' object has no attribute 'input'
2025/08/29 04:03:39 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[2, 1, 2, 2, 6, 5, 5, 6, 6, 5, 5, 6, 2, 2, 1, 2], [1, 6, 6, 1, 5, 6, 5, 2, 2, 5, 6, 5, 1, 6, 6, 1], [2, 6, 1, 6, 5, 5, 5, 2, 2, 5, 5, 5, 6, 1, 6, 2], [2, 1, 6, 6, 6, 2, 2, 2, 2, 2, 2, 6, 6, 6, 1, 2], [6, 5, 5, 6, 5, 8, 5, 7, 7, 5, 8, 5, 6, 5, 5, 6], [5, 6, 5, 2, 8, 8, 5, 8, 8, 3, 3, 3, 3, 3, 6, 5], [5, 5, 5, 2, 5, 5, 5, 8, 8, 3, 3, 3, 3, 3, 5, 5], [6, 2, 2, 2, 7, 8, 8, 8, 8, 3, 3, 3, 3, 3, 2, 6], [6, 2, 2, 2, 7, 8, 8, 8, 8, 3, 3, 3, 3, 3, 2, 6], [5, 5, 5, 2, 5, 5, 5, 8, 8, 3, 3, 3, 3, 3, 5, 5], [5, 6, 5, 2, 8, 8, 5, 8, 8, 5, 8, 8, 2, 5, 6, 5], [6, 5, 5, 6, 5, 8, 5, 7, 7, 5, 8, 5, 6, 5, 5, 6], [2, 1, 6, 6, 6, 2, 2, 2, 2, 2, 2, 6, 6, 6, 1, 2], [2, 6, 1, 6, 5, 5, 5, 2, 2, 5, 5, 5, 6, 1, 6, 2], [1, 6, 6, 1, 5, 6, 5, 2, 2, 5, 6, 5, 1, 6, 6, 1], [2, 1, 2, 2, 6, 5, 5, 6, 6, 5, 5, 6, 2, 2, 1, 2]], 'output': [[5, 8, 8, 2, 5], [5, 5, 5, 2, 5], [8, 8, 7, 2, 2], [8, 8, 7, 2, 2], [5, 5, 5, 2, 5]]}, {'input': [[8, 9, 9, 3, 3, 3, 3, 3, 2, 2, 7, 7, 8, 9, 9, 8], [9, 8, 9, 3, 3, 3, 3, 3, 2, 7, 1, 7, 9, 9, 8, 9], [9, 9, 8, 3, 3, 3, 3, 3, 7, 2, 7, 2, 2, 8, 9, 9], [8, 9, 2, 3, 3, 3, 3, 3, 1, 7, 2, 2, 9, 2, 9, 8], [7, 7, 2, 3, 3, 3, 3, 3, 7, 8, 7, 2, 2, 2, 7, 7], [7, 1, 7, 2, 7, 2, 7, 7, 7, 7, 2, 7, 2, 7, 1, 7], [2, 7, 2, 7, 8, 7, 2, 8, 8, 2, 7, 8, 7, 2, 7, 2], [2, 2, 7, 1, 7, 7, 8, 2, 2, 8, 7, 7, 1, 7, 2, 2], [2, 2, 7, 1, 7, 7, 8, 2, 2, 8, 7, 7, 1, 7, 2, 2], [2, 7, 2, 7, 8, 7, 2, 8, 8, 2, 7, 8, 7, 2, 7, 2], [7, 1, 7, 2, 7, 2, 7, 7, 7, 7, 2, 7, 2, 7, 1, 7], [7, 7, 2, 2, 2, 7, 8, 7, 7, 8, 7, 2, 2, 2, 7, 7], [8, 9, 2, 9, 2, 2, 7, 1, 1, 7, 2, 2, 9, 2, 9, 8], [9, 9, 8, 2, 2, 7, 2, 7, 7, 2, 7, 2, 2, 8, 9, 9], [9, 8, 9, 9, 7, 1, 7, 2, 2, 7, 1, 7, 9, 9, 8, 9], [8, 9, 9, 8, 7, 7, 2, 2, 2, 2, 7, 7, 8, 9, 9, 8]], 'output': [[8, 7, 7, 2, 2], [9, 7, 1, 7, 2], [2, 2, 7, 2, 7], [9, 2, 2, 7, 1], [2, 2, 7, 8, 7]]}, {'input': [[2, 2, 5, 2, 9, 9, 9, 3, 3, 3, 3, 3, 2, 5, 2, 2], [2, 5, 4, 4, 9, 5, 2, 3, 3, 3, 3, 3, 4, 4, 5, 2], [5, 4, 5, 4, 9, 2, 5, 3, 3, 3, 3, 3, 4, 5, 4, 5], [2, 4, 4, 4, 5, 9, 5, 3, 3, 3, 3, 3, 4, 4, 4, 2], [9, 9, 9, 5, 9, 6, 9, 3, 3, 3, 3, 3, 5, 9, 9, 9], [9, 5, 2, 9, 6, 6, 9, 9, 9, 9, 6, 6, 9, 2, 5, 9], [9, 2, 5, 5, 9, 9, 7, 9, 9, 7, 9, 9, 5, 5, 2, 9], [5, 9, 5, 2, 9, 9, 9, 6, 6, 9, 9, 9, 2, 5, 9, 5], [5, 9, 5, 2, 9, 9, 9, 6, 6, 9, 9, 9, 2, 5, 9, 5], [9, 2, 5, 5, 9, 9, 7, 9, 9, 7, 9, 9, 5, 5, 2, 9], [9, 5, 2, 9, 6, 6, 9, 9, 9, 9, 6, 6, 9, 2, 5, 9], [9, 9, 9, 5, 9, 6, 9, 9, 9, 9, 6, 9, 5, 9, 9, 9], [2, 4, 4, 4, 5, 9, 5, 2, 2, 5, 9, 5, 4, 4, 4, 2], [5, 4, 5, 4, 9, 2, 5, 5, 5, 5, 2, 9, 4, 5, 4, 5], [2, 5, 4, 4, 9, 5, 2, 9, 9, 2, 5, 9, 4, 4, 5, 2], [2, 2, 5, 2, 9, 9, 9, 5, 5, 9, 9, 9, 2, 5, 2, 2]], 'output': [[5, 5, 9, 9, 9], [9, 9, 2, 5, 9], [5, 5, 5, 2, 9], [2, 2, 5, 9, 5], [9, 9, 9, 6, 9]]}], 'test_inputs': [[[5, 5, 2, 5, 2, 5, 5, 5, 5, 5, 5, 2, 5, 2, 5, 5], [5, 2, 2, 5, 5, 5, 2, 2, 2, 2, 5, 5, 5, 2, 2, 5], [2, 2, 5, 8, 5, 2, 2, 5, 5, 2, 2, 5, 8, 5, 2, 2], [5, 5, 8, 5, 5, 2, 5, 5, 5, 5, 2, 5, 5, 8, 5, 5], [2, 5, 5, 5, 4, 6, 6, 9, 3, 3, 3, 3, 3, 5, 5, 2], [5, 5, 2, 2, 6, 6, 9, 9, 3, 3, 3, 3, 3, 2, 5, 5], [5, 2, 2, 5, 6, 9, 6, 9, 3, 3, 3, 3, 3, 2, 2, 5], [5, 2, 5, 5, 9, 9, 9, 9, 3, 3, 3, 3, 3, 5, 2, 5], [5, 2, 5, 5, 9, 9, 9, 9, 3, 3, 3, 3, 3, 5, 2, 5], [5, 2, 2, 5, 6, 9, 6, 9, 9, 6, 9, 6, 5, 2, 2, 5], [5, 5, 2, 2, 6, 6, 9, 9, 9, 9, 6, 6, 2, 2, 5, 5], [2, 5, 5, 5, 4, 6, 6, 9, 9, 6, 6, 4, 5, 5, 5, 2], [5, 5, 8, 5, 5, 2, 5, 5, 5, 5, 2, 5, 5, 8, 5, 5], [2, 2, 5, 8, 5, 2, 2, 5, 5, 2, 2, 5, 8, 5, 2, 2], [5, 2, 2, 5, 5, 5, 2, 2, 2, 2, 5, 5, 5, 2, 2, 5], [5, 5, 2, 5, 2, 5, 5, 5, 5, 5, 5, 2, 5, 2, 5, 5]]], 'test_outputs': [[[9, 6, 6, 4, 5], [9, 9, 6, 6, 2], [9, 6, 9, 6, 5], [9, 9, 9, 9, 5], [9, 9, 9, 9, 5]]]}) (input_keys={'test_inputs', 'training_examples'}): 'dict' object has no attribute 'input'
Traceback (most recent call last):
File "<string>", line 126, in forward
AttributeError: 'dict' object has no attribute 'input'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 144, in forward
AttributeError: 'dict' object has no attribute 'input'
2025/08/29 04:04:02 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [8, 8, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 8, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 8, 8, 8, 8, 8, 8, 8], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [8, 8, 8, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 8, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [8, 8, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 8, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [8, 8, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 8, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 8, 8, 8, 8, 8, 8, 8], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [8, 8, 8, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 8, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [8, 8, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 8, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 1, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}, {'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [8, 8, 8, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [8, 8, 8, 8, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [8, 8, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [8, 8, 8, 1, 1, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [8, 8, 8, 8, 1, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [8, 8, 1, 1, 1, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}, {'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [8, 0, 8, 0, 8, 0, 8, 0, 8, 0], [0, 8, 0, 8, 0, 8, 0, 8, 0, 8], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [8, 0, 8, 0, 0, 0, 0, 0, 0, 0], [0, 8, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [8, 0, 8, 0, 8, 0, 0, 0, 0, 0], [0, 8, 0, 8, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [8, 0, 8, 0, 8, 0, 8, 0, 8, 0], [0, 8, 0, 8, 0, 8, 0, 8, 0, 8], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [8, 0, 8, 0, 1, 0, 1, 0, 1, 0], [0, 8, 0, 1, 0, 1, 0, 1, 0, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [8, 0, 8, 0, 8, 0, 1, 0, 1, 0], [0, 8, 0, 8, 0, 1, 0, 1, 0, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}], 'test_inputs': [[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 8, 8, 8, 8, 8, 8, 8, 8], [8, 8, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 8, 8, 8, 8, 8, 8, 8, 8], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 8, 8, 0, 0, 0, 0, 0], [8, 8, 8, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 8, 8, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]], 'test_outputs': [[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 8, 8, 8, 8, 8, 8, 8, 8], [8, 8, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 8, 8, 8, 8, 8, 8, 8, 8], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 8, 8, 1, 1, 1, 1, 1], [8, 8, 8, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 8, 8, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]]}) (input_keys={'test_inputs', 'training_examples'}): 'dict' object has no attribute 'input'
Traceback (most recent call last):
File "<string>", line 126, in forward
AttributeError: 'dict' object has no attribute 'input'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 144, in forward
AttributeError: 'dict' object has no attribute 'input'
2025/08/29 04:04:21 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 8, 8, 8, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 8, 0, 8, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 8, 8, 8, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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[0, 0, 0, 0, 0, 0, 8, 8, 8, 0, 3, 3, 3, 0, 3, 3, 3, 0, 3, 3, 3], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 2, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 2, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 2, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}, {'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 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0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 1, 0, 0, 0, 4, 0, 0, 0, 4], [2, 2, 0, 2, 2, 2, 0, 2, 2, 2, 0, 1, 1, 1, 0, 4, 4, 4, 0, 4, 4], [2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 1, 0, 0, 0, 4, 0, 0, 0, 4], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 6, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 6, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 5, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1]]}], 'test_inputs': [[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 8, 8, 8, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 8, 0, 8, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 8, 0, 8, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]], 'test_outputs': [[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 4, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 4, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 8, 8, 8, 0, 2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2], [0, 0, 0, 0, 0, 0, 8, 0, 8, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2], [0, 0, 0, 0, 0, 0, 8, 0, 8, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 3, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 3, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 3, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 3, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 3, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]]}) (input_keys={'test_inputs', 'training_examples'}): 'dict' object has no attribute 'input'
Traceback (most recent call last):
File "<string>", line 126, in forward
AttributeError: 'dict' object has no attribute 'input'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 144, in forward
AttributeError: 'dict' object has no attribute 'input'
2025/08/29 04:04:51 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 04:04:55 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 8, 8, 8, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 8, 0, 8, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 8, 8, 8, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 8, 8, 8, 0, 3, 3, 3, 0, 3, 3, 3, 0, 3, 3, 3], [0, 0, 0, 0, 0, 0, 8, 0, 8, 0, 3, 0, 3, 0, 3, 0, 3, 0, 3, 0, 3], [0, 0, 0, 0, 0, 0, 8, 8, 8, 0, 3, 3, 3, 0, 3, 3, 3, 0, 3, 3, 3], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 2, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 2, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 2, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}, {'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 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0, 0, 0, 0, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 3, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]]}) (input_keys={'test_inputs', 'training_examples'}): 'dict' object has no attribute 'input'
Traceback (most recent call last):
File "<string>", line 126, in forward
AttributeError: 'dict' object has no attribute 'input'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 144, in forward
AttributeError: 'dict' object has no attribute 'input'
2025/08/29 04:05:21 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [8, 8, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 8, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 8, 8, 8, 8, 8, 8, 8], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [8, 8, 8, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 8, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [8, 8, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 8, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [8, 8, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 8, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 8, 8, 8, 8, 8, 8, 8], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [8, 8, 8, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 8, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [8, 8, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 8, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 1, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}, {'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [8, 8, 8, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [8, 8, 8, 8, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [8, 8, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [8, 8, 8, 1, 1, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [8, 8, 8, 8, 1, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [8, 8, 1, 1, 1, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}, {'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [8, 0, 8, 0, 8, 0, 8, 0, 8, 0], [0, 8, 0, 8, 0, 8, 0, 8, 0, 8], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [8, 0, 8, 0, 0, 0, 0, 0, 0, 0], [0, 8, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [8, 0, 8, 0, 8, 0, 0, 0, 0, 0], [0, 8, 0, 8, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [8, 0, 8, 0, 8, 0, 8, 0, 8, 0], [0, 8, 0, 8, 0, 8, 0, 8, 0, 8], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [8, 0, 8, 0, 1, 0, 1, 0, 1, 0], [0, 8, 0, 1, 0, 1, 0, 1, 0, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [8, 0, 8, 0, 8, 0, 1, 0, 1, 0], [0, 8, 0, 8, 0, 1, 0, 1, 0, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}], 'test_inputs': [[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 8, 8, 8, 8, 8, 8, 8, 8], [8, 8, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 8, 8, 8, 8, 8, 8, 8, 8], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 8, 8, 0, 0, 0, 0, 0], [8, 8, 8, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 8, 8, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]], 'test_outputs': [[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 8, 8, 8, 8, 8, 8, 8, 8], [8, 8, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 8, 8, 8, 8, 8, 8, 8, 8], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 8, 8, 1, 1, 1, 1, 1], [8, 8, 8, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 8, 8, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]]}) (input_keys={'test_inputs', 'training_examples'}): 'dict' object has no attribute 'input'
Traceback (most recent call last):
File "<string>", line 126, in forward
AttributeError: 'dict' object has no attribute 'input'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 144, in forward
AttributeError: 'dict' object has no attribute 'input'
2025/08/29 04:09:07 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[2, 1, 2, 2, 6, 5, 5, 6, 6, 5, 5, 6, 2, 2, 1, 2], [1, 6, 6, 1, 5, 6, 5, 2, 2, 5, 6, 5, 1, 6, 6, 1], [2, 6, 1, 6, 5, 5, 5, 2, 2, 5, 5, 5, 6, 1, 6, 2], [2, 1, 6, 6, 6, 2, 2, 2, 2, 2, 2, 6, 6, 6, 1, 2], [6, 5, 5, 6, 5, 8, 5, 7, 7, 5, 8, 5, 6, 5, 5, 6], [5, 6, 5, 2, 8, 8, 5, 8, 8, 3, 3, 3, 3, 3, 6, 5], [5, 5, 5, 2, 5, 5, 5, 8, 8, 3, 3, 3, 3, 3, 5, 5], [6, 2, 2, 2, 7, 8, 8, 8, 8, 3, 3, 3, 3, 3, 2, 6], [6, 2, 2, 2, 7, 8, 8, 8, 8, 3, 3, 3, 3, 3, 2, 6], [5, 5, 5, 2, 5, 5, 5, 8, 8, 3, 3, 3, 3, 3, 5, 5], [5, 6, 5, 2, 8, 8, 5, 8, 8, 5, 8, 8, 2, 5, 6, 5], [6, 5, 5, 6, 5, 8, 5, 7, 7, 5, 8, 5, 6, 5, 5, 6], [2, 1, 6, 6, 6, 2, 2, 2, 2, 2, 2, 6, 6, 6, 1, 2], [2, 6, 1, 6, 5, 5, 5, 2, 2, 5, 5, 5, 6, 1, 6, 2], [1, 6, 6, 1, 5, 6, 5, 2, 2, 5, 6, 5, 1, 6, 6, 1], [2, 1, 2, 2, 6, 5, 5, 6, 6, 5, 5, 6, 2, 2, 1, 2]], 'output': [[5, 8, 8, 2, 5], [5, 5, 5, 2, 5], [8, 8, 7, 2, 2], [8, 8, 7, 2, 2], [5, 5, 5, 2, 5]]}, {'input': [[8, 9, 9, 3, 3, 3, 3, 3, 2, 2, 7, 7, 8, 9, 9, 8], [9, 8, 9, 3, 3, 3, 3, 3, 2, 7, 1, 7, 9, 9, 8, 9], [9, 9, 8, 3, 3, 3, 3, 3, 7, 2, 7, 2, 2, 8, 9, 9], [8, 9, 2, 3, 3, 3, 3, 3, 1, 7, 2, 2, 9, 2, 9, 8], [7, 7, 2, 3, 3, 3, 3, 3, 7, 8, 7, 2, 2, 2, 7, 7], [7, 1, 7, 2, 7, 2, 7, 7, 7, 7, 2, 7, 2, 7, 1, 7], [2, 7, 2, 7, 8, 7, 2, 8, 8, 2, 7, 8, 7, 2, 7, 2], [2, 2, 7, 1, 7, 7, 8, 2, 2, 8, 7, 7, 1, 7, 2, 2], [2, 2, 7, 1, 7, 7, 8, 2, 2, 8, 7, 7, 1, 7, 2, 2], [2, 7, 2, 7, 8, 7, 2, 8, 8, 2, 7, 8, 7, 2, 7, 2], [7, 1, 7, 2, 7, 2, 7, 7, 7, 7, 2, 7, 2, 7, 1, 7], [7, 7, 2, 2, 2, 7, 8, 7, 7, 8, 7, 2, 2, 2, 7, 7], [8, 9, 2, 9, 2, 2, 7, 1, 1, 7, 2, 2, 9, 2, 9, 8], [9, 9, 8, 2, 2, 7, 2, 7, 7, 2, 7, 2, 2, 8, 9, 9], [9, 8, 9, 9, 7, 1, 7, 2, 2, 7, 1, 7, 9, 9, 8, 9], [8, 9, 9, 8, 7, 7, 2, 2, 2, 2, 7, 7, 8, 9, 9, 8]], 'output': [[8, 7, 7, 2, 2], [9, 7, 1, 7, 2], [2, 2, 7, 2, 7], [9, 2, 2, 7, 1], [2, 2, 7, 8, 7]]}, {'input': [[2, 2, 5, 2, 9, 9, 9, 3, 3, 3, 3, 3, 2, 5, 2, 2], [2, 5, 4, 4, 9, 5, 2, 3, 3, 3, 3, 3, 4, 4, 5, 2], [5, 4, 5, 4, 9, 2, 5, 3, 3, 3, 3, 3, 4, 5, 4, 5], [2, 4, 4, 4, 5, 9, 5, 3, 3, 3, 3, 3, 4, 4, 4, 2], [9, 9, 9, 5, 9, 6, 9, 3, 3, 3, 3, 3, 5, 9, 9, 9], [9, 5, 2, 9, 6, 6, 9, 9, 9, 9, 6, 6, 9, 2, 5, 9], [9, 2, 5, 5, 9, 9, 7, 9, 9, 7, 9, 9, 5, 5, 2, 9], [5, 9, 5, 2, 9, 9, 9, 6, 6, 9, 9, 9, 2, 5, 9, 5], [5, 9, 5, 2, 9, 9, 9, 6, 6, 9, 9, 9, 2, 5, 9, 5], [9, 2, 5, 5, 9, 9, 7, 9, 9, 7, 9, 9, 5, 5, 2, 9], [9, 5, 2, 9, 6, 6, 9, 9, 9, 9, 6, 6, 9, 2, 5, 9], [9, 9, 9, 5, 9, 6, 9, 9, 9, 9, 6, 9, 5, 9, 9, 9], [2, 4, 4, 4, 5, 9, 5, 2, 2, 5, 9, 5, 4, 4, 4, 2], [5, 4, 5, 4, 9, 2, 5, 5, 5, 5, 2, 9, 4, 5, 4, 5], [2, 5, 4, 4, 9, 5, 2, 9, 9, 2, 5, 9, 4, 4, 5, 2], [2, 2, 5, 2, 9, 9, 9, 5, 5, 9, 9, 9, 2, 5, 2, 2]], 'output': [[5, 5, 9, 9, 9], [9, 9, 2, 5, 9], [5, 5, 5, 2, 9], [2, 2, 5, 9, 5], [9, 9, 9, 6, 9]]}], 'test_inputs': [[[5, 5, 2, 5, 2, 5, 5, 5, 5, 5, 5, 2, 5, 2, 5, 5], [5, 2, 2, 5, 5, 5, 2, 2, 2, 2, 5, 5, 5, 2, 2, 5], [2, 2, 5, 8, 5, 2, 2, 5, 5, 2, 2, 5, 8, 5, 2, 2], [5, 5, 8, 5, 5, 2, 5, 5, 5, 5, 2, 5, 5, 8, 5, 5], [2, 5, 5, 5, 4, 6, 6, 9, 3, 3, 3, 3, 3, 5, 5, 2], [5, 5, 2, 2, 6, 6, 9, 9, 3, 3, 3, 3, 3, 2, 5, 5], [5, 2, 2, 5, 6, 9, 6, 9, 3, 3, 3, 3, 3, 2, 2, 5], [5, 2, 5, 5, 9, 9, 9, 9, 3, 3, 3, 3, 3, 5, 2, 5], [5, 2, 5, 5, 9, 9, 9, 9, 3, 3, 3, 3, 3, 5, 2, 5], [5, 2, 2, 5, 6, 9, 6, 9, 9, 6, 9, 6, 5, 2, 2, 5], [5, 5, 2, 2, 6, 6, 9, 9, 9, 9, 6, 6, 2, 2, 5, 5], [2, 5, 5, 5, 4, 6, 6, 9, 9, 6, 6, 4, 5, 5, 5, 2], [5, 5, 8, 5, 5, 2, 5, 5, 5, 5, 2, 5, 5, 8, 5, 5], [2, 2, 5, 8, 5, 2, 2, 5, 5, 2, 2, 5, 8, 5, 2, 2], [5, 2, 2, 5, 5, 5, 2, 2, 2, 2, 5, 5, 5, 2, 2, 5], [5, 5, 2, 5, 2, 5, 5, 5, 5, 5, 5, 2, 5, 2, 5, 5]]], 'test_outputs': [[[9, 6, 6, 4, 5], [9, 9, 6, 6, 2], [9, 6, 9, 6, 5], [9, 9, 9, 9, 5], [9, 9, 9, 9, 5]]]}) (input_keys={'test_inputs', 'training_examples'}): 'dict' object has no attribute 'input'
Traceback (most recent call last):
File "<string>", line 126, in forward
AttributeError: 'dict' object has no attribute 'input'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 144, in forward
AttributeError: 'dict' object has no attribute 'input'
2025/08/29 04:09:07 INFO dspy.evaluate.evaluate: Average Metric: 0.0 / 3 (0.0%)
GEPA Optimization: 28%|██████████████████████████████▉ | 1127/4000 [6:35:11<63:32:00, 79.61s/rollouts]Iteration 65: New subsample score is not better, skipping
Iteration 66: Selected program 1 score: 0.63
Average Metric: 1.00 / 3 (33.3%): : 4it [04:01, 60.31s/it] 2025/08/29 04:13:08 INFO dspy.evaluate.evaluate: Average Metric: 1.0 / 3 (33.3%)
Iteration 66: Proposed new text for program: import dspy
from typing import List
import pydantic
import traceback
# Define the type for a matrix for clarity
MATRIX = List[List[int]]
class TrainingExample(pydantic.BaseModel):
"""A Pydantic model for a single training example, containing an input and output matrix."""
input: MATRIX
output: MATRIX
class GeneratePythonFunctionSignature(dspy.Signature):
"""
Analyzes pairs of input/output matrices to deduce the transformation rule and express it as a Python function.
You are an expert programmer and a specialist in abstract visual reasoning. Your task is to analyze training examples, each with an 'input' and 'output' matrix.
1. **Reason Step-by-Step:** First, in the 'reasoning' field, articulate the transformation rule in clear natural language. Describe the pattern, logic, and any intermediate steps.
2. **Write a Python Function:** Based on your reasoning, write a single, complete, and self-contained Python function named `transform_matrix`.
**Function Requirements:**
- It must be named `transform_matrix`.
- It must accept one argument: `matrix` (a list of lists of integers).
- It must return a new matrix (a list of lists of integers) representing the transformed output.
- The function must be pure; it should not rely on any external state or variables defined outside of its scope.
- You can use standard Python libraries, but avoid dependencies outside the standard library.
- Ensure the code is robust and handles edge cases observed in the examples (e.g., empty matrices).
**Example of a valid function format:**
```python
def transform_matrix(matrix: list[list[int]]) -> list[list[int]]:
# Your transformation logic here
# ...
return new_matrix
```
"""
training_examples: List[TrainingExample] = dspy.InputField(description="A list of input and output examples demonstrating the task.")
feedback: str = dspy.InputField(description="Feedback on the previous attempt's code, indicating which examples failed. Use this to correct your function.", default="")
reasoning: str = dspy.OutputField(description="A step-by-step explanation of the logic behind the transformation.")
python_function: str = dspy.OutputField(description="A self-contained Python function `transform_matrix(matrix)` that implements the rule, enclosed in markdown code fences.")
class ARCProgram(dspy.Module):
"""A program that infers a Python function from examples, verifies it, and then applies it."""
def __init__(self, max_retries=2):
super().__init__()
# Use ChainOfThought to encourage reasoning before generating code.
self.code_generator = dspy.ChainOfThought(GeneratePythonFunctionSignature)
self.max_retries = max_retries
def _extract_python_code(self, text: str) -> str:
"""Extracts Python code from a markdown block."""
if "```python" in text:
return text.split("```python\n")[1].split("```")[0]
return text
def _get_zeroed_matrix(self, matrix: MATRIX) -> MATRIX:
"""Creates a zero-filled matrix of the same dimensions as the input."""
if not matrix or not isinstance(matrix, list) or not isinstance(matrix[0], list):
return []
return [[0] * len(matrix[0]) for _ in range(len(matrix))]
def forward(self, training_examples: List[TrainingExample], test_inputs: List[MATRIX]) -> dspy.Prediction:
feedback = ""
transform_func = None
for attempt in range(self.max_retries):
try:
# 1. Generate the Python function.
prediction = self.code_generator(training_examples=training_examples, feedback=feedback)
code_to_exec = self._extract_python_code(prediction.python_function)
# 2. Execute the generated code to define the function.
local_scope = {}
exec(code_to_exec, globals(), local_scope)
current_func = local_scope.get('transform_matrix')
if not callable(current_func):
raise ValueError("`transform_matrix` function not found or is not callable.")
# 3. Verify the function against all training examples.
is_correct = True
failures = []
for i, example in enumerate(training_examples):
input_matrix = example.input
expected_output = example.output
actual_output = current_func(input_matrix)
if actual_output != expected_output:
is_correct = False
failures.append(f" - Example {i+1} failed.\n - Expected: {expected_output}\n - Got: {actual_output}")
if is_correct:
transform_func = current_func
break # Success, exit the retry loop.
else:
feedback = f"The previously generated function failed on some training examples. Please analyze the failures and provide a corrected function.\nDetails of failures:\n" + "\n".join(failures)
if attempt == self.max_retries - 1:
transform_func = current_func # Keep the last attempt
except Exception as e:
feedback = f"The previous code attempt failed with an error during execution or verification: {traceback.format_exc()}. Please generate a new, valid Python function."
if attempt == self.max_retries - 1:
# All retries failed, proceed to fallback.
break
# 4. Apply the verified (or last-attempted) function to test inputs.
if callable(transform_func):
all_test_outputs = []
for test_matrix in test_inputs:
try:
result_matrix = transform_func(test_matrix)
all_test_outputs.append(result_matrix)
except Exception:
# Fallback for individual test case failure during application.
all_test_outputs.append(self._get_zeroed_matrix(test_matrix))
return dspy.Prediction(test_outputs=all_test_outputs)
else:
# Fallback if no function could be generated at all.
return dspy.Prediction(test_outputs=[self._get_zeroed_matrix(m) for m in test_inputs])
# The final 'program' object uses a more robust code-generation and verification strategy.
program = ARCProgram()
2025/08/29 04:17:52 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 04:19:31 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 04:19:54 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 04:21:32 INFO dspy.evaluate.evaluate: Average Metric: 0.0 / 3 (0.0%)
GEPA Optimization: 28%|███████████████████████████████▏ | 1133/4000 [6:47:35<70:18:01, 88.27s/rollouts]Iteration 66: New subsample score is not better, skipping
Iteration 67: Selected program 0 score: 0.67
Average Metric: 3.00 / 3 (100.0%): 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [03:14<00:00, 64.96s/it]2025/08/29 04:24:47 INFO dspy.evaluate.evaluate: Average Metric: 3.0 / 3 (100.0%)
GEPA Optimization: 28%|███████████████████████████████▏ | 1136/4000 [6:50:50<67:57:46, 85.43s/rollouts]
Iteration 67: All subsample scores perfect. Skipping.
Iteration 67: Reflective mutation did not propose a new candidate
Iteration 68: Selected program 2 score: 0.605
Average Metric: 3.00 / 3 (100.0%): : 5it [04:58, 59.69s/it] 2025/08/29 04:29:45 INFO dspy.evaluate.evaluate: Average Metric: 3.0 / 3 (100.0%)
GEPA Optimization: 28%|███████████████████████████████▎ | 1139/4000 [6:55:49<69:33:09, 87.52s/rollouts]
Iteration 68: All subsample scores perfect. Skipping.
Iteration 68: Reflective mutation did not propose a new candidate
Iteration 69: Selected program 1 score: 0.63
Average Metric: 2.00 / 3 (66.7%): 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [03:49<00:00, 76.54s/it]2025/08/29 04:33:35 INFO dspy.evaluate.evaluate: Average Metric: 2.0 / 3 (66.7%)
Iteration 69: Proposed new text for program: import dspy
from typing import List
import pydantic
import math # Import math for use in exec
# Define the type for a matrix for clarity
MATRIX = List[List[int]]
class TrainingExample(pydantic.BaseModel):
"""A Pydantic model for a single training example, containing an input and output matrix."""
input: MATRIX
output: MATRIX
class InferRuleSignature(dspy.Signature):
"""
Analyzes pairs of input/output matrices to deduce the transformation rule.
You are an expert analyst of abstract visual reasoning puzzles. Your task is to carefully examine the provided training examples. Each example consists of an 'input' matrix and a corresponding 'output' matrix.
Your goal is to deduce the single, consistent transformation rule that converts each input to its output. Describe this rule in clear, unambiguous, step-by-step natural language. The description must be precise enough for an expert Python programmer to implement it.
Consider various possible transformation patterns, including but not limited to:
- Grid-level properties: Analyze the relationship between input/output grid dimensions (height, width).
- Geometric operations: rotation, reflection, scaling, repetition, shifting.
- Color/value transformations: changing specific numbers to others, conditional changes.
- Object-based logic: identifying shapes or objects and applying rules based on their properties (size, color, position).
- Fill/completion patterns: such as flood fills or completing a grid based on object boundaries.
- Bounding box or frame logic: creating structures based on the extents of objects.
"""
training_examples: List[TrainingExample] = dspy.InputField(description="A list of input and output examples demonstrating the task.")
transformation_rule: str = dspy.OutputField(description="A step-by-step natural language description of the transformation rule.")
class GenerateCodeSignature(dspy.Signature):
"""
Given a natural language transformation rule and example input/output pairs, write a Python function to implement the rule.
You are an expert Python programmer. Your task is to write a single, self-contained Python function `transform_matrix` that implements the provided transformation rule.
- The function signature must be exactly: `def transform_matrix(matrix: list[list[int]]) -> list[list[int]]:`
- The function must be self-contained. You can use the `math` library if needed.
- Do NOT include any code outside of this function definition. No example calls, no `if __name__ == "__main__":`, and no explanatory text or markdown. Your output must be only the Python code for the function.
- Analyze the provided examples to understand the data structures and ensure your code handles them correctly.
"""
transformation_rule: str = dspy.InputField(description="The natural language rule to implement.")
training_examples: List[TrainingExample] = dspy.InputField(description="Examples to understand data formats and validate the rule's logic.")
python_code: str = dspy.OutputField(description="A string containing ONLY the Python function `transform_matrix`.")
class ARCProgram(dspy.Module):
"""A program that infers a rule, generates code for it, and then executes the code."""
def __init__(self):
super().__init__()
self.rule_inferrer = dspy.ChainOfThought(InferRuleSignature)
self.code_generator = dspy.Predict(GenerateCodeSignature)
def forward(self, training_examples: List[TrainingExample], test_inputs: List[MATRIX]) -> dspy.Prediction:
"""
Infers a rule, generates code, and executes it for each test input.
Args:
training_examples: A list of TrainingExample objects.
test_inputs: A list of input matrices to be solved.
Returns:
A dspy.Prediction object with the 'test_outputs' field populated.
"""
# 1. Infer the transformation rule from the training examples.
inferred = self.rule_inferrer(training_examples=training_examples)
rule = inferred.transformation_rule
# 2. Generate a Python function that implements the rule.
generated = self.code_generator(transformation_rule=rule, training_examples=training_examples)
code_str = generated.python_code
all_test_outputs = []
try:
# 3. Prepare and execute the generated code.
# Clean up potential markdown fences from the LLM's output.
if code_str.startswith("```python"):
code_str = code_str[9:]
if code_str.endswith("```"):
code_str = code_str[:-3]
local_scope = {}
exec(code_str, {"math": math}, local_scope)
transform_func = local_scope['transform_matrix']
# 4. Apply the generated function to each test input.
for test_matrix in test_inputs:
try:
result_matrix = transform_func(test_matrix)
all_test_outputs.append(result_matrix)
except Exception:
# Fallback for failure on a single test case.
if test_matrix and test_matrix[0]:
all_test_outputs.append([([0] * len(test_matrix[0])) for _ in range(len(test_matrix))])
else:
all_test_outputs.append([])
except Exception:
# Fallback if code generation or exec fails entirely.
for test_matrix in test_inputs:
if test_matrix and test_matrix[0]:
all_test_outputs.append([([0] * len(test_matrix[0])) for _ in range(len(test_matrix))])
else:
all_test_outputs.append([])
return dspy.Prediction(test_outputs=all_test_outputs)
# The final 'program' object is an instance of our robust, multi-step module.
program = ARCProgram()
2025/08/29 04:39:52 INFO dspy.evaluate.evaluate: Average Metric: 2.0 / 3 (66.7%)
GEPA Optimization: 29%|███████████████████████████████▍ | 1145/4000 [7:05:56<72:37:57, 91.59s/rollouts]Iteration 69: New subsample score is not better, skipping
Iteration 70: Selected program 0 score: 0.67
Average Metric: 1.00 / 3 (33.3%): 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 69.14it/s]2025/08/29 04:39:52 INFO dspy.evaluate.evaluate: Average Metric: 1.0 / 3 (33.3%)
Iteration 70: Proposed new text for program: import dspy
from typing import List
import pydantic
import traceback
import copy
# Define the type for a grid/matrix as a list of lists of integers.
MATRIX = List[List[int]]
class TrainingExample(pydantic.BaseModel):
"""A Pydantic model for a single training example, containing an input and output grid."""
input: MATRIX
output: MATRIX
class GenerateTransformationFunction(dspy.Signature):
"""
You are an expert programmer solving visual puzzles from the Abstraction and Reasoning Corpus (ARC).
Your goal is to analyze a set of training examples, each consisting of an input grid and an output grid.
Based on this analysis, you must write a single, self-contained Python function named `transform_grid` that implements the observed transformation.
**Function Requirements:**
- The function must be named exactly `transform_grid`.
- It must accept one argument: `grid`, which is a 2D list of integers (the input grid).
- It must return a new 2D list of integers representing the transformed grid.
- The function should not rely on any external libraries unless they are standard (e.g., `copy`). If you need a library like numpy, import it inside the function.
- Do not modify the input grid in place; create a copy if necessary.
**Analysis Strategy:**
1. **Observe Core Patterns:** Look for simple, recurring patterns like geometric transformations (rotation, reflection, scaling), color changes, object manipulation (copying, moving, recoloring), pattern propagation, or flood-fills.
2. **Decompose the Problem:** Break down the transformation into logical steps. For example, "first, find all objects of color blue, then for each object, reflect it horizontally."
3. **Generalize:** The logic must be general enough to work for all training examples and, by extension, the unseen test inputs.
4. **Code Implementation:** Translate your logic into a clear and correct Python function. Ensure your code is robust and handles edge cases observed in the examples.
"""
training_examples: List[TrainingExample] = dspy.InputField(desc="A list of input/output grid pairs demonstrating the transformation rule.")
test_input_grid: MATRIX = dspy.InputField(desc="A single test input grid to which the transformation should be applicable. Use its properties (e.g., dimensions) to ensure your code is general.")
reasoning: str = dspy.OutputField(desc="Step-by-step thinking process to deduce the transformation rule and how to implement it in Python.")
python_code: str = dspy.OutputField(desc="A string containing the complete, self-contained Python function `transform_grid(grid)`.")
class ARCSolver(dspy.Module):
"""A DSPy module that solves ARC-like tasks by generating and executing Python code."""
def __init__(self):
super().__init__()
# Use ChainOfThought to encourage the LM to reason before writing code.
self.code_generator = dspy.ChainOfThought(GenerateTransformationFunction)
def forward(self, training_examples: List[TrainingExample], test_inputs: List[MATRIX]):
"""
Generates and executes a transformation function for each test input.
The overall signature of this method matches the original problem's requirements.
"""
generated_outputs = []
# We only need to generate the code once, assuming the transformation
# rule is the same for all test inputs in a given task.
# We pass the first test input as a representative example for the LM.
prediction = self.code_generator(training_examples=training_examples, test_input_grid=test_inputs[0])
python_code = prediction.python_code
# Prepare a local scope for executing the generated code safely.
local_scope = {}
transform_function = None
try:
# The LM might wrap the code in markdown, so we extract it.
if "```python" in python_code:
python_code = python_code.split("```python")[1].split("```")[0].strip()
# Execute the generated Python code to define the function in our scope.
exec(python_code, globals(), local_scope)
transform_function = local_scope.get('transform_grid')
if not (transform_function and callable(transform_function)):
print("Warning: `transform_grid` function not found or not callable. Using fallback.")
transform_function = None # Ensure fallback is used.
except Exception as e:
print(f"Error executing generated code: {e}")
print(f"Traceback: {traceback.format_exc()}")
# Fallback will be used as transform_function is None.
# Now, apply the (potentially successfully generated) function to all test inputs.
for test_input in test_inputs:
if transform_function:
try:
# Call the function with a deep copy to avoid modifying the original input.
output_grid = transform_function(copy.deepcopy(test_input))
generated_outputs.append(output_grid)
except Exception as e:
print(f"Error applying `transform_grid` to a test input: {e}")
# If the function fails on one input, fall back for that specific input.
generated_outputs.append(copy.deepcopy(test_input))
else:
# Fallback: if code generation or execution failed, return the original input.
generated_outputs.append(copy.deepcopy(test_input))
return dspy.Prediction(test_outputs=generated_outputs)
# The final program object is an instance of our new, more robust custom module.
program = ARCSolver()
2025/08/29 04:44:35 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 04:46:17 INFO dspy.evaluate.evaluate: Average Metric: 2.0 / 3 (66.7%)
Error applying `transform_grid` to a test input: name 'collections' is not defined
Error applying `transform_grid` to a test input: name 'collections' is not defined
Error applying `transform_grid` to a test input: name 'collections' is not defined
Error applying `transform_grid` to a test input: name 'collections' is not defined
Error applying `transform_grid` to a test input: name 'collections' is not defined
Error applying `transform_grid` to a test input: name 'collections' is not defined
Error applying `transform_grid` to a test input: name 'collections' is not defined
Error applying `transform_grid` to a test input: name 'collections' is not defined
Error applying `transform_grid` to a test input: name 'collections' is not defined
Error applying `transform_grid` to a test input: name 'collections' is not defined
Error applying `transform_grid` to a test input: name 'collections' is not defined
Error applying `transform_grid` to a test input: name 'collections' is not defined
Error applying `transform_grid` to a test input: name 'collections' is not defined
Error applying `transform_grid` to a test input: name 'collections' is not defined
Error applying `transform_grid` to a test input: name 'collections' is not defined
Error applying `transform_grid` to a test input: name 'collections' is not defined
Error applying `transform_grid` to a test input: name 'collections' is not defined
Error applying `transform_grid` to a test input: name 'collections' is not defined
Error applying `transform_grid` to a test input: name 'collections' is not defined
2025/08/29 04:50:27 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
Error executing generated code: argument of type 'NoneType' is not iterable
Traceback: Traceback (most recent call last):
File "<string>", line 65, in forward
TypeError: argument of type 'NoneType' is not iterable
2025/08/29 04:50:31 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 04:50:31 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 04:50:33 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 04:50:35 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 04:50:37 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
Error applying `transform_grid` to a test input: name 'collections' is not defined
Error applying `transform_grid` to a test input: name 'collections' is not defined
Error applying `transform_grid` to a test input: name 'collections' is not defined
2025/08/29 04:51:46 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 04:51:47 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
Error executing generated code: argument of type 'NoneType' is not iterable
Traceback: Traceback (most recent call last):
File "<string>", line 65, in forward
TypeError: argument of type 'NoneType' is not iterable
Error applying `transform_grid` to a test input: name 'collections' is not defined
2025/08/29 04:52:13 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
Error applying `transform_grid` to a test input: name 'collections' is not defined
Error applying `transform_grid` to a test input: name 'collections' is not defined
Error applying `transform_grid` to a test input: name 'collections' is not defined
Error applying `transform_grid` to a test input: cannot access local variable 'cell' where it is not associated with a value
2025/08/29 04:52:40 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
Error applying `transform_grid` to a test input: name 'collections' is not defined
Error applying `transform_grid` to a test input: name 'collections' is not defined
2025/08/29 04:53:13 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
Error applying `transform_grid` to a test input: name 'collections' is not defined
2025/08/29 04:53:27 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
Error executing generated code: argument of type 'NoneType' is not iterable
Traceback: Traceback (most recent call last):
File "<string>", line 65, in forward
TypeError: argument of type 'NoneType' is not iterable
Error applying `transform_grid` to a test input: name 'collections' is not defined
Error applying `transform_grid` to a test input: name 'collections' is not defined
Error applying `transform_grid` to a test input: name 'collections' is not defined
2025/08/29 04:54:48 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 04:54:48 WARNING dspy.adapters.json_adapter: Failed to use structured output format, falling back to JSON mode.
Error applying `transform_grid` to a test input: name 'collections' is not defined
Error applying `transform_grid` to a test input: name 'collections' is not defined
Error applying `transform_grid` to a test input: name 'collections' is not defined
2025/08/29 04:56:21 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 04:56:21 WARNING dspy.adapters.json_adapter: Failed to use structured output format, falling back to JSON mode.
2025/08/29 04:56:53 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 04:56:54 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 04:56:54 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 04:56:54 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 04:56:54 WARNING dspy.adapters.json_adapter: Failed to use structured output format, falling back to JSON mode.
2025/08/29 04:56:54 WARNING dspy.adapters.json_adapter: Failed to use structured output format, falling back to JSON mode.
Error applying `transform_grid` to a test input: name 'collections' is not defined
2025/08/29 04:57:18 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 04:57:18 WARNING dspy.adapters.json_adapter: Failed to use structured output format, falling back to JSON mode.
Error applying `transform_grid` to a test input: name 'collections' is not defined
Error applying `transform_grid` to a test input: name 'collections' is not defined
2025/08/29 04:59:55 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 04:59:55 WARNING dspy.adapters.json_adapter: Failed to use structured output format, falling back to JSON mode.
Error applying `transform_grid` to a test input: name 'collections' is not defined
2025/08/29 05:00:05 INFO dspy.evaluate.evaluate: Average Metric: 122.0 / 200 (61.0%)
GEPA Optimization: 34%|█████████████████████████████████████▍ | 1351/4000 [7:26:09<8:22:16, 11.38s/rollouts]Error applying `transform_grid` to a test input: name 'collections' is not defined
Iteration 70: Full valset score for new program: 0.61
Iteration 70: Full train_val score for new program: 0.61
Iteration 70: Individual valset scores for new program: [False, True, False, True, True, True, True, True, True, True, True, True, True, False, True, True, False, True, True, True, True, True, True, False, True, False, False, True, True, True, False, False, False, True, False, True, False, True, False, True, False, True, True, False, False, True, False, False, True, True, True, True, True, True, True, False, True, True, False, True, False, False, False, True, True, False, False, True, False, False, True, True, True, True, True, False, True, True, True, True, False, True, True, True, True, True, True, True, True, False, True, True, True, True, True, True, False, False, True, True, True, True, True, False, True, False, True, False, False, True, False, False, False, True, True, True, True, True, True, False, True, False, True, False, False, True, False, True, False, False, False, False, True, False, True, False, False, False, True, True, True, True, True, False, True, False, False, False, True, True, True, False, True, True, True, False, False, True, False, False, False, True, True, False, False, True, True, True, True, False, False, True, False, False, True, False, False, True, False, True, False, True, True, False, False, True, True, True, True, False, True, True, True, True, True, False, True, False, False, True]
Iteration 70: New valset pareto front scores: [True, True, True, True, True, True, True, True, True, True, True, True, True, False, True, True, False, True, True, True, True, True, True, True, True, True, False, True, True, True, True, False, True, True, True, True, False, True, True, True, False, True, True, True, False, True, False, False, True, True, True, True, True, True, True, 0, True, True, False, True, True, True, False, True, True, False, True, True, 0, False, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, False, True, True, True, True, True, True, True, False, True, True, True, True, True, False, True, True, True, True, True, True, False, True, True, True, True, True, True, True, True, True, True, True, True, False, False, True, False, True, True, True, True, True, True, True, True, False, True, True, True, True, True, True, True, False, True, False, True, False, True, True, True, False, True, True, True, True, True, True, True, False, True, True, True, True, False, True, True, True, True, False, True, True, False, True, True, True, False, True, False, True, True, True, True, False, False, True, True, True, True, True, True, True, True, True, True, False, True, True, False, True]
Iteration 70: Full valset pareto front score: 0.815
Iteration 70: Updated valset pareto front programs: [{1, 3}, {0, 1, 2, 3, 4}, {0}, {0, 2, 3, 4}, {0, 1, 2, 3, 4}, {0, 4}, {0, 1, 2, 3, 4}, {0, 1, 3, 4}, {0, 1, 2, 3, 4}, {0, 2, 3, 4}, {0, 1, 2, 3, 4}, {0, 1, 2, 3, 4}, {0, 1, 2, 3, 4}, {0, 1, 2, 3, 4}, {0, 1, 2, 3, 4}, {0, 1, 2, 3, 4}, {0, 1, 2, 3, 4}, {1, 4}, {0, 1, 2, 3, 4}, {0, 2, 4}, {0, 1, 2, 3, 4}, {0, 2, 3, 4}, {0, 1, 2, 3, 4}, {1}, {0, 3, 4}, {0, 1, 3}, {0, 1, 2, 3, 4}, {0, 4}, {0, 1, 2, 3, 4}, {0, 1, 2, 3, 4}, {1}, {0, 1, 2, 3, 4}, {2}, {0, 1, 2, 3, 4}, {0, 1, 2, 3}, {0, 1, 2, 3, 4}, {0, 1, 2, 3, 4}, {0, 1, 2, 3, 4}, {2}, {0, 2, 3, 4}, {0, 1, 2, 3, 4}, {0, 1, 2, 3, 4}, {0, 1, 2, 3, 4}, {1}, {0, 1, 2, 3, 4}, {0, 1, 2, 3, 4}, {0, 1, 2, 3, 4}, {0, 1, 2, 3, 4}, {0, 1, 2, 3, 4}, {0, 1, 2, 3, 4}, {0, 1, 2, 3, 4}, {0, 1, 2, 3, 4}, {0, 1, 3, 4}, {0, 1, 2, 3, 4}, {0, 1, 2, 3, 4}, {0, 1, 2, 3, 4}, {0, 1, 3, 4}, {0, 1, 2, 3, 4}, {0, 1, 2, 3, 4}, {0, 1, 2, 3, 4}, {0, 1, 2, 3}, {0, 1}, {0, 1, 2, 3, 4}, {0, 1, 2, 3, 4}, {4}, {0, 1, 2, 3, 4}, {1, 2, 3}, {0, 1, 3, 4}, {0, 1, 2, 3, 4}, {0, 1, 2, 3, 4}, {0, 1, 2, 3, 4}, {0, 1, 2, 3, 4}, {0, 1, 2, 3, 4}, {2, 3, 4}, {0, 1, 2, 3, 4}, {1, 2, 3}, {0, 3, 4}, {4}, {0, 1, 2, 3, 4}, {0, 2, 3, 4}, {0, 2}, {1, 2, 3, 4}, {0, 1, 2, 3, 4}, {0, 1, 2, 3, 4}, {0, 1, 2, 3, 4}, {0, 1, 2, 3, 4}, {0, 1, 2, 4}, {0, 1, 2, 3, 4}, {0, 3, 4}, {0, 1, 2, 3, 4}, {0, 1, 2, 4}, {0, 1, 2, 3, 4}, {0, 1, 2, 3, 4}, {0, 1, 2, 3, 4}, {0, 1, 2, 3, 4}, {0, 1, 2, 3, 4}, {1, 3}, {0, 1, 2, 3, 4}, {0, 3, 4}, {0, 1, 2, 3, 4}, {0, 1, 2, 3, 4}, {0, 1, 2, 3, 4}, {0, 4}, {0, 1, 2, 3, 4}, {0, 1, 2, 3, 4}, {0, 1, 2, 3}, {1, 2, 3, 4}, {0, 1, 2, 3}, {1, 2}, {0, 1, 4}, {0, 1, 2, 3, 4}, {0, 2}, {1, 3}, {2, 4}, {0, 1, 2, 3, 4}, {3, 4}, {0, 1, 2, 3, 4}, {2, 3, 4}, {0, 1, 2, 3, 4}, {0, 1, 2, 3}, {2, 3, 4}, {0, 1}, {0, 1, 2, 3, 4}, {0, 1, 2, 3, 4}, {0, 1, 2, 3, 4}, {0, 1, 2, 3, 4}, {0, 1, 2, 3, 4}, {0, 1, 2, 3, 4}, {0, 1, 2, 3}, {0, 1, 2, 3}, {2}, {0, 1, 3}, {0, 1, 2, 3, 4}, {0, 1, 3}, {0, 1, 2, 3, 4}, {0, 1, 2, 3, 4}, {0, 1, 3}, {3}, {1, 2, 3, 4}, {1, 2, 3, 4}, {0, 1, 2, 3, 4}, {0, 1, 2, 3, 4}, {0, 1, 2, 3, 4}, {0, 1, 2, 3, 4}, {0, 1, 2, 3, 4}, {0, 1, 2, 3, 4}, {0, 2, 3}, {0, 1, 2, 3, 4}, {0, 1, 2, 3, 4}, {0, 1, 2, 3, 4}, {4}, {0, 1, 2, 3, 4}, {0, 1, 2, 3, 4}, {0, 1, 3, 4}, {0, 1, 3, 4}, {0, 1, 2, 3}, {0, 3}, {0, 1, 2, 3, 4}, {0, 1, 2, 3}, {0, 1, 2, 3, 4}, {1}, {0, 1, 2, 3, 4}, {0, 4}, {0, 3}, {0, 1, 2, 3, 4}, {0, 1, 2, 3, 4}, {0, 1, 2, 3, 4}, {0, 1, 2, 4}, {0, 1, 2, 4}, {0, 1, 2, 3, 4}, {0}, {0, 1, 2, 3, 4}, {0, 1, 2, 3, 4}, {0, 1, 3}, {0, 1, 2, 3, 4}, {1, 2, 3}, {0, 1, 2, 3, 4}, {0, 1, 2, 3, 4}, {0, 1, 2, 3, 4}, {0, 1, 3, 4}, {0, 2}, {0, 1, 2, 3, 4}, {0, 1, 2, 3, 4}, {0, 1, 2, 3, 4}, {0, 1, 2, 3, 4}, {0, 1, 2, 3, 4}, {0, 1, 2, 3, 4}, {0, 1, 2, 3, 4}, {0, 1, 2, 4}, {0}, {0, 1, 2, 3, 4}, {0, 1, 2, 3, 4}, {0, 1, 2, 3, 4}, {1, 2, 4}, {0, 1, 2, 3, 4}, {0, 1, 2, 3, 4}, {0, 1, 2, 3, 4}, {0}, {0, 1, 2, 3, 4}, {0, 2, 3, 4}]
Iteration 70: Best valset aggregate score so far: 0.67
Iteration 70: Best program as per aggregate score on train_val: 0
Iteration 70: Best program as per aggregate score on valset: 0
Iteration 70: Best score on valset: 0.67
Iteration 70: Best score on train_val: 0.67
Iteration 70: Linear pareto front program index: 0
Iteration 70: New program candidate index: 4
Iteration 71: Selected program 4 score: 0.61
0%| | 0/3 [00:00<?, ?it/s]2025/08/29 05:00:28 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 05:00:28 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[8, 8, 8, 0, 0, 0, 0, 0, 0], [1, 8, 8, 0, 8, 1, 8, 0, 0], [8, 8, 8, 0, 1, 1, 8, 0, 0], [0, 0, 0, 0, 8, 8, 8, 0, 0], [0, 8, 8, 1, 0, 0, 0, 0, 0], [0, 8, 8, 8, 0, 0, 8, 1, 8], [0, 8, 1, 8, 0, 0, 1, 8, 1], [0, 0, 0, 0, 0, 0, 1, 8, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[8, 1, 8], [1, 8, 1], [1, 8, 1]]}, {'input': [[0, 8, 8, 1, 0, 0, 0, 0, 0], [0, 8, 1, 8, 0, 8, 1, 8, 0], [0, 8, 8, 8, 0, 1, 8, 8, 0], [0, 0, 0, 0, 0, 8, 8, 1, 0], [0, 0, 8, 1, 8, 0, 0, 0, 0], [0, 0, 1, 1, 8, 0, 0, 0, 0], [0, 0, 8, 8, 1, 0, 8, 8, 8], [0, 0, 0, 0, 0, 0, 8, 8, 8], [0, 0, 0, 0, 0, 0, 1, 8, 8]], 'output': [[8, 1, 8], [1, 1, 8], [8, 8, 1]]}, {'input': [[0, 0, 0, 0, 8, 8, 8, 0, 0], [8, 8, 8, 0, 8, 8, 8, 0, 0], [8, 8, 8, 0, 1, 8, 8, 0, 0], [8, 8, 8, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 8, 1, 8], [8, 1, 8, 0, 0, 0, 1, 1, 8], [8, 8, 1, 0, 0, 0, 1, 8, 1], [1, 8, 8, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[8, 1, 8], [1, 1, 8], [1, 8, 1]]}, {'input': [[0, 0, 1, 1, 8, 0, 0, 0, 0], [0, 0, 8, 8, 1, 0, 8, 1, 1], [0, 0, 1, 1, 8, 0, 1, 1, 1], [0, 0, 0, 0, 0, 0, 8, 1, 8], [8, 8, 8, 0, 0, 0, 0, 0, 0], [8, 8, 1, 0, 8, 1, 8, 0, 0], [1, 8, 8, 0, 1, 8, 8, 0, 0], [0, 0, 0, 0, 8, 8, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[8, 1, 1], [1, 1, 1], [8, 1, 8]]}], 'test_inputs': [[[8, 8, 8, 0, 0, 0, 8, 1, 8], [8, 8, 8, 0, 0, 0, 1, 8, 1], [1, 8, 8, 0, 0, 0, 8, 1, 8], [0, 0, 0, 8, 1, 8, 0, 0, 0], [0, 0, 0, 8, 8, 1, 0, 0, 0], [0, 0, 0, 1, 8, 8, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 1, 8], [0, 0, 0, 0, 0, 0, 8, 1, 1], [0, 0, 0, 0, 0, 0, 1, 1, 8]]], 'test_outputs': [[[1, 1, 8], [8, 1, 1], [1, 1, 8]]]}) (input_keys={'test_inputs', 'training_examples'}): Adapter JSONAdapter failed to parse the LM response.
LM Response: {
"reasoning": "1. **Initial Observation:** The input grids are 9x9 and the output grids are 3x3. This strongly suggests that the transformation works by dividing the 9x9 input grid into a 3x3 grid of 3x3 subgrids, and each subgrid is mapped to a single pixel in the output grid.\n\n2. **Color Analysis:** The input grids contain colors 0, 1, and 8. The output grids only contain colors 1 and 8. Color 0 appears to be a background color that is ignored in the main calculation.\n\n3. **Formulating a Hypothesis (Majority Vote):** A simple first hypothesis is that the output pixel's color is determined by the majority color (between 1 and 8) within the corresponding 3x3 subgrid. Let's denote the count of color 1 as `count1` and color 8 as `count8`.\n - If `count1 > count8`, output is 1.\n - If `count8 > count1`, output is 8.\n - If `count1 == count8`, a tie-breaking rule is needed.\n Testing this hypothesis across the examples shows it fails frequently. In many cases, the output color is the *minority* color. This indicates the rule is more complex.\n\n4. **Searching for a Pattern:** The rule seems to switch between majority and minority vote. The trigger for this switch must be a property of the subgrid or its position. I explored several properties:\n - The parity of the subgrid's coordinates `(i, j)`.\n - The parity of the total count of non-zero cells (`count1 + count8`).\n - The parity of `count1` or `count8` individually.\n\n5. **Discovering the Core Rule (Parity of `count1`):** After extensive testing, a promising pattern emerged related to the parity of `count1`. The rule `output = 1 if count1 is odd else 8` works for a significant number of cases (5 out of 9 in the first example).\n\n6. **Refining the Rule (Positional Inversion):** The cases where the `count1` parity rule failed in the first example were `(0,0)`, `(1,0)`, `(1,1)`, and `(2,2)`. Notice that three of these, `(0,0)`, `(1,1)`, and `(2,2)`, lie on the main diagonal of the 3x3 grid of subgrids. This suggests a positional modification to the rule.\n - Let's hypothesize: The base rule is `output = 1 if count1 is odd else 8`.\n - However, if the subgrid is on the main diagonal (`i == j`), the rule is inverted: `output = 8 if count1 is odd else 1`.\n\n7. **Final Verification:** Let's test this refined hypothesis on the first training example:\n - `(0,0)`: Diagonal. `count1=1` (odd). Inverted rule -> `8`. Correct.\n - `(0,1)`: Off-diagonal. `count1=3` (odd). Base rule -> `1`. Correct.\n - `(0,2)`: Off-diagonal. `count1=0` (even). Base rule -> `8`. Correct.\n - `(1,0)`: Off-diagonal. `count1=0` (even). Base rule -> `8`. Actual is `1`. This case fails.\n - `(1,1)`: Diagonal. `count1=1` (odd). Inverted rule -> `8`. Correct.\n - `(1,2)`: Off-diagonal. `count1=1` (odd). Base rule -> `1`. Correct.\n - `(2,0)`: Off-diagonal. `count1=1` (odd). Base rule -> `1`. Correct.\n - `(2,1)`: Off-diagonal. `count1=0` (even). Base rule -> `8`. Correct.\n - `(2,2)`: Diagonal. `count1=4` (even). Inverted rule -> `1`. Correct.\n\n This rule works for 8 out of 9 subgrids in the first example. While not perfect, it is the most consistent and simplest rule found after analyzing all examples. The single failure might indicate an additional, more subtle condition, but this rule captures the primary logic. Applying this rule to the other examples shows it performs better than other simple hypotheses. I will proceed with implementing this rule as the most likely solution.\n\n8. **Implementation Plan:**\n - Create an empty 3x3 output grid.\n - Iterate through the 3x3 grid of subgrids using indices `i` (for rows) and `j` (for columns).\n - For each subgrid at `(i, j)`:\n - Extract the 3x3 subgrid from the input.\n - Count the occurrences of the number 1 (`count1`).\n - Check if the subgrid is on the main diagonal (`i == j`).\n - Apply the base rule or the inverted rule based on the diagonal check and the parity of `count1`.\n - Assign the
Expected to find output fields in the LM response: [reasoning, python_code]
Actual output fields parsed from the LM response: [reasoning]
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/adapters/chat_adapter.py", line 38, in __call__
return super().__call__(lm, lm_kwargs, signature, demos, inputs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/adapters/base.py", line 128, in __call__
return self._call_postprocess(processed_signature, signature, outputs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/adapters/base.py", line 89, in _call_postprocess
value = self.parse(processed_signature, text)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/adapters/chat_adapter.py", line 197, in parse
raise AdapterParseError(
dspy.utils.exceptions.AdapterParseError: Adapter ChatAdapter failed to parse the LM response.
LM Response: [[ ## reasoning ## ]]
1. **Analyze Input and Output:** The input grids are 9x9, and the output grids are 3x3. This strongly suggests that the transformation operates on 3x3 sub-grids (or blocks) of the input to produce a single cell in the output. The output grid's cell at `(r, c)` corresponds to the input grid's 3x3 block starting at `(r*3, c*3)`.
2. **Formulate Initial Hypothesis (Majority Color):** A simple first guess is that the output color is the majority color (ignoring the background color 0) within the corresponding 3x3 sub-grid. Let's count the number of blue cells (color 1), `c1`, and red cells (color 8), `c8`, in each sub-grid.
* Hypothesis: If `c1 > c8`, output is 1. If `c8 > c1`, output is 8. If `c1 == c8`, a tie-breaker is needed.
3. **Test the Majority Color Hypothesis:**
* Let's test this on the first training example.
* Sub-grid (0,0): `c1=1, c8=8`. `c8 > c1`, so output should be 8. The actual output is 8. Correct.
* Sub-grid (0,1): `c1=2, c8=1`. `c1 > c8`, so output should be 1. The actual output is 1. Correct.
* Sub-grid (1,0): `c1=0, c8=4`. `c8 > c1`, so output should be 8. The actual output is 1. **Incorrect.**
* This hypothesis is wrong. The counts `c1` and `c8` alone are not sufficient to determine the output color.
4. **Look for a Deeper Pattern (Parity and Position):** Since the content of the sub-grid is not enough, let's consider other properties. The fact that two all-zero sub-grids in example 3 produce different outputs (sub-grid (1,1) -> 1, sub-grid (2,1) -> 8) is a crucial clue. This implies the position of the sub-grid, `(r, c)`, must be part of the rule.
5. **Investigate Parity:** Let's explore rules based on parity (even/odd).
* Let's check the parity of the difference in counts: `(c1 - c8) % 2`.
* Let's check the parity of the sub-grid's position, for which `(r + c) % 2` (a checkerboard pattern) is a common feature in ARC puzzles.
6. **Develop a Combined Hypothesis:** Let's try to combine these parity observations into a single rule.
* **Case 1: `(c1 - c8)` is odd.**
* Let's re-examine the examples where `c1 - c8` is odd.
* Ex1 (0,0): `c1-c8=-7` (odd). `
Expected to find output fields in the LM response: [reasoning, python_code]
Actual output fields parsed from the LM response: [reasoning]
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/adapters/json_adapter.py", line 78, in __call__
return super().__call__(lm, lm_kwargs, signature, demos, inputs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/adapters/chat_adapter.py", line 46, in __call__
raise e
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/adapters/chat_adapter.py", line 38, in __call__
return super().__call__(lm, lm_kwargs, signature, demos, inputs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/adapters/base.py", line 128, in __call__
return self._call_postprocess(processed_signature, signature, outputs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/adapters/base.py", line 89, in _call_postprocess
value = self.parse(processed_signature, text)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/adapters/json_adapter.py", line 172, in parse
raise AdapterParseError(
dspy.utils.exceptions.AdapterParseError: Adapter JSONAdapter failed to parse the LM response.
LM Response: {
"reasoning": "1. **Analyze Input/Output Structure:** The input grids are 9x9, and the output grids are 3x3. This strongly suggests that the transformation operates on 3x3 subgrids of the input to produce a single pixel in the output. The 9x9 input can be seen as a 3x3 grid of these subgrids.\n\n2. **Identify Key Features:** The grids contain three colors: 0 (black), 1 (blue), and 8 (red). The output grids only contain colors 1 and 8. This implies the rule for each subgrid is to decide between outputting a 1 or an 8.\n\n3. **Formulate a Hypothesis (Subgrid to Pixel Rule):** The decision for each output pixel likely depends on the properties of the corresponding 3x3 input subgrid. Let's analyze the properties. A simple property is the count of each non-black color. Let `C1` be the count of color 1 and `C8` be the count of color 8 within a subgrid.\n\n4. **Test Initial Hypotheses:**\n * **Majority Rule:** Does the color with the higher count win? This fails; in several cases, the color with the lower count determines the output.\n * **Parity of Counts:** Let's consider the parity (even or odd) of `C1` and `C8`. Let `p1 = C1 % 2` and `p8 = C8 % 2`. A rule based solely on `p1` and `p8` (e.g., `output = 1 if p1 == p8 else 8`) is inconsistent across different examples and even within the same example. This indicates another factor is involved.\n\n5. **Incorporate Positional Information:** The rule must also depend on the position of the subgrid. Let the subgrid's position be `(r, c)` where `r` and `c` range from 0 to 2. A common positional feature in ARC is the parity of the sum of coordinates, `pc = (r + c) % 2`, which creates a chessboard pattern.\n\n6. **Synthesize the Final Rule:** The most likely rule combines these three parity features: `p1`, `p8`, and `pc`. Through trial and error, a consistent logical expression is sought. Let's test the boolean expression `(p1 XOR p8) == pc`. We can map this boolean result to the output colors. Let's say if the expression is true, the output is 1, and if false, the output is 8.\n\n * Let's verify this rule: `output = 1 if (p1 ^ p8) == pc else 8`.\n * **Example 1, Subgrid (0,0):** `r=0, c=0`. `pc=0`. `C1=1, C8=8`. `p1=1, p8=0`. `p1^p8 = 1`. `1 == 0` is false. Output should be 8. **Correct.**\n * **Example 1, Subgrid (0,1):** `r=0, c=1`. `pc=1`. `C1=3, C8=1`. `p1=1, p8=1`. `p1^p8 = 0`. `0 == 1` is false. Output should be 8. Actual output is 1. **This rule fails.**\n\n7. **Refine the Final Rule:** Let's try another combination: `output = 1 if p1 == (p8 ^ pc) else 8`.\n * **Example 1, Subgrid (0,0):** `r=0, c=0`. `pc=0`. `C1=1, C8=8`. `p1=1, p8=0`. `p8^pc = 0`. `p1 == 0` is false. Output should be 8. **Correct.**\n * **Example 1, Subgrid (0,1):** `r=0, c=1`. `pc=1`. `C1=3, C8=1`. `p1=1, p8=1`. `p8^pc = 0`. `p1 == 0` is false. Output should be 8. Actual output is 1. **This rule also fails.**\n\n8. **Discover the Correct Rule:** After extensive testing of boolean combinations, the correct rule is found to be: **The output color is 8 if the parity of the count of color 8 is equal to the parity of the subgrid's column index `c`. Otherwise, the output is 1.** This can be expressed as `output = 8 if (C8 % 2) == (c % 2) else 1`. Let's verify this.\n * **Example 1, Subgrid (0,0):** `c=0`. `pc_col=0`. `C8=8`. `p8=0`. `p8 == pc_col` is true. Output 8. **Correct.**\n * **Example 1, Subgrid (0,1):** `c=1`. `pc_col=1`. `C8=1`. `p8=1`. `p8 == pc_col` is true. Output 8. Actual is 1. **This rule also fails.**\n\n9. **Final Re-evaluation and Correct Rule Discovery:** The interaction is more subtle. Let's re-examine the simplest cases (all-black subgrids). The output follows a chessboard pattern based on `r+c`. This suggests a base pattern which is then modified. The modification rule seems to be a flip if `p1 != p8`. Let's formalize this: `base_color = 1 if (r+c)%2 == 0 else 8`. `flip = (p1 != p8)`. If `flip` is true, the output is the other color. If `flip` is false, the output is the `base_color`. Let's test this.\n * **Example 1, (0,0):** `r+c=0`(E). Base=1. `p1=1, p8=0`. `p1!=p8` is true (flip). Output is 8. **Correct.**\n * **Example 1, (0,1):** `r+c=1`(O). Base=8. `p1=1, p8=1`. `p1!=p8` is false (no flip). Output is 8. Actual is 1. **Fails.**\n\n10. **The Actual Correct Rule:** The rule is a specific boolean function of the three parities. The output is 1 if `(p1 == p8) == ((r+c)%2 == 0)`. Otherwise, the output is 8. Let's verify:\n * **Example 1, (0,0):** `p1=1, p8=0`. `p1==p8` is F. `r+c=0`. `(r+c)%2==0` is T. `F == T` is false. Output 8. **Correct.**\n * **Example 1, (0,1):** `p1=1, p8=1`. `p1==p8` is T. `r+c=1`. `(r+c)%2==0` is F. `T == F` is false. Output 8. Actual is 1. **Fails.**\n\n11. **Final Attempt at a Simple Rule:** Let's try `output = 1 if (p1 + p8 + c) % 2 == 1 else 8`.\n * **Example 1, (0,0):** `p1=1, p8=0, c=0`. `(1+0+0)%2=1`. Output 1. Actual 8. **Fails.**\n * Let's flip it: `output = 8 if (p1 + p8 + c) % 2 == 1 else 1`.\n * **Example 1, (0,0):** `(1+0+0)%2=1`. Output 8. **Correct.**\n * **Example 1, (0,1):** `p1=1, p8=1, c=1`. `(1+1+1)%2=1`. Output 8. Actual 1. **Fails.**\n\n12. **The Correct Rule (Re-discovered):** The output color is 1 if `(C1 % 2) == (r % 2)`. Otherwise, the output is 8. Let's test this simple rule.\n * **Example 1, (0,0):** `r=0`. `pr=0`. `C1=1`. `p1=1`. `1==0` is F. Output 8. **Correct.**\n * **Example 1, (0,1):** `r=0`. `pr=0`. `C1=3`. `p1=1`. `1==0` is F. Output 8. Actual 1. **Fails.**\n\nLet's try `output = 1 if (C8 % 2) == (r % 2) else 8`.\n * **Example 1, (0,0):** `r=0`. `pr=0`. `C8=8`. `p8=0`. `0==0` is T. Output 1. Actual 8. **Fails.**\n\nAfter exhausting simple rules, a more complex but correct one is found: The output is 1 if `(C1 % 2) XOR ((r+c) % 2) == 0`. Otherwise, the output is 8. This is equivalent to `output = 1 if p1 == pc else 8`.\n * **Example 1, (0,0):** `p1=1, pc=0`. `1==0` is F. Output 8. **Correct.**\n * **Example 1, (0,1):** `p1=1, pc=1`. `1==1` is T. Output 1. **Correct.**\n * **Example 1, (0,2):** `p1=0, pc=0`. `0==0` is T. Output 1. Actual 8. **Fails.**\n\nLet's try `output = 1 if (C8 % 2) == ((r+c) % 2) else 8`.\n * **Example 1, (0,0):** `p8=0, pc=0`. `0==0` is T. Output 1. Actual 8. **Fails.**\n\nLet's try `output = 1 if (C1%2 + C8%2)%2 == ((r+c)%2) else 8`.\n * **Example 1, (0,0):** `p1^p8=1, pc=0`. `1==0` is F. Output 8. **Correct.**\n * **Example 1, (0,1):** `p1^p8=0, pc=1`. `0==1` is F. Output 8. Actual 1. **Fails.**\n\nLet's try `output = 1 if (C1%2 + C8%2)%2 != ((r+c)%2) else 8`.\n * **Example 1, (0,0):** `p1^p8=1, pc=0`. `1!=0` is T. Output 1. Actual 8. **Fails.**\n\nLet's try `output = 1 if (C1%2) != ((r+c)%2) else 8`.\n * **Example 1, (0,0):** `p1=1, pc=0`. `1!=0` is T. Output 1. Actual 8. **Fails.**\n\nLet's try `output = 1 if (C8%2) != ((r+c)%2) else 8`.\n * **Example 1, (0,0):** `p8=0, pc=0`. `0!=0` is F. Output 8. **Correct.**\n * **Example 1, (0,1):** `p8=1, pc=1`. `1!=1` is F. Output 8. Actual 1. **Fails.**\n\nLet's try `output = 1 if (C1%2 + C8%2 + r + c)%2 == 0 else 8`.\n * **Example 1, (0,0):** `1+0+0+0=1`. `1%2=1`. Output 8. **Correct.**\n * **Example 1, (0,1):** `1+1+0+1=3`. `3%2=1`. Output 8. Actual 1. **Fails.**\n\nLet's try `output = 1 if (C1%2 + C8%2 + r + c)%2 == 1 else 8`.\n * **Example 1, (0,0):** `1+0+0+0=1`. `1%2=1`. Output 1. Actual 8. **Fails.**\n\nLet's try `output = 1 if (C1%2 + r)%2 == (C8%2 + c)%2 else 8`.\n * **Example 1, (0,0):** `(1+0)%2=1`. `(0+0)%2=0`. `1==0` is F. Output 8. **Correct.**\n * **Example 1, (0,1):** `(1+0)%2=1`. `(1+1)%2=0`. `1==0` is F. Output 8. Actual 1. **Fails.**\n\nLet's try `output = 1 if (C1%2 + c)%2 == (C8%2 + r)%2 else 8`.\n * **Example 1, (0,0):** `(1+0)%2=1`. `(0+0)%2=0`. `1==0` is F. Output 8. **Correct.**\n * **Example 1, (0,1):** `(1+1)%2=0`. `(1+0)%2=1`. `0==1` is F. Output 8. Actual 1. **Fails.**\n\nLet's try `output = 1 if (C1%2 + C8%2)%2 == (r%2) else 8`.\n * **Example 1, (0,0):** `(1+0)%2=1`. `0%2=0`. `1==0` is F. Output 8. **Correct.**\n * **Example 1, (0,1):** `(1+1)%2=0`. `0%2=0`. `0==0` is T. Output 1. **Correct.**\n * **Example 1, (0,2):** `(0+0)%2=0`. `0%2=0`. `0==0` is T. Output 1. Actual 8. **Fails.**\n\nLet's try `output = 1 if (C1%2 + C8%2)%2 == (c%2) else 8`.\n * **Example 1, (0,0):** `(1+0)%2=1`. `0%2=0`. `1==0` is F. Output 8. **Correct.**\n * **Example 1, (0,1):** `(1+1)%2=0`. `1%2=1`. `0==1` is F. Output 8. Actual 1. **Fails.**\n\nLet's try `output = 1 if (C1%2) == (C8%2) else 8`.\n * **Example 1, (0,0):** `1==0` is F. Output 8. **Correct.**\n * **Example 1, (0,1):** `1==1` is T. Output 1. **Correct.**\n * **Example 1, (0,2):** `0==0` is T. Output 1. Actual 8. **Fails.**\n\nLet's try `output = 1 if (C1%2) != (C8%2) else 8`.\n * **Example 1, (0,0):** `1!=0` is T. Output 1. Actual 8. **Fails.**\n\nLet's try `output = 1 if (C1%2 + C8%2 + r)%2 == 0 else 8`.\n * **Example 1, (0,0):** `1+0+0=1`. `1%2=1`. Output 8. **Correct.**\n * **Example 1, (0,1):** `1+1+0=2`. `2%2=0`. Output 1. **Correct.**\n * **Example 1, (0,2):** `0+0+0=0`. `0%2=0`. Output 1. Actual 8. **Fails.**\n\nLet's try `output = 1 if (C1%2 + C8%2 + c)%2 == 0 else 8`.\n * **Example 1, (0,0):** `1+0+0=1`. `1%2=1`. Output 8. **Correct.**\
Expected to find output fields in the LM response: [reasoning, python_code]
Actual output fields parsed from the LM response: [reasoning]
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 56, in forward
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/predict/chain_of_thought.py", line 37, in forward
return self.predict(**kwargs)
^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/predict/predict.py", line 86, in __call__
return super().__call__(**kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/predict/predict.py", line 175, in forward
completions = adapter(lm, lm_kwargs=config, signature=signature, demos=demos, inputs=kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/adapters/chat_adapter.py", line 47, in __call__
return JSONAdapter()(lm, lm_kwargs, signature, demos, inputs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/adapters/json_adapter.py", line 82, in __call__
return super().__call__(lm, lm_kwargs, signature, demos, inputs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/adapters/chat_adapter.py", line 46, in __call__
raise e
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/adapters/chat_adapter.py", line 38, in __call__
return super().__call__(lm, lm_kwargs, signature, demos, inputs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/adapters/base.py", line 128, in __call__
return self._call_postprocess(processed_signature, signature, outputs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/adapters/base.py", line 89, in _call_postprocess
value = self.parse(processed_signature, text)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/adapters/json_adapter.py", line 172, in parse
raise AdapterParseError(
dspy.utils.exceptions.AdapterParseError: Adapter JSONAdapter failed to parse the LM response.
LM Response: {
"reasoning": "1. **Initial Observation:** The input grids are 9x9 and the output grids are 3x3. This strongly suggests that the transformation works by dividing the 9x9 input grid into a 3x3 grid of 3x3 subgrids, and each subgrid is mapped to a single pixel in the output grid.\n\n2. **Color Analysis:** The input grids contain colors 0, 1, and 8. The output grids only contain colors 1 and 8. Color 0 appears to be a background color that is ignored in the main calculation.\n\n3. **Formulating a Hypothesis (Majority Vote):** A simple first hypothesis is that the output pixel's color is determined by the majority color (between 1 and 8) within the corresponding 3x3 subgrid. Let's denote the count of color 1 as `count1` and color 8 as `count8`.\n - If `count1 > count8`, output is 1.\n - If `count8 > count1`, output is 8.\n - If `count1 == count8`, a tie-breaking rule is needed.\n Testing this hypothesis across the examples shows it fails frequently. In many cases, the output color is the *minority* color. This indicates the rule is more complex.\n\n4. **Searching for a Pattern:** The rule seems to switch between majority and minority vote. The trigger for this switch must be a property of the subgrid or its position. I explored several properties:\n - The parity of the subgrid's coordinates `(i, j)`.\n - The parity of the total count of non-zero cells (`count1 + count8`).\n - The parity of `count1` or `count8` individually.\n\n5. **Discovering the Core Rule (Parity of `count1`):** After extensive testing, a promising pattern emerged related to the parity of `count1`. The rule `output = 1 if count1 is odd else 8` works for a significant number of cases (5 out of 9 in the first example).\n\n6. **Refining the Rule (Positional Inversion):** The cases where the `count1` parity rule failed in the first example were `(0,0)`, `(1,0)`, `(1,1)`, and `(2,2)`. Notice that three of these, `(0,0)`, `(1,1)`, and `(2,2)`, lie on the main diagonal of the 3x3 grid of subgrids. This suggests a positional modification to the rule.\n - Let's hypothesize: The base rule is `output = 1 if count1 is odd else 8`.\n - However, if the subgrid is on the main diagonal (`i == j`), the rule is inverted: `output = 8 if count1 is odd else 1`.\n\n7. **Final Verification:** Let's test this refined hypothesis on the first training example:\n - `(0,0)`: Diagonal. `count1=1` (odd). Inverted rule -> `8`. Correct.\n - `(0,1)`: Off-diagonal. `count1=3` (odd). Base rule -> `1`. Correct.\n - `(0,2)`: Off-diagonal. `count1=0` (even). Base rule -> `8`. Correct.\n - `(1,0)`: Off-diagonal. `count1=0` (even). Base rule -> `8`. Actual is `1`. This case fails.\n - `(1,1)`: Diagonal. `count1=1` (odd). Inverted rule -> `8`. Correct.\n - `(1,2)`: Off-diagonal. `count1=1` (odd). Base rule -> `1`. Correct.\n - `(2,0)`: Off-diagonal. `count1=1` (odd). Base rule -> `1`. Correct.\n - `(2,1)`: Off-diagonal. `count1=0` (even). Base rule -> `8`. Correct.\n - `(2,2)`: Diagonal. `count1=4` (even). Inverted rule -> `1`. Correct.\n\n This rule works for 8 out of 9 subgrids in the first example. While not perfect, it is the most consistent and simplest rule found after analyzing all examples. The single failure might indicate an additional, more subtle condition, but this rule captures the primary logic. Applying this rule to the other examples shows it performs better than other simple hypotheses. I will proceed with implementing this rule as the most likely solution.\n\n8. **Implementation Plan:**\n - Create an empty 3x3 output grid.\n - Iterate through the 3x3 grid of subgrids using indices `i` (for rows) and `j` (for columns).\n - For each subgrid at `(i, j)`:\n - Extract the 3x3 subgrid from the input.\n - Count the occurrences of the number 1 (`count1`).\n - Check if the subgrid is on the main diagonal (`i == j`).\n - Apply the base rule or the inverted rule based on the diagonal check and the parity of `count1`.\n - Assign the
Expected to find output fields in the LM response: [reasoning, python_code]
Actual output fields parsed from the LM response: [reasoning]
Average Metric: 1.00 / 1 (100.0%): 33%|█████████████████████████████████████ | 1/3 [00:29<00:59, 29.62s/it]Error applying `transform_grid` to a test input: name 'collections' is not defined
Average Metric: 1.00 / 2 (50.0%): 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [04:00<00:00, 87.39s/it]2025/08/29 05:04:18 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
Average Metric: 1.00 / 2 (50.0%): : 4it [05:08, 79.78s/it] Error applying `transform_grid` to a test input: name 'collections' is not defined
Average Metric: 1.00 / 3 (33.3%): : 5it [05:43, 68.67s/it]2025/08/29 05:05:49 INFO dspy.evaluate.evaluate: Average Metric: 1.0 / 3 (33.3%)
Iteration 71: Proposed new text for program: import dspy
from typing import List, Optional
import pydantic
import traceback
import copy
# Define the type for a grid/matrix as a list of lists of integers.
MATRIX = List[List[int]]
class TrainingExample(pydantic.BaseModel):
"""A Pydantic model for a single training example, containing an input and output grid."""
input: MATRIX
output: MATRIX
# --- Signature for the initial code generation ---
class GenerateTransformationFunction(dspy.Signature):
"""
You are an expert programmer solving visual puzzles from the Abstraction and Reasoning Corpus (ARC).
Your goal is to analyze a set of training examples, each consisting of an input grid and an output grid.
Based on this analysis, you must write a single, self-contained Python function named `transform_grid` that implements the observed transformation.
**Function Requirements:**
- The function must be named exactly `transform_grid`.
- It must accept one argument: `grid`, which is a 2D list of integers (the input grid).
- It must return a new 2D list of integers representing the transformed grid.
- The function should not rely on any external libraries unless they are standard (e.g., `copy`). If you need a library like numpy, import it inside the function.
- Do not modify the input grid in place; create a copy if necessary.
**Analysis Strategy:**
1. **Observe Core Patterns:** Look for simple, recurring patterns like geometric transformations (rotation, reflection, scaling), color changes, object manipulation (copying, moving, recoloring), pattern propagation, or flood-fills.
2. **Decompose the Problem:** Break down the transformation into logical steps. For example, "first, find all objects of color blue, then for each object, reflect it horizontally."
3. **Generalize:** The logic must be general enough to work for all training examples and, by extension, the unseen test inputs.
4. **Code Implementation:** Translate your logic into a clear and correct Python function. Ensure your code is robust and handles edge cases observed in the examples.
"""
training_examples: List[TrainingExample] = dspy.InputField(desc="A list of input/output grid pairs demonstrating the transformation rule.")
test_input_grid: MATRIX = dspy.InputField(desc="A single test input grid to which the transformation should be applicable. Use its properties (e.g., dimensions) to ensure your code is general.")
reasoning: str = dspy.OutputField(desc="Step-by-step thinking process to deduce the transformation rule and how to implement it in Python.")
python_code: str = dspy.OutputField(desc="A string containing the complete, self-contained Python function `transform_grid(grid)`.")
# --- Signature for refining the code based on feedback ---
class RefineTransformationFunction(dspy.Signature):
"""
You are an expert programmer debugging a Python function for an ARC visual puzzle.
You previously wrote a function, but it failed to correctly transform one of the training examples.
Your task is to analyze the original problem, the faulty code, and the specific error feedback to produce a corrected version of the function.
**Debugging Strategy:**
1. **Understand the Goal:** Re-read the `training_examples` to ensure you understand the required transformation.
2. **Analyze the Error:** Carefully examine the `feedback`. It will tell you which training example failed, what the function produced, and what the correct output should have been.
3. **Isolate the Bug:** Compare the incorrect output with the expected output to pinpoint the logical error in your `previous_code`. Did you misinterpret a pattern? Is there an off-by-one error? Does your logic fail on an edge case present in that specific example?
4. **Correct the Code:** Rewrite the `transform_grid` function to fix the bug. Ensure the new logic correctly handles the failing example while still being general enough for all other examples.
5. **Maintain Requirements:** The corrected function must still be named `transform_grid`, be self-contained, and meet all the original requirements.
"""
training_examples: List[TrainingExample] = dspy.InputField(desc="The original list of input/output grid pairs.")
test_input_grid: MATRIX = dspy.InputField(desc="The original representative test input grid.")
previous_code: str = dspy.InputField(desc="The Python code for `transform_grid` that produced an error.")
feedback: str = dspy.InputField(desc="Detailed feedback on why the code failed, including the specific training example, the incorrect output, and the expected output.")
reasoning: str = dspy.OutputField(desc="Step-by-step thinking process analyzing the bug and explaining the fix.")
python_code: str = dspy.OutputField(desc="A string containing the complete, corrected, self-contained Python function `transform_grid(grid)`.")
# --- The improved custom module with a self-correction loop ---
class ARCSolver(dspy.Module):
"""
A DSPy module that solves ARC tasks by generating, verifying, and refining Python code.
It uses a generate-and-test loop to improve the robustness of the generated solution.
"""
def __init__(self, max_attempts=3):
super().__init__()
self.max_attempts = max_attempts
self.code_generator = dspy.ChainOfThought(GenerateTransformationFunction)
self.code_refiner = dspy.ChainOfThought(RefineTransformationFunction)
def _verify_code(self, python_code: str, training_examples: List[TrainingExample]):
"""
Executes the generated code and verifies it against all training examples.
Returns the callable function if successful, otherwise returns a feedback string.
"""
local_scope = {}
# The LM might wrap the code in markdown, so we extract it.
if "```python" in python_code:
python_code = python_code.split("```python")[1].split("```")[0].strip()
try:
exec(python_code, globals(), local_scope)
transform_function = local_scope.get('transform_grid')
if not (transform_function and callable(transform_function)):
return None, "Error: `transform_grid` function not found or not callable after execution."
for i, example in enumerate(training_examples):
# Use deepcopy to prevent the function from modifying the original input data
input_copy = copy.deepcopy(example.input)
try:
predicted_output = transform_function(input_copy)
if predicted_output != example.output:
feedback = (
f"Verification failed on training example {i}.\n"
f"Input Grid:\n{example.input}\n"
f"Expected Output:\n{example.output}\n"
f"Generated Output:\n{predicted_output}"
)
return None, feedback
except Exception as e:
feedback = (
f"An exception occurred during verification on training example {i}: {e}\n"
f"Traceback: {traceback.format_exc()}"
)
return None, feedback
# All examples passed
return transform_function, None
except Exception as e:
feedback = f"A syntax error or other exception occurred during `exec`: {e}\nTraceback: {traceback.format_exc()}"
return None, feedback
def forward(self, training_examples: List[TrainingExample], test_inputs: List[MATRIX]):
"""
Generates, verifies, and refines a transformation function, then applies it.
"""
# Initial code generation
prediction = self.code_generator(training_examples=training_examples, test_input_grid=test_inputs[0])
python_code = prediction.python_code
transform_function = None
for attempt in range(self.max_attempts):
# Verify the current version of the code
verified_function, feedback = self._verify_code(python_code, training_examples)
if verified_function:
# Success! The code works on all training examples.
transform_function = verified_function
break
# If verification failed and we have attempts left, refine the code
if attempt < self.max_attempts - 1:
print(f"Attempt {attempt + 1} failed. Feedback: {feedback}")
print("Attempting to refine the code...")
refinement = self.code_refiner(
training_examples=training_examples,
test_input_grid=test_inputs[0],
previous_code=python_code,
feedback=feedback
)
python_code = refinement.python_code
else:
print(f"Final attempt failed. Could not produce a valid function. Feedback: {feedback}")
# Apply the final verified function to all test inputs
generated_outputs = []
for test_input in test_inputs:
if transform_function:
try:
output_grid = transform_function(copy.deepcopy(test_input))
generated_outputs.append(output_grid)
except Exception as e:
print(f"Error applying final `transform_grid` to a test input: {e}")
# Fallback for this specific input if the function fails at runtime
generated_outputs.append(copy.deepcopy(test_input))
else:
# Fallback: if no valid function was ever created, return original inputs.
generated_outputs.append(copy.deepcopy(test_input))
return dspy.Prediction(test_outputs=generated_outputs)
# The final program object is an instance of our new, more robust custom module.
program = ARCSolver()
Attempt 1 failed. Feedback: A syntax error or other exception occurred during `exec`: 'dict' object has no attribute 'input'
Traceback: Traceback (most recent call last):
File "<string>", line 93, in _verify_code
AttributeError: 'dict' object has no attribute 'input'
Attempting to refine the code...
Attempt 1 failed. Feedback: A syntax error or other exception occurred during `exec`: 'dict' object has no attribute 'input'
Traceback: Traceback (most recent call last):
File "<string>", line 93, in _verify_code
AttributeError: 'dict' object has no attribute 'input'
Attempting to refine the code...
Attempt 1 failed. Feedback: A syntax error or other exception occurred during `exec`: 'dict' object has no attribute 'input'
Traceback: Traceback (most recent call last):
File "<string>", line 93, in _verify_code
AttributeError: 'dict' object has no attribute 'input'
Attempting to refine the code...
Attempt 2 failed. Feedback: A syntax error or other exception occurred during `exec`: 'dict' object has no attribute 'input'
Traceback: Traceback (most recent call last):
File "<string>", line 93, in _verify_code
AttributeError: 'dict' object has no attribute 'input'
Attempting to refine the code...
Attempt 2 failed. Feedback: A syntax error or other exception occurred during `exec`: 'dict' object has no attribute 'input'
Traceback: Traceback (most recent call last):
File "<string>", line 93, in _verify_code
AttributeError: 'dict' object has no attribute 'input'
Attempting to refine the code...
Attempt 2 failed. Feedback: A syntax error or other exception occurred during `exec`: 'dict' object has no attribute 'input'
Traceback: Traceback (most recent call last):
File "<string>", line 93, in _verify_code
AttributeError: 'dict' object has no attribute 'input'
Attempting to refine the code...
2025/08/29 05:08:23 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 05:08:59 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
Attempt 1 failed. Feedback: A syntax error or other exception occurred during `exec`: 'dict' object has no attribute 'input'
Traceback: Traceback (most recent call last):
File "<string>", line 93, in _verify_code
AttributeError: 'dict' object has no attribute 'input'
Attempt 1 failed. Feedback: A syntax error or other exception occurred during `exec`: 'dict' object has no attribute 'input'
Traceback: Traceback (most recent call last):
File "<string>", line 93, in _verify_code
AttributeError: 'dict' object has no attribute 'input'
Attempting to refine the code...
Attempting to refine the code...
Attempt 2 failed. Feedback: A syntax error or other exception occurred during `exec`: 'dict' object has no attribute 'input'
Traceback: Traceback (most recent call last):
File "<string>", line 93, in _verify_code
AttributeError: 'dict' object has no attribute 'input'
Attempting to refine the code...
Attempt 2 failed. Feedback: A syntax error or other exception occurred during `exec`: 'dict' object has no attribute 'input'
Traceback: Traceback (most recent call last):
File "<string>", line 93, in _verify_code
AttributeError: 'dict' object has no attribute 'input'
Attempting to refine the code...
Attempt 1 failed. Feedback: A syntax error or other exception occurred during `exec`: 'dict' object has no attribute 'input'
Traceback: Traceback (most recent call last):
File "<string>", line 93, in _verify_code
AttributeError: 'dict' object has no attribute 'input'
Attempting to refine the code...
Final attempt failed. Could not produce a valid function. Feedback: A syntax error or other exception occurred during `exec`: 'dict' object has no attribute 'input'
Traceback: Traceback (most recent call last):
File "<string>", line 93, in _verify_code
AttributeError: 'dict' object has no attribute 'input'
Final attempt failed. Could not produce a valid function. Feedback: A syntax error or other exception occurred during `exec`: 'dict' object has no attribute 'input'
Traceback: Traceback (most recent call last):
File "<string>", line 93, in _verify_code
AttributeError: 'dict' object has no attribute 'input'
2025/08/29 05:10:22 INFO dspy.evaluate.evaluate: Average Metric: 0.0 / 3 (0.0%)
GEPA Optimization: 34%|█████████████████████████████████████▎ | 1357/4000 [7:36:26<10:52:10, 14.81s/rollouts]Final attempt failed. Could not produce a valid function. Feedback: A syntax error or other exception occurred during `exec`: 'dict' object has no attribute 'input'
Traceback: Traceback (most recent call last):
File "<string>", line 93, in _verify_code
AttributeError: 'dict' object has no attribute 'input'
Iteration 71: New subsample score is not better, skipping
Iteration 72: Selected program 4 score: 0.61
0%| | 0/3 [00:00<?, ?it/s]Final attempt failed. Could not produce a valid function. Feedback: A syntax error or other exception occurred during `exec`: 'dict' object has no attribute 'input'
Traceback: Traceback (most recent call last):
File "<string>", line 93, in _verify_code
AttributeError: 'dict' object has no attribute 'input'
Final attempt failed. Could not produce a valid function. Feedback: A syntax error or other exception occurred during `exec`: 'dict' object has no attribute 'input'
Traceback: Traceback (most recent call last):
File "<string>", line 93, in _verify_code
AttributeError: 'dict' object has no attribute 'input'
Average Metric: 2.00 / 2 (100.0%): 67%|██████████████████████████████████████████████████████████████████████████ | 2/3 [00:51<00:24, 24.45s/it]Attempt 2 failed. Feedback: A syntax error or other exception occurred during `exec`: 'dict' object has no attribute 'input'
Traceback: Traceback (most recent call last):
File "<string>", line 93, in _verify_code
AttributeError: 'dict' object has no attribute 'input'
Attempting to refine the code...
Average Metric: 2.00 / 3 (66.7%): 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [03:47<00:00, 75.67s/it]2025/08/29 05:14:09 INFO dspy.evaluate.evaluate: Average Metric: 2.0 / 3 (66.7%)
Final attempt failed. Could not produce a valid function. Feedback: A syntax error or other exception occurred during `exec`: 'dict' object has no attribute 'input'
Traceback: Traceback (most recent call last):
File "<string>", line 93, in _verify_code
AttributeError: 'dict' object has no attribute 'input'
2025/08/29 05:15:09 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[0, 0, 0, 0, 3], [0, 8, 8, 0, 3], [0, 8, 8, 0, 3], [0, 0, 0, 0, 3], [3, 3, 3, 3, 3]], 'output': [[2, 0, 0, 0, 0, 0, 0, 2, 3, 3], [0, 2, 0, 0, 0, 0, 2, 0, 3, 3], [0, 0, 8, 8, 8, 8, 0, 0, 3, 3], [0, 0, 8, 8, 8, 8, 0, 0, 3, 3], [0, 0, 8, 8, 8, 8, 0, 0, 3, 3], [0, 0, 8, 8, 8, 8, 0, 0, 3, 3], [0, 2, 0, 0, 0, 0, 2, 0, 3, 3], [2, 0, 0, 0, 0, 0, 0, 2, 3, 3], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3]]}, {'input': [[0, 0, 0, 0, 7], [4, 4, 0, 0, 7], [4, 4, 0, 0, 6], [0, 0, 0, 0, 6], [7, 7, 6, 6, 6]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 7, 7, 7], [0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 7, 7, 7], [0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 7, 7, 7], [4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 7, 7, 7], [4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 7, 7, 7], [4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 7, 7, 7], [4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 6, 6, 6], [4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 6, 6, 6], [4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 6, 6, 6], [0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 6, 6, 6], [0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 6, 6, 6], [0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 6, 6, 6], [7, 7, 7, 7, 7, 7, 6, 6, 6, 6, 6, 6, 6, 6, 6], [7, 7, 7, 7, 7, 7, 6, 6, 6, 6, 6, 6, 6, 6, 6], [7, 7, 7, 7, 7, 7, 6, 6, 6, 6, 6, 6, 6, 6, 6]]}, {'input': [[0, 0, 0, 0, 9], [0, 1, 1, 0, 9], [0, 1, 1, 0, 3], [0, 0, 0, 0, 3], [9, 9, 3, 3, 4]], 'output': [[2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 9, 9, 9, 9], [0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 9, 9, 9, 9], [0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 9, 9, 9, 9], [0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 9, 9, 9, 9], [0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 9, 9, 9, 9], [0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 9, 9, 9, 9], [0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 9, 9, 9, 9], [0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 9, 9, 9, 9], [0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 3, 3, 3, 3], [0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 3, 3, 3, 3], [0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 3, 3, 3, 3], [0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 3, 3, 3, 3], [0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 3, 3, 3, 3], [0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 3, 3, 3, 3], [0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 3, 3, 3, 3], [2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 3, 3, 3, 3], [9, 9, 9, 9, 9, 9, 9, 9, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4], [9, 9, 9, 9, 9, 9, 9, 9, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4], [9, 9, 9, 9, 9, 9, 9, 9, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4], [9, 9, 9, 9, 9, 9, 9, 9, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4]]}], 'test_inputs': [[[0, 6, 6, 0, 8], [0, 6, 6, 0, 8], [0, 0, 0, 0, 1], [0, 0, 0, 0, 7], [8, 8, 1, 7, 9]]], 'test_outputs': [[[0, 0, 0, 0, 0, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8], [0, 0, 0, 0, 0, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8], [0, 0, 0, 0, 0, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8], [0, 0, 0, 0, 0, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8], [0, 0, 0, 0, 0, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8], [0, 0, 0, 0, 0, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8], [0, 0, 0, 0, 0, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8], [0, 0, 0, 0, 0, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8], [0, 0, 0, 0, 0, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8], [0, 0, 0, 0, 0, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8], [0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 1, 1, 1, 1, 1], [0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 1, 1, 1, 1, 1], [0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 1, 1, 1, 1, 1], [0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 1, 1, 1, 1, 1], [2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 7, 7, 7, 7], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 7, 7, 7, 7], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 7, 7, 7, 7], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 7, 7, 7, 7], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 7, 7, 7, 7], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 1, 1, 1, 1, 1, 7, 7, 7, 7, 7, 9, 9, 9, 9, 9], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 1, 1, 1, 1, 1, 7, 7, 7, 7, 7, 9, 9, 9, 9, 9], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 1, 1, 1, 1, 1, 7, 7, 7, 7, 7, 9, 9, 9, 9, 9], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 1, 1, 1, 1, 1, 7, 7, 7, 7, 7, 9, 9, 9, 9, 9], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 1, 1, 1, 1, 1, 7, 7, 7, 7, 7, 9, 9, 9, 9, 9]]]}) (input_keys={'test_inputs', 'training_examples'}): 'dict' object has no attribute 'input'
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 117, in forward
AttributeError: 'dict' object has no attribute 'input'
2025/08/29 05:15:09 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[3, 3, 0], [7, 4, 0], [0, 0, 4]], 'output': [[3, 3, 3, 3, 3, 3, 0, 0, 0], [3, 3, 3, 3, 3, 3, 0, 0, 0], [3, 3, 3, 3, 3, 3, 0, 0, 0], [7, 7, 7, 4, 4, 4, 0, 0, 0], [7, 7, 7, 4, 4, 4, 0, 0, 0], [7, 7, 7, 4, 4, 4, 0, 0, 0], [0, 0, 0, 0, 0, 0, 4, 4, 4], [0, 0, 0, 0, 0, 0, 4, 4, 4], [0, 0, 0, 0, 0, 0, 4, 4, 4]]}, {'input': [[3, 0, 2], [0, 2, 2], [0, 0, 3]], 'output': [[3, 3, 3, 0, 0, 0, 2, 2, 2], [3, 3, 3, 0, 0, 0, 2, 2, 2], [3, 3, 3, 0, 0, 0, 2, 2, 2], [0, 0, 0, 2, 2, 2, 2, 2, 2], [0, 0, 0, 2, 2, 2, 2, 2, 2], [0, 0, 0, 2, 2, 2, 2, 2, 2], [0, 0, 0, 0, 0, 0, 3, 3, 3], [0, 0, 0, 0, 0, 0, 3, 3, 3], [0, 0, 0, 0, 0, 0, 3, 3, 3]]}], 'test_inputs': [[[0, 1, 0], [0, 0, 6], [6, 1, 0]]], 'test_outputs': [[[0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 6, 6, 6], [0, 0, 0, 0, 0, 0, 6, 6, 6], [0, 0, 0, 0, 0, 0, 6, 6, 6], [6, 6, 6, 1, 1, 1, 0, 0, 0], [6, 6, 6, 1, 1, 1, 0, 0, 0], [6, 6, 6, 1, 1, 1, 0, 0, 0]]]}) (input_keys={'test_inputs', 'training_examples'}): 'dict' object has no attribute 'input'
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 117, in forward
AttributeError: 'dict' object has no attribute 'input'
2025/08/29 05:15:09 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 5, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 5, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 5, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 1, 1, 1, 0], [0, 0, 0, 0, 0, 1, 5, 1, 0], [0, 0, 0, 0, 0, 1, 1, 1, 0], [0, 0, 1, 1, 1, 0, 0, 0, 0], [0, 0, 1, 5, 1, 0, 0, 0, 0], [0, 0, 1, 1, 1, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0, 0, 0], [1, 5, 1, 0, 0, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0, 0, 0]]}, {'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 5, 0], [0, 0, 0, 5, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 5, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 5, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 1, 1, 1], [0, 0, 1, 1, 1, 0, 1, 5, 1], [0, 0, 1, 5, 1, 0, 1, 1, 1], [0, 0, 1, 1, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 1, 1], [0, 0, 0, 0, 0, 0, 1, 5, 1], [0, 0, 1, 1, 1, 0, 1, 1, 1], [0, 0, 1, 5, 1, 0, 0, 0, 0], [0, 0, 1, 1, 1, 0, 0, 0, 0]]}], 'test_inputs': [[[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 5, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 5, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 5, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 5, 0, 0, 0, 5, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]]], 'test_outputs': [[[1, 1, 1, 0, 0, 0, 0, 0, 0], [1, 5, 1, 0, 0, 0, 1, 1, 1], [1, 1, 1, 0, 0, 0, 1, 5, 1], [0, 0, 1, 1, 1, 0, 1, 1, 1], [0, 0, 1, 5, 1, 0, 0, 0, 0], [0, 0, 1, 1, 1, 0, 0, 0, 0], [1, 1, 1, 0, 1, 1, 1, 0, 0], [1, 5, 1, 0, 1, 5, 1, 0, 0], [1, 1, 1, 0, 1, 1, 1, 0, 0]]]}) (input_keys={'test_inputs', 'training_examples'}): 'dict' object has no attribute 'input'
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 117, in forward
AttributeError: 'dict' object has no attribute 'input'
2025/08/29 05:15:09 INFO dspy.evaluate.evaluate: Average Metric: 0.0 / 3 (0.0%)
GEPA Optimization: 34%|█████████████████████████████████████▍ | 1363/4000 [7:41:13<12:04:24, 16.48s/rollouts]Iteration 72: Proposed new text for program: import dspy
from typing import List, Optional, Tuple
import pydantic
import traceback
import copy
# Define the type for a grid/matrix as a list of lists of integers.
MATRIX = List[List[int]]
class TrainingExample(pydantic.BaseModel):
"""A Pydantic model for a single training example, containing an input and output grid."""
input: MATRIX
output: MATRIX
class GenerateTransformationFunction(dspy.Signature):
"""
You are an expert programmer solving visual puzzles from the Abstraction and Reasoning Corpus (ARC).
Your goal is to analyze a set of training examples, each consisting of an input grid and an output grid.
Based on this analysis, you must write a single, self-contained Python function named `transform_grid` that implements the observed transformation.
**Function Requirements:**
- The function must be named exactly `transform_grid`.
- It must accept one argument: `grid`, which is a 2D list of integers (the input grid).
- It must return a new 2D list of integers representing the transformed grid.
- The function should not rely on any external libraries unless they are standard (e.g., `copy`). If you need a library like numpy, import it inside the function.
- Do not modify the input grid in place; create a copy if necessary.
**Analysis Strategy:**
1. **Observe Core Patterns:** Look for simple, recurring patterns like geometric transformations (rotation, reflection, scaling), color changes, object manipulation (copying, moving, recoloring), pattern propagation, or flood-fills.
2. **Decompose the Problem:** Break down the transformation into logical steps. For example, "first, find all objects of color blue, then for each object, reflect it horizontally."
3. **Generalize:** The logic must be general enough to work for all training examples and, by extension, the unseen test inputs.
4. **Code Implementation:** Translate your logic into a clear and correct Python function. Ensure your code is robust and handles edge cases observed in the examples.
"""
training_examples: List[TrainingExample] = dspy.InputField(desc="A list of input/output grid pairs demonstrating the transformation rule.")
test_input_grid: MATRIX = dspy.InputField(desc="A single test input grid to which the transformation should be applicable. Use its properties (e.g., dimensions) to ensure your code is general.")
reasoning: str = dspy.OutputField(desc="Step-by-step thinking process to deduce the transformation rule and how to implement it in Python.")
python_code: str = dspy.OutputField(desc="A string containing the complete, self-contained Python function `transform_grid(grid)`.")
class RefineTransformationFunction(dspy.Signature):
"""
You are an expert programmer debugging a Python function for an ARC puzzle.
You previously wrote a function that failed on a specific training example.
Analyze the faulty code, the input it failed on, the incorrect output it produced, and the expected output.
Then, rewrite the function to fix the bug. The new function must work for this example and all previous ones.
"""
training_examples: List[TrainingExample] = dspy.InputField(desc="The full list of training examples for context.")
faulty_python_code: str = dspy.InputField(desc="The previous Python code that failed.")
failing_input_grid: MATRIX = dspy.InputField(desc="The specific input grid on which the code failed.")
incorrect_output_grid: MATRIX = dspy.InputField(desc="The incorrect output produced by the faulty code.")
expected_output_grid: MATRIX = dspy.InputField(desc="The correct output for the failing input grid.")
reasoning: str = dspy.OutputField(desc="Step-by-step analysis of the bug and the plan to fix it.")
python_code: str = dspy.OutputField(desc="The corrected, self-contained Python function `transform_grid(grid)`.")
class ARCSolver(dspy.Module):
"""A DSPy module that solves ARC tasks via iterative code generation, validation, and refinement."""
def __init__(self, max_attempts=3):
super().__init__()
self.max_attempts = max_attempts
self.code_generator = dspy.ChainOfThought(GenerateTransformationFunction)
self.code_refiner = dspy.ChainOfThought(RefineTransformationFunction)
def _execute_and_test_code(
self, python_code: str, training_examples: List[TrainingExample]
) -> Tuple[Optional[callable], bool, Optional[TrainingExample], Optional[MATRIX]]:
"""
Executes the generated code and tests it against all training examples.
Returns the function, a success flag, and details of the first failure.
"""
local_scope = {}
try:
if "```python" in python_code:
python_code = python_code.split("```python")[1].split("```")[0].strip()
exec(python_code, globals(), local_scope)
transform_function = local_scope.get('transform_grid')
if not (transform_function and callable(transform_function)):
print("Warning: `transform_grid` function not found or not callable.")
return None, False, None, None
for example in training_examples:
try:
predicted_output = transform_function(copy.deepcopy(example.input))
if predicted_output != example.output:
print(f"Validation failed for one training example.")
return transform_function, False, example, predicted_output
except Exception as e:
print(f"Error during validation on a training example: {e}")
return transform_function, False, example, None # Failed due to runtime error
return transform_function, True, None, None # All examples passed
except Exception as e:
print(f"Error executing generated code: {e}")
print(f"Traceback: {traceback.format_exc()}")
return None, False, None, None
def forward(self, training_examples: List[TrainingExample], test_inputs: List[MATRIX]):
python_code = None
failing_example = None
incorrect_output = None
transform_function = None
for attempt in range(self.max_attempts):
print(f"--- Attempt {attempt + 1}/{self.max_attempts} ---")
# 1. Generate code (either from scratch or by refining)
if attempt == 0:
prediction = self.code_generator(
training_examples=training_examples,
test_input_grid=test_inputs[0]
)
python_code = prediction.python_code
else:
prediction = self.code_refiner(
training_examples=training_examples,
faulty_python_code=python_code,
failing_input_grid=failing_example.input,
incorrect_output_grid=incorrect_output,
expected_output_grid=failing_example.output
)
python_code = prediction.python_code
# 2. Execute and test the generated code
func, is_correct, fail_ex, wrong_out = self._execute_and_test_code(python_code, training_examples)
if is_correct:
print("--- Code validation successful! ---")
transform_function = func
break # Exit the loop on success
else:
print("Code failed validation. Will attempt to refine.")
failing_example = fail_ex
incorrect_output = wrong_out
# The loop will continue to the next attempt
# 3. Apply the final function or fallback
generated_outputs = []
if transform_function:
for test_input in test_inputs:
try:
output_grid = transform_function(copy.deepcopy(test_input))
generated_outputs.append(output_grid)
except Exception as e:
print(f"Error applying validated `transform_grid` to a test input: {e}")
generated_outputs.append(copy.deepcopy(test_input))
else:
print("--- Max attempts reached. Could not generate a valid function. Using fallback. ---")
for test_input in test_inputs:
generated_outputs.append(copy.deepcopy(test_input))
return dspy.Prediction(test_outputs=generated_outputs)
# The final program object is an instance of our new, more robust custom module.
program = ARCSolver()
--- Attempt 1/3 ---
--- Attempt 1/3 ---
--- Attempt 1/3 ---
Error during validation on a training example: 'dict' object has no attribute 'input'
Code failed validation. Will attempt to refine.
--- Attempt 2/3 ---
Error during validation on a training example: 'dict' object has no attribute 'input'
Code failed validation. Will attempt to refine.
--- Attempt 2/3 ---
Error during validation on a training example: 'dict' object has no attribute 'input'
Code failed validation. Will attempt to refine.
--- Attempt 2/3 ---
Iteration 72: New subsample score is not better, skipping
Iteration 73: Selected program 3 score: 0.645
Average Metric: 3.00 / 3 (100.0%): 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [02:24<00:00, 48.24s/it]2025/08/29 05:17:34 INFO dspy.evaluate.evaluate: Average Metric: 3.0 / 3 (100.0%)
GEPA Optimization: 34%|█████████████████████████████████████▌ | 1366/4000 [7:43:38<12:52:26, 17.60s/rollouts]
Iteration 73: All subsample scores perfect. Skipping.
Iteration 73: Reflective mutation did not propose a new candidate
Iteration 74: Selected program 0 score: 0.67
Average Metric: 3.00 / 3 (100.0%): 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 169.59it/s]2025/08/29 05:17:34 INFO dspy.evaluate.evaluate: Average Metric: 3.0 / 3 (100.0%)
Iteration 74: All subsample scores perfect. Skipping.
Iteration 74: Reflective mutation did not propose a new candidate
Iteration 75: Selected program 1 score: 0.63
Average Metric: 3.00 / 3 (100.0%): 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [01:54<00:00, 38.17s/it]2025/08/29 05:19:28 INFO dspy.evaluate.evaluate: Average Metric: 3.0 / 3 (100.0%)
GEPA Optimization: 34%|█████████████████████████████████████▋ | 1372/4000 [7:45:32<12:56:39, 17.73s/rollouts]
Iteration 75: All subsample scores perfect. Skipping.
Iteration 75: Reflective mutation did not propose a new candidate
Iteration 76: Selected program 2 score: 0.605
Average Metric: 2.00 / 3 (66.7%): 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [03:43<00:00, 74.66s/it]2025/08/29 05:23:12 INFO dspy.evaluate.evaluate: Average Metric: 2.0 / 3 (66.7%)
Iteration 76: Proposed new text for program: import dspy
from typing import List
import pydantic
import copy
# Define type aliases and pydantic models for clarity and structure.
MATRIX = List[List[int]]
class TrainingExample(pydantic.BaseModel):
input: MATRIX
output: MATRIX
class GenerateHypothesis(dspy.Signature):
"""
Analyze a series of input/output matrix pairs from the Abstraction and Reasoning Corpus (ARC).
Based on these examples, deduce the underlying transformation rule.
Describe this rule as a clear, step-by-step natural language hypothesis.
**Successful Strategies to Consider:**
- **Object Permanence:** Do grid dimensions change? Are objects added, removed, or just moved?
- **Color Transformations:** Look for rules that change colors based on their value or their neighbors.
- **Spatial Reasoning:** Analyze shapes, positions, and relationships. Common patterns include filling enclosed areas, drawing lines between points, moving objects, detecting symmetry, or repeating patterns.
- **Iterative Processes:** Some rules might be applied repeatedly.
- **Decomposition:** Break down the transformation into smaller, independent steps if possible. For example, "1. Find all red squares. 2. For each red square, draw a blue line to the nearest edge."
"""
training_examples: List[TrainingExample] = dspy.InputField(desc="A list of input/output pairs demonstrating the transformation rule.")
hypothesis: str = dspy.OutputField(desc="A clear, step-by-step natural language description of the transformation rule.")
class GeneratePythonFunctionFromHypothesis(dspy.Signature):
"""
You are an expert programmer. Your task is to write a single, self-contained Python function based on a given hypothesis and example matrix pairs.
**Function Requirements:**
- The function must be named `transform_matrix`.
- It must accept one argument: `matrix`, which is a list of lists of integers.
- It must return a list of lists of integers (the transformed grid).
- The function should not use any external libraries except for `copy` if needed (e.g., `import copy; new_matrix = copy.deepcopy(matrix)`).
- Your output must be ONLY the Python code for the function. Do not include any explanations, comments outside the function, or markdown formatting like ```python.
**Example of a Correctly Formatted Output:**
def transform_matrix(matrix: list[list[int]]) -> list[list[int]]:
# Your implementation here based on the hypothesis.
# For example, find the most frequent color and fill the grid.
import copy
new_matrix = copy.deepcopy(matrix)
# ... logic ...
return new_matrix
"""
hypothesis: str = dspy.InputField(desc="The natural language hypothesis describing the transformation rule.")
training_examples: List[TrainingExample] = dspy.InputField(desc="The original examples, for reference.")
python_function: str = dspy.OutputField(desc="A string containing a single Python function `transform_matrix` that implements the rule.")
class ARCSolver(dspy.Module):
"""A module that solves ARC tasks by first generating a hypothesis and then generating code."""
def __init__(self):
super().__init__()
# Decompose the problem: 1) Reason about the rule, 2) Write code for the rule.
# ChainOfThought is used for hypothesis generation to encourage structured reasoning.
self.generate_hypothesis = dspy.ChainOfThought(GenerateHypothesis)
self.generate_code = dspy.Predict(GeneratePythonFunctionFromHypothesis)
def forward(self, training_examples: List[TrainingExample], test_inputs: List[MATRIX]):
# Step 1: Generate a natural language hypothesis about the transformation rule.
prediction = self.generate_hypothesis(training_examples=training_examples)
hypothesis = prediction.hypothesis
# Step 2: Generate Python code based on the clear, focused hypothesis.
# Providing the examples again helps the LM ground the code in concrete data.
code_prediction = self.generate_code(hypothesis=hypothesis, training_examples=training_examples)
python_code = code_prediction.python_function
# Prepare a dictionary to hold the executed function.
local_scope = {}
# Create fallback outputs in case of any failure.
fallback_outputs = [copy.deepcopy(matrix) for matrix in test_inputs]
try:
# Step 3: Execute the generated code string to define the function.
exec(python_code, globals(), local_scope)
transform_func = local_scope.get('transform_matrix')
if not callable(transform_func):
return dspy.Prediction(test_outputs=fallback_outputs)
# Step 4: Apply the generated function to each test input.
solved_outputs = []
for test_matrix in test_inputs:
try:
input_copy = copy.deepcopy(test_matrix)
result = transform_func(input_copy)
solved_outputs.append(result)
except Exception:
# If the function fails on a specific test case, append the original
# matrix as a fallback for that case and continue.
solved_outputs.append(copy.deepcopy(test_matrix))
return dspy.Prediction(test_outputs=solved_outputs)
except Exception:
# If code generation or `exec` fails entirely, return the original inputs.
return dspy.Prediction(test_outputs=fallback_outputs)
# The overall task signature remains the same, defining the final input/output of the program.
class SolveTaskSignature(dspy.Signature):
"""
Given a set of training examples demonstrating a matrix transformation,
apply the same transformation to a new set of test matrices.
"""
training_examples: List[TrainingExample] = dspy.InputField(description="Input and output examples demonstrating the task to be performed.")
test_inputs: List[MATRIX] = dspy.InputField(description="Input matrices to be solved following the task described in the training examples.")
test_outputs: List[MATRIX] = dspy.OutputField(description="Output matrices corresponding to the test inputs.")
# The final program is an instance of our new, more robust module.
program = ARCSolver()
2025/08/29 05:26:36 INFO dspy.evaluate.evaluate: Average Metric: 2.0 / 3 (66.7%)
GEPA Optimization: 34%|█████████████████████████████████████▉ | 1378/4000 [7:52:39<17:23:42, 23.88s/rollouts]Iteration 76: New subsample score is not better, skipping
Iteration 77: Selected program 1 score: 0.63
Average Metric: 2.00 / 3 (66.7%): 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [04:46<00:00, 95.58s/it]2025/08/29 05:31:22 INFO dspy.evaluate.evaluate: Average Metric: 2.0 / 3 (66.7%)
Iteration 77: Proposed new text for program: import dspy
from typing import List
import pydantic
import traceback
# Define the type for a matrix for clarity
MATRIX = List[List[int]]
class TrainingExample(pydantic.BaseModel):
"""A Pydantic model for a single training example, containing an input and output matrix."""
input: MATRIX
output: MATRIX
class GeneratePythonRuleSignature(dspy.Signature):
"""
Analyzes pairs of input/output matrices to deduce the transformation rule and express it as a Python function.
You are an expert programmer and analyst of abstract visual reasoning puzzles. Your task is to carefully examine the provided training examples. Each example consists of an 'input' matrix and a corresponding 'output' matrix.
Your goal is to deduce the single, consistent transformation rule that converts each input to its output. Then, you must write a self-contained Python function named `transform_matrix` that implements this rule.
The function must:
- Be named exactly `transform_matrix`.
- Accept a single argument, `matrix`, which is a list of lists of integers (the input grid).
- Return a list of lists of integers (the transformed output grid).
- Not use any external libraries (like numpy or pandas). Standard Python libraries are allowed.
- Contain all logic required for the transformation.
Analyze the patterns carefully. Consider geometric operations (rotation, reflection), color/value transformations, object-based logic (identifying shapes, properties), or grid-based patterns (counting, partitioning). Your generated code should be robust enough to solve a new, unseen test input based on the same rule.
Example of a valid function format:
```python
def transform_matrix(matrix: list[list[int]]) -> list[list[int]]:
# Your transformation logic here
# e.g., create a new_matrix of the same dimensions
# ...
return new_matrix
```
"""
training_examples: List[TrainingExample] = dspy.InputField(description="A list of input and output examples demonstrating the task.")
python_function: str = dspy.OutputField(description="A self-contained Python function `transform_matrix` that implements the transformation rule.", prefix="```python\n")
class ARCProgram(dspy.Module):
"""A program that infers a rule as a Python function and then executes it to solve test inputs."""
def __init__(self):
super().__init__()
# Use ChainOfThought for the complex reasoning task of generating a Python function.
self.rule_generator = dspy.ChainOfThought(GeneratePythonRuleSignature)
def forward(self, training_examples: List[TrainingExample], test_inputs: List[MATRIX]) -> dspy.Prediction:
"""
Infers a rule as a Python function from training examples and applies it to each test input.
Args:
training_examples: A list of TrainingExample objects.
test_inputs: A list of input matrices to be solved.
Returns:
A dspy.Prediction object with the 'test_outputs' field populated.
"""
# 1. Generate the Python function that encapsulates the transformation rule.
generated = self.rule_generator(training_examples=training_examples)
python_code = generated.python_function
# Clean up the generated code, removing markdown fences if they exist.
if python_code.startswith("```python"):
python_code = python_code[len("```python"):].strip()
if python_code.endswith("```"):
python_code = python_code[:-len("```")].strip()
all_test_outputs = []
# 2. Prepare the execution environment for the generated function.
local_namespace = {}
try:
exec(python_code, globals(), local_namespace)
transformer_func = local_namespace.get('transform_matrix')
if not callable(transformer_func):
raise ValueError("`transform_matrix` function not found or is not callable in the generated code.")
# 3. Iterate through each test input and apply the generated function.
for test_matrix in test_inputs:
try:
# Create a deep copy to prevent the function from modifying the original test input list
matrix_copy = [row[:] for row in test_matrix]
result_matrix = transformer_func(matrix_copy)
all_test_outputs.append(result_matrix)
except Exception:
# Fallback for a single failing test case during execution.
if test_matrix:
all_test_outputs.append([([0] * len(test_matrix[0])) for _ in range(len(test_matrix))])
else:
all_test_outputs.append([])
except Exception:
# Fallback if code compilation (`exec`) fails or the function is invalid.
# Generate empty/zero outputs for all test cases.
for test_matrix in test_inputs:
if test_matrix and test_matrix[0]:
all_test_outputs.append([([0] * len(test_matrix[0])) for _ in range(len(test_matrix))])
else:
all_test_outputs.append([])
# 4. Return the collected outputs in a single Prediction object.
return dspy.Prediction(test_outputs=all_test_outputs)
# The final 'program' object is an instance of our robust, code-generating module.
program = ARCProgram()
2025/08/29 05:34:07 INFO dspy.evaluate.evaluate: Average Metric: 3.0 / 3 (100.0%)
2025/08/29 05:37:49 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 05:37:49 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 8, 0, 0, 0, 0, 8, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 8, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 8, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 8, 8, 8, 8, 8, 8, 0, 0, 0], [0, 0, 0, 0, 0, 0, 8, 0, 0, 0], [0, 0, 0, 0, 0, 0, 8, 0, 0, 0], [0, 0, 0, 0, 0, 0, 8, 0, 0, 0], [0, 0, 0, 0, 8, 0, 8, 0, 0, 0], [0, 0, 0, 0, 0, 0, 8, 0, 0, 0], [0, 0, 0, 0, 0, 0, 8, 0, 0, 0]]}, {'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 8, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 8, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 8, 0, 0, 0, 8], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 8, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 8, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 8, 0, 0, 0, 0, 0, 0, 0], [0, 0, 8, 0, 0, 0, 0, 0, 0, 0], [0, 0, 8, 0, 0, 0, 0, 0, 0, 0], [0, 0, 8, 0, 0, 8, 8, 8, 8, 8], [0, 0, 8, 0, 0, 0, 0, 0, 0, 0], [0, 0, 8, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}, {'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 8, 0, 0, 0, 8, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 8, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 8, 0, 0, 0, 8], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 8, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 8, 8, 8, 8, 8, 0, 0, 0, 0], [0, 8, 0, 0, 0, 8, 0, 0, 0, 0], [0, 8, 0, 0, 0, 8, 0, 0, 0, 0], [0, 8, 0, 0, 0, 8, 0, 0, 0, 0], [0, 0, 0, 0, 0, 8, 0, 0, 0, 0], [0, 0, 0, 0, 0, 8, 0, 0, 0, 0], [0, 0, 0, 0, 0, 8, 8, 8, 8, 8], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 8, 0, 0, 0, 0, 0, 0, 0]]}], 'test_inputs': [[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 8, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 8, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 8, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 8, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 8, 0, 0, 8, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]], 'test_outputs': [[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 8, 0, 0, 0, 0, 0, 0, 0, 0], [0, 8, 0, 8, 0, 0, 0, 0, 0, 0], [0, 8, 0, 0, 0, 0, 0, 8, 0, 0], [0, 8, 0, 0, 0, 0, 0, 8, 0, 0], [0, 8, 0, 0, 0, 0, 0, 8, 0, 0], [0, 0, 0, 0, 0, 0, 0, 8, 0, 0], [0, 0, 0, 0, 0, 0, 0, 8, 0, 0], [0, 0, 0, 0, 8, 8, 8, 8, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]]}) (input_keys={'test_inputs', 'training_examples'}): 'NoneType' object has no attribute 'startswith'
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 66, in forward
AttributeError: 'NoneType' object has no attribute 'startswith'
2025/08/29 05:38:07 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 05:38:07 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[2, 2, 2, 3, 0], [2, 2, 2, 3, 0], [2, 2, 2, 3, 0], [3, 3, 3, 3, 0], [0, 0, 0, 0, 0]], 'output': [[2, 2, 2, 3, 2, 2, 2, 3, 2, 2], [2, 2, 2, 3, 2, 2, 2, 3, 2, 2], [2, 2, 2, 3, 2, 2, 2, 3, 2, 2], [3, 3, 3, 3, 2, 2, 2, 3, 2, 2], [2, 2, 2, 2, 2, 2, 2, 3, 2, 2], [2, 2, 2, 2, 2, 2, 2, 3, 2, 2], [2, 2, 2, 2, 2, 2, 2, 3, 2, 2], [3, 3, 3, 3, 3, 3, 3, 3, 2, 2], [2, 2, 2, 2, 2, 2, 2, 2, 2, 2], [2, 2, 2, 2, 2, 2, 2, 2, 2, 2]]}, {'input': [[1, 1, 4, 6, 0], [1, 1, 4, 6, 0], [4, 4, 4, 6, 0], [6, 6, 6, 6, 0], [0, 0, 0, 0, 0]], 'output': [[1, 1, 4, 6, 1, 1, 4, 6, 1, 1], [1, 1, 4, 6, 1, 1, 4, 6, 1, 1], [4, 4, 4, 6, 1, 1, 4, 6, 1, 1], [6, 6, 6, 6, 1, 1, 4, 6, 1, 1], [1, 1, 1, 1, 1, 1, 4, 6, 1, 1], [1, 1, 1, 1, 1, 1, 4, 6, 1, 1], [4, 4, 4, 4, 4, 4, 4, 6, 1, 1], [6, 6, 6, 6, 6, 6, 6, 6, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}, {'input': [[2, 3, 4, 1, 6], [3, 3, 4, 1, 6], [4, 4, 4, 1, 6], [1, 1, 1, 1, 6], [6, 6, 6, 6, 6]], 'output': [[2, 3, 4, 1, 6, 2, 3, 4, 1, 6], [3, 3, 4, 1, 6, 2, 3, 4, 1, 6], [4, 4, 4, 1, 6, 2, 3, 4, 1, 6], [1, 1, 1, 1, 6, 2, 3, 4, 1, 6], [6, 6, 6, 6, 6, 2, 3, 4, 1, 6], [2, 2, 2, 2, 2, 2, 3, 4, 1, 6], [3, 3, 3, 3, 3, 3, 3, 4, 1, 6], [4, 4, 4, 4, 4, 4, 4, 4, 1, 6], [1, 1, 1, 1, 1, 1, 1, 1, 1, 6], [6, 6, 6, 6, 6, 6, 6, 6, 6, 6]]}], 'test_inputs': [[[7, 7, 3, 2, 2], [7, 7, 3, 2, 2], [3, 3, 3, 2, 2], [2, 2, 2, 2, 2], [2, 2, 2, 2, 2]]], 'test_outputs': [[[7, 7, 3, 2, 2, 7, 7, 3, 2, 2], [7, 7, 3, 2, 2, 7, 7, 3, 2, 2], [3, 3, 3, 2, 2, 7, 7, 3, 2, 2], [2, 2, 2, 2, 2, 7, 7, 3, 2, 2], [2, 2, 2, 2, 2, 7, 7, 3, 2, 2], [7, 7, 7, 7, 7, 7, 7, 3, 2, 2], [7, 7, 7, 7, 7, 7, 7, 3, 2, 2], [3, 3, 3, 3, 3, 3, 3, 3, 2, 2], [2, 2, 2, 2, 2, 2, 2, 2, 2, 2], [2, 2, 2, 2, 2, 2, 2, 2, 2, 2]]]}) (input_keys={'test_inputs', 'training_examples'}): 'NoneType' object has no attribute 'startswith'
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 66, in forward
AttributeError: 'NoneType' object has no attribute 'startswith'
2025/08/29 05:38:15 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 05:38:15 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[0, 0, 0, 0, 0, 0, 0, 0, 6, 6, 5, 5, 0, 1, 0, 0, 0, 0, 1, 0, 5, 5, 6, 6, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 5, 0, 0, 6, 6, 5, 0, 1, 0, 0, 7, 7, 0, 0, 1, 0, 5, 6, 6, 0, 0, 5, 0, 0, 0], [0, 0, 7, 7, 0, 0, 0, 7, 5, 5, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 5, 5, 7, 0, 0, 0, 7, 7], [0, 0, 7, 7, 0, 0, 7, 0, 5, 0, 0, 4, 0, 7, 0, 2, 2, 0, 7, 0, 4, 0, 0, 5, 0, 7, 0, 0, 7, 7], [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 7, 0, 4, 0, 0, 4, 0, 7, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 5, 0, 0, 0, 0, 0, 0, 1, 0, 0, 7, 0, 7, 0, 0, 0, 0, 7, 0, 7, 0, 9, 9, 9, 9, 9, 9, 9, 9], [0, 0, 0, 7, 0, 0, 3, 0, 0, 0, 0, 0, 4, 0, 8, 0, 0, 8, 0, 4, 0, 0, 9, 9, 9, 9, 9, 9, 9, 9], [0, 0, 7, 0, 0, 0, 0, 0, 0, 7, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 9, 9, 9, 9, 9, 9, 9, 9], [9, 9, 9, 9, 9, 9, 9, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 9, 9, 9, 9, 9, 9, 9, 9], [9, 9, 9, 9, 9, 9, 9, 7, 0, 8, 0, 0, 9, 9, 9, 9, 9, 9, 9, 0, 0, 0, 9, 9, 9, 9, 9, 9, 9, 9], [9, 9, 9, 9, 9, 9, 9, 0, 4, 0, 0, 5, 9, 9, 9, 9, 9, 9, 9, 0, 5, 0, 9, 9, 9, 9, 9, 9, 9, 9], [5, 0, 0, 4, 0, 7, 0, 2, 0, 0, 5, 0, 9, 9, 9, 9, 9, 9, 9, 0, 0, 5, 9, 9, 9, 9, 9, 9, 4, 0], [0, 1, 0, 0, 7, 0, 4, 0, 0, 0, 0, 0, 9, 9, 9, 9, 9, 9, 9, 1, 0, 0, 9, 9, 9, 9, 9, 9, 0, 0], [1, 0, 0, 7, 0, 7, 0, 0, 0, 0, 0, 0, 9, 9, 9, 9, 9, 9, 9, 0, 0, 0, 0, 0, 0, 0, 7, 0, 7, 0], [0, 0, 0, 0, 4, 0, 8, 0, 0, 0, 0, 7, 9, 9, 9, 9, 9, 9, 9, 0, 7, 0, 0, 0, 0, 8, 0, 4, 0, 0], [0, 7, 0, 2, 0, 0, 0, 0, 0, 0, 7, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 7, 0, 0, 0, 0, 0, 0, 2, 0], [0, 7, 0, 2, 0, 0, 0, 0, 0, 0, 7, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 7, 0, 0, 0, 0, 0, 0, 2, 0], [0, 0, 0, 0, 4, 0, 8, 0, 0, 0, 0, 7, 0, 1, 1, 0, 0, 1, 1, 0, 7, 0, 0, 0, 0, 8, 0, 4, 0, 0], [1, 0, 0, 7, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 7, 0], [0, 1, 0, 0, 7, 0, 4, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 4, 0, 7, 0, 0], [5, 0, 0, 4, 0, 7, 0, 2, 0, 0, 5, 0, 0, 0, 7, 0, 0, 7, 0, 0, 0, 5, 0, 0, 2, 0, 7, 0, 4, 0], [5, 5, 4, 0, 0, 0, 0, 0, 4, 0, 0, 5, 0, 0, 0, 7, 7, 0, 0, 0, 5, 0, 0, 4, 0, 0, 0, 0, 0, 4], [6, 6, 5, 0, 1, 0, 0, 7, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 7, 0, 0, 1, 0, 5], [6, 6, 5, 5, 0, 1, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 1, 0, 5, 5], [0, 0, 7, 0, 0, 0, 0, 0, 0, 7, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 7, 0, 0, 0, 0, 0, 0, 7], [0, 0, 0, 7, 0, 0, 3, 0, 0, 0, 0, 0, 4, 0, 8, 0, 0, 8, 0, 4, 0, 0, 0, 0, 0, 3, 0, 0, 7, 0], [0, 5, 0, 0, 0, 0, 0, 0, 1, 0, 0, 7, 0, 7, 0, 0, 0, 0, 7, 0, 7, 0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 7, 0, 4, 0, 0, 4, 0, 7, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 7, 7, 0, 0, 7, 0, 5, 0, 0, 4, 0, 7, 0, 2, 2, 0, 7, 0, 4, 0, 0, 5, 0, 7, 0, 0, 7, 7], [0, 0, 7, 7, 0, 0, 0, 7, 5, 5, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 5, 5, 7, 0, 0, 0, 7, 7]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 6, 6, 5, 5, 0, 1, 0, 0, 0, 0, 1, 0, 5, 5, 6, 6, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 5, 0, 0, 6, 6, 5, 0, 1, 0, 0, 7, 7, 0, 0, 1, 0, 5, 6, 6, 0, 0, 5, 0, 0, 0], [0, 0, 7, 7, 0, 0, 0, 7, 5, 5, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 5, 5, 7, 0, 0, 0, 7, 7], [0, 0, 7, 7, 0, 0, 7, 0, 5, 0, 0, 4, 0, 7, 0, 2, 2, 0, 7, 0, 4, 0, 0, 5, 0, 7, 0, 0, 7, 7], [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 7, 0, 4, 0, 0, 4, 0, 7, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 5, 0, 0, 0, 0, 0, 0, 1, 0, 0, 7, 0, 7, 0, 0, 0, 0, 7, 0, 7, 0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 7, 0, 0, 3, 0, 0, 0, 0, 0, 4, 0, 8, 0, 0, 8, 0, 4, 0, 0, 0, 0, 0, 3, 0, 0, 7, 0], [0, 0, 7, 0, 0, 0, 0, 0, 0, 7, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 7, 0, 0, 0, 0, 0, 0, 7], [6, 6, 5, 5, 0, 1, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 1, 0, 5, 5], [6, 6, 5, 0, 1, 0, 0, 7, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 7, 0, 0, 1, 0, 5], [5, 5, 4, 0, 0, 0, 0, 0, 4, 0, 0, 5, 0, 0, 0, 7, 7, 0, 0, 0, 5, 0, 0, 4, 0, 0, 0, 0, 0, 4], [5, 0, 0, 4, 0, 7, 0, 2, 0, 0, 5, 0, 0, 0, 7, 0, 0, 7, 0, 0, 0, 5, 0, 0, 2, 0, 7, 0, 4, 0], [0, 1, 0, 0, 7, 0, 4, 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0, 0, 4, 3, 0, 0, 0, 0, 0, 6, 6, 0, 0], [0, 8, 0, 0, 6, 6, 0, 0, 6, 0, 3, 0, 0, 4, 3, 0, 0, 3, 4, 0, 0, 3, 0, 6, 0, 0, 6, 6, 0, 0], [6, 6, 7, 7, 0, 0, 6, 0, 0, 5, 0, 0, 0, 3, 6, 6, 6, 6, 3, 0, 0, 0, 5, 0, 0, 6, 0, 0, 7, 7], [0, 6, 7, 7, 0, 0, 0, 6, 0, 0, 0, 0, 3, 0, 0, 6, 6, 0, 0, 3, 0, 0, 0, 0, 6, 0, 0, 0, 7, 7], [3, 3, 6, 6, 8, 8, 0, 0, 0, 6, 0, 5, 0, 0, 0, 3, 3, 0, 0, 0, 5, 0, 6, 0, 0, 0, 8, 8, 6, 6], [0, 3, 0, 6, 0, 8, 0, 0, 0, 0, 0, 0, 6, 0, 3, 0, 0, 3, 0, 6, 0, 0, 0, 0, 0, 0, 8, 0, 6, 0], [1, 1, 8, 0, 6, 6, 0, 2, 0, 0, 6, 0, 0, 0, 5, 0, 0, 5, 0, 0, 0, 6, 0, 0, 2, 0, 6, 6, 0, 8], [1, 1, 0, 8, 0, 6, 2, 0, 0, 0, 0, 6, 0, 0, 5, 5, 5, 5, 0, 0, 6, 0, 0, 0, 0, 2, 6, 0, 8, 0], [7, 7, 1, 1, 0, 5, 6, 6, 8, 8, 0, 0, 6, 6, 0, 0, 0, 0, 6, 6, 0, 0, 8, 8, 6, 6, 5, 0, 1, 1], [7, 7, 1, 1, 0, 0, 0, 6, 0, 8, 0, 0, 6, 6, 0, 0, 0, 0, 9, 9, 9, 9, 9, 9, 9, 0, 0, 0, 1, 1], [0, 0, 0, 0, 1, 1, 8, 0, 6, 6, 7, 7, 0, 0, 6, 0, 0, 6, 9, 9, 9, 9, 9, 9, 9, 8, 1, 1, 0, 0], [7, 0, 2, 0, 1, 1, 0, 8, 0, 6, 7, 7, 0, 0, 0, 6, 6, 0, 0, 0, 7, 7, 6, 0, 8, 0, 1, 1, 0, 2]]], 'test_outputs': [[[8, 0, 7, 0, 7, 7, 1, 1, 0, 3, 0, 6, 0, 8, 0, 0, 0, 0, 8, 0, 6, 0, 3, 0, 1, 1, 7, 7, 0, 7], [0, 8, 0, 0, 7, 7, 1, 1, 3, 3, 6, 6, 8, 8, 0, 0, 0, 0, 8, 8, 6, 6, 3, 3, 1, 1, 7, 7, 0, 0], [7, 0, 2, 0, 1, 1, 0, 8, 0, 6, 7, 7, 0, 0, 0, 6, 6, 0, 0, 0, 7, 7, 6, 0, 8, 0, 1, 1, 0, 2], [0, 0, 0, 0, 1, 1, 8, 0, 6, 6, 7, 7, 0, 0, 6, 0, 0, 6, 0, 0, 7, 7, 6, 6, 0, 8, 1, 1, 0, 0], [7, 7, 1, 1, 0, 0, 0, 6, 0, 8, 0, 0, 6, 6, 0, 0, 0, 0, 6, 6, 0, 0, 8, 0, 6, 0, 0, 0, 1, 1], [7, 7, 1, 1, 0, 5, 6, 6, 8, 8, 0, 0, 6, 6, 0, 0, 0, 0, 6, 6, 0, 0, 8, 8, 6, 6, 5, 0, 1, 1], [1, 1, 0, 8, 0, 6, 2, 0, 0, 0, 0, 6, 0, 0, 5, 5, 5, 5, 0, 0, 6, 0, 0, 0, 0, 2, 6, 0, 8, 0], [1, 1, 8, 0, 6, 6, 0, 2, 0, 0, 6, 0, 0, 0, 5, 0, 0, 5, 0, 0, 0, 6, 0, 0, 2, 0, 6, 6, 0, 8], [0, 3, 0, 6, 0, 8, 0, 0, 0, 0, 0, 0, 6, 0, 3, 0, 0, 3, 0, 6, 0, 0, 0, 0, 0, 0, 8, 0, 6, 0], [3, 3, 6, 6, 8, 8, 0, 0, 0, 6, 0, 5, 0, 0, 0, 3, 3, 0, 0, 0, 5, 0, 6, 0, 0, 0, 8, 8, 6, 6], [0, 6, 7, 7, 0, 0, 0, 6, 0, 0, 0, 0, 3, 0, 0, 6, 6, 0, 0, 3, 0, 0, 0, 0, 6, 0, 0, 0, 7, 7], [6, 6, 7, 7, 0, 0, 6, 0, 0, 5, 0, 0, 0, 3, 6, 6, 6, 6, 3, 0, 0, 0, 5, 0, 0, 6, 0, 0, 7, 7], [0, 8, 0, 0, 6, 6, 0, 0, 6, 0, 3, 0, 0, 4, 3, 0, 0, 3, 4, 0, 0, 3, 0, 6, 0, 0, 6, 6, 0, 0], [8, 8, 0, 0, 6, 6, 0, 0, 0, 0, 0, 3, 4, 0, 0, 0, 0, 0, 0, 4, 3, 0, 0, 0, 0, 0, 6, 6, 0, 0], [0, 0, 0, 6, 0, 0, 5, 5, 3, 0, 0, 6, 3, 0, 2, 0, 0, 2, 0, 3, 6, 0, 0, 3, 5, 5, 0, 0, 6, 0], [0, 0, 6, 0, 0, 0, 5, 0, 0, 3, 6, 6, 0, 0, 0, 2, 2, 0, 0, 0, 6, 6, 3, 0, 0, 5, 0, 0, 0, 6], [0, 0, 6, 0, 0, 0, 5, 0, 0, 3, 6, 6, 0, 0, 0, 2, 2, 0, 0, 0, 6, 6, 3, 0, 0, 5, 0, 0, 0, 6], [0, 0, 0, 6, 0, 0, 5, 5, 3, 0, 0, 6, 3, 0, 2, 0, 0, 2, 0, 3, 6, 0, 0, 3, 5, 5, 0, 0, 6, 0], [8, 8, 0, 0, 6, 6, 0, 0, 0, 0, 0, 3, 4, 0, 0, 0, 0, 0, 0, 4, 3, 0, 0, 0, 0, 0, 6, 6, 0, 0], [0, 8, 0, 0, 6, 6, 0, 0, 6, 0, 3, 0, 0, 4, 3, 0, 0, 3, 4, 0, 0, 3, 0, 6, 0, 0, 6, 6, 0, 0], [6, 6, 7, 7, 0, 0, 6, 0, 0, 5, 0, 0, 0, 3, 6, 6, 6, 6, 3, 0, 0, 0, 5, 0, 0, 6, 0, 0, 7, 7], [0, 6, 7, 7, 0, 0, 0, 6, 0, 0, 0, 0, 3, 0, 0, 6, 6, 0, 0, 3, 0, 0, 0, 0, 6, 0, 0, 0, 7, 7], [3, 3, 6, 6, 8, 8, 0, 0, 0, 6, 0, 5, 0, 0, 0, 3, 3, 0, 0, 0, 5, 0, 6, 0, 0, 0, 8, 8, 6, 6], [0, 3, 0, 6, 0, 8, 0, 0, 0, 0, 0, 0, 6, 0, 3, 0, 0, 3, 0, 6, 0, 0, 0, 0, 0, 0, 8, 0, 6, 0], [1, 1, 8, 0, 6, 6, 0, 2, 0, 0, 6, 0, 0, 0, 5, 0, 0, 5, 0, 0, 0, 6, 0, 0, 2, 0, 6, 6, 0, 8], [1, 1, 0, 8, 0, 6, 2, 0, 0, 0, 0, 6, 0, 0, 5, 5, 5, 5, 0, 0, 6, 0, 0, 0, 0, 2, 6, 0, 8, 0], [7, 7, 1, 1, 0, 5, 6, 6, 8, 8, 0, 0, 6, 6, 0, 0, 0, 0, 6, 6, 0, 0, 8, 8, 6, 6, 5, 0, 1, 1], [7, 7, 1, 1, 0, 0, 0, 6, 0, 8, 0, 0, 6, 6, 0, 0, 0, 0, 6, 6, 0, 0, 8, 0, 6, 0, 0, 0, 1, 1], [0, 0, 0, 0, 1, 1, 8, 0, 6, 6, 7, 7, 0, 0, 6, 0, 0, 6, 0, 0, 7, 7, 6, 6, 0, 8, 1, 1, 0, 0], [7, 0, 2, 0, 1, 1, 0, 8, 0, 6, 7, 7, 0, 0, 0, 6, 6, 0, 0, 0, 7, 7, 6, 0, 8, 0, 1, 1, 0, 2]]]}) (input_keys={'test_inputs', 'training_examples'}): 'NoneType' object has no attribute 'startswith'
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 66, in forward
AttributeError: 'NoneType' object has no attribute 'startswith'
2025/08/29 05:38:22 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 05:38:22 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[0, 0, 0, 0, 8, 3, 3, 3, 3, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0], [0, 0, 0, 0, 8, 3, 3, 3, 3, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0], [0, 0, 0, 0, 8, 3, 3, 3, 3, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0], [0, 0, 0, 0, 8, 3, 3, 3, 3, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [3, 3, 3, 3, 8, 6, 6, 6, 6, 8, 3, 3, 3, 3, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0], [3, 3, 3, 3, 8, 6, 6, 6, 6, 8, 3, 3, 3, 3, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0], [3, 3, 3, 3, 8, 6, 6, 6, 6, 8, 3, 3, 3, 3, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0], [3, 3, 3, 3, 8, 6, 6, 6, 6, 8, 3, 3, 3, 3, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [0, 0, 0, 0, 8, 3, 3, 3, 3, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0], [0, 0, 0, 0, 8, 3, 3, 3, 3, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0], [0, 0, 0, 0, 8, 3, 3, 3, 3, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0], [0, 0, 0, 0, 8, 3, 3, 3, 3, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0], [0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0], [0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0], [0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 6, 6, 6, 6, 8, 0, 0, 0, 0], [0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 6, 6, 6, 6, 8, 0, 0, 0, 0], [0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 6, 6, 6, 6, 8, 0, 0, 0, 0], [0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 6, 6, 6, 6, 8, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 8, 3, 3, 3, 3, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0], [0, 0, 0, 0, 8, 3, 3, 3, 3, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0], [0, 0, 0, 0, 8, 3, 3, 3, 3, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0], [0, 0, 0, 0, 8, 3, 3, 3, 3, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [3, 3, 3, 3, 8, 6, 6, 6, 6, 8, 3, 3, 3, 3, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0], [3, 3, 3, 3, 8, 6, 6, 6, 6, 8, 3, 3, 3, 3, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0], [3, 3, 3, 3, 8, 6, 6, 6, 6, 8, 3, 3, 3, 3, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0], [3, 3, 3, 3, 8, 6, 6, 6, 6, 8, 3, 3, 3, 3, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [0, 0, 0, 0, 8, 3, 3, 3, 3, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0], [0, 0, 0, 0, 8, 3, 3, 3, 3, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0], [0, 0, 0, 0, 8, 3, 3, 3, 3, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0], [0, 0, 0, 0, 8, 3, 3, 3, 3, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 3, 3, 3, 3, 8, 0, 0, 0, 0], [0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 3, 3, 3, 3, 8, 0, 0, 0, 0], [0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 3, 3, 3, 3, 8, 0, 0, 0, 0], [0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 3, 3, 3, 3, 8, 0, 0, 0, 0], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 3, 3, 3, 3, 8, 6, 6, 6, 6, 8, 3, 3, 3, 3], [0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 3, 3, 3, 3, 8, 6, 6, 6, 6, 8, 3, 3, 3, 3], [0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 3, 3, 3, 3, 8, 6, 6, 6, 6, 8, 3, 3, 3, 3], [0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 3, 3, 3, 3, 8, 6, 6, 6, 6, 8, 3, 3, 3, 3]]}, {'input': [[0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 4, 4, 4], [0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 4, 4, 4], [0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 4, 4, 4], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3], [0, 0, 0, 3, 0, 0, 0, 3, 6, 6, 6, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0], [0, 0, 0, 3, 0, 0, 0, 3, 6, 6, 6, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0], [0, 0, 0, 3, 0, 0, 0, 3, 6, 6, 6, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3], [0, 0, 0, 3, 6, 6, 6, 3, 4, 4, 4, 3, 6, 6, 6, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0], [0, 0, 0, 3, 6, 6, 6, 3, 4, 4, 4, 3, 6, 6, 6, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0], [0, 0, 0, 3, 6, 6, 6, 3, 4, 4, 4, 3, 6, 6, 6, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3], [0, 0, 0, 3, 0, 0, 0, 3, 6, 6, 6, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0], [0, 0, 0, 3, 0, 0, 0, 3, 6, 6, 6, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0], [0, 0, 0, 3, 0, 0, 0, 3, 6, 6, 6, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 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4, 0, 0, 4, 0, 0], [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4], [0, 0, 4, 0, 0, 4, 0, 0, 4, 0, 0, 4, 0, 0, 4, 0, 0, 4, 0, 0, 4, 0, 0, 4, 0, 0, 4, 0, 0], [0, 0, 4, 0, 0, 4, 0, 0, 4, 0, 0, 4, 0, 0, 4, 0, 0, 4, 0, 0, 4, 0, 0, 4, 0, 0, 4, 0, 0]]], 'test_outputs': [[[0, 0, 4, 0, 0, 4, 0, 0, 4, 0, 0, 4, 0, 0, 4, 0, 0, 4, 8, 8, 4, 3, 3, 4, 8, 8, 4, 0, 0], [0, 0, 4, 0, 0, 4, 0, 0, 4, 0, 0, 4, 0, 0, 4, 0, 0, 4, 8, 8, 4, 3, 3, 4, 8, 8, 4, 0, 0], [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4], [0, 0, 4, 0, 0, 4, 0, 0, 4, 0, 0, 4, 0, 0, 4, 0, 0, 4, 3, 3, 4, 6, 6, 4, 3, 3, 4, 0, 0], [0, 0, 4, 0, 0, 4, 0, 0, 4, 0, 0, 4, 0, 0, 4, 0, 0, 4, 3, 3, 4, 6, 6, 4, 3, 3, 4, 0, 0], [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4], [0, 0, 4, 0, 0, 4, 8, 8, 4, 3, 3, 4, 8, 8, 4, 0, 0, 4, 8, 8, 4, 3, 3, 4, 8, 8, 4, 0, 0], [0, 0, 4, 0, 0, 4, 8, 8, 4, 3, 3, 4, 8, 8, 4, 0, 0, 4, 8, 8, 4, 3, 3, 4, 8, 8, 4, 0, 0], [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4], [0, 0, 4, 0, 0, 4, 3, 3, 4, 6, 6, 4, 3, 3, 4, 0, 0, 4, 0, 0, 4, 0, 0, 4, 0, 0, 4, 0, 0], [0, 0, 4, 0, 0, 4, 3, 3, 4, 6, 6, 4, 3, 3, 4, 0, 0, 4, 0, 0, 4, 0, 0, 4, 0, 0, 4, 0, 0], [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4], [0, 0, 4, 0, 0, 4, 8, 8, 4, 3, 3, 4, 8, 8, 4, 0, 0, 4, 0, 0, 4, 0, 0, 4, 0, 0, 4, 0, 0], [0, 0, 4, 0, 0, 4, 8, 8, 4, 3, 3, 4, 8, 8, 4, 0, 0, 4, 0, 0, 4, 0, 0, 4, 0, 0, 4, 0, 0], [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4], [0, 0, 4, 0, 0, 4, 0, 0, 4, 0, 0, 4, 0, 0, 4, 0, 0, 4, 0, 0, 4, 0, 0, 4, 0, 0, 4, 0, 0], [0, 0, 4, 0, 0, 4, 0, 0, 4, 0, 0, 4, 0, 0, 4, 0, 0, 4, 0, 0, 4, 0, 0, 4, 0, 0, 4, 0, 0], [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4], [0, 0, 4, 0, 0, 4, 0, 0, 4, 0, 0, 4, 8, 8, 4, 3, 3, 4, 8, 8, 4, 0, 0, 4, 0, 0, 4, 0, 0], [0, 0, 4, 0, 0, 4, 0, 0, 4, 0, 0, 4, 8, 8, 4, 3, 3, 4, 8, 8, 4, 0, 0, 4, 0, 0, 4, 0, 0], [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4], [3, 3, 4, 8, 8, 4, 0, 0, 4, 0, 0, 4, 3, 3, 4, 6, 6, 4, 3, 3, 4, 0, 0, 4, 0, 0, 4, 0, 0], [3, 3, 4, 8, 8, 4, 0, 0, 4, 0, 0, 4, 3, 3, 4, 6, 6, 4, 3, 3, 4, 0, 0, 4, 0, 0, 4, 0, 0], [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4], [6, 6, 4, 3, 3, 4, 0, 0, 4, 0, 0, 4, 8, 8, 4, 3, 3, 4, 8, 8, 4, 0, 0, 4, 0, 0, 4, 0, 0], [6, 6, 4, 3, 3, 4, 0, 0, 4, 0, 0, 4, 8, 8, 4, 3, 3, 4, 8, 8, 4, 0, 0, 4, 0, 0, 4, 0, 0], [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4], [3, 3, 4, 8, 8, 4, 0, 0, 4, 0, 0, 4, 0, 0, 4, 0, 0, 4, 0, 0, 4, 0, 0, 4, 0, 0, 4, 0, 0], [3, 3, 4, 8, 8, 4, 0, 0, 4, 0, 0, 4, 0, 0, 4, 0, 0, 4, 0, 0, 4, 0, 0, 4, 0, 0, 4, 0, 0]]]}) (input_keys={'test_inputs', 'training_examples'}): 'NoneType' object has no attribute 'startswith'
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 66, in forward
AttributeError: 'NoneType' object has no attribute 'startswith'
2025/08/29 05:38:24 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 05:38:30 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 05:39:43 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 05:39:43 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[5, 5, 5, 5, 0, 5, 5, 5, 0, 5], [0, 0, 5, 5, 5, 5, 5, 5, 5, 5], [0, 5, 5, 5, 5, 5, 0, 0, 5, 0], [5, 5, 0, 5, 5, 5, 5, 0, 5, 0], [5, 5, 5, 5, 0, 0, 5, 5, 5, 5], [0, 5, 0, 5, 5, 5, 5, 0, 5, 0], [0, 5, 5, 5, 0, 0, 5, 5, 5, 0], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [5, 5, 5, 5, 5, 5, 5, 5, 5, 0], [0, 5, 5, 5, 5, 5, 5, 0, 5, 0]], 'output': [[5, 5, 5, 5, 3, 5, 5, 5, 3, 5], [1, 1, 5, 5, 5, 5, 5, 5, 5, 5], [1, 5, 5, 5, 5, 5, 1, 1, 5, 2], [5, 5, 3, 5, 5, 5, 5, 1, 5, 2], [5, 5, 5, 5, 2, 2, 5, 5, 5, 5], [2, 5, 3, 5, 5, 5, 5, 3, 5, 2], [2, 5, 5, 5, 2, 2, 5, 5, 5, 2], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [5, 5, 5, 5, 5, 5, 5, 5, 5, 2], [3, 5, 5, 5, 5, 5, 5, 3, 5, 2]]}, {'input': [[5, 5, 5, 5, 5, 0, 0, 5, 5, 5], [0, 0, 5, 0, 5, 5, 5, 5, 5, 0], [5, 5, 5, 5, 5, 0, 5, 0, 0, 5], [5, 0, 5, 5, 5, 0, 5, 5, 5, 5], [5, 5, 5, 5, 5, 5, 5, 5, 0, 5], [5, 5, 5, 5, 0, 5, 5, 5, 5, 5], [0, 0, 5, 5, 0, 5, 0, 0, 5, 0], [5, 5, 5, 5, 5, 5, 5, 0, 5, 5], [0, 5, 5, 5, 5, 5, 0, 5, 5, 0], [0, 0, 5, 5, 5, 5, 5, 5, 0, 5]], 'output': [[5, 5, 5, 5, 5, 2, 2, 5, 5, 5], [2, 2, 5, 3, 5, 5, 5, 5, 5, 3], [5, 5, 5, 5, 5, 2, 5, 2, 2, 5], [5, 3, 5, 5, 5, 2, 5, 5, 5, 5], [5, 5, 5, 5, 5, 5, 5, 5, 3, 5], [5, 5, 5, 5, 2, 5, 5, 5, 5, 5], [2, 2, 5, 5, 2, 5, 1, 1, 5, 3], [5, 5, 5, 5, 5, 5, 5, 1, 5, 5], [1, 5, 5, 5, 5, 5, 3, 5, 5, 3], [1, 1, 5, 5, 5, 5, 5, 5, 3, 5]]}, {'input': [[0, 0, 5, 5, 0, 5, 5, 5, 0, 5], [5, 5, 0, 0, 5, 5, 5, 5, 0, 5], [5, 0, 5, 0, 5, 0, 5, 5, 0, 5], [5, 0, 5, 5, 0, 5, 5, 5, 5, 5], [5, 5, 5, 0, 0, 5, 5, 0, 5, 0], [5, 5, 0, 5, 5, 5, 5, 0, 5, 0], [5, 5, 0, 5, 5, 0, 5, 5, 5, 5], [5, 5, 5, 0, 5, 5, 5, 5, 5, 5], [5, 0, 5, 5, 5, 0, 5, 0, 5, 5], [5, 5, 0, 5, 5, 5, 5, 5, 5, 5]], 'output': [[2, 2, 5, 5, 3, 5, 5, 5, 1, 5], [5, 5, 1, 1, 5, 5, 5, 5, 1, 5], [5, 2, 5, 1, 5, 3, 5, 5, 1, 5], [5, 2, 5, 5, 1, 5, 5, 5, 5, 5], [5, 5, 5, 1, 1, 5, 5, 2, 5, 2], [5, 5, 2, 5, 5, 5, 5, 2, 5, 2], [5, 5, 2, 5, 5, 3, 5, 5, 5, 5], [5, 5, 5, 3, 5, 5, 5, 5, 5, 5], [5, 3, 5, 5, 5, 3, 5, 3, 5, 5], [5, 5, 3, 5, 5, 5, 5, 5, 5, 5]]}], 'test_inputs': [[[0, 5, 5, 5, 5, 5, 0, 0, 5, 5], [5, 5, 5, 0, 5, 5, 0, 5, 0, 5], [5, 5, 0, 5, 5, 5, 5, 5, 0, 5], [5, 0, 0, 5, 5, 5, 5, 5, 5, 5], [0, 5, 5, 5, 5, 5, 0, 5, 5, 5], [0, 5, 5, 0, 5, 5, 0, 5, 0, 0], [5, 5, 0, 5, 5, 5, 5, 5, 0, 5], [5, 5, 0, 5, 5, 5, 5, 5, 5, 0], [0, 0, 5, 5, 5, 5, 0, 5, 5, 5], [5, 5, 5, 5, 0, 5, 0, 0, 5, 0]]], 'test_outputs': [[[3, 5, 5, 5, 5, 5, 1, 1, 5, 5], [5, 5, 5, 3, 5, 5, 1, 5, 2, 5], [5, 5, 1, 5, 5, 5, 5, 5, 2, 5], [5, 1, 1, 5, 5, 5, 5, 5, 5, 5], [2, 5, 5, 5, 5, 5, 2, 5, 5, 5], [2, 5, 5, 3, 5, 5, 2, 5, 1, 1], [5, 5, 2, 5, 5, 5, 5, 5, 1, 5], [5, 5, 2, 5, 5, 5, 5, 5, 5, 3], [2, 2, 5, 5, 5, 5, 1, 5, 5, 5], [5, 5, 5, 5, 3, 5, 1, 1, 5, 3]]]}) (input_keys={'test_inputs', 'training_examples'}): 'NoneType' object has no attribute 'startswith'
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 66, in forward
AttributeError: 'NoneType' object has no attribute 'startswith'
2025/08/29 05:39:45 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 05:39:51 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 05:39:51 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 2, 0, 0, 0, 0, 0, 0, 0], [0, 2, 0, 2, 0, 0, 0, 0, 0], [0, 2, 0, 2, 0, 2, 0, 0, 0], [0, 2, 0, 2, 0, 2, 0, 2, 0], [0, 2, 0, 2, 0, 2, 0, 2, 0], [0, 2, 0, 2, 0, 2, 0, 2, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 2, 0, 0, 0, 0, 0, 0, 0], [0, 2, 0, 2, 0, 0, 0, 0, 0], [0, 2, 0, 2, 0, 2, 0, 0, 0], [0, 8, 0, 2, 0, 2, 0, 2, 0], [0, 8, 0, 8, 0, 8, 0, 2, 0], [0, 8, 0, 8, 0, 8, 0, 8, 0]]}, {'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 2, 0, 0, 0, 0, 0, 0, 0], [0, 2, 0, 0, 0, 0, 0, 2, 0], [0, 2, 0, 2, 0, 0, 0, 2, 0], [0, 2, 0, 2, 0, 0, 0, 2, 0], [0, 2, 0, 2, 0, 2, 0, 2, 0], [0, 2, 0, 2, 0, 2, 0, 2, 0], [0, 2, 0, 2, 0, 2, 0, 2, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 2, 0, 0, 0, 0, 0, 0, 0], [0, 2, 0, 0, 0, 0, 0, 2, 0], [0, 2, 0, 2, 0, 0, 0, 2, 0], [0, 2, 0, 2, 0, 0, 0, 2, 0], [0, 8, 0, 2, 0, 2, 0, 8, 0], [0, 8, 0, 8, 0, 2, 0, 8, 0], [0, 8, 0, 8, 0, 8, 0, 8, 0]]}, {'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 2, 0], [0, 2, 0, 0, 0, 0, 0, 2, 0], [0, 2, 0, 0, 0, 0, 0, 2, 0], [0, 2, 0, 0, 0, 0, 0, 2, 0], [0, 2, 0, 0, 0, 2, 0, 2, 0], [0, 2, 0, 2, 0, 2, 0, 2, 0], [0, 2, 0, 2, 0, 2, 0, 2, 0], [0, 2, 0, 2, 0, 2, 0, 2, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 2, 0], [0, 2, 0, 0, 0, 0, 0, 2, 0], [0, 2, 0, 0, 0, 0, 0, 2, 0], [0, 2, 0, 0, 0, 0, 0, 2, 0], [0, 2, 0, 0, 0, 2, 0, 8, 0], [0, 8, 0, 2, 0, 2, 0, 8, 0], [0, 8, 0, 2, 0, 8, 0, 8, 0], [0, 8, 0, 8, 0, 8, 0, 8, 0]]}], 'test_inputs': [[[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 2, 0, 0, 0, 0, 0, 0, 0], [0, 2, 0, 2, 0, 0, 0, 0, 0], [0, 2, 0, 2, 0, 0, 0, 0, 0], [0, 2, 0, 2, 0, 0, 0, 0, 0], [0, 2, 0, 2, 0, 0, 0, 0, 0], [0, 2, 0, 2, 0, 0, 0, 2, 0], [0, 2, 0, 2, 0, 0, 0, 2, 0], [0, 2, 0, 2, 0, 0, 0, 2, 0], [0, 2, 0, 2, 0, 2, 0, 2, 0], [0, 2, 0, 2, 0, 2, 0, 2, 0]]], 'test_outputs': [[[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 2, 0, 0, 0, 0, 0, 0, 0], [0, 2, 0, 2, 0, 0, 0, 0, 0], [0, 2, 0, 2, 0, 0, 0, 0, 0], [0, 2, 0, 2, 0, 0, 0, 0, 0], [0, 2, 0, 2, 0, 0, 0, 0, 0], [0, 8, 0, 2, 0, 0, 0, 2, 0], [0, 8, 0, 8, 0, 0, 0, 2, 0], [0, 8, 0, 8, 0, 0, 0, 2, 0], [0, 8, 0, 8, 0, 2, 0, 8, 0], [0, 8, 0, 8, 0, 8, 0, 8, 0]]]}) (input_keys={'test_inputs', 'training_examples'}): 'NoneType' object has no attribute 'startswith'
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 66, in forward
AttributeError: 'NoneType' object has no attribute 'startswith'
2025/08/29 05:39:53 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 05:39:53 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 2, 2, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 2, 2, 1, 2, 2, 1, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 2, 2, 0, 2, 2, 0, 2, 2, 0, 2, 2, 0, 2, 2], [1, 2, 2, 1, 2, 2, 1, 2, 2, 1, 2, 2, 1, 2, 2]]}, {'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0], [3, 3, 2, 1, 3, 3, 2, 1, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2], [0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2], [3, 3, 2, 1, 3, 3, 2, 1, 3, 3, 2, 1, 3, 3, 2]]}, {'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [4, 0, 0, 0, 0, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0], [4, 3, 0, 0, 3, 4, 4, 3, 0, 0, 0, 0, 0, 0, 0], [4, 3, 2, 2, 3, 4, 4, 3, 2, 2, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [4, 0, 0, 0, 0, 4, 4, 0, 0, 0, 0, 4, 4, 0, 0], [4, 3, 0, 0, 3, 4, 4, 3, 0, 0, 3, 4, 4, 3, 0], [4, 3, 2, 2, 3, 4, 4, 3, 2, 2, 3, 4, 4, 3, 2]]}], 'test_inputs': [[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0], [6, 2, 2, 0, 6, 2, 2, 0, 6, 2, 0, 0, 0, 0, 0], [6, 6, 2, 3, 6, 6, 2, 3, 6, 6, 0, 0, 0, 0, 0]]], 'test_outputs': [[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2], [6, 2, 2, 0, 6, 2, 2, 0, 6, 2, 2, 0, 6, 2, 2], [6, 6, 2, 3, 6, 6, 2, 3, 6, 6, 2, 3, 6, 6, 2]]]}) (input_keys={'test_inputs', 'training_examples'}): 'NoneType' object has no attribute 'startswith'
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 66, in forward
AttributeError: 'NoneType' object has no attribute 'startswith'
2025/08/29 05:41:27 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 05:43:47 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 05:43:47 WARNING dspy.adapters.json_adapter: Failed to use structured output format, falling back to JSON mode.
2025/08/29 05:44:17 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 05:44:17 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 05:44:17 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 05:44:17 WARNING dspy.adapters.json_adapter: Failed to use structured output format, falling back to JSON mode.
2025/08/29 05:45:56 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 05:45:56 WARNING dspy.adapters.json_adapter: Failed to use structured output format, falling back to JSON mode.
2025/08/29 05:48:12 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 05:48:12 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 05:48:12 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 05:48:12 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 05:48:12 WARNING dspy.adapters.json_adapter: Failed to use structured output format, falling back to JSON mode.
2025/08/29 05:48:12 WARNING dspy.adapters.json_adapter: Failed to use structured output format, falling back to JSON mode.
2025/08/29 05:50:13 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 05:52:06 INFO dspy.evaluate.evaluate: Average Metric: 126.0 / 200 (63.0%)
GEPA Optimization: 40%|███████████████████████████████████████████▉ | 1584/4000 [8:18:10<6:38:53, 9.91s/rollouts]Iteration 77: Full valset score for new program: 0.63
Iteration 77: Full train_val score for new program: 0.63
Iteration 77: Individual valset scores for new program: [True, True, False, True, True, False, True, True, True, True, True, True, True, False, False, True, False, True, True, False, True, True, True, True, False, False, False, 0.0, True, True, False, False, False, True, True, True, False, True, True, True, False, True, True, False, False, 0.0, False, False, True, False, False, True, True, True, True, 0.0, True, True, False, True, False, True, False, True, False, False, True, True, 0.0, False, True, True, True, True, True, True, True, False, True, True, False, True, True, True, True, True, True, True, False, False, True, True, True, True, True, True, False, False, False, True, True, True, False, False, True, True, True, True, False, True, False, True, False, False, True, True, True, False, True, True, True, False, True, False, False, True, False, True, True, True, 0.0, 0.0, True, True, 0.0, False, False, True, True, True, True, False, True, False, True, False, True, False, True, True, False, False, True, True, True, True, True, True, False, False, False, True, True, True, False, True, True, True, True, False, False, True, False, True, True, True, False, True, False, True, False, True, True, False, False, True, True, True, True, True, True, True, True, True, True, False, True, False, False, True]
Iteration 77: New valset pareto front scores: [True, True, True, True, True, True, True, True, True, True, True, True, True, False, True, True, False, True, True, True, True, True, True, True, True, True, False, True, True, True, True, False, True, True, True, True, False, True, True, True, False, True, True, True, False, True, False, False, True, True, True, True, True, True, True, 0, True, True, False, True, True, True, False, True, True, False, True, True, 0, False, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, False, True, True, True, True, True, True, True, False, True, True, True, True, True, False, True, True, True, True, True, True, False, True, True, True, True, True, True, True, True, True, True, True, True, False, False, True, False, True, True, True, True, True, True, True, True, False, True, True, True, True, True, True, True, False, True, False, True, False, True, True, True, False, True, True, True, True, True, True, True, False, True, True, True, True, False, True, True, True, True, False, True, True, False, True, True, True, False, True, False, True, True, True, True, False, False, True, True, True, True, True, True, True, True, True, True, False, True, True, False, True]
Iteration 77: Full valset pareto front score: 0.815
Iteration 77: Updated valset pareto front programs: [{1, 3, 5}, {0, 1, 2, 3, 4, 5}, {0}, {0, 2, 3, 4, 5}, {0, 1, 2, 3, 4, 5}, {0, 4}, {0, 1, 2, 3, 4, 5}, {0, 1, 3, 4, 5}, {0, 1, 2, 3, 4, 5}, {0, 2, 3, 4, 5}, {0, 1, 2, 3, 4, 5}, {0, 1, 2, 3, 4, 5}, {0, 1, 2, 3, 4, 5}, {0, 1, 2, 3, 4, 5}, {0, 1, 2, 3, 4}, {0, 1, 2, 3, 4, 5}, {0, 1, 2, 3, 4, 5}, {1, 4, 5}, {0, 1, 2, 3, 4, 5}, {0, 2, 4}, {0, 1, 2, 3, 4, 5}, {0, 2, 3, 4, 5}, {0, 1, 2, 3, 4, 5}, {1, 5}, {0, 3, 4}, {0, 1, 3}, {0, 1, 2, 3, 4, 5}, {0, 4}, {0, 1, 2, 3, 4, 5}, {0, 1, 2, 3, 4, 5}, {1}, {0, 1, 2, 3, 4, 5}, {2}, {0, 1, 2, 3, 4, 5}, {0, 1, 2, 3, 5}, {0, 1, 2, 3, 4, 5}, {0, 1, 2, 3, 4, 5}, {0, 1, 2, 3, 4, 5}, {2, 5}, {0, 2, 3, 4, 5}, {0, 1, 2, 3, 4, 5}, {0, 1, 2, 3, 4, 5}, {0, 1, 2, 3, 4, 5}, {1}, {0, 1, 2, 3, 4, 5}, {0, 1, 2, 3, 4}, {0, 1, 2, 3, 4, 5}, {0, 1, 2, 3, 4, 5}, {0, 1, 2, 3, 4, 5}, {0, 1, 2, 3, 4}, {0, 1, 2, 3, 4}, {0, 1, 2, 3, 4, 5}, {0, 1, 3, 4, 5}, {0, 1, 2, 3, 4, 5}, {0, 1, 2, 3, 4, 5}, {0, 1, 2, 3, 4, 5}, {0, 1, 3, 4, 5}, {0, 1, 2, 3, 4, 5}, {0, 1, 2, 3, 4, 5}, {0, 1, 2, 3, 4, 5}, {0, 1, 2, 3}, {0, 1, 5}, {0, 1, 2, 3, 4, 5}, {0, 1, 2, 3, 4, 5}, {4}, {0, 1, 2, 3, 4, 5}, {1, 2, 3, 5}, {0, 1, 3, 4, 5}, {0, 1, 2, 3, 4, 5}, {0, 1, 2, 3, 4, 5}, {0, 1, 2, 3, 4, 5}, {0, 1, 2, 3, 4, 5}, {0, 1, 2, 3, 4, 5}, {2, 3, 4, 5}, {0, 1, 2, 3, 4, 5}, {1, 2, 3, 5}, {0, 3, 4, 5}, {4}, {0, 1, 2, 3, 4, 5}, {0, 2, 3, 4, 5}, {0, 2}, {1, 2, 3, 4, 5}, {0, 1, 2, 3, 4, 5}, {0, 1, 2, 3, 4, 5}, {0, 1, 2, 3, 4, 5}, {0, 1, 2, 3, 4, 5}, {0, 1, 2, 4, 5}, {0, 1, 2, 3, 4, 5}, {0, 3, 4}, {0, 1, 2, 3, 4, 5}, {0, 1, 2, 4, 5}, {0, 1, 2, 3, 4, 5}, {0, 1, 2, 3, 4, 5}, {0, 1, 2, 3, 4, 5}, {0, 1, 2, 3, 4, 5}, {0, 1, 2, 3, 4, 5}, {1, 3}, {0, 1, 2, 3, 4, 5}, {0, 3, 4}, {0, 1, 2, 3, 4, 5}, {0, 1, 2, 3, 4, 5}, {0, 1, 2, 3, 4, 5}, {0, 4}, {0, 1, 2, 3, 4, 5}, {0, 1, 2, 3, 4, 5}, {0, 1, 2, 3, 5}, {1, 2, 3, 4, 5}, {0, 1, 2, 3, 5}, {1, 2}, {0, 1, 4, 5}, {0, 1, 2, 3, 4, 5}, {0, 2, 5}, {1, 3}, {2, 4}, {0, 1, 2, 3, 4, 5}, {3, 4, 5}, {0, 1, 2, 3, 4, 5}, {2, 3, 4}, {0, 1, 2, 3, 4, 5}, {0, 1, 2, 3, 5}, {2, 3, 4, 5}, {0, 1}, {0, 1, 2, 3, 4, 5}, {0, 1, 2, 3, 4, 5}, {0, 1, 2, 3, 4, 5}, {0, 1, 2, 3, 4, 5}, {0, 1, 2, 3, 4, 5}, {0, 1, 2, 3, 4, 5}, {0, 1, 2, 3, 5}, {0, 1, 2, 3, 5}, {2}, {0, 1, 3}, {0, 1, 2, 3, 4, 5}, {0, 1, 3, 5}, {0, 1, 2, 3, 4}, {0, 1, 2, 3, 4, 5}, {0, 1, 3}, {3, 5}, {1, 2, 3, 4, 5}, {1, 2, 3, 4, 5}, {0, 1, 2, 3, 4, 5}, {0, 1, 2, 3, 4}, {0, 1, 2, 3, 4, 5}, {0, 1, 2, 3, 4, 5}, {0, 1, 2, 3, 4, 5}, {0, 1, 2, 3, 4, 5}, {0, 2, 3, 5}, {0, 1, 2, 3, 4, 5}, {0, 1, 2, 3, 4, 5}, {0, 1, 2, 3, 4, 5}, {4}, {0, 1, 2, 3, 4, 5}, {0, 1, 2, 3, 4, 5}, {0, 1, 3, 4, 5}, {0, 1, 3, 4, 5}, {0, 1, 2, 3, 5}, {0, 3, 5}, {0, 1, 2, 3, 4, 5}, {0, 1, 2, 3}, {0, 1, 2, 3, 4, 5}, {1}, {0, 1, 2, 3, 4, 5}, {0, 4, 5}, {0, 3, 5}, {0, 1, 2, 3, 4, 5}, {0, 1, 2, 3, 4, 5}, {0, 1, 2, 3, 4, 5}, {0, 1, 2, 4, 5}, {0, 1, 2, 4, 5}, {0, 1, 2, 3, 4, 5}, {0}, {0, 1, 2, 3, 4, 5}, {0, 1, 2, 3, 4, 5}, {0, 1, 3, 5}, {0, 1, 2, 3, 4, 5}, {1, 2, 3, 5}, {0, 1, 2, 3, 4, 5}, {0, 1, 2, 3, 4, 5}, {0, 1, 2, 3, 4, 5}, {0, 1, 3, 4, 5}, {0, 2}, {0, 1, 2, 3, 4, 5}, {0, 1, 2, 3, 4, 5}, {0, 1, 2, 3, 4, 5}, {0, 1, 2, 3, 4, 5}, {0, 1, 2, 3, 4, 5}, {0, 1, 2, 3, 4, 5}, {0, 1, 2, 3, 4, 5}, {0, 1, 2, 4, 5}, {0, 5}, {0, 1, 2, 3, 4, 5}, {0, 1, 2, 3, 4, 5}, {0, 1, 2, 3, 4, 5}, {1, 2, 4, 5}, {0, 1, 2, 3, 4, 5}, {0, 1, 2, 3, 4, 5}, {0, 1, 2, 3, 4, 5}, {0}, {0, 1, 2, 3, 4, 5}, {0, 2, 3, 4, 5}]
Iteration 77: Best valset aggregate score so far: 0.67
Iteration 77: Best program as per aggregate score on train_val: 0
Iteration 77: Best program as per aggregate score on valset: 0
Iteration 77: Best score on valset: 0.67
Iteration 77: Best score on train_val: 0.67
Iteration 77: Linear pareto front program index: 0
Iteration 77: New program candidate index: 5
Iteration 78: Selected program 3 score: 0.645
Average Metric: 2.00 / 3 (66.7%): 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [06:22<00:00, 127.50s/it]2025/08/29 05:58:29 INFO dspy.evaluate.evaluate: Average Metric: 2.0 / 3 (66.7%)
2025/08/29 05:59:46 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[0, 0, 8, 0], [0, 8, 0, 8], [0, 0, 8, 0]], 'output': [[0, 0, 8, 0, 0, 8, 0, 0], [0, 8, 0, 8, 8, 0, 8, 0], [0, 0, 8, 0, 0, 8, 0, 0], [0, 0, 8, 0, 0, 8, 0, 0], [0, 8, 0, 8, 8, 0, 8, 0], [0, 0, 8, 0, 0, 8, 0, 0]]}, {'input': [[0, 0, 3, 3], [0, 3, 0, 3], [3, 3, 3, 0]], 'output': [[0, 0, 3, 3, 3, 3, 0, 0], [0, 3, 0, 3, 3, 0, 3, 0], [3, 3, 3, 0, 0, 3, 3, 3], [3, 3, 3, 0, 0, 3, 3, 3], [0, 3, 0, 3, 3, 0, 3, 0], [0, 0, 3, 3, 3, 3, 0, 0]]}, {'input': [[3, 3, 3, 3], [3, 0, 0, 0], [3, 0, 0, 0]], 'output': [[3, 3, 3, 3, 3, 3, 3, 3], [3, 0, 0, 0, 0, 0, 0, 3], [3, 0, 0, 0, 0, 0, 0, 3], [3, 0, 0, 0, 0, 0, 0, 3], [3, 0, 0, 0, 0, 0, 0, 3], [3, 3, 3, 3, 3, 3, 3, 3]]}], 'test_inputs': [[[4, 0, 0, 0], [0, 0, 0, 4], [4, 4, 0, 0]]], 'test_outputs': [[[4, 0, 0, 0, 0, 0, 0, 4], [0, 0, 0, 4, 4, 0, 0, 0], [4, 4, 0, 0, 0, 0, 4, 4], [4, 4, 0, 0, 0, 0, 4, 4], [0, 0, 0, 4, 4, 0, 0, 0], [4, 0, 0, 0, 0, 0, 0, 4]]]}) (input_keys={'test_inputs', 'training_examples'}): 'dict' object has no attribute 'dict'
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 117, in forward
File "<string>", line 117, in <listcomp>
AttributeError: 'dict' object has no attribute 'dict'
2025/08/29 05:59:46 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[3, 3, 3, 3, 3, 3, 3, 4, 3, 4, 3], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3], [3, 3, 3, 3, 1, 3, 3, 3, 3, 3, 3], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3], [3, 3, 1, 3, 3, 3, 1, 4, 3, 4, 3], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3], [3, 3, 3, 3, 1, 3, 2, 3, 3, 3, 2], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3], [8, 3, 3, 3, 8, 3, 3, 3, 3, 3, 3], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3], [8, 3, 3, 3, 8, 3, 2, 3, 3, 3, 2], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3]], 'output': [[2, 4, 1, 4, 2], [8, 3, 3, 3, 8], [1, 3, 3, 3, 1], [8, 3, 3, 3, 8], [2, 4, 1, 4, 2]]}, {'input': [[1, 1, 1, 1, 1, 1, 1, 1], [1, 8, 1, 8, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1], [1, 8, 1, 8, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 3, 1, 1, 1], [1, 1, 1, 3, 1, 3, 1, 1], [1, 1, 1, 1, 3, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1]], 'output': [[8, 3, 8], [3, 1, 3], [8, 3, 8]]}, {'input': [[4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4], [4, 4, 1, 4, 4, 4, 1, 4, 4, 4, 4, 4, 4, 4], [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4], [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4], [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4], [4, 4, 1, 4, 4, 4, 1, 4, 4, 7, 4, 4, 4, 4], [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4], [4, 4, 4, 4, 4, 4, 4, 7, 4, 4, 4, 7, 4, 4], [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4], [4, 4, 4, 4, 4, 4, 4, 4, 4, 7, 4, 4, 4, 4], [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4], [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4]], 'output': [[1, 4, 7, 4, 1], [4, 4, 4, 4, 4], [7, 4, 4, 4, 7], [4, 4, 4, 4, 4], [1, 4, 7, 4, 1]]}], 'test_inputs': [[[8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [8, 8, 8, 8, 8, 8, 1, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [8, 8, 8, 1, 8, 8, 8, 8, 8, 1, 8, 8, 8, 8, 8, 8, 8, 8, 8], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 2, 8, 8, 8, 8, 8, 2, 8], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [8, 8, 8, 8, 8, 8, 1, 8, 8, 8, 8, 2, 8, 8, 8, 8, 8, 2, 8], [8, 8, 8, 3, 8, 8, 8, 8, 8, 3, 8, 8, 8, 8, 8, 8, 8, 8, 8], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 6, 8, 6, 8, 8], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [8, 8, 8, 3, 8, 8, 8, 8, 8, 3, 8, 8, 8, 8, 8, 8, 8, 8, 8], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 6, 8, 6, 8, 8], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8]]], 'test_outputs': [[[3, 8, 6, 1, 6, 8, 3], [8, 8, 8, 8, 8, 8, 8], [2, 8, 8, 8, 8, 8, 2], [1, 8, 8, 8, 8, 8, 1], [2, 8, 8, 8, 8, 8, 2], [8, 8, 8, 8, 8, 8, 8], [3, 8, 6, 1, 6, 8, 3]]]}) (input_keys={'test_inputs', 'training_examples'}): 'dict' object has no attribute 'dict'
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 117, in forward
File "<string>", line 117, in <listcomp>
AttributeError: 'dict' object has no attribute 'dict'
2025/08/29 05:59:46 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 2, 0, 0, 0], [0, 0, 0, 0, 0, 7, 1, 6, 0, 0], [0, 0, 0, 0, 0, 0, 8, 0, 0, 2], [0, 0, 0, 0, 0, 0, 0, 0, 7, 1], [0, 0, 0, 2, 0, 0, 0, 0, 0, 8], [0, 0, 7, 1, 6, 0, 0, 0, 0, 0], [0, 0, 0, 8, 0, 0, 0, 2, 0, 0], [0, 0, 0, 0, 0, 0, 7, 1, 6, 0], [0, 2, 0, 0, 0, 0, 0, 8, 0, 0], [7, 1, 6, 0, 0, 0, 0, 0, 0, 0]]}, {'input': [[0, 0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 1]], 'output': [[0, 0, 0, 0, 7, 1, 6, 0, 0, 0], [2, 0, 0, 0, 0, 8, 0, 0, 0, 0], [1, 6, 0, 0, 0, 0, 0, 0, 0, 2], [8, 0, 0, 0, 0, 0, 0, 0, 7, 1], [0, 0, 0, 0, 0, 2, 0, 0, 0, 8], [0, 0, 0, 0, 7, 1, 6, 0, 0, 0], [0, 0, 0, 0, 0, 8, 0, 0, 0, 0], [0, 0, 2, 0, 0, 0, 0, 0, 0, 0], [0, 7, 1, 6, 0, 0, 0, 0, 0, 2], [0, 0, 8, 0, 0, 0, 0, 0, 7, 1]]}], 'test_inputs': [[[0, 1, 0, 0, 0, 0, 0, 0, 0, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0, 0]]], 'test_outputs': [[[7, 1, 6, 0, 0, 0, 0, 0, 7, 1], [0, 8, 0, 0, 0, 0, 0, 2, 0, 8], [0, 0, 0, 2, 0, 0, 7, 1, 6, 0], [0, 0, 7, 1, 6, 0, 0, 8, 0, 0], [0, 0, 0, 8, 0, 0, 0, 0, 0, 0], [2, 0, 0, 0, 0, 0, 0, 2, 0, 0], [1, 6, 0, 0, 0, 0, 7, 1, 6, 0], [8, 0, 0, 0, 0, 0, 0, 8, 0, 0], [0, 0, 0, 0, 2, 0, 0, 0, 0, 0], [0, 0, 0, 7, 1, 6, 0, 0, 0, 0]]]}) (input_keys={'test_inputs', 'training_examples'}): 'dict' object has no attribute 'dict'
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 117, in forward
File "<string>", line 117, in <listcomp>
AttributeError: 'dict' object has no attribute 'dict'
2025/08/29 05:59:46 INFO dspy.evaluate.evaluate: Average Metric: 0.0 / 3 (0.0%)
GEPA Optimization: 40%|████████████████████████████████████████████ | 1590/4000 [8:25:50<8:09:30, 12.19s/rollouts]Iteration 78: Proposed new text for program: import dspy
from typing import List, Optional, Callable, Tuple
import pydantic
import copy
import json
# Define type aliases and pydantic models for clarity and structure.
MATRIX = List[List[int]]
class TrainingExample(pydantic.BaseModel):
input: MATRIX
output: MATRIX
# --- Signatures ---
class DeduceRuleSignature(dspy.Signature):
"""
Analyze a series of input/output matrix pairs from the ARC challenge.
Deduce the single, underlying transformation rule that applies to all pairs.
Describe this rule clearly, concisely, and unambiguously in natural language.
The rule must be general enough to explain every single input/output pair provided.
**Successful Strategies:**
- Focus on high-level concepts: Instead of pixel-by-pixel changes, think about objects, shapes, colors, symmetry, repetition, counting, or filling.
- Be precise: "Fill the area enclosed by the blue border with red" is better than "fill something". "Reflect the largest contiguous non-black object horizontally" is better than "move the object".
- Step-by-step: If the transformation is complex, break it down into a logical sequence of simple operations. For example: "1. Find all red squares. 2. For each red square, draw a blue line from it to the top edge of the grid."
"""
training_examples: str = dspy.InputField(desc="A JSON string of input/output pairs demonstrating the transformation rule.")
rule_description: str = dspy.OutputField(desc="A clear, natural language description of the transformation rule.")
class GeneratePythonFunctionFromRuleSignature(dspy.Signature):
"""
You are an expert programmer. Your task is to write a single, self-contained Python function based on a provided rule description and example matrix pairs.
**Primary Instruction:**
Your main goal is to faithfully implement the logic described in the `rule_description`. The `training_examples` are for context and to understand the data format, but the `rule_description` is your primary source of truth for the algorithm. Do not invent new logic not present in the rule description.
**Function Requirements:**
- The function must be named `transform_matrix`.
- It must accept one argument: `matrix`, which is a list of lists of integers.
- It must return a list of lists of integers.
- It should not use any external libraries except for `copy` if needed (e.g., `import copy; new_matrix = copy.deepcopy(matrix)`).
- Your output must be ONLY the Python code for the function. Do not include any explanations, comments outside the function, or markdown formatting like ```python.
**Example of a Correctly Formatted Output:**
def transform_matrix(matrix: list[list[int]]) -> list[list[int]]:
# Implementation based on the rule_description
import copy
new_matrix = copy.deepcopy(matrix)
# ... rest of the logic ...
return new_matrix
"""
training_examples: str = dspy.InputField(desc="A JSON string of input/output pairs for context.")
rule_description: str = dspy.InputField(desc="The natural language description of the rule to implement.")
python_function: str = dspy.OutputField(desc="A string containing a single Python function `transform_matrix` that implements the rule.")
class RefineFunctionSignature(dspy.Signature):
"""
You are an expert programmer debugging code for an ARC puzzle. A previously generated function failed to correctly transform the training examples. Your task is to fix it.
Analyze the original examples, the faulty rule, the faulty code, and the specific feedback detailing the mismatch. Produce a new, corrected Python function `transform_matrix` that successfully passes all training examples.
Your output must be ONLY the Python code for the function. Do not repeat the same mistake. Focus on the discrepancies highlighted in the feedback.
"""
training_examples: str = dspy.InputField(desc="A JSON string of the original input/output pairs.")
failed_rule: str = dspy.InputField(desc="The natural language rule that was being implemented.")
failed_code: str = dspy.InputField(desc="The Python code that failed verification.")
feedback: str = dspy.InputField(desc="Specific details about which training example failed and the difference between the expected and actual output.")
python_function: str = dspy.OutputField(desc="A string containing the corrected Python function `transform_matrix`.")
# --- Custom Module with Self-Correction ---
class ARCSolver(dspy.Module):
"""A module that solves ARC tasks by deducing a rule, generating code, and then verifying and refining the code."""
def __init__(self, max_attempts: int = 2):
super().__init__()
self.max_attempts = max_attempts
self.rule_deducer = dspy.ChainOfThought(DeduceRuleSignature)
self.code_generator = dspy.Predict(GeneratePythonFunctionFromRuleSignature)
self.code_refiner = dspy.Predict(RefineFunctionSignature)
def _execute_code(self, python_code: str) -> Optional[Callable[[MATRIX], MATRIX]]:
"""Executes the generated Python code in a safe scope and returns the function."""
local_scope = {}
try:
exec(python_code, globals(), local_scope)
transform_func = local_scope.get('transform_matrix')
if callable(transform_func):
return transform_func
except Exception:
return None
return None
def _verify_function(self, func: Callable, examples: List[TrainingExample]) -> Tuple[bool, str]:
"""Verifies the function against all training examples."""
for i, example in enumerate(examples):
try:
input_copy = copy.deepcopy(example.input)
actual_output = func(input_copy)
if actual_output != example.output:
feedback = (
f"Verification failed on training example {i}.\n"
f"Input:\n{json.dumps(example.input)}\n"
f"Expected Output:\n{json.dumps(example.output)}\n"
f"Actual Output:\n{json.dumps(actual_output)}"
)
return False, feedback
except Exception as e:
feedback = f"Verification failed on training example {i} with an exception: {str(e)}"
return False, feedback
return True, "All training examples passed."
def forward(self, training_examples: List[TrainingExample], test_inputs: List[MATRIX]):
# Pydantic models are not directly serializable for LM prompts, convert to JSON string.
examples_str = json.dumps([ex.dict() for ex in training_examples])
rule_description = ""
python_code = ""
feedback = "No feedback yet."
transform_func = None
for attempt in range(self.max_attempts):
if attempt == 0:
# First attempt: Deduce rule and generate code
deduction = self.rule_deducer(training_examples=examples_str)
rule_description = deduction.rule_description
generation = self.code_generator(
training_examples=examples_str,
rule_description=rule_description
)
python_code = generation.python_function
else:
# Subsequent attempts: Refine the code based on feedback
refinement = self.code_refiner(
training_examples=examples_str,
failed_rule=rule_description,
failed_code=python_code,
feedback=feedback
)
python_code = refinement.python_function
transform_func = self._execute_code(python_code)
if transform_func:
is_correct, feedback = self._verify_function(transform_func, training_examples)
if is_correct:
break # Successfully verified, exit the loop
else:
feedback = "Generated code could not be executed or did not define 'transform_matrix'."
# After the loop, apply the best function we have
fallback_outputs = [copy.deepcopy(matrix) for matrix in test_inputs]
if not transform_func:
return dspy.Prediction(test_outputs=fallback_outputs)
solved_outputs = []
for test_matrix in test_inputs:
try:
input_copy = copy.deepcopy(test_matrix)
result = transform_func(input_copy)
solved_outputs.append(result)
except Exception:
solved_outputs.append(copy.deepcopy(test_matrix))
return dspy.Prediction(test_outputs=solved_outputs)
# The overall task signature defines the final input/output of the program.
class SolveTaskSignature(dspy.Signature):
"""
Given a set of training examples demonstrating a matrix transformation,
apply the same transformation to a new set of test matrices.
"""
training_examples: List[TrainingExample] = dspy.InputField(description="Input and output examples demonstrating the task to be performed.")
test_inputs: List[MATRIX] = dspy.InputField(description="Input matrices to be solved following the task described in the training examples.")
test_outputs: List[MATRIX] = dspy.OutputField(description="Output matrices corresponding to the test inputs.")
# The final program is an instance of our new, self-correcting module.
program = ARCSolver()
Iteration 78: New subsample score is not better, skipping
Iteration 79: Selected program 2 score: 0.605
Average Metric: 1.00 / 1 (100.0%): 0%| | 0/3 [00:00<?, ?it/s]2025/08/29 06:04:06 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
Average Metric: 1.00 / 2 (50.0%): 67%|██████████████████████████████████████████████████████████████████████████ | 2/3 [04:19<02:09, 129.91s/it]2025/08/29 06:04:06 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
Average Metric: 1.00 / 3 (33.3%): : 4it [05:54, 88.60s/it] 2025/08/29 06:05:41 INFO dspy.evaluate.evaluate: Average Metric: 1.0 / 3 (33.3%)
2025/08/29 06:06:06 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
Iteration 79: Proposed new text for program: import dspy
from typing import List
import pydantic
import copy
# Define type aliases and pydantic models for clarity and structure.
MATRIX = List[List[int]]
class TrainingExample(pydantic.BaseModel):
input: MATRIX
output: MATRIX
class GeneratePythonFunction(dspy.Signature):
"""
You are an expert programmer. Your task is to analyze a series of input and output matrix pairs from the Abstraction and Reasoning Corpus (ARC). Based on these examples, you must deduce the underlying transformation rule and write a single, self-contained Python function that implements this rule.
**Your Thought Process:**
1. First, in a step-by-step manner, describe your reasoning and the logic of the transformation you have deduced from the examples. This is your hypothesis.
2. Then, based on your reasoning, write the Python function.
**Function Requirements:**
- The function must be named `transform_matrix`.
- It must accept one argument: `matrix`, which is a list of lists of integers (representing the input grid).
- It must return a list of lists of integers (representing the transformed output grid).
- The function should not use any external libraries except for `copy` if needed (e.g., `import copy; new_matrix = copy.deepcopy(matrix)`).
- Your output for the function must be ONLY the Python code. Do not include any explanations, comments outside the function, or markdown formatting like ```python.
**Successful Strategies to Consider:**
- **Object Permanence:** Most transformations preserve the grid dimensions. However, some tasks require resizing the output grid.
- **Color Transformations:** Look for rules that change colors based on their value or their neighbors.
- **Spatial Reasoning:** Analyze shapes, positions, and relationships. Common patterns include filling enclosed areas, drawing lines, moving objects, mirroring, or detecting symmetry.
- **Iterative Processes:** Some rules are applied repeatedly until the grid no longer changes. Consider using a `while` loop that continues as long as modifications are being made in a pass.
- **Neighborhoods:** Often, a cell's new value depends on its immediate neighbors (e.g., von Neumann or Moore neighborhoods).
"""
training_examples: List[TrainingExample] = dspy.InputField(desc="A list of input/output pairs demonstrating the transformation rule.")
reasoning: str = dspy.OutputField(desc="A step-by-step explanation of the transformation rule deduced from the examples.")
python_function: str = dspy.OutputField(desc="A string containing a single Python function `transform_matrix` that implements the rule.")
class ARCSolver(dspy.Module):
"""A module that solves ARC tasks by generating, verifying, and executing Python code."""
def __init__(self):
super().__init__()
# Use ChainOfThought to encourage the LM to reason before generating code.
self.code_generator = dspy.ChainOfThought(GeneratePythonFunction)
def _verify_code(self, python_code: str, training_examples: List[TrainingExample]):
"""
Safely executes the generated Python code and verifies its correctness
against all training examples.
Returns the callable function if verification passes, otherwise None.
"""
if not python_code or "def transform_matrix" not in python_code:
return None
local_scope = {}
try:
exec(python_code, globals(), local_scope)
transform_func = local_scope.get('transform_matrix')
if not callable(transform_func):
return None
# Test the function against all training examples.
for example in training_examples:
input_copy = copy.deepcopy(example.input)
predicted_output = transform_func(input_copy)
if predicted_output != example.output:
# The generated code does not work for all training examples.
return None
# If all examples pass, the function is valid.
return transform_func
except Exception:
# The code failed to execute or failed during execution.
return None
def forward(self, training_examples: List[TrainingExample], test_inputs: List[MATRIX]):
# Step 1: Generate reasoning and the Python function.
prediction = self.code_generator(training_examples=training_examples)
python_code = prediction.python_function
# Step 2: Verify the generated code against the training examples.
# This is a critical self-correction/validation step.
transform_func = self._verify_code(python_code, training_examples)
# Prepare fallback outputs in case of any failure.
fallback_outputs = [copy.deepcopy(matrix) for matrix in test_inputs]
if not transform_func:
# If code is invalid or fails verification, return original inputs as a fallback.
return dspy.Prediction(test_outputs=fallback_outputs)
# Step 3: If verification passes, apply the trusted function to test inputs.
try:
solved_outputs = []
for test_matrix in test_inputs:
# Use a deepcopy to prevent the function from modifying the original input list.
input_copy = copy.deepcopy(test_matrix)
result = transform_func(input_copy)
solved_outputs.append(result)
return dspy.Prediction(test_outputs=solved_outputs)
except Exception:
# If the verified function still fails on unseen test data, fallback.
return dspy.Prediction(test_outputs=fallback_outputs)
# The overall task signature defines the final input/output of the program.
class SolveTaskSignature(dspy.Signature):
"""
Given a set of training examples demonstrating a matrix transformation,
apply the same transformation to a new set of test matrices.
"""
training_examples: List[TrainingExample] = dspy.InputField(description="Input and output examples demonstrating the task to be performed.")
test_inputs: List[MATRIX] = dspy.InputField(description="Input matrices to be solved following the task described in the training examples.")
test_outputs: List[MATRIX] = dspy.OutputField(description="Output matrices corresponding to the test inputs.")
# The final program is an instance of our new, more robust module.
program = ARCSolver()
2025/08/29 06:10:29 INFO dspy.evaluate.evaluate: Average Metric: 0.0 / 3 (0.0%)
GEPA Optimization: 40%|███████████████████████████████████████████▉ | 1596/4000 [8:36:32<11:05:01, 16.60s/rollouts]Iteration 79: New subsample score is not better, skipping
Iteration 80: Selected program 4 score: 0.61
Average Metric: 2.00 / 2 (100.0%): 67%|██████████████████████████████████████████████████████████████████████████ | 2/3 [01:01<00:29, 29.52s/it]Error applying `transform_grid` to a test input: name 'collections' is not defined
Average Metric: 2.00 / 3 (66.7%): 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [03:45<00:00, 75.20s/it]2025/08/29 06:14:14 INFO dspy.evaluate.evaluate: Average Metric: 2.0 / 3 (66.7%)
Iteration 80: Proposed new text for program: import dspy
from typing import List, Optional
import pydantic
import traceback
import copy
# Define the type for a grid/matrix as a list of lists of integers.
MATRIX = List[List[int]]
class TrainingExample(pydantic.BaseModel):
"""A Pydantic model for a single training example, containing an input and output grid."""
input: MATRIX
output: MATRIX
# --- Signature for Initial Code Generation ---
class GenerateTransformationFunction(dspy.Signature):
"""
You are an expert programmer solving visual puzzles from the Abstraction and Reasoning Corpus (ARC).
Your goal is to analyze a set of training examples, each consisting of an input grid and an output grid.
Based on this analysis, you must write a single, self-contained Python function named `transform_grid` that implements the observed transformation.
**Function Requirements:**
- The function must be named exactly `transform_grid`.
- It must accept one argument: `grid`, which is a 2D list of integers (the input grid).
- It must return a new 2D list of integers representing the transformed grid.
- The function should not rely on any external libraries unless they are standard (e.g., `copy`). If you need a library like numpy, import it inside the function.
- Do not modify the input grid in place; create a copy if necessary.
**Analysis Strategy:**
1. **Observe Core Patterns:** Look for simple, recurring patterns like geometric transformations (rotation, reflection, scaling), color changes, object manipulation (copying, moving, recoloring), pattern propagation, or flood-fills.
2. **Decompose the Problem:** Break down the transformation into logical steps. For example, "first, find all objects of color blue, then for each object, reflect it horizontally."
3. **Generalize:** The logic must be general enough to work for all training examples and, by extension, the unseen test inputs. Pay close attention to grid dimensions, as they can vary between training and test cases.
4. **Code Implementation:** Translate your logic into a clear and correct Python function. Your generated code will be automatically tested against the training examples. Ensure it is robust and handles all cases observed.
"""
training_examples: List[TrainingExample] = dspy.InputField(desc="A list of input/output grid pairs demonstrating the transformation rule.")
test_input_grid: MATRIX = dspy.InputField(desc="A single test input grid to which the transformation should be applicable. Use its properties (e.g., dimensions) to ensure your code is general.")
reasoning: str = dspy.OutputField(desc="Step-by-step thinking process to deduce the transformation rule and how to implement it in Python.")
python_code: str = dspy.OutputField(desc="A string containing the complete, self-contained Python function `transform_grid(grid)`.")
# --- Signature for Code Refinement/Debugging ---
class RefineTransformationFunction(dspy.Signature):
"""
You are an expert Python programmer acting as a debugger. You have been given a Python function that failed to correctly solve a visual puzzle from the Abstraction and Reasoning Corpus (ARC).
Your task is to analyze the faulty code, the input that caused the failure, the incorrect output it produced, and the expected correct output.
Based on this analysis, you must identify the bug and provide a corrected version of the function.
**Debugging Strategy:**
1. **Understand the Goal:** Briefly review the training examples to understand the intended transformation.
2. **Analyze the Failure:** Compare the `generated_output` with the `expected_output` for the `failed_input`. What is the specific discrepancy? (e.g., wrong colors, incorrect shapes, off-by-one errors, etc.). If an `error_traceback` is provided, use it to find the exact line of the crash.
3. **Pinpoint the Bug:** Read through the `previous_code` and identify the part of the logic that led to the failure.
4. **Propose a Fix:** Explain your reasoning for the fix.
5. **Rewrite the Code:** Provide the complete, corrected, self-contained `transform_grid` function. Do not just provide a snippet.
"""
training_examples: List[TrainingExample] = dspy.InputField(desc="The original list of input/output grid pairs demonstrating the transformation rule.")
previous_code: str = dspy.InputField(desc="The previous version of the Python code that failed verification.")
failed_input: MATRIX = dspy.InputField(desc="The specific input grid from the training set on which the code failed.")
generated_output: Optional[MATRIX] = dspy.InputField(desc="The incorrect output grid produced by the previous code. This will be None if the code crashed.")
expected_output: MATRIX = dspy.InputField(desc="The correct, expected output grid for the failed input.")
error_traceback: Optional[str] = dspy.InputField(desc="The Python error traceback if the code crashed during execution. This will be None if the code ran but produced the wrong output.")
reasoning: str = dspy.OutputField(desc="Step-by-step reasoning that identifies the bug and explains the fix.")
python_code: str = dspy.OutputField(desc="A string containing the complete, corrected, self-contained Python function `transform_grid(grid)`.")
# --- The Improved Custom Module with a Refinement Loop ---
class ARCRefineSolver(dspy.Module):
"""A DSPy module that solves ARC tasks by generating, verifying, and refining Python code."""
def __init__(self, max_retries=2):
super().__init__()
self.max_retries = max_retries
self.code_generator = dspy.ChainOfThought(GenerateTransformationFunction)
self.code_refiner = dspy.ChainOfThought(RefineTransformationFunction)
def forward(self, training_examples: List[TrainingExample], test_inputs: List[MATRIX]):
# 1. Initial Code Generation
prediction = self.code_generator(training_examples=training_examples, test_input_grid=test_inputs[0])
python_code = prediction.python_code
transform_function = None
# 2. Verification and Refinement Loop
for attempt in range(self.max_retries + 1):
local_scope = {}
current_transform_function = None
compilation_error = None
try:
code_to_exec = python_code
if "```python" in code_to_exec:
code_to_exec = code_to_exec.split("```python")[1].split("```")[0].strip()
exec(code_to_exec, globals(), local_scope)
current_transform_function = local_scope.get('transform_grid')
if not callable(current_transform_function):
current_transform_function = None
compilation_error = "Function 'transform_grid' not found or not callable after execution."
except Exception as e:
compilation_error = traceback.format_exc()
if compilation_error or not current_transform_function:
if attempt < self.max_retries:
refinement = self.code_refiner(
training_examples=training_examples,
previous_code=python_code,
failed_input=training_examples[0].input,
generated_output=None,
expected_output=training_examples[0].output,
error_traceback=compilation_error
)
python_code = refinement.python_code
continue
else:
break
verified = True
for example in training_examples:
try:
generated_output = current_transform_function(copy.deepcopy(example.input))
if generated_output != example.output:
verified = False
if attempt < self.max_retries:
refinement = self.code_refiner(
training_examples=training_examples,
previous_code=python_code,
failed_input=example.input,
generated_output=generated_output,
expected_output=example.output,
error_traceback=None
)
python_code = refinement.python_code
break
except Exception:
verified = False
if attempt < self.max_retries:
refinement = self.code_refiner(
training_examples=training_examples,
previous_code=python_code,
failed_input=example.input,
generated_output=None,
expected_output=example.output,
error_traceback=traceback.format_exc()
)
python_code = refinement.python_code
break
if verified:
transform_function = current_transform_function
break
# 3. Final Application to Test Inputs
generated_outputs = []
for test_input in test_inputs:
if transform_function:
try:
output_grid = transform_function(copy.deepcopy(test_input))
generated_outputs.append(output_grid)
except Exception:
generated_outputs.append(copy.deepcopy(test_input))
else:
generated_outputs.append(copy.deepcopy(test_input))
return dspy.Prediction(test_outputs=generated_outputs)
# The final program object is an instance of our new, more robust custom module.
program = ARCRefineSolver()
2025/08/29 06:15:50 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0], [2, 0, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 5, 5, 0, 0, 0, 0, 5, 5, 0, 0], [2, 0, 5, 5, 0, 0, 0, 0, 5, 5, 0, 0], [2, 0, 5, 5, 0, 0, 0, 0, 5, 5, 0, 0], [1, 0, 0, 0, 0, 0, 0, 0, 5, 5, 0, 0], [1, 0, 0, 0, 5, 5, 5, 0, 5, 5, 0, 0], [1, 0, 0, 0, 5, 5, 5, 0, 5, 5, 0, 0], [2, 0, 0, 0, 5, 5, 5, 0, 5, 5, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0], [2, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0], [2, 0, 2, 2, 0, 0, 0, 0, 2, 2, 0, 0], [2, 0, 2, 2, 0, 0, 0, 0, 2, 2, 0, 0], [1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0], [1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0], [1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0], [2, 0, 0, 0, 2, 2, 2, 0, 2, 2, 0, 0]]}, {'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [3, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0], [3, 5, 5, 5, 5, 0, 0, 5, 5, 5, 5, 5], [4, 5, 5, 5, 5, 0, 0, 5, 5, 5, 5, 5], [4, 5, 5, 5, 5, 0, 0, 5, 5, 5, 5, 5], [3, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5], [4, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 5], [3, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 5], [3, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 5], [3, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 5], [4, 0, 0, 0, 5, 5, 5, 0, 0, 0, 0, 0], [4, 0, 0, 0, 5, 5, 5, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0], [3, 3, 3, 3, 3, 0, 0, 3, 3, 3, 3, 3], [4, 4, 4, 4, 4, 0, 0, 4, 4, 4, 4, 4], [4, 4, 4, 4, 4, 0, 0, 4, 4, 4, 4, 4], [3, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3], [4, 0, 0, 0, 4, 4, 4, 4, 4, 4, 4, 4], [3, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3], [3, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3], [3, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3], [4, 0, 0, 0, 4, 4, 4, 0, 0, 0, 0, 0], [4, 0, 0, 0, 4, 4, 4, 0, 0, 0, 0, 0]]}], 'test_inputs': [[[1, 0, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0], [8, 0, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0], [1, 0, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0], [1, 0, 5, 5, 5, 5, 5, 5, 0, 5, 5, 5], [7, 0, 5, 5, 5, 5, 5, 5, 0, 5, 5, 5], [7, 0, 5, 5, 5, 5, 5, 5, 0, 5, 5, 5], [7, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5], [7, 0, 0, 0, 0, 0, 5, 5, 5, 0, 0, 0], [8, 0, 0, 0, 0, 0, 5, 5, 5, 0, 0, 0], [8, 0, 5, 5, 5, 0, 5, 5, 5, 0, 0, 0], [8, 0, 5, 5, 5, 0, 5, 5, 5, 0, 0, 0], [8, 0, 5, 5, 5, 0, 5, 5, 5, 0, 0, 0]]], 'test_outputs': [[[1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0], [8, 0, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0], [1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0], [1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1], [7, 0, 7, 7, 7, 7, 7, 7, 0, 7, 7, 7], [7, 0, 7, 7, 7, 7, 7, 7, 0, 7, 7, 7], [7, 0, 0, 0, 0, 0, 7, 7, 7, 7, 7, 7], [7, 0, 0, 0, 0, 0, 7, 7, 7, 0, 0, 0], [8, 0, 0, 0, 0, 0, 8, 8, 8, 0, 0, 0], [8, 0, 8, 8, 8, 0, 8, 8, 8, 0, 0, 0], [8, 0, 8, 8, 8, 0, 8, 8, 8, 0, 0, 0], [8, 0, 8, 8, 8, 0, 8, 8, 8, 0, 0, 0]]]}) (input_keys={'test_inputs', 'training_examples'}): 'dict' object has no attribute 'input'
Traceback (most recent call last):
File "<string>", line 115, in forward
AttributeError: 'dict' object has no attribute 'input'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 135, in forward
AttributeError: 'dict' object has no attribute 'input'
Error applying `transform_grid` to a test input: name 'collections' is not defined
2025/08/29 06:16:46 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[5, 5, 5, 5, 0, 5, 5, 5, 5, 0, 5, 5, 5, 5], [5, 5, 5, 5, 0, 5, 0, 0, 5, 0, 0, 5, 5, 0], [5, 5, 5, 5, 0, 5, 0, 0, 5, 0, 0, 5, 5, 0], [5, 5, 5, 5, 0, 5, 5, 5, 5, 0, 5, 5, 5, 5]], 'output': [[2, 2, 2], [8, 8, 8], [3, 3, 3]]}, {'input': [[5, 5, 5, 5, 0, 5, 5, 5, 5, 0, 5, 5, 5, 5], [0, 5, 5, 0, 0, 5, 5, 5, 5, 0, 5, 5, 5, 5], [0, 5, 5, 0, 0, 5, 0, 0, 5, 0, 5, 5, 5, 5], [5, 5, 5, 5, 0, 5, 0, 0, 5, 0, 5, 5, 5, 5]], 'output': [[3, 3, 3], [4, 4, 4], [2, 2, 2]]}, {'input': [[5, 5, 5, 5, 0, 5, 5, 5, 5, 0, 5, 5, 5, 5], [5, 0, 0, 5, 0, 5, 5, 5, 5, 0, 5, 5, 5, 5], [5, 0, 0, 5, 0, 5, 5, 5, 5, 0, 5, 0, 0, 5], [5, 5, 5, 5, 0, 5, 5, 5, 5, 0, 5, 0, 0, 5]], 'output': [[8, 8, 8], [2, 2, 2], [4, 4, 4]]}, {'input': [[5, 5, 5, 5, 0, 5, 5, 5, 5, 0, 5, 5, 5, 5], [5, 5, 5, 5, 0, 5, 5, 5, 5, 0, 5, 5, 5, 5], [5, 5, 5, 5, 0, 5, 0, 0, 5, 0, 5, 5, 5, 5], [5, 5, 5, 5, 0, 5, 0, 0, 5, 0, 5, 5, 5, 5]], 'output': [[2, 2, 2], [4, 4, 4], [2, 2, 2]]}], 'test_inputs': [[[5, 5, 5, 5, 0, 5, 5, 5, 5, 0, 5, 5, 5, 5], [5, 5, 5, 5, 0, 0, 5, 5, 0, 0, 5, 0, 0, 5], [5, 0, 0, 5, 0, 0, 5, 5, 0, 0, 5, 0, 0, 5], [5, 0, 0, 5, 0, 5, 5, 5, 5, 0, 5, 5, 5, 5]]], 'test_outputs': [[[4, 4, 4], [3, 3, 3], [8, 8, 8]]]}) (input_keys={'test_inputs', 'training_examples'}): 'dict' object has no attribute 'input'
Traceback (most recent call last):
File "<string>", line 115, in forward
AttributeError: 'dict' object has no attribute 'input'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 135, in forward
AttributeError: 'dict' object has no attribute 'input'
2025/08/29 06:21:38 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 06:22:18 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 2, 1, 1, 2, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 2, 2, 0, 0, 0, 0, 2, 2, 0], [0, 0, 0, 2, 2, 0, 0, 0, 0, 2, 2, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 2, 1, 1, 2, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 2, 2, 1, 1, 1, 1, 2, 2, 0], [0, 0, 0, 2, 2, 1, 1, 1, 1, 2, 2, 0], [0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0], [0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0], [0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0]]}, {'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 1, 1, 0, 0, 0, 0, 0, 2, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0], [0, 0, 2, 0, 0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}, {'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 1, 1, 2, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0], [0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0], [0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 1, 1, 2, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 0, 0], [1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 0, 0], [1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}], 'test_inputs': [[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0], [0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]], 'test_outputs': [[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 2, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 2, 2, 2, 0], [0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 2, 2, 2, 0], [0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 2, 2, 2, 0], [0, 1, 1, 0, 0, 2, 2, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0], [0, 1, 1, 0, 0, 2, 2, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0], [0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0], [0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]]}) (input_keys={'test_inputs', 'training_examples'}): 'dict' object has no attribute 'input'
Traceback (most recent call last):
File "<string>", line 115, in forward
AttributeError: 'dict' object has no attribute 'input'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 135, in forward
AttributeError: 'dict' object has no attribute 'input'
2025/08/29 06:22:34 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 2, 1, 1, 2, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 2, 2, 0, 0, 0, 0, 2, 2, 0], [0, 0, 0, 2, 2, 0, 0, 0, 0, 2, 2, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 2, 1, 1, 2, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 2, 2, 1, 1, 1, 1, 2, 2, 0], [0, 0, 0, 2, 2, 1, 1, 1, 1, 2, 2, 0], [0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0], [0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0], [0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0]]}, {'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 1, 1, 0, 0, 0, 0, 0, 2, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0], [0, 0, 2, 0, 0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}, {'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 1, 1, 2, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0], [0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0], [0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 1, 1, 2, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 0, 0], [1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 0, 0], [1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}], 'test_inputs': [[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0], [0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]], 'test_outputs': [[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 2, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 2, 2, 2, 0], [0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 2, 2, 2, 0], [0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 2, 2, 2, 0], [0, 1, 1, 0, 0, 2, 2, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0], [0, 1, 1, 0, 0, 2, 2, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0], [0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0], [0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]]}) (input_keys={'test_inputs', 'training_examples'}): 'dict' object has no attribute 'input'
Traceback (most recent call last):
File "<string>", line 115, in forward
AttributeError: 'dict' object has no attribute 'input'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 135, in forward
AttributeError: 'dict' object has no attribute 'input'
2025/08/29 06:23:22 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 06:23:22 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 2, 1, 1, 2, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 2, 2, 0, 0, 0, 0, 2, 2, 0], [0, 0, 0, 2, 2, 0, 0, 0, 0, 2, 2, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 2, 1, 1, 2, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 2, 2, 1, 1, 1, 1, 2, 2, 0], [0, 0, 0, 2, 2, 1, 1, 1, 1, 2, 2, 0], [0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0], [0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0], [0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0]]}, {'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 1, 1, 0, 0, 0, 0, 0, 2, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0], [0, 0, 2, 0, 0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}, {'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 1, 1, 2, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0], [0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0], [0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 1, 1, 2, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 0, 0], [1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 0, 0], [1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}], 'test_inputs': [[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0], [0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]], 'test_outputs': [[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 2, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 2, 2, 2, 0], [0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 2, 2, 2, 0], [0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 2, 2, 2, 0], [0, 1, 1, 0, 0, 2, 2, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0], [0, 1, 1, 0, 0, 2, 2, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0], [0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0], [0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]]}) (input_keys={'test_inputs', 'training_examples'}): 'dict' object has no attribute 'input'
Traceback (most recent call last):
File "<string>", line 115, in forward
AttributeError: 'dict' object has no attribute 'input'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 135, in forward
AttributeError: 'dict' object has no attribute 'input'
2025/08/29 06:24:46 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 2, 1, 1, 2, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 2, 2, 0, 0, 0, 0, 2, 2, 0], [0, 0, 0, 2, 2, 0, 0, 0, 0, 2, 2, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 2, 1, 1, 2, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 2, 2, 1, 1, 1, 1, 2, 2, 0], [0, 0, 0, 2, 2, 1, 1, 1, 1, 2, 2, 0], [0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0], [0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0], [0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0]]}, {'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 1, 1, 0, 0, 0, 0, 0, 2, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0], [0, 0, 2, 0, 0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}, {'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 1, 1, 2, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0], [0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0], [0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 1, 1, 2, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 0, 0], [1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 0, 0], [1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}], 'test_inputs': [[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0], [0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]], 'test_outputs': [[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 2, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 2, 2, 2, 0], [0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 2, 2, 2, 0], [0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 2, 2, 2, 0], [0, 1, 1, 0, 0, 2, 2, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0], [0, 1, 1, 0, 0, 2, 2, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0], [0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0], [0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]]}) (input_keys={'test_inputs', 'training_examples'}): 'dict' object has no attribute 'input'
Traceback (most recent call last):
File "<string>", line 115, in forward
AttributeError: 'dict' object has no attribute 'input'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 135, in forward
AttributeError: 'dict' object has no attribute 'input'
2025/08/29 06:25:16 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 2, 1, 1, 2, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 2, 2, 0, 0, 0, 0, 2, 2, 0], [0, 0, 0, 2, 2, 0, 0, 0, 0, 2, 2, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 2, 1, 1, 2, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 2, 2, 1, 1, 1, 1, 2, 2, 0], [0, 0, 0, 2, 2, 1, 1, 1, 1, 2, 2, 0], [0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0], [0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0], [0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0]]}, {'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 1, 1, 0, 0, 0, 0, 0, 2, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0], [0, 0, 2, 0, 0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}, {'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 1, 1, 2, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0], [0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0], [0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 1, 1, 2, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 0, 0], [1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 0, 0], [1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}], 'test_inputs': [[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0], [0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]], 'test_outputs': [[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 2, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 2, 2, 2, 0], [0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 2, 2, 2, 0], [0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 2, 2, 2, 0], [0, 1, 1, 0, 0, 2, 2, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0], [0, 1, 1, 0, 0, 2, 2, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0], [0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0], [0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]]}) (input_keys={'test_inputs', 'training_examples'}): 'dict' object has no attribute 'input'
Traceback (most recent call last):
File "<string>", line 115, in forward
AttributeError: 'dict' object has no attribute 'input'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 135, in forward
AttributeError: 'dict' object has no attribute 'input'
2025/08/29 06:25:16 INFO dspy.evaluate.evaluate: Average Metric: 0.0 / 3 (0.0%)
GEPA Optimization: 40%|████████████████████████████████████████████ | 1602/4000 [8:51:20<16:30:25, 24.78s/rollouts]Iteration 80: New subsample score is not better, skipping
Iteration 81: Selected program 1 score: 0.63
Average Metric: 2.00 / 3 (66.7%): 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [01:41<00:00, 33.71s/it]2025/08/29 06:26:58 INFO dspy.evaluate.evaluate: Average Metric: 2.0 / 3 (66.7%)
Iteration 81: Proposed new text for program: import dspy
from typing import List
import pydantic
import copy
# Define the type for a matrix for clarity
MATRIX = List[List[int]]
class TrainingExample(pydantic.BaseModel):
"""A Pydantic model for a single training example, containing an input and output matrix."""
input: MATRIX
output: MATRIX
class InferRuleAsCodeSignature(dspy.Signature):
"""
Analyzes pairs of input/output matrices to deduce the transformation rule and express it as a Python function.
You are an expert programmer and analyst of abstract visual reasoning puzzles. Your task is to carefully examine the provided training examples. Each example consists of an 'input' matrix and a corresponding 'output' matrix.
Your goal is to deduce the single, consistent transformation rule that converts each input to its output. You must then write a standalone Python function named `transform_matrix` that takes one argument (a matrix as a list of lists of integers) and returns the transformed matrix.
**Function Requirements:**
- The function must be named `transform_matrix`.
- It must accept a single argument: `matrix`.
- It must return the transformed matrix (a list of lists of integers).
- The function must be self-contained. Do not use any external libraries like numpy or pandas. You can use the `copy` library for `copy.deepcopy` if needed.
- Your output must be ONLY the Python code for the function, as a single string. Do not include any example usage, explanation, or markdown formatting.
**Successful Strategies to Consider:**
- **Cell-wise transformations:** Does each cell's output value depend only on its input value? This often involves creating a simple mapping or dictionary.
- **Geometric operations:** Look for patterns of rotation, reflection (horizontal or vertical), repetition, or shifting of the entire grid or objects within it.
- **Object-based logic:** Identify distinct objects or shapes (contiguous blocks of non-zero colors). The rule might depend on the properties of these objects (size, color, position, count) or their relative positions.
- **Row/Column operations:** The transformation might involve copying, deleting, or reordering entire rows or columns. For example, creating a vertical palindrome from the input rows.
- **Positional logic:** The rule might depend on the absolute (row, col) or relative position of cells. For example, coloring columns based on the vertical position of their topmost non-zero cell.
"""
training_examples: List[TrainingExample] = dspy.InputField(description="A list of input and output examples demonstrating the task.")
python_function: str = dspy.OutputField(description="A string containing the complete Python function `transform_matrix`.")
class ARCProgram(dspy.Module):
"""A program that infers a rule as Python code and then executes it to solve test inputs."""
def __init__(self):
super().__init__()
# Use ChainOfThought for the complex reasoning task of inferring a rule as code.
self.rule_inferrer = dspy.ChainOfThought(InferRuleAsCodeSignature)
def forward(self, training_examples: List[TrainingExample], test_inputs: List[MATRIX]) -> dspy.Prediction:
"""
Infers a rule as a Python function from training examples and executes it for each test input.
Args:
training_examples: A list of TrainingExample objects.
test_inputs: A list of input matrices to be solved.
Returns:
A dspy.Prediction object with the 'test_outputs' field populated.
"""
# 1. Infer the transformation rule as a Python function string.
inferred = self.rule_inferrer(training_examples=training_examples)
function_string = inferred.python_function
all_test_outputs = []
try:
# 2. Prepare a scope for executing the generated function string.
# We provide 'copy' so the LM can use `copy.deepcopy` for safety.
execution_scope = {'copy': copy}
# 3. Execute the string to define the function within our scope.
exec(function_string, execution_scope)
transform_func = execution_scope['transform_matrix']
# 4. Apply the now-defined function to each test input.
for test_matrix in test_inputs:
# Pass a deep copy to prevent the function from modifying the original input.
input_copy = copy.deepcopy(test_matrix)
result_matrix = transform_func(input_copy)
all_test_outputs.append(result_matrix)
except Exception as e:
# Fallback strategy: If code generation or execution fails, return placeholders.
print(f"Code generation or execution failed: {e}")
print(f"--- Failing code ---\n{function_string}\n--------------------")
for test_matrix in test_inputs:
# Return a matrix of zeros with the same dimensions as the input.
if test_matrix and test_matrix[0]:
all_test_outputs.append([([0] * len(test_matrix[0])) for _ in range(len(test_matrix))])
else:
all_test_outputs.append([])
# 5. Return the collected outputs in a single Prediction object.
return dspy.Prediction(test_outputs=all_test_outputs)
# The final 'program' object is an instance of our robust, code-executing module.
program = ARCProgram()
Code generation or execution failed: invalid syntax (<string>, line 1)
--- Failing code ---
```python
def transform_matrix(matrix):
"""
Transforms a matrix by applying a fixed substitution cipher to each cell's value.
"""
# The substitution mapping derived from the training examples.
substitution_map = {
1: 5,
2: 6,
3: 4,
4: 3,
5: 1,
6: 2,
8: 9,
9: 8
}
# Get the dimensions of the matrix.
height = len(matrix)
width = len(matrix[0]) if height > 0 else 0
# Create a new matrix to store the output.
output_matrix = [[0 for _ in range(width)] for _ in range(height)]
# Iterate through each cell of the input matrix.
for r in range(height):
for c in range(width):
original_value = matrix[r][c]
# Use the map to get the new value. If the original value is not in the map,
# default to using the original value itself.
output_matrix[r][c] = substitution_map.get(original_value, original_value)
return output_matrix
```
--------------------
Code generation or execution failed: invalid syntax (<string>, line 1)
--- Failing code ---
```python
import copy
def transform_matrix(matrix):
"""
Transforms the matrix by re-coloring non-zero columns based on the vertical
position of their topmost non-zero cell.
"""
height = len(matrix)
if not height:
return []
width = len(matrix[0])
if not width:
return [[] for _ in range(height)]
# Step 1: Find the topmost non-zero cell for each column that has one.
# Store as a list of tuples (top_row_index, column_index).
col_order_info = []
for c in range(width):
for r in range(height):
if matrix[r][c] != 0:
col_order_info.append((r, c))
break # Found the topmost for this column, move to the next.
# Step 2: Sort the list. Python's default tuple sort will order by the
# first element (top_row_index), which is what we want.
col_order_info.sort()
# Step 3: Create a mapping from column index to its new rank-based value.
col_to_new_value = {}
for i, (top_row, col_idx) in enumerate(col_order_info):
# The new value is the rank (index in sorted list) + 1.
col_to_new_value[col_idx] = i + 1
# Step 4: Create the output matrix by applying the transformation.
output_matrix = copy.deepcopy(matrix)
for r in range(height):
for c in range(width):
# If the cell is non-zero, replace its value with the new mapped value.
if output_matrix[r][c] != 0:
output_matrix[r][c] = col_to_new_value[c]
return output_matrix
```
--------------------
2025/08/29 06:28:51 INFO dspy.evaluate.evaluate: Average Metric: 0.0 / 3 (0.0%)
GEPA Optimization: 40%|████████████████████████████████████████████▏ | 1608/4000 [8:54:54<17:03:33, 25.67s/rollouts]Code generation or execution failed: invalid syntax (<string>, line 1)
--- Failing code ---
```python
def transform_matrix(matrix):
"""
Transforms the input matrix by vertically expanding it based on a specific palindromic pattern of its rows.
The transformation rule is as follows:
1. Let N be the number of rows in the input matrix.
2. A base palindromic sequence of row indices `P` is created, going from 0 to N-1 and back down to 0.
(e.g., for N=4, P = [0, 1, 2, 3, 2, 1, 0]).
3. The final sequence of indices `S` is formed by concatenating `P` with `P` excluding its first element.
(e.g., for N=4, S = P + P[1:]).
4. The output matrix is constructed by taking rows from the input matrix corresponding to the indices in S.
"""
num_rows = len(matrix)
if num_rows == 0:
return []
# Create the forward part of the palindrome, e.g., [0, 1, 2] for N=3.
forward_part = list(range(num_rows))
# Create the backward part of the palindrome, e.g., [1, 0] for N=3.
backward_part = list(range(num_rows - 2, -1, -1))
# Combine to form the full palindromic sequence P.
palindromic_indices = forward_part + backward_part
# The final sequence is P concatenated with P starting from its second element.
final_indices = palindromic_indices + palindromic_indices[1:]
# Build the output matrix by selecting rows from the input matrix based on the final index sequence.
output_matrix = [matrix[i] for i in final_indices]
return output_matrix
```
--------------------
Iteration 81: New subsample score is not better, skipping
Iteration 82: Selected program 0 score: 0.67
Average Metric: 1.00 / 3 (33.3%): 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [02:43<00:00, 54.54s/it]2025/08/29 06:31:34 INFO dspy.evaluate.evaluate: Average Metric: 1.0 / 3 (33.3%)
2025/08/29 06:32:22 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[0, 7, 7, 7, 1, 0, 4, 0, 4], [7, 7, 7, 0, 1, 4, 4, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 4], [7, 0, 0, 0, 1, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 0, 0, 0, 1, 6, 6, 6, 0], [0, 0, 8, 8, 1, 0, 0, 0, 0], [8, 0, 8, 0, 1, 6, 0, 0, 6], [0, 0, 0, 8, 1, 0, 0, 0, 0]], 'output': [[6, 7, 7, 7], [7, 7, 7, 8], [8, 0, 8, 4], [7, 0, 0, 8]]}, {'input': [[7, 7, 7, 0, 1, 0, 4, 0, 0], [7, 0, 7, 0, 1, 4, 0, 4, 4], [0, 7, 0, 7, 1, 4, 0, 4, 4], [0, 0, 0, 7, 1, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 0, 8, 0, 1, 6, 0, 0, 6], [0, 0, 0, 0, 1, 6, 0, 0, 0], [0, 0, 0, 0, 1, 6, 6, 0, 6], [8, 8, 8, 0, 1, 6, 0, 6, 6]], 'output': [[7, 7, 7, 6], [7, 0, 7, 4], [4, 7, 4, 7], [8, 8, 8, 7]]}, {'input': [[0, 0, 7, 7, 1, 0, 4, 4, 0], [0, 0, 0, 7, 1, 0, 0, 4, 4], [7, 7, 7, 7, 1, 0, 0, 0, 4], [0, 7, 0, 0, 1, 0, 4, 4, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 0, 8, 8, 1, 0, 6, 6, 6], [0, 0, 0, 0, 1, 0, 0, 6, 0], [0, 0, 0, 8, 1, 6, 0, 6, 0], [8, 0, 0, 0, 1, 6, 6, 0, 0]], 'output': [[0, 4, 7, 7], [0, 0, 4, 7], [7, 7, 7, 7], [8, 7, 4, 0]]}, {'input': [[7, 7, 0, 0, 1, 4, 4, 0, 4], [7, 0, 7, 0, 1, 4, 0, 0, 0], [7, 0, 0, 7, 1, 4, 4, 4, 0], [7, 0, 7, 7, 1, 4, 0, 4, 4], [1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 0, 8, 0, 1, 0, 0, 0, 0], [0, 0, 8, 0, 1, 6, 6, 0, 0], [0, 0, 8, 0, 1, 0, 6, 6, 6], [0, 8, 0, 8, 1, 0, 6, 6, 0]], 'output': [[7, 7, 8, 4], [7, 6, 7, 0], [7, 4, 4, 7], [7, 8, 7, 7]]}, {'input': [[7, 7, 0, 0, 1, 0, 0, 0, 4], [7, 0, 0, 0, 1, 4, 4, 4, 4], [7, 0, 7, 0, 1, 4, 0, 0, 0], [0, 7, 7, 0, 1, 4, 4, 4, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1], [8, 0, 8, 0, 1, 6, 6, 6, 6], [0, 0, 8, 8, 1, 0, 0, 6, 0], [0, 0, 0, 0, 1, 0, 6, 0, 6], [8, 8, 8, 8, 1, 0, 0, 0, 6]], 'output': [[7, 7, 8, 4], [7, 4, 4, 4], [7, 6, 7, 6], [4, 7, 7, 8]]}, {'input': [[7, 0, 0, 7, 1, 4, 4, 4, 0], [0, 7, 7, 7, 1, 4, 4, 0, 4], [7, 7, 7, 0, 1, 4, 4, 0, 4], [7, 7, 7, 0, 1, 0, 4, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1], [8, 8, 0, 8, 1, 6, 6, 6, 6], [0, 8, 8, 8, 1, 0, 0, 0, 6], [0, 8, 0, 8, 1, 0, 0, 6, 0], [8, 8, 0, 8, 1, 0, 6, 0, 0]], 'output': [[7, 4, 4, 7], [4, 7, 7, 7], [7, 7, 7, 4], [7, 7, 7, 8]]}], 'test_inputs': [[[7, 7, 7, 0, 1, 0, 0, 4, 0], [0, 7, 7, 0, 1, 4, 4, 0, 4], [7, 7, 7, 7, 1, 0, 4, 0, 4], [7, 0, 0, 0, 1, 4, 0, 4, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 0, 0, 8, 1, 0, 6, 0, 6], [8, 0, 0, 8, 1, 6, 0, 0, 6], [8, 0, 8, 0, 1, 6, 6, 6, 6], [0, 8, 0, 8, 1, 0, 6, 0, 0]]], 'test_outputs': [[[7, 7, 7, 8], [4, 7, 7, 4], [7, 7, 7, 7], [7, 8, 4, 8]]]}) (input_keys={'test_inputs', 'training_examples'}): 'dict' object has no attribute 'dict'
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 67, in forward
File "<string>", line 67, in <listcomp>
AttributeError: 'dict' object has no attribute 'dict'
2025/08/29 06:32:22 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 8, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 8, 3, 3, 3, 3, 3, 3, 8, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}, {'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0], [0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 3, 0, 0, 0, 8, 0, 0], [0, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0], [0, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0], [0, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0], [0, 0, 0, 0, 3, 0, 0, 0, 8, 0, 0], [0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}, {'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 8, 0, 0, 0, 0, 0, 0, 0, 8, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 8, 3, 3, 3, 3, 3, 3, 3, 8, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 8, 3, 3, 3, 3, 8, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}, {'input': [[0, 0, 0, 0, 0, 0], [0, 0, 8, 0, 0, 0], [0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], [0, 0, 8, 0, 0, 0], [0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0], [0, 0, 8, 0, 0, 0], [0, 0, 3, 0, 0, 0], [0, 0, 3, 0, 0, 0], [0, 0, 3, 0, 0, 0], [0, 0, 3, 0, 0, 0], [0, 0, 3, 0, 0, 0], [0, 0, 8, 0, 0, 0], [0, 0, 0, 0, 0, 0]]}, {'input': [[0, 0, 0], [0, 8, 0], [0, 0, 0]], 'output': [[0, 0, 0], [0, 8, 0], [0, 0, 0]]}, {'input': [[0, 0, 0, 0, 0, 0], [0, 8, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 8, 0], [0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0], [0, 8, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 8, 0], [0, 0, 0, 0, 0, 0]]}, {'input': [[0, 0, 0, 0, 0, 0], [0, 0, 0, 8, 0, 0], [0, 0, 0, 0, 0, 0], [0, 8, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], [0, 0, 0, 8, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0], [0, 0, 0, 8, 0, 0], [0, 0, 0, 3, 0, 0], [0, 8, 0, 3, 0, 0], [0, 0, 0, 3, 0, 0], [0, 0, 0, 3, 0, 0], [0, 0, 0, 8, 0, 0]]}, {'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 8], [0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 3, 0, 0, 8, 3, 3, 3, 8], [0, 8, 0, 3, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}], 'test_inputs': [[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0], [0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]], 'test_outputs': [[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 8, 3, 3, 3, 3, 3, 3, 3, 8, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 8, 3, 3, 3, 3, 3, 8], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 3, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0], [0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]]}) (input_keys={'test_inputs', 'training_examples'}): 'dict' object has no attribute 'dict'
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 67, in forward
File "<string>", line 67, in <listcomp>
AttributeError: 'dict' object has no attribute 'dict'
2025/08/29 06:32:22 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0, 0], [0, 0, 0, 0, 1, 1, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 1, 0], [0, 0, 0, 0, 1, 1, 1, 1, 1, 0], [0, 0, 0, 0, 1, 1, 0, 1, 1, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 1, 1, 0, 0, 0, 0], [0, 0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0, 0], [0, 0, 0, 0, 1, 1, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 1, 0], [0, 2, 2, 0, 1, 1, 1, 1, 1, 0], [0, 2, 2, 2, 1, 1, 0, 1, 1, 0], [0, 2, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 1, 1, 0, 0, 0, 0], [0, 0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}, {'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 1, 1], [0, 0, 0, 0, 0, 1, 0, 0, 1, 1], [0, 0, 0, 0, 0, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 1, 0, 0, 1, 1], [0, 0, 0, 0, 0, 1, 0, 0, 1, 1], [0, 0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 0, 1, 1, 1, 1, 1, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 2, 2, 0, 0, 1, 0, 0, 1, 1], [0, 2, 2, 0, 0, 1, 0, 0, 1, 1], [0, 2, 2, 2, 2, 1, 1, 1, 1, 1], [0, 2, 2, 0, 0, 1, 0, 0, 1, 1], [0, 2, 2, 0, 0, 1, 0, 0, 1, 1], [0, 0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 0, 1, 1, 1, 1, 1, 0, 0]]}, {'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 1, 1, 1, 0, 0, 1], [0, 0, 0, 0, 0, 1, 0, 1, 0, 1], [0, 0, 0, 0, 0, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 1, 0, 1, 0, 1], [0, 0, 0, 0, 1, 1, 1, 0, 0, 1], [0, 0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 1, 1, 1, 1, 1, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 2, 0, 0, 1, 1, 1, 0, 0, 1], [0, 2, 0, 2, 0, 1, 0, 1, 0, 1], [0, 2, 2, 2, 2, 1, 1, 1, 1, 1], [0, 2, 0, 2, 0, 1, 0, 1, 0, 1], [0, 2, 0, 0, 1, 1, 1, 0, 0, 1], [0, 0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 1, 1, 1, 1, 1, 0, 0]]}], 'test_inputs': [[[0, 0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0, 0], [0, 0, 0, 0, 1, 1, 0, 0, 1, 0], [0, 0, 0, 0, 1, 0, 0, 0, 1, 0], [0, 0, 0, 0, 1, 1, 1, 1, 1, 1], [0, 0, 0, 0, 1, 1, 0, 1, 1, 0], [0, 0, 0, 0, 0, 1, 0, 0, 1, 0], [0, 0, 0, 0, 1, 1, 0, 0, 0, 0], [0, 0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0, 0]]], 'test_outputs': [[[0, 0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0, 0], [0, 0, 0, 0, 1, 1, 0, 0, 1, 0], [0, 2, 0, 0, 1, 0, 0, 0, 1, 0], [0, 2, 2, 0, 1, 1, 1, 1, 1, 1], [2, 2, 2, 2, 1, 1, 0, 1, 1, 0], [0, 2, 0, 0, 0, 1, 0, 0, 1, 0], [0, 2, 0, 0, 1, 1, 0, 0, 0, 0], [0, 0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0, 0]]]}) (input_keys={'test_inputs', 'training_examples'}): 'dict' object has no attribute 'dict'
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 67, in forward
File "<string>", line 67, in <listcomp>
AttributeError: 'dict' object has no attribute 'dict'
2025/08/29 06:32:22 INFO dspy.evaluate.evaluate: Average Metric: 0.0 / 3 (0.0%)
GEPA Optimization: 40%|████████████████████████████████████████████▍ | 1614/4000 [8:58:26<17:40:51, 26.68s/rollouts]Iteration 82: Proposed new text for program: import dspy
from typing import List
import pydantic
import json
import traceback
# Define the matrix type for clarity and use in Pydantic models
MATRIX = List[List[int]]
class TrainingExample(pydantic.BaseModel):
"""A Pydantic model for a single input-output training pair."""
input: MATRIX
output: MATRIX
class SolveTaskSignature(dspy.Signature):
"""
You are an expert in solving abstract reasoning challenges.
Given a set of training examples that demonstrate a specific transformation rule on 2D grids of integers, your goal is to deduce this rule and apply it to a new set of test inputs.
The transformations can involve patterns related to:
- Colors and counts of pixels
- Geometric shapes, symmetry, repetition, and scaling
- Positional logic and object relationships
- Algorithmic operations like filling, drawing lines, or moving objects
Your final output must be the transformed test matrices.
"""
training_examples: List[TrainingExample] = dspy.InputField(description="A list of input/output pairs demonstrating the transformation rule.")
test_inputs: List[MATRIX] = dspy.InputField(description="A list of input matrices to be transformed according to the rule.")
test_outputs: List[MATRIX] = dspy.OutputField(description="The corresponding output matrices for the test inputs.")
class GenerateRuleSignature(dspy.Signature):
"""
Analyze the provided input/output grid pairs to deduce the underlying transformation rule.
Describe the rule in a clear, step-by-step, and unambiguous manner.
Focus on what operations are performed to get from an input grid to its corresponding output grid.
Be precise about conditions, colors, and locations. The rule should be general enough to apply to new, unseen grids.
"""
training_examples: str = dspy.InputField(desc="String representation of training examples.")
deduced_rule: str = dspy.OutputField(desc="A step-by-step description of the transformation rule.")
class GeneratePythonCodeSignature(dspy.Signature):
"""
Given a description of a transformation rule for 2D grids, write a single Python function to implement it.
**Requirements:**
1. The function must be named `transform_matrix`.
2. It must accept exactly one argument: `matrix`, which is a 2D list of integers (the input grid).
3. It must return a new 2D list of integers representing the transformed grid.
4. The function should be self-contained. You can use standard Python libraries like `copy` (e.g., `import copy; new_matrix = copy.deepcopy(matrix)` to avoid modifying the input).
5. Do NOT use external libraries like numpy or pandas.
6. The code should ONLY contain the function definition. Do not include any example usage, calls to the function, or markdown fences.
"""
deduced_rule: str = dspy.InputField(desc="The rule to be implemented.")
python_code: str = dspy.OutputField(desc="A Python function `transform_matrix(matrix)` that implements the rule.")
class ARCSolver(dspy.Module):
"""A DSPy module that solves ARC tasks by generating and executing code."""
def __init__(self):
super().__init__()
self.rule_generator = dspy.ChainOfThought(GenerateRuleSignature)
self.code_generator = dspy.ChainOfThought(GeneratePythonCodeSignature)
self.fallback_predictor = dspy.Predict(SolveTaskSignature, max_tokens=4096)
def forward(self, training_examples: List[TrainingExample], test_inputs: List[MATRIX]):
# Convert Pydantic objects to a compact string for the prompt
examples_str = json.dumps([ex.dict() for ex in training_examples])
try:
# Step 1: Deduce the rule from examples
rule_prediction = self.rule_generator(training_examples=examples_str)
deduced_rule = rule_prediction.deduced_rule
# Step 2: Generate Python code from the rule
code_prediction = self.code_generator(deduced_rule=deduced_rule)
python_code = code_prediction.python_code
# Step 3: Execute the generated code
local_scope = {}
exec(python_code, globals(), local_scope)
transform_func = local_scope['transform_matrix']
test_outputs = [transform_func(test_input) for test_input in test_inputs]
return dspy.Prediction(
reasoning=f"Rule: {deduced_rule}\n---\nCode:\n{python_code}",
test_outputs=test_outputs
)
except Exception as e:
print(f"Code generation or execution failed: {e}")
print(f"Traceback: {traceback.format_exc()}")
print("Falling back to direct prediction.")
# Fallback strategy: use a simple predictor
fallback_prediction = self.fallback_predictor(
training_examples=training_examples,
test_inputs=test_inputs
)
reasoning = "Code-based approach failed. "
if hasattr(fallback_prediction, 'reasoning'):
reasoning += f"Fallback reasoning: {fallback_prediction.reasoning}"
else:
reasoning += "No reasoning available from fallback."
return dspy.Prediction(
reasoning=reasoning,
test_outputs=fallback_prediction.test_outputs
)
program = ARCSolver()
Iteration 82: New subsample score is not better, skipping
Iteration 83: Selected program 2 score: 0.605
Average Metric: 2.00 / 3 (66.7%): : 4it [06:37, 99.35s/it] 2025/08/29 06:39:00 INFO dspy.evaluate.evaluate: Average Metric: 2.0 / 3 (66.7%)
Iteration 83: Proposed new text for program: import dspy
from typing import List, Optional
import pydantic
import copy
import traceback
# Define type aliases and pydantic models for clarity and structure.
MATRIX = List[List[int]]
class TrainingExample(pydantic.BaseModel):
input: MATRIX
output: MATRIX
# --- New Signatures for a Multi-Step, Refinable Process ---
class ProposeRule(dspy.Signature):
"""
Analyze a series of input/output matrix pairs from the Abstraction and Reasoning Corpus (ARC).
Based on these examples, deduce the underlying transformation rule and describe it clearly in English.
**Analysis Steps:**
1. **Grid Properties:** Compare the input and output grid dimensions. Do they change?
2. **Color & Pixel Mapping:** Analyze how pixel colors are transformed. Is it a simple replacement? Does it depend on position or neighbors?
3. **Object Analysis:** Identify distinct objects or shapes. How are they created, destroyed, moved, rotated, or modified?
4. **Global Patterns:** Look for global properties like symmetry, repetition, or filling enclosed areas.
5. **Synthesize the Rule:** Combine your observations into a concise, step-by-step transformation rule.
"""
training_examples: List[TrainingExample] = dspy.InputField(desc="A list of input/output pairs demonstrating the transformation rule.")
english_rule: str = dspy.OutputField(desc="A clear, step-by-step English description of the transformation rule.")
class GeneratePythonFunction(dspy.Signature):
"""
You are an expert programmer. Your task is to write a single, self-contained Python function based on an English description of a transformation rule and a series of examples. If you are given previous code that failed, analyze the feedback and fix the bug.
**Function Requirements:**
- The function must be named `transform_matrix`.
- It must accept one argument: `matrix`, which is a list of lists of integers (representing the input grid).
- It must return a list of lists of integers (representing the transformed output grid).
- The function should not use any external libraries except for `copy` if needed (e.g., `import copy; new_matrix = copy.deepcopy(matrix)`).
- Your output must be ONLY the Python code for the function. Do not include any explanations, comments outside the function, or markdown formatting like ```python.
**Successful Strategies to Consider:**
- **Object Permanence:** Most transformations preserve the grid dimensions.
- **Color Transformations:** Look for rules that change colors based on their value or their neighbors.
- **Spatial Reasoning:** Analyze shapes, positions, and relationships. Common patterns include filling enclosed areas, drawing lines, moving objects, or detecting symmetry.
- **Iterative Processes:** Some rules are applied repeatedly until the grid no longer changes. Consider using a `while` loop that continues as long as modifications are being made in a pass.
- **Neighborhoods:** Often, a cell's new value depends on its immediate neighbors (e.g., von Neumann or Moore neighborhoods).
"""
training_examples: List[TrainingExample] = dspy.InputField(desc="A list of input/output pairs demonstrating the transformation rule.")
english_rule: str = dspy.InputField(desc="The English description of the rule to implement.")
previous_code: Optional[str] = dspy.InputField(desc="A previous, incorrect version of the code. Analyze it to avoid repeating mistakes.")
feedback: Optional[str] = dspy.InputField(desc="Feedback explaining why the previous code was wrong. Use this to correct the logic.")
python_function: str = dspy.OutputField(desc="A string containing a single Python function `transform_matrix` that implements the rule.")
class ARCSolver(dspy.Module):
"""A module that solves ARC tasks by proposing a rule, generating code, and refining it based on tests."""
def __init__(self, max_retries=2):
super().__init__()
self.max_retries = max_retries
self.propose_rule = dspy.ChainOfThought(ProposeRule)
self.code_generator = dspy.Predict(GeneratePythonFunction)
def _execute_and_verify(self, code_str: str, examples: List[TrainingExample]) -> (bool, str):
"""
Executes the generated code against training examples to verify its correctness.
Returns a boolean for success and a feedback string.
"""
local_scope = {}
try:
exec(code_str, globals(), local_scope)
transform_func = local_scope.get('transform_matrix')
if not callable(transform_func):
return False, "Failed to define the `transform_matrix` function. Ensure the function is named correctly and has no syntax errors."
for i, example in enumerate(examples):
input_copy = copy.deepcopy(example.input)
try:
predicted_output = transform_func(input_copy)
if predicted_output != example.output:
feedback = f"Verification failed on training example {i}.\n"
feedback += f"Input:\n{example.input}\n"
feedback += f"Expected Output:\n{example.output}\n"
feedback += f"Actual Output:\n{predicted_output}\n"
return False, feedback
except Exception:
error_trace = traceback.format_exc()
feedback = f"An exception occurred while running the function on training example {i}:\n{error_trace}"
return False, feedback
return True, "All training examples passed."
except Exception:
error_trace = traceback.format_exc()
return False, f"A syntax error or other exception occurred during `exec`:\n{error_trace}"
def forward(self, training_examples: List[TrainingExample], test_inputs: List[MATRIX]):
# Step 1: Propose a rule in English.
rule_prediction = self.propose_rule(training_examples=training_examples)
english_rule = rule_prediction.english_rule
# Step 2: Iteratively generate and refine code.
current_code = None
feedback = "No feedback yet. This is the first attempt."
previous_code = None
for attempt in range(self.max_retries):
# Generate code based on the rule and any feedback from previous failures.
code_prediction = self.code_generator(
training_examples=training_examples,
english_rule=english_rule,
previous_code=previous_code,
feedback=feedback
)
current_code = code_prediction.python_function
# Verify the new code against the training examples.
is_correct, feedback_str = self._execute_and_verify(current_code, training_examples)
if is_correct:
break # Code is correct, exit the loop.
# If incorrect, the feedback will be used in the next iteration.
feedback = feedback_str
previous_code = current_code
# If loop finished without success, one last attempt to fix the last known buggy code
if not self._execute_and_verify(current_code, training_examples)[0]:
code_prediction = self.code_generator(
training_examples=training_examples,
english_rule=english_rule,
previous_code=previous_code,
feedback=feedback
)
current_code = code_prediction.python_function
# Step 3: Execute the final code on the test inputs.
local_scope = {}
fallback_outputs = [copy.deepcopy(matrix) for matrix in test_inputs]
try:
exec(current_code, globals(), local_scope)
transform_func = local_scope.get('transform_matrix')
if not callable(transform_func):
return dspy.Prediction(test_outputs=fallback_outputs)
solved_outputs = []
for test_matrix in test_inputs:
try:
input_copy = copy.deepcopy(test_matrix)
result = transform_func(input_copy)
solved_outputs.append(result)
except Exception:
solved_outputs.append(copy.deepcopy(test_matrix))
return dspy.Prediction(test_outputs=solved_outputs)
except Exception:
return dspy.Prediction(test_outputs=fallback_outputs)
# The overall task signature remains the same, defining the program's external interface.
class SolveTaskSignature(dspy.Signature):
"""
Given a set of training examples demonstrating a matrix transformation,
apply the same transformation to a new set of test matrices.
"""
training_examples: List[TrainingExample] = dspy.InputField(description="Input and output examples demonstrating the task to be performed.")
test_inputs: List[MATRIX] = dspy.InputField(description="Input matrices to be solved following the task described in the training examples.")
test_outputs: List[MATRIX] = dspy.OutputField(description="Output matrices corresponding to the test inputs.")
# The final program is an instance of our new, more robust module.
program = ARCSolver()
2025/08/29 06:47:40 INFO dspy.evaluate.evaluate: Average Metric: 2.0 / 3 (66.7%)
GEPA Optimization: 40%|████████████████████████████████████████████▌ | 1620/4000 [9:13:43<28:28:43, 43.08s/rollouts]Iteration 83: New subsample score is not better, skipping
Iteration 84: Selected program 3 score: 0.645
Average Metric: 3.00 / 3 (100.0%): 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [02:50<00:00, 56.92s/it]2025/08/29 06:50:30 INFO dspy.evaluate.evaluate: Average Metric: 3.0 / 3 (100.0%)
GEPA Optimization: 41%|████████████████████████████████████████████▋ | 1623/4000 [9:16:34<29:13:12, 44.25s/rollouts]
Iteration 84: All subsample scores perfect. Skipping.
Iteration 84: Reflective mutation did not propose a new candidate
Iteration 85: Selected program 4 score: 0.61
Average Metric: 1.00 / 1 (100.0%): 33%|█████████████████████████████████████ | 1/3 [00:32<01:04, 32.38s/it]Error applying `transform_grid` to a test input: name 'collections' is not defined
Average Metric: 1.00 / 2 (50.0%): 67%|██████████████████████████████████████████████████████████████████████████ | 2/3 [03:38<02:02, 122.90s/it]Error applying `transform_grid` to a test input: name 'collections' is not defined
Average Metric: 1.00 / 2 (50.0%): 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [04:00<00:00, 76.94s/it]Error applying `transform_grid` to a test input: name 'collections' is not defined
Average Metric: 1.00 / 3 (33.3%): : 4it [04:01, 60.30s/it] 2025/08/29 06:54:32 INFO dspy.evaluate.evaluate: Average Metric: 1.0 / 3 (33.3%)
Iteration 85: Proposed new text for program: import dspy
from typing import List, Optional
import pydantic
import traceback
import copy
# Define the type for a grid/matrix as a list of lists of integers.
MATRIX = List[List[int]]
class TrainingExample(pydantic.BaseModel):
"""A Pydantic model for a single training example, containing an input and output grid."""
input: MATRIX
output: MATRIX
class GenerateTransformationFunction(dspy.Signature):
"""
You are an expert programmer solving visual puzzles from the Abstraction and Reasoning Corpus (ARC).
Your goal is to analyze a set of training examples, each consisting of an input grid and an output grid.
Based on this analysis, you must write a single, self-contained Python function named `transform_grid` that implements the observed transformation.
**Function Requirements:**
- The function must be named exactly `transform_grid`.
- It must accept one argument: `grid`, which is a 2D list of integers (the input grid).
- It must return a new 2D list of integers representing the transformed grid.
- The function should not rely on any external libraries unless they are standard (e.g., `copy`, `collections`). If you need a library like numpy, import it inside the function.
- Do not modify the input grid in place; create a copy if necessary.
**Analysis Strategy:**
1. **Observe Core Patterns:** Look for simple, recurring patterns like geometric transformations (rotation, reflection, scaling), color changes, object manipulation (copying, moving, recoloring), pattern propagation, or flood-fills.
2. **Decompose the Problem:** Break down the transformation into logical steps. For example, "first, find all objects of color blue, then for each object, reflect it horizontally."
3. **Generalize:** The logic must be general enough to work for all training examples and, by extension, the unseen test inputs.
4. **Code Implementation:** Translate your logic into a clear and correct Python function. Ensure your code is robust and handles edge cases observed in the examples.
"""
training_examples: List[TrainingExample] = dspy.InputField(desc="A list of input/output grid pairs demonstrating the transformation rule.")
test_input_grid: MATRIX = dspy.InputField(desc="A single test input grid to which the transformation should be applicable. Use its properties (e.g., dimensions) to ensure your code is general.")
reasoning: str = dspy.OutputField(desc="Step-by-step thinking process to deduce the transformation rule and how to implement it in Python.")
python_code: str = dspy.OutputField(desc="A string containing the complete, self-contained Python function `transform_grid(grid)`.")
class RefineTransformationFunction(dspy.Signature):
"""
You are an expert Python programmer debugging a function for an ARC visual puzzle.
You are given a `transform_grid` function that failed on a specific training example.
Your task is to analyze the error, identify the bug, and provide a corrected version of the function.
**Debugging Strategy:**
1. **Understand the Goal:** Re-read the `training_examples` to understand the required transformation.
2. **Analyze the Failure:** Examine the `failed_example_input`, the `expected_output`, and the `actual_output` from the faulty code. This is your primary clue. What is the discrepancy?
3. **Pinpoint the Bug:** Read the `previous_code` and identify the logical error that led to the failure.
4. **Formulate a Fix:** Determine how to modify the code to correct the bug and handle all examples correctly.
5. **Implement the Fix:** Write the new, complete, self-contained `transform_grid` function. Do not just provide a patch; provide the full function.
"""
training_examples: List[TrainingExample] = dspy.InputField(desc="The full list of original training examples.")
previous_code: str = dspy.InputField(desc="The previous, incorrect Python code for `transform_grid`.")
failed_example_input: MATRIX = dspy.InputField(desc="The specific input grid from the training set where the previous code failed.")
expected_output: MATRIX = dspy.InputField(desc="The correct output for the failed example.")
actual_output: str = dspy.InputField(desc="The incorrect output or error traceback generated by the previous code.")
reasoning: str = dspy.OutputField(desc="A step-by-step analysis of the bug and the plan for the fix.")
python_code: str = dspy.OutputField(desc="The complete, corrected, self-contained Python function `transform_grid(grid)`.")
class ARCSolver(dspy.Module):
"""A DSPy module that solves ARC tasks via a generate-test-refine loop."""
def __init__(self, max_attempts=2):
super().__init__()
self.max_attempts = max_attempts
self.code_generator = dspy.ChainOfThought(GenerateTransformationFunction)
self.code_refiner = dspy.ChainOfThought(RefineTransformationFunction)
def _verify_code(self, python_code, training_examples):
"""Safely executes and verifies the generated code against training examples."""
local_scope = {}
try:
# The LM might wrap the code in markdown, so we extract it.
if "```python" in python_code:
code_to_exec = python_code.split("```python")[1].split("```")[0].strip()
else:
code_to_exec = python_code
exec(code_to_exec, globals(), local_scope)
transform_function = local_scope.get('transform_grid')
if not (transform_function and callable(transform_function)):
return None, "Code executed, but `transform_grid` function not found or not callable.", None
for example in training_examples:
input_copy = copy.deepcopy(example.input)
actual_output = transform_function(input_copy)
if actual_output != example.output:
error_details = {
"failed_example": example,
"actual_output": str(actual_output)
}
return None, "Verification failed: output mismatch.", error_details
return transform_function, "Verification successful.", None
except Exception as e:
error_details = {
"failed_example": None, # The error might not be tied to a specific example
"actual_output": f"Error during code execution: {e}\nTraceback: {traceback.format_exc()}"
}
return None, "Exception during code execution or verification.", error_details
def forward(self, training_examples: List[TrainingExample], test_inputs: List[MATRIX]):
"""
Generates, tests, and refines a transformation function, then applies it.
"""
transform_function = None
last_code = ""
error_details = None
for attempt in range(self.max_attempts):
if attempt == 0:
# First attempt: Generate code from scratch
prediction = self.code_generator(
training_examples=training_examples,
test_input_grid=test_inputs[0]
)
last_code = prediction.python_code
else:
# Subsequent attempts: Refine the previous code
failed_example = error_details['failed_example']
prediction = self.code_refiner(
training_examples=training_examples,
previous_code=last_code,
failed_example_input=failed_example.input if failed_example else [[]],
expected_output=failed_example.output if failed_example else [[]],
actual_output=error_details['actual_output']
)
last_code = prediction.python_code
# Verify the newly generated or refined code
verified_function, status, new_error_details = self._verify_code(last_code, training_examples)
if verified_function:
transform_function = verified_function
break # Success, exit the loop
else:
error_details = new_error_details # Store details for the next refinement attempt
# Apply the final verified function (if any) to all test inputs
generated_outputs = []
if transform_function:
for test_input in test_inputs:
try:
output_grid = transform_function(copy.deepcopy(test_input))
generated_outputs.append(output_grid)
except Exception:
# If the verified function fails on a test input, fallback for that input
generated_outputs.append(copy.deepcopy(test_input))
else:
# Fallback for all inputs if no function was successfully verified
for test_input in test_inputs:
generated_outputs.append(copy.deepcopy(test_input))
return dspy.Prediction(test_outputs=generated_outputs)
# The final program object is an instance of our new, more robust custom module.
program = ARCSolver()
Error applying `transform_grid` to a test input: name 'collections' is not defined
2025/08/29 06:59:47 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 06:59:55 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 07:01:46 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 07:01:55 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 07:04:56 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 07:04:56 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 07:05:52 INFO dspy.evaluate.evaluate: Average Metric: 0.0 / 3 (0.0%)
GEPA Optimization: 41%|████████████████████████████████████████████▊ | 1629/4000 [9:31:56<43:12:12, 65.60s/rollouts]Iteration 85: New subsample score is not better, skipping
Iteration 86: Selected program 4 score: 0.61
Average Metric: 2.00 / 3 (66.7%): 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [01:43<00:00, 34.61s/it]2025/08/29 07:07:36 INFO dspy.evaluate.evaluate: Average Metric: 2.0 / 3 (66.7%)
Iteration 86: Proposed new text for program: import dspy
from typing import List, Optional, Any
import pydantic
import traceback
import copy
# Define the type for a grid/matrix as a list of lists of integers.
MATRIX = List[List[int]]
class TrainingExample(pydantic.BaseModel):
"""A Pydantic model for a single training example, containing an input and output grid."""
input: MATRIX
output: MATRIX
class GenerateTransformationFunction(dspy.Signature):
"""
You are an expert programmer solving visual puzzles from the Abstraction and Reasoning Corpus (ARC).
Your goal is to analyze a set of training examples, each consisting of an input grid and an output grid.
Based on this analysis, you must write a single, self-contained Python function named `transform_grid` that implements the observed transformation.
**Function Requirements:**
- The function must be named exactly `transform_grid`.
- It must accept one argument: `grid`, which is a 2D list of integers (the input grid).
- It must return a new 2D list of integers representing the transformed grid.
- The function should not rely on any external libraries unless they are standard (e.g., `copy`, `collections`). If you need a library like numpy, import it inside the function.
- Do not modify the input grid in place; create a copy if necessary.
**Analysis Strategy & Successful Patterns:**
1. **Observe Core Patterns:** Look for simple, recurring patterns like geometric transformations (rotation, reflection), color changes, or object manipulation.
2. **Decompose the Problem:** Break down the transformation into logical steps. For example, "first, find all objects of color blue, then for each object, reflect it horizontally."
3. **Generalize:** The logic must be general enough to work for all training examples and, by extension, the unseen test inputs.
4. **Consider Structural Clues:**
- **Dividers:** Look for lines (e.g., color 8) that partition the grid into sub-regions. The transformation might apply different rules to different regions.
- **Markers:** A unique, single pixel (e.g., color 5) might be a reference point. The transformation could be relative to this marker's position.
- **Shape Proximity:** The rule might involve finding the shape of a certain color that is closest to a marker or another feature.
5. **Think about Subgrid Extraction:** The output might be a small, fixed-size (e.g., 3x3) subgrid extracted from the input. The challenge is to find the rule that determines the top-left corner of this extraction (e.g., based on a marker or the bounding box of a specific shape).
6. **Code Implementation:** Translate your logic into a clear and correct Python function. Ensure your code is robust and handles edge cases observed in the examples.
"""
training_examples: List[TrainingExample] = dspy.InputField(desc="A list of input/output grid pairs demonstrating the transformation rule.")
test_input_grid: MATRIX = dspy.InputField(desc="A single test input grid to which the transformation should be applicable. Use its properties (e.g., dimensions) to ensure your code is general.")
reasoning: str = dspy.OutputField(desc="Step-by-step thinking process to deduce the transformation rule and how to implement it in Python.")
python_code: str = dspy.OutputField(desc="A string containing the complete, self-contained Python function `transform_grid(grid)`.")
class CorrectCode(dspy.Signature):
"""
You are a programming expert debugging a Python function for an ARC puzzle.
You are given the original problem (training examples), the previous attempt at writing the code, and the error message or feedback from running it.
Your task is to provide a corrected, self-contained Python function `transform_grid`.
Analyze the feedback and the faulty code to identify the bug and fix it.
Provide only the complete, corrected function in a single code block.
"""
training_examples: List[TrainingExample] = dspy.InputField(desc="The original list of input/output grid pairs.")
test_input_grid: MATRIX = dspy.InputField(desc="The representative test input grid.")
faulty_code: str = dspy.InputField(desc="The previous Python code that failed.")
feedback: str = dspy.InputField(desc="The error message or a description of why the code is wrong (e.g., failed a test case).")
corrected_python_code: str = dspy.OutputField(desc="The complete, corrected Python function `transform_grid(grid)`.")
class SelfCorrectingARCSolver(dspy.Module):
"""A DSPy module that solves ARC tasks by generating, validating, and correcting Python code."""
def __init__(self, max_attempts=3):
super().__init__()
self.max_attempts = max_attempts
self.code_generator = dspy.ChainOfThought(GenerateTransformationFunction)
self.code_corrector = dspy.Predict(CorrectCode)
def _execute_and_validate(self, code_str: str, training_examples: List[TrainingExample]) -> tuple[Optional[callable], str]:
"""Tries to execute the generated code and validates it against training examples."""
local_scope = {}
# Sanitize code string
if "```python" in code_str:
code_str = code_str.split("```python")[1].split("```")[0].strip()
try:
exec(code_str, globals(), local_scope)
transform_function = local_scope.get('transform_grid')
if not (transform_function and callable(transform_function)):
return None, "Validation failed: `transform_grid` function not found or not callable."
except Exception as e:
return None, f"Code failed to execute. Error: {traceback.format_exc()}"
# Validate against all training examples
for i, example in enumerate(training_examples):
try:
# Use deepcopy to prevent the function from modifying the original input
output = transform_function(copy.deepcopy(example.input))
if output != example.output:
feedback = (
f"Validation failed on training example {i}.\n"
f"Input:\n{example.input}\n"
f"Expected Output:\n{example.output}\n"
f"Actual Output:\n{output}"
)
return None, feedback
except Exception as e:
return None, f"Function failed during execution on training example {i}. Error: {traceback.format_exc()}"
return transform_function, "Validation successful."
def forward(self, training_examples: List[TrainingExample], test_inputs: List[MATRIX]):
"""Generates, validates, and applies a transformation function."""
# Initial code generation
prediction = self.code_generator(training_examples=training_examples, test_input_grid=test_inputs[0])
python_code = prediction.python_code
transform_function = None
for attempt in range(self.max_attempts):
validated_func, feedback = self._execute_and_validate(python_code, training_examples)
if validated_func:
transform_function = validated_func
break # Success
# If validation fails and we have attempts left, try to correct the code
if attempt < self.max_attempts - 1:
correction = self.code_corrector(
training_examples=training_examples,
test_input_grid=test_inputs[0],
faulty_code=python_code,
feedback=feedback
)
python_code = correction.corrected_python_code
# Apply the validated function to all test inputs
generated_outputs = []
if transform_function:
for test_input in test_inputs:
try:
output_grid = transform_function(copy.deepcopy(test_input))
generated_outputs.append(output_grid)
except Exception:
# Fallback for this specific input if the function fails unexpectedly
generated_outputs.append(copy.deepcopy(test_input))
else:
# Fallback if no valid function could be generated after all attempts
for test_input in test_inputs:
generated_outputs.append(copy.deepcopy(test_input))
return dspy.Prediction(test_outputs=generated_outputs)
# The final program object is an instance of our new, self-correcting module.
program = SelfCorrectingARCSolver()
2025/08/29 07:15:26 INFO dspy.evaluate.evaluate: Average Metric: 0.0 / 3 (0.0%)
GEPA Optimization: 41%|████████████████████████████████████████████▉ | 1635/4000 [9:41:30<47:24:18, 72.16s/rollouts]Iteration 86: New subsample score is not better, skipping
Iteration 87: Selected program 0 score: 0.67
Average Metric: 2.00 / 3 (66.7%): 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [03:26<00:00, 68.67s/it]2025/08/29 07:18:52 INFO dspy.evaluate.evaluate: Average Metric: 2.0 / 3 (66.7%)
2025/08/29 07:19:31 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[0, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0], [2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3], [2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 3], [2, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3], [2, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 3], [2, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3], [2, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 3], [2, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 2, 0, 0, 3], [2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3], [0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0]], 'output': [[0, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0], [2, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 3], [2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3], [2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3], [2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3], [2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3], [2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3], [2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3], [2, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 3], [0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0]]}, {'input': [[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0], [2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4], [2, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 4], [2, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 4], [2, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 4], [2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4], [2, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 4], [2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4], [2, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 4], [2, 0, 0, 0, 0, 1, 0, 0, 7, 0, 0, 4], [2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4], [0, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 0]], 'output': [[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0], [2, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 4], [2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4], [2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4], [2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4], [2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4], [2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4], [2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4], [2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4], [2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4], [2, 0, 0, 0, 0, 0, 0, 0, 7, 7, 0, 4], [0, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 0]]}, {'input': [[0, 6, 6, 6, 6, 6, 6, 6, 6, 6, 0], [4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8], [4, 0, 2, 0, 0, 0, 0, 0, 0, 0, 8], [4, 0, 0, 0, 0, 0, 0, 0, 6, 0, 8], [4, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8], [4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8], [4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8], [4, 0, 0, 4, 0, 0, 0, 0, 0, 0, 8], [4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8], [4, 0, 0, 0, 0, 0, 8, 0, 0, 0, 8], [4, 0, 8, 0, 0, 0, 0, 0, 0, 0, 8], [4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8], [4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8], [0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0]], 'output': [[0, 6, 6, 6, 6, 6, 6, 6, 6, 6, 0], [4, 0, 0, 0, 0, 0, 0, 0, 6, 0, 8], [4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8], [4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8], [4, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8], [4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8], [4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8], [4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 8], [4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8], [4, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8], [4, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8], [4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8], [4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8], [0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0]]}], 'test_inputs': [[[0, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0], [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2], [1, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 1, 0, 0, 0, 2], [1, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 2], [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2], [1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 7, 0, 0, 8, 0, 0, 2], [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2], [1, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2], [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2], [1, 0, 0, 0, 0, 6, 0, 0, 4, 0, 0, 0, 0, 0, 4, 0, 2], [1, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 2], [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2], [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2], [0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0]]], 'test_outputs': [[[0, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0], [1, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 4, 0, 2], [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2], [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2], [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2], [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2], [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2], [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2], [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2], [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2], [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2], [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2], [1, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 2], [0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0]]]}) (input_keys={'test_inputs', 'training_examples'}): 'dict' object has no attribute 'dict'
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 87, in forward
File "<string>", line 87, in <listcomp>
AttributeError: 'dict' object has no attribute 'dict'
2025/08/29 07:19:31 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 2, 1, 2, 2, 1, 1, 2, 1, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0], [0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0], [0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 1, 1, 2, 2, 1, 1, 2, 1, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}, {'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 1, 2, 2, 1, 1, 0, 0, 0, 0], [0, 0, 1, 0, 2, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 2, 0, 1, 0, 0, 0, 2, 0, 0, 0, 0], [0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 1, 0, 2, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 2, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 1, 1, 1, 1, 2, 1, 2, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}, {'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 2, 1, 1, 2, 1, 1, 1, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 1, 1, 2, 1, 2, 1, 1, 2, 2, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 1, 1, 2, 1, 1, 2, 2, 1, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}], 'test_inputs': [[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]], 'test_outputs': [[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 2, 1, 1, 2, 1, 2, 1, 1, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0], [0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 1, 2, 1, 2, 1, 2, 2, 1, 1, 0, 0], [0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 1, 2, 1, 1, 2, 1, 2, 1, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]]}) (input_keys={'test_inputs', 'training_examples'}): 'dict' object has no attribute 'dict'
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 87, in forward
File "<string>", line 87, in <listcomp>
AttributeError: 'dict' object has no attribute 'dict'
2025/08/29 07:19:31 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[0, 6, 6, 6, 0, 0, 0, 0], [0, 6, 0, 6, 0, 0, 0, 0], [0, 0, 0, 0, 0, 6, 6, 6], [0, 0, 0, 0, 0, 6, 0, 6], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 6, 6, 6, 0, 0, 0, 0], [0, 6, 0, 6, 0, 0, 0, 0], [0, 0, 0, 0, 0, 6, 6, 6], [0, 0, 0, 0, 0, 6, 0, 6], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 4, 0, 0, 0, 4, 0]]}, {'input': [[0, 3, 3, 3, 0], [0, 3, 0, 3, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0]], 'output': [[0, 3, 3, 3, 0], [0, 3, 0, 3, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 4, 0, 0]]}, {'input': [[0, 0, 0, 0, 0, 0, 0], [0, 8, 8, 8, 0, 0, 0], [0, 8, 0, 8, 6, 6, 6], [0, 0, 0, 0, 6, 0, 6], [0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0], [0, 8, 8, 8, 0, 0, 0], [0, 8, 0, 8, 6, 6, 6], [0, 0, 0, 0, 6, 0, 6], [0, 0, 4, 0, 0, 4, 0]]}], 'test_inputs': [[[0, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0], [0, 5, 0, 5, 0, 8, 8, 8, 0, 0, 0], [0, 0, 0, 0, 0, 8, 0, 8, 3, 3, 3], [0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 3], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]], 'test_outputs': [[[0, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0], [0, 5, 0, 5, 0, 8, 8, 8, 0, 0, 0], [0, 0, 0, 0, 0, 8, 0, 8, 3, 3, 3], [0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 3], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 4, 0, 0, 0, 4, 0, 0, 4, 0]]]}) (input_keys={'test_inputs', 'training_examples'}): 'dict' object has no attribute 'dict'
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 87, in forward
File "<string>", line 87, in <listcomp>
AttributeError: 'dict' object has no attribute 'dict'
2025/08/29 07:19:31 INFO dspy.evaluate.evaluate: Average Metric: 0.0 / 3 (0.0%)
GEPA Optimization: 41%|█████████████████████████████████████████████▏ | 1641/4000 [9:45:35<42:25:00, 64.73s/rollouts]Iteration 87: Proposed new text for program: import dspy
from typing import List
import pydantic
import json
import re
# Define the type for a matrix for clarity
MATRIX = List[List[int]]
# Pydantic model for a single training example, ensuring structured data
class TrainingExample(pydantic.BaseModel):
input: MATRIX
output: MATRIX
# --- Signature for Deducing the Rule ---
class DeduceRuleSignature(dspy.Signature):
"""
Analyzes pairs of input and output grids to deduce the transformation rule.
Your goal is to find the most general and simplest rule that explains all transformations.
Look for patterns, pixel-wise operations, object movements, shape detection, color changes, or geometric transformations.
Describe the logic clearly, concisely, and step-by-step, so that a programmer could implement it.
Focus on the "what" and "how" of the transformation.
"""
training_examples: str = dspy.InputField(desc="A JSON string of input-output grid pairs.")
rule_description: str = dspy.OutputField(desc="A clear, step-by-step description of the transformation rule.")
# --- Signature for Generating Python Code from a Rule ---
class GenerateCodeSignature(dspy.Signature):
"""
Writes a Python function to implement a described grid transformation rule.
You are an expert Python programmer. Your task is to write a single, self-contained Python function that implements the given rule.
- The function must be named `transform_matrix`.
- It must accept a single argument: `matrix` (a list of lists of integers).
- It must return the transformed matrix (a list of lists of integers).
- Do NOT write any code outside this function definition (e.g., no imports, no example calls).
- You can use standard Python libraries like `copy.deepcopy` if needed, but assume they are already imported.
- The input matrix can be modified in-place or a new one can be created and returned.
"""
rule_description: str = dspy.InputField(desc="The natural language description of the rule to implement.")
training_examples: str = dspy.InputField(desc="A JSON string of examples to provide context on data structures.")
python_code: str = dspy.OutputField(desc="A string containing only the Python function `def transform_matrix(matrix): ...`.")
# --- The Main Custom Module ---
class SolveWithGeneratedCode(dspy.Module):
"""
A module that solves tasks by first generating Python code to represent the task's logic
and then executing that code.
"""
def __init__(self):
super().__init__()
# Module to deduce the rule from examples
self.deduce_rule = dspy.ChainOfThought(DeduceRuleSignature)
# Module to generate code from the rule
self.generate_code = dspy.Predict(GenerateCodeSignature)
# Fallback module in case code generation or execution fails
self.fallback = dspy.ChainOfThought(SolveTaskSignature)
def execute_generated_code(self, code_str: str, test_inputs: List[MATRIX]) -> List[MATRIX]:
"""
Safely executes the generated Python code string.
"""
# Isolate the function definition
code = f"import copy\n{code_str}"
# Prepare a local scope for exec
local_scope = {}
exec(code, globals(), local_scope)
if 'transform_matrix' not in local_scope:
raise ValueError("Generated code did not define 'transform_matrix' function.")
transform_func = local_scope['transform_matrix']
# Apply the function to each test input
results = []
for test_input in test_inputs:
# Use a deepcopy to prevent modification of the original input list
import copy
input_copy = copy.deepcopy(test_input)
results.append(transform_func(input_copy))
return results
def forward(self, training_examples: List[TrainingExample], test_inputs: List[MATRIX]):
# Serialize examples to a string for the prompt
examples_str = json.dumps([ex.dict() for ex in training_examples])
try:
# 1. Deduce the rule
rule_pred = self.deduce_rule(training_examples=examples_str)
# 2. Generate Python code based on the rule
code_pred = self.generate_code(
rule_description=rule_pred.rule_description,
training_examples=examples_str
)
# Clean up the generated code (remove markdown fences)
code_str = re.sub(r"```python\n|```", "", code_pred.python_code).strip()
# 3. Execute the code
test_outputs = self.execute_generated_code(code_str, test_inputs)
# Set trace for transparency
dspy.Suggest(
True,
"Successfully generated and executed Python code to solve the task.",
rule=rule_pred.rule_description,
generated_code=code_str
)
except Exception as e:
# If any step fails, use the fallback mechanism
dspy.Suggest(
False,
f"Code generation/execution failed with error: {e}. Using fallback.",
)
fallback_pred = self.fallback(
training_examples=training_examples,
test_inputs=test_inputs
)
return dspy.Prediction(test_outputs=fallback_pred.test_outputs)
return dspy.Prediction(test_outputs=test_outputs)
# --- The Original Signature (used by the fallback module) ---
class SolveTaskSignature(dspy.Signature):
"""
Given a set of training examples demonstrating a task, solve the same task for new test inputs.
Analyze the training examples to understand the transformation rule, then apply that rule to each test input to produce the corresponding output.
"""
training_examples: List[TrainingExample] = dspy.InputField(description="Input and output examples demonstrating the task to be performed.")
test_inputs: List[MATRIX] = dspy.InputField(description="Input matrices to be solved following the task described in the training examples.")
test_outputs: List[MATRIX] = dspy.OutputField(description="Output matrices corresponding to the test inputs.")
# Final program assignment
program = SolveWithGeneratedCode()
Iteration 87: New subsample score is not better, skipping
Iteration 88: Selected program 1 score: 0.63
Average Metric: 1.00 / 3 (33.3%): 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [02:19<00:00, 46.54s/it]2025/08/29 07:21:51 INFO dspy.evaluate.evaluate: Average Metric: 1.0 / 3 (33.3%)
Iteration 88: Proposed new text for program: import dspy
from typing import List
import pydantic
# Define the type for a matrix for clarity
MATRIX = List[List[int]]
class TrainingExample(pydantic.BaseModel):
"""A Pydantic model for a single training example, containing an input and output matrix."""
input: MATRIX
output: MATRIX
class InferRuleAsCodeSignature(dspy.Signature):
"""
Analyzes pairs of input/output matrices to deduce the transformation rule and express it as a Python function.
You are an expert programmer and analyst of abstract visual reasoning puzzles. Your task is to carefully examine the provided training examples. Each example consists of an 'input' matrix and a corresponding 'output' matrix.
Your goal is to deduce the single, consistent transformation rule that converts each input to its output. Then, you must write a self-contained Python function named `transform` that implements this rule.
**Function Requirements:**
- The function signature must be: `def transform(matrix: list[list[int]]) -> list[list[int]]:`
- The function should not rely on any external libraries unless they are standard (e.g., `copy`). If you use them, include the import inside the function.
- The function must be entirely self-contained within the returned code string.
- The function should handle various matrix sizes and values gracefully.
**Example Output Format:**
```python
def transform(matrix: list[list[int]]) -> list[list[int]]:
# A simple rule: swap values 1 and 2
import copy
new_matrix = copy.deepcopy(matrix)
for r, row in enumerate(new_matrix):
for c, val in enumerate(row):
if val == 1:
new_matrix[r][c] = 2
elif val == 2:
new_matrix[r][c] = 1
return new_matrix
```
Now, analyze the provided examples and generate the `transform` function.
"""
training_examples: List[TrainingExample] = dspy.InputField(description="A list of input and output examples demonstrating the task.")
python_code: str = dspy.OutputField(description="A string containing a self-contained Python function `transform(matrix)` that implements the rule.")
class ARCProgram(dspy.Module):
"""A program that first infers a rule as Python code and then executes it to solve test inputs."""
def __init__(self):
super().__init__()
# Use ChainOfThought for the complex reasoning task of generating code from examples.
self.rule_inferrer = dspy.ChainOfThought(InferRuleAsCodeSignature)
def forward(self, training_examples: List[TrainingExample], test_inputs: List[MATRIX]) -> dspy.Prediction:
"""
Infers a rule as a Python function from training examples and applies it to each test input.
Args:
training_examples: A list of TrainingExample objects.
test_inputs: A list of input matrices to be solved.
Returns:
A dspy.Prediction object with the 'test_outputs' field populated.
"""
# 1. Infer the transformation rule once as a Python function string.
inferred = self.rule_inferrer(training_examples=training_examples)
code = inferred.python_code
all_test_outputs = []
transform_func = None
# 2. Prepare the execution environment and define the function.
try:
# Extract the function code, handling potential markdown fences
if "```python" in code:
code = code.split("```python")[1].split("```")[0].strip()
elif "```" in code:
code = code.split("```")[1].strip()
local_scope = {}
exec(code, globals(), local_scope)
transform_func = local_scope.get('transform')
if not callable(transform_func):
# The generated code did not define the 'transform' function correctly.
transform_func = None
except Exception:
# Catch syntax errors or other issues with the generated code.
transform_func = None
# 3. Iterate through each test input and apply the function.
for test_matrix in test_inputs:
output_matrix = None
if transform_func:
try:
# Execute the generated function on the test matrix.
output_matrix = transform_func(test_matrix)
except Exception:
# The function failed during execution.
output_matrix = None
# Fallback strategy: if anything fails, append a zero-filled matrix.
if output_matrix is None:
if test_matrix and isinstance(test_matrix, list) and len(test_matrix) > 0 and isinstance(test_matrix[0], list):
output_matrix = [[0] * len(test_matrix[0]) for _ in range(len(test_matrix))]
else:
output_matrix = [] # Handle empty or invalid input case.
all_test_outputs.append(output_matrix)
# 4. Return the collected outputs in a single Prediction object.
return dspy.Prediction(test_outputs=all_test_outputs)
# The final 'program' object is an instance of our robust, code-generating module.
program = ARCProgram()
2025/08/29 07:24:28 INFO dspy.evaluate.evaluate: Average Metric: 1.0 / 3 (33.3%)
GEPA Optimization: 41%|█████████████████████████████████████████████▎ | 1647/4000 [9:50:31<39:46:18, 60.85s/rollouts]Iteration 88: New subsample score is not better, skipping
Iteration 89: Selected program 1 score: 0.63
Average Metric: 2.00 / 3 (66.7%): 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [06:38<00:00, 132.94s/it]2025/08/29 07:31:07 INFO dspy.evaluate.evaluate: Average Metric: 2.0 / 3 (66.7%)
Iteration 89: Proposed new text for program: import dspy
from typing import List
import pydantic
import re
import traceback
import copy
# Define the type for a matrix for clarity
MATRIX = List[List[int]]
class TrainingExample(pydantic.BaseModel):
"""A Pydantic model for a single training example, containing an input and output matrix."""
input: MATRIX
output: MATRIX
# --- Signature for Step 1: Inferring the rule in natural language ---
class InferRuleSignature(dspy.Signature):
"""
Analyzes pairs of input/output matrices to deduce the transformation rule.
You are an expert analyst of abstract visual reasoning puzzles. Your task is to carefully examine the provided training examples. Each example consists of an 'input' matrix and a corresponding 'output' matrix.
Your goal is to deduce the single, consistent transformation rule that converts each input to its output. Describe this rule in clear, unambiguous, step-by-step natural language. The description must be precise enough for a programmer to implement it.
Consider various possible transformation patterns, including but not limited to:
- Geometric operations: rotation, reflection, scaling, repetition, shifting.
- Color/value transformations: changing specific numbers to others, conditional changes.
- Object-based logic: identifying shapes or objects and applying rules based on their properties (size, color, position).
- Fill/completion patterns: such as flood fills or completing a grid based on object boundaries.
- Bounding box or frame logic: creating structures based on the extents of objects.
- Cellular automata-like rules based on neighbor counts.
"""
training_examples: List[TrainingExample] = dspy.InputField(description="A list of input and output examples demonstrating the task.")
transformation_rule: str = dspy.OutputField(description="A step-by-step natural language description of the transformation rule.")
# --- Signature for Step 2: Generating Python code from the rule ---
class GeneratePythonCodeSignature(dspy.Signature):
"""
Translates a natural language transformation rule into a Python function.
You are an expert Python programmer specializing in matrix manipulations for abstract reasoning tasks.
Your task is to write a single Python function named `transform_matrix` that implements the given natural language `transformation_rule`.
**Function Requirements:**
- The function signature MUST be: `def transform_matrix(matrix: list[list[int]]) -> list[list[int]]:`
- The function must be self-contained and not rely on any global variables.
- Use standard libraries like `copy` if needed. Avoid libraries like `numpy` or `pandas`.
- The function must take one argument: `matrix`, a list of lists of integers.
- The function must return a new list of lists of integers representing the transformed matrix.
- Do NOT include any code outside the function definition (e.g., example usage, print statements).
- Your output must contain ONLY the Python code for the function, enclosed in a single markdown code block.
**Example Output Format:**
```python
import copy
def transform_matrix(matrix: list[list[int]]) -> list[list[int]]:
# Your implementation here
new_matrix = copy.deepcopy(matrix)
# ... logic to modify new_matrix ...
return new_matrix
```
First, think step-by-step about how to implement the logic described in the rule. Then, write the code.
"""
transformation_rule: str = dspy.InputField(description="The natural language rule to implement.")
training_examples: List[TrainingExample] = dspy.InputField(description="The original examples, for context and to guide implementation.")
python_code: str = dspy.OutputField(description="A Python function `transform_matrix` that implements the rule, enclosed in a markdown block.")
# --- The Improved Custom Module ---
class ARCCodeProgram(dspy.Module):
"""A program that infers a rule, generates Python code to implement it, and then executes that code."""
def __init__(self):
super().__init__()
self.rule_inferrer = dspy.ChainOfThought(InferRuleSignature)
self.code_generator = dspy.Predict(GeneratePythonCodeSignature)
def _extract_python_code(self, text: str) -> str:
"""Extracts Python code from a markdown block."""
match = re.search(r"```python\n(.*?)\n```", text, re.DOTALL)
if match:
return match.group(1)
# Fallback for code that isn't in a markdown block
if "def transform_matrix" in text:
return text
return ""
def _execute_code(self, code: str, test_inputs: List[MATRIX]) -> List[MATRIX]:
"""Executes the generated Python code on all test inputs with robust error handling."""
all_outputs = []
if not code:
# Fallback if no code was generated
return [self._get_fallback_matrix(m) for m in test_inputs]
try:
scope = {}
exec(code, scope)
transform_func = scope.get('transform_matrix')
if not callable(transform_func):
raise ValueError("`transform_matrix` function not found in generated code.")
for test_matrix in test_inputs:
try:
# Pass a deep copy to prevent the function from modifying the original input
matrix_copy = copy.deepcopy(test_matrix)
result_matrix = transform_func(matrix_copy)
all_outputs.append(result_matrix)
except Exception as e:
print(f"Execution of generated code failed for one test case: {e}")
traceback.print_exc()
all_outputs.append(self._get_fallback_matrix(test_matrix))
except Exception as e:
print(f"Code generation or compilation failed: {e}")
traceback.print_exc()
# Fallback if the entire code is invalid
all_outputs = [self._get_fallback_matrix(m) for m in test_inputs]
return all_outputs
def _get_fallback_matrix(self, matrix: MATRIX) -> MATRIX:
"""Creates a fallback matrix of zeros with the same dimensions as the input."""
if matrix and isinstance(matrix, list) and isinstance(matrix[0], list):
return [[0] * len(matrix[0]) for _ in range(len(matrix))]
return []
def forward(self, training_examples: List[TrainingExample], test_inputs: List[MATRIX]) -> dspy.Prediction:
"""
Infers a rule, generates code, and applies it to each test input.
"""
# 1. Infer the transformation rule in natural language.
inferred = self.rule_inferrer(training_examples=training_examples)
rule = inferred.transformation_rule
# 2. Generate Python code to implement the rule.
generated = self.code_generator(transformation_rule=rule, training_examples=training_examples)
code_block = generated.python_code
# 3. Extract the code from the LM's output.
python_code = self._extract_python_code(code_block)
# 4. Execute the generated code on all test inputs.
all_test_outputs = self._execute_code(python_code, test_inputs)
# 5. Return the collected outputs in a Prediction object.
return dspy.Prediction(test_outputs=all_test_outputs)
# The final 'program' object is an instance of our robust, code-generating module.
program = ARCCodeProgram()
2025/08/29 07:40:19 INFO dspy.evaluate.evaluate: Average Metric: 1.0 / 3 (33.3%)
GEPA Optimization: 41%|█████████████████████████████████████████████ | 1653/4000 [10:06:23<56:36:44, 86.84s/rollouts]Iteration 89: New subsample score is not better, skipping
Iteration 90: Selected program 2 score: 0.605
Average Metric: 2.00 / 3 (66.7%): : 4it [03:15, 48.80s/it] 2025/08/29 07:43:35 INFO dspy.evaluate.evaluate: Average Metric: 2.0 / 3 (66.7%)
Iteration 90: Proposed new text for program: import dspy
from typing import List
import pydantic
import copy
# Define type aliases and pydantic models for clarity and structure.
MATRIX = List[List[int]]
class TrainingExample(pydantic.BaseModel):
input: MATRIX
output: MATRIX
class HypothesizeRule(dspy.Signature):
"""
Analyze the provided input/output matrix pairs from the Abstraction and Reasoning Corpus (ARC).
Deduce the underlying transformation rule that converts each input matrix to its corresponding output matrix.
Describe this rule in clear, step-by-step, unambiguous English.
Focus on the logic and the sequence of operations, not on Python code implementation.
"""
training_examples: List[TrainingExample] = dspy.InputField(desc="A list of input/output pairs demonstrating the transformation rule.")
rule_description: str = dspy.OutputField(desc="A step-by-step English description of the transformation rule.")
class ImplementRule(dspy.Signature):
"""
You are an expert programmer. Your task is to write a single, self-contained Python function based on a provided rule description and example pairs.
**Function Requirements:**
- The function must be named `transform_matrix`.
- It must accept one argument: `matrix`, which is a list of lists of integers (representing the input grid).
- It must return a list of lists of integers (representing the transformed output grid).
- The function should not use any external libraries except for `copy` if needed (e.g., `import copy; new_matrix = copy.deepcopy(matrix)`).
- Your output must be ONLY the Python code for the function. Do not include any explanations, comments outside the function, or markdown formatting like ```python.
**Successful Strategies to Consider:**
- **Object Permanence:** Most transformations preserve the grid dimensions.
- **Color Transformations:** Look for rules that change colors based on their value or their neighbors.
- **Spatial Reasoning:** Analyze shapes, positions, and relationships. Common patterns include filling enclosed areas, drawing lines, moving objects, or detecting symmetry.
- **Iterative Processes:** Some rules are applied repeatedly until the grid no longer changes. Consider using a `while` loop that continues as long as modifications are being made in a pass.
- **Neighborhoods:** Often, a cell's new value depends on its immediate neighbors (e.g., von Neumann or Moore neighborhoods).
**Example of a Correctly Formatted Output:**
def transform_matrix(matrix: list[list[int]]) -> list[list[int]]:
# Your implementation here
# For example, find the most frequent color and fill the grid
from collections import Counter
import itertools
if not matrix or not matrix[0]:
return []
counts = Counter(itertools.chain.from_iterable(matrix))
if counts:
# Handle ties by picking the smaller number value
most_common_color = sorted(counts.items(), key=lambda item: (-item[1], item[0]))[0][0]
else:
return []
height = len(matrix)
width = len(matrix[0])
return [[most_common_color for _ in range(width)] for _ in range(height)]
"""
rule_description: str = dspy.InputField(desc="The English description of the rule to implement.")
training_examples: List[TrainingExample] = dspy.InputField(desc="A list of input/output pairs to use as a reference for implementation.")
python_function: str = dspy.OutputField(desc="A string containing a single Python function `transform_matrix` that implements the rule.")
class ARCSolver(dspy.Module):
"""A module that solves ARC tasks by first hypothesizing a rule, then generating and executing Python code."""
def __init__(self):
super().__init__()
# Decompose the task: 1) Hypothesize, 2) Implement.
# ChainOfThought is used for hypothesizing to encourage more detailed reasoning.
self.rule_hypothesizer = dspy.ChainOfThought(HypothesizeRule)
self.code_implementer = dspy.Predict(ImplementRule)
def forward(self, training_examples: List[TrainingExample], test_inputs: List[MATRIX]):
# Step 1: Generate an English description of the transformation rule.
hypothesis = self.rule_hypothesizer(training_examples=training_examples)
# Step 2: Generate Python code based on the rule description and examples.
prediction = self.code_implementer(
rule_description=hypothesis.rule_description,
training_examples=training_examples
)
python_code = prediction.python_function
# Prepare a dictionary to hold the executed function.
local_scope = {}
# Create fallback outputs in case of any failure.
fallback_outputs = [copy.deepcopy(matrix) for matrix in test_inputs]
try:
# Step 3: Execute the generated code string to define the function.
exec(python_code, globals(), local_scope)
transform_func = local_scope.get('transform_matrix')
if not callable(transform_func):
return dspy.Prediction(test_outputs=fallback_outputs)
# Step 4: Apply the generated function to each test input.
solved_outputs = []
for test_matrix in test_inputs:
try:
input_copy = copy.deepcopy(test_matrix)
result = transform_func(input_copy)
solved_outputs.append(result)
except Exception:
solved_outputs.append(copy.deepcopy(test_matrix))
return dspy.Prediction(test_outputs=solved_outputs)
except Exception:
return dspy.Prediction(test_outputs=fallback_outputs)
# The overall task signature, defining the final inputs and outputs of the program.
class SolveTaskSignature(dspy.Signature):
"""
Given a set of training examples demonstrating a matrix transformation,
apply the same transformation to a new set of test matrices.
"""
training_examples: List[TrainingExample] = dspy.InputField(description="Input and output examples demonstrating the task to be performed.")
test_inputs: List[MATRIX] = dspy.InputField(description="Input matrices to be solved following the task described in the training examples.")
test_outputs: List[MATRIX] = dspy.OutputField(description="Output matrices corresponding to the test inputs.")
# The final program is an instance of our new, more robust module.
program = ARCSolver()
2025/08/29 07:46:51 INFO dspy.evaluate.evaluate: Average Metric: 3.0 / 3 (100.0%)
2025/08/29 07:50:39 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 07:51:14 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 07:51:37 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 07:54:31 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 07:54:39 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 07:54:57 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 07:55:15 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 07:57:31 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 07:58:57 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 07:59:32 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 08:01:17 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 08:02:04 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 08:02:04 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 08:04:05 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 08:04:05 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 08:05:19 INFO dspy.evaluate.evaluate: Average Metric: 135.0 / 200 (67.5%)
GEPA Optimization: 46%|███████████████████████████████████████████████████ | 1859/4000 [10:31:22<7:41:56, 12.95s/rollouts]Iteration 90: New program is on the linear pareto front
Iteration 90: Full valset score for new program: 0.675
Iteration 90: Full train_val score for new program: 0.675
Iteration 90: Individual valset scores for new program: [False, True, False, True, True, False, True, True, True, True, True, True, True, False, True, True, False, False, True, True, True, True, True, False, True, True, False, False, True, True, False, False, False, True, True, True, False, True, False, True, False, True, True, False, False, True, False, False, True, True, True, True, True, True, True, False, True, True, False, True, False, False, False, True, True, False, True, True, False, False, True, True, True, True, True, True, True, False, True, False, True, True, True, True, True, True, True, True, True, False, True, True, True, True, True, True, False, False, True, True, True, True, True, False, True, False, True, True, False, True, False, False, False, False, True, False, True, True, True, True, True, False, True, False, False, True, False, True, True, True, False, False, True, True, True, False, True, True, True, True, True, True, True, False, True, False, True, False, True, True, False, False, True, True, True, True, True, True, True, False, False, True, True, False, False, True, True, True, True, False, False, True, True, True, True, True, False, True, False, True, False, True, True, False, False, True, True, True, True, True, True, True, True, True, True, True, True, False, False, True]
Iteration 90: New valset pareto front scores: [True, True, True, True, True, True, True, True, True, True, True, True, True, False, True, True, False, True, True, True, True, True, True, True, True, True, False, True, True, True, True, False, True, True, True, True, False, True, True, True, False, True, True, True, False, True, False, False, True, True, True, True, True, True, True, 0, True, True, False, True, True, True, False, True, True, False, True, True, 0, False, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, False, True, True, True, True, True, True, True, False, True, True, True, True, True, False, True, True, True, True, True, True, False, True, True, True, True, True, True, True, True, True, True, True, True, False, False, True, False, True, True, True, True, True, True, True, True, False, True, True, True, True, True, True, True, False, True, False, True, False, True, True, True, False, True, True, True, True, True, True, True, False, True, True, True, True, False, True, True, True, True, False, True, True, True, True, True, True, False, True, False, True, True, True, True, False, False, True, True, True, True, True, True, True, True, True, True, True, True, True, False, True]
Iteration 90: Full valset pareto front score: 0.825
Iteration 90: Updated valset pareto front programs: [{1, 3, 5}, {0, 1, 2, 3, 4, 5, 6}, {0}, {0, 2, 3, 4, 5, 6}, {0, 1, 2, 3, 4, 5, 6}, {0, 4}, {0, 1, 2, 3, 4, 5, 6}, {0, 1, 3, 4, 5, 6}, {0, 1, 2, 3, 4, 5, 6}, {0, 2, 3, 4, 5, 6}, {0, 1, 2, 3, 4, 5, 6}, {0, 1, 2, 3, 4, 5, 6}, {0, 1, 2, 3, 4, 5, 6}, {0, 1, 2, 3, 4, 5, 6}, {0, 1, 2, 3, 4, 6}, {0, 1, 2, 3, 4, 5, 6}, {0, 1, 2, 3, 4, 5, 6}, {1, 4, 5}, {0, 1, 2, 3, 4, 5, 6}, {0, 2, 4, 6}, {0, 1, 2, 3, 4, 5, 6}, {0, 2, 3, 4, 5, 6}, {0, 1, 2, 3, 4, 5, 6}, {1, 5}, {0, 3, 4, 6}, {0, 1, 3, 6}, {0, 1, 2, 3, 4, 5, 6}, {0, 4}, {0, 1, 2, 3, 4, 5, 6}, {0, 1, 2, 3, 4, 5, 6}, {1}, {0, 1, 2, 3, 4, 5, 6}, {2}, {0, 1, 2, 3, 4, 5, 6}, {0, 1, 2, 3, 5, 6}, {0, 1, 2, 3, 4, 5, 6}, {0, 1, 2, 3, 4, 5, 6}, {0, 1, 2, 3, 4, 5, 6}, {2, 5}, {0, 2, 3, 4, 5, 6}, {0, 1, 2, 3, 4, 5, 6}, {0, 1, 2, 3, 4, 5, 6}, {0, 1, 2, 3, 4, 5, 6}, {1}, {0, 1, 2, 3, 4, 5, 6}, {0, 1, 2, 3, 4, 6}, {0, 1, 2, 3, 4, 5, 6}, {0, 1, 2, 3, 4, 5, 6}, {0, 1, 2, 3, 4, 5, 6}, {0, 1, 2, 3, 4, 6}, {0, 1, 2, 3, 4, 6}, {0, 1, 2, 3, 4, 5, 6}, {0, 1, 3, 4, 5, 6}, {0, 1, 2, 3, 4, 5, 6}, {0, 1, 2, 3, 4, 5, 6}, {0, 1, 2, 3, 4, 5, 6}, {0, 1, 3, 4, 5, 6}, {0, 1, 2, 3, 4, 5, 6}, {0, 1, 2, 3, 4, 5, 6}, {0, 1, 2, 3, 4, 5, 6}, {0, 1, 2, 3}, {0, 1, 5}, {0, 1, 2, 3, 4, 5, 6}, {0, 1, 2, 3, 4, 5, 6}, {4, 6}, {0, 1, 2, 3, 4, 5, 6}, {1, 2, 3, 5, 6}, {0, 1, 3, 4, 5, 6}, {0, 1, 2, 3, 4, 5, 6}, {0, 1, 2, 3, 4, 5, 6}, {0, 1, 2, 3, 4, 5, 6}, {0, 1, 2, 3, 4, 5, 6}, {0, 1, 2, 3, 4, 5, 6}, {2, 3, 4, 5, 6}, {0, 1, 2, 3, 4, 5, 6}, {1, 2, 3, 5, 6}, {0, 3, 4, 5, 6}, {4}, {0, 1, 2, 3, 4, 5, 6}, {0, 2, 3, 4, 5}, {0, 2, 6}, {1, 2, 3, 4, 5, 6}, {0, 1, 2, 3, 4, 5, 6}, {0, 1, 2, 3, 4, 5, 6}, {0, 1, 2, 3, 4, 5, 6}, {0, 1, 2, 3, 4, 5, 6}, {0, 1, 2, 4, 5, 6}, {0, 1, 2, 3, 4, 5, 6}, {0, 3, 4, 6}, {0, 1, 2, 3, 4, 5, 6}, {0, 1, 2, 4, 5, 6}, {0, 1, 2, 3, 4, 5, 6}, {0, 1, 2, 3, 4, 5, 6}, {0, 1, 2, 3, 4, 5, 6}, {0, 1, 2, 3, 4, 5, 6}, {0, 1, 2, 3, 4, 5, 6}, {1, 3}, {0, 1, 2, 3, 4, 5, 6}, {0, 3, 4, 6}, {0, 1, 2, 3, 4, 5, 6}, {0, 1, 2, 3, 4, 5, 6}, {0, 1, 2, 3, 4, 5, 6}, {0, 4, 6}, {0, 1, 2, 3, 4, 5, 6}, {0, 1, 2, 3, 4, 5, 6}, {0, 1, 2, 3, 5}, {1, 2, 3, 4, 5, 6}, {0, 1, 2, 3, 5, 6}, {1, 2}, {0, 1, 4, 5, 6}, {0, 1, 2, 3, 4, 5, 6}, {0, 2, 5}, {1, 3}, {2, 4}, {0, 1, 2, 3, 4, 5, 6}, {3, 4, 5}, {0, 1, 2, 3, 4, 5, 6}, {2, 3, 4, 6}, {0, 1, 2, 3, 4, 5, 6}, {0, 1, 2, 3, 5, 6}, {2, 3, 4, 5, 6}, {0, 1}, {0, 1, 2, 3, 4, 5, 6}, {0, 1, 2, 3, 4, 5, 6}, {0, 1, 2, 3, 4, 5, 6}, {0, 1, 2, 3, 4, 5, 6}, {0, 1, 2, 3, 4, 5, 6}, {0, 1, 2, 3, 4, 5, 6}, {0, 1, 2, 3, 5, 6}, {0, 1, 2, 3, 5, 6}, {2}, {0, 1, 3}, {0, 1, 2, 3, 4, 5, 6}, {0, 1, 3, 5, 6}, {0, 1, 2, 3, 4, 6}, {0, 1, 2, 3, 4, 5, 6}, {0, 1, 3, 6}, {3, 5, 6}, {1, 2, 3, 4, 5, 6}, {1, 2, 3, 4, 5, 6}, {0, 1, 2, 3, 4, 5, 6}, {0, 1, 2, 3, 4, 6}, {0, 1, 2, 3, 4, 5, 6}, {0, 1, 2, 3, 4, 5, 6}, {0, 1, 2, 3, 4, 5, 6}, {0, 1, 2, 3, 4, 5, 6}, {0, 2, 3, 5, 6}, {0, 1, 2, 3, 4, 5, 6}, {0, 1, 2, 3, 4, 5, 6}, {0, 1, 2, 3, 4, 5, 6}, {4}, {0, 1, 2, 3, 4, 5, 6}, {0, 1, 2, 3, 4, 5, 6}, {0, 1, 3, 4, 5, 6}, {0, 1, 3, 4, 5, 6}, {0, 1, 2, 3, 5, 6}, {0, 3, 5, 6}, {0, 1, 2, 3, 4, 5, 6}, {0, 1, 2, 3, 6}, {0, 1, 2, 3, 4, 5, 6}, {1}, {0, 1, 2, 3, 4, 5, 6}, {0, 4, 5, 6}, {0, 3, 5}, {0, 1, 2, 3, 4, 5, 6}, {0, 1, 2, 3, 4, 5, 6}, {0, 1, 2, 3, 4, 5, 6}, {0, 1, 2, 4, 5, 6}, {0, 1, 2, 4, 5, 6}, {0, 1, 2, 3, 4, 5, 6}, {0}, {0, 1, 2, 3, 4, 5, 6}, {6}, {0, 1, 3, 5, 6}, {0, 1, 2, 3, 4, 5, 6}, {1, 2, 3, 5, 6}, {0, 1, 2, 3, 4, 5, 6}, {0, 1, 2, 3, 4, 5, 6}, {0, 1, 2, 3, 4, 5, 6}, {0, 1, 3, 4, 5, 6}, {0, 2}, {0, 1, 2, 3, 4, 5, 6}, {0, 1, 2, 3, 4, 5, 6}, {0, 1, 2, 3, 4, 5, 6}, {0, 1, 2, 3, 4, 5, 6}, {0, 1, 2, 3, 4, 5, 6}, {0, 1, 2, 3, 4, 5, 6}, {0, 1, 2, 3, 4, 5, 6}, {0, 1, 2, 4, 5, 6}, {0, 5, 6}, {0, 1, 2, 3, 4, 5, 6}, {0, 1, 2, 3, 4, 5, 6}, {0, 1, 2, 3, 4, 5, 6}, {1, 2, 4, 5, 6}, {0, 1, 2, 3, 4, 5, 6}, {6}, {0, 1, 2, 3, 4, 5, 6}, {0}, {0, 1, 2, 3, 4, 5, 6}, {0, 2, 3, 4, 5, 6}]
Iteration 90: Best valset aggregate score so far: 0.675
Iteration 90: Best program as per aggregate score on train_val: 6
Iteration 90: Best program as per aggregate score on valset: 6
Iteration 90: Best score on valset: 0.675
Iteration 90: Best score on train_val: 0.675
Iteration 90: Linear pareto front program index: 6
Iteration 90: New program candidate index: 6
Iteration 91: Selected program 1 score: 0.63
Average Metric: 2.00 / 3 (66.7%): 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [02:09<00:00, 43.00s/it]2025/08/29 08:07:28 INFO dspy.evaluate.evaluate: Average Metric: 2.0 / 3 (66.7%)
Iteration 91: Proposed new text for program: import dspy
from typing import List
import pydantic
import traceback
# Define the type for a matrix for clarity
MATRIX = List[List[int]]
class TrainingExample(pydantic.BaseModel):
"""A Pydantic model for a single training example, containing an input and output matrix."""
input: MATRIX
output: MATRIX
class InferRuleSignature(dspy.Signature):
"""
Analyzes pairs of input/output matrices to deduce the transformation rule.
You are an expert analyst of abstract visual reasoning puzzles. Your task is to carefully examine the provided training examples. Each example consists of an 'input' matrix and a corresponding 'output' matrix.
Your goal is to deduce the single, consistent transformation rule that converts each input to its output. Describe this rule in clear, unambiguous, step-by-step natural language. The description must be precise enough for a programmer to translate it directly into Python code to solve a new, unseen test input.
Consider various possible transformation patterns, including but not limited to:
- Geometric operations: rotation, reflection, scaling, repetition, shifting.
- Color/value transformations: changing specific numbers to others, conditional changes.
- Object-based logic: identifying shapes or objects and applying rules based on their properties (size, color, position).
- Tiling or pattern repetition: identifying a core pattern and using it to construct the output.
- Algorithmic paths: such as a "bouncing particle" or pathfinding logic.
"""
training_examples: List[TrainingExample] = dspy.InputField(description="A list of input and output examples demonstrating the task.")
transformation_rule: str = dspy.OutputField(description="A step-by-step natural language description of the transformation rule.")
class GenerateCodeSignature(dspy.Signature):
"""
Translates a natural language transformation rule into a Python function.
You are an expert Python programmer. Your task is to write a single, self-contained Python function named `transform_matrix` that implements the given transformation rule.
- The function must accept one argument: `matrix`, which is a list of lists of integers.
- It must return the transformed matrix, also as a list of lists of integers.
- You can use standard Python libraries like `copy` or `math` if needed.
- The `test_input` is provided as context for dimensions and values, but your function should be general enough to work for any valid input.
**Crucially, your output must be ONLY the Python code for the function. Do not include any explanations, example usage, or markdown code fences like ```python.**
"""
transformation_rule: str = dspy.InputField(description="The natural language rule to implement.")
test_input: MATRIX = dspy.InputField(description="An example input matrix for context (e.g., for dimensions).")
python_code: str = dspy.OutputField(description="The complete Python code for the `transform_matrix` function.")
class ARCProgram(dspy.Module):
"""A program that infers a rule, generates code to implement it, and then executes the code."""
def __init__(self):
super().__init__()
self.rule_inferrer = dspy.ChainOfThought(InferRuleSignature)
self.code_generator = dspy.Predict(GenerateCodeSignature)
def execute_generated_code(self, code_str: str, test_matrix: MATRIX) -> MATRIX:
"""Safely executes the generated Python code."""
# Create a deep copy to avoid modifying the original input matrix
import copy
matrix_copy = copy.deepcopy(test_matrix)
# Prepare a local scope for exec
local_scope = {}
# Execute the code string to define the function in the local scope
exec(code_str, globals(), local_scope)
# Retrieve the function from the scope
transform_func = local_scope.get('transform_matrix')
if not callable(transform_func):
raise ValueError("Generated code did not define a callable function named 'transform_matrix'.")
# Call the generated function and return its result
return transform_func(matrix_copy)
def forward(self, training_examples: List[TrainingExample], test_inputs: List[MATRIX]) -> dspy.Prediction:
"""
Infers a rule, generates code, and applies it to each test input.
Args:
training_examples: A list of TrainingExample objects.
test_inputs: A list of input matrices to be solved.
Returns:
A dspy.Prediction object with the 'test_outputs' field populated.
"""
# 1. Infer the transformation rule once based on all training examples.
inferred = self.rule_inferrer(training_examples=training_examples)
rule = inferred.transformation_rule
all_test_outputs = []
# 2. Iterate through each test input.
for test_matrix in test_inputs:
try:
# 2a. Generate Python code for the rule, using the current test matrix for context.
generated = self.code_generator(transformation_rule=rule, test_input=test_matrix)
code = generated.python_code
# 2b. Execute the generated code.
output_matrix = self.execute_generated_code(code, test_matrix)
all_test_outputs.append(output_matrix)
except Exception as e:
# Fallback strategy: if code generation or execution fails, append a zero-filled matrix
# of the same dimensions to maintain the correct number of outputs.
# print(f"An error occurred: {e}")
# traceback.print_exc()
if test_matrix and len(test_matrix) > 0 and len(test_matrix[0]) > 0:
all_test_outputs.append([([0] * len(test_matrix[0])) for _ in range(len(test_matrix))])
else:
all_test_outputs.append([])
# 3. Return the collected outputs in a single Prediction object.
return dspy.Prediction(test_outputs=all_test_outputs)
# The final 'program' object is an instance of our robust, multi-step module.
program = ARCProgram()
2025/08/29 08:11:37 INFO dspy.evaluate.evaluate: Average Metric: 2.0 / 3 (66.7%)
GEPA Optimization: 47%|███████████████████████████████████████████████████▎ | 1865/4000 [10:37:41<8:46:58, 14.81s/rollouts]Iteration 91: New subsample score is not better, skipping
Iteration 92: Selected program 1 score: 0.63
Average Metric: 2.00 / 3 (66.7%): 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [02:45<00:00, 55.14s/it]2025/08/29 08:14:22 INFO dspy.evaluate.evaluate: Average Metric: 2.0 / 3 (66.7%)
Iteration 92: Proposed new text for program: import dspy
from typing import List, Any
import pydantic
import traceback
# Define the type for a matrix for clarity
MATRIX = List[List[int]]
class TrainingExample(pydantic.BaseModel):
"""A Pydantic model for a single training example, containing an input and output matrix."""
input: MATRIX
output: MATRIX
class InferRuleSignature(dspy.Signature):
"""
Analyzes pairs of input/output matrices to deduce the transformation rule.
You are an expert analyst of abstract visual reasoning puzzles. Your task is to carefully examine the provided training examples. Each example consists of an 'input' matrix and a corresponding 'output' matrix.
Your goal is to deduce the single, consistent transformation rule that converts each input to its output. Describe this rule in clear, unambiguous, step-by-step natural language. The description must be precise enough for another AI or a programmer to follow it to solve a new, unseen test input.
Consider various possible transformation patterns, including but not limited to:
- Geometric operations: rotation, reflection, scaling, repetition, shifting.
- Color/value transformations: changing specific numbers to others, conditional changes.
- Object-based logic: identifying shapes or objects and applying rules based on their properties (size, color, position).
- Cropping or subgrid extraction: such as finding a bounding box or selecting a specific region.
- Fill/completion patterns: such as flood fills or completing a grid based on object boundaries.
- Bounding box or frame logic: creating structures based on the extents of objects.
"""
training_examples: List[TrainingExample] = dspy.InputField(description="A list of input and output examples demonstrating the task.")
transformation_rule: str = dspy.OutputField(description="A step-by-step natural language description of the transformation rule.")
class GeneratePythonCodeSignature(dspy.Signature):
"""
Converts a natural language transformation rule into a Python function.
You are an expert Python programmer. Your task is to write a single Python function named `transform_matrix` that implements the given natural language `transformation_rule`.
**Function Requirements:**
- The function must be named exactly `transform_matrix`.
- It must accept one argument: `matrix: List[List[int]]`.
- It must return the transformed matrix as a `List[List[int]]`.
- You can use standard Python libraries. Do not use external libraries like numpy or pandas.
- The `training_examples` are provided for context, allowing you to mentally check if your function logic would produce the correct output for the given inputs.
**Output Format:**
- Your output must be ONLY the Python code for the function.
- Do NOT include any explanations, comments outside the function, or markdown formatting like ```python ... ```.
"""
transformation_rule: str = dspy.InputField(description="The natural language rule to implement.")
training_examples: List[TrainingExample] = dspy.InputField(description="Example input/output pairs to validate the logic.")
python_code: str = dspy.OutputField(description="A string containing only the Python code for the `transform_matrix` function.")
class ApplyRuleSignature(dspy.Signature):
"""
Applies a given transformation rule to a single test input matrix.
You are an expert in meticulously following instructions to transform matrices. You will be given a specific, detailed transformation rule and a single test input matrix.
Your task is to:
1. Carefully read and understand the provided rule.
2. Apply the rule step-by-step to the test input matrix.
3. Produce the final output matrix.
**Crucially, your output must be ONLY the resulting matrix, formatted as a valid JSON list of lists of integers. Do not include any extra text, explanations, or markdown formatting.**
"""
transformation_rule: str = dspy.InputField(description="The natural language rule to apply.")
test_input: MATRIX = dspy.InputField(description="The input matrix to be transformed.")
test_output: MATRIX = dspy.OutputField(description="The resulting matrix after applying the rule, as a list of lists of integers.")
class ARCProgram(dspy.Module):
"""A program that infers a rule, generates code to apply it, and falls back to direct application if needed."""
def __init__(self):
super().__init__()
self.rule_inferrer = dspy.ChainOfThought(InferRuleSignature)
self.code_generator = dspy.Predict(GeneratePythonCodeSignature)
self.rule_applier_fallback = dspy.Predict(ApplyRuleSignature)
def forward(self, training_examples: List[TrainingExample], test_inputs: List[MATRIX]) -> dspy.Prediction:
"""
Infers a rule, generates and executes code, and applies it to each test input.
Args:
training_examples: A list of TrainingExample objects.
test_inputs: A list of input matrices to be solved.
Returns:
A dspy.Prediction object with the 'test_outputs' field populated.
"""
# 1. Infer the transformation rule once.
inferred = self.rule_inferrer(training_examples=training_examples)
rule = inferred.transformation_rule
all_test_outputs = []
# 2. Attempt to generate and execute Python code for the rule.
try:
generated = self.code_generator(transformation_rule=rule, training_examples=training_examples)
python_code = generated.python_code
# Prepare a namespace for safe execution of the generated code.
local_namespace = {}
exec(python_code, globals(), local_namespace)
transform_func = local_namespace['transform_matrix']
# Apply the generated function to each test input.
for test_matrix in test_inputs:
# The generated function might have its own errors.
try:
output = transform_func(test_matrix)
all_test_outputs.append(output)
except Exception:
# If a single execution fails, we must re-raise to trigger the main fallback.
raise
# If we successfully process all test cases with code, we are done.
return dspy.Prediction(test_outputs=all_test_outputs)
except Exception:
# 3. Fallback Strategy: If code generation or execution fails, revert to direct LM application.
all_test_outputs = []
for test_matrix in test_inputs:
try:
result = self.rule_applier_fallback(transformation_rule=rule, test_input=test_matrix)
all_test_outputs.append(result.test_output)
except Exception:
# If the fallback also fails, append a default empty/zero matrix.
if test_matrix and test_matrix[0]:
all_test_outputs.append([([0] * len(test_matrix[0])) for _ in range(len(test_matrix))])
else:
all_test_outputs.append([])
return dspy.Prediction(test_outputs=all_test_outputs)
# The final 'program' object is an instance of our robust, multi-step module.
program = ARCProgram()
2025/08/29 08:21:57 INFO dspy.evaluate.evaluate: Average Metric: 3.0 / 3 (100.0%)
2025/08/29 08:26:05 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 08:27:49 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 10:15:58 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 10:17:14 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 10:17:35 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 10:17:35 WARNING dspy.adapters.json_adapter: Failed to use structured output format, falling back to JSON mode.
2025/08/29 10:24:09 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 10:24:24 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 10:24:24 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 10:26:24 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 10:27:50 INFO dspy.evaluate.evaluate: Average Metric: 135.0 / 200 (67.5%)
GEPA Optimization: 52%|████████████████████████████████████████████████████████▍ | 2071/4000 [12:53:53<16:32:32, 30.87s/rollouts]Iteration 92: Full valset score for new program: 0.675
Iteration 92: Full train_val score for new program: 0.675
Iteration 92: Individual valset scores for new program: [False, True, False, True, True, False, True, True, True, True, True, True, True, False, False, True, False, True, True, True, True, True, True, True, False, True, False, True, True, True, False, False, False, True, True, True, False, True, False, True, False, True, True, False, False, False, False, False, True, True, True, True, True, True, True, False, True, True, False, True, False, False, False, True, True, False, True, True, False, False, True, True, True, True, True, True, True, False, True, True, False, True, True, True, True, True, True, True, True, False, True, True, True, True, True, True, False, True, True, True, True, False, True, False, True, True, True, True, False, False, False, False, False, False, True, True, True, True, False, True, True, True, True, False, False, True, False, True, True, True, True, False, True, True, True, True, True, True, True, True, True, True, True, False, True, False, True, False, True, True, False, True, True, True, True, True, False, True, True, False, False, False, True, False, False, True, True, True, True, False, True, True, False, True, False, False, False, True, False, True, True, True, True, False, False, True, True, True, True, True, True, True, True, True, True, False, True, False, False, True]
Iteration 92: New valset pareto front scores: [True, True, True, True, True, True, True, True, True, True, True, True, True, False, True, True, False, True, True, True, True, True, True, True, True, True, False, True, True, True, True, False, True, True, True, True, False, True, True, True, False, True, True, True, False, True, False, False, True, True, True, True, True, True, True, 0, True, True, False, True, True, True, False, True, True, False, True, True, 0, False, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, False, True, True, True, True, True, True, True, True, True, True, True, True, True, False, True, True, True, True, True, True, False, True, True, True, True, True, True, True, True, True, True, True, True, False, False, True, False, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, False, True, False, True, False, True, True, True, True, True, True, True, True, True, True, True, False, True, True, True, True, False, True, True, True, True, False, True, True, True, True, True, True, False, True, False, True, True, True, True, False, False, True, True, True, True, True, True, True, True, True, True, True, True, True, False, True]
Iteration 92: Full valset pareto front score: 0.84
Iteration 92: Updated valset pareto front programs: [{1, 3, 5}, {0, 1, 2, 3, 4, 5, 6, 7}, {0}, {0, 2, 3, 4, 5, 6, 7}, {0, 1, 2, 3, 4, 5, 6, 7}, {0, 4}, {0, 1, 2, 3, 4, 5, 6, 7}, {0, 1, 3, 4, 5, 6, 7}, {0, 1, 2, 3, 4, 5, 6, 7}, {0, 2, 3, 4, 5, 6, 7}, {0, 1, 2, 3, 4, 5, 6, 7}, {0, 1, 2, 3, 4, 5, 6, 7}, {0, 1, 2, 3, 4, 5, 6, 7}, {0, 1, 2, 3, 4, 5, 6, 7}, {0, 1, 2, 3, 4, 6}, {0, 1, 2, 3, 4, 5, 6, 7}, {0, 1, 2, 3, 4, 5, 6, 7}, {1, 4, 5, 7}, {0, 1, 2, 3, 4, 5, 6, 7}, {0, 2, 4, 6, 7}, {0, 1, 2, 3, 4, 5, 6, 7}, {0, 2, 3, 4, 5, 6, 7}, {0, 1, 2, 3, 4, 5, 6, 7}, {1, 5, 7}, {0, 3, 4, 6}, {0, 1, 3, 6, 7}, {0, 1, 2, 3, 4, 5, 6, 7}, {0, 4, 7}, {0, 1, 2, 3, 4, 5, 6, 7}, {0, 1, 2, 3, 4, 5, 6, 7}, {1}, {0, 1, 2, 3, 4, 5, 6, 7}, {2}, {0, 1, 2, 3, 4, 5, 6, 7}, {0, 1, 2, 3, 5, 6, 7}, {0, 1, 2, 3, 4, 5, 6, 7}, {0, 1, 2, 3, 4, 5, 6, 7}, {0, 1, 2, 3, 4, 5, 6, 7}, {2, 5}, {0, 2, 3, 4, 5, 6, 7}, {0, 1, 2, 3, 4, 5, 6, 7}, {0, 1, 2, 3, 4, 5, 6, 7}, {0, 1, 2, 3, 4, 5, 6, 7}, {1}, {0, 1, 2, 3, 4, 5, 6, 7}, {0, 1, 2, 3, 4, 6}, {0, 1, 2, 3, 4, 5, 6, 7}, {0, 1, 2, 3, 4, 5, 6, 7}, {0, 1, 2, 3, 4, 5, 6, 7}, {0, 1, 2, 3, 4, 6, 7}, {0, 1, 2, 3, 4, 6, 7}, {0, 1, 2, 3, 4, 5, 6, 7}, {0, 1, 3, 4, 5, 6, 7}, {0, 1, 2, 3, 4, 5, 6, 7}, {0, 1, 2, 3, 4, 5, 6, 7}, {0, 1, 2, 3, 4, 5, 6, 7}, {0, 1, 3, 4, 5, 6, 7}, {0, 1, 2, 3, 4, 5, 6, 7}, {0, 1, 2, 3, 4, 5, 6, 7}, {0, 1, 2, 3, 4, 5, 6, 7}, {0, 1, 2, 3}, {0, 1, 5}, {0, 1, 2, 3, 4, 5, 6, 7}, {0, 1, 2, 3, 4, 5, 6, 7}, {4, 6, 7}, {0, 1, 2, 3, 4, 5, 6, 7}, {1, 2, 3, 5, 6, 7}, {0, 1, 3, 4, 5, 6, 7}, {0, 1, 2, 3, 4, 5, 6, 7}, {0, 1, 2, 3, 4, 5, 6, 7}, {0, 1, 2, 3, 4, 5, 6, 7}, {0, 1, 2, 3, 4, 5, 6, 7}, {0, 1, 2, 3, 4, 5, 6, 7}, {2, 3, 4, 5, 6, 7}, {0, 1, 2, 3, 4, 5, 6, 7}, {1, 2, 3, 5, 6, 7}, {0, 3, 4, 5, 6, 7}, {4}, {0, 1, 2, 3, 4, 5, 6, 7}, {0, 2, 3, 4, 5, 7}, {0, 2, 6}, {1, 2, 3, 4, 5, 6, 7}, {0, 1, 2, 3, 4, 5, 6, 7}, {0, 1, 2, 3, 4, 5, 6, 7}, {0, 1, 2, 3, 4, 5, 6, 7}, {0, 1, 2, 3, 4, 5, 6, 7}, {0, 1, 2, 4, 5, 6, 7}, {0, 1, 2, 3, 4, 5, 6, 7}, {0, 3, 4, 6, 7}, {0, 1, 2, 3, 4, 5, 6, 7}, {0, 1, 2, 4, 5, 6, 7}, {0, 1, 2, 3, 4, 5, 6, 7}, {0, 1, 2, 3, 4, 5, 6, 7}, {0, 1, 2, 3, 4, 5, 6, 7}, {0, 1, 2, 3, 4, 5, 6, 7}, {0, 1, 2, 3, 4, 5, 6, 7}, {1, 3}, {7}, {0, 3, 4, 6, 7}, {0, 1, 2, 3, 4, 5, 6, 7}, {0, 1, 2, 3, 4, 5, 6, 7}, {0, 1, 2, 3, 4, 5, 6}, {0, 4, 6, 7}, {0, 1, 2, 3, 4, 5, 6, 7}, {0, 1, 2, 3, 4, 5, 6, 7}, {0, 1, 2, 3, 5, 7}, {1, 2, 3, 4, 5, 6, 7}, {0, 1, 2, 3, 5, 6, 7}, {1, 2}, {0, 1, 4, 5, 6}, {0, 1, 2, 3, 4, 5, 6, 7}, {0, 2, 5}, {1, 3}, {2, 4}, {0, 1, 2, 3, 4, 5, 6, 7}, {3, 4, 5, 7}, {0, 1, 2, 3, 4, 5, 6, 7}, {2, 3, 4, 6, 7}, {0, 1, 2, 3, 4, 5, 6}, {0, 1, 2, 3, 5, 6, 7}, {2, 3, 4, 5, 6, 7}, {0, 1, 7}, {0, 1, 2, 3, 4, 5, 6, 7}, {0, 1, 2, 3, 4, 5, 6, 7}, {0, 1, 2, 3, 4, 5, 6, 7}, {0, 1, 2, 3, 4, 5, 6, 7}, {0, 1, 2, 3, 4, 5, 6, 7}, {0, 1, 2, 3, 4, 5, 6, 7}, {0, 1, 2, 3, 5, 6, 7}, {0, 1, 2, 3, 5, 6, 7}, {2, 7}, {0, 1, 3}, {0, 1, 2, 3, 4, 5, 6, 7}, {0, 1, 3, 5, 6, 7}, {0, 1, 2, 3, 4, 6, 7}, {7}, {0, 1, 3, 6, 7}, {3, 5, 6, 7}, {1, 2, 3, 4, 5, 6, 7}, {1, 2, 3, 4, 5, 6, 7}, {0, 1, 2, 3, 4, 5, 6, 7}, {0, 1, 2, 3, 4, 6, 7}, {0, 1, 2, 3, 4, 5, 6, 7}, {0, 1, 2, 3, 4, 5, 6, 7}, {0, 1, 2, 3, 4, 5, 6, 7}, {0, 1, 2, 3, 4, 5, 6, 7}, {0, 2, 3, 5, 6, 7}, {0, 1, 2, 3, 4, 5, 6, 7}, {0, 1, 2, 3, 4, 5, 6, 7}, {0, 1, 2, 3, 4, 5, 6, 7}, {4}, {7}, {0, 1, 2, 3, 4, 5, 6, 7}, {0, 1, 3, 4, 5, 6, 7}, {0, 1, 3, 4, 5, 6, 7}, {0, 1, 2, 3, 5, 6, 7}, {0, 3, 5, 6}, {0, 1, 2, 3, 4, 5, 6, 7}, {0, 1, 2, 3, 6, 7}, {0, 1, 2, 3, 4, 5, 6, 7}, {1}, {0, 1, 2, 3, 4, 5, 6}, {0, 4, 5, 6, 7}, {0, 3, 5}, {0, 1, 2, 3, 4, 5, 6, 7}, {0, 1, 2, 3, 4, 5, 6, 7}, {0, 1, 2, 3, 4, 5, 6, 7}, {0, 1, 2, 4, 5, 6, 7}, {0, 1, 2, 4, 5, 6, 7}, {0, 1, 2, 3, 4, 5, 6, 7}, {0, 7}, {0, 1, 2, 3, 4, 5, 6, 7}, {6}, {0, 1, 3, 5, 6, 7}, {0, 1, 2, 3, 4, 5, 6}, {1, 2, 3, 5, 6}, {0, 1, 2, 3, 4, 5, 6, 7}, {0, 1, 2, 3, 4, 5, 6, 7}, {0, 1, 2, 3, 4, 5, 6, 7}, {0, 1, 3, 4, 5, 6, 7}, {0, 2, 7}, {0, 1, 2, 3, 4, 5, 6, 7}, {0, 1, 2, 3, 4, 5, 6, 7}, {0, 1, 2, 3, 4, 5, 6, 7}, {0, 1, 2, 3, 4, 5, 6, 7}, {0, 1, 2, 3, 4, 5, 6, 7}, {0, 1, 2, 3, 4, 5, 6, 7}, {0, 1, 2, 3, 4, 5, 6, 7}, {0, 1, 2, 4, 5, 6, 7}, {0, 5, 6, 7}, {0, 1, 2, 3, 4, 5, 6, 7}, {0, 1, 2, 3, 4, 5, 6, 7}, {0, 1, 2, 3, 4, 5, 6, 7}, {1, 2, 4, 5, 6, 7}, {0, 1, 2, 3, 4, 5, 6, 7}, {6}, {0, 1, 2, 3, 4, 5, 6, 7}, {0}, {0, 1, 2, 3, 4, 5, 6, 7}, {0, 2, 3, 4, 5, 6, 7}]
Iteration 92: Best valset aggregate score so far: 0.675
Iteration 92: Best program as per aggregate score on train_val: 6
Iteration 92: Best program as per aggregate score on valset: 6
Iteration 92: Best score on valset: 0.675
Iteration 92: Best score on train_val: 0.675
Iteration 92: Linear pareto front program index: 6
Iteration 92: New program candidate index: 7
Iteration 93: Selected program 0 score: 0.67
Average Metric: 2.00 / 3 (66.7%): 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 63.91it/s]2025/08/29 10:27:50 INFO dspy.evaluate.evaluate: Average Metric: 2.0 / 3 (66.7%)
Iteration 93: Proposed new text for program: import dspy
from typing import List
import pydantic
import traceback
# Define the basic data structures using Pydantic and type hints.
MATRIX = List[List[int]]
class TrainingExample(pydantic.BaseModel):
"""Represents a single input-output pair demonstrating the task."""
input: MATRIX
output: MATRIX
class GeneratePythonFunction(dspy.Signature):
"""
Analyzes training examples of matrix transformations to deduce the underlying rule.
Based on this rule, it generates a self-contained Python function to solve the task.
**Instructions for the AI Model:**
1. **Analyze Deeply:** Carefully examine the `training_examples`. Look for patterns related to colors, shapes, object counts, positions, sizes, and spatial relationships (e.g., symmetry, rotation, containment).
2. **Reason Step-by-Step:** In the `reasoning` field, articulate the discovered rule clearly. Break down your logic. If you find multiple components, explain how they are identified and ordered.
3. **Generate a Python Function:** Write a single, complete Python function named `transform_matrix`.
- This function must accept one argument: `matrix` (a list of lists of integers).
- It must return the transformed matrix (a list of lists of integers).
- The function must be entirely self-contained. Do NOT use any external libraries like numpy or pandas. All logic must be implemented using standard Python libraries.
4. **Generalize:** The function should implement the general rule, not just solve the specific examples. It will be tested on new, unseen inputs that follow the same pattern.
"""
training_examples: List[TrainingExample] = dspy.InputField(
desc="A list of input-output pairs demonstrating the transformation rule."
)
test_inputs: List[MATRIX] = dspy.InputField(
desc="A list of input matrices to which the transformation rule must be applied."
)
reasoning: str = dspy.OutputField(
desc="A detailed, step-by-step explanation of the discovered transformation rule."
)
python_function: str = dspy.OutputField(
desc="A self-contained Python function `transform_matrix(matrix)` that implements the rule."
)
class CodeGeneratingSolver(dspy.Module):
"""A DSPy module that solves ARC-like tasks by generating and executing Python code."""
def __init__(self):
super().__init__()
# Use ChainOfThought to encourage the LM to reason before generating the code.
self.code_generator = dspy.ChainOfThought(GeneratePythonFunction)
def forward(self, training_examples: List[TrainingExample], test_inputs: List[MATRIX]):
# Step 1: Generate the reasoning and the Python function from the LM.
prediction = self.code_generator(training_examples=training_examples, test_inputs=test_inputs)
generated_code = prediction.python_function
predicted_outputs = []
# Prepare a scope for exec to run in.
local_scope = {}
try:
# Step 2: Execute the generated Python code string.
# This defines the `transform_matrix` function within `local_scope`.
exec(generated_code, globals(), local_scope)
transform_func = local_scope.get('transform_matrix')
if not callable(transform_func):
raise ValueError("`transform_matrix` function not found or is not callable in the generated code.")
# Step 3: Apply the generated function to each test input.
for test_matrix in test_inputs:
# The function might modify the input, so pass a copy.
input_copy = [row[:] for row in test_matrix]
result = transform_func(input_copy)
predicted_outputs.append(result)
except Exception as e:
print(f"Error executing generated code: {e}")
print(f"Traceback: {traceback.format_exc()}")
# Fallback strategy: If code execution fails, return the original test inputs.
# This prevents the program from crashing and provides a neutral, though likely incorrect, answer.
predicted_outputs = test_inputs
# Return the final prediction in the required format.
return dspy.Prediction(test_outputs=predicted_outputs)
# The final program is an instance of our new, improved module.
program = CodeGeneratingSolver()
Error executing generated code: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 61, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Error executing generated code: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 61, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
2025/08/29 10:30:21 INFO dspy.evaluate.evaluate: Average Metric: 0.0 / 3 (0.0%)
GEPA Optimization: 52%|████████████████████████████████████████████████████████▌ | 2077/4000 [12:56:24<16:24:40, 30.72s/rollouts]Error executing generated code: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 61, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Iteration 93: New subsample score is not better, skipping
Iteration 94: Selected program 1 score: 0.63
Average Metric: 1.00 / 3 (33.3%): : 4it [04:00, 60.17s/it] 2025/08/29 10:34:21 INFO dspy.evaluate.evaluate: Average Metric: 1.0 / 3 (33.3%)
Iteration 94: Proposed new text for program: import dspy
from typing import List
import pydantic
import re
import traceback
# Define the type for a matrix for clarity
MATRIX = List[List[int]]
class TrainingExample(pydantic.BaseModel):
"""A Pydantic model for a single training example, containing an input and output matrix."""
input: MATRIX
output: MATRIX
# --- Signature for Step 1: Inferring the Rule ---
# This signature is already strong. We will slightly enhance the docstring to guide
# the LM towards creating a more algorithm-like description.
class InferRuleSignature(dspy.Signature):
"""
Analyzes pairs of input/output matrices to deduce the transformation rule.
You are an expert analyst of abstract visual reasoning puzzles. Your task is to carefully examine the provided training examples. Each example consists of an 'input' matrix and a corresponding 'output' matrix.
Your goal is to deduce the single, consistent transformation rule that converts each input to its output. Describe this rule in clear, unambiguous, step-by-step natural language. The description must be precise, logical, and algorithmic, as if you were explaining it to a programmer who will implement it in code.
Consider various possible transformation patterns, including but not limited to:
- Geometric operations: rotation, reflection, scaling, repetition, shifting.
- Color/value transformations: changing specific numbers to others, conditional changes.
- Object-based logic: identifying shapes or objects and applying rules based on their properties (size, color, position, neighbors).
- Fill/completion patterns: such as flood fills or completing a grid based on object boundaries.
- Bounding box or frame logic: creating structures based on the extents of objects.
- Grid manipulation: splitting, merging, superimposing parts of the grid.
**Successful Strategy:** Think like a programmer. Break down the problem into logical steps. Your final rule should be a clear algorithm that can be translated into code.
"""
training_examples: List[TrainingExample] = dspy.InputField(description="A list of input and output examples demonstrating the task.")
transformation_rule: str = dspy.OutputField(description="A step-by-step natural language description of the transformation rule, written as a clear algorithm.")
# --- New Signature for Step 2: Generating Python Code from the Rule ---
# This replaces the unreliable 'ApplyRuleSignature'.
class GenerateCodeSignature(dspy.Signature):
"""
Converts a natural language transformation rule into a Python function.
You are an expert Python programmer specializing in matrix manipulations and algorithms. You will be given a detailed, step-by-step transformation rule and an example input matrix.
Your task is to write a single Python function named `transform_matrix` that takes one argument, `matrix` (a list of lists of integers), and returns the transformed matrix (a list of lists of integers). This function must precisely implement the logic described in the provided rule. You can import common libraries like `numpy` if needed.
**Crucially, your output must be ONLY the Python code for the function, enclosed in a single markdown code block (```python ... ```). Do not include any other text, explanations, or comments outside the function body unless necessary for the code's logic.**
"""
transformation_rule: str = dspy.InputField(description="The natural language algorithm to implement.")
test_input: MATRIX = dspy.InputField(description="An example input matrix to help understand the required data structures and dimensions.")
python_code: str = dspy.OutputField(description="A Python function `transform_matrix(matrix)` that implements the rule, enclosed in a markdown block.")
# --- The Improved Custom Module ---
class ARCProgram(dspy.Module):
"""A program that infers a rule, generates code to implement it, and executes the code."""
def __init__(self):
super().__init__()
# Use ChainOfThought for the complex reasoning task of rule inference.
self.rule_inferrer = dspy.ChainOfThought(InferRuleSignature)
# Use a simple Predict for the structured task of generating code from the rule.
self.code_generator = dspy.Predict(GenerateCodeSignature)
def _execute_generated_code(self, code_str: str, test_matrix: MATRIX) -> MATRIX:
"""Safely parses and executes the generated Python code."""
# 1. Extract code from markdown block.
match = re.search(r"```python\n(.*?)\n```", code_str, re.DOTALL)
if not match:
# Fallback for code not in markdown, if it looks like code.
if "def transform_matrix" in code_str:
code_to_exec = code_str
else:
raise ValueError("Could not find a Python code block in the LM's output.")
else:
code_to_exec = match.group(1)
# 2. Prepare a local scope for safe execution.
local_scope = {}
# 3. Execute the code to define the function within the local scope.
exec(code_to_exec, globals(), local_scope)
# 4. Check if the required function was defined.
if 'transform_matrix' not in local_scope:
raise NameError("The generated code did not define the 'transform_matrix' function.")
transform_func = local_scope['transform_matrix']
# 5. Call the function with a deep copy of the input to prevent mutation issues.
matrix_copy = [row[:] for row in test_matrix]
return transform_func(matrix_copy)
def forward(self, training_examples: List[TrainingExample], test_inputs: List[MATRIX]) -> dspy.Prediction:
"""
Infers a rule, generates code, and applies it to each test input.
Args:
training_examples: A list of TrainingExample objects.
test_inputs: A list of input matrices to be solved.
Returns:
A dspy.Prediction object with the 'test_outputs' field populated.
"""
# 1. Infer the transformation rule once based on all training examples.
inferred = self.rule_inferrer(training_examples=training_examples)
rule = inferred.transformation_rule
all_test_outputs = []
# 2. Iterate through each test input.
for test_matrix in test_inputs:
try:
# 2a. Generate Python code that implements the rule.
# Providing the test_input gives the LM context on matrix dimensions.
generated = self.code_generator(transformation_rule=rule, test_input=test_matrix)
code = generated.python_code
# 2b. Execute the generated code to get the output matrix.
output_matrix = self._execute_generated_code(code, test_matrix)
all_test_outputs.append(output_matrix)
except Exception as e:
# Fallback strategy: If code generation or execution fails,
# log the error and append a minimal placeholder to avoid crashing.
# This ensures the output list has the correct number of elements.
print(f"An error occurred while processing a test case: {e}")
print(f"Traceback: {traceback.format_exc()}")
all_test_outputs.append([[0]]) # A minimal 1x1 grid as a failure indicator.
# 3. Return the collected outputs in a single Prediction object.
return dspy.Prediction(test_outputs=all_test_outputs)
# The final 'program' object is an instance of our robust, multi-step module.
program = ARCProgram()
2025/08/29 10:39:59 INFO dspy.evaluate.evaluate: Average Metric: 1.0 / 3 (33.3%)
GEPA Optimization: 52%|████████████████████████████████████████████████████████▊ | 2083/4000 [13:06:03<17:37:16, 33.09s/rollouts]An error occurred while processing a test case: name 'copy' is not defined
Traceback: Traceback (most recent call last):
File "<string>", line 119, in forward
File "<string>", line 92, in _execute_generated_code
File "<string>", line 7, in transform_matrix
NameError: name 'copy' is not defined
Iteration 94: New subsample score is not better, skipping
Iteration 95: Selected program 1 score: 0.63
Average Metric: 1.00 / 1 (100.0%): 33%|█████████████████████████████████████ | 1/3 [00:00<00:00, 8.52it/s]An error occurred while processing a test case: name 'copy' is not defined
Traceback: Traceback (most recent call last):
File "<string>", line 119, in forward
File "<string>", line 92, in _execute_generated_code
File "<string>", line 7, in transform_matrix
NameError: name 'copy' is not defined
Average Metric: 2.00 / 2 (100.0%): 67%|██████████████████████████████████████████████████████████████████████████ | 2/3 [00:38<00:22, 22.46s/it]An error occurred while processing a test case: name 'copy' is not defined
Traceback: Traceback (most recent call last):
File "<string>", line 119, in forward
File "<string>", line 92, in _execute_generated_code
File "<string>", line 9, in transform_matrix
NameError: name 'copy' is not defined
Average Metric: 3.00 / 3 (100.0%): 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [02:03<00:00, 41.14s/it]2025/08/29 10:42:03 INFO dspy.evaluate.evaluate: Average Metric: 3.0 / 3 (100.0%)
GEPA Optimization: 52%|████████████████████████████████████████████████████████▊ | 2086/4000 [13:08:06<17:42:04, 33.29s/rollouts]
Iteration 95: All subsample scores perfect. Skipping.
Iteration 95: Reflective mutation did not propose a new candidate
Iteration 96: Selected program 0 score: 0.67
Average Metric: 2.00 / 3 (66.7%): 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [05:29<00:00, 109.93s/it]2025/08/29 10:47:33 INFO dspy.evaluate.evaluate: Average Metric: 2.0 / 3 (66.7%)
2025/08/29 10:48:18 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0], [0, 5, 5, 5, 5, 5, 5, 0, 4, 4, 4, 4, 4, 4, 4], [0, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0], [0, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0], [0, 5, 5, 5, 5, 5, 5, 0, 8, 8, 8, 8, 8, 8, 8], [0, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6]], 'output': [[2, 2, 2, 2, 2, 2], [1, 1, 1, 1, 1, 1], [3, 3, 3, 3, 3, 3], [4, 4, 4, 4, 4, 4], [8, 8, 8, 8, 8, 8], [6, 6, 6, 6, 6, 6]]}, {'input': [[0, 0, 0, 0, 0, 2, 0, 0, 4, 0], [0, 5, 5, 5, 0, 2, 0, 0, 4, 0], [0, 5, 5, 5, 0, 2, 0, 0, 4, 0], [0, 5, 5, 5, 0, 2, 0, 0, 4, 0], [0, 0, 0, 0, 0, 2, 0, 0, 4, 0], [0, 0, 1, 0, 0, 2, 0, 0, 4, 0], [0, 0, 1, 0, 0, 2, 0, 0, 4, 0], [0, 0, 1, 0, 0, 2, 0, 0, 4, 0], [0, 0, 1, 0, 0, 2, 0, 0, 4, 0], [0, 0, 1, 0, 0, 2, 0, 0, 4, 0], [0, 0, 1, 0, 0, 2, 0, 0, 4, 0], [0, 0, 1, 0, 0, 2, 0, 0, 4, 0]], 'output': [[1, 2, 4], [1, 2, 4], [1, 2, 4]]}, {'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [2, 2, 2, 2, 2, 0, 5, 5, 5, 5, 0, 2], [0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 0, 0], [0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 0, 0], [8, 8, 8, 8, 8, 0, 5, 5, 5, 5, 0, 8], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[2, 2, 2, 2], [8, 8, 8, 8], [4, 4, 4, 4], [1, 1, 1, 1]]}], 'test_inputs': [[[2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2], [0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0], [3, 3, 3, 3, 3, 3, 3, 0, 5, 5, 5, 5, 5, 5, 5, 0, 3, 3, 3], [0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0], [8, 8, 8, 8, 8, 8, 8, 0, 5, 5, 5, 5, 5, 5, 5, 0, 8, 8, 8], [0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7]]], 'test_outputs': [[[2, 2, 2, 2, 2, 2, 2], [3, 3, 3, 3, 3, 3, 3], [8, 8, 8, 8, 8, 8, 8], [4, 4, 4, 4, 4, 4, 4], [6, 6, 6, 6, 6, 6, 6], [1, 1, 1, 1, 1, 1, 1], [7, 7, 7, 7, 7, 7, 7]]]}) (input_keys={'test_inputs', 'training_examples'}): 'dict' object has no attribute 'model_dump'
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 61, in forward
File "<string>", line 61, in <listcomp>
AttributeError: 'dict' object has no attribute 'model_dump'
2025/08/29 10:48:18 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[0, 7, 7], [7, 7, 7], [0, 7, 7]], 'output': [[0, 0, 0, 0, 7, 7, 0, 7, 7], [0, 0, 0, 7, 7, 7, 7, 7, 7], [0, 0, 0, 0, 7, 7, 0, 7, 7], [0, 7, 7, 0, 7, 7, 0, 7, 7], [7, 7, 7, 7, 7, 7, 7, 7, 7], [0, 7, 7, 0, 7, 7, 0, 7, 7], [0, 0, 0, 0, 7, 7, 0, 7, 7], [0, 0, 0, 7, 7, 7, 7, 7, 7], [0, 0, 0, 0, 7, 7, 0, 7, 7]]}, {'input': [[4, 0, 4], [0, 0, 0], [0, 4, 0]], 'output': [[4, 0, 4, 0, 0, 0, 4, 0, 4], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 4, 0, 0, 0, 0, 0, 4, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 4, 0, 4, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 4, 0, 0, 0, 0]]}, {'input': [[0, 0, 0], [0, 0, 2], [2, 0, 2]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 2], [0, 0, 0, 0, 0, 0, 2, 0, 2], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 2, 0, 0, 0, 0, 0, 2], [2, 0, 2, 0, 0, 0, 2, 0, 2]]}, {'input': [[6, 6, 0], [6, 0, 0], [0, 6, 6]], 'output': [[6, 6, 0, 6, 6, 0, 0, 0, 0], [6, 0, 0, 6, 0, 0, 0, 0, 0], [0, 6, 6, 0, 6, 6, 0, 0, 0], [6, 6, 0, 0, 0, 0, 0, 0, 0], [6, 0, 0, 0, 0, 0, 0, 0, 0], [0, 6, 6, 0, 0, 0, 0, 0, 0], [0, 0, 0, 6, 6, 0, 6, 6, 0], [0, 0, 0, 6, 0, 0, 6, 0, 0], [0, 0, 0, 0, 6, 6, 0, 6, 6]]}, {'input': [[2, 2, 2], [0, 0, 0], [0, 2, 2]], 'output': [[2, 2, 2, 2, 2, 2, 2, 2, 2], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 2, 2, 0, 2, 2, 0, 2, 2], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 2, 2, 2, 2, 2, 2], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 2, 2, 0, 2, 2]]}], 'test_inputs': [[[7, 0, 7], [7, 0, 7], [7, 7, 0]]], 'test_outputs': [[[7, 0, 7, 0, 0, 0, 7, 0, 7], [7, 0, 7, 0, 0, 0, 7, 0, 7], [7, 7, 0, 0, 0, 0, 7, 7, 0], [7, 0, 7, 0, 0, 0, 7, 0, 7], [7, 0, 7, 0, 0, 0, 7, 0, 7], [7, 7, 0, 0, 0, 0, 7, 7, 0], [7, 0, 7, 7, 0, 7, 0, 0, 0], [7, 0, 7, 7, 0, 7, 0, 0, 0], [7, 7, 0, 7, 7, 0, 0, 0, 0]]]}) (input_keys={'test_inputs', 'training_examples'}): 'dict' object has no attribute 'model_dump'
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 61, in forward
File "<string>", line 61, in <listcomp>
AttributeError: 'dict' object has no attribute 'model_dump'
2025/08/29 10:48:18 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[7, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 7, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 7, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 7, 5, 5, 0, 0, 0, 0], [0, 0, 0, 0, 7, 5, 0, 0, 0, 0], [0, 0, 0, 0, 0, 7, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 7, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 7, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 7, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 7]], 'output': [[7, 0, 0, 0, 7, 0, 0, 0, 0, 0], [0, 7, 0, 0, 0, 7, 0, 0, 0, 0], [0, 0, 7, 0, 0, 0, 7, 0, 0, 0], [0, 0, 0, 7, 0, 0, 0, 7, 0, 0], [0, 0, 0, 0, 7, 0, 0, 0, 7, 0], [0, 0, 0, 0, 0, 7, 0, 0, 0, 7], [0, 0, 0, 0, 0, 0, 7, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 7, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 7, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 7]]}, {'input': [[0, 0, 0, 0, 0, 9, 0, 0, 0, 0], [0, 0, 0, 0, 0, 5, 9, 0, 0, 0], [0, 0, 0, 0, 0, 5, 5, 9, 0, 0], [0, 0, 0, 0, 0, 5, 5, 5, 9, 0], [0, 0, 0, 0, 0, 5, 5, 5, 5, 9], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 9, 0, 0, 0, 0], [9, 0, 0, 0, 0, 0, 9, 0, 0, 0], [0, 9, 0, 0, 0, 0, 0, 9, 0, 0], [0, 0, 9, 0, 0, 0, 0, 0, 9, 0], [0, 0, 0, 9, 0, 0, 0, 0, 0, 9], [0, 0, 0, 0, 9, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 9, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 9, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 9, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 9, 0]]}, {'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [2, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 2, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 2, 5, 5, 0, 0, 0, 0, 0], [0, 0, 0, 2, 5, 0, 0, 0, 0, 0], [0, 0, 0, 5, 2, 0, 0, 0, 0, 0], [0, 0, 0, 5, 5, 2, 0, 0, 0, 0], [0, 0, 0, 5, 5, 5, 2, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 2, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 2, 0]], 'output': [[0, 0, 0, 2, 0, 0, 0, 0, 0, 0], [2, 0, 0, 0, 2, 0, 0, 0, 0, 0], [0, 2, 0, 0, 0, 2, 0, 0, 0, 0], [0, 0, 2, 0, 0, 0, 2, 0, 0, 0], [0, 0, 0, 2, 0, 0, 0, 2, 0, 0], [0, 0, 0, 0, 2, 0, 0, 0, 2, 0], [2, 0, 0, 0, 0, 2, 0, 0, 0, 2], [0, 2, 0, 0, 0, 0, 2, 0, 0, 0], [0, 0, 2, 0, 0, 0, 0, 2, 0, 0], [0, 0, 0, 2, 0, 0, 0, 0, 2, 0]]}], 'test_inputs': [[[0, 1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 5, 5, 0, 0, 0, 0, 0], [0, 0, 0, 1, 5, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 5, 1, 0, 0, 0, 0], [0, 0, 0, 0, 5, 5, 1, 0, 0, 0], [0, 0, 0, 0, 5, 5, 5, 1, 0, 0], [0, 0, 0, 0, 5, 5, 5, 5, 1, 0], [0, 0, 0, 0, 5, 5, 5, 5, 5, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]], 'test_outputs': [[[0, 1, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 1, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0, 1], [0, 0, 0, 0, 0, 0, 1, 0, 0, 0], [1, 0, 0, 0, 0, 0, 0, 1, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0, 1, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0, 1], [0, 0, 0, 1, 0, 0, 0, 0, 0, 0]]]}) (input_keys={'test_inputs', 'training_examples'}): 'dict' object has no attribute 'model_dump'
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 61, in forward
File "<string>", line 61, in <listcomp>
AttributeError: 'dict' object has no attribute 'model_dump'
2025/08/29 10:48:18 INFO dspy.evaluate.evaluate: Average Metric: 0.0 / 3 (0.0%)
GEPA Optimization: 52%|█████████████████████████████████████████████████████████ | 2092/4000 [13:14:22<18:41:07, 35.26s/rollouts]Iteration 96: Proposed new text for program: import dspy
from typing import List
import pydantic
import re
import json
# Define the data structures for matrices and training examples.
# Using pydantic.BaseModel and type hints helps DSPy handle structured data.
MATRIX = List[List[int]]
class TrainingExample(pydantic.BaseModel):
input: MATRIX
output: MATRIX
# This is the overall signature for the task, which our custom module will fulfill.
class SolveTaskSignature(dspy.Signature):
"""Given a list of training examples demonstrating a task, and a list of test inputs, solve the task for the test inputs."""
training_examples: List[TrainingExample] = dspy.InputField(description="Input and output examples demonstrating the task to be performed.")
test_inputs: List[MATRIX] = dspy.InputField(description="Input matrices to be solved following the task described in the training examples.")
test_outputs: List[MATRIX] = dspy.OutputField(description="Output matrices corresponding to the test inputs.")
# This new signature is for the code generation sub-task. It's more focused.
class GeneratePythonFunction(dspy.Signature):
"""
Analyzes pairs of input/output matrices to deduce a transformation rule.
First, it describes the rule in English.
Second, it writes a self-contained Python function `transform_matrix` that implements this rule.
**Successful Strategies to Consider:**
- Look for geometric patterns: scaling, tiling, repeating, fractals (like in the 3x3 -> 9x9 example).
- Identify object-based rules: extract shapes (e.g., horizontal lines, vertical lines, blocks), filter them, and rearrange them based on properties like color or position.
- Check for conditional logic: the rule might change based on the number or type of shapes present (e.g., more horizontal lines vs. vertical lines).
- Consider pixel-wise operations, reflections, rotations, and shifts.
- The Python function must be pure, self-contained, and not rely on any external libraries.
"""
training_examples: str = dspy.InputField(description="A JSON string of input-output matrix pairs.")
rule_description: str = dspy.OutputField(description="A step-by-step description of the transformation rule in plain English.")
python_function: str = dspy.OutputField(description="A single, self-contained Python function named `transform_matrix` that takes one argument (an input matrix) and returns the transformed matrix. The function must be enclosed in a markdown code block (```python...```).")
class SolveTaskWithCode(dspy.Module):
"""A module that solves tasks by generating and executing Python code."""
def __init__(self):
super().__init__()
# A module to generate the rule and the Python function.
self.code_generator = dspy.ChainOfThought(GeneratePythonFunction)
# A fallback module in case the code generation or execution fails.
self.fallback_solver = dspy.ChainOfThought(SolveTaskSignature)
def _extract_python_code(self, text: str) -> str | None:
"""Extracts Python code from a markdown block."""
match = re.search(r"```python\n(.*?)\n```", text, re.DOTALL)
if match:
return match.group(1)
# Fallback for code without markdown fences
if "def transform_matrix" in text:
return text
return None
def forward(self, training_examples: List[TrainingExample], test_inputs: List[MATRIX]):
# Convert Pydantic objects to a JSON string for a cleaner prompt.
examples_str = json.dumps([ex.model_dump() for ex in training_examples])
try:
# 1. Generate the rule and Python code from the examples.
prediction = self.code_generator(training_examples=examples_str)
code_str = self._extract_python_code(prediction.python_function)
if not code_str:
raise ValueError("No Python code block found in the LM's output.")
# 2. Execute the generated code string to define the function in a local scope.
local_scope = {}
exec(code_str, globals(), local_scope)
transform_matrix_func = local_scope.get('transform_matrix')
if not callable(transform_matrix_func):
raise ValueError("`transform_matrix` function not found or is not callable.")
# 3. Apply the dynamically created function to all test inputs.
final_outputs = [transform_matrix_func(matrix) for matrix in test_inputs]
return dspy.Prediction(test_outputs=final_outputs)
except Exception as e:
# 4. If any step fails, use the original, simpler approach as a fallback.
print(f"Code generation or execution failed: {e}. Using fallback solver.")
return self.fallback_solver(training_examples=training_examples, test_inputs=test_inputs)
# The final program is an instance of our new, more robust module.
program = SolveTaskWithCode()
Iteration 96: New subsample score is not better, skipping
Iteration 97: Selected program 1 score: 0.63
Average Metric: 0.00 / 1 (0.0%): 33%|█████████████████████████████████████▋ | 1/3 [00:00<00:00, 6.84it/s]2025/08/29 10:50:01 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 10:50:01 WARNING dspy.adapters.json_adapter: Failed to use structured output format, falling back to JSON mode.
Average Metric: 0.00 / 3 (0.0%): : 4it [03:47, 56.90s/it] 2025/08/29 10:52:06 INFO dspy.evaluate.evaluate: Average Metric: 0.0 / 3 (0.0%)
Iteration 97: Proposed new text for program: import dspy
from typing import List
import pydantic
import copy
# Define the type for a matrix for clarity
MATRIX = List[List[int]]
class TrainingExample(pydantic.BaseModel):
"""A Pydantic model for a single training example, containing an input and output matrix."""
input: MATRIX
output: MATRIX
class InferRuleSignature(dspy.Signature):
"""
Analyzes pairs of input/output matrices to deduce the transformation rule.
You are an expert analyst of abstract visual reasoning puzzles. Your task is to carefully examine the provided training examples. Each example consists of an 'input' matrix and a corresponding 'output' matrix.
Your goal is to deduce the single, consistent transformation rule that converts each input to its output. Describe this rule in clear, unambiguous, step-by-step natural language. The description must be precise and algorithmic, as if you were explaining it to a programmer who will implement it.
Consider various possible transformation patterns, including but not limited to:
- Geometric operations: rotation, reflection, scaling, repetition, shifting.
- Color/value transformations: changing specific numbers to others, conditional changes.
- Object-based logic: identifying shapes or objects and applying rules based on their properties (size, color, position).
- Fill/completion patterns: such as flood fills or completing a grid based on object boundaries.
- Bounding box or frame logic: creating structures based on the extents of objects.
- Iterative processes that repeat until no more changes occur.
"""
training_examples: List[TrainingExample] = dspy.InputField(description="A list of input and output examples demonstrating the task.")
transformation_rule: str = dspy.OutputField(description="A step-by-step natural language description of the transformation rule, suitable for implementation in code.")
class GenerateCodeSignature(dspy.Signature):
"""
Translates a natural language transformation rule into a self-contained Python function.
You are an expert programmer specializing in algorithmic tasks and data manipulation, particularly with nested lists representing matrices.
Your task is to write a single Python function named `transform_matrix` that implements the given natural language rule.
**Function Requirements:**
- The function must be named exactly `transform_matrix`.
- It must accept a single argument: `matrix` (a list of lists of integers).
- It must return the transformed matrix (a list of lists of integers).
- The function must be entirely self-contained. Do not assume any global variables exist.
- You can use standard Python libraries. If you use a library like `numpy`, you must include the `import numpy as np` statement *inside* the function body to ensure it is self-contained.
- Your output must be ONLY the Python code for the function. Do not include any explanations, markdown formatting, or example usage calls.
"""
transformation_rule: str = dspy.InputField(description="The natural language rule to be converted into a Python function.")
python_function: str = dspy.OutputField(description="A string containing only the Python code for the `transform_matrix` function.")
class ARCProgram(dspy.Module):
"""A program that infers a rule, generates code for it, and then executes the code."""
def __init__(self):
super().__init__()
self.rule_inferrer = dspy.ChainOfThought(InferRuleSignature)
self.code_generator = dspy.Predict(GenerateCodeSignature)
def forward(self, training_examples: List[TrainingExample], test_inputs: List[MATRIX]) -> dspy.Prediction:
"""
Infers a rule, generates a function to apply it, and executes it on each test input.
Args:
training_examples: A list of TrainingExample objects.
test_inputs: A list of input matrices to be solved.
Returns:
A dspy.Prediction object with the 'test_outputs' field populated.
"""
# 1. Infer the natural language rule from the examples.
inferred = self.rule_inferrer(training_examples=training_examples)
rule = inferred.transformation_rule
# 2. Generate a Python function from the natural language rule.
generated_code = self.code_generator(transformation_rule=rule)
function_string = generated_code.python_function
all_test_outputs = []
# 3. Try to execute the generated function for each test input.
try:
# Create a local scope to execute the function string in.
local_scope = {}
exec(function_string, globals(), local_scope)
transform_func = local_scope['transform_matrix']
for test_matrix in test_inputs:
# Use a deep copy to prevent the function from modifying the original input list.
input_copy = copy.deepcopy(test_matrix)
try:
# Apply the generated function.
result_matrix = transform_func(input_copy)
all_test_outputs.append(result_matrix)
except Exception as e:
# Fallback if the function fails on a specific matrix.
print(f"Generated function failed during execution on a test case: {e}")
if test_matrix and test_matrix[0]:
all_test_outputs.append([([0] * len(test_matrix[0])) for _ in range(len(test_matrix))])
else:
all_test_outputs.append([])
except Exception as e:
# Fallback if the generated code is invalid (e.g., syntax error, wrong function name).
print(f"Failed to execute or find generated function: {e}")
for test_matrix in test_inputs:
if test_matrix and test_matrix[0]:
all_test_outputs.append([([0] * len(test_matrix[0])) for _ in range(len(test_matrix))])
else:
all_test_outputs.append([])
return dspy.Prediction(test_outputs=all_test_outputs)
# The final 'program' object is an instance of our robust, multi-step module.
program = ARCProgram()
2025/08/29 10:58:29 INFO dspy.evaluate.evaluate: Average Metric: 0.0 / 3 (0.0%)
GEPA Optimization: 52%|█████████████████████████████████████████████████████████▏ | 2098/4000 [13:24:33<21:41:42, 41.06s/rollouts]Iteration 97: New subsample score is not better, skipping
Iteration 98: Selected program 6 score: 0.675
Average Metric: 2.00 / 3 (66.7%): : 4it [04:23, 65.99s/it] 2025/08/29 11:02:53 INFO dspy.evaluate.evaluate: Average Metric: 2.0 / 3 (66.7%)
Iteration 98: Proposed new text for program: import dspy
from typing import List, Optional
import pydantic
import copy
# Define type aliases and pydantic models for clarity and structure.
MATRIX = List[List[int]]
class TrainingExample(pydantic.BaseModel):
input: MATRIX
output: MATRIX
class HypothesizeRule(dspy.Signature):
"""
Analyze the provided input/output matrix pairs from the Abstraction and Reasoning Corpus (ARC).
Deduce the underlying transformation rule that converts each input matrix to its corresponding output matrix.
Describe this rule in clear, step-by-step, unambiguous English.
**Successful Strategies to Consider:**
- Strive for the simplest, most generalizable rule that explains all examples. Overly complex rules tailored only to the training data may fail on test cases.
- Consider transformations based on object properties (color, shape, size), spatial relationships (position, containment, symmetry), or logical operations.
- Do not describe a Python implementation, but focus on the abstract logic of the transformation.
"""
training_examples: List[TrainingExample] = dspy.InputField(desc="A list of input/output pairs demonstrating the transformation rule.")
rule_description: str = dspy.OutputField(desc="A step-by-step English description of the transformation rule.")
class ImplementRule(dspy.Signature):
"""
You are an expert programmer. Your task is to write a single, self-contained Python function based on a provided rule description and example pairs.
**Function Requirements:**
- The function must be named `transform_matrix`.
- It must accept one argument: `matrix`, which is a list of lists of integers (representing the input grid).
- It must return a list of lists of integers (representing the transformed output grid).
- The function should not use any external libraries except for `copy` if needed (e.g., `import copy; new_matrix = copy.deepcopy(matrix)`).
- Your output must be ONLY the Python code for the function. Do not include any explanations, comments outside the function, or markdown formatting like ```python.
- Your implementation will be automatically verified against the training examples. Ensure it correctly reproduces the output for every provided training pair.
**Example of a Correctly Formatted Output:**
def transform_matrix(matrix: list[list[int]]) -> list[list[int]]:
# Your implementation here
# For example, find the most frequent color and fill the grid
from collections import Counter
import itertools
if not matrix or not matrix[0]:
return []
counts = Counter(itertools.chain.from_iterable(matrix))
if counts:
# Handle ties by picking the smaller number value
most_common_color = sorted(counts.items(), key=lambda item: (-item[1], item[0]))[0][0]
else:
return []
height = len(matrix)
width = len(matrix[0])
return [[most_common_color for _ in range(width)] for _ in range(height)]
"""
rule_description: str = dspy.InputField(desc="The English description of the rule to implement.")
training_examples: List[TrainingExample] = dspy.InputField(desc="A list of input/output pairs to use as a reference for implementation.")
python_function: str = dspy.OutputField(desc="A string containing a single Python function `transform_matrix` that implements the rule.")
class RefineCode(dspy.Signature):
"""
You are an expert programmer debugging a function. You are given a rule description, training examples, a buggy Python function, and feedback on why it's wrong.
Your task is to analyze the discrepancy between the function's output and the expected output for the training examples and provide a corrected version of the function.
The refined function must adhere to the same requirements as the original: be named `transform_matrix`, be self-contained, and use no external libraries besides `copy`.
Output ONLY the complete, corrected Python function code.
"""
rule_description: str = dspy.InputField(desc="The English description of the transformation rule.")
training_examples: List[TrainingExample] = dspy.InputField(desc="A list of input/output pairs for reference.")
buggy_python_function: str = dspy.InputField(desc="The Python function that failed verification.")
error_feedback: str = dspy.InputField(desc="Detailed explanation of which training examples failed and how the function's output differed from the expected output.")
refined_python_function: str = dspy.OutputField(desc="A string containing the complete, corrected Python function `transform_matrix`.")
class ARCSolver(dspy.Module):
"""A module that solves ARC tasks with a hypothesis, implementation, and refinement loop."""
def __init__(self, max_attempts=2):
super().__init__()
self.max_attempts = max_attempts
self.rule_hypothesizer = dspy.ChainOfThought(HypothesizeRule)
self.code_implementer = dspy.Predict(ImplementRule)
self.code_refiner = dspy.ChainOfThought(RefineCode)
def forward(self, training_examples: List[TrainingExample], test_inputs: List[MATRIX]):
# Step 1: Generate an English description of the transformation rule.
hypothesis = self.rule_hypothesizer(training_examples=training_examples)
# Step 2: Generate the initial Python code.
prediction = self.code_implementer(
rule_description=hypothesis.rule_description,
training_examples=training_examples
)
python_code = prediction.python_function
# Step 3: Iteratively verify and refine the code.
for attempt in range(self.max_attempts):
local_scope = {}
verification_errors = []
try:
exec(python_code, globals(), local_scope)
transform_func = local_scope.get('transform_matrix')
if not callable(transform_func):
raise ValueError("`transform_matrix` function not found or not callable.")
# Verify against training examples
for i, example in enumerate(training_examples):
input_copy = copy.deepcopy(example.input)
try:
result = transform_func(input_copy)
if result != example.output:
error_detail = f"Input:\n{example.input}\nProduced Output:\n{result}\nExpected Output:\n{example.output}"
verification_errors.append(f"Failed on training example {i}:\n{error_detail}")
except Exception as e:
verification_errors.append(f"Threw an exception on training example {i}: {e}")
if not verification_errors:
# Code is correct, break the loop
break
except Exception as e:
verification_errors.append(f"Code execution failed: {e}")
# If we are here, verification failed. Prepare for refinement.
if attempt < self.max_attempts - 1:
feedback = "The code failed verification.\n" + "\n".join(verification_errors)
refinement = self.code_refiner(
rule_description=hypothesis.rule_description,
training_examples=training_examples,
buggy_python_function=python_code,
error_feedback=feedback
)
python_code = refinement.refined_python_function
# Step 4: Execute the final code and apply to test inputs.
final_local_scope = {}
fallback_outputs = [copy.deepcopy(matrix) for matrix in test_inputs]
try:
exec(python_code, globals(), final_local_scope)
final_transform_func = final_local_scope.get('transform_matrix')
if not callable(final_transform_func):
return dspy.Prediction(test_outputs=fallback_outputs)
solved_outputs = []
for test_matrix in test_inputs:
try:
input_copy = copy.deepcopy(test_matrix)
result = final_transform_func(input_copy)
solved_outputs.append(result)
except Exception:
solved_outputs.append(copy.deepcopy(test_matrix))
return dspy.Prediction(test_outputs=solved_outputs)
except Exception:
return dspy.Prediction(test_outputs=fallback_outputs)
# The overall task signature, defining the final inputs and outputs of the program.
class SolveTaskSignature(dspy.Signature):
"""
Given a set of training examples demonstrating a matrix transformation,
apply the same transformation to a new set of test matrices.
"""
training_examples: List[TrainingExample] = dspy.InputField(description="Input and output examples demonstrating the task to be performed.")
test_inputs: List[MATRIX] = dspy.InputField(description="Input matrices to be solved following the task described in the training examples.")
test_outputs: List[MATRIX] = dspy.OutputField(description="Output matrices corresponding to the test inputs.")
# The final program is an instance of our new, more robust module.
program = ARCSolver()
2025/08/29 11:09:01 INFO dspy.evaluate.evaluate: Average Metric: 2.0 / 3 (66.7%)
GEPA Optimization: 53%|█████████████████████████████████████████████████████████▎ | 2104/4000 [13:35:05<25:22:33, 48.18s/rollouts]Iteration 98: New subsample score is not better, skipping
Iteration 99: Selected program 7 score: 0.675
Average Metric: 1.00 / 1 (100.0%): 67%|██████████████████████████████████████████████████████████████████████████ | 2/3 [03:12<01:21, 81.11s/it]2025/08/29 11:12:49 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 11:14:48 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
Average Metric: 1.00 / 2 (50.0%): 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [07:06<00:00, 150.61s/it]2025/08/29 11:16:07 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
Average Metric: 2.00 / 3 (66.7%): : 4it [07:35, 113.97s/it] 2025/08/29 11:16:37 INFO dspy.evaluate.evaluate: Average Metric: 2.0 / 3 (66.7%)
Iteration 99: Proposed new text for program: import dspy
from typing import List
import pydantic
import copy
# Define the type for a matrix for clarity
MATRIX = List[List[int]]
class TrainingExample(pydantic.BaseModel):
"""A Pydantic model for a single training example, containing an input and output matrix."""
input: MATRIX
output: MATRIX
class InferRuleSignature(dspy.Signature):
"""
Analyzes pairs of input/output matrices to deduce the transformation rule.
You are an expert analyst of abstract visual reasoning puzzles. Your task is to carefully examine the provided training examples. Each example consists of an 'input' matrix and a corresponding 'output' matrix.
Your goal is to deduce the single, consistent transformation rule that converts each input to its output. Describe this rule in clear, unambiguous, step-by-step natural language. The description must be precise enough for another AI or a programmer to follow it to solve a new, unseen test input.
**Successful Strategies:**
1. First, analyze the global properties and structure. Look for patterns like symmetry, frames, containers, or repetition across all examples.
2. Next, identify key objects or shapes and their properties (color, size, position).
3. Finally, deduce the specific transformation logic. How are objects created, destroyed, moved, or recolored based on the properties you identified?
Consider various possible transformation patterns, including but not limited to:
- Geometric operations: rotation, reflection, scaling, repetition, shifting.
- Color/value transformations: changing specific numbers to others, conditional changes based on neighbors.
- Object-based logic: identifying shapes or objects and applying rules based on their properties.
- Cropping or subgrid extraction: such as finding a bounding box or selecting a specific region.
- Fill/completion patterns: such as flood fills or completing a grid based on object boundaries.
- Bounding box or frame logic: creating structures based on the extents of objects.
"""
training_examples: List[TrainingExample] = dspy.InputField(description="A list of input and output examples demonstrating the task.")
transformation_rule: str = dspy.OutputField(description="A step-by-step natural language description of the transformation rule.")
class GeneratePythonCodeSignature(dspy.Signature):
"""
Converts a natural language transformation rule into a Python function.
You are an expert Python programmer. Your task is to write a single Python function named `transform_matrix` that implements the given natural language `transformation_rule`.
**Function Requirements:**
- The function must be named exactly `transform_matrix`.
- It must accept one argument: `matrix: List[List[int]]`.
- It must return the transformed matrix as a `List[List[int]]`.
- You can use standard Python libraries. Do not use external libraries like numpy or pandas.
- The `training_examples` are provided for context, allowing you to mentally check if your function logic would produce the correct output for the given inputs.
**Pitfalls to Avoid:**
- Do not make assumptions about grid size; your code should be general.
- Carefully handle edge cases, such as empty grids or cases where a pattern is not found.
- Ensure your implementation matches the rule for ALL training examples, not just the first one.
**Output Format:**
- Your output must be ONLY the Python code for the function.
- Do NOT include any explanations, comments outside the function, or markdown formatting like ```python ... ```.
"""
transformation_rule: str = dspy.InputField(description="The natural language rule to implement.")
training_examples: List[TrainingExample] = dspy.InputField(description="Example input/output pairs to validate the logic.")
python_code: str = dspy.OutputField(description="A string containing only the Python code for the `transform_matrix` function.")
class ApplyRuleSignature(dspy.Signature):
"""
Applies a given transformation rule to a single test input matrix.
You are an expert in meticulously following instructions to transform matrices. You will be given a specific, detailed transformation rule and a single test input matrix.
Your task is to:
1. Carefully read and understand the provided rule.
2. Apply the rule step-by-step to the test input matrix.
3. Produce the final output matrix.
**Crucially, your output must be ONLY the resulting matrix, formatted as a valid JSON list of lists of integers. Do not include any extra text, explanations, or markdown formatting.**
"""
transformation_rule: str = dspy.InputField(description="The natural language rule to apply.")
test_input: MATRIX = dspy.InputField(description="The input matrix to be transformed.")
test_output: MATRIX = dspy.OutputField(description="The resulting matrix after applying the rule, as a list of lists of integers.")
class ARCProgram(dspy.Module):
"""A program that infers a rule, generates and verifies code, and falls back to direct application."""
def __init__(self):
super().__init__()
self.rule_inferrer = dspy.ChainOfThought(InferRuleSignature)
self.code_generator = dspy.Predict(GeneratePythonCodeSignature)
self.rule_applier_fallback = dspy.Predict(ApplyRuleSignature)
def forward(self, training_examples: List[TrainingExample], test_inputs: List[MATRIX]) -> dspy.Prediction:
"""
Infers a rule, generates and verifies code, and applies it to each test input.
Args:
training_examples: A list of TrainingExample objects.
test_inputs: A list of input matrices to be solved.
Returns:
A dspy.Prediction object with the 'test_outputs' field populated.
"""
# 1. Infer the transformation rule once.
inferred = self.rule_inferrer(training_examples=training_examples)
rule = inferred.transformation_rule
transform_func = None
# 2. Attempt to generate and VERIFY Python code for the rule.
try:
generated = self.code_generator(transformation_rule=rule, training_examples=training_examples)
python_code = generated.python_code
local_namespace = {}
exec(python_code, globals(), local_namespace)
candidate_func = local_namespace['transform_matrix']
is_verified = True
for example in training_examples:
try:
input_copy = copy.deepcopy(example.input)
predicted_output = candidate_func(input_copy)
if predicted_output != example.output:
is_verified = False
break
except Exception:
is_verified = False
break
if is_verified:
transform_func = candidate_func
except Exception:
# Catches errors in code generation, syntax, or exec. `transform_func` remains None.
pass
# 3. Apply the verified function or fall back.
all_test_outputs = []
if transform_func:
# Strategy A: Verified code exists. Use it with a per-input fallback.
for test_matrix in test_inputs:
try:
test_matrix_copy = copy.deepcopy(test_matrix)
output = transform_func(test_matrix_copy)
all_test_outputs.append(output)
except Exception:
# If verified code fails on a test case, fall back for that case only.
all_test_outputs.append(self._fallback_for_single_matrix(rule, test_matrix))
else:
# Strategy B: No verified code. Use fallback for all.
for test_matrix in test_inputs:
all_test_outputs.append(self._fallback_for_single_matrix(rule, test_matrix))
return dspy.Prediction(test_outputs=all_test_outputs)
def _fallback_for_single_matrix(self, rule: str, matrix: MATRIX) -> MATRIX:
"""Helper method to apply the LM fallback and handle its potential errors."""
try:
result = self.rule_applier_fallback(transformation_rule=rule, test_input=matrix)
return result.test_output
except Exception:
# If the fallback also fails, return a default empty/zero matrix.
if matrix and matrix[0]:
return [[0] * len(matrix[0]) for _ in range(len(matrix))]
return []
# The final 'program' object is an instance of our robust, multi-step module.
program = ARCProgram()
2025/08/29 12:07:07 INFO dspy.evaluate.evaluate: Average Metric: 2.0 / 3 (66.7%)
GEPA Optimization: 53%|████████████████████████████████████████████████████████▉ | 2110/4000 [14:33:11<63:32:12, 121.02s/rollouts]Iteration 99: New subsample score is not better, skipping
Iteration 100: Selected program 2 score: 0.605
Average Metric: 1.00 / 2 (50.0%): : 5it [04:28, 28.69s/it] 2025/08/29 12:12:53 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
Average Metric: 1.00 / 3 (33.3%): : 6it [05:45, 57.65s/it]2025/08/29 12:12:53 INFO dspy.evaluate.evaluate: Average Metric: 1.0 / 3 (33.3%)
Iteration 100: Proposed new text for program: import dspy
from typing import List
import pydantic
import copy
# Define type aliases and pydantic models for clarity and structure.
MATRIX = List[List[int]]
class TrainingExample(pydantic.BaseModel):
input: MATRIX
output: MATRIX
# --- Step 1: A Signature to deduce the transformation rule in natural language ---
class HypothesizeRule(dspy.Signature):
"""
Analyzes pairs of input/output matrices from the Abstraction and Reasoning Corpus (ARC)
and deduces the underlying transformation rule. Describe the rule in clear, step-by-step
natural language.
**Successful Strategies to Consider:**
- **Grid Properties:** Analyze changes in dimensions, colors, and object counts.
- **Object Transformations:** Identify objects/shapes and describe how they are moved, rotated, scaled, colored, or combined.
- **Pattern Recognition:** Look for patterns like symmetry, repetition, or subgrid extraction. For example, is the output a small subgrid from the input?
- **Conditional Logic:** The rule might depend on specific conditions, like the color of a neighboring cell or the number of objects present.
"""
training_examples: List[TrainingExample] = dspy.InputField(desc="A list of input/output pairs demonstrating the transformation rule.")
hypothesis: str = dspy.OutputField(desc="A clear, step-by-step natural language description of the transformation rule.")
# --- Step 2: A Signature to generate Python code based on the deduced rule ---
class ImplementRuleInPython(dspy.Signature):
"""
You are an expert programmer. Your task is to write a single, self-contained Python function based on a provided hypothesis about a matrix transformation rule. Use the training examples for context and to verify your logic.
**Function Requirements:**
- The function must be named `transform_matrix`.
- It must accept one argument: `matrix`, which is a list of lists of integers.
- It must return a list of lists of integers (the transformed grid).
- The function should not use any external libraries except for `copy`.
- Your output must be ONLY the Python code for the function. Do not include explanations or markdown formatting.
**Pitfalls to Avoid:**
- **Hardcoding:** Do not hardcode values from the examples. The function must be general.
- **Index Errors:** Be extremely careful with grid boundaries and coordinates.
- **Incorrect Logic:** Ensure your code perfectly matches the logic described in the hypothesis.
"""
hypothesis: str = dspy.InputField(desc="The natural language description of the rule to implement.")
training_examples: List[TrainingExample] = dspy.InputField(desc="The original examples, for context and verification.")
python_function: str = dspy.OutputField(desc="A string containing a single Python function `transform_matrix` that implements the rule.")
# --- The Improved Custom Module with a Two-Step Pipeline ---
class ARCSolver(dspy.Module):
"""A module that solves ARC tasks by first hypothesizing a rule, then generating and executing Python code."""
def __init__(self):
super().__init__()
# Decompose the problem: 1) Reason about the rule, 2) Implement the rule.
# ChainOfThought encourages more detailed reasoning for the hypothesis step.
self.rule_hypothesizer = dspy.ChainOfThought(HypothesizeRule)
self.code_generator = dspy.Predict(ImplementRuleInPython)
def forward(self, training_examples: List[TrainingExample], test_inputs: List[MATRIX]):
# Create fallback outputs in case of any failure.
fallback_outputs = [copy.deepcopy(matrix) for matrix in test_inputs]
try:
# Step 1: Generate a natural language hypothesis about the transformation rule.
prediction = self.rule_hypothesizer(training_examples=training_examples)
hypothesis = prediction.hypothesis
# Step 2: Generate Python code based on the hypothesis.
code_prediction = self.code_generator(hypothesis=hypothesis, training_examples=training_examples)
python_code = code_prediction.python_function
if not python_code or not isinstance(python_code, str):
# If code generation fails (e.g., returns None), use the fallback.
return dspy.Prediction(test_outputs=fallback_outputs)
# Step 3: Execute the generated code and apply it to test inputs.
local_scope = {}
exec(python_code, globals(), local_scope)
transform_func = local_scope.get('transform_matrix')
if not callable(transform_func):
# If function definition failed or the name is wrong, use the fallback.
return dspy.Prediction(test_outputs=fallback_outputs)
solved_outputs = []
for test_matrix in test_inputs:
try:
# Use a deepcopy to prevent the function from modifying the original input list.
input_copy = copy.deepcopy(test_matrix)
result = transform_func(input_copy)
solved_outputs.append(result)
except Exception:
# If the function fails on a specific test case, append the original
# matrix as a fallback for that case and continue.
solved_outputs.append(copy.deepcopy(test_matrix))
return dspy.Prediction(test_outputs=solved_outputs)
except Exception:
# If any step in the pipeline fails catastrophically, return the original inputs.
return dspy.Prediction(test_outputs=fallback_outputs)
# The overall task signature, defining the final inputs and outputs of the program.
class SolveTaskSignature(dspy.Signature):
"""
Given a set of training examples demonstrating a matrix transformation,
apply the same transformation to a new set of test matrices.
"""
training_examples: List[TrainingExample] = dspy.InputField(description="Input and output examples demonstrating the task to be performed.")
test_inputs: List[MATRIX] = dspy.InputField(description="Input matrices to be solved following the task described in the training examples.")
test_outputs: List[MATRIX] = dspy.OutputField(description="Output matrices corresponding to the test inputs.")
# The final program is an instance of our new, more robust module.
program = ARCSolver()
2025/08/29 12:14:30 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 12:15:20 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 12:17:12 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 12:17:34 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 12:20:45 INFO dspy.evaluate.evaluate: Average Metric: 3.0 / 3 (100.0%)
2025/08/29 12:21:27 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 12:23:58 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 12:24:40 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 12:24:59 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 12:25:04 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 12:25:48 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 12:28:17 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 12:29:11 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 12:35:41 INFO dspy.evaluate.evaluate: Average Metric: 124.0 / 200 (62.0%)
GEPA Optimization: 58%|███████████████████████████████████████████████████████████████ | 2316/4000 [15:01:44<10:44:00, 22.95s/rollouts]Iteration 100: Full valset score for new program: 0.62
Iteration 100: Full train_val score for new program: 0.62
Iteration 100: Individual valset scores for new program: [True, True, False, True, False, False, False, True, True, True, True, True, True, True, False, True, False, False, True, False, True, True, True, False, True, True, False, False, True, True, False, False, False, True, True, True, False, True, False, True, False, True, True, False, False, True, False, True, True, True, False, True, True, True, True, False, True, True, False, True, True, False, False, True, True, False, True, True, False, False, True, True, True, True, True, True, False, False, True, True, True, True, True, True, True, True, True, False, False, False, True, True, True, True, True, True, True, False, False, True, True, True, False, False, True, True, True, True, False, True, False, False, False, False, True, True, True, True, True, False, False, False, True, False, False, False, False, True, True, True, False, False, True, True, True, True, True, False, True, False, True, True, True, False, True, False, False, False, True, True, False, True, True, True, True, True, True, False, False, False, False, True, True, False, False, True, True, False, False, False, True, True, False, True, True, True, False, True, False, True, True, True, True, False, False, True, True, True, True, True, True, True, False, True, True, False, True, False, False, True]
Iteration 100: New valset pareto front scores: [True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, False, True, True, True, True, True, True, True, True, True, False, True, True, True, True, False, True, True, True, True, False, True, True, True, False, True, True, True, False, True, False, True, True, True, True, True, True, True, True, 0, True, True, False, True, True, True, False, True, True, False, True, True, 0, False, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, False, True, True, True, True, True, True, True, True, True, True, True, True, True, False, True, True, True, True, True, True, False, True, True, True, True, True, True, True, True, True, True, True, True, False, False, True, False, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, False, True, False, True, False, True, True, True, True, True, True, True, True, True, True, True, False, True, True, True, True, False, True, True, True, True, False, True, True, True, True, True, True, False, True, False, True, True, True, True, False, False, True, True, True, True, True, True, True, True, True, True, True, True, True, False, True]
Iteration 100: Full valset pareto front score: 0.85
Iteration 100: Updated valset pareto front programs: [{8, 1, 3, 5}, {0, 1, 2, 3, 4, 5, 6, 7, 8}, {0}, {0, 2, 3, 4, 5, 6, 7, 8}, {0, 1, 2, 3, 4, 5, 6, 7}, {0, 4}, {0, 1, 2, 3, 4, 5, 6, 7}, {0, 1, 3, 4, 5, 6, 7, 8}, {0, 1, 2, 3, 4, 5, 6, 7, 8}, {0, 2, 3, 4, 5, 6, 7, 8}, {0, 1, 2, 3, 4, 5, 6, 7, 8}, {0, 1, 2, 3, 4, 5, 6, 7, 8}, {0, 1, 2, 3, 4, 5, 6, 7, 8}, {8}, {0, 1, 2, 3, 4, 6}, {0, 1, 2, 3, 4, 5, 6, 7, 8}, {0, 1, 2, 3, 4, 5, 6, 7, 8}, {1, 4, 5, 7}, {0, 1, 2, 3, 4, 5, 6, 7, 8}, {0, 2, 4, 6, 7}, {0, 1, 2, 3, 4, 5, 6, 7, 8}, {0, 2, 3, 4, 5, 6, 7, 8}, {0, 1, 2, 3, 4, 5, 6, 7, 8}, {1, 5, 7}, {0, 3, 4, 6, 8}, {0, 1, 3, 6, 7, 8}, {0, 1, 2, 3, 4, 5, 6, 7, 8}, {0, 4, 7}, {0, 1, 2, 3, 4, 5, 6, 7, 8}, {0, 1, 2, 3, 4, 5, 6, 7, 8}, {1}, {0, 1, 2, 3, 4, 5, 6, 7, 8}, {2}, {0, 1, 2, 3, 4, 5, 6, 7, 8}, {0, 1, 2, 3, 5, 6, 7, 8}, {0, 1, 2, 3, 4, 5, 6, 7, 8}, {0, 1, 2, 3, 4, 5, 6, 7, 8}, {0, 1, 2, 3, 4, 5, 6, 7, 8}, {2, 5}, {0, 2, 3, 4, 5, 6, 7, 8}, {0, 1, 2, 3, 4, 5, 6, 7, 8}, {0, 1, 2, 3, 4, 5, 6, 7, 8}, {0, 1, 2, 3, 4, 5, 6, 7, 8}, {1}, {0, 1, 2, 3, 4, 5, 6, 7, 8}, {0, 1, 2, 3, 4, 6, 8}, {0, 1, 2, 3, 4, 5, 6, 7, 8}, {8}, {0, 1, 2, 3, 4, 5, 6, 7, 8}, {0, 1, 2, 3, 4, 6, 7, 8}, {0, 1, 2, 3, 4, 6, 7}, {0, 1, 2, 3, 4, 5, 6, 7, 8}, {0, 1, 3, 4, 5, 6, 7, 8}, {0, 1, 2, 3, 4, 5, 6, 7, 8}, {0, 1, 2, 3, 4, 5, 6, 7, 8}, {0, 1, 2, 3, 4, 5, 6, 7, 8}, {0, 1, 3, 4, 5, 6, 7, 8}, {0, 1, 2, 3, 4, 5, 6, 7, 8}, {0, 1, 2, 3, 4, 5, 6, 7, 8}, {0, 1, 2, 3, 4, 5, 6, 7, 8}, {0, 1, 2, 3, 8}, {0, 1, 5}, {0, 1, 2, 3, 4, 5, 6, 7, 8}, {0, 1, 2, 3, 4, 5, 6, 7, 8}, {8, 4, 6, 7}, {0, 1, 2, 3, 4, 5, 6, 7, 8}, {1, 2, 3, 5, 6, 7, 8}, {0, 1, 3, 4, 5, 6, 7, 8}, {0, 1, 2, 3, 4, 5, 6, 7, 8}, {0, 1, 2, 3, 4, 5, 6, 7, 8}, {0, 1, 2, 3, 4, 5, 6, 7, 8}, {0, 1, 2, 3, 4, 5, 6, 7, 8}, {0, 1, 2, 3, 4, 5, 6, 7, 8}, {2, 3, 4, 5, 6, 7, 8}, {0, 1, 2, 3, 4, 5, 6, 7, 8}, {1, 2, 3, 5, 6, 7, 8}, {0, 3, 4, 5, 6, 7}, {4}, {0, 1, 2, 3, 4, 5, 6, 7, 8}, {0, 2, 3, 4, 5, 7, 8}, {0, 8, 2, 6}, {1, 2, 3, 4, 5, 6, 7, 8}, {0, 1, 2, 3, 4, 5, 6, 7, 8}, {0, 1, 2, 3, 4, 5, 6, 7, 8}, {0, 1, 2, 3, 4, 5, 6, 7, 8}, {0, 1, 2, 3, 4, 5, 6, 7, 8}, {0, 1, 2, 4, 5, 6, 7, 8}, {0, 1, 2, 3, 4, 5, 6, 7}, {0, 3, 4, 6, 7}, {0, 1, 2, 3, 4, 5, 6, 7, 8}, {0, 1, 2, 4, 5, 6, 7, 8}, {0, 1, 2, 3, 4, 5, 6, 7, 8}, {0, 1, 2, 3, 4, 5, 6, 7, 8}, {0, 1, 2, 3, 4, 5, 6, 7, 8}, {0, 1, 2, 3, 4, 5, 6, 7, 8}, {0, 1, 2, 3, 4, 5, 6, 7, 8}, {8, 1, 3}, {7}, {0, 3, 4, 6, 7}, {0, 1, 2, 3, 4, 5, 6, 7, 8}, {0, 1, 2, 3, 4, 5, 6, 7, 8}, {0, 1, 2, 3, 4, 5, 6, 8}, {0, 4, 6, 7}, {0, 1, 2, 3, 4, 5, 6, 7, 8}, {0, 1, 2, 3, 4, 5, 6, 7, 8}, {0, 1, 2, 3, 5, 7, 8}, {1, 2, 3, 4, 5, 6, 7, 8}, {0, 1, 2, 3, 5, 6, 7, 8}, {1, 2}, {0, 1, 4, 5, 6, 8}, {0, 1, 2, 3, 4, 5, 6, 7, 8}, {0, 2, 5}, {1, 3}, {2, 4}, {0, 1, 2, 3, 4, 5, 6, 7, 8}, {3, 4, 5, 7, 8}, {0, 1, 2, 3, 4, 5, 6, 7, 8}, {2, 3, 4, 6, 7, 8}, {0, 1, 2, 3, 4, 5, 6, 8}, {0, 1, 2, 3, 5, 6, 7}, {2, 3, 4, 5, 6, 7}, {0, 1, 7}, {0, 1, 2, 3, 4, 5, 6, 7, 8}, {0, 1, 2, 3, 4, 5, 6, 7, 8}, {0, 1, 2, 3, 4, 5, 6, 7, 8}, {0, 1, 2, 3, 4, 5, 6, 7}, {0, 1, 2, 3, 4, 5, 6, 7, 8}, {0, 1, 2, 3, 4, 5, 6, 7, 8}, {0, 1, 2, 3, 5, 6, 7, 8}, {0, 1, 2, 3, 5, 6, 7, 8}, {2, 7}, {0, 1, 3}, {0, 1, 2, 3, 4, 5, 6, 7, 8}, {0, 1, 3, 5, 6, 7, 8}, {0, 1, 2, 3, 4, 6, 7, 8}, {8, 7}, {0, 1, 3, 6, 7, 8}, {3, 5, 6, 7}, {1, 2, 3, 4, 5, 6, 7, 8}, {1, 2, 3, 4, 5, 6, 7}, {0, 1, 2, 3, 4, 5, 6, 7, 8}, {0, 1, 2, 3, 4, 6, 7, 8}, {0, 1, 2, 3, 4, 5, 6, 7, 8}, {0, 1, 2, 3, 4, 5, 6, 7, 8}, {0, 1, 2, 3, 4, 5, 6, 7, 8}, {0, 1, 2, 3, 4, 5, 6, 7, 8}, {0, 2, 3, 5, 6, 7}, {0, 1, 2, 3, 4, 5, 6, 7, 8}, {0, 1, 2, 3, 4, 5, 6, 7, 8}, {0, 1, 2, 3, 4, 5, 6, 7, 8}, {4}, {8, 7}, {0, 1, 2, 3, 4, 5, 6, 7, 8}, {0, 1, 3, 4, 5, 6, 7, 8}, {0, 1, 3, 4, 5, 6, 7, 8}, {0, 1, 2, 3, 5, 6, 7, 8}, {0, 3, 5, 6, 8}, {0, 1, 2, 3, 4, 5, 6, 7}, {0, 1, 2, 3, 6, 7}, {0, 1, 2, 3, 4, 5, 6, 7, 8}, {1}, {0, 1, 2, 3, 4, 5, 6, 8}, {0, 4, 5, 6, 7, 8}, {0, 3, 5}, {0, 1, 2, 3, 4, 5, 6, 7, 8}, {0, 1, 2, 3, 4, 5, 6, 7, 8}, {0, 1, 2, 3, 4, 5, 6, 7, 8}, {0, 1, 2, 4, 5, 6, 7}, {0, 1, 2, 4, 5, 6, 7}, {0, 1, 2, 3, 4, 5, 6, 7, 8}, {0, 8, 7}, {0, 1, 2, 3, 4, 5, 6, 7, 8}, {6}, {0, 1, 3, 5, 6, 7, 8}, {0, 1, 2, 3, 4, 5, 6, 8}, {1, 2, 3, 5, 6, 8}, {0, 1, 2, 3, 4, 5, 6, 7, 8}, {0, 1, 2, 3, 4, 5, 6, 7, 8}, {0, 1, 2, 3, 4, 5, 6, 7, 8}, {0, 1, 3, 4, 5, 6, 7, 8}, {0, 8, 2, 7}, {0, 1, 2, 3, 4, 5, 6, 7, 8}, {0, 1, 2, 3, 4, 5, 6, 7, 8}, {0, 1, 2, 3, 4, 5, 6, 7, 8}, {0, 1, 2, 3, 4, 5, 6, 7, 8}, {0, 1, 2, 3, 4, 5, 6, 7, 8}, {0, 1, 2, 3, 4, 5, 6, 7, 8}, {0, 1, 2, 3, 4, 5, 6, 7, 8}, {0, 1, 2, 4, 5, 6, 7, 8}, {0, 5, 6, 7, 8}, {0, 1, 2, 3, 4, 5, 6, 7, 8}, {0, 1, 2, 3, 4, 5, 6, 7, 8}, {0, 1, 2, 3, 4, 5, 6, 7}, {1, 2, 4, 5, 6, 7, 8}, {0, 1, 2, 3, 4, 5, 6, 7, 8}, {6}, {0, 1, 2, 3, 4, 5, 6, 7, 8}, {0}, {0, 1, 2, 3, 4, 5, 6, 7, 8}, {0, 2, 3, 4, 5, 6, 7, 8}]
Iteration 100: Best valset aggregate score so far: 0.675
Iteration 100: Best program as per aggregate score on train_val: 6
Iteration 100: Best program as per aggregate score on valset: 6
Iteration 100: Best score on valset: 0.675
Iteration 100: Best score on train_val: 0.675
Iteration 100: Linear pareto front program index: 6
Iteration 100: New program candidate index: 8
Iteration 101: Selected program 4 score: 0.61
Average Metric: 1.00 / 1 (100.0%): 33%|████████████████████████████████████▋ | 1/3 [01:45<03:30, 105.39s/it]Error applying `transform_grid` to a test input: name 'collections' is not defined
Average Metric: 1.00 / 2 (50.0%): 67%|██████████████████████████████████████████████████████████████████████████ | 2/3 [03:37<01:49, 109.32s/it]Error applying `transform_grid` to a test input: name 'collections' is not defined
Average Metric: 1.00 / 3 (33.3%): : 4it [05:37, 84.29s/it] 2025/08/29 12:41:18 INFO dspy.evaluate.evaluate: Average Metric: 1.0 / 3 (33.3%)
Iteration 101: Proposed new text for program: import dspy
from typing import List
import pydantic
import traceback
import copy
import collections
# Define the type for a grid/matrix as a list of lists of integers.
MATRIX = List[List[int]]
class TrainingExample(pydantic.BaseModel):
"""A Pydantic model for a single training example, containing an input and output grid."""
input: MATRIX
output: MATRIX
# --- Decomposed Signatures for a Multi-Step Approach ---
class HypothesizeTransformation(dspy.Signature):
"""
Analyze the provided training examples from the Abstraction and Reasoning Corpus (ARC).
Your goal is to propose a single, concise, high-level transformation rule (a hypothesis) that explains the change from input to output grids.
**Strategy:**
1. Look for the simplest possible explanation first.
2. Consider patterns like: color filling, object manipulation (moving, copying, scaling), periodic tiling, drawing lines, or symmetry.
3. Express the rule in one or two clear sentences. Do not explain how to code it, just state the rule itself.
**Example Hypotheses:**
- "Fill all zero-valued cells with the most frequent non-zero color from the input grid."
- "Find the repeating 3x3 pattern from the top-left corner and tile the entire grid with it to fill in the zeros."
- "For every horizontal segment of zeros between two different colors, fill the first half of the segment with the left color and the second half with the right color."
"""
training_examples: List[TrainingExample] = dspy.InputField(desc="A list of input/output grid pairs demonstrating the transformation rule.")
hypothesis: str = dspy.OutputField(desc="A concise, high-level description of the transformation rule.")
class ImplementTransformation(dspy.Signature):
"""
You are an expert programmer solving visual puzzles from the Abstraction and Reasoning Corpus (ARC).
Your primary goal is to write a single, self-contained Python function named `transform_grid` that correctly implements the given transformation **Hypothesis**.
Use the provided `training_examples` to understand the context, handle specific values, and verify edge cases related to the hypothesis.
**Function Requirements:**
- The function must be named exactly `transform_grid`.
- It must accept one argument: `grid`, which is a 2D list of integers (the input grid).
- It must return a new 2D list of integers representing the transformed grid.
- The function should not rely on any external libraries unless they are standard (e.g., `copy`, `collections`). If you need a library like numpy, import it inside the function.
- Do not modify the input grid in place; create a copy if necessary.
- Ensure your code is robust and correctly implements the logic described in the hypothesis.
"""
training_examples: List[TrainingExample] = dspy.InputField(desc="A list of input/output grid pairs for context and edge cases.")
hypothesis: str = dspy.InputField(desc="The high-level transformation rule to implement.")
reasoning: str = dspy.OutputField(desc="Step-by-step thinking process on how to translate the hypothesis into Python code, considering the examples.")
python_code: str = dspy.OutputField(desc="A string containing the complete, self-contained Python function `transform_grid(grid)`.")
# --- New Custom Module with "Hypothesize, Implement, Verify" Loop ---
class ARCCodeGeneratorAndTester(dspy.Module):
"""
A DSPy module that solves ARC tasks by generating, testing, and refining Python code.
It follows a "Hypothesize, Implement, Verify" loop to increase robustness.
"""
def __init__(self, max_attempts=3):
super().__init__()
self.max_attempts = max_attempts
# Module for generating a high-level hypothesis.
self.generate_hypothesis = dspy.Predict(HypothesizeTransformation)
# Module for implementing the hypothesis as code.
self.implement_hypothesis = dspy.ChainOfThought(ImplementTransformation)
def verify_code(self, code_str: str, training_examples: List[TrainingExample]):
"""
Safely executes the generated code and verifies its correctness against all training examples.
Returns the callable function if verification passes, otherwise returns None.
"""
local_scope = {}
try:
# The LM might wrap the code in markdown, so we extract it.
if "```python" in code_str:
code_str = code_str.split("```python")[1].split("```")[0].strip()
exec(code_str, globals(), local_scope)
transform_function = local_scope.get('transform_grid')
if not (transform_function and callable(transform_function)):
print("Verification failed: `transform_grid` function not found or not callable.")
return None
# Test the function against all training examples.
for example in training_examples:
input_copy = copy.deepcopy(example.input)
predicted_output = transform_function(input_copy)
if predicted_output != example.output:
print("Verification failed: Output mismatch for a training example.")
return None
print("Verification successful: Code passed all training examples.")
return transform_function
except Exception as e:
print(f"Verification failed: Error during code execution or testing: {e}")
return None
def forward(self, training_examples: List[TrainingExample], test_inputs: List[MATRIX]):
verified_transform_function = None
for attempt in range(self.max_attempts):
print(f"--- Attempt {attempt + 1}/{self.max_attempts} ---")
# 1. Hypothesize: Generate a high-level plan.
prediction = self.generate_hypothesis(training_examples=training_examples)
hypothesis = prediction.hypothesis
print(f"Generated Hypothesis: {hypothesis}")
# 2. Implement: Generate code based on the hypothesis.
code_prediction = self.implement_hypothesis(
training_examples=training_examples,
hypothesis=hypothesis
)
python_code = code_prediction.python_code
# 3. Verify: Test the generated code against training examples.
verified_transform_function = self.verify_code(python_code, training_examples)
if verified_transform_function:
break # Found a working solution.
# 4. Apply or Fallback
generated_outputs = []
if verified_transform_function:
print("Applying verified function to test inputs.")
for test_input in test_inputs:
try:
output_grid = verified_transform_function(copy.deepcopy(test_input))
generated_outputs.append(output_grid)
except Exception as e:
print(f"Error applying verified function to a test input: {e}. Falling back for this input.")
generated_outputs.append(copy.deepcopy(test_input))
else:
print("Failed to generate and verify a working function. Using fallback for all test inputs.")
for test_input in test_inputs:
generated_outputs.append(copy.deepcopy(test_input))
return dspy.Prediction(test_outputs=generated_outputs)
# The final program object is an instance of our new, more robust custom module.
program = ARCCodeGeneratorAndTester()
--- Attempt 1/3 ---
--- Attempt 1/3 ---
--- Attempt 1/3 ---
Error applying `transform_grid` to a test input: name 'collections' is not defined
Generated Hypothesis: Fill the zero-valued cells in each column by continuing the periodic vertical pattern established by the non-zero cells within that same column.
--- Attempt 1/3 ------ Attempt 1/3 ---
--- Attempt 1/3 ---
Generated Hypothesis: Fill the zero-valued cells in each column by continuing the periodic vertical pattern established by the non-zero cells within that same column.
2025/08/29 12:44:24 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
Error executing generated code: argument of type 'NoneType' is not iterable
Traceback: Traceback (most recent call last):
File "<string>", line 65, in forward
TypeError: argument of type 'NoneType' is not iterable
Generated Hypothesis: Fill each black cell with the color of the nearest non-black cell in the input grid, using Manhattan distance (sum of horizontal and vertical steps). In case of a tie for the nearest cell, the one that is located top-most, and then left-most, determines the color.
Verification failed: Error during code execution or testing: 'dict' object has no attribute 'input'
--- Attempt 2/3 ---
Generated Hypothesis: Fill the zero-valued cells in each column by continuing the periodic vertical pattern established by the non-zero cells within that same column.
Verification failed: Error during code execution or testing: 'dict' object has no attribute 'input'
--- Attempt 3/3 ---
Generated Hypothesis: Fill the zero-valued cells in each column by continuing the periodic vertical pattern established by the non-zero cells within that same column.
Verification failed: Error during code execution or testing: 'dict' object has no attribute 'input'
Failed to generate and verify a working function. Using fallback for all test inputs.
Verification failed: Error during code execution or testing: 'dict' object has no attribute 'input'
--- Attempt 2/3 ---
Generated Hypothesis: Fill the zero-valued cells in each column by continuing the periodic vertical pattern established by the non-zero cells within that same column.
Verification failed: Error during code execution or testing: 'dict' object has no attribute 'input'
--- Attempt 3/3 ---
Generated Hypothesis: Fill the zero-valued cells in each column by continuing the periodic vertical pattern established by the non-zero cells within that same column.
Verification failed: Error during code execution or testing: 'dict' object has no attribute 'input'
Failed to generate and verify a working function. Using fallback for all test inputs.
Verification failed: Error during code execution or testing: 'dict' object has no attribute 'input'
--- Attempt 2/3 ---
Generated Hypothesis: Fill each black cell with the color of the nearest non-black cell in the input grid, using Manhattan distance (sum of horizontal and vertical steps). In case of a tie for the nearest cell, the one that is located top-most, and then left-most, determines the color.
Verification failed: Error during code execution or testing: 'dict' object has no attribute 'input'
--- Attempt 3/3 ---
Generated Hypothesis: Fill each black cell with the color of the nearest non-black cell in the input grid, using Manhattan distance (sum of horizontal and vertical steps). In case of a tie for the nearest cell, the one that is located top-most, and then left-most, determines the color.
Verification failed: Error during code execution or testing: 'dict' object has no attribute 'input'
Failed to generate and verify a working function. Using fallback for all test inputs.
--- Attempt 1/3 ---
--- Attempt 1/3 ---
Generated Hypothesis: Fill each black cell with the color of the nearest non-black cell in the input grid, using Manhattan distance (sum of horizontal and vertical steps). In case of a tie for the nearest cell, the one that is located top-most, and then left-most, determines the color.
Verification failed: Error during code execution or testing: 'dict' object has no attribute 'input'
--- Attempt 2/3 ---
Generated Hypothesis: Fill each black cell with the color of the nearest non-black cell in the input grid, using Manhattan distance (sum of horizontal and vertical steps). In case of a tie for the nearest cell, the one that is located top-most, and then left-most, determines the color.
Verification failed: Error during code execution or testing: 'dict' object has no attribute 'input'
--- Attempt 3/3 ---
Generated Hypothesis: Fill each black cell with the color of the nearest non-black cell in the input grid, using Manhattan distance (sum of horizontal and vertical steps). In case of a tie for the nearest cell, the one that is located top-most, and then left-most, determines the color.
Verification failed: Error during code execution or testing: 'dict' object has no attribute 'input'
Failed to generate and verify a working function. Using fallback for all test inputs.
Generated Hypothesis: For each cell that is black in the input, its color in the output is determined by looking down its column. If a non-black cell is found in the input grid below the current cell, the black cell takes the color of the highest of these non-black cells. If there are no non-black cells below it, the cell is colored based on its row's parity: even-indexed rows are colored light blue (5), and odd-indexed rows are colored with the non-black color from the input.
Generated Hypothesis: Each zero-valued cell is replaced by the color that appears most frequently among its eight neighbors (including diagonals).
Generated Hypothesis: For each row, if it contains any non-zero pixels from the input, its zero-valued cells are filled with a pattern that alternates between blue (5) and the main input color based on the column index. If a row is entirely zero in the input, it is filled with a solid color, which alternates between the main input color and blue based on the row index.
2025/08/29 12:48:42 INFO dspy.evaluate.evaluate: Average Metric: 0.0 / 3 (0.0%)
GEPA Optimization: 58%|███████████████████████████████████████████████████████████████▎ | 2322/4000 [15:14:46<12:26:36, 26.70s/rollouts]Verification failed: Error during code execution or testing: 'dict' object has no attribute 'input'
--- Attempt 2/3 ---
Generated Hypothesis: For each cell that is black in the input, its color in the output is determined by looking down its column. If a non-black cell is found in the input grid below the current cell, the black cell takes the color of the highest of these non-black cells. If there are no non-black cells below it, the cell is colored based on its row's parity: even-indexed rows are colored light blue (5), and odd-indexed rows are colored with the non-black color from the input.
Verification failed: Error during code execution or testing: 'dict' object has no attribute 'input'
--- Attempt 3/3 ---
Generated Hypothesis: For each cell that is black in the input, its color in the output is determined by looking down its column. If a non-black cell is found in the input grid below the current cell, the black cell takes the color of the highest of these non-black cells. If there are no non-black cells below it, the cell is colored based on its row's parity: even-indexed rows are colored light blue (5), and odd-indexed rows are colored with the non-black color from the input.
Verification failed: Error during code execution or testing: 'dict' object has no attribute 'input'
Failed to generate and verify a working function. Using fallback for all test inputs.
Iteration 101: New subsample score is not better, skipping
Iteration 102: Selected program 0 score: 0.67
Average Metric: 1.00 / 2 (50.0%): 33%|█████████████████████████████████████▎ | 1/3 [00:00<00:00, 44.01it/s]Verification failed: Error during code execution or testing: 'dict' object has no attribute 'input'
--- Attempt 2/3 ---
Generated Hypothesis: Each zero-valued cell is replaced by the color that appears most frequently among its eight neighbors (including diagonals).
Verification failed: Error during code execution or testing: 'dict' object has no attribute 'input'
--- Attempt 3/3 ---
Generated Hypothesis: Each zero-valued cell is replaced by the color that appears most frequently among its eight neighbors (including diagonals).
Verification failed: Error during code execution or testing: 'dict' object has no attribute 'input'
Failed to generate and verify a working function. Using fallback for all test inputs.
Generated Hypothesis: Create a background of horizontal stripes, alternating between the most frequent non-zero color from the input (C) and color 5. If C is greater than 5, the top row is C; otherwise, the top row is 5. The final output is the original input's non-zero pixels overlaid onto this background.
Average Metric: 2.00 / 3 (66.7%): 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [01:12<00:00, 24.14s/it]2025/08/29 12:49:55 INFO dspy.evaluate.evaluate: Average Metric: 2.0 / 3 (66.7%)
Verification failed: Error during code execution or testing: 'dict' object has no attribute 'input'
--- Attempt 2/3 ---
Generated Hypothesis: For each cell that is black in the input, its color in the output is determined by looking down its column. If a non-black cell is found in the input grid below the current cell, the black cell takes the color of the highest of these non-black cells. If there are no non-black cells below it, the cell is colored based on its row's parity: even-indexed rows are colored light blue (5), and odd-indexed rows are colored with the non-black color from the input.
Verification failed: Error during code execution or testing: 'dict' object has no attribute 'input'
--- Attempt 3/3 ---
Generated Hypothesis: For each cell that is black in the input, its color in the output is determined by looking down its column. If a non-black cell is found in the input grid below the current cell, the black cell takes the color of the highest of these non-black cells. If there are no non-black cells below it, the cell is colored based on its row's parity: even-indexed rows are colored light blue (5), and odd-indexed rows are colored with the non-black color from the input.
Verification failed: Error during code execution or testing: 'dict' object has no attribute 'input'
Failed to generate and verify a working function. Using fallback for all test inputs.
Iteration 102: Proposed new text for program: import dspy
from typing import List
import pydantic
import copy
# Define the data structures for matrices and training examples.
# Using pydantic.BaseModel ensures structured data that DSPy can serialize.
MATRIX = List[List[int]]
class TrainingExample(pydantic.BaseModel):
input: MATRIX
output: MATRIX
class DeduceRuleSignature(dspy.Signature):
"""
Your task is to analyze pairs of input/output grids from the Abstract Reasoning Corpus (ARC) and deduce the transformation rule. You must then express this rule in two ways: a natural language description and a Python function.
**1. Analyze the Training Examples:**
Carefully examine the `training_examples`, which show "before" (input) and "after" (output) states of a grid. Identify the logical pattern or transformation that connects them.
**2. Write a Natural Language Description:**
In the `rule_description` field, provide a clear, step-by-step explanation of the transformation logic. This should be understandable by a human.
**3. Write a Python Function:**
In the `python_function` field, provide a single, self-contained Python function `transform_matrix(matrix: list[list[int]]) -> list[list[int]]`.
- This function must implement the rule you described.
- It must accept one argument: a 2D list of integers (`matrix`).
- It must return the transformed 2D list of integers.
- **IMPORTANT**: Do NOT use any external libraries like numpy. You can use the standard `copy` library for deep copies if needed to avoid modifying the input matrix in place. The function must be entirely self-contained.
**Common Transformation Patterns to Look For:**
- Object manipulation: moving, rotating, scaling, copying, or deleting shapes/objects.
- Filling patterns: filling enclosed areas or shapes based on properties like size, color, or the properties of the enclosing border.
- Neighborhood-based changes: modifying a cell's value based on its adjacent or nearby cells.
- Symmetry and repetition: creating or completing patterns based on symmetry or repeating motifs.
"""
training_examples: List[TrainingExample] = dspy.InputField(
desc="A list of input-output pairs demonstrating the task."
)
rule_description: str = dspy.OutputField(
desc="A step-by-step natural language explanation of the transformation rule."
)
python_function: str = dspy.OutputField(
desc="A single, self-contained Python function `transform_matrix(matrix)` that implements the rule."
)
class ARCsolver(dspy.Module):
"""A DSPy module that solves ARC tasks by generating and executing Python code."""
def __init__(self):
super().__init__()
# The Predict module is tasked with generating the rule and the code.
self.deduce_rule = dspy.Predict(DeduceRuleSignature)
def forward(self, training_examples: List[TrainingExample], test_inputs: List[MATRIX]) -> dspy.Prediction:
"""
Takes training examples and test inputs, generates a solution function,
and applies it to the test inputs.
"""
# Step 1: Deduce the rule and generate the Python function from the examples.
prediction = self.deduce_rule(training_examples=training_examples)
python_code = prediction.python_function
# Step 2: Prepare a safe environment to execute the generated code.
local_namespace = {}
# The 'copy' module is often useful for grid manipulations.
exec_globals = {'copy': copy}
# Step 3: Execute the generated code to define the function, with a fallback.
try:
exec(python_code, exec_globals, local_namespace)
transform_func = local_namespace['transform_matrix']
# Apply the dynamically created function to all test inputs.
test_outputs = [transform_func(matrix) for matrix in test_inputs]
except Exception as e:
# Fallback strategy: If code generation or execution fails,
# return the original inputs as a safe default. This prevents crashing.
print(f"Code execution failed with error: {e}. Returning original inputs as fallback.")
test_outputs = test_inputs
# Step 4: Return the final prediction in the required format.
return dspy.Prediction(test_outputs=test_outputs)
# The final 'program' is an instance of our advanced ARCsolver module.
program = ARCsolver()
Code execution failed with error: invalid syntax (<string>, line 1). Returning original inputs as fallback.
Code execution failed with error: invalid syntax (<string>, line 1). Returning original inputs as fallback.
2025/08/29 12:51:38 INFO dspy.evaluate.evaluate: Average Metric: 0.0 / 3 (0.0%)
GEPA Optimization: 58%|███████████████████████████████████████████████████████████████▍ | 2328/4000 [15:17:42<12:27:25, 26.82s/rollouts]Code execution failed with error: invalid syntax (<string>, line 1). Returning original inputs as fallback.
Iteration 102: New subsample score is not better, skipping
Iteration 103: Selected program 1 score: 0.63
Average Metric: 0.00 / 1 (0.0%): 33%|█████████████████████████████████████▋ | 1/3 [00:00<00:00, 7.65it/s]Verification failed: Error during code execution or testing: 'dict' object has no attribute 'input'
--- Attempt 2/3 ---
Generated Hypothesis: For each cell that is black in the input, its color in the output is determined by looking down its column. If a non-black cell is found in the input grid below the current cell, the black cell takes the color of the highest of these non-black cells. If there are no non-black cells below it, the cell is colored based on its row's parity: even-indexed rows are colored light blue (5), and odd-indexed rows are colored with the non-black color from the input.
Verification failed: Error during code execution or testing: 'dict' object has no attribute 'input'
--- Attempt 3/3 ---
Generated Hypothesis: For each cell that is black in the input, its color in the output is determined by looking down its column. If a non-black cell is found in the input grid below the current cell, the black cell takes the color of the highest of these non-black cells. If there are no non-black cells below it, the cell is colored based on its row's parity: even-indexed rows are colored light blue (5), and odd-indexed rows are colored with the non-black color from the input.
Verification failed: Error during code execution or testing: 'dict' object has no attribute 'input'
Failed to generate and verify a working function. Using fallback for all test inputs.
Average Metric: 1.00 / 3 (33.3%): 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [03:51<00:00, 77.15s/it]2025/08/29 12:55:30 INFO dspy.evaluate.evaluate: Average Metric: 1.0 / 3 (33.3%)
Iteration 103: Proposed new text for program: import dspy
from typing import List, Any
import pydantic
import traceback
# Define the type for a matrix for clarity
MATRIX = List[List[int]]
class TrainingExample(pydantic.BaseModel):
"""A Pydantic model for a single training example, containing an input and output matrix."""
input: MATRIX
output: MATRIX
class InferRuleSignature(dspy.Signature):
"""
Analyzes pairs of input/output matrices to deduce the transformation rule.
You are an expert analyst of abstract visual reasoning puzzles. Your task is to carefully examine the provided training examples. Each example consists of an 'input' matrix and a corresponding 'output' matrix.
Your goal is to deduce the single, consistent transformation rule that converts each input to its output. Describe this rule in clear, unambiguous, step-by-step natural language. The description must be precise enough for an expert Python programmer to implement it without further clarification.
Consider various possible transformation patterns, including but not limited to:
- Simple value substitutions (e.g., change all 6s to 2s).
- Geometric operations: rotation, reflection, scaling, repetition, shifting.
- Object-based logic: identifying shapes or objects and applying rules based on their properties (size, color, position, aspect ratio of bounding box).
- Cropping and framing: Cropping the input to the bounding box of the largest object, or the object of a specific color.
- Color logic: Swapping the most frequent color with the least frequent color.
- Fill/completion patterns: such as flood fills or completing a grid based on object boundaries.
Focus on creating a generalizable, algorithmic description.
"""
training_examples: List[TrainingExample] = dspy.InputField(description="A list of input and output examples demonstrating the task.")
transformation_rule: str = dspy.OutputField(description="A step-by-step natural language description of the transformation rule.")
class GenerateCodeSignature(dspy.Signature):
"""
Writes a Python function to implement a transformation rule for matrices.
You are an expert Python programmer specializing in algorithmic tasks. You will be given a natural language description of a rule that transforms a matrix (list of lists of integers) and the original training examples that the rule was derived from.
Your task is to write a single Python function named `transform_matrix` that implements this rule.
**Function Requirements:**
- The function must be named exactly `transform_matrix`.
- It must accept one argument: `matrix` (a list of lists of integers).
- It must return a new matrix (a list of lists of integers).
- You CANNOT use any external libraries like numpy. You must implement all logic using standard Python data structures and control flow.
- The function should be self-contained. Do not define helper functions outside of its scope unless absolutely necessary (e.g., via nesting).
**Output Format:**
- Your output must be ONLY the Python code for the function.
- Do not include any explanations, comments, or surrounding text.
- Do not include the function call, just the definition.
- Start with `def transform_matrix(matrix):` and end with the last line of the function's code.
"""
transformation_rule: str = dspy.InputField(description="The natural language rule to implement.")
training_examples: List[TrainingExample] = dspy.InputField(description="The original examples used to derive the rule, for context and validation.")
python_code: str = dspy.OutputField(description="A string containing a single Python function `transform_matrix` that implements the rule.")
class ARCProgram(dspy.Module):
"""A program that infers a rule, generates code for it, and then executes the code."""
def __init__(self):
super().__init__()
self.rule_inferrer = dspy.ChainOfThought(InferRuleSignature)
self.code_generator = dspy.Predict(GenerateCodeSignature)
def forward(self, training_examples: List[TrainingExample], test_inputs: List[MATRIX]) -> dspy.Prediction:
"""
Infers a rule, generates Python code, and applies it to each test input.
Args:
training_examples: A list of TrainingExample objects.
test_inputs: A list of input matrices to be solved.
Returns:
A dspy.Prediction object with the 'test_outputs' field populated.
"""
# 1. Infer the transformation rule from the examples.
inferred = self.rule_inferrer(training_examples=training_examples)
rule = inferred.transformation_rule
# 2. Generate a Python function that implements the rule.
generated = self.code_generator(transformation_rule=rule, training_examples=training_examples)
code = generated.python_code
# Prepare a safe execution environment and a list for outputs.
all_test_outputs = []
transform_func = None
# 3. Execute the generated code to define the function.
try:
# The generated code is often wrapped in markdown backticks.
if code.strip().startswith("```python"):
code = code.strip()[9:].strip()
if code.strip().startswith("```"):
code = code.strip()[3:].strip()
if code.strip().endswith("```"):
code = code.strip()[:-3].strip()
# Define a local scope for exec to run in.
local_scope = {}
exec(code, globals(), local_scope)
transform_func = local_scope.get('transform_matrix')
except Exception as e:
print(f"Failed to compile generated code: {e}")
print(f"Generated code:\n{code}")
traceback.print_exc()
# 4. Apply the function to each test input.
for test_matrix in test_inputs:
# Default fallback output is a zero-filled matrix of the same size.
if test_matrix and test_matrix[0]:
fallback_output = [[0] * len(test_matrix[0]) for _ in range(len(test_matrix))]
else:
fallback_output = []
if transform_func:
try:
# Apply the dynamically created function.
result = transform_func(test_matrix)
all_test_outputs.append(result)
except Exception as e:
print(f"Execution of transform_matrix failed for an input: {e}")
traceback.print_exc()
all_test_outputs.append(fallback_output)
else:
# If function definition failed, append fallback for all inputs.
all_test_outputs.append(fallback_output)
return dspy.Prediction(test_outputs=all_test_outputs)
# The final 'program' object is an instance of our robust, multi-step module.
program = ARCProgram()
2025/08/29 12:57:20 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 13:00:03 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 13:00:23 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 13:02:32 INFO dspy.evaluate.evaluate: Average Metric: 2.0 / 3 (66.7%)
2025/08/29 13:06:22 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 13:06:33 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 13:06:36 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 13:07:02 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 13:08:48 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 13:09:06 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
Traceback (most recent call last):
File "<string>", line 93, in forward
AttributeError: 'NoneType' object has no attribute 'strip'
Failed to compile generated code: 'NoneType' object has no attribute 'strip'
Generated code:
None
2025/08/29 13:09:16 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
Execution of transform_matrix failed for an input: (2, 4)
Traceback (most recent call last):
File "<string>", line 121, in forward
File "<string>", line 118, in transform_matrix
KeyError: (2, 4)
2025/08/29 13:11:00 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 13:11:45 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 13:12:08 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 13:12:16 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
Traceback (most recent call last):
File "<string>", line 121, in forward
File "<string>", line 69, in transform_matrix
IndexError: list index out of range
2025/08/29 13:16:38 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 13:16:38 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 13:16:38 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
Execution of transform_matrix failed for an input: list index out of range
2025/08/29 13:18:15 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
Traceback (most recent call last):
File "<string>", line 93, in forward
AttributeError: 'NoneType' object has no attribute 'strip'
Failed to compile generated code: 'NoneType' object has no attribute 'strip'
Generated code:
None
2025/08/29 13:18:38 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 13:18:38 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 13:18:38 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
Traceback (most recent call last):
File "<string>", line 93, in forward
AttributeError: 'NoneType' object has no attribute 'strip'
Failed to compile generated code: 'NoneType' object has no attribute 'strip'
Generated code:
None
2025/08/29 13:20:38 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 13:21:42 INFO dspy.evaluate.evaluate: Average Metric: 134.0 / 200 (67.0%)
GEPA Optimization: 63%|█████████████████████████████████████████████████████████████████████▋ | 2534/4000 [15:47:46<5:46:24, 14.18s/rollouts]Iteration 103: Full valset score for new program: 0.67
Iteration 103: Full train_val score for new program: 0.67
Iteration 103: Individual valset scores for new program: [True, True, True, True, True, False, True, True, True, True, True, True, True, False, True, True, False, True, True, False, True, True, True, True, True, True, False, False, True, True, False, False, False, True, True, True, False, True, False, True, False, True, True, False, False, True, False, False, True, True, False, True, True, True, True, False, True, True, False, True, False, False, False, True, False, False, True, True, False, False, True, True, True, True, True, True, True, False, True, True, False, True, True, True, True, True, False, False, False, False, True, True, True, True, True, True, False, False, True, True, True, True, True, False, True, False, True, True, False, False, False, False, False, True, True, False, True, True, False, True, True, True, True, False, False, True, False, True, True, True, False, True, True, True, True, True, True, False, True, True, True, True, True, False, True, True, True, False, True, True, False, True, True, True, False, True, True, True, True, False, False, True, True, False, False, True, True, True, True, False, True, True, True, True, True, True, False, True, False, False, True, True, True, False, True, True, True, True, True, True, True, True, False, False, True, False, True, False, False, True]
Iteration 103: New valset pareto front scores: [True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, False, True, True, True, True, True, True, True, True, True, False, True, True, True, True, False, True, True, True, True, False, True, True, True, False, True, True, True, False, True, False, True, True, True, True, True, True, True, True, 0, True, True, False, True, True, True, False, True, True, False, True, True, 0, False, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, False, True, True, True, True, True, True, True, True, True, True, True, True, True, False, True, True, True, True, True, True, False, True, True, True, True, True, True, True, True, True, True, True, True, False, False, True, False, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, False, True, True, True, False, True, True, True, True, True, True, True, True, True, True, True, False, True, True, True, True, False, True, True, True, True, False, True, True, True, True, True, True, False, True, False, True, True, True, True, False, True, True, True, True, True, True, True, True, True, True, True, True, True, True, False, True]
Iteration 103: Full valset pareto front score: 0.86
Iteration 103: Updated valset pareto front programs: [{1, 3, 5, 8, 9}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, {0, 9}, {0, 2, 3, 4, 5, 6, 7, 8, 9}, {0, 1, 2, 3, 4, 5, 6, 7, 9}, {0, 4}, {0, 1, 2, 3, 4, 5, 6, 7, 9}, {0, 1, 3, 4, 5, 6, 7, 8, 9}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, {0, 2, 3, 4, 5, 6, 7, 8, 9}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, {8}, {0, 1, 2, 3, 4, 6, 9}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, {1, 4, 5, 7, 9}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, {0, 2, 4, 6, 7}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, {0, 2, 3, 4, 5, 6, 7, 8, 9}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, {1, 5, 9, 7}, {0, 3, 4, 6, 8, 9}, {0, 1, 3, 6, 7, 8, 9}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, {0, 4, 7}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, {1}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, {2}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, {0, 1, 2, 3, 5, 6, 7, 8, 9}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, {2, 5}, {0, 2, 3, 4, 5, 6, 7, 8, 9}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, {1}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, {0, 1, 2, 3, 4, 6, 8, 9}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, {8}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, {0, 1, 2, 3, 4, 6, 7, 8, 9}, {0, 1, 2, 3, 4, 6, 7}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, {0, 1, 3, 4, 5, 6, 7, 8, 9}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, {0, 1, 3, 4, 5, 6, 7, 8, 9}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, {0, 1, 2, 3, 8}, {0, 1, 5}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, {8, 4, 6, 7}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, {1, 2, 3, 5, 6, 7, 8, 9}, {0, 1, 3, 4, 5, 6, 7, 8, 9}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, {2, 3, 4, 5, 6, 7, 8, 9}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, {1, 2, 3, 5, 6, 7, 8, 9}, {0, 3, 4, 5, 6, 7, 9}, {4}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, {0, 2, 3, 4, 5, 7, 8, 9}, {0, 8, 2, 6}, {1, 2, 3, 4, 5, 6, 7, 8, 9}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, {0, 1, 2, 4, 5, 6, 7, 8}, {0, 1, 2, 3, 4, 5, 6, 7}, {0, 3, 4, 6, 7}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, {0, 1, 2, 4, 5, 6, 7, 8, 9}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, {8, 1, 3}, {7}, {0, 3, 4, 6, 7, 9}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, {0, 1, 2, 3, 4, 5, 6, 8, 9}, {0, 4, 6, 7, 9}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, {0, 1, 2, 3, 5, 7, 8}, {1, 2, 3, 4, 5, 6, 7, 8, 9}, {0, 1, 2, 3, 5, 6, 7, 8, 9}, {1, 2}, {0, 1, 4, 5, 6, 8}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, {0, 2, 5}, {1, 3}, {9, 2, 4}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, {3, 4, 5, 7, 8}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, {2, 3, 4, 6, 7, 8, 9}, {0, 1, 2, 3, 4, 5, 6, 8}, {0, 1, 2, 3, 5, 6, 7, 9}, {2, 3, 4, 5, 6, 7, 9}, {0, 1, 9, 7}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, {0, 1, 2, 3, 4, 5, 6, 7, 9}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, {0, 1, 2, 3, 5, 6, 7, 8, 9}, {0, 1, 2, 3, 5, 6, 7, 8, 9}, {2, 7}, {0, 1, 3, 9}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, {0, 1, 3, 5, 6, 7, 8, 9}, {0, 1, 2, 3, 4, 6, 7, 8, 9}, {8, 9, 7}, {0, 1, 3, 6, 7, 8, 9}, {3, 5, 6, 7}, {1, 2, 3, 4, 5, 6, 7, 8, 9}, {1, 2, 3, 4, 5, 6, 7, 9}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, {0, 1, 2, 3, 4, 6, 7, 8, 9}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, {9}, {0, 2, 3, 5, 6, 7, 9}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, {4}, {8, 9, 7}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, {0, 1, 3, 4, 5, 6, 7, 8, 9}, {0, 1, 3, 4, 5, 6, 7, 8}, {0, 1, 2, 3, 5, 6, 7, 8, 9}, {0, 3, 5, 6, 8, 9}, {0, 1, 2, 3, 4, 5, 6, 7, 9}, {0, 1, 2, 3, 6, 7, 9}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, {1}, {0, 1, 2, 3, 4, 5, 6, 8, 9}, {0, 4, 5, 6, 7, 8, 9}, {0, 3, 5}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, {0, 1, 2, 4, 5, 6, 7, 9}, {0, 1, 2, 4, 5, 6, 7, 9}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, {0, 8, 9, 7}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, {9, 6}, {0, 1, 3, 5, 6, 7, 8, 9}, {0, 1, 2, 3, 4, 5, 6, 8, 9}, {1, 2, 3, 5, 6, 8, 9}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, {0, 1, 3, 4, 5, 6, 7, 8}, {0, 2, 7, 8, 9}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, {9}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, {0, 1, 2, 4, 5, 6, 7, 8, 9}, {0, 5, 6, 7, 8, 9}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, {0, 1, 2, 3, 4, 5, 6, 7}, {1, 2, 4, 5, 6, 7, 8}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, {6}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, {0}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, {0, 2, 3, 4, 5, 6, 7, 8, 9}]
Iteration 103: Best valset aggregate score so far: 0.675
Iteration 103: Best program as per aggregate score on train_val: 6
Iteration 103: Best program as per aggregate score on valset: 6
Iteration 103: Best score on valset: 0.675
Iteration 103: Best score on train_val: 0.675
Iteration 103: Linear pareto front program index: 6
Iteration 103: New program candidate index: 9
Iteration 104: Selected program 6 score: 0.675
Average Metric: 2.00 / 3 (66.7%): : 4it [02:54, 43.66s/it] 2025/08/29 13:24:37 INFO dspy.evaluate.evaluate: Average Metric: 2.0 / 3 (66.7%)
Iteration 104: Proposed new text for program: import dspy
from typing import List
import pydantic
import copy
import traceback
# Define type aliases and pydantic models for clarity and structure.
MATRIX = List[List[int]]
class TrainingExample(pydantic.BaseModel):
input: MATRIX
output: MATRIX
class HypothesizeRule(dspy.Signature):
"""
Analyze the provided input/output matrix pairs from the Abstraction and Reasoning Corpus (ARC).
Deduce the underlying transformation rule that converts each input matrix to its corresponding output matrix.
Describe this rule in clear, step-by-step, unambiguous English.
Focus on the logic and the sequence of operations, not on Python code implementation.
"""
training_examples: List[TrainingExample] = dspy.InputField(desc="A list of input/output pairs demonstrating the transformation rule.")
rule_description: str = dspy.OutputField(desc="A step-by-step English description of the transformation rule.")
class ImplementRule(dspy.Signature):
"""
You are an expert programmer. Your task is to write a single, self-contained Python function based on a provided rule description and example pairs.
**Function Requirements:**
- The function must be named `transform_matrix`.
- It must accept one argument: `matrix`, which is a list of lists of integers (representing the input grid).
- It must return a list of lists of integers (representing the transformed output grid).
- The function should not use any external libraries except for `copy` if needed (e.g., `import copy; new_matrix = copy.deepcopy(matrix)`).
- Your output must be ONLY the Python code for the function. Do not include any explanations, comments outside the function, or markdown formatting like ```python.
**Successful Strategies to Consider:**
- **Object Permanence:** Most transformations preserve the grid dimensions.
- **Color Transformations:** Look for rules that change colors based on their value or their neighbors.
- **Spatial Reasoning:** Analyze shapes, positions, and relationships. Common patterns include filling enclosed areas, drawing lines, moving objects, or detecting symmetry.
- **Iterative Processes:** Some rules are applied repeatedly until the grid no longer changes.
- **Neighborhoods:** Often, a cell's new value depends on its immediate neighbors.
**Pitfalls to Avoid:**
- **Overlooking Subtle Constraints:** A rule might say "draw a blue line," but this could implicitly mean "change only black cells to blue," leaving other existing colors untouched. Always check for these implicit conditions in the examples.
- **Hardcoding:** The solution must be general enough to work for all examples, not just the first one.
**Example of a Correctly Formatted Output:**
def transform_matrix(matrix: list[list[int]]) -> list[list[int]]:
# Your implementation here
# For example, find the most frequent color and fill the grid
from collections import Counter
import itertools
if not matrix or not matrix[0]:
return []
counts = Counter(itertools.chain.from_iterable(matrix))
if counts:
# Handle ties by picking the smaller number value
most_common_color = sorted(counts.items(), key=lambda item: (-item[1], item[0]))[0][0]
else:
return []
height = len(matrix)
width = len(matrix[0])
return [[most_common_color for _ in range(width)] for _ in range(height)]
"""
rule_description: str = dspy.InputField(desc="The English description of the rule to implement.")
training_examples: List[TrainingExample] = dspy.InputField(desc="A list of input/output pairs to use as a reference for implementation.")
python_function: str = dspy.OutputField(desc="A string containing a single Python function `transform_matrix` that implements the rule.")
class RefineCode(dspy.Signature):
"""
You are a debugging expert. You are given a rule description, a set of training examples,
a Python function that fails on at least one example, and detailed feedback on the failure.
Your task is to analyze the faulty code and feedback, identify the bug, and provide a corrected version.
**Function Requirements (same as ImplementRule):**
- The function must be named `transform_matrix`.
- It must accept one argument: `matrix`.
- It must return a list of lists of integers.
- It should not use any external libraries except for `copy`.
- Your output must be ONLY the Python code for the function.
**Debugging Strategy:**
1. Carefully read the feedback to understand the exact failure point.
2. Compare the logic in the `faulty_code` with the `rule_description`.
3. Trace the execution of the `faulty_code` on the failing example to pinpoint the error.
4. Rewrite the function to correct the bug while preserving the correct parts of the logic.
"""
rule_description: str = dspy.InputField(desc="The English description of the rule.")
training_examples: List[TrainingExample] = dspy.InputField(desc="A list of input/output pairs for reference.")
faulty_code: str = dspy.InputField(desc="The Python code that failed verification.")
feedback: str = dspy.InputField(desc="Detailed feedback on which training example failed and the difference between the code's output and the expected output.")
corrected_python_function: str = dspy.OutputField(desc="A string containing the corrected `transform_matrix` function.")
class ARCSolver(dspy.Module):
"""A module that solves ARC tasks with a hypothesis, implementation, and refinement loop."""
def __init__(self, max_attempts=3):
super().__init__()
self.max_attempts = max_attempts
self.rule_hypothesizer = dspy.ChainOfThought(HypothesizeRule)
self.code_implementer = dspy.Predict(ImplementRule)
self.code_refiner = dspy.Predict(RefineCode)
def _verify_code(self, python_code: str, training_examples: List[TrainingExample]):
"""Executes the code against training examples and returns success status and feedback."""
try:
local_scope = {}
exec(python_code, globals(), local_scope)
transform_func = local_scope.get('transform_matrix')
if not callable(transform_func):
return False, "Code did not define a callable function `transform_matrix`."
for i, example in enumerate(training_examples):
input_matrix = copy.deepcopy(example.input)
expected_output = example.output
try:
actual_output = transform_func(input_matrix)
if actual_output != expected_output:
feedback = (
f"Verification failed on training example {i}.\n"
f"Input:\n{example.input}\n"
f"Expected Output:\n{expected_output}\n"
f"Actual Output:\n{actual_output}"
)
return False, feedback
except Exception:
tb = traceback.format_exc()
return False, f"Runtime error on training example {i}:\n{tb}"
return True, "All training examples passed."
except Exception:
tb = traceback.format_exc()
return False, f"Failed to execute the generated code:\n{tb}"
def _execute_on_test(self, python_code: str, test_inputs: List[MATRIX]):
"""Executes the verified code on the final test inputs."""
try:
local_scope = {}
exec(python_code, globals(), local_scope)
transform_func = local_scope.get('transform_matrix')
if not callable(transform_func):
raise ValueError("Final code is not a callable function.")
solved_outputs = []
for test_matrix in test_inputs:
input_copy = copy.deepcopy(test_matrix)
result = transform_func(input_copy)
solved_outputs.append(result)
return solved_outputs
except Exception:
# If final execution fails, return fallback
return [copy.deepcopy(matrix) for matrix in test_inputs]
def forward(self, training_examples: List[TrainingExample], test_inputs: List[MATRIX]):
hypothesis = self.rule_hypothesizer(training_examples=training_examples)
rule_description = hypothesis.rule_description
python_code = ""
feedback = ""
for attempt in range(self.max_attempts):
if attempt == 0:
prediction = self.code_implementer(
rule_description=rule_description,
training_examples=training_examples
)
python_code = prediction.python_function
else:
refinement = self.code_refiner(
rule_description=rule_description,
training_examples=training_examples,
faulty_code=python_code,
feedback=feedback
)
python_code = refinement.corrected_python_function
is_verified, feedback = self._verify_code(python_code, training_examples)
if is_verified:
solved_outputs = self._execute_on_test(python_code, test_inputs)
return dspy.Prediction(test_outputs=solved_outputs)
# Fallback if all attempts fail
fallback_outputs = [copy.deepcopy(matrix) for matrix in test_inputs]
return dspy.Prediction(test_outputs=fallback_outputs)
# The overall task signature, defining the final inputs and outputs of the program.
class SolveTaskSignature(dspy.Signature):
"""
Given a set of training examples demonstrating a matrix transformation,
apply the same transformation to a new set of test matrices.
"""
training_examples: List[TrainingExample] = dspy.InputField(description="Input and output examples demonstrating the task to be performed.")
test_inputs: List[MATRIX] = dspy.InputField(description="Input matrices to be solved following the task described in the training examples.")
test_outputs: List[MATRIX] = dspy.OutputField(description="Output matrices corresponding to the test inputs.")
# The final program is an instance of our new, more robust module.
program = ARCSolver()
2025/08/29 13:29:48 INFO dspy.evaluate.evaluate: Average Metric: 0.0 / 3 (0.0%)
GEPA Optimization: 64%|█████████████████████████████████████████████████████████████████████▊ | 2540/4000 [15:55:52<6:30:59, 16.07s/rollouts]Iteration 104: New subsample score is not better, skipping
Iteration 105: Selected program 2 score: 0.605
Average Metric: 2.00 / 3 (66.7%): : 4it [06:23, 95.89s/it] 2025/08/29 13:36:12 INFO dspy.evaluate.evaluate: Average Metric: 2.0 / 3 (66.7%)
Iteration 105: Proposed new text for program: import dspy
from typing import List, Optional
import pydantic
import copy
# Define type aliases and pydantic models for clarity and structure.
MATRIX = List[List[int]]
class TrainingExample(pydantic.BaseModel):
input: MATRIX
output: MATRIX
class GeneratePythonFunction(dspy.Signature):
"""
You are an expert programmer. Your task is to analyze a series of input and output matrix pairs from the Abstraction and Reasoning Corpus (ARC). Based on these examples, you must deduce the underlying transformation rule and write a single, self-contained Python function that implements this rule.
**Function Requirements:**
- The function must be named `transform_matrix`.
- It must accept one argument: `matrix`, which is a list of lists of integers (representing the input grid).
- It must return a list of lists of integers (representing the transformed output grid).
- The function should not use any external libraries except for `copy` if needed (e.g., `import copy; new_matrix = copy.deepcopy(matrix)`).
- Your output must be ONLY the Python code for the function. Do not include any explanations, comments outside the function, or markdown formatting like ```python.
**Successful Strategies to Consider:**
- **Object Permanence:** Most transformations preserve the grid dimensions.
- **Color Transformations:** Look for rules that change colors based on their value or their neighbors.
- **Spatial Reasoning:** Analyze shapes, positions, and relationships. Common patterns include filling enclosed areas, drawing lines, moving objects, or detecting symmetry.
- **Iterative Processes:** Some rules are applied repeatedly until the grid no longer changes. Consider using a `while` loop that continues as long as modifications are being made in a pass.
- **Neighborhoods:** Often, a cell's new value depends on its immediate neighbors (e.g., von Neumann or Moore neighborhoods).
**Example of a Correctly Formatted Output:**
def transform_matrix(matrix: list[list[int]]) -> list[list[int]]:
# Your implementation here
# For example, find the most frequent color and fill the grid
from collections import Counter
import itertools
if not matrix or not matrix[0]:
return []
counts = Counter(itertools.chain.from_iterable(matrix))
if counts:
# Handle ties by picking the smaller number value
most_common_color = sorted(counts.items(), key=lambda item: (-item[1], item[0]))[0][0]
else:
return []
height = len(matrix)
width = len(matrix[0])
return [[most_common_color for _ in range(width)] for _ in range(height)]
"""
training_examples: List[TrainingExample] = dspy.InputField(desc="A list of input/output pairs demonstrating the transformation rule.")
python_function: str = dspy.OutputField(desc="A string containing a single Python function `transform_matrix` that implements the rule.")
class RefinePythonFunction(dspy.Signature):
"""
You are an expert programmer debugging a Python function for an ARC task. You previously generated a function that failed to correctly solve the provided training examples. Your task is to analyze the faulty code and the feedback detailing the error, then provide a corrected version of the Python function.
**Function Requirements:**
- The corrected function must still be named `transform_matrix`.
- It must adhere to all the original requirements (e.g., single argument, returns a matrix, no external libraries other than `copy`).
- Your output must be ONLY the corrected Python code. Do not include explanations or markdown.
"""
training_examples: List[TrainingExample] = dspy.InputField(desc="The original list of input/output pairs.")
previous_code: str = dspy.InputField(desc="The Python code that failed verification.")
feedback: str = dspy.InputField(desc="Detailed feedback on why the code failed, including the specific training example it failed on.")
python_function: str = dspy.OutputField(desc="A string containing the corrected Python function `transform_matrix`.")
class ARCSolver(dspy.Module):
"""A module that solves ARC tasks by generating, verifying, and refining Python code."""
def __init__(self, max_attempts: int = 3):
super().__init__()
self.max_attempts = max_attempts
self.code_generator = dspy.Predict(GeneratePythonFunction)
self.code_refiner = dspy.Predict(RefinePythonFunction)
def forward(self, training_examples: List[TrainingExample], test_inputs: List[MATRIX]):
python_code = None
feedback = None
verified_func = None
fallback_outputs = [copy.deepcopy(matrix) for matrix in test_inputs]
for attempt in range(self.max_attempts):
# Step 1: Generate or Refine Code
if attempt == 0:
# First attempt: generate from scratch
prediction = self.code_generator(training_examples=training_examples)
python_code = prediction.python_function
else:
# Subsequent attempts: refine the previous code
prediction = self.code_refiner(
training_examples=training_examples,
previous_code=python_code,
feedback=feedback
)
python_code = prediction.python_function
# Step 2: Verify the generated function against training examples
local_scope = {}
try:
exec(python_code, globals(), local_scope)
transform_func = local_scope.get('transform_matrix')
if not callable(transform_func):
feedback = "Error: The generated code did not define a callable function named 'transform_matrix'."
continue
# Verification loop
is_verified = True
for i, example in enumerate(training_examples):
input_copy = copy.deepcopy(example.input)
try:
result = transform_func(input_copy)
if result != example.output:
is_verified = False
feedback = f"Verification failed on training example {i}.\nInput:\n{example.input}\n\nGenerated Output:\n{result}\n\nExpected Output:\n{example.output}"
break
except Exception as e:
is_verified = False
feedback = f"Verification failed on training example {i} with a runtime error: {e}"
break
if is_verified:
verified_func = transform_func
break # Exit the refinement loop on success
except Exception as e:
feedback = f"Code execution failed with error: {e}"
continue # Try to refine
# Step 3: Execute the verified function on test inputs
if verified_func:
solved_outputs = []
for test_matrix in test_inputs:
try:
input_copy = copy.deepcopy(test_matrix)
result = verified_func(input_copy)
solved_outputs.append(result)
except Exception:
# If the verified function fails on a test case, fallback for that case.
solved_outputs.append(copy.deepcopy(test_matrix))
return dspy.Prediction(test_outputs=solved_outputs)
else:
# If no function could be verified after all attempts, return the fallback.
return dspy.Prediction(test_outputs=fallback_outputs)
# The overall task signature remains the same.
class SolveTaskSignature(dspy.Signature):
"""
Given a set of training examples demonstrating a matrix transformation,
apply the same transformation to a new set of test matrices.
"""
training_examples: List[TrainingExample] = dspy.InputField(description="Input and output examples demonstrating the task to be performed.")
test_inputs: List[MATRIX] = dspy.InputField(description="Input matrices to be solved following the task described in the training examples.")
test_outputs: List[MATRIX] = dspy.OutputField(description="Output matrices corresponding to the test inputs.")
# The final program is an instance of our new, more robust module.
program = ARCSolver()
2025/08/29 13:45:26 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 13:45:26 INFO dspy.evaluate.evaluate: Average Metric: 0.0 / 3 (0.0%)
GEPA Optimization: 64%|██████████████████████████████████████████████████████████████████████ | 2546/4000 [16:11:30<8:41:30, 21.52s/rollouts]Iteration 105: New subsample score is not better, skipping
Iteration 106: Selected program 6 score: 0.675
Average Metric: 2.00 / 3 (66.7%): : 4it [04:43, 70.94s/it] 2025/08/29 13:50:10 INFO dspy.evaluate.evaluate: Average Metric: 2.0 / 3 (66.7%)
Iteration 106: Proposed new text for program: import dspy
from typing import List, Optional, Callable
import pydantic
import copy
# Define type aliases and pydantic models for clarity and structure.
MATRIX = List[List[int]]
class TrainingExample(pydantic.BaseModel):
input: MATRIX
output: MATRIX
# --- Signatures ---
class HypothesizeRule(dspy.Signature):
"""
Analyze the provided input/output matrix pairs from the Abstraction and Reasoning Corpus (ARC).
Deduce the underlying transformation rule that converts each input matrix to its corresponding output matrix.
Describe this rule in clear, step-by-step, unambiguous English.
**Reasoning Process:**
1. **Overall Change:** First, describe the high-level visual change. Do colors change? Do shapes move? Does the grid size change? Is the output a summary of the input?
2. **Hypothesis Formulation:** Based on the overall change, propose a specific, testable hypothesis about the transformation rule.
3. **Hypothesis Verification:** Mentally (or by writing it down) walk through each training example and apply your hypothesized rule. Does it correctly produce the output for every single example? If not, refine your hypothesis.
4. **Final Rule Description:** Once you have a verified hypothesis, write down the final rule as a clear, step-by-step algorithm in English. Focus on the logic and the sequence of operations, not on Python code implementation.
"""
training_examples: List[TrainingExample] = dspy.InputField(desc="A list of input/output pairs demonstrating the transformation rule.")
rule_description: str = dspy.OutputField(desc="A step-by-step English description of the transformation rule.")
class ImplementRule(dspy.Signature):
"""
You are an expert programmer. Your task is to write a single, self-contained Python function based on a provided rule description and example pairs.
**Function Requirements:**
- The function must be named `transform_matrix`.
- It must accept one argument: `matrix`, which is a list of lists of integers (representing the input grid).
- It must return a list of lists of integers (representing the transformed output grid).
- The function should not use any external libraries except for `copy` if needed (e.g., `import copy; new_matrix = copy.deepcopy(matrix)`).
- Your output must be ONLY the Python code for the function. Do not include any explanations, comments outside the function, or markdown formatting like ```python.
**Successful Strategies to Consider:**
- **Shape Property Analysis:** The rule might depend on a unique property of one shape. For example, find the only shape that is not a solid rectangle by comparing its pixel count to its bounding box area.
- **Grid Partitioning & Key-Finding:** Look for patterns that divide the grid into subgrids. The rule might involve finding a 'key' subgrid based on some property (e.g., the one with the fewest colored cells, or the lowest sum of colors) and using that key to transform the other subgrids.
- **Template Matching:** A shape (often enclosed in another) can serve as a "template". The task may be to find other shapes that match this template (potentially with rotation/reflection) and modify them.
- **Neighborhoods:** Often, a cell's new value depends on its immediate neighbors (e.g., von Neumann or Moore neighborhoods).
- **Iterative Processes:** Some rules are applied repeatedly until the grid no longer changes. Consider using a `while` loop that continues as long as modifications are being made in a pass.
**Example of a Correctly Formatted Output:**
def transform_matrix(matrix: list[list[int]]) -> list[list[int]]:
# Your implementation here
# For example, find the most frequent color and fill the grid
from collections import Counter
import itertools
if not matrix or not matrix[0]:
return []
counts = Counter(itertools.chain.from_iterable(matrix))
if counts:
# Handle ties by picking the smaller number value
most_common_color = sorted(counts.items(), key=lambda item: (-item[1], item[0]))[0][0]
else:
return []
height = len(matrix)
width = len(matrix[0])
return [[most_common_color for _ in range(width)] for _ in range(height)]
"""
rule_description: str = dspy.InputField(desc="The English description of the rule to implement.")
training_examples: List[TrainingExample] = dspy.InputField(desc="A list of input/output pairs to use as a reference for implementation.")
python_function: str = dspy.OutputField(desc="A string containing a single Python function `transform_matrix` that implements the rule.")
class RefineCode(dspy.Signature):
"""
You are an expert Python debugger. You are given a rule description, a Python function that fails to implement it correctly, and a specific training example that demonstrates the failure (input, incorrect output, and expected output).
Your task is to analyze the code, identify the bug based on the failing example, and provide a corrected version of the entire Python function.
The corrected function must still be named `transform_matrix`, be self-contained, and adhere to the original requirements (no external libraries except `copy`).
Focus on fixing the logic error. Do not just hardcode the solution for the failing example. The fix should be general.
Your output must be ONLY the Python code for the corrected function.
"""
rule_description: str = dspy.InputField(desc="The English description of the rule.")
training_examples: List[TrainingExample] = dspy.InputField(desc="All training examples for context.")
faulty_code: str = dspy.InputField(desc="The Python function that produced an incorrect output.")
failed_input: MATRIX = dspy.InputField(desc="The specific input matrix on which the function failed.")
incorrect_output: MATRIX = dspy.InputField(desc="The incorrect output produced by the faulty code.")
expected_output: MATRIX = dspy.InputField(desc="The correct output that the function should have produced.")
corrected_python_function: str = dspy.OutputField(desc="A string containing the corrected Python function `transform_matrix`.")
# --- Custom Module with Self-Correction ---
class ARCSolver(dspy.Module):
"""A module that solves ARC tasks by hypothesizing a rule, generating code, and refining it based on feedback."""
def __init__(self, max_refinements: int = 2):
super().__init__()
self.max_refinements = max_refinements
# Decompose the task: 1) Hypothesize, 2) Implement, 3) Refine.
self.rule_hypothesizer = dspy.ChainOfThought(HypothesizeRule)
self.code_implementer = dspy.Predict(ImplementRule)
self.code_refiner = dspy.Predict(RefineCode)
def _execute_code(self, code_str: str) -> Optional[Callable[[MATRIX], MATRIX]]:
"""Safely execute the generated Python code string and return the transform function."""
try:
local_scope = {}
exec(code_str, globals(), local_scope)
transform_func = local_scope.get('transform_matrix')
if callable(transform_func):
return transform_func
except Exception:
pass
return None
def forward(self, training_examples: List[TrainingExample], test_inputs: List[MATRIX]):
# Create fallback outputs in case of any failure.
fallback_outputs = [copy.deepcopy(matrix) for matrix in test_inputs]
# Step 1: Generate an English description of the transformation rule.
hypothesis = self.rule_hypothesizer(training_examples=training_examples)
# Step 2: Generate the initial Python code.
implementation = self.code_implementer(
rule_description=hypothesis.rule_description,
training_examples=training_examples
)
python_code = implementation.python_function
# Step 3: Verify and refine the code using the training examples.
for i in range(self.max_refinements + 1):
transform_func = self._execute_code(python_code)
if not transform_func:
# If code is syntactically incorrect and fails to execute, we can't proceed with it.
break
# Verification loop
failed_example = None
incorrect_output = None
is_correct_on_all = True
for example in training_examples:
try:
input_copy = copy.deepcopy(example.input)
predicted_output = transform_func(input_copy)
if predicted_output != example.output:
is_correct_on_all = False
failed_example = example
incorrect_output = predicted_output
break # Exit verification loop on first failure
except Exception:
# Runtime error in the generated code
is_correct_on_all = False
failed_example = example
incorrect_output = [[]] # Placeholder for runtime error
break
if is_correct_on_all:
# Code is verified, break the refinement loop.
break
# If code failed and we have refinements left, try to refine it.
if i < self.max_refinements and failed_example:
refinement = self.code_refiner(
rule_description=hypothesis.rule_description,
training_examples=training_examples,
faulty_code=python_code,
failed_input=failed_example.input,
incorrect_output=incorrect_output,
expected_output=failed_example.output
)
python_code = refinement.corrected_python_function
else:
# No more refinements left or no specific failure to report.
break
# Step 4: Execute the final version of the code on the test inputs.
final_transform_func = self._execute_code(python_code)
if not final_transform_func:
return dspy.Prediction(test_outputs=fallback_outputs)
solved_outputs = []
for test_matrix in test_inputs:
try:
input_copy = copy.deepcopy(test_matrix)
result = final_transform_func(input_copy)
solved_outputs.append(result)
except Exception:
# If the final function fails on test data, use the input as a fallback for that specific case.
solved_outputs.append(copy.deepcopy(test_matrix))
return dspy.Prediction(test_outputs=solved_outputs)
# The overall task signature, defining the final inputs and outputs of the program.
class SolveTaskSignature(dspy.Signature):
"""
Given a set of training examples demonstrating a matrix transformation,
apply the same transformation to a new set of test matrices.
"""
training_examples: List[TrainingExample] = dspy.InputField(description="Input and output examples demonstrating the task to be performed.")
test_inputs: List[MATRIX] = dspy.InputField(description="Input matrices to be solved following the task described in the training examples.")
test_outputs: List[MATRIX] = dspy.OutputField(description="Output matrices corresponding to the test inputs.")
# The final program is an instance of our new, more robust module.
program = ARCSolver()
2025/08/29 13:52:55 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 6, 6, 6, 0, 0, 0, 0, 3, 3, 3, 0, 0], [0, 6, 0, 6, 0, 0, 0, 0, 3, 3, 3, 0, 0], [0, 6, 0, 6, 0, 1, 1, 0, 3, 3, 3, 0, 0], [0, 6, 6, 6, 0, 1, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 7, 7, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 7, 7, 0], [0, 0, 0, 2, 2, 2, 2, 2, 0, 7, 7, 7, 0], [0, 0, 0, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0], [0, 0, 0, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [4, 4, 4, 0, 0, 0, 0, 0, 8, 8, 8, 8, 0], [4, 4, 4, 0, 0, 0, 0, 0, 8, 8, 8, 8, 0], [0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[6]]}, {'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 7, 7, 7], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 7, 7, 7], [8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 7, 7, 7, 7], [8, 8, 8, 8, 8, 0, 0, 5, 5, 5, 5, 0, 0, 7, 7, 7, 7], [8, 8, 8, 8, 8, 0, 0, 5, 5, 5, 5, 0, 0, 7, 7, 7, 7], [0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 0, 0, 7, 7, 7, 7], [0, 0, 0, 2, 2, 2, 0, 5, 0, 0, 5, 0, 0, 0, 0, 0, 0], [0, 0, 0, 2, 2, 2, 0, 5, 0, 0, 5, 0, 0, 0, 0, 0, 0], [0, 0, 0, 2, 2, 2, 0, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0], [0, 0, 0, 2, 2, 2, 0, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0], [0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 4, 4, 4, 4, 4, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4, 4, 4, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4, 4, 4, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4, 4, 4, 0, 0]], 'output': [[5]]}, {'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 3, 3, 3, 3, 3, 0, 0], [0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 3, 3, 3, 3, 3, 0, 0], [0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 3, 3, 3, 3, 3, 0, 0], [0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 3, 3, 3, 3, 3, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 2, 2, 2, 2, 2, 2, 2, 0, 0, 7, 7, 7, 7, 7, 0], [0, 0, 2, 2, 2, 2, 2, 2, 2, 0, 0, 7, 7, 7, 7, 7, 0], [0, 0, 2, 0, 0, 0, 2, 2, 2, 0, 0, 7, 7, 7, 7, 7, 0], [0, 0, 2, 0, 0, 0, 2, 2, 2, 0, 0, 7, 7, 7, 7, 7, 0], [0, 0, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[2]]}], 'test_inputs': [[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 0], [2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 3, 3, 3, 3, 0], [2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 3, 3, 3, 3, 0], [2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 3, 3, 3, 3, 0], [2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 3, 3, 3, 3, 0], [2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0], [2, 2, 2, 2, 2, 2, 2, 2, 0, 4, 4, 4, 4, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4, 4, 0, 0], [0, 5, 5, 5, 0, 0, 0, 0, 0, 4, 4, 4, 4, 0, 0], [0, 5, 5, 5, 8, 8, 8, 8, 0, 4, 4, 4, 4, 0, 0], [0, 5, 5, 5, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 8, 8, 8, 8, 0, 0, 7, 7, 7, 7, 0], [0, 0, 0, 0, 8, 8, 8, 8, 0, 0, 7, 0, 0, 7, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 7, 7, 7, 0]]], 'test_outputs': [[[7]]]}) (input_keys={'test_inputs', 'training_examples'}): 'dict' object has no attribute 'input'
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 166, in forward
AttributeError: 'dict' object has no attribute 'input'
2025/08/29 13:54:14 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0], [5, 0, 0, 2, 0, 0, 5, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0], [5, 0, 2, 2, 2, 0, 5, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0], [5, 0, 0, 2, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0], [5, 0, 0, 2, 0, 0, 5, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0], [5, 0, 2, 2, 2, 0, 5, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0], [5, 0, 0, 2, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}, {'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 5, 0, 3, 3, 3, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 5, 0, 3, 3, 3, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 5, 0, 3, 3, 3, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 5, 0, 3, 3, 3, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 5, 0, 3, 3, 3, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 5, 0, 3, 3, 3, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 3, 3, 3, 0, 0], [0, 0, 0, 3, 3, 3, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 3, 3, 3, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}, {'input': [[0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5], [0, 0, 2, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5], [0, 0, 2, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 5, 0, 2, 2, 2, 0, 5], [0, 0, 2, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 5, 0, 2, 2, 2, 0, 5], [0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5], [5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5], [0, 0, 2, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5], [0, 0, 2, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 5, 0, 2, 2, 2, 0, 5], [0, 0, 2, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 5, 0, 2, 2, 2, 0, 5], [0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5], [5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 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3, 3, 3, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 5, 0, 3, 3, 3, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0], [0, 5, 5, 5, 5, 5, 5, 5, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 3, 3, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 3, 3, 0, 0], [0, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 5, 0, 3, 3, 3, 3, 0], [0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0], [0, 5, 0, 3, 3, 3, 0, 5, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5], [0, 5, 0, 3, 3, 3, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 5, 0, 3, 3, 3, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0], [0, 5, 5, 5, 5, 5, 5, 5, 0, 0, 3, 3, 3, 0, 0, 0, 1, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 0, 0, 1, 1, 1, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}], 'test_inputs': [[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 2, 2, 2, 0, 0], [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 5, 0, 0, 2, 2, 2, 0, 0], [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 5], [0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 0], [0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0], [0, 0, 2, 2, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 2, 0, 0, 5, 0], [0, 2, 2, 2, 2, 0, 5, 0, 0, 0, 0, 0, 5, 0, 2, 2, 2, 0, 5, 0], [0, 2, 2, 2, 2, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 2, 0, 0, 5, 0], [0, 0, 2, 2, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0], [0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 0]]], 'test_outputs': [[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 2, 2, 2, 0, 0], [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 5, 0, 0, 2, 2, 2, 0, 0], [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 5], [0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 2, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0], [0, 2, 2, 2, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0], [0, 0, 2, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 0], [0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0], [0, 0, 2, 2, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 2, 0, 0, 5, 0], [0, 2, 2, 2, 2, 0, 5, 0, 0, 0, 0, 0, 5, 0, 2, 2, 2, 0, 5, 0], [0, 2, 2, 2, 2, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 2, 0, 0, 5, 0], [0, 0, 2, 2, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0], [0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 0]]]}) (input_keys={'test_inputs', 'training_examples'}): 'dict' object has no attribute 'input'
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 166, in forward
AttributeError: 'dict' object has no attribute 'input'
2025/08/29 13:54:50 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0], [5, 0, 0, 2, 0, 0, 5, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0], [5, 0, 2, 2, 2, 0, 5, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0], [5, 0, 0, 2, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 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0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}, {'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 5, 0, 3, 3, 3, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 5, 0, 3, 3, 3, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 5, 0, 3, 3, 3, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 5, 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0, 0], [0, 2, 2, 2, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0], [0, 0, 2, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 0], [0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0], [0, 0, 2, 2, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 2, 0, 0, 5, 0], [0, 2, 2, 2, 2, 0, 5, 0, 0, 0, 0, 0, 5, 0, 2, 2, 2, 0, 5, 0], [0, 2, 2, 2, 2, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 2, 0, 0, 5, 0], [0, 0, 2, 2, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 0], [0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 0]]]}) (input_keys={'test_inputs', 'training_examples'}): 'dict' object has no attribute 'input'
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 166, in forward
AttributeError: 'dict' object has no attribute 'input'
2025/08/29 13:58:41 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[2, 0, 0, 5, 0, 6, 2, 5, 0, 0, 4], [0, 4, 3, 5, 4, 0, 8, 5, 3, 0, 6], [6, 0, 0, 5, 3, 0, 0, 5, 8, 0, 2], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [3, 8, 0, 5, 6, 2, 0, 5, 0, 4, 8], [0, 0, 4, 5, 0, 0, 4, 5, 6, 0, 0], [6, 2, 0, 5, 3, 8, 0, 5, 0, 3, 2], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [0, 3, 6, 5, 0, 2, 0, 5, 0, 6, 0], [2, 0, 0, 5, 4, 0, 8, 5, 0, 0, 8], [8, 0, 4, 5, 6, 3, 0, 5, 2, 3, 4]], 'output': [[2, 2, 2, 5, 0, 0, 0, 5, 0, 0, 0], [2, 2, 2, 5, 0, 0, 0, 5, 0, 0, 0], [2, 2, 2, 5, 0, 0, 0, 5, 0, 0, 0], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [0, 0, 0, 5, 4, 4, 4, 5, 3, 3, 3], [0, 0, 0, 5, 4, 4, 4, 5, 3, 3, 3], [0, 0, 0, 5, 4, 4, 4, 5, 3, 3, 3], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [6, 6, 6, 5, 0, 0, 0, 5, 0, 0, 0], [6, 6, 6, 5, 0, 0, 0, 5, 0, 0, 0], [6, 6, 6, 5, 0, 0, 0, 5, 0, 0, 0]]}, {'input': [[2, 0, 3, 5, 4, 6, 0, 5, 0, 6, 0], [0, 0, 8, 5, 0, 0, 2, 5, 4, 0, 3], [4, 6, 0, 5, 3, 8, 0, 5, 2, 0, 8], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [4, 0, 8, 5, 0, 0, 2, 5, 0, 6, 4], [0, 0, 2, 5, 0, 3, 0, 5, 3, 0, 0], [3, 0, 6, 5, 4, 0, 6, 5, 8, 0, 2], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [3, 6, 0, 5, 0, 8, 4, 5, 2, 0, 0], [0, 8, 4, 5, 2, 0, 0, 5, 8, 0, 3], [2, 0, 0, 5, 0, 3, 6, 5, 6, 4, 0]], 'output': [[0, 0, 0, 5, 0, 0, 0, 5, 2, 2, 2], [0, 0, 0, 5, 0, 0, 0, 5, 2, 2, 2], [0, 0, 0, 5, 0, 0, 0, 5, 2, 2, 2], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [0, 0, 0, 5, 3, 3, 3, 5, 0, 0, 0], [0, 0, 0, 5, 3, 3, 3, 5, 0, 0, 0], [0, 0, 0, 5, 3, 3, 3, 5, 0, 0, 0], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [4, 4, 4, 5, 0, 0, 0, 5, 6, 6, 6], [4, 4, 4, 5, 0, 0, 0, 5, 6, 6, 6], [4, 4, 4, 5, 0, 0, 0, 5, 6, 6, 6]]}, {'input': [[0, 3, 0, 5, 0, 6, 3, 5, 0, 6, 2], [6, 0, 4, 5, 2, 8, 0, 5, 0, 0, 8], [0, 2, 8, 5, 0, 4, 0, 5, 3, 0, 4], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [0, 2, 0, 5, 4, 0, 3, 5, 3, 4, 0], [4, 0, 8, 5, 2, 0, 6, 5, 0, 0, 2], [3, 6, 0, 5, 0, 8, 0, 5, 8, 6, 0], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [6, 3, 0, 5, 0, 3, 0, 5, 0, 0, 3], [0, 0, 2, 5, 0, 6, 4, 5, 2, 8, 0], [8, 4, 0, 5, 2, 0, 0, 5, 4, 0, 6]], 'output': [[0, 0, 0, 5, 3, 3, 3, 5, 0, 0, 0], [0, 0, 0, 5, 3, 3, 3, 5, 0, 0, 0], [0, 0, 0, 5, 3, 3, 3, 5, 0, 0, 0], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [0, 0, 0, 5, 6, 6, 6, 5, 4, 4, 4], [0, 0, 0, 5, 6, 6, 6, 5, 4, 4, 4], [0, 0, 0, 5, 6, 6, 6, 5, 4, 4, 4], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [2, 2, 2, 5, 0, 0, 0, 5, 0, 0, 0], [2, 2, 2, 5, 0, 0, 0, 5, 0, 0, 0], [2, 2, 2, 5, 0, 0, 0, 5, 0, 0, 0]]}, {'input': [[3, 8, 4, 5, 4, 6, 0, 5, 2, 0, 8], [0, 0, 0, 5, 8, 0, 3, 5, 6, 0, 3], [6, 2, 0, 5, 0, 2, 0, 5, 4, 0, 0], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [0, 4, 2, 5, 8, 0, 3, 5, 0, 4, 0], [0, 8, 6, 5, 0, 0, 4, 5, 0, 2, 6], [0, 3, 0, 5, 2, 6, 0, 5, 0, 3, 0], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [0, 6, 0, 5, 6, 2, 0, 5, 3, 6, 0], [3, 0, 8, 5, 0, 8, 3, 5, 0, 0, 4], [4, 2, 0, 5, 0, 0, 4, 5, 2, 0, 8]], 'output': [[0, 0, 0, 5, 4, 4, 4, 5, 0, 0, 0], [0, 0, 0, 5, 4, 4, 4, 5, 0, 0, 0], [0, 0, 0, 5, 4, 4, 4, 5, 0, 0, 0], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [0, 0, 0, 5, 2, 2, 2, 5, 6, 6, 6], [0, 0, 0, 5, 2, 2, 2, 5, 6, 6, 6], [0, 0, 0, 5, 2, 2, 2, 5, 6, 6, 6], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [0, 0, 0, 5, 3, 3, 3, 5, 0, 0, 0], [0, 0, 0, 5, 3, 3, 3, 5, 0, 0, 0], [0, 0, 0, 5, 3, 3, 3, 5, 0, 0, 0]]}], 'test_inputs': [[[6, 4, 0, 5, 0, 3, 0, 5, 0, 4, 0], [0, 0, 3, 5, 2, 8, 6, 5, 8, 0, 2], [2, 0, 8, 5, 4, 0, 0, 5, 6, 3, 0], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [2, 0, 0, 5, 0, 3, 0, 5, 3, 6, 2], [3, 4, 6, 5, 8, 4, 2, 5, 0, 0, 4], [0, 8, 0, 5, 0, 0, 6, 5, 8, 0, 0], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [0, 2, 4, 5, 0, 6, 4, 5, 0, 2, 8], [0, 6, 3, 5, 0, 0, 3, 5, 4, 0, 6], [0, 0, 0, 5, 2, 0, 8, 5, 3, 0, 0]]], 'test_outputs': [[[0, 0, 0, 5, 2, 2, 2, 5, 4, 4, 4], [0, 0, 0, 5, 2, 2, 2, 5, 4, 4, 4], [0, 0, 0, 5, 2, 2, 2, 5, 4, 4, 4], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [0, 0, 0, 5, 6, 6, 6, 5, 3, 3, 3], [0, 0, 0, 5, 6, 6, 6, 5, 3, 3, 3], [0, 0, 0, 5, 6, 6, 6, 5, 3, 3, 3], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0], [0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0], [0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0]]]}) (input_keys={'test_inputs', 'training_examples'}): 'dict' object has no attribute 'input'
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 166, in forward
AttributeError: 'dict' object has no attribute 'input'
2025/08/29 13:59:47 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[2, 0, 0, 5, 0, 6, 2, 5, 0, 0, 4], [0, 4, 3, 5, 4, 0, 8, 5, 3, 0, 6], [6, 0, 0, 5, 3, 0, 0, 5, 8, 0, 2], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [3, 8, 0, 5, 6, 2, 0, 5, 0, 4, 8], [0, 0, 4, 5, 0, 0, 4, 5, 6, 0, 0], [6, 2, 0, 5, 3, 8, 0, 5, 0, 3, 2], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [0, 3, 6, 5, 0, 2, 0, 5, 0, 6, 0], [2, 0, 0, 5, 4, 0, 8, 5, 0, 0, 8], [8, 0, 4, 5, 6, 3, 0, 5, 2, 3, 4]], 'output': [[2, 2, 2, 5, 0, 0, 0, 5, 0, 0, 0], [2, 2, 2, 5, 0, 0, 0, 5, 0, 0, 0], [2, 2, 2, 5, 0, 0, 0, 5, 0, 0, 0], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [0, 0, 0, 5, 4, 4, 4, 5, 3, 3, 3], [0, 0, 0, 5, 4, 4, 4, 5, 3, 3, 3], [0, 0, 0, 5, 4, 4, 4, 5, 3, 3, 3], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [6, 6, 6, 5, 0, 0, 0, 5, 0, 0, 0], [6, 6, 6, 5, 0, 0, 0, 5, 0, 0, 0], [6, 6, 6, 5, 0, 0, 0, 5, 0, 0, 0]]}, {'input': [[2, 0, 3, 5, 4, 6, 0, 5, 0, 6, 0], [0, 0, 8, 5, 0, 0, 2, 5, 4, 0, 3], [4, 6, 0, 5, 3, 8, 0, 5, 2, 0, 8], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [4, 0, 8, 5, 0, 0, 2, 5, 0, 6, 4], [0, 0, 2, 5, 0, 3, 0, 5, 3, 0, 0], [3, 0, 6, 5, 4, 0, 6, 5, 8, 0, 2], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [3, 6, 0, 5, 0, 8, 4, 5, 2, 0, 0], [0, 8, 4, 5, 2, 0, 0, 5, 8, 0, 3], [2, 0, 0, 5, 0, 3, 6, 5, 6, 4, 0]], 'output': [[0, 0, 0, 5, 0, 0, 0, 5, 2, 2, 2], [0, 0, 0, 5, 0, 0, 0, 5, 2, 2, 2], [0, 0, 0, 5, 0, 0, 0, 5, 2, 2, 2], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [0, 0, 0, 5, 3, 3, 3, 5, 0, 0, 0], [0, 0, 0, 5, 3, 3, 3, 5, 0, 0, 0], [0, 0, 0, 5, 3, 3, 3, 5, 0, 0, 0], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [4, 4, 4, 5, 0, 0, 0, 5, 6, 6, 6], [4, 4, 4, 5, 0, 0, 0, 5, 6, 6, 6], [4, 4, 4, 5, 0, 0, 0, 5, 6, 6, 6]]}, {'input': [[0, 3, 0, 5, 0, 6, 3, 5, 0, 6, 2], [6, 0, 4, 5, 2, 8, 0, 5, 0, 0, 8], [0, 2, 8, 5, 0, 4, 0, 5, 3, 0, 4], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [0, 2, 0, 5, 4, 0, 3, 5, 3, 4, 0], [4, 0, 8, 5, 2, 0, 6, 5, 0, 0, 2], [3, 6, 0, 5, 0, 8, 0, 5, 8, 6, 0], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [6, 3, 0, 5, 0, 3, 0, 5, 0, 0, 3], [0, 0, 2, 5, 0, 6, 4, 5, 2, 8, 0], [8, 4, 0, 5, 2, 0, 0, 5, 4, 0, 6]], 'output': [[0, 0, 0, 5, 3, 3, 3, 5, 0, 0, 0], [0, 0, 0, 5, 3, 3, 3, 5, 0, 0, 0], [0, 0, 0, 5, 3, 3, 3, 5, 0, 0, 0], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [0, 0, 0, 5, 6, 6, 6, 5, 4, 4, 4], [0, 0, 0, 5, 6, 6, 6, 5, 4, 4, 4], [0, 0, 0, 5, 6, 6, 6, 5, 4, 4, 4], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [2, 2, 2, 5, 0, 0, 0, 5, 0, 0, 0], [2, 2, 2, 5, 0, 0, 0, 5, 0, 0, 0], [2, 2, 2, 5, 0, 0, 0, 5, 0, 0, 0]]}, {'input': [[3, 8, 4, 5, 4, 6, 0, 5, 2, 0, 8], [0, 0, 0, 5, 8, 0, 3, 5, 6, 0, 3], [6, 2, 0, 5, 0, 2, 0, 5, 4, 0, 0], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [0, 4, 2, 5, 8, 0, 3, 5, 0, 4, 0], [0, 8, 6, 5, 0, 0, 4, 5, 0, 2, 6], [0, 3, 0, 5, 2, 6, 0, 5, 0, 3, 0], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [0, 6, 0, 5, 6, 2, 0, 5, 3, 6, 0], [3, 0, 8, 5, 0, 8, 3, 5, 0, 0, 4], [4, 2, 0, 5, 0, 0, 4, 5, 2, 0, 8]], 'output': [[0, 0, 0, 5, 4, 4, 4, 5, 0, 0, 0], [0, 0, 0, 5, 4, 4, 4, 5, 0, 0, 0], [0, 0, 0, 5, 4, 4, 4, 5, 0, 0, 0], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [0, 0, 0, 5, 2, 2, 2, 5, 6, 6, 6], [0, 0, 0, 5, 2, 2, 2, 5, 6, 6, 6], [0, 0, 0, 5, 2, 2, 2, 5, 6, 6, 6], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [0, 0, 0, 5, 3, 3, 3, 5, 0, 0, 0], [0, 0, 0, 5, 3, 3, 3, 5, 0, 0, 0], [0, 0, 0, 5, 3, 3, 3, 5, 0, 0, 0]]}], 'test_inputs': [[[6, 4, 0, 5, 0, 3, 0, 5, 0, 4, 0], [0, 0, 3, 5, 2, 8, 6, 5, 8, 0, 2], [2, 0, 8, 5, 4, 0, 0, 5, 6, 3, 0], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [2, 0, 0, 5, 0, 3, 0, 5, 3, 6, 2], [3, 4, 6, 5, 8, 4, 2, 5, 0, 0, 4], [0, 8, 0, 5, 0, 0, 6, 5, 8, 0, 0], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [0, 2, 4, 5, 0, 6, 4, 5, 0, 2, 8], [0, 6, 3, 5, 0, 0, 3, 5, 4, 0, 6], [0, 0, 0, 5, 2, 0, 8, 5, 3, 0, 0]]], 'test_outputs': [[[0, 0, 0, 5, 2, 2, 2, 5, 4, 4, 4], [0, 0, 0, 5, 2, 2, 2, 5, 4, 4, 4], [0, 0, 0, 5, 2, 2, 2, 5, 4, 4, 4], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [0, 0, 0, 5, 6, 6, 6, 5, 3, 3, 3], [0, 0, 0, 5, 6, 6, 6, 5, 3, 3, 3], [0, 0, 0, 5, 6, 6, 6, 5, 3, 3, 3], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0], [0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0], [0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0]]]}) (input_keys={'test_inputs', 'training_examples'}): 'dict' object has no attribute 'input'
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 166, in forward
AttributeError: 'dict' object has no attribute 'input'
2025/08/29 14:02:12 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[2, 0, 0, 5, 0, 6, 2, 5, 0, 0, 4], [0, 4, 3, 5, 4, 0, 8, 5, 3, 0, 6], [6, 0, 0, 5, 3, 0, 0, 5, 8, 0, 2], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [3, 8, 0, 5, 6, 2, 0, 5, 0, 4, 8], [0, 0, 4, 5, 0, 0, 4, 5, 6, 0, 0], [6, 2, 0, 5, 3, 8, 0, 5, 0, 3, 2], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [0, 3, 6, 5, 0, 2, 0, 5, 0, 6, 0], [2, 0, 0, 5, 4, 0, 8, 5, 0, 0, 8], [8, 0, 4, 5, 6, 3, 0, 5, 2, 3, 4]], 'output': [[2, 2, 2, 5, 0, 0, 0, 5, 0, 0, 0], [2, 2, 2, 5, 0, 0, 0, 5, 0, 0, 0], [2, 2, 2, 5, 0, 0, 0, 5, 0, 0, 0], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [0, 0, 0, 5, 4, 4, 4, 5, 3, 3, 3], [0, 0, 0, 5, 4, 4, 4, 5, 3, 3, 3], [0, 0, 0, 5, 4, 4, 4, 5, 3, 3, 3], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [6, 6, 6, 5, 0, 0, 0, 5, 0, 0, 0], [6, 6, 6, 5, 0, 0, 0, 5, 0, 0, 0], [6, 6, 6, 5, 0, 0, 0, 5, 0, 0, 0]]}, {'input': [[2, 0, 3, 5, 4, 6, 0, 5, 0, 6, 0], [0, 0, 8, 5, 0, 0, 2, 5, 4, 0, 3], [4, 6, 0, 5, 3, 8, 0, 5, 2, 0, 8], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [4, 0, 8, 5, 0, 0, 2, 5, 0, 6, 4], [0, 0, 2, 5, 0, 3, 0, 5, 3, 0, 0], [3, 0, 6, 5, 4, 0, 6, 5, 8, 0, 2], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [3, 6, 0, 5, 0, 8, 4, 5, 2, 0, 0], [0, 8, 4, 5, 2, 0, 0, 5, 8, 0, 3], [2, 0, 0, 5, 0, 3, 6, 5, 6, 4, 0]], 'output': [[0, 0, 0, 5, 0, 0, 0, 5, 2, 2, 2], [0, 0, 0, 5, 0, 0, 0, 5, 2, 2, 2], [0, 0, 0, 5, 0, 0, 0, 5, 2, 2, 2], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [0, 0, 0, 5, 3, 3, 3, 5, 0, 0, 0], [0, 0, 0, 5, 3, 3, 3, 5, 0, 0, 0], [0, 0, 0, 5, 3, 3, 3, 5, 0, 0, 0], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [4, 4, 4, 5, 0, 0, 0, 5, 6, 6, 6], [4, 4, 4, 5, 0, 0, 0, 5, 6, 6, 6], [4, 4, 4, 5, 0, 0, 0, 5, 6, 6, 6]]}, {'input': [[0, 3, 0, 5, 0, 6, 3, 5, 0, 6, 2], [6, 0, 4, 5, 2, 8, 0, 5, 0, 0, 8], [0, 2, 8, 5, 0, 4, 0, 5, 3, 0, 4], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [0, 2, 0, 5, 4, 0, 3, 5, 3, 4, 0], [4, 0, 8, 5, 2, 0, 6, 5, 0, 0, 2], [3, 6, 0, 5, 0, 8, 0, 5, 8, 6, 0], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [6, 3, 0, 5, 0, 3, 0, 5, 0, 0, 3], [0, 0, 2, 5, 0, 6, 4, 5, 2, 8, 0], [8, 4, 0, 5, 2, 0, 0, 5, 4, 0, 6]], 'output': [[0, 0, 0, 5, 3, 3, 3, 5, 0, 0, 0], [0, 0, 0, 5, 3, 3, 3, 5, 0, 0, 0], [0, 0, 0, 5, 3, 3, 3, 5, 0, 0, 0], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [0, 0, 0, 5, 6, 6, 6, 5, 4, 4, 4], [0, 0, 0, 5, 6, 6, 6, 5, 4, 4, 4], [0, 0, 0, 5, 6, 6, 6, 5, 4, 4, 4], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [2, 2, 2, 5, 0, 0, 0, 5, 0, 0, 0], [2, 2, 2, 5, 0, 0, 0, 5, 0, 0, 0], [2, 2, 2, 5, 0, 0, 0, 5, 0, 0, 0]]}, {'input': [[3, 8, 4, 5, 4, 6, 0, 5, 2, 0, 8], [0, 0, 0, 5, 8, 0, 3, 5, 6, 0, 3], [6, 2, 0, 5, 0, 2, 0, 5, 4, 0, 0], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [0, 4, 2, 5, 8, 0, 3, 5, 0, 4, 0], [0, 8, 6, 5, 0, 0, 4, 5, 0, 2, 6], [0, 3, 0, 5, 2, 6, 0, 5, 0, 3, 0], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [0, 6, 0, 5, 6, 2, 0, 5, 3, 6, 0], [3, 0, 8, 5, 0, 8, 3, 5, 0, 0, 4], [4, 2, 0, 5, 0, 0, 4, 5, 2, 0, 8]], 'output': [[0, 0, 0, 5, 4, 4, 4, 5, 0, 0, 0], [0, 0, 0, 5, 4, 4, 4, 5, 0, 0, 0], [0, 0, 0, 5, 4, 4, 4, 5, 0, 0, 0], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [0, 0, 0, 5, 2, 2, 2, 5, 6, 6, 6], [0, 0, 0, 5, 2, 2, 2, 5, 6, 6, 6], [0, 0, 0, 5, 2, 2, 2, 5, 6, 6, 6], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [0, 0, 0, 5, 3, 3, 3, 5, 0, 0, 0], [0, 0, 0, 5, 3, 3, 3, 5, 0, 0, 0], [0, 0, 0, 5, 3, 3, 3, 5, 0, 0, 0]]}], 'test_inputs': [[[6, 4, 0, 5, 0, 3, 0, 5, 0, 4, 0], [0, 0, 3, 5, 2, 8, 6, 5, 8, 0, 2], [2, 0, 8, 5, 4, 0, 0, 5, 6, 3, 0], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [2, 0, 0, 5, 0, 3, 0, 5, 3, 6, 2], [3, 4, 6, 5, 8, 4, 2, 5, 0, 0, 4], [0, 8, 0, 5, 0, 0, 6, 5, 8, 0, 0], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [0, 2, 4, 5, 0, 6, 4, 5, 0, 2, 8], [0, 6, 3, 5, 0, 0, 3, 5, 4, 0, 6], [0, 0, 0, 5, 2, 0, 8, 5, 3, 0, 0]]], 'test_outputs': [[[0, 0, 0, 5, 2, 2, 2, 5, 4, 4, 4], [0, 0, 0, 5, 2, 2, 2, 5, 4, 4, 4], [0, 0, 0, 5, 2, 2, 2, 5, 4, 4, 4], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [0, 0, 0, 5, 6, 6, 6, 5, 3, 3, 3], [0, 0, 0, 5, 6, 6, 6, 5, 3, 3, 3], [0, 0, 0, 5, 6, 6, 6, 5, 3, 3, 3], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0], [0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0], [0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0]]]}) (input_keys={'test_inputs', 'training_examples'}): 'dict' object has no attribute 'input'
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 166, in forward
AttributeError: 'dict' object has no attribute 'input'
2025/08/29 14:02:22 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[2, 0, 0, 5, 0, 6, 2, 5, 0, 0, 4], [0, 4, 3, 5, 4, 0, 8, 5, 3, 0, 6], [6, 0, 0, 5, 3, 0, 0, 5, 8, 0, 2], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [3, 8, 0, 5, 6, 2, 0, 5, 0, 4, 8], [0, 0, 4, 5, 0, 0, 4, 5, 6, 0, 0], [6, 2, 0, 5, 3, 8, 0, 5, 0, 3, 2], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [0, 3, 6, 5, 0, 2, 0, 5, 0, 6, 0], [2, 0, 0, 5, 4, 0, 8, 5, 0, 0, 8], [8, 0, 4, 5, 6, 3, 0, 5, 2, 3, 4]], 'output': [[2, 2, 2, 5, 0, 0, 0, 5, 0, 0, 0], [2, 2, 2, 5, 0, 0, 0, 5, 0, 0, 0], [2, 2, 2, 5, 0, 0, 0, 5, 0, 0, 0], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [0, 0, 0, 5, 4, 4, 4, 5, 3, 3, 3], [0, 0, 0, 5, 4, 4, 4, 5, 3, 3, 3], [0, 0, 0, 5, 4, 4, 4, 5, 3, 3, 3], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [6, 6, 6, 5, 0, 0, 0, 5, 0, 0, 0], [6, 6, 6, 5, 0, 0, 0, 5, 0, 0, 0], [6, 6, 6, 5, 0, 0, 0, 5, 0, 0, 0]]}, {'input': [[2, 0, 3, 5, 4, 6, 0, 5, 0, 6, 0], [0, 0, 8, 5, 0, 0, 2, 5, 4, 0, 3], [4, 6, 0, 5, 3, 8, 0, 5, 2, 0, 8], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [4, 0, 8, 5, 0, 0, 2, 5, 0, 6, 4], [0, 0, 2, 5, 0, 3, 0, 5, 3, 0, 0], [3, 0, 6, 5, 4, 0, 6, 5, 8, 0, 2], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [3, 6, 0, 5, 0, 8, 4, 5, 2, 0, 0], [0, 8, 4, 5, 2, 0, 0, 5, 8, 0, 3], [2, 0, 0, 5, 0, 3, 6, 5, 6, 4, 0]], 'output': [[0, 0, 0, 5, 0, 0, 0, 5, 2, 2, 2], [0, 0, 0, 5, 0, 0, 0, 5, 2, 2, 2], [0, 0, 0, 5, 0, 0, 0, 5, 2, 2, 2], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [0, 0, 0, 5, 3, 3, 3, 5, 0, 0, 0], [0, 0, 0, 5, 3, 3, 3, 5, 0, 0, 0], [0, 0, 0, 5, 3, 3, 3, 5, 0, 0, 0], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [4, 4, 4, 5, 0, 0, 0, 5, 6, 6, 6], [4, 4, 4, 5, 0, 0, 0, 5, 6, 6, 6], [4, 4, 4, 5, 0, 0, 0, 5, 6, 6, 6]]}, {'input': [[0, 3, 0, 5, 0, 6, 3, 5, 0, 6, 2], [6, 0, 4, 5, 2, 8, 0, 5, 0, 0, 8], [0, 2, 8, 5, 0, 4, 0, 5, 3, 0, 4], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [0, 2, 0, 5, 4, 0, 3, 5, 3, 4, 0], [4, 0, 8, 5, 2, 0, 6, 5, 0, 0, 2], [3, 6, 0, 5, 0, 8, 0, 5, 8, 6, 0], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [6, 3, 0, 5, 0, 3, 0, 5, 0, 0, 3], [0, 0, 2, 5, 0, 6, 4, 5, 2, 8, 0], [8, 4, 0, 5, 2, 0, 0, 5, 4, 0, 6]], 'output': [[0, 0, 0, 5, 3, 3, 3, 5, 0, 0, 0], [0, 0, 0, 5, 3, 3, 3, 5, 0, 0, 0], [0, 0, 0, 5, 3, 3, 3, 5, 0, 0, 0], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [0, 0, 0, 5, 6, 6, 6, 5, 4, 4, 4], [0, 0, 0, 5, 6, 6, 6, 5, 4, 4, 4], [0, 0, 0, 5, 6, 6, 6, 5, 4, 4, 4], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [2, 2, 2, 5, 0, 0, 0, 5, 0, 0, 0], [2, 2, 2, 5, 0, 0, 0, 5, 0, 0, 0], [2, 2, 2, 5, 0, 0, 0, 5, 0, 0, 0]]}, {'input': [[3, 8, 4, 5, 4, 6, 0, 5, 2, 0, 8], [0, 0, 0, 5, 8, 0, 3, 5, 6, 0, 3], [6, 2, 0, 5, 0, 2, 0, 5, 4, 0, 0], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [0, 4, 2, 5, 8, 0, 3, 5, 0, 4, 0], [0, 8, 6, 5, 0, 0, 4, 5, 0, 2, 6], [0, 3, 0, 5, 2, 6, 0, 5, 0, 3, 0], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [0, 6, 0, 5, 6, 2, 0, 5, 3, 6, 0], [3, 0, 8, 5, 0, 8, 3, 5, 0, 0, 4], [4, 2, 0, 5, 0, 0, 4, 5, 2, 0, 8]], 'output': [[0, 0, 0, 5, 4, 4, 4, 5, 0, 0, 0], [0, 0, 0, 5, 4, 4, 4, 5, 0, 0, 0], [0, 0, 0, 5, 4, 4, 4, 5, 0, 0, 0], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [0, 0, 0, 5, 2, 2, 2, 5, 6, 6, 6], [0, 0, 0, 5, 2, 2, 2, 5, 6, 6, 6], [0, 0, 0, 5, 2, 2, 2, 5, 6, 6, 6], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [0, 0, 0, 5, 3, 3, 3, 5, 0, 0, 0], [0, 0, 0, 5, 3, 3, 3, 5, 0, 0, 0], [0, 0, 0, 5, 3, 3, 3, 5, 0, 0, 0]]}], 'test_inputs': [[[6, 4, 0, 5, 0, 3, 0, 5, 0, 4, 0], [0, 0, 3, 5, 2, 8, 6, 5, 8, 0, 2], [2, 0, 8, 5, 4, 0, 0, 5, 6, 3, 0], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [2, 0, 0, 5, 0, 3, 0, 5, 3, 6, 2], [3, 4, 6, 5, 8, 4, 2, 5, 0, 0, 4], [0, 8, 0, 5, 0, 0, 6, 5, 8, 0, 0], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [0, 2, 4, 5, 0, 6, 4, 5, 0, 2, 8], [0, 6, 3, 5, 0, 0, 3, 5, 4, 0, 6], [0, 0, 0, 5, 2, 0, 8, 5, 3, 0, 0]]], 'test_outputs': [[[0, 0, 0, 5, 2, 2, 2, 5, 4, 4, 4], [0, 0, 0, 5, 2, 2, 2, 5, 4, 4, 4], [0, 0, 0, 5, 2, 2, 2, 5, 4, 4, 4], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [0, 0, 0, 5, 6, 6, 6, 5, 3, 3, 3], [0, 0, 0, 5, 6, 6, 6, 5, 3, 3, 3], [0, 0, 0, 5, 6, 6, 6, 5, 3, 3, 3], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0], [0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0], [0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0]]]}) (input_keys={'test_inputs', 'training_examples'}): 'dict' object has no attribute 'input'
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 166, in forward
AttributeError: 'dict' object has no attribute 'input'
2025/08/29 14:03:10 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[2, 0, 0, 5, 0, 6, 2, 5, 0, 0, 4], [0, 4, 3, 5, 4, 0, 8, 5, 3, 0, 6], [6, 0, 0, 5, 3, 0, 0, 5, 8, 0, 2], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [3, 8, 0, 5, 6, 2, 0, 5, 0, 4, 8], [0, 0, 4, 5, 0, 0, 4, 5, 6, 0, 0], [6, 2, 0, 5, 3, 8, 0, 5, 0, 3, 2], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [0, 3, 6, 5, 0, 2, 0, 5, 0, 6, 0], [2, 0, 0, 5, 4, 0, 8, 5, 0, 0, 8], [8, 0, 4, 5, 6, 3, 0, 5, 2, 3, 4]], 'output': [[2, 2, 2, 5, 0, 0, 0, 5, 0, 0, 0], [2, 2, 2, 5, 0, 0, 0, 5, 0, 0, 0], [2, 2, 2, 5, 0, 0, 0, 5, 0, 0, 0], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [0, 0, 0, 5, 4, 4, 4, 5, 3, 3, 3], [0, 0, 0, 5, 4, 4, 4, 5, 3, 3, 3], [0, 0, 0, 5, 4, 4, 4, 5, 3, 3, 3], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [6, 6, 6, 5, 0, 0, 0, 5, 0, 0, 0], [6, 6, 6, 5, 0, 0, 0, 5, 0, 0, 0], [6, 6, 6, 5, 0, 0, 0, 5, 0, 0, 0]]}, {'input': [[2, 0, 3, 5, 4, 6, 0, 5, 0, 6, 0], [0, 0, 8, 5, 0, 0, 2, 5, 4, 0, 3], [4, 6, 0, 5, 3, 8, 0, 5, 2, 0, 8], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [4, 0, 8, 5, 0, 0, 2, 5, 0, 6, 4], [0, 0, 2, 5, 0, 3, 0, 5, 3, 0, 0], [3, 0, 6, 5, 4, 0, 6, 5, 8, 0, 2], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [3, 6, 0, 5, 0, 8, 4, 5, 2, 0, 0], [0, 8, 4, 5, 2, 0, 0, 5, 8, 0, 3], [2, 0, 0, 5, 0, 3, 6, 5, 6, 4, 0]], 'output': [[0, 0, 0, 5, 0, 0, 0, 5, 2, 2, 2], [0, 0, 0, 5, 0, 0, 0, 5, 2, 2, 2], [0, 0, 0, 5, 0, 0, 0, 5, 2, 2, 2], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [0, 0, 0, 5, 3, 3, 3, 5, 0, 0, 0], [0, 0, 0, 5, 3, 3, 3, 5, 0, 0, 0], [0, 0, 0, 5, 3, 3, 3, 5, 0, 0, 0], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [4, 4, 4, 5, 0, 0, 0, 5, 6, 6, 6], [4, 4, 4, 5, 0, 0, 0, 5, 6, 6, 6], [4, 4, 4, 5, 0, 0, 0, 5, 6, 6, 6]]}, {'input': [[0, 3, 0, 5, 0, 6, 3, 5, 0, 6, 2], [6, 0, 4, 5, 2, 8, 0, 5, 0, 0, 8], [0, 2, 8, 5, 0, 4, 0, 5, 3, 0, 4], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [0, 2, 0, 5, 4, 0, 3, 5, 3, 4, 0], [4, 0, 8, 5, 2, 0, 6, 5, 0, 0, 2], [3, 6, 0, 5, 0, 8, 0, 5, 8, 6, 0], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [6, 3, 0, 5, 0, 3, 0, 5, 0, 0, 3], [0, 0, 2, 5, 0, 6, 4, 5, 2, 8, 0], [8, 4, 0, 5, 2, 0, 0, 5, 4, 0, 6]], 'output': [[0, 0, 0, 5, 3, 3, 3, 5, 0, 0, 0], [0, 0, 0, 5, 3, 3, 3, 5, 0, 0, 0], [0, 0, 0, 5, 3, 3, 3, 5, 0, 0, 0], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [0, 0, 0, 5, 6, 6, 6, 5, 4, 4, 4], [0, 0, 0, 5, 6, 6, 6, 5, 4, 4, 4], [0, 0, 0, 5, 6, 6, 6, 5, 4, 4, 4], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [2, 2, 2, 5, 0, 0, 0, 5, 0, 0, 0], [2, 2, 2, 5, 0, 0, 0, 5, 0, 0, 0], [2, 2, 2, 5, 0, 0, 0, 5, 0, 0, 0]]}, {'input': [[3, 8, 4, 5, 4, 6, 0, 5, 2, 0, 8], [0, 0, 0, 5, 8, 0, 3, 5, 6, 0, 3], [6, 2, 0, 5, 0, 2, 0, 5, 4, 0, 0], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [0, 4, 2, 5, 8, 0, 3, 5, 0, 4, 0], [0, 8, 6, 5, 0, 0, 4, 5, 0, 2, 6], [0, 3, 0, 5, 2, 6, 0, 5, 0, 3, 0], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [0, 6, 0, 5, 6, 2, 0, 5, 3, 6, 0], [3, 0, 8, 5, 0, 8, 3, 5, 0, 0, 4], [4, 2, 0, 5, 0, 0, 4, 5, 2, 0, 8]], 'output': [[0, 0, 0, 5, 4, 4, 4, 5, 0, 0, 0], [0, 0, 0, 5, 4, 4, 4, 5, 0, 0, 0], [0, 0, 0, 5, 4, 4, 4, 5, 0, 0, 0], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [0, 0, 0, 5, 2, 2, 2, 5, 6, 6, 6], [0, 0, 0, 5, 2, 2, 2, 5, 6, 6, 6], [0, 0, 0, 5, 2, 2, 2, 5, 6, 6, 6], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [0, 0, 0, 5, 3, 3, 3, 5, 0, 0, 0], [0, 0, 0, 5, 3, 3, 3, 5, 0, 0, 0], [0, 0, 0, 5, 3, 3, 3, 5, 0, 0, 0]]}], 'test_inputs': [[[6, 4, 0, 5, 0, 3, 0, 5, 0, 4, 0], [0, 0, 3, 5, 2, 8, 6, 5, 8, 0, 2], [2, 0, 8, 5, 4, 0, 0, 5, 6, 3, 0], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [2, 0, 0, 5, 0, 3, 0, 5, 3, 6, 2], [3, 4, 6, 5, 8, 4, 2, 5, 0, 0, 4], [0, 8, 0, 5, 0, 0, 6, 5, 8, 0, 0], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [0, 2, 4, 5, 0, 6, 4, 5, 0, 2, 8], [0, 6, 3, 5, 0, 0, 3, 5, 4, 0, 6], [0, 0, 0, 5, 2, 0, 8, 5, 3, 0, 0]]], 'test_outputs': [[[0, 0, 0, 5, 2, 2, 2, 5, 4, 4, 4], [0, 0, 0, 5, 2, 2, 2, 5, 4, 4, 4], [0, 0, 0, 5, 2, 2, 2, 5, 4, 4, 4], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [0, 0, 0, 5, 6, 6, 6, 5, 3, 3, 3], [0, 0, 0, 5, 6, 6, 6, 5, 3, 3, 3], [0, 0, 0, 5, 6, 6, 6, 5, 3, 3, 3], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0], [0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0], [0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0]]]}) (input_keys={'test_inputs', 'training_examples'}): 'dict' object has no attribute 'input'
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 166, in forward
AttributeError: 'dict' object has no attribute 'input'
2025/08/29 14:03:34 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[2, 0, 0, 5, 0, 6, 2, 5, 0, 0, 4], [0, 4, 3, 5, 4, 0, 8, 5, 3, 0, 6], [6, 0, 0, 5, 3, 0, 0, 5, 8, 0, 2], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [3, 8, 0, 5, 6, 2, 0, 5, 0, 4, 8], [0, 0, 4, 5, 0, 0, 4, 5, 6, 0, 0], [6, 2, 0, 5, 3, 8, 0, 5, 0, 3, 2], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [0, 3, 6, 5, 0, 2, 0, 5, 0, 6, 0], [2, 0, 0, 5, 4, 0, 8, 5, 0, 0, 8], [8, 0, 4, 5, 6, 3, 0, 5, 2, 3, 4]], 'output': [[2, 2, 2, 5, 0, 0, 0, 5, 0, 0, 0], [2, 2, 2, 5, 0, 0, 0, 5, 0, 0, 0], [2, 2, 2, 5, 0, 0, 0, 5, 0, 0, 0], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [0, 0, 0, 5, 4, 4, 4, 5, 3, 3, 3], [0, 0, 0, 5, 4, 4, 4, 5, 3, 3, 3], [0, 0, 0, 5, 4, 4, 4, 5, 3, 3, 3], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [6, 6, 6, 5, 0, 0, 0, 5, 0, 0, 0], [6, 6, 6, 5, 0, 0, 0, 5, 0, 0, 0], [6, 6, 6, 5, 0, 0, 0, 5, 0, 0, 0]]}, {'input': [[2, 0, 3, 5, 4, 6, 0, 5, 0, 6, 0], [0, 0, 8, 5, 0, 0, 2, 5, 4, 0, 3], [4, 6, 0, 5, 3, 8, 0, 5, 2, 0, 8], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [4, 0, 8, 5, 0, 0, 2, 5, 0, 6, 4], [0, 0, 2, 5, 0, 3, 0, 5, 3, 0, 0], [3, 0, 6, 5, 4, 0, 6, 5, 8, 0, 2], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [3, 6, 0, 5, 0, 8, 4, 5, 2, 0, 0], [0, 8, 4, 5, 2, 0, 0, 5, 8, 0, 3], [2, 0, 0, 5, 0, 3, 6, 5, 6, 4, 0]], 'output': [[0, 0, 0, 5, 0, 0, 0, 5, 2, 2, 2], [0, 0, 0, 5, 0, 0, 0, 5, 2, 2, 2], [0, 0, 0, 5, 0, 0, 0, 5, 2, 2, 2], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [0, 0, 0, 5, 3, 3, 3, 5, 0, 0, 0], [0, 0, 0, 5, 3, 3, 3, 5, 0, 0, 0], [0, 0, 0, 5, 3, 3, 3, 5, 0, 0, 0], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [4, 4, 4, 5, 0, 0, 0, 5, 6, 6, 6], [4, 4, 4, 5, 0, 0, 0, 5, 6, 6, 6], [4, 4, 4, 5, 0, 0, 0, 5, 6, 6, 6]]}, {'input': [[0, 3, 0, 5, 0, 6, 3, 5, 0, 6, 2], [6, 0, 4, 5, 2, 8, 0, 5, 0, 0, 8], [0, 2, 8, 5, 0, 4, 0, 5, 3, 0, 4], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [0, 2, 0, 5, 4, 0, 3, 5, 3, 4, 0], [4, 0, 8, 5, 2, 0, 6, 5, 0, 0, 2], [3, 6, 0, 5, 0, 8, 0, 5, 8, 6, 0], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [6, 3, 0, 5, 0, 3, 0, 5, 0, 0, 3], [0, 0, 2, 5, 0, 6, 4, 5, 2, 8, 0], [8, 4, 0, 5, 2, 0, 0, 5, 4, 0, 6]], 'output': [[0, 0, 0, 5, 3, 3, 3, 5, 0, 0, 0], [0, 0, 0, 5, 3, 3, 3, 5, 0, 0, 0], [0, 0, 0, 5, 3, 3, 3, 5, 0, 0, 0], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [0, 0, 0, 5, 6, 6, 6, 5, 4, 4, 4], [0, 0, 0, 5, 6, 6, 6, 5, 4, 4, 4], [0, 0, 0, 5, 6, 6, 6, 5, 4, 4, 4], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [2, 2, 2, 5, 0, 0, 0, 5, 0, 0, 0], [2, 2, 2, 5, 0, 0, 0, 5, 0, 0, 0], [2, 2, 2, 5, 0, 0, 0, 5, 0, 0, 0]]}, {'input': [[3, 8, 4, 5, 4, 6, 0, 5, 2, 0, 8], [0, 0, 0, 5, 8, 0, 3, 5, 6, 0, 3], [6, 2, 0, 5, 0, 2, 0, 5, 4, 0, 0], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [0, 4, 2, 5, 8, 0, 3, 5, 0, 4, 0], [0, 8, 6, 5, 0, 0, 4, 5, 0, 2, 6], [0, 3, 0, 5, 2, 6, 0, 5, 0, 3, 0], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [0, 6, 0, 5, 6, 2, 0, 5, 3, 6, 0], [3, 0, 8, 5, 0, 8, 3, 5, 0, 0, 4], [4, 2, 0, 5, 0, 0, 4, 5, 2, 0, 8]], 'output': [[0, 0, 0, 5, 4, 4, 4, 5, 0, 0, 0], [0, 0, 0, 5, 4, 4, 4, 5, 0, 0, 0], [0, 0, 0, 5, 4, 4, 4, 5, 0, 0, 0], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [0, 0, 0, 5, 2, 2, 2, 5, 6, 6, 6], [0, 0, 0, 5, 2, 2, 2, 5, 6, 6, 6], [0, 0, 0, 5, 2, 2, 2, 5, 6, 6, 6], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [0, 0, 0, 5, 3, 3, 3, 5, 0, 0, 0], [0, 0, 0, 5, 3, 3, 3, 5, 0, 0, 0], [0, 0, 0, 5, 3, 3, 3, 5, 0, 0, 0]]}], 'test_inputs': [[[6, 4, 0, 5, 0, 3, 0, 5, 0, 4, 0], [0, 0, 3, 5, 2, 8, 6, 5, 8, 0, 2], [2, 0, 8, 5, 4, 0, 0, 5, 6, 3, 0], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [2, 0, 0, 5, 0, 3, 0, 5, 3, 6, 2], [3, 4, 6, 5, 8, 4, 2, 5, 0, 0, 4], [0, 8, 0, 5, 0, 0, 6, 5, 8, 0, 0], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [0, 2, 4, 5, 0, 6, 4, 5, 0, 2, 8], [0, 6, 3, 5, 0, 0, 3, 5, 4, 0, 6], [0, 0, 0, 5, 2, 0, 8, 5, 3, 0, 0]]], 'test_outputs': [[[0, 0, 0, 5, 2, 2, 2, 5, 4, 4, 4], [0, 0, 0, 5, 2, 2, 2, 5, 4, 4, 4], [0, 0, 0, 5, 2, 2, 2, 5, 4, 4, 4], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [0, 0, 0, 5, 6, 6, 6, 5, 3, 3, 3], [0, 0, 0, 5, 6, 6, 6, 5, 3, 3, 3], [0, 0, 0, 5, 6, 6, 6, 5, 3, 3, 3], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0], [0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0], [0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0]]]}) (input_keys={'test_inputs', 'training_examples'}): 'dict' object has no attribute 'input'
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 166, in forward
AttributeError: 'dict' object has no attribute 'input'
2025/08/29 14:04:21 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[2, 0, 0, 5, 0, 6, 2, 5, 0, 0, 4], [0, 4, 3, 5, 4, 0, 8, 5, 3, 0, 6], [6, 0, 0, 5, 3, 0, 0, 5, 8, 0, 2], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [3, 8, 0, 5, 6, 2, 0, 5, 0, 4, 8], [0, 0, 4, 5, 0, 0, 4, 5, 6, 0, 0], [6, 2, 0, 5, 3, 8, 0, 5, 0, 3, 2], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [0, 3, 6, 5, 0, 2, 0, 5, 0, 6, 0], [2, 0, 0, 5, 4, 0, 8, 5, 0, 0, 8], [8, 0, 4, 5, 6, 3, 0, 5, 2, 3, 4]], 'output': [[2, 2, 2, 5, 0, 0, 0, 5, 0, 0, 0], [2, 2, 2, 5, 0, 0, 0, 5, 0, 0, 0], [2, 2, 2, 5, 0, 0, 0, 5, 0, 0, 0], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [0, 0, 0, 5, 4, 4, 4, 5, 3, 3, 3], [0, 0, 0, 5, 4, 4, 4, 5, 3, 3, 3], [0, 0, 0, 5, 4, 4, 4, 5, 3, 3, 3], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [6, 6, 6, 5, 0, 0, 0, 5, 0, 0, 0], [6, 6, 6, 5, 0, 0, 0, 5, 0, 0, 0], [6, 6, 6, 5, 0, 0, 0, 5, 0, 0, 0]]}, {'input': [[2, 0, 3, 5, 4, 6, 0, 5, 0, 6, 0], [0, 0, 8, 5, 0, 0, 2, 5, 4, 0, 3], [4, 6, 0, 5, 3, 8, 0, 5, 2, 0, 8], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [4, 0, 8, 5, 0, 0, 2, 5, 0, 6, 4], [0, 0, 2, 5, 0, 3, 0, 5, 3, 0, 0], [3, 0, 6, 5, 4, 0, 6, 5, 8, 0, 2], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [3, 6, 0, 5, 0, 8, 4, 5, 2, 0, 0], [0, 8, 4, 5, 2, 0, 0, 5, 8, 0, 3], [2, 0, 0, 5, 0, 3, 6, 5, 6, 4, 0]], 'output': [[0, 0, 0, 5, 0, 0, 0, 5, 2, 2, 2], [0, 0, 0, 5, 0, 0, 0, 5, 2, 2, 2], [0, 0, 0, 5, 0, 0, 0, 5, 2, 2, 2], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [0, 0, 0, 5, 3, 3, 3, 5, 0, 0, 0], [0, 0, 0, 5, 3, 3, 3, 5, 0, 0, 0], [0, 0, 0, 5, 3, 3, 3, 5, 0, 0, 0], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [4, 4, 4, 5, 0, 0, 0, 5, 6, 6, 6], [4, 4, 4, 5, 0, 0, 0, 5, 6, 6, 6], [4, 4, 4, 5, 0, 0, 0, 5, 6, 6, 6]]}, {'input': [[0, 3, 0, 5, 0, 6, 3, 5, 0, 6, 2], [6, 0, 4, 5, 2, 8, 0, 5, 0, 0, 8], [0, 2, 8, 5, 0, 4, 0, 5, 3, 0, 4], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [0, 2, 0, 5, 4, 0, 3, 5, 3, 4, 0], [4, 0, 8, 5, 2, 0, 6, 5, 0, 0, 2], [3, 6, 0, 5, 0, 8, 0, 5, 8, 6, 0], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [6, 3, 0, 5, 0, 3, 0, 5, 0, 0, 3], [0, 0, 2, 5, 0, 6, 4, 5, 2, 8, 0], [8, 4, 0, 5, 2, 0, 0, 5, 4, 0, 6]], 'output': [[0, 0, 0, 5, 3, 3, 3, 5, 0, 0, 0], [0, 0, 0, 5, 3, 3, 3, 5, 0, 0, 0], [0, 0, 0, 5, 3, 3, 3, 5, 0, 0, 0], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [0, 0, 0, 5, 6, 6, 6, 5, 4, 4, 4], [0, 0, 0, 5, 6, 6, 6, 5, 4, 4, 4], [0, 0, 0, 5, 6, 6, 6, 5, 4, 4, 4], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [2, 2, 2, 5, 0, 0, 0, 5, 0, 0, 0], [2, 2, 2, 5, 0, 0, 0, 5, 0, 0, 0], [2, 2, 2, 5, 0, 0, 0, 5, 0, 0, 0]]}, {'input': [[3, 8, 4, 5, 4, 6, 0, 5, 2, 0, 8], [0, 0, 0, 5, 8, 0, 3, 5, 6, 0, 3], [6, 2, 0, 5, 0, 2, 0, 5, 4, 0, 0], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [0, 4, 2, 5, 8, 0, 3, 5, 0, 4, 0], [0, 8, 6, 5, 0, 0, 4, 5, 0, 2, 6], [0, 3, 0, 5, 2, 6, 0, 5, 0, 3, 0], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [0, 6, 0, 5, 6, 2, 0, 5, 3, 6, 0], [3, 0, 8, 5, 0, 8, 3, 5, 0, 0, 4], [4, 2, 0, 5, 0, 0, 4, 5, 2, 0, 8]], 'output': [[0, 0, 0, 5, 4, 4, 4, 5, 0, 0, 0], [0, 0, 0, 5, 4, 4, 4, 5, 0, 0, 0], [0, 0, 0, 5, 4, 4, 4, 5, 0, 0, 0], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [0, 0, 0, 5, 2, 2, 2, 5, 6, 6, 6], [0, 0, 0, 5, 2, 2, 2, 5, 6, 6, 6], [0, 0, 0, 5, 2, 2, 2, 5, 6, 6, 6], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [0, 0, 0, 5, 3, 3, 3, 5, 0, 0, 0], [0, 0, 0, 5, 3, 3, 3, 5, 0, 0, 0], [0, 0, 0, 5, 3, 3, 3, 5, 0, 0, 0]]}], 'test_inputs': [[[6, 4, 0, 5, 0, 3, 0, 5, 0, 4, 0], [0, 0, 3, 5, 2, 8, 6, 5, 8, 0, 2], [2, 0, 8, 5, 4, 0, 0, 5, 6, 3, 0], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [2, 0, 0, 5, 0, 3, 0, 5, 3, 6, 2], [3, 4, 6, 5, 8, 4, 2, 5, 0, 0, 4], [0, 8, 0, 5, 0, 0, 6, 5, 8, 0, 0], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [0, 2, 4, 5, 0, 6, 4, 5, 0, 2, 8], [0, 6, 3, 5, 0, 0, 3, 5, 4, 0, 6], [0, 0, 0, 5, 2, 0, 8, 5, 3, 0, 0]]], 'test_outputs': [[[0, 0, 0, 5, 2, 2, 2, 5, 4, 4, 4], [0, 0, 0, 5, 2, 2, 2, 5, 4, 4, 4], [0, 0, 0, 5, 2, 2, 2, 5, 4, 4, 4], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [0, 0, 0, 5, 6, 6, 6, 5, 3, 3, 3], [0, 0, 0, 5, 6, 6, 6, 5, 3, 3, 3], [0, 0, 0, 5, 6, 6, 6, 5, 3, 3, 3], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0], [0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0], [0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0]]]}) (input_keys={'test_inputs', 'training_examples'}): 'dict' object has no attribute 'input'
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 166, in forward
AttributeError: 'dict' object has no attribute 'input'
2025/08/29 14:04:21 INFO dspy.evaluate.evaluate: Average Metric: 0.0 / 3 (0.0%)
GEPA Optimization: 64%|█████████████████████████████████████████████████████████████████████▌ | 2552/4000 [16:30:24<12:11:58, 30.33s/rollouts]Iteration 106: New subsample score is not better, skipping
Iteration 107: Selected program 4 score: 0.61
Average Metric: 2.00 / 3 (66.7%): 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [03:00<00:00, 60.18s/it]2025/08/29 14:07:21 INFO dspy.evaluate.evaluate: Average Metric: 2.0 / 3 (66.7%)
Iteration 107: Proposed new text for program: import dspy
from typing import List, Optional
import pydantic
import traceback
import copy
# Define the type for a grid/matrix as a list of lists of integers.
MATRIX = List[List[int]]
class TrainingExample(pydantic.BaseModel):
"""A Pydantic model for a single training example, containing an input and output grid."""
input: MATRIX
output: MATRIX
class GenerateTransformationFunction(dspy.Signature):
"""
You are an expert programmer solving visual puzzles from the Abstraction and Reasoning Corpus (ARC).
Your goal is to analyze a set of training examples, each consisting of an input grid and an output grid.
Based on this analysis, you must write a single, self-contained Python function named `transform_grid` that implements the observed transformation.
**Function Requirements:**
- The function must be named exactly `transform_grid`.
- It must accept one argument: `grid`, which is a 2D list of integers (the input grid).
- It must return a new 2D list of integers representing the transformed grid.
- The function should not rely on any external libraries unless they are standard (e.g., `copy`). If you need a library like numpy, import it inside the function.
- Do not modify the input grid in place; create a copy if necessary.
**Analysis Strategy:**
1. **Observe Core Patterns:** Look for simple, recurring patterns like geometric transformations (rotation, reflection, scaling), color changes, object manipulation (copying, moving, recoloring), pattern propagation, or flood-fills.
2. **Decompose the Problem:** Break down the transformation into logical steps. For example, "first, find all objects of color blue, then for each object, reflect it horizontally."
3. **Generalize:** The logic must be general enough to work for all training examples and, by extension, the unseen test inputs.
4. **Handle Boundaries:** Pay close attention to grid boundaries. Off-by-one errors are common. Ensure your loops and checks for neighbors (e.g., `grid[r-1]`, `grid[c+1]`) do not go out of bounds.
5. **Code Implementation:** Translate your logic into a clear and correct Python function.
"""
training_examples: List[TrainingExample] = dspy.InputField(desc="A list of input/output grid pairs demonstrating the transformation rule.")
test_input_grid: MATRIX = dspy.InputField(desc="A single test input grid to which the transformation should be applicable. Use its properties (e.g., dimensions) to ensure your code is general.")
reasoning: str = dspy.OutputField(desc="Step-by-step thinking process to deduce the transformation rule and how to implement it in Python.")
python_code: str = dspy.OutputField(desc="A string containing the complete, self-contained Python function `transform_grid(grid)`.")
class DebugTransformationFunction(dspy.Signature):
"""
You are a debugging expert. You will be given a Python function that failed to correctly solve a visual puzzle. Your task is to analyze the error and provide a corrected version of the function.
**Analysis Strategy:**
1. **Review the Goal:** Understand the intended transformation from the `training_examples`.
2. **Analyze the Failure:**
* If an `error_traceback` is provided, it indicates a crash. Identify the line and the cause (e.g., `IndexError`, `TypeError`).
* If `actual_output` and `expected_output` are provided, it's a logical error. Perform a diff between them to see *what* is wrong (e.g., wrong colors, shapes in wrong places, missing transformations).
3. **Isolate the Bug:** Read the `buggy_code` to pinpoint the part of the logic that would lead to the observed failure. For example, an incorrect loop boundary could cause an `IndexError` or miss parts of the grid. A flawed conditional might apply a transformation incorrectly.
4. **Propose a Fix:** Write a corrected version of the function. Do not just hardcode a solution for the failing example. Your fix must be a general improvement to the logic that respects the overall pattern.
5. **Explain Your Reasoning:** Clearly describe the bug you found and how your new code fixes it.
"""
training_examples: List[TrainingExample] = dspy.InputField(desc="The original examples to understand the goal.")
buggy_code: str = dspy.InputField(desc="The Python code that failed.")
failing_input: MATRIX = dspy.InputField(desc="The specific input grid that caused the failure.")
expected_output: MATRIX = dspy.InputField(desc="The correct output for the failing input.")
actual_output: Optional[MATRIX] = dspy.InputField(desc="The incorrect output produced by the buggy code. Will be None if the code crashed.", default=None)
error_traceback: Optional[str] = dspy.InputField(desc="The Python traceback if the code crashed. Will be None if it was a logical error.", default=None)
reasoning: str = dspy.OutputField(desc="A step-by-step analysis of the bug and the plan to fix it.")
fixed_python_code: str = dspy.OutputField(desc="The complete, corrected, self-contained Python function `transform_grid(grid)`.")
class ARCRefineSolver(dspy.Module):
"""A DSPy module that solves ARC tasks by generating, verifying, and refining Python code."""
def __init__(self, max_retries=2):
super().__init__()
self.max_retries = max_retries
self.generator = dspy.ChainOfThought(GenerateTransformationFunction)
self.debugger = dspy.ChainOfThought(DebugTransformationFunction)
def _execute_code(self, code_str: str):
"""Safely executes generated code and returns the transform function or an error."""
try:
if "```python" in code_str:
code_str = code_str.split("```python")[1].split("```")[0].strip()
local_scope = {}
exec(code_str, globals(), local_scope)
transform_function = local_scope.get('transform_grid')
if transform_function and callable(transform_function):
return transform_function, None
else:
return None, "Function `transform_grid` not found or not callable."
except Exception as e:
return None, traceback.format_exc()
def forward(self, training_examples: List[TrainingExample], test_inputs: List[MATRIX]):
# 1. Initial Code Generation
gen_pred = self.generator(training_examples=training_examples, test_input_grid=test_inputs[0])
python_code = gen_pred.python_code
# 2. Verification and Refinement Loop
for attempt in range(self.max_retries):
transform_function, exec_error = self._execute_code(python_code)
if exec_error:
# This case is rare but handles syntax errors in generated code.
# We'll pick the first training example to report the error against.
debug_pred = self.debugger(
training_examples=training_examples,
buggy_code=python_code,
failing_input=training_examples[0].input,
expected_output=training_examples[0].output,
error_traceback=exec_error
)
python_code = debug_pred.fixed_python_code
continue
# Verify against all training examples
is_correct = True
for example in training_examples:
try:
actual_output = transform_function(copy.deepcopy(example.input))
if actual_output != example.output:
# Logical error found
debug_pred = self.debugger(
training_examples=training_examples,
buggy_code=python_code,
failing_input=example.input,
expected_output=example.output,
actual_output=actual_output
)
python_code = debug_pred.fixed_python_code
is_correct = False
break
except Exception:
# Runtime error found
debug_pred = self.debugger(
training_examples=training_examples,
buggy_code=python_code,
failing_input=example.input,
expected_output=example.output,
error_traceback=traceback.format_exc()
)
python_code = debug_pred.fixed_python_code
is_correct = False
break
if is_correct:
break # Code passed all checks
# 3. Final Execution on Test Inputs
final_transform_function, _ = self._execute_code(python_code)
generated_outputs = []
for test_input in test_inputs:
if final_transform_function:
try:
output_grid = final_transform_function(copy.deepcopy(test_input))
generated_outputs.append(output_grid)
except Exception as e:
print(f"Error applying final `transform_grid` to a test input: {e}")
generated_outputs.append(copy.deepcopy(test_input)) # Fallback
else:
generated_outputs.append(copy.deepcopy(test_input)) # Fallback
return dspy.Prediction(test_outputs=generated_outputs)
program = ARCRefineSolver()
2025/08/29 14:08:44 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1], [2, 0, 0, 0, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2], [3, 0, 0, 0, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3], [4, 0, 0, 0, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4], [5, 0, 0, 0, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5], [1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 0, 0, 5, 1], [2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 0, 0, 1, 2], [3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3], [4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4], [5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 0, 0, 0, 0, 4, 5], [1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 0, 0, 0, 0, 5, 1], [2, 3, 4, 5, 1, 2, 0, 0, 0, 1, 0, 0, 0, 0, 1, 2], [3, 4, 5, 1, 2, 3, 0, 0, 0, 0, 3, 4, 5, 1, 2, 3], [4, 5, 1, 2, 3, 4, 0, 0, 0, 0, 4, 5, 1, 2, 3, 4], [5, 1, 2, 3, 4, 5, 0, 0, 0, 0, 5, 1, 2, 3, 4, 5], [1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1]], 'output': [[1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1], [2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2], [3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3], [4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4], [5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5], [1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1], [2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2], [3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3], [4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4], [5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5], [1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1], [2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2], [3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3], [4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4], [5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5], [1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1]]}, {'input': [[1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4], [2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5], [3, 4, 5, 6, 1, 2, 0, 0, 5, 6, 1, 2, 3, 4, 5, 6], [4, 5, 6, 1, 2, 0, 0, 0, 6, 1, 2, 3, 4, 5, 6, 1], [5, 6, 1, 2, 3, 0, 0, 0, 1, 2, 3, 4, 5, 6, 1, 2], [6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3], [1, 2, 3, 4, 5, 6, 1, 2, 0, 0, 0, 6, 1, 2, 3, 4], [2, 3, 4, 5, 6, 1, 2, 3, 0, 0, 0, 0, 2, 3, 4, 5], [3, 4, 5, 6, 1, 2, 3, 4, 0, 0, 0, 0, 3, 4, 5, 6], [0, 0, 0, 0, 2, 3, 4, 5, 0, 0, 0, 0, 4, 5, 6, 1], [0, 0, 0, 0, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2], [0, 0, 0, 0, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3], [0, 0, 0, 0, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4], [2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5], [3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6], [4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1]], 'output': [[1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4], [2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5], [3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6], [4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1], [5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2], [6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3], [1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4], [2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5], [3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6], [4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1], [5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2], [6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3], [1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4], [2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5], [3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6], [4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1]]}, {'input': [[1, 2, 3, 4, 5, 6, 7, 1, 2, 3, 4, 5, 6, 7, 1, 2], [2, 3, 4, 5, 6, 7, 1, 2, 3, 4, 5, 6, 7, 1, 2, 3], [3, 0, 0, 0, 0, 1, 2, 3, 4, 5, 6, 7, 1, 2, 3, 4], [4, 0, 0, 0, 0, 2, 3, 4, 5, 6, 7, 1, 2, 3, 4, 5], [5, 0, 0, 0, 0, 3, 4, 5, 6, 7, 1, 2, 3, 4, 5, 6], [6, 0, 0, 0, 0, 4, 5, 6, 7, 1, 2, 3, 4, 5, 6, 7], [7, 1, 2, 3, 4, 5, 6, 7, 1, 2, 3, 4, 5, 6, 7, 1], [1, 2, 3, 4, 5, 6, 7, 1, 2, 3, 4, 5, 6, 7, 1, 2], [2, 0, 0, 0, 0, 7, 1, 2, 3, 4, 5, 6, 7, 1, 2, 3], [3, 0, 0, 0, 0, 1, 2, 3, 4, 5, 6, 7, 1, 2, 3, 4], [4, 5, 6, 7, 1, 2, 3, 4, 5, 6, 7, 1, 0, 0, 4, 5], [5, 6, 7, 1, 2, 3, 4, 5, 6, 7, 1, 2, 0, 0, 5, 6], [6, 7, 1, 2, 3, 4, 5, 6, 7, 1, 2, 0, 0, 0, 0, 7], [7, 1, 2, 3, 4, 5, 6, 7, 1, 2, 3, 0, 0, 0, 0, 1], [1, 2, 3, 4, 5, 6, 7, 1, 2, 3, 4, 0, 0, 0, 0, 2], [2, 3, 4, 5, 6, 7, 1, 2, 3, 4, 5, 6, 7, 1, 2, 3]], 'output': [[1, 2, 3, 4, 5, 6, 7, 1, 2, 3, 4, 5, 6, 7, 1, 2], [2, 3, 4, 5, 6, 7, 1, 2, 3, 4, 5, 6, 7, 1, 2, 3], [3, 4, 5, 6, 7, 1, 2, 3, 4, 5, 6, 7, 1, 2, 3, 4], [4, 5, 6, 7, 1, 2, 3, 4, 5, 6, 7, 1, 2, 3, 4, 5], [5, 6, 7, 1, 2, 3, 4, 5, 6, 7, 1, 2, 3, 4, 5, 6], [6, 7, 1, 2, 3, 4, 5, 6, 7, 1, 2, 3, 4, 5, 6, 7], [7, 1, 2, 3, 4, 5, 6, 7, 1, 2, 3, 4, 5, 6, 7, 1], [1, 2, 3, 4, 5, 6, 7, 1, 2, 3, 4, 5, 6, 7, 1, 2], [2, 3, 4, 5, 6, 7, 1, 2, 3, 4, 5, 6, 7, 1, 2, 3], [3, 4, 5, 6, 7, 1, 2, 3, 4, 5, 6, 7, 1, 2, 3, 4], [4, 5, 6, 7, 1, 2, 3, 4, 5, 6, 7, 1, 2, 3, 4, 5], [5, 6, 7, 1, 2, 3, 4, 5, 6, 7, 1, 2, 3, 4, 5, 6], [6, 7, 1, 2, 3, 4, 5, 6, 7, 1, 2, 3, 4, 5, 6, 7], [7, 1, 2, 3, 4, 5, 6, 7, 1, 2, 3, 4, 5, 6, 7, 1], [1, 2, 3, 4, 5, 6, 7, 1, 2, 3, 4, 5, 6, 7, 1, 2], [2, 3, 4, 5, 6, 7, 1, 2, 3, 4, 5, 6, 7, 1, 2, 3]]}], 'test_inputs': [[[1, 2, 3, 4, 5, 6, 7, 0, 0, 0, 3, 4, 5, 6, 7, 8], [2, 3, 4, 5, 6, 7, 8, 0, 0, 0, 4, 5, 6, 7, 8, 1], [3, 4, 5, 6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8, 1, 2], [4, 5, 6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3], [5, 6, 0, 0, 0, 0, 3, 4, 5, 6, 7, 8, 1, 2, 3, 4], [6, 7, 0, 0, 0, 0, 0, 0, 0, 7, 8, 1, 2, 3, 4, 5], [7, 8, 0, 0, 0, 0, 0, 0, 0, 8, 1, 2, 3, 4, 5, 6], [8, 1, 0, 0, 0, 0, 0, 0, 0, 1, 2, 3, 4, 5, 6, 7], [1, 2, 3, 4, 5, 0, 0, 0, 0, 2, 3, 4, 5, 6, 7, 8], [2, 3, 4, 5, 6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8, 1], [3, 4, 5, 6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8, 1, 2], [4, 5, 6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3], [5, 6, 7, 8, 1, 2, 3, 0, 0, 6, 7, 8, 1, 2, 3, 4], [6, 7, 8, 1, 2, 3, 4, 0, 0, 7, 8, 1, 2, 3, 4, 5], [7, 8, 1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3, 4, 5, 6], [8, 1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3, 4, 5, 6, 7]]], 'test_outputs': [[[1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8], [2, 3, 4, 5, 6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8, 1], [3, 4, 5, 6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8, 1, 2], [4, 5, 6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3], [5, 6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3, 4], [6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3, 4, 5], [7, 8, 1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3, 4, 5, 6], [8, 1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3, 4, 5, 6, 7], [1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8], [2, 3, 4, 5, 6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8, 1], [3, 4, 5, 6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8, 1, 2], [4, 5, 6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3], [5, 6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3, 4], [6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3, 4, 5], [7, 8, 1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3, 4, 5, 6], [8, 1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3, 4, 5, 6, 7]]]}) (input_keys={'test_inputs', 'training_examples'}): 'dict' object has no attribute 'input'
Traceback (most recent call last):
File "<string>", line 113, in forward
AttributeError: 'dict' object has no attribute 'input'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 131, in forward
AttributeError: 'dict' object has no attribute 'input'
2025/08/29 14:09:22 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 5, 0, 0, 0, 0, 0, 0], [0, 0, 5, 0, 0, 0, 5, 0, 0], [5, 0, 5, 0, 0, 0, 5, 0, 0], [5, 0, 5, 0, 0, 0, 5, 0, 0], [5, 0, 5, 0, 5, 0, 5, 0, 0], [5, 0, 5, 0, 5, 0, 5, 0, 5], [5, 0, 5, 0, 5, 0, 5, 0, 5], [5, 0, 5, 0, 5, 0, 5, 0, 5]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 2], [0, 0, 1, 0, 0, 0, 0, 0, 2], [0, 0, 1, 0, 0, 0, 0, 0, 2]]}, {'input': [[0, 0, 0, 0, 5, 0, 0, 0, 0], [0, 0, 0, 0, 5, 0, 0, 0, 0], [5, 0, 0, 0, 5, 0, 0, 0, 0], [5, 0, 0, 0, 5, 0, 5, 0, 0], [5, 0, 0, 0, 5, 0, 5, 0, 0], [5, 0, 0, 0, 5, 0, 5, 0, 0], [5, 0, 0, 0, 5, 0, 5, 0, 0], [5, 0, 5, 0, 5, 0, 5, 0, 0], [5, 0, 5, 0, 5, 0, 5, 0, 0]], 'output': [[0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 2, 0, 1, 0, 0, 0, 0], [0, 0, 2, 0, 1, 0, 0, 0, 0]]}], 'test_inputs': [[[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 5, 0], [0, 0, 0, 5, 0, 0, 0, 5, 0], [0, 0, 0, 5, 0, 0, 0, 5, 0], [0, 0, 0, 5, 0, 5, 0, 5, 0], [0, 0, 0, 5, 0, 5, 0, 5, 0], [0, 0, 0, 5, 0, 5, 0, 5, 0], [0, 0, 0, 5, 0, 5, 0, 5, 0], [0, 5, 0, 5, 0, 5, 0, 5, 0]]], 'test_outputs': [[[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 1, 0], [0, 0, 0, 0, 0, 0, 0, 1, 0], [0, 0, 0, 0, 0, 0, 0, 1, 0], [0, 0, 0, 0, 0, 0, 0, 1, 0], [0, 0, 0, 0, 0, 0, 0, 1, 0], [0, 0, 0, 0, 0, 0, 0, 1, 0], [0, 0, 0, 0, 0, 0, 0, 1, 0], [0, 2, 0, 0, 0, 0, 0, 1, 0]]]}) (input_keys={'test_inputs', 'training_examples'}): 'dict' object has no attribute 'input'
Traceback (most recent call last):
File "<string>", line 113, in forward
AttributeError: 'dict' object has no attribute 'input'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 131, in forward
AttributeError: 'dict' object has no attribute 'input'
2025/08/29 14:10:53 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[5, 0, 0, 5, 0, 0, 5, 5, 5, 5, 5, 5, 0, 0, 5, 5, 5, 5, 0, 0], [5, 0, 5, 5, 5, 5, 5, 5, 0, 0, 5, 5, 5, 5, 0, 5, 5, 0, 5, 5], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 5, 5], [0, 5, 5, 5, 5, 5, 0, 0, 0, 5, 0, 5, 5, 0, 5, 5, 0, 0, 0, 5], [5, 5, 5, 5, 5, 5, 0, 5, 0, 0, 5, 5, 5, 0, 0, 0, 5, 5, 0, 5], [0, 5, 0, 5, 0, 5, 0, 0, 5, 0, 5, 0, 5, 0, 5, 0, 5, 5, 5, 5], [5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 5, 0, 5, 5, 5, 0, 0, 0, 5], [0, 0, 0, 5, 5, 5, 0, 0, 0, 5, 5, 5, 0, 5, 0, 5, 0, 0, 0, 5], [5, 5, 0, 0, 5, 0, 0, 5, 5, 5, 5, 0, 0, 5, 0, 5, 0, 0, 0, 5], [0, 0, 5, 0, 0, 0, 5, 5, 0, 5, 5, 5, 5, 0, 5, 5, 5, 0, 5, 5], [5, 5, 5, 0, 5, 5, 5, 5, 5, 0, 0, 5, 0, 0, 5, 5, 5, 5, 5, 5], [5, 0, 5, 5, 5, 5, 5, 0, 5, 5, 5, 5, 0, 5, 0, 5, 5, 5, 0, 5], [5, 0, 0, 5, 5, 5, 5, 0, 0, 5, 5, 5, 0, 5, 5, 5, 5, 5, 5, 5], [5, 5, 0, 5, 5, 5, 5, 5, 5, 5, 5, 0, 5, 5, 5, 0, 5, 5, 0, 5], [0, 0, 5, 5, 5, 5, 0, 5, 5, 0, 5, 5, 5, 5, 0, 5, 5, 5, 0, 5], [5, 0, 0, 5, 0, 5, 0, 0, 0, 5, 5, 5, 0, 5, 0, 5, 5, 0, 5, 0], [0, 5, 0, 5, 0, 5, 5, 0, 0, 5, 0, 0, 5, 0, 5, 0, 0, 0, 5, 0], [5, 5, 5, 5, 5, 0, 5, 5, 5, 5, 5, 0, 0, 0, 5, 0, 5, 5, 0, 5], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 5, 5, 0, 5, 5], [5, 5, 5, 0, 0, 5, 5, 5, 5, 0, 5, 5, 0, 5, 0, 5, 0, 0, 0, 5]], 'output': [[5, 0, 0, 5, 0, 0, 5, 5, 5, 5, 5, 5, 0, 0, 5, 5, 5, 5, 0, 0], [5, 0, 5, 5, 5, 5, 5, 5, 0, 0, 5, 5, 5, 5, 0, 5, 5, 0, 5, 5], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 5, 5], [0, 5, 5, 5, 5, 5, 0, 0, 0, 5, 0, 5, 5, 0, 5, 5, 0, 0, 0, 5], [5, 5, 5, 5, 5, 5, 0, 5, 0, 0, 5, 5, 5, 0, 0, 0, 5, 5, 0, 5], [0, 5, 0, 5, 0, 5, 0, 0, 5, 0, 5, 0, 5, 0, 5, 0, 5, 5, 5, 5], [5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 5, 0, 5, 5, 5, 1, 1, 1, 5], [0, 0, 0, 5, 5, 5, 0, 0, 0, 5, 5, 5, 0, 5, 0, 5, 1, 1, 1, 5], [5, 5, 0, 0, 5, 0, 0, 5, 5, 5, 5, 0, 0, 5, 0, 5, 1, 1, 1, 5], [0, 0, 5, 0, 0, 0, 5, 5, 0, 5, 5, 5, 5, 0, 5, 5, 5, 0, 5, 5], [5, 5, 5, 0, 5, 5, 5, 5, 5, 0, 0, 5, 0, 0, 5, 5, 5, 5, 5, 5], [5, 0, 5, 5, 5, 5, 5, 0, 5, 5, 5, 5, 0, 5, 0, 5, 5, 5, 0, 5], [5, 0, 0, 5, 5, 5, 5, 0, 0, 5, 5, 5, 0, 5, 5, 5, 5, 5, 5, 5], [5, 5, 0, 5, 5, 5, 5, 5, 5, 5, 5, 0, 5, 5, 5, 0, 5, 5, 0, 5], [0, 0, 5, 5, 5, 5, 0, 5, 5, 0, 5, 5, 5, 5, 0, 5, 5, 5, 0, 5], [5, 0, 0, 5, 0, 5, 0, 0, 0, 5, 5, 5, 0, 5, 0, 5, 5, 0, 5, 0], [0, 5, 0, 5, 0, 5, 5, 0, 0, 5, 0, 0, 5, 0, 5, 0, 0, 0, 5, 0], [5, 5, 5, 5, 5, 0, 5, 5, 5, 5, 5, 0, 0, 0, 5, 0, 5, 5, 0, 5], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 5, 5, 0, 5, 5], [5, 5, 5, 0, 0, 5, 5, 5, 5, 0, 5, 5, 0, 5, 0, 5, 0, 0, 0, 5]]}, {'input': [[3, 3, 3, 3, 0, 3, 0, 3, 0, 3, 3, 0, 0, 3, 3, 3, 0, 3, 0, 0], [0, 0, 3, 3, 0, 0, 3, 0, 3, 3, 0, 3, 0, 3, 3, 0, 0, 3, 3, 0], [3, 3, 3, 3, 3, 0, 0, 3, 0, 0, 0, 3, 0, 3, 3, 0, 3, 3, 3, 3], [3, 0, 3, 3, 0, 0, 0, 0, 3, 0, 3, 3, 0, 3, 3, 3, 0, 3, 3, 0], [0, 0, 0, 3, 0, 3, 0, 3, 3, 3, 0, 3, 3, 3, 0, 3, 3, 3, 0, 0], [3, 3, 0, 0, 3, 3, 0, 3, 3, 3, 3, 0, 0, 3, 0, 3, 3, 3, 3, 0], [0, 3, 0, 0, 0, 0, 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0, 0, 3, 0, 0, 0, 3, 0, 3, 3, 0, 3, 3, 3, 3], [3, 0, 3, 3, 0, 0, 0, 0, 3, 0, 3, 3, 0, 3, 3, 3, 0, 3, 3, 0], [0, 0, 0, 3, 0, 3, 0, 3, 3, 3, 0, 3, 3, 3, 0, 3, 3, 3, 0, 0], [3, 3, 0, 0, 3, 3, 0, 3, 3, 3, 3, 0, 0, 3, 0, 3, 3, 3, 3, 0], [0, 3, 0, 1, 1, 1, 3, 3, 0, 3, 0, 0, 3, 0, 0, 0, 3, 0, 3, 0], [3, 0, 3, 1, 1, 1, 0, 0, 0, 3, 3, 3, 0, 3, 3, 3, 3, 3, 3, 3], [0, 3, 3, 1, 1, 1, 0, 3, 0, 3, 3, 0, 3, 3, 0, 0, 3, 3, 3, 3], [0, 0, 0, 3, 3, 0, 0, 3, 3, 3, 3, 3, 0, 3, 0, 3, 0, 3, 3, 3], [3, 0, 3, 3, 0, 3, 3, 3, 0, 0, 3, 0, 3, 0, 0, 0, 3, 3, 0, 3], [3, 0, 0, 3, 0, 0, 0, 3, 3, 3, 3, 0, 0, 3, 0, 3, 0, 3, 3, 3], [0, 3, 3, 0, 0, 0, 3, 3, 0, 3, 3, 3, 3, 0, 0, 3, 0, 0, 3, 3], [0, 0, 3, 0, 3, 3, 3, 3, 0, 0, 0, 3, 3, 3, 0, 0, 3, 0, 3, 0], [3, 0, 3, 3, 3, 0, 3, 3, 0, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 3], [0, 0, 3, 0, 3, 3, 0, 0, 3, 0, 3, 0, 3, 3, 0, 3, 3, 3, 0, 0], [3, 3, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 3, 0, 3, 0, 0, 0, 3, 3], [0, 3, 0, 3, 0, 0, 3, 3, 3, 0, 3, 3, 3, 0, 0, 3, 3, 0, 0, 0], [3, 0, 0, 3, 0, 3, 3, 0, 3, 0, 0, 3, 0, 0, 3, 3, 3, 3, 3, 3], [3, 0, 3, 3, 0, 3, 3, 3, 0, 0, 0, 3, 0, 3, 0, 3, 3, 3, 0, 3]]}, {'input': [[7, 0, 7, 7, 7, 7, 0, 7, 7, 0, 0, 7, 7, 0, 0, 7, 0, 7, 7, 7], [0, 0, 7, 0, 7, 0, 7, 0, 7, 7, 7, 0, 0, 0, 0, 7, 7, 0, 0, 7], [0, 0, 0, 0, 0, 7, 0, 0, 7, 7, 7, 7, 0, 7, 0, 0, 0, 0, 7, 0], [7, 0, 7, 0, 7, 0, 7, 7, 0, 0, 0, 7, 7, 0, 0, 7, 7, 0, 7, 0], [0, 0, 7, 0, 0, 7, 0, 0, 7, 0, 7, 7, 7, 7, 0, 0, 7, 0, 0, 7], [7, 7, 7, 7, 7, 7, 7, 7, 0, 7, 7, 0, 7, 7, 0, 0, 0, 7, 0, 7], [0, 0, 0, 7, 0, 7, 0, 0, 7, 7, 0, 7, 0, 7, 0, 0, 0, 0, 7, 7], [0, 7, 7, 7, 7, 0, 7, 0, 7, 0, 0, 7, 7, 7, 0, 0, 0, 0, 0, 7], [0, 0, 0, 7, 0, 0, 0, 0, 7, 7, 7, 0, 0, 7, 7, 0, 0, 0, 7, 7], [7, 7, 0, 7, 7, 7, 0, 7, 0, 0, 7, 0, 7, 7, 0, 7, 7, 0, 7, 0], [7, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 7, 0, 0, 0, 0, 7, 7, 0], [7, 7, 0, 0, 7, 7, 7, 0, 7, 7, 7, 7, 0, 7, 0, 0, 7, 7, 7, 7], [0, 7, 0, 7, 7, 7, 0, 0, 0, 7, 7, 0, 7, 7, 0, 7, 0, 0, 7, 7], [0, 0, 7, 7, 0, 7, 7, 7, 7, 7, 0, 7, 7, 0, 7, 7, 7, 0, 7, 7], [0, 0, 7, 7, 7, 0, 7, 0, 7, 7, 0, 7, 0, 7, 7, 7, 0, 7, 7, 7], [7, 0, 7, 7, 7, 0, 7, 0, 7, 7, 7, 7, 7, 0, 0, 7, 7, 7, 0, 0], [7, 7, 7, 0, 0, 0, 7, 7, 7, 0, 7, 7, 0, 7, 0, 7, 0, 0, 0, 0], [7, 7, 7, 0, 0, 0, 7, 0, 7, 7, 0, 7, 0, 0, 7, 0, 0, 0, 0, 0], [7, 0, 0, 0, 0, 0, 7, 7, 0, 7, 0, 0, 0, 7, 0, 7, 7, 7, 0, 7], [0, 7, 7, 0, 7, 7, 0, 7, 0, 0, 7, 7, 7, 7, 0, 0, 7, 0, 7, 7]], 'output': [[7, 0, 7, 7, 7, 7, 0, 7, 7, 0, 0, 7, 7, 0, 0, 7, 0, 7, 7, 7], [0, 0, 7, 0, 7, 0, 7, 0, 7, 7, 7, 0, 0, 0, 0, 7, 7, 0, 0, 7], [0, 0, 0, 0, 0, 7, 0, 0, 7, 7, 7, 7, 0, 7, 0, 0, 0, 0, 7, 0], [7, 0, 7, 0, 7, 0, 7, 7, 0, 0, 0, 7, 7, 0, 0, 7, 7, 0, 7, 0], [0, 0, 7, 0, 0, 7, 0, 0, 7, 0, 7, 7, 7, 7, 0, 0, 7, 0, 0, 7], [7, 7, 7, 7, 7, 7, 7, 7, 0, 7, 7, 0, 7, 7, 1, 1, 1, 7, 0, 7], [0, 0, 0, 7, 0, 7, 0, 0, 7, 7, 0, 7, 0, 7, 1, 1, 1, 0, 7, 7], [0, 7, 7, 7, 7, 0, 7, 0, 7, 0, 0, 7, 7, 7, 1, 1, 1, 0, 0, 7], [0, 0, 0, 7, 0, 0, 0, 0, 7, 7, 7, 0, 0, 7, 7, 0, 0, 0, 7, 7], [7, 7, 0, 7, 7, 7, 0, 7, 0, 0, 7, 0, 7, 7, 0, 7, 7, 0, 7, 0], [7, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 7, 0, 0, 0, 0, 7, 7, 0], [7, 7, 0, 0, 7, 7, 7, 0, 7, 7, 7, 7, 0, 7, 0, 0, 7, 7, 7, 7], [0, 7, 0, 7, 7, 7, 0, 0, 0, 7, 7, 0, 7, 7, 0, 7, 0, 0, 7, 7], [0, 0, 7, 7, 0, 7, 7, 7, 7, 7, 0, 7, 7, 0, 7, 7, 7, 0, 7, 7], [0, 0, 7, 7, 7, 0, 7, 0, 7, 7, 0, 7, 0, 7, 7, 7, 0, 7, 7, 7], [7, 0, 7, 7, 7, 0, 7, 0, 7, 7, 7, 7, 7, 0, 0, 7, 7, 7, 0, 0], [7, 7, 7, 1, 1, 1, 7, 7, 7, 0, 7, 7, 0, 7, 0, 7, 0, 0, 0, 0], [7, 7, 7, 1, 1, 1, 7, 0, 7, 7, 0, 7, 0, 0, 7, 0, 0, 0, 0, 0], [7, 0, 0, 1, 1, 1, 7, 7, 0, 7, 0, 0, 0, 7, 0, 7, 7, 7, 0, 7], [0, 7, 7, 0, 7, 7, 0, 7, 0, 0, 7, 7, 7, 7, 0, 0, 7, 0, 7, 7]]}], 'test_inputs': [[[0, 4, 0, 4, 4, 0, 4, 4, 4, 0, 0, 0, 4, 0, 4, 4, 4, 4, 4, 0], [0, 0, 4, 4, 0, 0, 4, 0, 4, 4, 0, 0, 0, 0, 4, 4, 4, 4, 4, 0], [4, 4, 4, 0, 0, 4, 0, 4, 0, 4, 0, 4, 4, 4, 4, 4, 4, 0, 4, 0], [4, 4, 0, 4, 0, 0, 4, 0, 0, 0, 0, 0, 0, 4, 4, 4, 0, 4, 0, 0], [4, 0, 0, 4, 4, 0, 4, 4, 4, 4, 4, 4, 4, 0, 4, 4, 0, 4, 0, 4], [4, 4, 0, 0, 4, 0, 0, 4, 4, 4, 4, 4, 4, 0, 0, 4, 4, 0, 4, 0], [0, 0, 0, 4, 0, 0, 0, 0, 4, 4, 4, 4, 4, 0, 4, 0, 4, 4, 0, 4], [4, 0, 4, 4, 0, 0, 0, 4, 4, 0, 0, 0, 0, 4, 4, 0, 0, 0, 0, 0], [0, 4, 4, 4, 0, 0, 0, 4, 4, 4, 0, 0, 4, 0, 4, 4, 4, 0, 0, 0], [4, 0, 0, 0, 4, 4, 0, 0, 4, 0, 0, 4, 0, 4, 4, 4, 0, 4, 0, 4], [0, 0, 0, 4, 0, 4, 0, 4, 4, 4, 0, 0, 4, 0, 4, 4, 4, 0, 4, 4], [0, 4, 4, 0, 0, 4, 4, 4, 4, 0, 0, 0, 4, 4, 4, 4, 4, 0, 4, 0], [0, 0, 4, 0, 0, 4, 0, 0, 4, 0, 0, 4, 0, 4, 4, 0, 0, 0, 4, 4], [4, 0, 4, 4, 0, 4, 0, 0, 4, 4, 4, 0, 0, 0, 0, 4, 4, 4, 0, 0], [0, 4, 4, 4, 4, 0, 0, 4, 0, 4, 0, 0, 4, 4, 0, 4, 4, 4, 4, 4], [4, 0, 0, 4, 4, 0, 4, 0, 4, 0, 0, 4, 0, 4, 0, 4, 0, 4, 0, 0], [4, 4, 0, 4, 0, 4, 0, 4, 4, 0, 0, 4, 4, 4, 0, 0, 0, 0, 4, 4], [4, 0, 0, 0, 0, 4, 4, 0, 4, 4, 0, 4, 0, 4, 0, 0, 0, 4, 4, 4], [0, 0, 0, 0, 0, 4, 4, 4, 4, 0, 4, 0, 0, 4, 0, 0, 0, 0, 0, 0], [4, 4, 0, 0, 0, 0, 0, 4, 4, 0, 0, 0, 4, 0, 4, 0, 4, 0, 4, 4]]], 'test_outputs': [[[0, 4, 0, 4, 4, 0, 4, 4, 4, 0, 0, 0, 4, 0, 4, 4, 4, 4, 4, 0], [0, 0, 4, 4, 0, 0, 4, 0, 4, 4, 0, 0, 0, 0, 4, 4, 4, 4, 4, 0], [4, 4, 4, 0, 0, 4, 0, 4, 0, 4, 0, 4, 4, 4, 4, 4, 4, 0, 4, 0], [4, 4, 0, 4, 0, 0, 4, 0, 0, 0, 0, 0, 0, 4, 4, 4, 0, 4, 0, 0], [4, 0, 0, 4, 4, 0, 4, 4, 4, 4, 4, 4, 4, 0, 4, 4, 0, 4, 0, 4], [4, 4, 0, 0, 4, 0, 0, 4, 4, 4, 4, 4, 4, 0, 0, 4, 4, 0, 4, 0], [0, 0, 0, 4, 1, 1, 1, 0, 4, 4, 4, 4, 4, 0, 4, 0, 4, 4, 0, 4], [4, 0, 4, 4, 1, 1, 1, 4, 4, 0, 0, 0, 0, 4, 4, 0, 0, 0, 0, 0], [0, 4, 4, 4, 1, 1, 1, 4, 4, 4, 0, 0, 4, 0, 4, 4, 4, 0, 0, 0], [4, 0, 0, 0, 4, 4, 0, 0, 4, 0, 0, 4, 0, 4, 4, 4, 0, 4, 0, 4], [0, 0, 0, 4, 0, 4, 0, 4, 4, 4, 0, 0, 4, 0, 4, 4, 4, 0, 4, 4], [0, 4, 4, 0, 0, 4, 4, 4, 4, 0, 0, 0, 4, 4, 4, 4, 4, 0, 4, 0], [0, 0, 4, 0, 0, 4, 0, 0, 4, 0, 0, 4, 0, 4, 4, 0, 0, 0, 4, 4], [4, 0, 4, 4, 0, 4, 0, 0, 4, 4, 4, 0, 0, 0, 0, 4, 4, 4, 0, 0], [0, 4, 4, 4, 4, 0, 0, 4, 0, 4, 0, 0, 4, 4, 0, 4, 4, 4, 4, 4], [4, 0, 0, 4, 4, 0, 4, 0, 4, 0, 0, 4, 0, 4, 0, 4, 0, 4, 0, 0], [4, 4, 0, 4, 0, 4, 0, 4, 4, 0, 0, 4, 4, 4, 1, 1, 1, 0, 4, 4], [4, 0, 1, 1, 1, 4, 4, 0, 4, 4, 0, 4, 0, 4, 1, 1, 1, 4, 4, 4], [0, 0, 1, 1, 1, 4, 4, 4, 4, 0, 4, 0, 0, 4, 1, 1, 1, 0, 0, 0], [4, 4, 1, 1, 1, 0, 0, 4, 4, 0, 0, 0, 4, 0, 4, 0, 4, 0, 4, 4]]]}) (input_keys={'test_inputs', 'training_examples'}): 'dict' object has no attribute 'input'
Traceback (most recent call last):
File "<string>", line 113, in forward
AttributeError: 'dict' object has no attribute 'input'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 131, in forward
AttributeError: 'dict' object has no attribute 'input'
2025/08/29 14:12:11 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[5, 0, 0, 5, 0, 0, 5, 5, 5, 5, 5, 5, 0, 0, 5, 5, 5, 5, 0, 0], [5, 0, 5, 5, 5, 5, 5, 5, 0, 0, 5, 5, 5, 5, 0, 5, 5, 0, 5, 5], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 5, 5], [0, 5, 5, 5, 5, 5, 0, 0, 0, 5, 0, 5, 5, 0, 5, 5, 0, 0, 0, 5], [5, 5, 5, 5, 5, 5, 0, 5, 0, 0, 5, 5, 5, 0, 0, 0, 5, 5, 0, 5], [0, 5, 0, 5, 0, 5, 0, 0, 5, 0, 5, 0, 5, 0, 5, 0, 5, 5, 5, 5], [5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 5, 0, 5, 5, 5, 0, 0, 0, 5], [0, 0, 0, 5, 5, 5, 0, 0, 0, 5, 5, 5, 0, 5, 0, 5, 0, 0, 0, 5], [5, 5, 0, 0, 5, 0, 0, 5, 5, 5, 5, 0, 0, 5, 0, 5, 0, 0, 0, 5], [0, 0, 5, 0, 0, 0, 5, 5, 0, 5, 5, 5, 5, 0, 5, 5, 5, 0, 5, 5], [5, 5, 5, 0, 5, 5, 5, 5, 5, 0, 0, 5, 0, 0, 5, 5, 5, 5, 5, 5], [5, 0, 5, 5, 5, 5, 5, 0, 5, 5, 5, 5, 0, 5, 0, 5, 5, 5, 0, 5], [5, 0, 0, 5, 5, 5, 5, 0, 0, 5, 5, 5, 0, 5, 5, 5, 5, 5, 5, 5], [5, 5, 0, 5, 5, 5, 5, 5, 5, 5, 5, 0, 5, 5, 5, 0, 5, 5, 0, 5], [0, 0, 5, 5, 5, 5, 0, 5, 5, 0, 5, 5, 5, 5, 0, 5, 5, 5, 0, 5], [5, 0, 0, 5, 0, 5, 0, 0, 0, 5, 5, 5, 0, 5, 0, 5, 5, 0, 5, 0], [0, 5, 0, 5, 0, 5, 5, 0, 0, 5, 0, 0, 5, 0, 5, 0, 0, 0, 5, 0], [5, 5, 5, 5, 5, 0, 5, 5, 5, 5, 5, 0, 0, 0, 5, 0, 5, 5, 0, 5], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 5, 5, 0, 5, 5], [5, 5, 5, 0, 0, 5, 5, 5, 5, 0, 5, 5, 0, 5, 0, 5, 0, 0, 0, 5]], 'output': [[5, 0, 0, 5, 0, 0, 5, 5, 5, 5, 5, 5, 0, 0, 5, 5, 5, 5, 0, 0], [5, 0, 5, 5, 5, 5, 5, 5, 0, 0, 5, 5, 5, 5, 0, 5, 5, 0, 5, 5], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 5, 5], [0, 5, 5, 5, 5, 5, 0, 0, 0, 5, 0, 5, 5, 0, 5, 5, 0, 0, 0, 5], [5, 5, 5, 5, 5, 5, 0, 5, 0, 0, 5, 5, 5, 0, 0, 0, 5, 5, 0, 5], [0, 5, 0, 5, 0, 5, 0, 0, 5, 0, 5, 0, 5, 0, 5, 0, 5, 5, 5, 5], [5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 5, 0, 5, 5, 5, 1, 1, 1, 5], [0, 0, 0, 5, 5, 5, 0, 0, 0, 5, 5, 5, 0, 5, 0, 5, 1, 1, 1, 5], [5, 5, 0, 0, 5, 0, 0, 5, 5, 5, 5, 0, 0, 5, 0, 5, 1, 1, 1, 5], [0, 0, 5, 0, 0, 0, 5, 5, 0, 5, 5, 5, 5, 0, 5, 5, 5, 0, 5, 5], [5, 5, 5, 0, 5, 5, 5, 5, 5, 0, 0, 5, 0, 0, 5, 5, 5, 5, 5, 5], [5, 0, 5, 5, 5, 5, 5, 0, 5, 5, 5, 5, 0, 5, 0, 5, 5, 5, 0, 5], [5, 0, 0, 5, 5, 5, 5, 0, 0, 5, 5, 5, 0, 5, 5, 5, 5, 5, 5, 5], [5, 5, 0, 5, 5, 5, 5, 5, 5, 5, 5, 0, 5, 5, 5, 0, 5, 5, 0, 5], [0, 0, 5, 5, 5, 5, 0, 5, 5, 0, 5, 5, 5, 5, 0, 5, 5, 5, 0, 5], [5, 0, 0, 5, 0, 5, 0, 0, 0, 5, 5, 5, 0, 5, 0, 5, 5, 0, 5, 0], [0, 5, 0, 5, 0, 5, 5, 0, 0, 5, 0, 0, 5, 0, 5, 0, 0, 0, 5, 0], [5, 5, 5, 5, 5, 0, 5, 5, 5, 5, 5, 0, 0, 0, 5, 0, 5, 5, 0, 5], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 5, 5, 0, 5, 5], [5, 5, 5, 0, 0, 5, 5, 5, 5, 0, 5, 5, 0, 5, 0, 5, 0, 0, 0, 5]]}, {'input': [[3, 3, 3, 3, 0, 3, 0, 3, 0, 3, 3, 0, 0, 3, 3, 3, 0, 3, 0, 0], [0, 0, 3, 3, 0, 0, 3, 0, 3, 3, 0, 3, 0, 3, 3, 0, 0, 3, 3, 0], [3, 3, 3, 3, 3, 0, 0, 3, 0, 0, 0, 3, 0, 3, 3, 0, 3, 3, 3, 3], [3, 0, 3, 3, 0, 0, 0, 0, 3, 0, 3, 3, 0, 3, 3, 3, 0, 3, 3, 0], [0, 0, 0, 3, 0, 3, 0, 3, 3, 3, 0, 3, 3, 3, 0, 3, 3, 3, 0, 0], [3, 3, 0, 0, 3, 3, 0, 3, 3, 3, 3, 0, 0, 3, 0, 3, 3, 3, 3, 0], [0, 3, 0, 0, 0, 0, 3, 3, 0, 3, 0, 0, 3, 0, 0, 0, 3, 0, 3, 0], [3, 0, 3, 0, 0, 0, 0, 0, 0, 3, 3, 3, 0, 3, 3, 3, 3, 3, 3, 3], [0, 3, 3, 0, 0, 0, 0, 3, 0, 3, 3, 0, 3, 3, 0, 0, 3, 3, 3, 3], [0, 0, 0, 3, 3, 0, 0, 3, 3, 3, 3, 3, 0, 3, 0, 3, 0, 3, 3, 3], [3, 0, 3, 3, 0, 3, 3, 3, 0, 0, 3, 0, 3, 0, 0, 0, 3, 3, 0, 3], [3, 0, 0, 3, 0, 0, 0, 3, 3, 3, 3, 0, 0, 3, 0, 3, 0, 3, 3, 3], [0, 3, 3, 0, 0, 0, 3, 3, 0, 3, 3, 3, 3, 0, 0, 3, 0, 0, 3, 3], [0, 0, 3, 0, 3, 3, 3, 3, 0, 0, 0, 3, 3, 3, 0, 0, 3, 0, 3, 0], [3, 0, 3, 3, 3, 0, 3, 3, 0, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 3], [0, 0, 3, 0, 3, 3, 0, 0, 3, 0, 3, 0, 3, 3, 0, 3, 3, 3, 0, 0], [3, 3, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 3, 0, 3, 0, 0, 0, 3, 3], [0, 3, 0, 3, 0, 0, 3, 3, 3, 0, 3, 3, 3, 0, 0, 3, 3, 0, 0, 0], [3, 0, 0, 3, 0, 3, 3, 0, 3, 0, 0, 3, 0, 0, 3, 3, 3, 3, 3, 3], [3, 0, 3, 3, 0, 3, 3, 3, 0, 0, 0, 3, 0, 3, 0, 3, 3, 3, 0, 3]], 'output': [[3, 3, 3, 3, 0, 3, 0, 3, 0, 3, 3, 0, 0, 3, 3, 3, 0, 3, 0, 0], [0, 0, 3, 3, 0, 0, 3, 0, 3, 3, 0, 3, 0, 3, 3, 0, 0, 3, 3, 0], [3, 3, 3, 3, 3, 0, 0, 3, 0, 0, 0, 3, 0, 3, 3, 0, 3, 3, 3, 3], [3, 0, 3, 3, 0, 0, 0, 0, 3, 0, 3, 3, 0, 3, 3, 3, 0, 3, 3, 0], [0, 0, 0, 3, 0, 3, 0, 3, 3, 3, 0, 3, 3, 3, 0, 3, 3, 3, 0, 0], [3, 3, 0, 0, 3, 3, 0, 3, 3, 3, 3, 0, 0, 3, 0, 3, 3, 3, 3, 0], [0, 3, 0, 1, 1, 1, 3, 3, 0, 3, 0, 0, 3, 0, 0, 0, 3, 0, 3, 0], [3, 0, 3, 1, 1, 1, 0, 0, 0, 3, 3, 3, 0, 3, 3, 3, 3, 3, 3, 3], [0, 3, 3, 1, 1, 1, 0, 3, 0, 3, 3, 0, 3, 3, 0, 0, 3, 3, 3, 3], [0, 0, 0, 3, 3, 0, 0, 3, 3, 3, 3, 3, 0, 3, 0, 3, 0, 3, 3, 3], [3, 0, 3, 3, 0, 3, 3, 3, 0, 0, 3, 0, 3, 0, 0, 0, 3, 3, 0, 3], [3, 0, 0, 3, 0, 0, 0, 3, 3, 3, 3, 0, 0, 3, 0, 3, 0, 3, 3, 3], [0, 3, 3, 0, 0, 0, 3, 3, 0, 3, 3, 3, 3, 0, 0, 3, 0, 0, 3, 3], [0, 0, 3, 0, 3, 3, 3, 3, 0, 0, 0, 3, 3, 3, 0, 0, 3, 0, 3, 0], [3, 0, 3, 3, 3, 0, 3, 3, 0, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 3], [0, 0, 3, 0, 3, 3, 0, 0, 3, 0, 3, 0, 3, 3, 0, 3, 3, 3, 0, 0], [3, 3, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 3, 0, 3, 0, 0, 0, 3, 3], [0, 3, 0, 3, 0, 0, 3, 3, 3, 0, 3, 3, 3, 0, 0, 3, 3, 0, 0, 0], [3, 0, 0, 3, 0, 3, 3, 0, 3, 0, 0, 3, 0, 0, 3, 3, 3, 3, 3, 3], [3, 0, 3, 3, 0, 3, 3, 3, 0, 0, 0, 3, 0, 3, 0, 3, 3, 3, 0, 3]]}, {'input': [[7, 0, 7, 7, 7, 7, 0, 7, 7, 0, 0, 7, 7, 0, 0, 7, 0, 7, 7, 7], [0, 0, 7, 0, 7, 0, 7, 0, 7, 7, 7, 0, 0, 0, 0, 7, 7, 0, 0, 7], [0, 0, 0, 0, 0, 7, 0, 0, 7, 7, 7, 7, 0, 7, 0, 0, 0, 0, 7, 0], [7, 0, 7, 0, 7, 0, 7, 7, 0, 0, 0, 7, 7, 0, 0, 7, 7, 0, 7, 0], [0, 0, 7, 0, 0, 7, 0, 0, 7, 0, 7, 7, 7, 7, 0, 0, 7, 0, 0, 7], [7, 7, 7, 7, 7, 7, 7, 7, 0, 7, 7, 0, 7, 7, 0, 0, 0, 7, 0, 7], [0, 0, 0, 7, 0, 7, 0, 0, 7, 7, 0, 7, 0, 7, 0, 0, 0, 0, 7, 7], [0, 7, 7, 7, 7, 0, 7, 0, 7, 0, 0, 7, 7, 7, 0, 0, 0, 0, 0, 7], [0, 0, 0, 7, 0, 0, 0, 0, 7, 7, 7, 0, 0, 7, 7, 0, 0, 0, 7, 7], [7, 7, 0, 7, 7, 7, 0, 7, 0, 0, 7, 0, 7, 7, 0, 7, 7, 0, 7, 0], [7, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 7, 0, 0, 0, 0, 7, 7, 0], [7, 7, 0, 0, 7, 7, 7, 0, 7, 7, 7, 7, 0, 7, 0, 0, 7, 7, 7, 7], [0, 7, 0, 7, 7, 7, 0, 0, 0, 7, 7, 0, 7, 7, 0, 7, 0, 0, 7, 7], [0, 0, 7, 7, 0, 7, 7, 7, 7, 7, 0, 7, 7, 0, 7, 7, 7, 0, 7, 7], [0, 0, 7, 7, 7, 0, 7, 0, 7, 7, 0, 7, 0, 7, 7, 7, 0, 7, 7, 7], [7, 0, 7, 7, 7, 0, 7, 0, 7, 7, 7, 7, 7, 0, 0, 7, 7, 7, 0, 0], [7, 7, 7, 0, 0, 0, 7, 7, 7, 0, 7, 7, 0, 7, 0, 7, 0, 0, 0, 0], [7, 7, 7, 0, 0, 0, 7, 0, 7, 7, 0, 7, 0, 0, 7, 0, 0, 0, 0, 0], [7, 0, 0, 0, 0, 0, 7, 7, 0, 7, 0, 0, 0, 7, 0, 7, 7, 7, 0, 7], [0, 7, 7, 0, 7, 7, 0, 7, 0, 0, 7, 7, 7, 7, 0, 0, 7, 0, 7, 7]], 'output': [[7, 0, 7, 7, 7, 7, 0, 7, 7, 0, 0, 7, 7, 0, 0, 7, 0, 7, 7, 7], [0, 0, 7, 0, 7, 0, 7, 0, 7, 7, 7, 0, 0, 0, 0, 7, 7, 0, 0, 7], [0, 0, 0, 0, 0, 7, 0, 0, 7, 7, 7, 7, 0, 7, 0, 0, 0, 0, 7, 0], [7, 0, 7, 0, 7, 0, 7, 7, 0, 0, 0, 7, 7, 0, 0, 7, 7, 0, 7, 0], [0, 0, 7, 0, 0, 7, 0, 0, 7, 0, 7, 7, 7, 7, 0, 0, 7, 0, 0, 7], [7, 7, 7, 7, 7, 7, 7, 7, 0, 7, 7, 0, 7, 7, 1, 1, 1, 7, 0, 7], [0, 0, 0, 7, 0, 7, 0, 0, 7, 7, 0, 7, 0, 7, 1, 1, 1, 0, 7, 7], [0, 7, 7, 7, 7, 0, 7, 0, 7, 0, 0, 7, 7, 7, 1, 1, 1, 0, 0, 7], [0, 0, 0, 7, 0, 0, 0, 0, 7, 7, 7, 0, 0, 7, 7, 0, 0, 0, 7, 7], [7, 7, 0, 7, 7, 7, 0, 7, 0, 0, 7, 0, 7, 7, 0, 7, 7, 0, 7, 0], [7, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 7, 0, 0, 0, 0, 7, 7, 0], [7, 7, 0, 0, 7, 7, 7, 0, 7, 7, 7, 7, 0, 7, 0, 0, 7, 7, 7, 7], [0, 7, 0, 7, 7, 7, 0, 0, 0, 7, 7, 0, 7, 7, 0, 7, 0, 0, 7, 7], [0, 0, 7, 7, 0, 7, 7, 7, 7, 7, 0, 7, 7, 0, 7, 7, 7, 0, 7, 7], [0, 0, 7, 7, 7, 0, 7, 0, 7, 7, 0, 7, 0, 7, 7, 7, 0, 7, 7, 7], [7, 0, 7, 7, 7, 0, 7, 0, 7, 7, 7, 7, 7, 0, 0, 7, 7, 7, 0, 0], [7, 7, 7, 1, 1, 1, 7, 7, 7, 0, 7, 7, 0, 7, 0, 7, 0, 0, 0, 0], [7, 7, 7, 1, 1, 1, 7, 0, 7, 7, 0, 7, 0, 0, 7, 0, 0, 0, 0, 0], [7, 0, 0, 1, 1, 1, 7, 7, 0, 7, 0, 0, 0, 7, 0, 7, 7, 7, 0, 7], [0, 7, 7, 0, 7, 7, 0, 7, 0, 0, 7, 7, 7, 7, 0, 0, 7, 0, 7, 7]]}], 'test_inputs': [[[0, 4, 0, 4, 4, 0, 4, 4, 4, 0, 0, 0, 4, 0, 4, 4, 4, 4, 4, 0], [0, 0, 4, 4, 0, 0, 4, 0, 4, 4, 0, 0, 0, 0, 4, 4, 4, 4, 4, 0], [4, 4, 4, 0, 0, 4, 0, 4, 0, 4, 0, 4, 4, 4, 4, 4, 4, 0, 4, 0], [4, 4, 0, 4, 0, 0, 4, 0, 0, 0, 0, 0, 0, 4, 4, 4, 0, 4, 0, 0], [4, 0, 0, 4, 4, 0, 4, 4, 4, 4, 4, 4, 4, 0, 4, 4, 0, 4, 0, 4], [4, 4, 0, 0, 4, 0, 0, 4, 4, 4, 4, 4, 4, 0, 0, 4, 4, 0, 4, 0], [0, 0, 0, 4, 0, 0, 0, 0, 4, 4, 4, 4, 4, 0, 4, 0, 4, 4, 0, 4], [4, 0, 4, 4, 0, 0, 0, 4, 4, 0, 0, 0, 0, 4, 4, 0, 0, 0, 0, 0], [0, 4, 4, 4, 0, 0, 0, 4, 4, 4, 0, 0, 4, 0, 4, 4, 4, 0, 0, 0], [4, 0, 0, 0, 4, 4, 0, 0, 4, 0, 0, 4, 0, 4, 4, 4, 0, 4, 0, 4], [0, 0, 0, 4, 0, 4, 0, 4, 4, 4, 0, 0, 4, 0, 4, 4, 4, 0, 4, 4], [0, 4, 4, 0, 0, 4, 4, 4, 4, 0, 0, 0, 4, 4, 4, 4, 4, 0, 4, 0], [0, 0, 4, 0, 0, 4, 0, 0, 4, 0, 0, 4, 0, 4, 4, 0, 0, 0, 4, 4], [4, 0, 4, 4, 0, 4, 0, 0, 4, 4, 4, 0, 0, 0, 0, 4, 4, 4, 0, 0], [0, 4, 4, 4, 4, 0, 0, 4, 0, 4, 0, 0, 4, 4, 0, 4, 4, 4, 4, 4], [4, 0, 0, 4, 4, 0, 4, 0, 4, 0, 0, 4, 0, 4, 0, 4, 0, 4, 0, 0], [4, 4, 0, 4, 0, 4, 0, 4, 4, 0, 0, 4, 4, 4, 0, 0, 0, 0, 4, 4], [4, 0, 0, 0, 0, 4, 4, 0, 4, 4, 0, 4, 0, 4, 0, 0, 0, 4, 4, 4], [0, 0, 0, 0, 0, 4, 4, 4, 4, 0, 4, 0, 0, 4, 0, 0, 0, 0, 0, 0], [4, 4, 0, 0, 0, 0, 0, 4, 4, 0, 0, 0, 4, 0, 4, 0, 4, 0, 4, 4]]], 'test_outputs': [[[0, 4, 0, 4, 4, 0, 4, 4, 4, 0, 0, 0, 4, 0, 4, 4, 4, 4, 4, 0], [0, 0, 4, 4, 0, 0, 4, 0, 4, 4, 0, 0, 0, 0, 4, 4, 4, 4, 4, 0], [4, 4, 4, 0, 0, 4, 0, 4, 0, 4, 0, 4, 4, 4, 4, 4, 4, 0, 4, 0], [4, 4, 0, 4, 0, 0, 4, 0, 0, 0, 0, 0, 0, 4, 4, 4, 0, 4, 0, 0], [4, 0, 0, 4, 4, 0, 4, 4, 4, 4, 4, 4, 4, 0, 4, 4, 0, 4, 0, 4], [4, 4, 0, 0, 4, 0, 0, 4, 4, 4, 4, 4, 4, 0, 0, 4, 4, 0, 4, 0], [0, 0, 0, 4, 1, 1, 1, 0, 4, 4, 4, 4, 4, 0, 4, 0, 4, 4, 0, 4], [4, 0, 4, 4, 1, 1, 1, 4, 4, 0, 0, 0, 0, 4, 4, 0, 0, 0, 0, 0], [0, 4, 4, 4, 1, 1, 1, 4, 4, 4, 0, 0, 4, 0, 4, 4, 4, 0, 0, 0], [4, 0, 0, 0, 4, 4, 0, 0, 4, 0, 0, 4, 0, 4, 4, 4, 0, 4, 0, 4], [0, 0, 0, 4, 0, 4, 0, 4, 4, 4, 0, 0, 4, 0, 4, 4, 4, 0, 4, 4], [0, 4, 4, 0, 0, 4, 4, 4, 4, 0, 0, 0, 4, 4, 4, 4, 4, 0, 4, 0], [0, 0, 4, 0, 0, 4, 0, 0, 4, 0, 0, 4, 0, 4, 4, 0, 0, 0, 4, 4], [4, 0, 4, 4, 0, 4, 0, 0, 4, 4, 4, 0, 0, 0, 0, 4, 4, 4, 0, 0], [0, 4, 4, 4, 4, 0, 0, 4, 0, 4, 0, 0, 4, 4, 0, 4, 4, 4, 4, 4], [4, 0, 0, 4, 4, 0, 4, 0, 4, 0, 0, 4, 0, 4, 0, 4, 0, 4, 0, 0], [4, 4, 0, 4, 0, 4, 0, 4, 4, 0, 0, 4, 4, 4, 1, 1, 1, 0, 4, 4], [4, 0, 1, 1, 1, 4, 4, 0, 4, 4, 0, 4, 0, 4, 1, 1, 1, 4, 4, 4], [0, 0, 1, 1, 1, 4, 4, 4, 4, 0, 4, 0, 0, 4, 1, 1, 1, 0, 0, 0], [4, 4, 1, 1, 1, 0, 0, 4, 4, 0, 0, 0, 4, 0, 4, 0, 4, 0, 4, 4]]]}) (input_keys={'test_inputs', 'training_examples'}): 'dict' object has no attribute 'input'
Traceback (most recent call last):
File "<string>", line 113, in forward
AttributeError: 'dict' object has no attribute 'input'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 131, in forward
AttributeError: 'dict' object has no attribute 'input'
2025/08/29 14:12:11 INFO dspy.evaluate.evaluate: Average Metric: 0.0 / 3 (0.0%)
GEPA Optimization: 64%|█████████████████████████████████████████████████████████████████████▋ | 2558/4000 [16:38:14<13:29:34, 33.69s/rollouts]Iteration 107: New subsample score is not better, skipping
Iteration 108: Selected program 6 score: 0.675
Average Metric: 2.00 / 3 (66.7%): : 4it [08:46, 131.75s/it] 2025/08/29 14:20:58 INFO dspy.evaluate.evaluate: Average Metric: 2.0 / 3 (66.7%)
Iteration 108: Proposed new text for program: import dspy
from typing import List, Tuple
import pydantic
import copy
# Define type aliases and pydantic models for clarity and structure.
MATRIX = List[List[int]]
class TrainingExample(pydantic.BaseModel):
input: MATRIX
output: MATRIX
class HypothesizeRule(dspy.Signature):
"""
Analyze the provided input/output matrix pairs from the Abstraction and Reasoning Corpus (ARC).
Deduce the underlying transformation rule that converts each input matrix to its corresponding output matrix.
Describe this rule in clear, step-by-step, unambiguous English.
Focus on the logic and the sequence of operations, not on Python code implementation.
"""
training_examples: List[TrainingExample] = dspy.InputField(desc="A list of input/output pairs demonstrating the transformation rule.")
rule_description: str = dspy.OutputField(desc="A step-by-step English description of the transformation rule.")
class ImplementRule(dspy.Signature):
"""
You are an expert programmer. Your task is to write a single, self-contained Python function based on a provided rule description and example pairs.
**Function Requirements:**
- The function must be named `transform_matrix`.
- It must accept one argument: `matrix`, which is a list of lists of integers (representing the input grid).
- It must return a list of lists of integers (representing the transformed output grid).
- The function should not use any external libraries except for `copy` if needed (e.g., `import copy; new_matrix = copy.deepcopy(matrix)`).
- Your output must be ONLY the Python code for the function. Do not include any explanations, comments outside the function, or markdown formatting like ```python.
**Successful Strategies to Consider:**
- **Decomposition:** For complex rules, break the logic into smaller helper functions within the main `transform_matrix` function. This is more robust than a single monolithic block of code.
- **Object Permanence:** Most transformations preserve the grid dimensions.
- **Color Transformations:** Look for rules that change colors based on their value or their neighbors.
- **Spatial Reasoning:** Analyze shapes, positions, and relationships. Common patterns include filling enclosed areas, drawing lines, moving objects, or detecting symmetry.
- **Neighborhoods:** Often, a cell's new value depends on its immediate neighbors (e.g., von Neumann or Moore neighborhoods).
**Example of a Correctly Formatted Output:**
def transform_matrix(matrix: list[list[int]]) -> list[list[int]]:
# Your implementation here
# For example, find the most frequent color and fill the grid
from collections import Counter
import itertools
if not matrix or not matrix[0]:
return []
counts = Counter(itertools.chain.from_iterable(matrix))
if counts:
# Handle ties by picking the smaller number value
most_common_color = sorted(counts.items(), key=lambda item: (-item[1], item[0]))[0][0]
else:
return []
height = len(matrix)
width = len(matrix[0])
return [[most_common_color for _ in range(width)] for _ in range(height)]
"""
rule_description: str = dspy.InputField(desc="The English description of the rule to implement.")
training_examples: List[TrainingExample] = dspy.InputField(desc="A list of input/output pairs to use as a reference for implementation.")
python_function: str = dspy.OutputField(desc="A string containing a single Python function `transform_matrix` that implements the rule.")
class RefineCode(dspy.Signature):
"""
You are an expert programmer and debugger. The provided Python code is an attempt to solve a matrix transformation task, but it fails on one of the training examples.
Your goal is to analyze the error feedback and the buggy code, then provide a corrected version.
**Analysis Steps:**
1. Thoroughly understand the original `rule_description`.
2. Examine the `buggy_code` to see how it tried to implement the rule.
3. Look at the `feedback`, which specifies the exact training example that failed, the incorrect output produced by the code, and the expected correct output.
4. Identify the logical error, bug, or edge case that was missed in the code.
5. Rewrite the entire function to fix the bug, ensuring it still adheres to the function requirements.
**Output Requirements:**
- The corrected function must be named `transform_matrix`.
- It must be a single, self-contained Python function.
- Your output must be ONLY the Python code for the function. Do not include any explanations or markdown formatting.
"""
rule_description: str = dspy.InputField(desc="The English description of the transformation rule.")
training_examples: List[TrainingExample] = dspy.InputField(desc="A list of all input/output pairs for reference.")
buggy_code: str = dspy.InputField(desc="The Python code that failed to pass the training examples.")
feedback: str = dspy.InputField(desc="Detailed feedback on which training example failed and the discrepancy between the actual and expected output.")
refined_python_function: str = dspy.OutputField(desc="A string containing the corrected `transform_matrix` Python function.")
class ARCSolver(dspy.Module):
"""A module that solves ARC tasks by hypothesizing a rule, then generating, testing, and refining Python code."""
def __init__(self, max_attempts=3):
super().__init__()
self.max_attempts = max_attempts
self.rule_hypothesizer = dspy.ChainOfThought(HypothesizeRule)
self.code_implementer = dspy.Predict(ImplementRule)
self.code_refiner = dspy.ChainOfThought(RefineCode)
def _verify_code(self, code_str: str, examples: List[TrainingExample]) -> Tuple[bool, str]:
"""Executes the code and verifies it against all training examples."""
try:
local_scope = {}
exec(code_str, globals(), local_scope)
transform_func = local_scope.get('transform_matrix')
if not callable(transform_func):
return False, "Code did not define a callable function 'transform_matrix'."
for i, example in enumerate(examples):
input_copy = copy.deepcopy(example.input)
result = transform_func(input_copy)
if result != example.output:
feedback = f"Failed on training example {i}.\nInput:\n{example.input}\n\nIncorrect Output:\n{result}\n\nExpected Output:\n{example.output}"
return False, feedback
return True, "All training examples passed."
except Exception as e:
return False, f"Code failed to execute. Error: {e!r}"
def forward(self, training_examples: List[TrainingExample], test_inputs: List[MATRIX]):
# Step 1: Generate an English description of the transformation rule.
hypothesis = self.rule_hypothesizer(training_examples=training_examples)
rule_description = hypothesis.rule_description
# Step 2: Iteratively generate and refine code.
current_code = None
last_feedback = ""
for attempt in range(self.max_attempts):
if attempt == 0:
# First attempt: generate from scratch.
prediction = self.code_implementer(
rule_description=rule_description,
training_examples=training_examples
)
current_code = prediction.python_function
else:
# Subsequent attempts: refine the last code.
refinement = self.code_refiner(
rule_description=rule_description,
training_examples=training_examples,
buggy_code=current_code,
feedback=last_feedback
)
current_code = refinement.refined_python_function
# Test the generated code against all training examples.
is_correct, last_feedback = self._verify_code(current_code, training_examples)
if is_correct:
break # Found a working solution.
# Fallback outputs in case of any failure after all attempts.
fallback_outputs = [copy.deepcopy(matrix) for matrix in test_inputs]
if not is_correct:
return dspy.Prediction(test_outputs=fallback_outputs)
# Step 3: Execute the final correct code on test inputs.
try:
local_scope = {}
exec(current_code, globals(), local_scope)
transform_func = local_scope.get('transform_matrix')
if not callable(transform_func):
return dspy.Prediction(test_outputs=fallback_outputs)
solved_outputs = []
for test_matrix in test_inputs:
try:
input_copy = copy.deepcopy(test_matrix)
result = transform_func(input_copy)
solved_outputs.append(result)
except Exception:
# If the function fails on a test case, return the input as output.
solved_outputs.append(copy.deepcopy(test_matrix))
return dspy.Prediction(test_outputs=solved_outputs)
except Exception:
return dspy.Prediction(test_outputs=fallback_outputs)
# The overall task signature, defining the final inputs and outputs of the program.
class SolveTaskSignature(dspy.Signature):
"""
Given a set of training examples demonstrating a matrix transformation,
apply the same transformation to a new set of test matrices.
"""
training_examples: List[TrainingExample] = dspy.InputField(description="Input and output examples demonstrating the task to be performed.")
test_inputs: List[MATRIX] = dspy.InputField(description="Input matrices to be solved following the task described in the training examples.")
test_outputs: List[MATRIX] = dspy.OutputField(description="Output matrices corresponding to the test inputs.")
# The final program is an instance of our new, more robust module.
program = ARCSolver()
2025/08/29 14:29:49 INFO dspy.evaluate.evaluate: Average Metric: 0.0 / 3 (0.0%)
GEPA Optimization: 64%|█████████████████████████████████████████████████████████████████████▊ | 2564/4000 [16:55:52<18:36:00, 46.63s/rollouts]Iteration 108: New subsample score is not better, skipping
Iteration 109: Selected program 6 score: 0.675
Average Metric: 1.00 / 3 (33.3%): 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [06:02<00:00, 120.79s/it]2025/08/29 14:35:51 INFO dspy.evaluate.evaluate: Average Metric: 1.0 / 3 (33.3%)
Iteration 109: Proposed new text for program: import dspy
from typing import List, Tuple, Any
import pydantic
import copy
import traceback
# Define type aliases and pydantic models for clarity and structure.
MATRIX = List[List[int]]
class TrainingExample(pydantic.BaseModel):
input: MATRIX
output: MATRIX
class HypothesizeRule(dspy.Signature):
"""
Analyze the provided input/output matrix pairs from the Abstraction and Reasoning Corpus (ARC).
Deduce the underlying transformation rule that converts each input matrix to its corresponding output matrix.
Describe this rule in clear, step-by-step, unambiguous English.
Focus on the logic and the sequence of operations, not on Python code implementation.
**Successful Strategies to Consider:**
- **Generalization:** Consider variations and edge cases. Does the rule apply once per grid, or potentially multiple times? Is the transformation dependent on specific colors, or the relative positions of shapes? Generalize from the examples to a rule that would also work on unseen, similar tasks.
- **Decomposition:** Break down complex transformations into simpler steps (e.g., "find all red objects," then "for each object, find its center," then "draw a blue line from the center to the edge").
"""
training_examples: List[TrainingExample] = dspy.InputField(desc="A list of input/output pairs demonstrating the transformation rule.")
rule_description: str = dspy.OutputField(desc="A step-by-step English description of the transformation rule.")
class ImplementRule(dspy.Signature):
"""
You are an expert programmer. Your task is to write a single, self-contained Python function based on a provided rule description and example pairs.
**Function Requirements:**
- The function must be named `transform_matrix`.
- It must accept one argument: `matrix`, which is a list of lists of integers (representing the input grid).
- It must return a list of lists of integers (representing the transformed output grid).
- The function should not use any external libraries except for `copy` if needed (e.g., `import copy; new_matrix = copy.deepcopy(matrix)`).
- Your output must be ONLY the Python code for the function. Do not include any explanations, comments outside the function, or markdown formatting like ```python.
**Pitfalls to Avoid:**
- **Overfitting to Examples:** Do not hardcode values (like matrix dimensions or specific coordinates) that only work for the provided examples. Your function must be general.
- **Assuming Single Application:** If a rule applies to a pattern in the examples (e.g., transforming a specific row), consider if that pattern could appear multiple times in a new input. Your code should handle all occurrences unless the rule explicitly states otherwise.
- **Incorrect Imports:** The only allowed library is `copy`. Do not use libraries like `numpy` or `pandas`.
"""
rule_description: str = dspy.InputField(desc="The English description of the rule to implement.")
training_examples: List[TrainingExample] = dspy.InputField(desc="A list of input/output pairs to use as a reference for implementation.")
python_function: str = dspy.OutputField(desc="A string containing a single Python function `transform_matrix` that implements the rule.")
class RefineCode(dspy.Signature):
"""
You are an expert Python programmer debugging and refining a function. You will be given a rule description, training examples, the buggy Python code, and specific feedback detailing why the code is wrong.
Your task is to return a corrected version of the Python function that fixes the bug.
The corrected function must adhere to the original rule description and pass the training examples.
Your output must be ONLY the Python code for the function. Do not include any explanations or markdown formatting.
"""
rule_description: str = dspy.InputField(desc="The English description of the transformation rule.")
training_examples: List[TrainingExample] = dspy.InputField(desc="The training examples the code must pass.")
buggy_code: str = dspy.InputField(desc="The Python code that failed.")
feedback: str = dspy.InputField(desc="The error message or description of the failure.")
refined_python_function: str = dspy.OutputField(desc="The corrected Python function code.")
class ARCSolver(dspy.Module):
"""A module that solves ARC tasks with a self-refining code generation pipeline."""
def __init__(self, max_refinement_attempts=2):
super().__init__()
self.max_refinement_attempts = max_refinement_attempts
self.rule_hypothesizer = dspy.ChainOfThought(HypothesizeRule)
self.code_implementer = dspy.Predict(ImplementRule)
self.code_refiner = dspy.ChainOfThought(RefineCode)
def _test_code(self, code_str: str, training_examples: List[TrainingExample]) -> Tuple[bool, Any, str]:
"""Tests the generated code against all training examples."""
local_scope = {}
try:
exec(code_str, globals(), local_scope)
transform_func = local_scope.get('transform_matrix')
if not callable(transform_func):
return False, None, "Failed to define a callable function named 'transform_matrix'."
except Exception as e:
return False, None, f"Code failed to execute (e.g., syntax error): {traceback.format_exc()}"
for i, example in enumerate(training_examples):
try:
input_copy = copy.deepcopy(example.input)
predicted_output = transform_func(input_copy)
if predicted_output != example.output:
feedback = f"Failed on training example #{i}. Input: {example.input}. Expected Output: {example.output}. Actual Output: {predicted_output}"
return False, None, feedback
except Exception as e:
feedback = f"Threw an exception on training example #{i}. Input: {example.input}. Error: {traceback.format_exc()}"
return False, None, feedback
return True, transform_func, "All training examples passed."
def forward(self, training_examples: List[TrainingExample], test_inputs: List[MATRIX]):
# Step 1: Generate an English description of the transformation rule.
hypothesis = self.rule_hypothesizer(training_examples=training_examples)
# Step 2: Generate the initial Python code.
prediction = self.code_implementer(
rule_description=hypothesis.rule_description,
training_examples=training_examples
)
python_code = prediction.python_function
# Step 3: Test and refine the code iteratively.
transform_func = None
for attempt in range(self.max_refinement_attempts):
is_correct, func, feedback = self._test_code(python_code, training_examples)
if is_correct:
transform_func = func
break
# If incorrect, attempt to refine the code.
refinement = self.code_refiner(
rule_description=hypothesis.rule_description,
training_examples=training_examples,
buggy_code=python_code,
feedback=feedback
)
python_code = refinement.refined_python_function
# Final check on the last refined code
if not transform_func:
is_correct, func, _ = self._test_code(python_code, training_examples)
if is_correct:
transform_func = func
# Step 4: Apply the validated function to test inputs.
fallback_outputs = [copy.deepcopy(matrix) for matrix in test_inputs]
if not callable(transform_func):
return dspy.Prediction(test_outputs=fallback_outputs)
try:
solved_outputs = []
for test_matrix in test_inputs:
try:
input_copy = copy.deepcopy(test_matrix)
result = transform_func(input_copy)
solved_outputs.append(result)
except Exception:
# If the function fails on a test case, fallback for that case only.
solved_outputs.append(copy.deepcopy(test_matrix))
return dspy.Prediction(test_outputs=solved_outputs)
except Exception:
return dspy.Prediction(test_outputs=fallback_outputs)
# The overall task signature, defining the final inputs and outputs of the program.
class SolveTaskSignature(dspy.Signature):
"""
Given a set of training examples demonstrating a matrix transformation,
apply the same transformation to a new set of test matrices.
"""
training_examples: List[TrainingExample] = dspy.InputField(description="Input and output examples demonstrating the task to be performed.")
test_inputs: List[MATRIX] = dspy.InputField(description="Input matrices to be solved following the task described in the training examples.")
test_outputs: List[MATRIX] = dspy.OutputField(description="Output matrices corresponding to the test inputs.")
# The final program is an instance of our new, more robust module.
program = ARCSolver()
2025/08/29 14:37:36 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[0, 2, 0, 0, 2], [2, 2, 0, 2, 2], [0, 0, 0, 0, 0], [0, 2, 0, 2, 2], [2, 2, 0, 2, 0]], 'output': [[2, 2], [2, 0]]}, {'input': [[1, 0, 0, 1, 0], [0, 1, 0, 0, 1], [0, 0, 0, 0, 0], [1, 0, 0, 1, 0], [1, 1, 0, 0, 1]], 'output': [[1, 0], [1, 1]]}, {'input': [[8, 8, 0, 0, 8], [8, 0, 0, 8, 0], [0, 0, 0, 0, 0], [8, 8, 0, 8, 8], [8, 0, 0, 8, 0]], 'output': [[0, 8], [8, 0]]}], 'test_inputs': [[[5, 5, 0, 5, 0], [0, 5, 0, 0, 5], [0, 0, 0, 0, 0], [5, 5, 0, 5, 5], [0, 5, 0, 0, 5]]], 'test_outputs': [[[5, 0], [0, 5]]]}) (input_keys={'test_inputs', 'training_examples'}): 'dict' object has no attribute 'input'
Traceback (most recent call last):
File "<string>", line 84, in _test_code
AttributeError: 'dict' object has no attribute 'input'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 109, in forward
File "<string>", line 90, in _test_code
AttributeError: 'dict' object has no attribute 'input'
2025/08/29 14:37:38 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 5, 2, 2, 2, 2, 2], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}, {'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [3, 3, 3, 3, 3, 5, 7, 7, 7, 7, 7], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}], 'test_inputs': [[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9]]], 'test_outputs': [[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [4, 4, 4, 4, 4, 5, 8, 8, 8, 8, 8], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [6, 6, 6, 6, 6, 5, 9, 9, 9, 9, 9]]]}) (input_keys={'test_inputs', 'training_examples'}): 'dict' object has no attribute 'input'
Traceback (most recent call last):
File "<string>", line 84, in _test_code
AttributeError: 'dict' object has no attribute 'input'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 109, in forward
File "<string>", line 90, in _test_code
AttributeError: 'dict' object has no attribute 'input'
2025/08/29 14:45:32 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 5, 5, 5, 5, 5, 5, 0, 0, 5, 0, 0, 0], [0, 5, 0, 0, 5, 5, 5, 5, 0, 0, 5, 0, 5, 0, 5, 0, 0, 5, 0, 0, 5, 5], [5, 0, 5, 5, 0, 5, 5, 5, 0, 0, 5, 5, 2, 0, 0, 0, 0, 0, 0, 0, 5, 0], [5, 0, 0, 5, 5, 0, 0, 0, 5, 0, 0, 0, 2, 5, 5, 5, 0, 5, 5, 5, 0, 0], [0, 0, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 2, 5, 5, 0, 0, 5, 0, 5, 5, 0], [0, 5, 0, 0, 5, 0, 0, 0, 5, 2, 5, 2, 5, 5, 5, 2, 5, 0, 5, 0, 0, 0], [0, 5, 5, 0, 5, 0, 0, 0, 0, 0, 5, 0, 2, 5, 0, 0, 5, 0, 0, 5, 5, 5], [0, 0, 0, 0, 0, 5, 0, 0, 0, 5, 5, 0, 2, 5, 0, 5, 5, 0, 5, 0, 0, 0], [5, 0, 0, 0, 0, 0, 5, 0, 5, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 5, 0, 5], [5, 0, 0, 5, 0, 0, 0, 0, 0, 5, 5, 0, 5, 5, 0, 0, 0, 0, 0, 5, 0, 0], [0, 5, 0, 5, 0, 5, 5, 5, 5, 5, 0, 0, 0, 0, 5, 0, 5, 5, 5, 0, 5, 5], [0, 5, 5, 0, 0, 5, 0, 0, 5, 0, 5, 5, 0, 5, 5, 0, 5, 5, 0, 0, 5, 5], [0, 0, 5, 5, 0, 2, 5, 5, 5, 0, 0, 5, 0, 0, 0, 0, 0, 5, 5, 0, 0, 0], [5, 0, 5, 0, 0, 5, 5, 5, 0, 0, 0, 0, 0, 5, 0, 0, 5, 5, 0, 0, 0, 5], [0, 0, 2, 5, 5, 2, 2, 2, 2, 0, 0, 0, 5, 5, 0, 5, 0, 0, 5, 0, 5, 0], [0, 5, 5, 0, 0, 5, 5, 0, 5, 0, 0, 5, 0, 0, 5, 5, 5, 0, 0, 0, 0, 0], [5, 0, 0, 0, 5, 2, 0, 5, 5, 0, 5, 0, 0, 5, 0, 0, 5, 5, 5, 0, 0, 0], [0, 0, 5, 5, 0, 2, 5, 0, 0, 0, 5, 0, 0, 0, 5, 5, 0, 0, 0, 5, 5, 5], [0, 5, 5, 5, 0, 0, 0, 5, 5, 5, 5, 0, 0, 5, 5, 0, 5, 0, 0, 0, 5, 5], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 5, 0, 5, 0, 0, 0, 5]], 'output': [[0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 5, 5, 5, 5, 5, 5, 0, 0, 5, 0, 0, 0], [0, 5, 0, 0, 5, 5, 5, 5, 0, 0, 5, 0, 5, 0, 5, 0, 0, 5, 0, 0, 5, 5], [5, 0, 5, 5, 0, 5, 5, 5, 0, 0, 5, 5, 2, 0, 0, 0, 0, 0, 0, 0, 5, 0], [5, 0, 0, 5, 5, 0, 0, 0, 5, 0, 0, 0, 2, 5, 5, 5, 0, 5, 5, 5, 0, 0], [0, 0, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 2, 5, 5, 0, 0, 5, 0, 5, 5, 0], [0, 5, 0, 0, 5, 0, 0, 0, 5, 2, 8, 2, 8, 8, 8, 2, 5, 0, 5, 0, 0, 0], [0, 5, 5, 0, 5, 0, 0, 0, 0, 0, 5, 0, 2, 5, 0, 0, 5, 0, 0, 5, 5, 5], [0, 0, 0, 0, 0, 5, 0, 0, 0, 5, 5, 0, 2, 5, 0, 5, 5, 0, 5, 0, 0, 0], [5, 0, 0, 0, 0, 0, 5, 0, 5, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 5, 0, 5], [5, 0, 0, 5, 0, 0, 0, 0, 0, 5, 5, 0, 5, 5, 0, 0, 0, 0, 0, 5, 0, 0], [0, 5, 0, 5, 0, 5, 5, 5, 5, 5, 0, 0, 0, 0, 5, 0, 5, 5, 5, 0, 5, 5], [0, 5, 5, 0, 0, 8, 0, 0, 5, 0, 5, 5, 0, 5, 5, 0, 5, 5, 0, 0, 5, 5], [0, 0, 5, 5, 0, 2, 5, 5, 5, 0, 0, 5, 0, 0, 0, 0, 0, 5, 5, 0, 0, 0], [5, 0, 5, 0, 0, 8, 5, 5, 0, 0, 0, 0, 0, 5, 0, 0, 5, 5, 0, 0, 0, 5], [0, 0, 2, 8, 8, 2, 2, 2, 2, 0, 0, 0, 5, 5, 0, 5, 0, 0, 5, 0, 5, 0], [0, 5, 5, 0, 0, 8, 5, 0, 5, 0, 0, 5, 0, 0, 5, 5, 5, 0, 0, 0, 0, 0], [5, 0, 0, 0, 5, 2, 0, 5, 5, 0, 5, 0, 0, 5, 0, 0, 5, 5, 5, 0, 0, 0], [0, 0, 5, 5, 0, 2, 5, 0, 0, 0, 5, 0, 0, 0, 5, 5, 0, 0, 0, 5, 5, 5], [0, 5, 5, 5, 0, 0, 0, 5, 5, 5, 5, 0, 0, 5, 5, 0, 5, 0, 0, 0, 5, 5], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 5, 0, 5, 0, 0, 0, 5]]}, {'input': [[0, 5, 0, 5, 0, 0, 0, 5, 5, 0, 5, 5, 0, 0, 0, 5, 5, 0, 5, 5], [5, 5, 0, 5, 5, 5, 0, 5, 5, 0, 5, 0, 0, 5, 0, 0, 0, 5, 5, 0], [0, 5, 0, 5, 5, 0, 5, 5, 0, 5, 0, 0, 5, 0, 0, 5, 0, 0, 5, 5], [5, 0, 0, 5, 5, 0, 2, 5, 0, 5, 0, 5, 0, 0, 0, 5, 5, 5, 5, 5], [0, 5, 0, 5, 2, 5, 2, 2, 2, 0, 5, 5, 0, 5, 0, 5, 5, 0, 0, 0], [5, 5, 0, 0, 5, 5, 2, 5, 5, 5, 0, 5, 0, 0, 5, 5, 0, 0, 0, 0], [0, 0, 5, 5, 0, 0, 5, 5, 0, 0, 5, 5, 0, 0, 5, 0, 0, 5, 0, 5], [0, 0, 0, 5, 0, 5, 0, 5, 5, 5, 0, 5, 5, 5, 0, 0, 5, 5, 0, 5], [5, 0, 0, 0, 5, 0, 0, 5, 5, 5, 5, 0, 5, 5, 5, 0, 0, 5, 0, 5], [5, 0, 0, 5, 0, 5, 5, 5, 0, 5, 5, 0, 5, 0, 5, 5, 5, 5, 5, 5], [5, 0, 5, 5, 0, 5, 5, 5, 5, 5, 0, 5, 2, 5, 2, 2, 2, 0, 0, 5], [0, 0, 5, 0, 0, 0, 0, 0, 0, 5, 5, 5, 0, 0, 5, 0, 0, 5, 0, 5], [0, 0, 5, 0, 0, 5, 0, 5, 5, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0], [5, 5, 0, 0, 5, 5, 0, 5, 0, 0, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0], [5, 5, 0, 0, 0, 5, 5, 5, 0, 5, 5, 0, 5, 5, 5, 5, 0, 0, 5, 5], [0, 0, 5, 0, 5, 5, 5, 2, 2, 5, 5, 0, 0, 5, 0, 0, 5, 5, 0, 0], [0, 5, 5, 0, 0, 5, 5, 2, 5, 0, 5, 5, 0, 0, 5, 0, 5, 5, 0, 0], [0, 0, 5, 0, 5, 0, 5, 5, 0, 5, 5, 5, 0, 0, 5, 0, 0, 0, 5, 0], [0, 0, 5, 0, 5, 5, 0, 5, 5, 5, 0, 5, 5, 5, 0, 5, 0, 0, 5, 5], [5, 5, 5, 0, 5, 0, 5, 0, 5, 5, 0, 0, 5, 5, 0, 0, 0, 0, 0, 5]], 'output': [[0, 5, 0, 5, 0, 0, 0, 5, 5, 0, 5, 5, 0, 0, 0, 5, 5, 0, 5, 5], [5, 5, 0, 5, 5, 5, 0, 5, 5, 0, 5, 0, 0, 5, 0, 0, 0, 5, 5, 0], [0, 5, 0, 5, 5, 0, 8, 5, 0, 5, 0, 0, 5, 0, 0, 5, 0, 0, 5, 5], [5, 0, 0, 5, 5, 0, 2, 5, 0, 5, 0, 5, 0, 0, 0, 5, 5, 5, 5, 5], [0, 5, 0, 5, 2, 8, 2, 2, 2, 0, 5, 5, 0, 5, 0, 5, 5, 0, 0, 0], [5, 5, 0, 0, 5, 5, 2, 5, 5, 5, 0, 5, 0, 0, 5, 5, 0, 0, 0, 0], [0, 0, 5, 5, 0, 0, 8, 5, 0, 0, 5, 5, 0, 0, 5, 0, 0, 5, 0, 5], [0, 0, 0, 5, 0, 5, 0, 5, 5, 5, 0, 5, 5, 5, 0, 0, 5, 5, 0, 5], [5, 0, 0, 0, 5, 0, 0, 5, 5, 5, 5, 0, 5, 5, 8, 0, 0, 5, 0, 5], [5, 0, 0, 5, 0, 5, 5, 5, 0, 5, 5, 0, 5, 0, 8, 5, 5, 5, 5, 5], [5, 0, 5, 5, 0, 5, 5, 5, 5, 5, 0, 5, 2, 8, 2, 2, 2, 0, 0, 5], [0, 0, 5, 0, 0, 0, 0, 0, 0, 5, 5, 5, 0, 0, 8, 0, 0, 5, 0, 5], [0, 0, 5, 0, 0, 5, 0, 5, 5, 0, 5, 5, 5, 5, 8, 5, 5, 5, 5, 0], [5, 5, 0, 0, 5, 5, 0, 8, 0, 0, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0], [5, 5, 0, 0, 0, 5, 5, 8, 0, 5, 5, 0, 5, 5, 5, 5, 0, 0, 5, 5], [0, 0, 5, 0, 5, 8, 8, 2, 2, 8, 5, 0, 0, 5, 0, 0, 5, 5, 0, 0], [0, 5, 5, 0, 0, 5, 5, 2, 5, 0, 5, 5, 0, 0, 5, 0, 5, 5, 0, 0], [0, 0, 5, 0, 5, 0, 5, 8, 0, 5, 5, 5, 0, 0, 5, 0, 0, 0, 5, 0], [0, 0, 5, 0, 5, 5, 0, 5, 5, 5, 0, 5, 5, 5, 0, 5, 0, 0, 5, 5], [5, 5, 5, 0, 5, 0, 5, 0, 5, 5, 0, 0, 5, 5, 0, 0, 0, 0, 0, 5]]}, {'input': [[0, 0, 5, 0, 5, 0, 5, 5, 5, 5, 0, 5, 5, 0, 0, 0, 5, 5, 0], [0, 0, 5, 5, 5, 0, 5, 5, 5, 5, 0, 0, 5, 5, 5, 5, 5, 0, 5], [0, 5, 5, 5, 0, 5, 0, 5, 5, 0, 0, 0, 5, 5, 5, 0, 5, 0, 0], [5, 5, 5, 5, 5, 0, 0, 0, 5, 5, 5, 5, 5, 5, 0, 0, 5, 0, 0], [5, 5, 0, 0, 0, 5, 5, 5, 0, 5, 5, 5, 5, 0, 0, 0, 5, 0, 0], [5, 0, 0, 0, 0, 0, 5, 0, 5, 0, 5, 2, 5, 0, 0, 5, 0, 5, 5], [5, 0, 5, 0, 0, 5, 5, 0, 5, 2, 2, 5, 2, 2, 5, 5, 0, 5, 0], [0, 5, 0, 5, 5, 5, 5, 5, 0, 5, 0, 5, 5, 5, 5, 0, 5, 5, 5], [5, 5, 5, 0, 5, 5, 5, 5, 0, 0, 5, 2, 5, 5, 5, 0, 0, 0, 0], [5, 2, 2, 5, 0, 0, 5, 0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0], [5, 2, 5, 5, 5, 0, 0, 5, 5, 5, 5, 0, 0, 5, 5, 5, 5, 0, 5], [0, 2, 5, 0, 5, 5, 0, 0, 5, 5, 5, 5, 5, 0, 5, 5, 5, 5, 0], [0, 5, 0, 0, 5, 5, 0, 0, 5, 5, 0, 0, 5, 0, 0, 5, 0, 0, 0], [5, 0, 0, 5, 5, 5, 5, 5, 0, 0, 5, 5, 5, 0, 5, 5, 5, 0, 5], [0, 5, 5, 5, 0, 0, 5, 0, 0, 0, 5, 0, 5, 5, 5, 5, 0, 0, 0], [5, 5, 0, 0, 5, 5, 5, 5, 0, 5, 5, 0, 5, 0, 5, 0, 0, 0, 0], [5, 0, 5, 0, 5, 0, 0, 0, 0, 0, 0, 5, 0, 0, 5, 0, 5, 0, 5], [0, 5, 5, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 5, 0, 5, 5, 5, 5]], 'output': [[0, 0, 5, 0, 5, 0, 5, 5, 5, 5, 0, 5, 5, 0, 0, 0, 5, 5, 0], [0, 0, 5, 5, 5, 0, 5, 5, 5, 5, 0, 0, 5, 5, 5, 5, 5, 0, 5], [0, 5, 5, 5, 0, 5, 0, 5, 5, 0, 0, 0, 5, 5, 5, 0, 5, 0, 0], [5, 5, 5, 5, 5, 0, 0, 0, 5, 5, 5, 5, 5, 5, 0, 0, 5, 0, 0], [5, 5, 0, 0, 0, 5, 5, 5, 0, 5, 5, 8, 5, 0, 0, 0, 5, 0, 0], [5, 0, 0, 0, 0, 0, 5, 0, 5, 0, 5, 2, 5, 0, 0, 5, 0, 5, 5], [5, 0, 5, 0, 0, 5, 5, 0, 5, 2, 2, 8, 2, 2, 5, 5, 0, 5, 0], [0, 8, 0, 5, 5, 5, 5, 5, 0, 5, 0, 8, 5, 5, 5, 0, 5, 5, 5], [5, 8, 5, 0, 5, 5, 5, 5, 0, 0, 5, 2, 5, 5, 5, 0, 0, 0, 0], [8, 2, 2, 8, 0, 0, 5, 0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0], [5, 2, 5, 5, 5, 0, 0, 5, 5, 5, 5, 0, 0, 5, 5, 5, 5, 0, 5], [0, 2, 5, 0, 5, 5, 0, 0, 5, 5, 5, 5, 5, 0, 5, 5, 5, 5, 0], [0, 5, 0, 0, 5, 5, 0, 0, 5, 5, 0, 0, 5, 0, 0, 5, 0, 0, 0], [5, 0, 0, 5, 5, 5, 5, 5, 0, 0, 5, 5, 5, 0, 5, 5, 5, 0, 5], [0, 5, 5, 5, 0, 0, 5, 0, 0, 0, 5, 0, 5, 5, 5, 5, 0, 0, 0], [5, 5, 0, 0, 5, 5, 5, 5, 0, 5, 5, 0, 5, 0, 5, 0, 0, 0, 0], [5, 0, 5, 0, 5, 0, 0, 0, 0, 0, 0, 5, 0, 0, 5, 0, 5, 0, 5], [0, 5, 5, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 5, 0, 5, 5, 5, 5]]}, {'input': [[0, 5, 0, 0, 0, 0, 5, 0, 0, 0, 0, 5], [5, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 5], [5, 0, 5, 0, 0, 5, 5, 0, 2, 0, 5, 0], [5, 5, 0, 0, 5, 0, 5, 0, 2, 5, 0, 5], [5, 0, 0, 5, 5, 5, 2, 5, 2, 2, 2, 0], [5, 5, 5, 0, 5, 5, 0, 5, 2, 0, 0, 5], [5, 5, 5, 0, 5, 0, 0, 5, 5, 0, 0, 0], [5, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0], [0, 5, 5, 0, 5, 0, 0, 0, 0, 5, 0, 0], [5, 0, 0, 0, 5, 5, 5, 5, 5, 0, 0, 0], [5, 0, 0, 0, 0, 5, 0, 0, 5, 5, 5, 5]], 'output': [[0, 5, 0, 0, 0, 0, 5, 0, 0, 0, 0, 5], [5, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 5], [5, 0, 5, 0, 0, 5, 5, 0, 2, 0, 5, 0], [5, 5, 0, 0, 5, 0, 5, 0, 2, 5, 0, 5], [5, 0, 0, 5, 5, 5, 2, 8, 2, 2, 2, 0], [5, 5, 5, 0, 5, 5, 0, 5, 2, 0, 0, 5], [5, 5, 5, 0, 5, 0, 0, 5, 8, 0, 0, 0], [5, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0], [0, 5, 5, 0, 5, 0, 0, 0, 0, 5, 0, 0], [5, 0, 0, 0, 5, 5, 5, 5, 5, 0, 0, 0], [5, 0, 0, 0, 0, 5, 0, 0, 5, 5, 5, 5]]}], 'test_inputs': [[[0, 5, 0, 5, 0, 0, 5, 5, 0, 5, 0, 0, 0, 5, 0, 5, 0, 0, 0, 5, 5, 0], [0, 5, 0, 5, 5, 0, 0, 0, 5, 5, 0, 0, 5, 5, 0, 0, 0, 0, 0, 5, 5, 5], [0, 0, 0, 0, 5, 5, 5, 0, 0, 0, 0, 5, 5, 0, 0, 5, 5, 0, 0, 5, 5, 5], [0, 0, 5, 5, 0, 5, 5, 5, 0, 5, 0, 5, 0, 5, 0, 5, 5, 0, 5, 5, 5, 0], [0, 5, 0, 5, 2, 2, 5, 2, 2, 5, 0, 0, 5, 0, 5, 5, 5, 0, 0, 5, 5, 0], [0, 0, 0, 5, 0, 5, 2, 5, 5, 5, 0, 5, 0, 0, 0, 0, 5, 5, 5, 5, 0, 0], [5, 5, 0, 0, 5, 5, 2, 0, 5, 5, 0, 0, 0, 5, 0, 0, 0, 5, 5, 5, 5, 5], [5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 0, 5, 0, 0, 5, 0, 5, 0], [5, 5, 5, 5, 5, 0, 0, 5, 5, 0, 5, 2, 5, 2, 5, 5, 0, 0, 5, 5, 5, 0], [0, 0, 0, 5, 5, 5, 0, 0, 5, 0, 0, 0, 5, 5, 0, 5, 5, 5, 0, 0, 0, 0], [0, 0, 0, 5, 5, 5, 0, 5, 0, 5, 0, 5, 5, 2, 5, 0, 5, 0, 0, 5, 5, 0], [0, 5, 5, 5, 0, 0, 0, 5, 5, 5, 5, 0, 0, 5, 0, 5, 5, 0, 0, 0, 5, 5], [5, 5, 0, 0, 5, 5, 5, 0, 0, 5, 5, 0, 5, 0, 5, 5, 0, 0, 5, 5, 0, 5], [0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 5, 5, 5, 5, 5, 0, 0, 5, 0, 5], [5, 5, 0, 5, 5, 2, 2, 2, 5, 5, 5, 0, 5, 5, 5, 0, 5, 0, 0, 5, 5, 0], [5, 0, 0, 0, 5, 2, 5, 0, 5, 0, 5, 0, 5, 5, 5, 5, 0, 0, 0, 0, 5, 5], [5, 5, 5, 0, 0, 2, 0, 5, 5, 0, 0, 2, 2, 2, 2, 2, 5, 0, 5, 0, 5, 5], [5, 0, 5, 0, 0, 5, 0, 5, 0, 0, 0, 0, 0, 5, 0, 5, 5, 5, 0, 5, 5, 0], [5, 5, 5, 5, 5, 0, 5, 0, 5, 5, 5, 5, 0, 5, 0, 0, 5, 5, 0, 5, 0, 5]]], 'test_outputs': [[[0, 5, 0, 5, 0, 0, 5, 5, 0, 5, 0, 0, 0, 5, 0, 5, 0, 0, 0, 5, 5, 0], [0, 5, 0, 5, 5, 0, 0, 0, 5, 5, 0, 0, 5, 5, 0, 0, 0, 0, 0, 5, 5, 5], [0, 0, 0, 0, 5, 5, 8, 0, 0, 0, 0, 5, 5, 0, 0, 5, 5, 0, 0, 5, 5, 5], [0, 0, 5, 5, 0, 5, 8, 5, 0, 5, 0, 5, 0, 5, 0, 5, 5, 0, 5, 5, 5, 0], [0, 5, 0, 5, 2, 2, 8, 2, 2, 5, 0, 0, 5, 0, 5, 5, 5, 0, 0, 5, 5, 0], [0, 0, 0, 5, 0, 5, 2, 5, 5, 5, 0, 5, 0, 0, 0, 0, 5, 5, 5, 5, 0, 0], [5, 5, 0, 0, 5, 5, 2, 0, 5, 5, 0, 0, 0, 8, 0, 0, 0, 5, 5, 5, 5, 5], [5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 8, 0, 5, 0, 0, 5, 0, 5, 0], [5, 5, 5, 5, 5, 0, 0, 5, 5, 0, 5, 2, 8, 2, 8, 8, 0, 0, 5, 5, 5, 0], [0, 0, 0, 5, 5, 5, 0, 0, 5, 0, 0, 0, 5, 8, 0, 5, 5, 5, 0, 0, 0, 0], [0, 0, 0, 5, 5, 5, 0, 5, 0, 5, 0, 5, 5, 2, 5, 0, 5, 0, 0, 5, 5, 0], [0, 5, 5, 5, 0, 0, 0, 5, 5, 5, 5, 0, 0, 5, 0, 5, 5, 0, 0, 0, 5, 5], [5, 5, 0, 0, 5, 8, 5, 0, 0, 5, 5, 0, 5, 0, 5, 5, 0, 0, 5, 5, 0, 5], [0, 0, 5, 5, 5, 8, 5, 5, 5, 5, 0, 0, 5, 5, 5, 5, 5, 0, 0, 5, 0, 5], [5, 5, 0, 8, 8, 2, 2, 2, 5, 5, 5, 0, 5, 8, 5, 0, 5, 0, 0, 5, 5, 0], [5, 0, 0, 0, 5, 2, 5, 0, 5, 0, 5, 0, 5, 8, 5, 5, 0, 0, 0, 0, 5, 5], [5, 5, 5, 0, 0, 2, 0, 5, 5, 0, 0, 2, 2, 2, 2, 2, 5, 0, 5, 0, 5, 5], [5, 0, 5, 0, 0, 5, 0, 5, 0, 0, 0, 0, 0, 8, 0, 5, 5, 5, 0, 5, 5, 0], [5, 5, 5, 5, 5, 0, 5, 0, 5, 5, 5, 5, 0, 8, 0, 0, 5, 5, 0, 5, 0, 5]]]}) (input_keys={'test_inputs', 'training_examples'}): 'dict' object has no attribute 'input'
Traceback (most recent call last):
File "<string>", line 84, in _test_code
AttributeError: 'dict' object has no attribute 'input'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 109, in forward
File "<string>", line 90, in _test_code
AttributeError: 'dict' object has no attribute 'input'
2025/08/29 14:45:36 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 5, 5, 5, 5, 5, 5, 0, 0, 5, 0, 0, 0], [0, 5, 0, 0, 5, 5, 5, 5, 0, 0, 5, 0, 5, 0, 5, 0, 0, 5, 0, 0, 5, 5], [5, 0, 5, 5, 0, 5, 5, 5, 0, 0, 5, 5, 2, 0, 0, 0, 0, 0, 0, 0, 5, 0], [5, 0, 0, 5, 5, 0, 0, 0, 5, 0, 0, 0, 2, 5, 5, 5, 0, 5, 5, 5, 0, 0], [0, 0, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 2, 5, 5, 0, 0, 5, 0, 5, 5, 0], [0, 5, 0, 0, 5, 0, 0, 0, 5, 2, 5, 2, 5, 5, 5, 2, 5, 0, 5, 0, 0, 0], [0, 5, 5, 0, 5, 0, 0, 0, 0, 0, 5, 0, 2, 5, 0, 0, 5, 0, 0, 5, 5, 5], [0, 0, 0, 0, 0, 5, 0, 0, 0, 5, 5, 0, 2, 5, 0, 5, 5, 0, 5, 0, 0, 0], [5, 0, 0, 0, 0, 0, 5, 0, 5, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 5, 0, 5], [5, 0, 0, 5, 0, 0, 0, 0, 0, 5, 5, 0, 5, 5, 0, 0, 0, 0, 0, 5, 0, 0], [0, 5, 0, 5, 0, 5, 5, 5, 5, 5, 0, 0, 0, 0, 5, 0, 5, 5, 5, 0, 5, 5], [0, 5, 5, 0, 0, 5, 0, 0, 5, 0, 5, 5, 0, 5, 5, 0, 5, 5, 0, 0, 5, 5], [0, 0, 5, 5, 0, 2, 5, 5, 5, 0, 0, 5, 0, 0, 0, 0, 0, 5, 5, 0, 0, 0], [5, 0, 5, 0, 0, 5, 5, 5, 0, 0, 0, 0, 0, 5, 0, 0, 5, 5, 0, 0, 0, 5], [0, 0, 2, 5, 5, 2, 2, 2, 2, 0, 0, 0, 5, 5, 0, 5, 0, 0, 5, 0, 5, 0], [0, 5, 5, 0, 0, 5, 5, 0, 5, 0, 0, 5, 0, 0, 5, 5, 5, 0, 0, 0, 0, 0], [5, 0, 0, 0, 5, 2, 0, 5, 5, 0, 5, 0, 0, 5, 0, 0, 5, 5, 5, 0, 0, 0], [0, 0, 5, 5, 0, 2, 5, 0, 0, 0, 5, 0, 0, 0, 5, 5, 0, 0, 0, 5, 5, 5], [0, 5, 5, 5, 0, 0, 0, 5, 5, 5, 5, 0, 0, 5, 5, 0, 5, 0, 0, 0, 5, 5], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 5, 0, 5, 0, 0, 0, 5]], 'output': [[0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 5, 5, 5, 5, 5, 5, 0, 0, 5, 0, 0, 0], [0, 5, 0, 0, 5, 5, 5, 5, 0, 0, 5, 0, 5, 0, 5, 0, 0, 5, 0, 0, 5, 5], [5, 0, 5, 5, 0, 5, 5, 5, 0, 0, 5, 5, 2, 0, 0, 0, 0, 0, 0, 0, 5, 0], [5, 0, 0, 5, 5, 0, 0, 0, 5, 0, 0, 0, 2, 5, 5, 5, 0, 5, 5, 5, 0, 0], [0, 0, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 2, 5, 5, 0, 0, 5, 0, 5, 5, 0], [0, 5, 0, 0, 5, 0, 0, 0, 5, 2, 8, 2, 8, 8, 8, 2, 5, 0, 5, 0, 0, 0], [0, 5, 5, 0, 5, 0, 0, 0, 0, 0, 5, 0, 2, 5, 0, 0, 5, 0, 0, 5, 5, 5], [0, 0, 0, 0, 0, 5, 0, 0, 0, 5, 5, 0, 2, 5, 0, 5, 5, 0, 5, 0, 0, 0], [5, 0, 0, 0, 0, 0, 5, 0, 5, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 5, 0, 5], [5, 0, 0, 5, 0, 0, 0, 0, 0, 5, 5, 0, 5, 5, 0, 0, 0, 0, 0, 5, 0, 0], [0, 5, 0, 5, 0, 5, 5, 5, 5, 5, 0, 0, 0, 0, 5, 0, 5, 5, 5, 0, 5, 5], [0, 5, 5, 0, 0, 8, 0, 0, 5, 0, 5, 5, 0, 5, 5, 0, 5, 5, 0, 0, 5, 5], [0, 0, 5, 5, 0, 2, 5, 5, 5, 0, 0, 5, 0, 0, 0, 0, 0, 5, 5, 0, 0, 0], [5, 0, 5, 0, 0, 8, 5, 5, 0, 0, 0, 0, 0, 5, 0, 0, 5, 5, 0, 0, 0, 5], [0, 0, 2, 8, 8, 2, 2, 2, 2, 0, 0, 0, 5, 5, 0, 5, 0, 0, 5, 0, 5, 0], [0, 5, 5, 0, 0, 8, 5, 0, 5, 0, 0, 5, 0, 0, 5, 5, 5, 0, 0, 0, 0, 0], [5, 0, 0, 0, 5, 2, 0, 5, 5, 0, 5, 0, 0, 5, 0, 0, 5, 5, 5, 0, 0, 0], [0, 0, 5, 5, 0, 2, 5, 0, 0, 0, 5, 0, 0, 0, 5, 5, 0, 0, 0, 5, 5, 5], [0, 5, 5, 5, 0, 0, 0, 5, 5, 5, 5, 0, 0, 5, 5, 0, 5, 0, 0, 0, 5, 5], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 5, 0, 5, 0, 0, 0, 5]]}, {'input': [[0, 5, 0, 5, 0, 0, 0, 5, 5, 0, 5, 5, 0, 0, 0, 5, 5, 0, 5, 5], [5, 5, 0, 5, 5, 5, 0, 5, 5, 0, 5, 0, 0, 5, 0, 0, 0, 5, 5, 0], [0, 5, 0, 5, 5, 0, 5, 5, 0, 5, 0, 0, 5, 0, 0, 5, 0, 0, 5, 5], [5, 0, 0, 5, 5, 0, 2, 5, 0, 5, 0, 5, 0, 0, 0, 5, 5, 5, 5, 5], [0, 5, 0, 5, 2, 5, 2, 2, 2, 0, 5, 5, 0, 5, 0, 5, 5, 0, 0, 0], [5, 5, 0, 0, 5, 5, 2, 5, 5, 5, 0, 5, 0, 0, 5, 5, 0, 0, 0, 0], [0, 0, 5, 5, 0, 0, 5, 5, 0, 0, 5, 5, 0, 0, 5, 0, 0, 5, 0, 5], [0, 0, 0, 5, 0, 5, 0, 5, 5, 5, 0, 5, 5, 5, 0, 0, 5, 5, 0, 5], [5, 0, 0, 0, 5, 0, 0, 5, 5, 5, 5, 0, 5, 5, 5, 0, 0, 5, 0, 5], [5, 0, 0, 5, 0, 5, 5, 5, 0, 5, 5, 0, 5, 0, 5, 5, 5, 5, 5, 5], [5, 0, 5, 5, 0, 5, 5, 5, 5, 5, 0, 5, 2, 5, 2, 2, 2, 0, 0, 5], [0, 0, 5, 0, 0, 0, 0, 0, 0, 5, 5, 5, 0, 0, 5, 0, 0, 5, 0, 5], [0, 0, 5, 0, 0, 5, 0, 5, 5, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0], [5, 5, 0, 0, 5, 5, 0, 5, 0, 0, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0], [5, 5, 0, 0, 0, 5, 5, 5, 0, 5, 5, 0, 5, 5, 5, 5, 0, 0, 5, 5], [0, 0, 5, 0, 5, 5, 5, 2, 2, 5, 5, 0, 0, 5, 0, 0, 5, 5, 0, 0], [0, 5, 5, 0, 0, 5, 5, 2, 5, 0, 5, 5, 0, 0, 5, 0, 5, 5, 0, 0], [0, 0, 5, 0, 5, 0, 5, 5, 0, 5, 5, 5, 0, 0, 5, 0, 0, 0, 5, 0], [0, 0, 5, 0, 5, 5, 0, 5, 5, 5, 0, 5, 5, 5, 0, 5, 0, 0, 5, 5], [5, 5, 5, 0, 5, 0, 5, 0, 5, 5, 0, 0, 5, 5, 0, 0, 0, 0, 0, 5]], 'output': [[0, 5, 0, 5, 0, 0, 0, 5, 5, 0, 5, 5, 0, 0, 0, 5, 5, 0, 5, 5], [5, 5, 0, 5, 5, 5, 0, 5, 5, 0, 5, 0, 0, 5, 0, 0, 0, 5, 5, 0], [0, 5, 0, 5, 5, 0, 8, 5, 0, 5, 0, 0, 5, 0, 0, 5, 0, 0, 5, 5], [5, 0, 0, 5, 5, 0, 2, 5, 0, 5, 0, 5, 0, 0, 0, 5, 5, 5, 5, 5], [0, 5, 0, 5, 2, 8, 2, 2, 2, 0, 5, 5, 0, 5, 0, 5, 5, 0, 0, 0], [5, 5, 0, 0, 5, 5, 2, 5, 5, 5, 0, 5, 0, 0, 5, 5, 0, 0, 0, 0], [0, 0, 5, 5, 0, 0, 8, 5, 0, 0, 5, 5, 0, 0, 5, 0, 0, 5, 0, 5], [0, 0, 0, 5, 0, 5, 0, 5, 5, 5, 0, 5, 5, 5, 0, 0, 5, 5, 0, 5], [5, 0, 0, 0, 5, 0, 0, 5, 5, 5, 5, 0, 5, 5, 8, 0, 0, 5, 0, 5], [5, 0, 0, 5, 0, 5, 5, 5, 0, 5, 5, 0, 5, 0, 8, 5, 5, 5, 5, 5], [5, 0, 5, 5, 0, 5, 5, 5, 5, 5, 0, 5, 2, 8, 2, 2, 2, 0, 0, 5], [0, 0, 5, 0, 0, 0, 0, 0, 0, 5, 5, 5, 0, 0, 8, 0, 0, 5, 0, 5], [0, 0, 5, 0, 0, 5, 0, 5, 5, 0, 5, 5, 5, 5, 8, 5, 5, 5, 5, 0], [5, 5, 0, 0, 5, 5, 0, 8, 0, 0, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0], [5, 5, 0, 0, 0, 5, 5, 8, 0, 5, 5, 0, 5, 5, 5, 5, 0, 0, 5, 5], [0, 0, 5, 0, 5, 8, 8, 2, 2, 8, 5, 0, 0, 5, 0, 0, 5, 5, 0, 0], [0, 5, 5, 0, 0, 5, 5, 2, 5, 0, 5, 5, 0, 0, 5, 0, 5, 5, 0, 0], [0, 0, 5, 0, 5, 0, 5, 8, 0, 5, 5, 5, 0, 0, 5, 0, 0, 0, 5, 0], [0, 0, 5, 0, 5, 5, 0, 5, 5, 5, 0, 5, 5, 5, 0, 5, 0, 0, 5, 5], [5, 5, 5, 0, 5, 0, 5, 0, 5, 5, 0, 0, 5, 5, 0, 0, 0, 0, 0, 5]]}, {'input': [[0, 0, 5, 0, 5, 0, 5, 5, 5, 5, 0, 5, 5, 0, 0, 0, 5, 5, 0], [0, 0, 5, 5, 5, 0, 5, 5, 5, 5, 0, 0, 5, 5, 5, 5, 5, 0, 5], [0, 5, 5, 5, 0, 5, 0, 5, 5, 0, 0, 0, 5, 5, 5, 0, 5, 0, 0], [5, 5, 5, 5, 5, 0, 0, 0, 5, 5, 5, 5, 5, 5, 0, 0, 5, 0, 0], [5, 5, 0, 0, 0, 5, 5, 5, 0, 5, 5, 5, 5, 0, 0, 0, 5, 0, 0], [5, 0, 0, 0, 0, 0, 5, 0, 5, 0, 5, 2, 5, 0, 0, 5, 0, 5, 5], [5, 0, 5, 0, 0, 5, 5, 0, 5, 2, 2, 5, 2, 2, 5, 5, 0, 5, 0], [0, 5, 0, 5, 5, 5, 5, 5, 0, 5, 0, 5, 5, 5, 5, 0, 5, 5, 5], [5, 5, 5, 0, 5, 5, 5, 5, 0, 0, 5, 2, 5, 5, 5, 0, 0, 0, 0], [5, 2, 2, 5, 0, 0, 5, 0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0], [5, 2, 5, 5, 5, 0, 0, 5, 5, 5, 5, 0, 0, 5, 5, 5, 5, 0, 5], [0, 2, 5, 0, 5, 5, 0, 0, 5, 5, 5, 5, 5, 0, 5, 5, 5, 5, 0], [0, 5, 0, 0, 5, 5, 0, 0, 5, 5, 0, 0, 5, 0, 0, 5, 0, 0, 0], [5, 0, 0, 5, 5, 5, 5, 5, 0, 0, 5, 5, 5, 0, 5, 5, 5, 0, 5], [0, 5, 5, 5, 0, 0, 5, 0, 0, 0, 5, 0, 5, 5, 5, 5, 0, 0, 0], [5, 5, 0, 0, 5, 5, 5, 5, 0, 5, 5, 0, 5, 0, 5, 0, 0, 0, 0], [5, 0, 5, 0, 5, 0, 0, 0, 0, 0, 0, 5, 0, 0, 5, 0, 5, 0, 5], [0, 5, 5, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 5, 0, 5, 5, 5, 5]], 'output': [[0, 0, 5, 0, 5, 0, 5, 5, 5, 5, 0, 5, 5, 0, 0, 0, 5, 5, 0], [0, 0, 5, 5, 5, 0, 5, 5, 5, 5, 0, 0, 5, 5, 5, 5, 5, 0, 5], [0, 5, 5, 5, 0, 5, 0, 5, 5, 0, 0, 0, 5, 5, 5, 0, 5, 0, 0], [5, 5, 5, 5, 5, 0, 0, 0, 5, 5, 5, 5, 5, 5, 0, 0, 5, 0, 0], [5, 5, 0, 0, 0, 5, 5, 5, 0, 5, 5, 8, 5, 0, 0, 0, 5, 0, 0], [5, 0, 0, 0, 0, 0, 5, 0, 5, 0, 5, 2, 5, 0, 0, 5, 0, 5, 5], [5, 0, 5, 0, 0, 5, 5, 0, 5, 2, 2, 8, 2, 2, 5, 5, 0, 5, 0], [0, 8, 0, 5, 5, 5, 5, 5, 0, 5, 0, 8, 5, 5, 5, 0, 5, 5, 5], [5, 8, 5, 0, 5, 5, 5, 5, 0, 0, 5, 2, 5, 5, 5, 0, 0, 0, 0], [8, 2, 2, 8, 0, 0, 5, 0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0], [5, 2, 5, 5, 5, 0, 0, 5, 5, 5, 5, 0, 0, 5, 5, 5, 5, 0, 5], [0, 2, 5, 0, 5, 5, 0, 0, 5, 5, 5, 5, 5, 0, 5, 5, 5, 5, 0], [0, 5, 0, 0, 5, 5, 0, 0, 5, 5, 0, 0, 5, 0, 0, 5, 0, 0, 0], [5, 0, 0, 5, 5, 5, 5, 5, 0, 0, 5, 5, 5, 0, 5, 5, 5, 0, 5], [0, 5, 5, 5, 0, 0, 5, 0, 0, 0, 5, 0, 5, 5, 5, 5, 0, 0, 0], [5, 5, 0, 0, 5, 5, 5, 5, 0, 5, 5, 0, 5, 0, 5, 0, 0, 0, 0], [5, 0, 5, 0, 5, 0, 0, 0, 0, 0, 0, 5, 0, 0, 5, 0, 5, 0, 5], [0, 5, 5, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 5, 0, 5, 5, 5, 5]]}, {'input': [[0, 5, 0, 0, 0, 0, 5, 0, 0, 0, 0, 5], [5, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 5], [5, 0, 5, 0, 0, 5, 5, 0, 2, 0, 5, 0], [5, 5, 0, 0, 5, 0, 5, 0, 2, 5, 0, 5], [5, 0, 0, 5, 5, 5, 2, 5, 2, 2, 2, 0], [5, 5, 5, 0, 5, 5, 0, 5, 2, 0, 0, 5], [5, 5, 5, 0, 5, 0, 0, 5, 5, 0, 0, 0], [5, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0], [0, 5, 5, 0, 5, 0, 0, 0, 0, 5, 0, 0], [5, 0, 0, 0, 5, 5, 5, 5, 5, 0, 0, 0], [5, 0, 0, 0, 0, 5, 0, 0, 5, 5, 5, 5]], 'output': [[0, 5, 0, 0, 0, 0, 5, 0, 0, 0, 0, 5], [5, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 5], [5, 0, 5, 0, 0, 5, 5, 0, 2, 0, 5, 0], [5, 5, 0, 0, 5, 0, 5, 0, 2, 5, 0, 5], [5, 0, 0, 5, 5, 5, 2, 8, 2, 2, 2, 0], [5, 5, 5, 0, 5, 5, 0, 5, 2, 0, 0, 5], [5, 5, 5, 0, 5, 0, 0, 5, 8, 0, 0, 0], [5, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0], [0, 5, 5, 0, 5, 0, 0, 0, 0, 5, 0, 0], [5, 0, 0, 0, 5, 5, 5, 5, 5, 0, 0, 0], [5, 0, 0, 0, 0, 5, 0, 0, 5, 5, 5, 5]]}], 'test_inputs': [[[0, 5, 0, 5, 0, 0, 5, 5, 0, 5, 0, 0, 0, 5, 0, 5, 0, 0, 0, 5, 5, 0], [0, 5, 0, 5, 5, 0, 0, 0, 5, 5, 0, 0, 5, 5, 0, 0, 0, 0, 0, 5, 5, 5], [0, 0, 0, 0, 5, 5, 5, 0, 0, 0, 0, 5, 5, 0, 0, 5, 5, 0, 0, 5, 5, 5], [0, 0, 5, 5, 0, 5, 5, 5, 0, 5, 0, 5, 0, 5, 0, 5, 5, 0, 5, 5, 5, 0], [0, 5, 0, 5, 2, 2, 5, 2, 2, 5, 0, 0, 5, 0, 5, 5, 5, 0, 0, 5, 5, 0], [0, 0, 0, 5, 0, 5, 2, 5, 5, 5, 0, 5, 0, 0, 0, 0, 5, 5, 5, 5, 0, 0], [5, 5, 0, 0, 5, 5, 2, 0, 5, 5, 0, 0, 0, 5, 0, 0, 0, 5, 5, 5, 5, 5], [5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 0, 5, 0, 0, 5, 0, 5, 0], [5, 5, 5, 5, 5, 0, 0, 5, 5, 0, 5, 2, 5, 2, 5, 5, 0, 0, 5, 5, 5, 0], [0, 0, 0, 5, 5, 5, 0, 0, 5, 0, 0, 0, 5, 5, 0, 5, 5, 5, 0, 0, 0, 0], [0, 0, 0, 5, 5, 5, 0, 5, 0, 5, 0, 5, 5, 2, 5, 0, 5, 0, 0, 5, 5, 0], [0, 5, 5, 5, 0, 0, 0, 5, 5, 5, 5, 0, 0, 5, 0, 5, 5, 0, 0, 0, 5, 5], [5, 5, 0, 0, 5, 5, 5, 0, 0, 5, 5, 0, 5, 0, 5, 5, 0, 0, 5, 5, 0, 5], [0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 5, 5, 5, 5, 5, 0, 0, 5, 0, 5], [5, 5, 0, 5, 5, 2, 2, 2, 5, 5, 5, 0, 5, 5, 5, 0, 5, 0, 0, 5, 5, 0], [5, 0, 0, 0, 5, 2, 5, 0, 5, 0, 5, 0, 5, 5, 5, 5, 0, 0, 0, 0, 5, 5], [5, 5, 5, 0, 0, 2, 0, 5, 5, 0, 0, 2, 2, 2, 2, 2, 5, 0, 5, 0, 5, 5], [5, 0, 5, 0, 0, 5, 0, 5, 0, 0, 0, 0, 0, 5, 0, 5, 5, 5, 0, 5, 5, 0], [5, 5, 5, 5, 5, 0, 5, 0, 5, 5, 5, 5, 0, 5, 0, 0, 5, 5, 0, 5, 0, 5]]], 'test_outputs': [[[0, 5, 0, 5, 0, 0, 5, 5, 0, 5, 0, 0, 0, 5, 0, 5, 0, 0, 0, 5, 5, 0], [0, 5, 0, 5, 5, 0, 0, 0, 5, 5, 0, 0, 5, 5, 0, 0, 0, 0, 0, 5, 5, 5], [0, 0, 0, 0, 5, 5, 8, 0, 0, 0, 0, 5, 5, 0, 0, 5, 5, 0, 0, 5, 5, 5], [0, 0, 5, 5, 0, 5, 8, 5, 0, 5, 0, 5, 0, 5, 0, 5, 5, 0, 5, 5, 5, 0], [0, 5, 0, 5, 2, 2, 8, 2, 2, 5, 0, 0, 5, 0, 5, 5, 5, 0, 0, 5, 5, 0], [0, 0, 0, 5, 0, 5, 2, 5, 5, 5, 0, 5, 0, 0, 0, 0, 5, 5, 5, 5, 0, 0], [5, 5, 0, 0, 5, 5, 2, 0, 5, 5, 0, 0, 0, 8, 0, 0, 0, 5, 5, 5, 5, 5], [5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 8, 0, 5, 0, 0, 5, 0, 5, 0], [5, 5, 5, 5, 5, 0, 0, 5, 5, 0, 5, 2, 8, 2, 8, 8, 0, 0, 5, 5, 5, 0], [0, 0, 0, 5, 5, 5, 0, 0, 5, 0, 0, 0, 5, 8, 0, 5, 5, 5, 0, 0, 0, 0], [0, 0, 0, 5, 5, 5, 0, 5, 0, 5, 0, 5, 5, 2, 5, 0, 5, 0, 0, 5, 5, 0], [0, 5, 5, 5, 0, 0, 0, 5, 5, 5, 5, 0, 0, 5, 0, 5, 5, 0, 0, 0, 5, 5], [5, 5, 0, 0, 5, 8, 5, 0, 0, 5, 5, 0, 5, 0, 5, 5, 0, 0, 5, 5, 0, 5], [0, 0, 5, 5, 5, 8, 5, 5, 5, 5, 0, 0, 5, 5, 5, 5, 5, 0, 0, 5, 0, 5], [5, 5, 0, 8, 8, 2, 2, 2, 5, 5, 5, 0, 5, 8, 5, 0, 5, 0, 0, 5, 5, 0], [5, 0, 0, 0, 5, 2, 5, 0, 5, 0, 5, 0, 5, 8, 5, 5, 0, 0, 0, 0, 5, 5], [5, 5, 5, 0, 0, 2, 0, 5, 5, 0, 0, 2, 2, 2, 2, 2, 5, 0, 5, 0, 5, 5], [5, 0, 5, 0, 0, 5, 0, 5, 0, 0, 0, 0, 0, 8, 0, 5, 5, 5, 0, 5, 5, 0], [5, 5, 5, 5, 5, 0, 5, 0, 5, 5, 5, 5, 0, 8, 0, 0, 5, 5, 0, 5, 0, 5]]]}) (input_keys={'test_inputs', 'training_examples'}): 'dict' object has no attribute 'input'
Traceback (most recent call last):
File "<string>", line 84, in _test_code
AttributeError: 'dict' object has no attribute 'input'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 109, in forward
File "<string>", line 90, in _test_code
AttributeError: 'dict' object has no attribute 'input'
2025/08/29 14:46:14 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 5, 5, 5, 5, 5, 5, 0, 0, 5, 0, 0, 0], [0, 5, 0, 0, 5, 5, 5, 5, 0, 0, 5, 0, 5, 0, 5, 0, 0, 5, 0, 0, 5, 5], [5, 0, 5, 5, 0, 5, 5, 5, 0, 0, 5, 5, 2, 0, 0, 0, 0, 0, 0, 0, 5, 0], [5, 0, 0, 5, 5, 0, 0, 0, 5, 0, 0, 0, 2, 5, 5, 5, 0, 5, 5, 5, 0, 0], [0, 0, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 2, 5, 5, 0, 0, 5, 0, 5, 5, 0], [0, 5, 0, 0, 5, 0, 0, 0, 5, 2, 5, 2, 5, 5, 5, 2, 5, 0, 5, 0, 0, 0], [0, 5, 5, 0, 5, 0, 0, 0, 0, 0, 5, 0, 2, 5, 0, 0, 5, 0, 0, 5, 5, 5], [0, 0, 0, 0, 0, 5, 0, 0, 0, 5, 5, 0, 2, 5, 0, 5, 5, 0, 5, 0, 0, 0], [5, 0, 0, 0, 0, 0, 5, 0, 5, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 5, 0, 5], [5, 0, 0, 5, 0, 0, 0, 0, 0, 5, 5, 0, 5, 5, 0, 0, 0, 0, 0, 5, 0, 0], [0, 5, 0, 5, 0, 5, 5, 5, 5, 5, 0, 0, 0, 0, 5, 0, 5, 5, 5, 0, 5, 5], [0, 5, 5, 0, 0, 5, 0, 0, 5, 0, 5, 5, 0, 5, 5, 0, 5, 5, 0, 0, 5, 5], [0, 0, 5, 5, 0, 2, 5, 5, 5, 0, 0, 5, 0, 0, 0, 0, 0, 5, 5, 0, 0, 0], [5, 0, 5, 0, 0, 5, 5, 5, 0, 0, 0, 0, 0, 5, 0, 0, 5, 5, 0, 0, 0, 5], [0, 0, 2, 5, 5, 2, 2, 2, 2, 0, 0, 0, 5, 5, 0, 5, 0, 0, 5, 0, 5, 0], [0, 5, 5, 0, 0, 5, 5, 0, 5, 0, 0, 5, 0, 0, 5, 5, 5, 0, 0, 0, 0, 0], [5, 0, 0, 0, 5, 2, 0, 5, 5, 0, 5, 0, 0, 5, 0, 0, 5, 5, 5, 0, 0, 0], [0, 0, 5, 5, 0, 2, 5, 0, 0, 0, 5, 0, 0, 0, 5, 5, 0, 0, 0, 5, 5, 5], [0, 5, 5, 5, 0, 0, 0, 5, 5, 5, 5, 0, 0, 5, 5, 0, 5, 0, 0, 0, 5, 5], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 5, 0, 5, 0, 0, 0, 5]], 'output': [[0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 5, 5, 5, 5, 5, 5, 0, 0, 5, 0, 0, 0], [0, 5, 0, 0, 5, 5, 5, 5, 0, 0, 5, 0, 5, 0, 5, 0, 0, 5, 0, 0, 5, 5], [5, 0, 5, 5, 0, 5, 5, 5, 0, 0, 5, 5, 2, 0, 0, 0, 0, 0, 0, 0, 5, 0], [5, 0, 0, 5, 5, 0, 0, 0, 5, 0, 0, 0, 2, 5, 5, 5, 0, 5, 5, 5, 0, 0], [0, 0, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 2, 5, 5, 0, 0, 5, 0, 5, 5, 0], [0, 5, 0, 0, 5, 0, 0, 0, 5, 2, 8, 2, 8, 8, 8, 2, 5, 0, 5, 0, 0, 0], [0, 5, 5, 0, 5, 0, 0, 0, 0, 0, 5, 0, 2, 5, 0, 0, 5, 0, 0, 5, 5, 5], [0, 0, 0, 0, 0, 5, 0, 0, 0, 5, 5, 0, 2, 5, 0, 5, 5, 0, 5, 0, 0, 0], [5, 0, 0, 0, 0, 0, 5, 0, 5, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 5, 0, 5], [5, 0, 0, 5, 0, 0, 0, 0, 0, 5, 5, 0, 5, 5, 0, 0, 0, 0, 0, 5, 0, 0], [0, 5, 0, 5, 0, 5, 5, 5, 5, 5, 0, 0, 0, 0, 5, 0, 5, 5, 5, 0, 5, 5], [0, 5, 5, 0, 0, 8, 0, 0, 5, 0, 5, 5, 0, 5, 5, 0, 5, 5, 0, 0, 5, 5], [0, 0, 5, 5, 0, 2, 5, 5, 5, 0, 0, 5, 0, 0, 0, 0, 0, 5, 5, 0, 0, 0], [5, 0, 5, 0, 0, 8, 5, 5, 0, 0, 0, 0, 0, 5, 0, 0, 5, 5, 0, 0, 0, 5], [0, 0, 2, 8, 8, 2, 2, 2, 2, 0, 0, 0, 5, 5, 0, 5, 0, 0, 5, 0, 5, 0], [0, 5, 5, 0, 0, 8, 5, 0, 5, 0, 0, 5, 0, 0, 5, 5, 5, 0, 0, 0, 0, 0], [5, 0, 0, 0, 5, 2, 0, 5, 5, 0, 5, 0, 0, 5, 0, 0, 5, 5, 5, 0, 0, 0], [0, 0, 5, 5, 0, 2, 5, 0, 0, 0, 5, 0, 0, 0, 5, 5, 0, 0, 0, 5, 5, 5], [0, 5, 5, 5, 0, 0, 0, 5, 5, 5, 5, 0, 0, 5, 5, 0, 5, 0, 0, 0, 5, 5], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 5, 0, 5, 0, 0, 0, 5]]}, {'input': [[0, 5, 0, 5, 0, 0, 0, 5, 5, 0, 5, 5, 0, 0, 0, 5, 5, 0, 5, 5], [5, 5, 0, 5, 5, 5, 0, 5, 5, 0, 5, 0, 0, 5, 0, 0, 0, 5, 5, 0], [0, 5, 0, 5, 5, 0, 5, 5, 0, 5, 0, 0, 5, 0, 0, 5, 0, 0, 5, 5], [5, 0, 0, 5, 5, 0, 2, 5, 0, 5, 0, 5, 0, 0, 0, 5, 5, 5, 5, 5], [0, 5, 0, 5, 2, 5, 2, 2, 2, 0, 5, 5, 0, 5, 0, 5, 5, 0, 0, 0], [5, 5, 0, 0, 5, 5, 2, 5, 5, 5, 0, 5, 0, 0, 5, 5, 0, 0, 0, 0], [0, 0, 5, 5, 0, 0, 5, 5, 0, 0, 5, 5, 0, 0, 5, 0, 0, 5, 0, 5], [0, 0, 0, 5, 0, 5, 0, 5, 5, 5, 0, 5, 5, 5, 0, 0, 5, 5, 0, 5], [5, 0, 0, 0, 5, 0, 0, 5, 5, 5, 5, 0, 5, 5, 5, 0, 0, 5, 0, 5], [5, 0, 0, 5, 0, 5, 5, 5, 0, 5, 5, 0, 5, 0, 5, 5, 5, 5, 5, 5], [5, 0, 5, 5, 0, 5, 5, 5, 5, 5, 0, 5, 2, 5, 2, 2, 2, 0, 0, 5], [0, 0, 5, 0, 0, 0, 0, 0, 0, 5, 5, 5, 0, 0, 5, 0, 0, 5, 0, 5], [0, 0, 5, 0, 0, 5, 0, 5, 5, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0], [5, 5, 0, 0, 5, 5, 0, 5, 0, 0, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0], [5, 5, 0, 0, 0, 5, 5, 5, 0, 5, 5, 0, 5, 5, 5, 5, 0, 0, 5, 5], [0, 0, 5, 0, 5, 5, 5, 2, 2, 5, 5, 0, 0, 5, 0, 0, 5, 5, 0, 0], [0, 5, 5, 0, 0, 5, 5, 2, 5, 0, 5, 5, 0, 0, 5, 0, 5, 5, 0, 0], [0, 0, 5, 0, 5, 0, 5, 5, 0, 5, 5, 5, 0, 0, 5, 0, 0, 0, 5, 0], [0, 0, 5, 0, 5, 5, 0, 5, 5, 5, 0, 5, 5, 5, 0, 5, 0, 0, 5, 5], [5, 5, 5, 0, 5, 0, 5, 0, 5, 5, 0, 0, 5, 5, 0, 0, 0, 0, 0, 5]], 'output': [[0, 5, 0, 5, 0, 0, 0, 5, 5, 0, 5, 5, 0, 0, 0, 5, 5, 0, 5, 5], [5, 5, 0, 5, 5, 5, 0, 5, 5, 0, 5, 0, 0, 5, 0, 0, 0, 5, 5, 0], [0, 5, 0, 5, 5, 0, 8, 5, 0, 5, 0, 0, 5, 0, 0, 5, 0, 0, 5, 5], [5, 0, 0, 5, 5, 0, 2, 5, 0, 5, 0, 5, 0, 0, 0, 5, 5, 5, 5, 5], [0, 5, 0, 5, 2, 8, 2, 2, 2, 0, 5, 5, 0, 5, 0, 5, 5, 0, 0, 0], [5, 5, 0, 0, 5, 5, 2, 5, 5, 5, 0, 5, 0, 0, 5, 5, 0, 0, 0, 0], [0, 0, 5, 5, 0, 0, 8, 5, 0, 0, 5, 5, 0, 0, 5, 0, 0, 5, 0, 5], [0, 0, 0, 5, 0, 5, 0, 5, 5, 5, 0, 5, 5, 5, 0, 0, 5, 5, 0, 5], [5, 0, 0, 0, 5, 0, 0, 5, 5, 5, 5, 0, 5, 5, 8, 0, 0, 5, 0, 5], [5, 0, 0, 5, 0, 5, 5, 5, 0, 5, 5, 0, 5, 0, 8, 5, 5, 5, 5, 5], [5, 0, 5, 5, 0, 5, 5, 5, 5, 5, 0, 5, 2, 8, 2, 2, 2, 0, 0, 5], [0, 0, 5, 0, 0, 0, 0, 0, 0, 5, 5, 5, 0, 0, 8, 0, 0, 5, 0, 5], [0, 0, 5, 0, 0, 5, 0, 5, 5, 0, 5, 5, 5, 5, 8, 5, 5, 5, 5, 0], [5, 5, 0, 0, 5, 5, 0, 8, 0, 0, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0], [5, 5, 0, 0, 0, 5, 5, 8, 0, 5, 5, 0, 5, 5, 5, 5, 0, 0, 5, 5], [0, 0, 5, 0, 5, 8, 8, 2, 2, 8, 5, 0, 0, 5, 0, 0, 5, 5, 0, 0], [0, 5, 5, 0, 0, 5, 5, 2, 5, 0, 5, 5, 0, 0, 5, 0, 5, 5, 0, 0], [0, 0, 5, 0, 5, 0, 5, 8, 0, 5, 5, 5, 0, 0, 5, 0, 0, 0, 5, 0], [0, 0, 5, 0, 5, 5, 0, 5, 5, 5, 0, 5, 5, 5, 0, 5, 0, 0, 5, 5], [5, 5, 5, 0, 5, 0, 5, 0, 5, 5, 0, 0, 5, 5, 0, 0, 0, 0, 0, 5]]}, {'input': [[0, 0, 5, 0, 5, 0, 5, 5, 5, 5, 0, 5, 5, 0, 0, 0, 5, 5, 0], [0, 0, 5, 5, 5, 0, 5, 5, 5, 5, 0, 0, 5, 5, 5, 5, 5, 0, 5], [0, 5, 5, 5, 0, 5, 0, 5, 5, 0, 0, 0, 5, 5, 5, 0, 5, 0, 0], [5, 5, 5, 5, 5, 0, 0, 0, 5, 5, 5, 5, 5, 5, 0, 0, 5, 0, 0], [5, 5, 0, 0, 0, 5, 5, 5, 0, 5, 5, 5, 5, 0, 0, 0, 5, 0, 0], [5, 0, 0, 0, 0, 0, 5, 0, 5, 0, 5, 2, 5, 0, 0, 5, 0, 5, 5], [5, 0, 5, 0, 0, 5, 5, 0, 5, 2, 2, 5, 2, 2, 5, 5, 0, 5, 0], [0, 5, 0, 5, 5, 5, 5, 5, 0, 5, 0, 5, 5, 5, 5, 0, 5, 5, 5], [5, 5, 5, 0, 5, 5, 5, 5, 0, 0, 5, 2, 5, 5, 5, 0, 0, 0, 0], [5, 2, 2, 5, 0, 0, 5, 0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0], [5, 2, 5, 5, 5, 0, 0, 5, 5, 5, 5, 0, 0, 5, 5, 5, 5, 0, 5], [0, 2, 5, 0, 5, 5, 0, 0, 5, 5, 5, 5, 5, 0, 5, 5, 5, 5, 0], [0, 5, 0, 0, 5, 5, 0, 0, 5, 5, 0, 0, 5, 0, 0, 5, 0, 0, 0], [5, 0, 0, 5, 5, 5, 5, 5, 0, 0, 5, 5, 5, 0, 5, 5, 5, 0, 5], [0, 5, 5, 5, 0, 0, 5, 0, 0, 0, 5, 0, 5, 5, 5, 5, 0, 0, 0], [5, 5, 0, 0, 5, 5, 5, 5, 0, 5, 5, 0, 5, 0, 5, 0, 0, 0, 0], [5, 0, 5, 0, 5, 0, 0, 0, 0, 0, 0, 5, 0, 0, 5, 0, 5, 0, 5], [0, 5, 5, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 5, 0, 5, 5, 5, 5]], 'output': [[0, 0, 5, 0, 5, 0, 5, 5, 5, 5, 0, 5, 5, 0, 0, 0, 5, 5, 0], [0, 0, 5, 5, 5, 0, 5, 5, 5, 5, 0, 0, 5, 5, 5, 5, 5, 0, 5], [0, 5, 5, 5, 0, 5, 0, 5, 5, 0, 0, 0, 5, 5, 5, 0, 5, 0, 0], [5, 5, 5, 5, 5, 0, 0, 0, 5, 5, 5, 5, 5, 5, 0, 0, 5, 0, 0], [5, 5, 0, 0, 0, 5, 5, 5, 0, 5, 5, 8, 5, 0, 0, 0, 5, 0, 0], [5, 0, 0, 0, 0, 0, 5, 0, 5, 0, 5, 2, 5, 0, 0, 5, 0, 5, 5], [5, 0, 5, 0, 0, 5, 5, 0, 5, 2, 2, 8, 2, 2, 5, 5, 0, 5, 0], [0, 8, 0, 5, 5, 5, 5, 5, 0, 5, 0, 8, 5, 5, 5, 0, 5, 5, 5], [5, 8, 5, 0, 5, 5, 5, 5, 0, 0, 5, 2, 5, 5, 5, 0, 0, 0, 0], [8, 2, 2, 8, 0, 0, 5, 0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0], [5, 2, 5, 5, 5, 0, 0, 5, 5, 5, 5, 0, 0, 5, 5, 5, 5, 0, 5], [0, 2, 5, 0, 5, 5, 0, 0, 5, 5, 5, 5, 5, 0, 5, 5, 5, 5, 0], [0, 5, 0, 0, 5, 5, 0, 0, 5, 5, 0, 0, 5, 0, 0, 5, 0, 0, 0], [5, 0, 0, 5, 5, 5, 5, 5, 0, 0, 5, 5, 5, 0, 5, 5, 5, 0, 5], [0, 5, 5, 5, 0, 0, 5, 0, 0, 0, 5, 0, 5, 5, 5, 5, 0, 0, 0], [5, 5, 0, 0, 5, 5, 5, 5, 0, 5, 5, 0, 5, 0, 5, 0, 0, 0, 0], [5, 0, 5, 0, 5, 0, 0, 0, 0, 0, 0, 5, 0, 0, 5, 0, 5, 0, 5], [0, 5, 5, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 5, 0, 5, 5, 5, 5]]}, {'input': [[0, 5, 0, 0, 0, 0, 5, 0, 0, 0, 0, 5], [5, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 5], [5, 0, 5, 0, 0, 5, 5, 0, 2, 0, 5, 0], [5, 5, 0, 0, 5, 0, 5, 0, 2, 5, 0, 5], [5, 0, 0, 5, 5, 5, 2, 5, 2, 2, 2, 0], [5, 5, 5, 0, 5, 5, 0, 5, 2, 0, 0, 5], [5, 5, 5, 0, 5, 0, 0, 5, 5, 0, 0, 0], [5, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0], [0, 5, 5, 0, 5, 0, 0, 0, 0, 5, 0, 0], [5, 0, 0, 0, 5, 5, 5, 5, 5, 0, 0, 0], [5, 0, 0, 0, 0, 5, 0, 0, 5, 5, 5, 5]], 'output': [[0, 5, 0, 0, 0, 0, 5, 0, 0, 0, 0, 5], [5, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 5], [5, 0, 5, 0, 0, 5, 5, 0, 2, 0, 5, 0], [5, 5, 0, 0, 5, 0, 5, 0, 2, 5, 0, 5], [5, 0, 0, 5, 5, 5, 2, 8, 2, 2, 2, 0], [5, 5, 5, 0, 5, 5, 0, 5, 2, 0, 0, 5], [5, 5, 5, 0, 5, 0, 0, 5, 8, 0, 0, 0], [5, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0], [0, 5, 5, 0, 5, 0, 0, 0, 0, 5, 0, 0], [5, 0, 0, 0, 5, 5, 5, 5, 5, 0, 0, 0], [5, 0, 0, 0, 0, 5, 0, 0, 5, 5, 5, 5]]}], 'test_inputs': [[[0, 5, 0, 5, 0, 0, 5, 5, 0, 5, 0, 0, 0, 5, 0, 5, 0, 0, 0, 5, 5, 0], [0, 5, 0, 5, 5, 0, 0, 0, 5, 5, 0, 0, 5, 5, 0, 0, 0, 0, 0, 5, 5, 5], [0, 0, 0, 0, 5, 5, 5, 0, 0, 0, 0, 5, 5, 0, 0, 5, 5, 0, 0, 5, 5, 5], [0, 0, 5, 5, 0, 5, 5, 5, 0, 5, 0, 5, 0, 5, 0, 5, 5, 0, 5, 5, 5, 0], [0, 5, 0, 5, 2, 2, 5, 2, 2, 5, 0, 0, 5, 0, 5, 5, 5, 0, 0, 5, 5, 0], [0, 0, 0, 5, 0, 5, 2, 5, 5, 5, 0, 5, 0, 0, 0, 0, 5, 5, 5, 5, 0, 0], [5, 5, 0, 0, 5, 5, 2, 0, 5, 5, 0, 0, 0, 5, 0, 0, 0, 5, 5, 5, 5, 5], [5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 0, 5, 0, 0, 5, 0, 5, 0], [5, 5, 5, 5, 5, 0, 0, 5, 5, 0, 5, 2, 5, 2, 5, 5, 0, 0, 5, 5, 5, 0], [0, 0, 0, 5, 5, 5, 0, 0, 5, 0, 0, 0, 5, 5, 0, 5, 5, 5, 0, 0, 0, 0], [0, 0, 0, 5, 5, 5, 0, 5, 0, 5, 0, 5, 5, 2, 5, 0, 5, 0, 0, 5, 5, 0], [0, 5, 5, 5, 0, 0, 0, 5, 5, 5, 5, 0, 0, 5, 0, 5, 5, 0, 0, 0, 5, 5], [5, 5, 0, 0, 5, 5, 5, 0, 0, 5, 5, 0, 5, 0, 5, 5, 0, 0, 5, 5, 0, 5], [0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 5, 5, 5, 5, 5, 0, 0, 5, 0, 5], [5, 5, 0, 5, 5, 2, 2, 2, 5, 5, 5, 0, 5, 5, 5, 0, 5, 0, 0, 5, 5, 0], [5, 0, 0, 0, 5, 2, 5, 0, 5, 0, 5, 0, 5, 5, 5, 5, 0, 0, 0, 0, 5, 5], [5, 5, 5, 0, 0, 2, 0, 5, 5, 0, 0, 2, 2, 2, 2, 2, 5, 0, 5, 0, 5, 5], [5, 0, 5, 0, 0, 5, 0, 5, 0, 0, 0, 0, 0, 5, 0, 5, 5, 5, 0, 5, 5, 0], [5, 5, 5, 5, 5, 0, 5, 0, 5, 5, 5, 5, 0, 5, 0, 0, 5, 5, 0, 5, 0, 5]]], 'test_outputs': [[[0, 5, 0, 5, 0, 0, 5, 5, 0, 5, 0, 0, 0, 5, 0, 5, 0, 0, 0, 5, 5, 0], [0, 5, 0, 5, 5, 0, 0, 0, 5, 5, 0, 0, 5, 5, 0, 0, 0, 0, 0, 5, 5, 5], [0, 0, 0, 0, 5, 5, 8, 0, 0, 0, 0, 5, 5, 0, 0, 5, 5, 0, 0, 5, 5, 5], [0, 0, 5, 5, 0, 5, 8, 5, 0, 5, 0, 5, 0, 5, 0, 5, 5, 0, 5, 5, 5, 0], [0, 5, 0, 5, 2, 2, 8, 2, 2, 5, 0, 0, 5, 0, 5, 5, 5, 0, 0, 5, 5, 0], [0, 0, 0, 5, 0, 5, 2, 5, 5, 5, 0, 5, 0, 0, 0, 0, 5, 5, 5, 5, 0, 0], [5, 5, 0, 0, 5, 5, 2, 0, 5, 5, 0, 0, 0, 8, 0, 0, 0, 5, 5, 5, 5, 5], [5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 8, 0, 5, 0, 0, 5, 0, 5, 0], [5, 5, 5, 5, 5, 0, 0, 5, 5, 0, 5, 2, 8, 2, 8, 8, 0, 0, 5, 5, 5, 0], [0, 0, 0, 5, 5, 5, 0, 0, 5, 0, 0, 0, 5, 8, 0, 5, 5, 5, 0, 0, 0, 0], [0, 0, 0, 5, 5, 5, 0, 5, 0, 5, 0, 5, 5, 2, 5, 0, 5, 0, 0, 5, 5, 0], [0, 5, 5, 5, 0, 0, 0, 5, 5, 5, 5, 0, 0, 5, 0, 5, 5, 0, 0, 0, 5, 5], [5, 5, 0, 0, 5, 8, 5, 0, 0, 5, 5, 0, 5, 0, 5, 5, 0, 0, 5, 5, 0, 5], [0, 0, 5, 5, 5, 8, 5, 5, 5, 5, 0, 0, 5, 5, 5, 5, 5, 0, 0, 5, 0, 5], [5, 5, 0, 8, 8, 2, 2, 2, 5, 5, 5, 0, 5, 8, 5, 0, 5, 0, 0, 5, 5, 0], [5, 0, 0, 0, 5, 2, 5, 0, 5, 0, 5, 0, 5, 8, 5, 5, 0, 0, 0, 0, 5, 5], [5, 5, 5, 0, 0, 2, 0, 5, 5, 0, 0, 2, 2, 2, 2, 2, 5, 0, 5, 0, 5, 5], [5, 0, 5, 0, 0, 5, 0, 5, 0, 0, 0, 0, 0, 8, 0, 5, 5, 5, 0, 5, 5, 0], [5, 5, 5, 5, 5, 0, 5, 0, 5, 5, 5, 5, 0, 8, 0, 0, 5, 5, 0, 5, 0, 5]]]}) (input_keys={'test_inputs', 'training_examples'}): 'dict' object has no attribute 'input'
Traceback (most recent call last):
File "<string>", line 84, in _test_code
AttributeError: 'dict' object has no attribute 'input'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 109, in forward
File "<string>", line 90, in _test_code
AttributeError: 'dict' object has no attribute 'input'
2025/08/29 14:46:34 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 5, 5, 5, 5, 5, 5, 0, 0, 5, 0, 0, 0], [0, 5, 0, 0, 5, 5, 5, 5, 0, 0, 5, 0, 5, 0, 5, 0, 0, 5, 0, 0, 5, 5], [5, 0, 5, 5, 0, 5, 5, 5, 0, 0, 5, 5, 2, 0, 0, 0, 0, 0, 0, 0, 5, 0], [5, 0, 0, 5, 5, 0, 0, 0, 5, 0, 0, 0, 2, 5, 5, 5, 0, 5, 5, 5, 0, 0], [0, 0, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 2, 5, 5, 0, 0, 5, 0, 5, 5, 0], [0, 5, 0, 0, 5, 0, 0, 0, 5, 2, 5, 2, 5, 5, 5, 2, 5, 0, 5, 0, 0, 0], [0, 5, 5, 0, 5, 0, 0, 0, 0, 0, 5, 0, 2, 5, 0, 0, 5, 0, 0, 5, 5, 5], [0, 0, 0, 0, 0, 5, 0, 0, 0, 5, 5, 0, 2, 5, 0, 5, 5, 0, 5, 0, 0, 0], [5, 0, 0, 0, 0, 0, 5, 0, 5, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 5, 0, 5], [5, 0, 0, 5, 0, 0, 0, 0, 0, 5, 5, 0, 5, 5, 0, 0, 0, 0, 0, 5, 0, 0], [0, 5, 0, 5, 0, 5, 5, 5, 5, 5, 0, 0, 0, 0, 5, 0, 5, 5, 5, 0, 5, 5], [0, 5, 5, 0, 0, 5, 0, 0, 5, 0, 5, 5, 0, 5, 5, 0, 5, 5, 0, 0, 5, 5], [0, 0, 5, 5, 0, 2, 5, 5, 5, 0, 0, 5, 0, 0, 0, 0, 0, 5, 5, 0, 0, 0], [5, 0, 5, 0, 0, 5, 5, 5, 0, 0, 0, 0, 0, 5, 0, 0, 5, 5, 0, 0, 0, 5], [0, 0, 2, 5, 5, 2, 2, 2, 2, 0, 0, 0, 5, 5, 0, 5, 0, 0, 5, 0, 5, 0], [0, 5, 5, 0, 0, 5, 5, 0, 5, 0, 0, 5, 0, 0, 5, 5, 5, 0, 0, 0, 0, 0], [5, 0, 0, 0, 5, 2, 0, 5, 5, 0, 5, 0, 0, 5, 0, 0, 5, 5, 5, 0, 0, 0], [0, 0, 5, 5, 0, 2, 5, 0, 0, 0, 5, 0, 0, 0, 5, 5, 0, 0, 0, 5, 5, 5], [0, 5, 5, 5, 0, 0, 0, 5, 5, 5, 5, 0, 0, 5, 5, 0, 5, 0, 0, 0, 5, 5], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 5, 0, 5, 0, 0, 0, 5]], 'output': [[0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 5, 5, 5, 5, 5, 5, 0, 0, 5, 0, 0, 0], [0, 5, 0, 0, 5, 5, 5, 5, 0, 0, 5, 0, 5, 0, 5, 0, 0, 5, 0, 0, 5, 5], [5, 0, 5, 5, 0, 5, 5, 5, 0, 0, 5, 5, 2, 0, 0, 0, 0, 0, 0, 0, 5, 0], [5, 0, 0, 5, 5, 0, 0, 0, 5, 0, 0, 0, 2, 5, 5, 5, 0, 5, 5, 5, 0, 0], [0, 0, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 2, 5, 5, 0, 0, 5, 0, 5, 5, 0], [0, 5, 0, 0, 5, 0, 0, 0, 5, 2, 8, 2, 8, 8, 8, 2, 5, 0, 5, 0, 0, 0], [0, 5, 5, 0, 5, 0, 0, 0, 0, 0, 5, 0, 2, 5, 0, 0, 5, 0, 0, 5, 5, 5], [0, 0, 0, 0, 0, 5, 0, 0, 0, 5, 5, 0, 2, 5, 0, 5, 5, 0, 5, 0, 0, 0], [5, 0, 0, 0, 0, 0, 5, 0, 5, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 5, 0, 5], [5, 0, 0, 5, 0, 0, 0, 0, 0, 5, 5, 0, 5, 5, 0, 0, 0, 0, 0, 5, 0, 0], [0, 5, 0, 5, 0, 5, 5, 5, 5, 5, 0, 0, 0, 0, 5, 0, 5, 5, 5, 0, 5, 5], [0, 5, 5, 0, 0, 8, 0, 0, 5, 0, 5, 5, 0, 5, 5, 0, 5, 5, 0, 0, 5, 5], [0, 0, 5, 5, 0, 2, 5, 5, 5, 0, 0, 5, 0, 0, 0, 0, 0, 5, 5, 0, 0, 0], [5, 0, 5, 0, 0, 8, 5, 5, 0, 0, 0, 0, 0, 5, 0, 0, 5, 5, 0, 0, 0, 5], [0, 0, 2, 8, 8, 2, 2, 2, 2, 0, 0, 0, 5, 5, 0, 5, 0, 0, 5, 0, 5, 0], [0, 5, 5, 0, 0, 8, 5, 0, 5, 0, 0, 5, 0, 0, 5, 5, 5, 0, 0, 0, 0, 0], [5, 0, 0, 0, 5, 2, 0, 5, 5, 0, 5, 0, 0, 5, 0, 0, 5, 5, 5, 0, 0, 0], [0, 0, 5, 5, 0, 2, 5, 0, 0, 0, 5, 0, 0, 0, 5, 5, 0, 0, 0, 5, 5, 5], [0, 5, 5, 5, 0, 0, 0, 5, 5, 5, 5, 0, 0, 5, 5, 0, 5, 0, 0, 0, 5, 5], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 5, 0, 5, 0, 0, 0, 5]]}, {'input': [[0, 5, 0, 5, 0, 0, 0, 5, 5, 0, 5, 5, 0, 0, 0, 5, 5, 0, 5, 5], [5, 5, 0, 5, 5, 5, 0, 5, 5, 0, 5, 0, 0, 5, 0, 0, 0, 5, 5, 0], [0, 5, 0, 5, 5, 0, 5, 5, 0, 5, 0, 0, 5, 0, 0, 5, 0, 0, 5, 5], [5, 0, 0, 5, 5, 0, 2, 5, 0, 5, 0, 5, 0, 0, 0, 5, 5, 5, 5, 5], [0, 5, 0, 5, 2, 5, 2, 2, 2, 0, 5, 5, 0, 5, 0, 5, 5, 0, 0, 0], [5, 5, 0, 0, 5, 5, 2, 5, 5, 5, 0, 5, 0, 0, 5, 5, 0, 0, 0, 0], [0, 0, 5, 5, 0, 0, 5, 5, 0, 0, 5, 5, 0, 0, 5, 0, 0, 5, 0, 5], [0, 0, 0, 5, 0, 5, 0, 5, 5, 5, 0, 5, 5, 5, 0, 0, 5, 5, 0, 5], [5, 0, 0, 0, 5, 0, 0, 5, 5, 5, 5, 0, 5, 5, 5, 0, 0, 5, 0, 5], [5, 0, 0, 5, 0, 5, 5, 5, 0, 5, 5, 0, 5, 0, 5, 5, 5, 5, 5, 5], [5, 0, 5, 5, 0, 5, 5, 5, 5, 5, 0, 5, 2, 5, 2, 2, 2, 0, 0, 5], [0, 0, 5, 0, 0, 0, 0, 0, 0, 5, 5, 5, 0, 0, 5, 0, 0, 5, 0, 5], [0, 0, 5, 0, 0, 5, 0, 5, 5, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0], [5, 5, 0, 0, 5, 5, 0, 5, 0, 0, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0], [5, 5, 0, 0, 0, 5, 5, 5, 0, 5, 5, 0, 5, 5, 5, 5, 0, 0, 5, 5], [0, 0, 5, 0, 5, 5, 5, 2, 2, 5, 5, 0, 0, 5, 0, 0, 5, 5, 0, 0], [0, 5, 5, 0, 0, 5, 5, 2, 5, 0, 5, 5, 0, 0, 5, 0, 5, 5, 0, 0], [0, 0, 5, 0, 5, 0, 5, 5, 0, 5, 5, 5, 0, 0, 5, 0, 0, 0, 5, 0], [0, 0, 5, 0, 5, 5, 0, 5, 5, 5, 0, 5, 5, 5, 0, 5, 0, 0, 5, 5], [5, 5, 5, 0, 5, 0, 5, 0, 5, 5, 0, 0, 5, 5, 0, 0, 0, 0, 0, 5]], 'output': [[0, 5, 0, 5, 0, 0, 0, 5, 5, 0, 5, 5, 0, 0, 0, 5, 5, 0, 5, 5], [5, 5, 0, 5, 5, 5, 0, 5, 5, 0, 5, 0, 0, 5, 0, 0, 0, 5, 5, 0], [0, 5, 0, 5, 5, 0, 8, 5, 0, 5, 0, 0, 5, 0, 0, 5, 0, 0, 5, 5], [5, 0, 0, 5, 5, 0, 2, 5, 0, 5, 0, 5, 0, 0, 0, 5, 5, 5, 5, 5], [0, 5, 0, 5, 2, 8, 2, 2, 2, 0, 5, 5, 0, 5, 0, 5, 5, 0, 0, 0], [5, 5, 0, 0, 5, 5, 2, 5, 5, 5, 0, 5, 0, 0, 5, 5, 0, 0, 0, 0], [0, 0, 5, 5, 0, 0, 8, 5, 0, 0, 5, 5, 0, 0, 5, 0, 0, 5, 0, 5], [0, 0, 0, 5, 0, 5, 0, 5, 5, 5, 0, 5, 5, 5, 0, 0, 5, 5, 0, 5], [5, 0, 0, 0, 5, 0, 0, 5, 5, 5, 5, 0, 5, 5, 8, 0, 0, 5, 0, 5], [5, 0, 0, 5, 0, 5, 5, 5, 0, 5, 5, 0, 5, 0, 8, 5, 5, 5, 5, 5], [5, 0, 5, 5, 0, 5, 5, 5, 5, 5, 0, 5, 2, 8, 2, 2, 2, 0, 0, 5], [0, 0, 5, 0, 0, 0, 0, 0, 0, 5, 5, 5, 0, 0, 8, 0, 0, 5, 0, 5], [0, 0, 5, 0, 0, 5, 0, 5, 5, 0, 5, 5, 5, 5, 8, 5, 5, 5, 5, 0], [5, 5, 0, 0, 5, 5, 0, 8, 0, 0, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0], [5, 5, 0, 0, 0, 5, 5, 8, 0, 5, 5, 0, 5, 5, 5, 5, 0, 0, 5, 5], [0, 0, 5, 0, 5, 8, 8, 2, 2, 8, 5, 0, 0, 5, 0, 0, 5, 5, 0, 0], [0, 5, 5, 0, 0, 5, 5, 2, 5, 0, 5, 5, 0, 0, 5, 0, 5, 5, 0, 0], [0, 0, 5, 0, 5, 0, 5, 8, 0, 5, 5, 5, 0, 0, 5, 0, 0, 0, 5, 0], [0, 0, 5, 0, 5, 5, 0, 5, 5, 5, 0, 5, 5, 5, 0, 5, 0, 0, 5, 5], [5, 5, 5, 0, 5, 0, 5, 0, 5, 5, 0, 0, 5, 5, 0, 0, 0, 0, 0, 5]]}, {'input': [[0, 0, 5, 0, 5, 0, 5, 5, 5, 5, 0, 5, 5, 0, 0, 0, 5, 5, 0], [0, 0, 5, 5, 5, 0, 5, 5, 5, 5, 0, 0, 5, 5, 5, 5, 5, 0, 5], [0, 5, 5, 5, 0, 5, 0, 5, 5, 0, 0, 0, 5, 5, 5, 0, 5, 0, 0], [5, 5, 5, 5, 5, 0, 0, 0, 5, 5, 5, 5, 5, 5, 0, 0, 5, 0, 0], [5, 5, 0, 0, 0, 5, 5, 5, 0, 5, 5, 5, 5, 0, 0, 0, 5, 0, 0], [5, 0, 0, 0, 0, 0, 5, 0, 5, 0, 5, 2, 5, 0, 0, 5, 0, 5, 5], [5, 0, 5, 0, 0, 5, 5, 0, 5, 2, 2, 5, 2, 2, 5, 5, 0, 5, 0], [0, 5, 0, 5, 5, 5, 5, 5, 0, 5, 0, 5, 5, 5, 5, 0, 5, 5, 5], [5, 5, 5, 0, 5, 5, 5, 5, 0, 0, 5, 2, 5, 5, 5, 0, 0, 0, 0], [5, 2, 2, 5, 0, 0, 5, 0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0], [5, 2, 5, 5, 5, 0, 0, 5, 5, 5, 5, 0, 0, 5, 5, 5, 5, 0, 5], [0, 2, 5, 0, 5, 5, 0, 0, 5, 5, 5, 5, 5, 0, 5, 5, 5, 5, 0], [0, 5, 0, 0, 5, 5, 0, 0, 5, 5, 0, 0, 5, 0, 0, 5, 0, 0, 0], [5, 0, 0, 5, 5, 5, 5, 5, 0, 0, 5, 5, 5, 0, 5, 5, 5, 0, 5], [0, 5, 5, 5, 0, 0, 5, 0, 0, 0, 5, 0, 5, 5, 5, 5, 0, 0, 0], [5, 5, 0, 0, 5, 5, 5, 5, 0, 5, 5, 0, 5, 0, 5, 0, 0, 0, 0], [5, 0, 5, 0, 5, 0, 0, 0, 0, 0, 0, 5, 0, 0, 5, 0, 5, 0, 5], [0, 5, 5, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 5, 0, 5, 5, 5, 5]], 'output': [[0, 0, 5, 0, 5, 0, 5, 5, 5, 5, 0, 5, 5, 0, 0, 0, 5, 5, 0], [0, 0, 5, 5, 5, 0, 5, 5, 5, 5, 0, 0, 5, 5, 5, 5, 5, 0, 5], [0, 5, 5, 5, 0, 5, 0, 5, 5, 0, 0, 0, 5, 5, 5, 0, 5, 0, 0], [5, 5, 5, 5, 5, 0, 0, 0, 5, 5, 5, 5, 5, 5, 0, 0, 5, 0, 0], [5, 5, 0, 0, 0, 5, 5, 5, 0, 5, 5, 8, 5, 0, 0, 0, 5, 0, 0], [5, 0, 0, 0, 0, 0, 5, 0, 5, 0, 5, 2, 5, 0, 0, 5, 0, 5, 5], [5, 0, 5, 0, 0, 5, 5, 0, 5, 2, 2, 8, 2, 2, 5, 5, 0, 5, 0], [0, 8, 0, 5, 5, 5, 5, 5, 0, 5, 0, 8, 5, 5, 5, 0, 5, 5, 5], [5, 8, 5, 0, 5, 5, 5, 5, 0, 0, 5, 2, 5, 5, 5, 0, 0, 0, 0], [8, 2, 2, 8, 0, 0, 5, 0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0], [5, 2, 5, 5, 5, 0, 0, 5, 5, 5, 5, 0, 0, 5, 5, 5, 5, 0, 5], [0, 2, 5, 0, 5, 5, 0, 0, 5, 5, 5, 5, 5, 0, 5, 5, 5, 5, 0], [0, 5, 0, 0, 5, 5, 0, 0, 5, 5, 0, 0, 5, 0, 0, 5, 0, 0, 0], [5, 0, 0, 5, 5, 5, 5, 5, 0, 0, 5, 5, 5, 0, 5, 5, 5, 0, 5], [0, 5, 5, 5, 0, 0, 5, 0, 0, 0, 5, 0, 5, 5, 5, 5, 0, 0, 0], [5, 5, 0, 0, 5, 5, 5, 5, 0, 5, 5, 0, 5, 0, 5, 0, 0, 0, 0], [5, 0, 5, 0, 5, 0, 0, 0, 0, 0, 0, 5, 0, 0, 5, 0, 5, 0, 5], [0, 5, 5, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 5, 0, 5, 5, 5, 5]]}, {'input': [[0, 5, 0, 0, 0, 0, 5, 0, 0, 0, 0, 5], [5, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 5], [5, 0, 5, 0, 0, 5, 5, 0, 2, 0, 5, 0], [5, 5, 0, 0, 5, 0, 5, 0, 2, 5, 0, 5], [5, 0, 0, 5, 5, 5, 2, 5, 2, 2, 2, 0], [5, 5, 5, 0, 5, 5, 0, 5, 2, 0, 0, 5], [5, 5, 5, 0, 5, 0, 0, 5, 5, 0, 0, 0], [5, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0], [0, 5, 5, 0, 5, 0, 0, 0, 0, 5, 0, 0], [5, 0, 0, 0, 5, 5, 5, 5, 5, 0, 0, 0], [5, 0, 0, 0, 0, 5, 0, 0, 5, 5, 5, 5]], 'output': [[0, 5, 0, 0, 0, 0, 5, 0, 0, 0, 0, 5], [5, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 5], [5, 0, 5, 0, 0, 5, 5, 0, 2, 0, 5, 0], [5, 5, 0, 0, 5, 0, 5, 0, 2, 5, 0, 5], [5, 0, 0, 5, 5, 5, 2, 8, 2, 2, 2, 0], [5, 5, 5, 0, 5, 5, 0, 5, 2, 0, 0, 5], [5, 5, 5, 0, 5, 0, 0, 5, 8, 0, 0, 0], [5, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0], [0, 5, 5, 0, 5, 0, 0, 0, 0, 5, 0, 0], [5, 0, 0, 0, 5, 5, 5, 5, 5, 0, 0, 0], [5, 0, 0, 0, 0, 5, 0, 0, 5, 5, 5, 5]]}], 'test_inputs': [[[0, 5, 0, 5, 0, 0, 5, 5, 0, 5, 0, 0, 0, 5, 0, 5, 0, 0, 0, 5, 5, 0], [0, 5, 0, 5, 5, 0, 0, 0, 5, 5, 0, 0, 5, 5, 0, 0, 0, 0, 0, 5, 5, 5], [0, 0, 0, 0, 5, 5, 5, 0, 0, 0, 0, 5, 5, 0, 0, 5, 5, 0, 0, 5, 5, 5], [0, 0, 5, 5, 0, 5, 5, 5, 0, 5, 0, 5, 0, 5, 0, 5, 5, 0, 5, 5, 5, 0], [0, 5, 0, 5, 2, 2, 5, 2, 2, 5, 0, 0, 5, 0, 5, 5, 5, 0, 0, 5, 5, 0], [0, 0, 0, 5, 0, 5, 2, 5, 5, 5, 0, 5, 0, 0, 0, 0, 5, 5, 5, 5, 0, 0], [5, 5, 0, 0, 5, 5, 2, 0, 5, 5, 0, 0, 0, 5, 0, 0, 0, 5, 5, 5, 5, 5], [5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 0, 5, 0, 0, 5, 0, 5, 0], [5, 5, 5, 5, 5, 0, 0, 5, 5, 0, 5, 2, 5, 2, 5, 5, 0, 0, 5, 5, 5, 0], [0, 0, 0, 5, 5, 5, 0, 0, 5, 0, 0, 0, 5, 5, 0, 5, 5, 5, 0, 0, 0, 0], [0, 0, 0, 5, 5, 5, 0, 5, 0, 5, 0, 5, 5, 2, 5, 0, 5, 0, 0, 5, 5, 0], [0, 5, 5, 5, 0, 0, 0, 5, 5, 5, 5, 0, 0, 5, 0, 5, 5, 0, 0, 0, 5, 5], [5, 5, 0, 0, 5, 5, 5, 0, 0, 5, 5, 0, 5, 0, 5, 5, 0, 0, 5, 5, 0, 5], [0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 5, 5, 5, 5, 5, 0, 0, 5, 0, 5], [5, 5, 0, 5, 5, 2, 2, 2, 5, 5, 5, 0, 5, 5, 5, 0, 5, 0, 0, 5, 5, 0], [5, 0, 0, 0, 5, 2, 5, 0, 5, 0, 5, 0, 5, 5, 5, 5, 0, 0, 0, 0, 5, 5], [5, 5, 5, 0, 0, 2, 0, 5, 5, 0, 0, 2, 2, 2, 2, 2, 5, 0, 5, 0, 5, 5], [5, 0, 5, 0, 0, 5, 0, 5, 0, 0, 0, 0, 0, 5, 0, 5, 5, 5, 0, 5, 5, 0], [5, 5, 5, 5, 5, 0, 5, 0, 5, 5, 5, 5, 0, 5, 0, 0, 5, 5, 0, 5, 0, 5]]], 'test_outputs': [[[0, 5, 0, 5, 0, 0, 5, 5, 0, 5, 0, 0, 0, 5, 0, 5, 0, 0, 0, 5, 5, 0], [0, 5, 0, 5, 5, 0, 0, 0, 5, 5, 0, 0, 5, 5, 0, 0, 0, 0, 0, 5, 5, 5], [0, 0, 0, 0, 5, 5, 8, 0, 0, 0, 0, 5, 5, 0, 0, 5, 5, 0, 0, 5, 5, 5], [0, 0, 5, 5, 0, 5, 8, 5, 0, 5, 0, 5, 0, 5, 0, 5, 5, 0, 5, 5, 5, 0], [0, 5, 0, 5, 2, 2, 8, 2, 2, 5, 0, 0, 5, 0, 5, 5, 5, 0, 0, 5, 5, 0], [0, 0, 0, 5, 0, 5, 2, 5, 5, 5, 0, 5, 0, 0, 0, 0, 5, 5, 5, 5, 0, 0], [5, 5, 0, 0, 5, 5, 2, 0, 5, 5, 0, 0, 0, 8, 0, 0, 0, 5, 5, 5, 5, 5], [5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 8, 0, 5, 0, 0, 5, 0, 5, 0], [5, 5, 5, 5, 5, 0, 0, 5, 5, 0, 5, 2, 8, 2, 8, 8, 0, 0, 5, 5, 5, 0], [0, 0, 0, 5, 5, 5, 0, 0, 5, 0, 0, 0, 5, 8, 0, 5, 5, 5, 0, 0, 0, 0], [0, 0, 0, 5, 5, 5, 0, 5, 0, 5, 0, 5, 5, 2, 5, 0, 5, 0, 0, 5, 5, 0], [0, 5, 5, 5, 0, 0, 0, 5, 5, 5, 5, 0, 0, 5, 0, 5, 5, 0, 0, 0, 5, 5], [5, 5, 0, 0, 5, 8, 5, 0, 0, 5, 5, 0, 5, 0, 5, 5, 0, 0, 5, 5, 0, 5], [0, 0, 5, 5, 5, 8, 5, 5, 5, 5, 0, 0, 5, 5, 5, 5, 5, 0, 0, 5, 0, 5], [5, 5, 0, 8, 8, 2, 2, 2, 5, 5, 5, 0, 5, 8, 5, 0, 5, 0, 0, 5, 5, 0], [5, 0, 0, 0, 5, 2, 5, 0, 5, 0, 5, 0, 5, 8, 5, 5, 0, 0, 0, 0, 5, 5], [5, 5, 5, 0, 0, 2, 0, 5, 5, 0, 0, 2, 2, 2, 2, 2, 5, 0, 5, 0, 5, 5], [5, 0, 5, 0, 0, 5, 0, 5, 0, 0, 0, 0, 0, 8, 0, 5, 5, 5, 0, 5, 5, 0], [5, 5, 5, 5, 5, 0, 5, 0, 5, 5, 5, 5, 0, 8, 0, 0, 5, 5, 0, 5, 0, 5]]]}) (input_keys={'test_inputs', 'training_examples'}): 'dict' object has no attribute 'input'
Traceback (most recent call last):
File "<string>", line 84, in _test_code
AttributeError: 'dict' object has no attribute 'input'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 109, in forward
File "<string>", line 90, in _test_code
AttributeError: 'dict' object has no attribute 'input'
2025/08/29 14:48:01 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 5, 5, 5, 5, 5, 5, 0, 0, 5, 0, 0, 0], [0, 5, 0, 0, 5, 5, 5, 5, 0, 0, 5, 0, 5, 0, 5, 0, 0, 5, 0, 0, 5, 5], [5, 0, 5, 5, 0, 5, 5, 5, 0, 0, 5, 5, 2, 0, 0, 0, 0, 0, 0, 0, 5, 0], [5, 0, 0, 5, 5, 0, 0, 0, 5, 0, 0, 0, 2, 5, 5, 5, 0, 5, 5, 5, 0, 0], [0, 0, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 2, 5, 5, 0, 0, 5, 0, 5, 5, 0], [0, 5, 0, 0, 5, 0, 0, 0, 5, 2, 5, 2, 5, 5, 5, 2, 5, 0, 5, 0, 0, 0], [0, 5, 5, 0, 5, 0, 0, 0, 0, 0, 5, 0, 2, 5, 0, 0, 5, 0, 0, 5, 5, 5], [0, 0, 0, 0, 0, 5, 0, 0, 0, 5, 5, 0, 2, 5, 0, 5, 5, 0, 5, 0, 0, 0], [5, 0, 0, 0, 0, 0, 5, 0, 5, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 5, 0, 5], [5, 0, 0, 5, 0, 0, 0, 0, 0, 5, 5, 0, 5, 5, 0, 0, 0, 0, 0, 5, 0, 0], [0, 5, 0, 5, 0, 5, 5, 5, 5, 5, 0, 0, 0, 0, 5, 0, 5, 5, 5, 0, 5, 5], [0, 5, 5, 0, 0, 5, 0, 0, 5, 0, 5, 5, 0, 5, 5, 0, 5, 5, 0, 0, 5, 5], [0, 0, 5, 5, 0, 2, 5, 5, 5, 0, 0, 5, 0, 0, 0, 0, 0, 5, 5, 0, 0, 0], [5, 0, 5, 0, 0, 5, 5, 5, 0, 0, 0, 0, 0, 5, 0, 0, 5, 5, 0, 0, 0, 5], [0, 0, 2, 5, 5, 2, 2, 2, 2, 0, 0, 0, 5, 5, 0, 5, 0, 0, 5, 0, 5, 0], [0, 5, 5, 0, 0, 5, 5, 0, 5, 0, 0, 5, 0, 0, 5, 5, 5, 0, 0, 0, 0, 0], [5, 0, 0, 0, 5, 2, 0, 5, 5, 0, 5, 0, 0, 5, 0, 0, 5, 5, 5, 0, 0, 0], [0, 0, 5, 5, 0, 2, 5, 0, 0, 0, 5, 0, 0, 0, 5, 5, 0, 0, 0, 5, 5, 5], [0, 5, 5, 5, 0, 0, 0, 5, 5, 5, 5, 0, 0, 5, 5, 0, 5, 0, 0, 0, 5, 5], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 5, 0, 5, 0, 0, 0, 5]], 'output': [[0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 5, 5, 5, 5, 5, 5, 0, 0, 5, 0, 0, 0], [0, 5, 0, 0, 5, 5, 5, 5, 0, 0, 5, 0, 5, 0, 5, 0, 0, 5, 0, 0, 5, 5], [5, 0, 5, 5, 0, 5, 5, 5, 0, 0, 5, 5, 2, 0, 0, 0, 0, 0, 0, 0, 5, 0], [5, 0, 0, 5, 5, 0, 0, 0, 5, 0, 0, 0, 2, 5, 5, 5, 0, 5, 5, 5, 0, 0], [0, 0, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 2, 5, 5, 0, 0, 5, 0, 5, 5, 0], [0, 5, 0, 0, 5, 0, 0, 0, 5, 2, 8, 2, 8, 8, 8, 2, 5, 0, 5, 0, 0, 0], [0, 5, 5, 0, 5, 0, 0, 0, 0, 0, 5, 0, 2, 5, 0, 0, 5, 0, 0, 5, 5, 5], [0, 0, 0, 0, 0, 5, 0, 0, 0, 5, 5, 0, 2, 5, 0, 5, 5, 0, 5, 0, 0, 0], [5, 0, 0, 0, 0, 0, 5, 0, 5, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 5, 0, 5], [5, 0, 0, 5, 0, 0, 0, 0, 0, 5, 5, 0, 5, 5, 0, 0, 0, 0, 0, 5, 0, 0], [0, 5, 0, 5, 0, 5, 5, 5, 5, 5, 0, 0, 0, 0, 5, 0, 5, 5, 5, 0, 5, 5], [0, 5, 5, 0, 0, 8, 0, 0, 5, 0, 5, 5, 0, 5, 5, 0, 5, 5, 0, 0, 5, 5], [0, 0, 5, 5, 0, 2, 5, 5, 5, 0, 0, 5, 0, 0, 0, 0, 0, 5, 5, 0, 0, 0], [5, 0, 5, 0, 0, 8, 5, 5, 0, 0, 0, 0, 0, 5, 0, 0, 5, 5, 0, 0, 0, 5], [0, 0, 2, 8, 8, 2, 2, 2, 2, 0, 0, 0, 5, 5, 0, 5, 0, 0, 5, 0, 5, 0], [0, 5, 5, 0, 0, 8, 5, 0, 5, 0, 0, 5, 0, 0, 5, 5, 5, 0, 0, 0, 0, 0], [5, 0, 0, 0, 5, 2, 0, 5, 5, 0, 5, 0, 0, 5, 0, 0, 5, 5, 5, 0, 0, 0], [0, 0, 5, 5, 0, 2, 5, 0, 0, 0, 5, 0, 0, 0, 5, 5, 0, 0, 0, 5, 5, 5], [0, 5, 5, 5, 0, 0, 0, 5, 5, 5, 5, 0, 0, 5, 5, 0, 5, 0, 0, 0, 5, 5], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 5, 0, 5, 0, 0, 0, 5]]}, {'input': [[0, 5, 0, 5, 0, 0, 0, 5, 5, 0, 5, 5, 0, 0, 0, 5, 5, 0, 5, 5], [5, 5, 0, 5, 5, 5, 0, 5, 5, 0, 5, 0, 0, 5, 0, 0, 0, 5, 5, 0], [0, 5, 0, 5, 5, 0, 5, 5, 0, 5, 0, 0, 5, 0, 0, 5, 0, 0, 5, 5], [5, 0, 0, 5, 5, 0, 2, 5, 0, 5, 0, 5, 0, 0, 0, 5, 5, 5, 5, 5], [0, 5, 0, 5, 2, 5, 2, 2, 2, 0, 5, 5, 0, 5, 0, 5, 5, 0, 0, 0], [5, 5, 0, 0, 5, 5, 2, 5, 5, 5, 0, 5, 0, 0, 5, 5, 0, 0, 0, 0], [0, 0, 5, 5, 0, 0, 5, 5, 0, 0, 5, 5, 0, 0, 5, 0, 0, 5, 0, 5], [0, 0, 0, 5, 0, 5, 0, 5, 5, 5, 0, 5, 5, 5, 0, 0, 5, 5, 0, 5], [5, 0, 0, 0, 5, 0, 0, 5, 5, 5, 5, 0, 5, 5, 5, 0, 0, 5, 0, 5], [5, 0, 0, 5, 0, 5, 5, 5, 0, 5, 5, 0, 5, 0, 5, 5, 5, 5, 5, 5], [5, 0, 5, 5, 0, 5, 5, 5, 5, 5, 0, 5, 2, 5, 2, 2, 2, 0, 0, 5], [0, 0, 5, 0, 0, 0, 0, 0, 0, 5, 5, 5, 0, 0, 5, 0, 0, 5, 0, 5], [0, 0, 5, 0, 0, 5, 0, 5, 5, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0], [5, 5, 0, 0, 5, 5, 0, 5, 0, 0, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0], [5, 5, 0, 0, 0, 5, 5, 5, 0, 5, 5, 0, 5, 5, 5, 5, 0, 0, 5, 5], [0, 0, 5, 0, 5, 5, 5, 2, 2, 5, 5, 0, 0, 5, 0, 0, 5, 5, 0, 0], [0, 5, 5, 0, 0, 5, 5, 2, 5, 0, 5, 5, 0, 0, 5, 0, 5, 5, 0, 0], [0, 0, 5, 0, 5, 0, 5, 5, 0, 5, 5, 5, 0, 0, 5, 0, 0, 0, 5, 0], [0, 0, 5, 0, 5, 5, 0, 5, 5, 5, 0, 5, 5, 5, 0, 5, 0, 0, 5, 5], [5, 5, 5, 0, 5, 0, 5, 0, 5, 5, 0, 0, 5, 5, 0, 0, 0, 0, 0, 5]], 'output': [[0, 5, 0, 5, 0, 0, 0, 5, 5, 0, 5, 5, 0, 0, 0, 5, 5, 0, 5, 5], [5, 5, 0, 5, 5, 5, 0, 5, 5, 0, 5, 0, 0, 5, 0, 0, 0, 5, 5, 0], [0, 5, 0, 5, 5, 0, 8, 5, 0, 5, 0, 0, 5, 0, 0, 5, 0, 0, 5, 5], [5, 0, 0, 5, 5, 0, 2, 5, 0, 5, 0, 5, 0, 0, 0, 5, 5, 5, 5, 5], [0, 5, 0, 5, 2, 8, 2, 2, 2, 0, 5, 5, 0, 5, 0, 5, 5, 0, 0, 0], [5, 5, 0, 0, 5, 5, 2, 5, 5, 5, 0, 5, 0, 0, 5, 5, 0, 0, 0, 0], [0, 0, 5, 5, 0, 0, 8, 5, 0, 0, 5, 5, 0, 0, 5, 0, 0, 5, 0, 5], [0, 0, 0, 5, 0, 5, 0, 5, 5, 5, 0, 5, 5, 5, 0, 0, 5, 5, 0, 5], [5, 0, 0, 0, 5, 0, 0, 5, 5, 5, 5, 0, 5, 5, 8, 0, 0, 5, 0, 5], [5, 0, 0, 5, 0, 5, 5, 5, 0, 5, 5, 0, 5, 0, 8, 5, 5, 5, 5, 5], [5, 0, 5, 5, 0, 5, 5, 5, 5, 5, 0, 5, 2, 8, 2, 2, 2, 0, 0, 5], [0, 0, 5, 0, 0, 0, 0, 0, 0, 5, 5, 5, 0, 0, 8, 0, 0, 5, 0, 5], [0, 0, 5, 0, 0, 5, 0, 5, 5, 0, 5, 5, 5, 5, 8, 5, 5, 5, 5, 0], [5, 5, 0, 0, 5, 5, 0, 8, 0, 0, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0], [5, 5, 0, 0, 0, 5, 5, 8, 0, 5, 5, 0, 5, 5, 5, 5, 0, 0, 5, 5], [0, 0, 5, 0, 5, 8, 8, 2, 2, 8, 5, 0, 0, 5, 0, 0, 5, 5, 0, 0], [0, 5, 5, 0, 0, 5, 5, 2, 5, 0, 5, 5, 0, 0, 5, 0, 5, 5, 0, 0], [0, 0, 5, 0, 5, 0, 5, 8, 0, 5, 5, 5, 0, 0, 5, 0, 0, 0, 5, 0], [0, 0, 5, 0, 5, 5, 0, 5, 5, 5, 0, 5, 5, 5, 0, 5, 0, 0, 5, 5], [5, 5, 5, 0, 5, 0, 5, 0, 5, 5, 0, 0, 5, 5, 0, 0, 0, 0, 0, 5]]}, {'input': [[0, 0, 5, 0, 5, 0, 5, 5, 5, 5, 0, 5, 5, 0, 0, 0, 5, 5, 0], [0, 0, 5, 5, 5, 0, 5, 5, 5, 5, 0, 0, 5, 5, 5, 5, 5, 0, 5], [0, 5, 5, 5, 0, 5, 0, 5, 5, 0, 0, 0, 5, 5, 5, 0, 5, 0, 0], [5, 5, 5, 5, 5, 0, 0, 0, 5, 5, 5, 5, 5, 5, 0, 0, 5, 0, 0], [5, 5, 0, 0, 0, 5, 5, 5, 0, 5, 5, 5, 5, 0, 0, 0, 5, 0, 0], [5, 0, 0, 0, 0, 0, 5, 0, 5, 0, 5, 2, 5, 0, 0, 5, 0, 5, 5], [5, 0, 5, 0, 0, 5, 5, 0, 5, 2, 2, 5, 2, 2, 5, 5, 0, 5, 0], [0, 5, 0, 5, 5, 5, 5, 5, 0, 5, 0, 5, 5, 5, 5, 0, 5, 5, 5], [5, 5, 5, 0, 5, 5, 5, 5, 0, 0, 5, 2, 5, 5, 5, 0, 0, 0, 0], [5, 2, 2, 5, 0, 0, 5, 0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0], [5, 2, 5, 5, 5, 0, 0, 5, 5, 5, 5, 0, 0, 5, 5, 5, 5, 0, 5], [0, 2, 5, 0, 5, 5, 0, 0, 5, 5, 5, 5, 5, 0, 5, 5, 5, 5, 0], [0, 5, 0, 0, 5, 5, 0, 0, 5, 5, 0, 0, 5, 0, 0, 5, 0, 0, 0], [5, 0, 0, 5, 5, 5, 5, 5, 0, 0, 5, 5, 5, 0, 5, 5, 5, 0, 5], [0, 5, 5, 5, 0, 0, 5, 0, 0, 0, 5, 0, 5, 5, 5, 5, 0, 0, 0], [5, 5, 0, 0, 5, 5, 5, 5, 0, 5, 5, 0, 5, 0, 5, 0, 0, 0, 0], [5, 0, 5, 0, 5, 0, 0, 0, 0, 0, 0, 5, 0, 0, 5, 0, 5, 0, 5], [0, 5, 5, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 5, 0, 5, 5, 5, 5]], 'output': [[0, 0, 5, 0, 5, 0, 5, 5, 5, 5, 0, 5, 5, 0, 0, 0, 5, 5, 0], [0, 0, 5, 5, 5, 0, 5, 5, 5, 5, 0, 0, 5, 5, 5, 5, 5, 0, 5], [0, 5, 5, 5, 0, 5, 0, 5, 5, 0, 0, 0, 5, 5, 5, 0, 5, 0, 0], [5, 5, 5, 5, 5, 0, 0, 0, 5, 5, 5, 5, 5, 5, 0, 0, 5, 0, 0], [5, 5, 0, 0, 0, 5, 5, 5, 0, 5, 5, 8, 5, 0, 0, 0, 5, 0, 0], [5, 0, 0, 0, 0, 0, 5, 0, 5, 0, 5, 2, 5, 0, 0, 5, 0, 5, 5], [5, 0, 5, 0, 0, 5, 5, 0, 5, 2, 2, 8, 2, 2, 5, 5, 0, 5, 0], [0, 8, 0, 5, 5, 5, 5, 5, 0, 5, 0, 8, 5, 5, 5, 0, 5, 5, 5], [5, 8, 5, 0, 5, 5, 5, 5, 0, 0, 5, 2, 5, 5, 5, 0, 0, 0, 0], [8, 2, 2, 8, 0, 0, 5, 0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0], [5, 2, 5, 5, 5, 0, 0, 5, 5, 5, 5, 0, 0, 5, 5, 5, 5, 0, 5], [0, 2, 5, 0, 5, 5, 0, 0, 5, 5, 5, 5, 5, 0, 5, 5, 5, 5, 0], [0, 5, 0, 0, 5, 5, 0, 0, 5, 5, 0, 0, 5, 0, 0, 5, 0, 0, 0], [5, 0, 0, 5, 5, 5, 5, 5, 0, 0, 5, 5, 5, 0, 5, 5, 5, 0, 5], [0, 5, 5, 5, 0, 0, 5, 0, 0, 0, 5, 0, 5, 5, 5, 5, 0, 0, 0], [5, 5, 0, 0, 5, 5, 5, 5, 0, 5, 5, 0, 5, 0, 5, 0, 0, 0, 0], [5, 0, 5, 0, 5, 0, 0, 0, 0, 0, 0, 5, 0, 0, 5, 0, 5, 0, 5], [0, 5, 5, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 5, 0, 5, 5, 5, 5]]}, {'input': [[0, 5, 0, 0, 0, 0, 5, 0, 0, 0, 0, 5], [5, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 5], [5, 0, 5, 0, 0, 5, 5, 0, 2, 0, 5, 0], [5, 5, 0, 0, 5, 0, 5, 0, 2, 5, 0, 5], [5, 0, 0, 5, 5, 5, 2, 5, 2, 2, 2, 0], [5, 5, 5, 0, 5, 5, 0, 5, 2, 0, 0, 5], [5, 5, 5, 0, 5, 0, 0, 5, 5, 0, 0, 0], [5, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0], [0, 5, 5, 0, 5, 0, 0, 0, 0, 5, 0, 0], [5, 0, 0, 0, 5, 5, 5, 5, 5, 0, 0, 0], [5, 0, 0, 0, 0, 5, 0, 0, 5, 5, 5, 5]], 'output': [[0, 5, 0, 0, 0, 0, 5, 0, 0, 0, 0, 5], [5, 0, 5, 0, 0, 0, 0, 0, 5, 0, 0, 5], [5, 0, 5, 0, 0, 5, 5, 0, 2, 0, 5, 0], [5, 5, 0, 0, 5, 0, 5, 0, 2, 5, 0, 5], [5, 0, 0, 5, 5, 5, 2, 8, 2, 2, 2, 0], [5, 5, 5, 0, 5, 5, 0, 5, 2, 0, 0, 5], [5, 5, 5, 0, 5, 0, 0, 5, 8, 0, 0, 0], [5, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0], [0, 5, 5, 0, 5, 0, 0, 0, 0, 5, 0, 0], [5, 0, 0, 0, 5, 5, 5, 5, 5, 0, 0, 0], [5, 0, 0, 0, 0, 5, 0, 0, 5, 5, 5, 5]]}], 'test_inputs': [[[0, 5, 0, 5, 0, 0, 5, 5, 0, 5, 0, 0, 0, 5, 0, 5, 0, 0, 0, 5, 5, 0], [0, 5, 0, 5, 5, 0, 0, 0, 5, 5, 0, 0, 5, 5, 0, 0, 0, 0, 0, 5, 5, 5], [0, 0, 0, 0, 5, 5, 5, 0, 0, 0, 0, 5, 5, 0, 0, 5, 5, 0, 0, 5, 5, 5], [0, 0, 5, 5, 0, 5, 5, 5, 0, 5, 0, 5, 0, 5, 0, 5, 5, 0, 5, 5, 5, 0], [0, 5, 0, 5, 2, 2, 5, 2, 2, 5, 0, 0, 5, 0, 5, 5, 5, 0, 0, 5, 5, 0], [0, 0, 0, 5, 0, 5, 2, 5, 5, 5, 0, 5, 0, 0, 0, 0, 5, 5, 5, 5, 0, 0], [5, 5, 0, 0, 5, 5, 2, 0, 5, 5, 0, 0, 0, 5, 0, 0, 0, 5, 5, 5, 5, 5], [5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 0, 5, 0, 0, 5, 0, 5, 0], [5, 5, 5, 5, 5, 0, 0, 5, 5, 0, 5, 2, 5, 2, 5, 5, 0, 0, 5, 5, 5, 0], [0, 0, 0, 5, 5, 5, 0, 0, 5, 0, 0, 0, 5, 5, 0, 5, 5, 5, 0, 0, 0, 0], [0, 0, 0, 5, 5, 5, 0, 5, 0, 5, 0, 5, 5, 2, 5, 0, 5, 0, 0, 5, 5, 0], [0, 5, 5, 5, 0, 0, 0, 5, 5, 5, 5, 0, 0, 5, 0, 5, 5, 0, 0, 0, 5, 5], [5, 5, 0, 0, 5, 5, 5, 0, 0, 5, 5, 0, 5, 0, 5, 5, 0, 0, 5, 5, 0, 5], [0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 5, 5, 5, 5, 5, 0, 0, 5, 0, 5], [5, 5, 0, 5, 5, 2, 2, 2, 5, 5, 5, 0, 5, 5, 5, 0, 5, 0, 0, 5, 5, 0], [5, 0, 0, 0, 5, 2, 5, 0, 5, 0, 5, 0, 5, 5, 5, 5, 0, 0, 0, 0, 5, 5], [5, 5, 5, 0, 0, 2, 0, 5, 5, 0, 0, 2, 2, 2, 2, 2, 5, 0, 5, 0, 5, 5], [5, 0, 5, 0, 0, 5, 0, 5, 0, 0, 0, 0, 0, 5, 0, 5, 5, 5, 0, 5, 5, 0], [5, 5, 5, 5, 5, 0, 5, 0, 5, 5, 5, 5, 0, 5, 0, 0, 5, 5, 0, 5, 0, 5]]], 'test_outputs': [[[0, 5, 0, 5, 0, 0, 5, 5, 0, 5, 0, 0, 0, 5, 0, 5, 0, 0, 0, 5, 5, 0], [0, 5, 0, 5, 5, 0, 0, 0, 5, 5, 0, 0, 5, 5, 0, 0, 0, 0, 0, 5, 5, 5], [0, 0, 0, 0, 5, 5, 8, 0, 0, 0, 0, 5, 5, 0, 0, 5, 5, 0, 0, 5, 5, 5], [0, 0, 5, 5, 0, 5, 8, 5, 0, 5, 0, 5, 0, 5, 0, 5, 5, 0, 5, 5, 5, 0], [0, 5, 0, 5, 2, 2, 8, 2, 2, 5, 0, 0, 5, 0, 5, 5, 5, 0, 0, 5, 5, 0], [0, 0, 0, 5, 0, 5, 2, 5, 5, 5, 0, 5, 0, 0, 0, 0, 5, 5, 5, 5, 0, 0], [5, 5, 0, 0, 5, 5, 2, 0, 5, 5, 0, 0, 0, 8, 0, 0, 0, 5, 5, 5, 5, 5], [5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 8, 0, 5, 0, 0, 5, 0, 5, 0], [5, 5, 5, 5, 5, 0, 0, 5, 5, 0, 5, 2, 8, 2, 8, 8, 0, 0, 5, 5, 5, 0], [0, 0, 0, 5, 5, 5, 0, 0, 5, 0, 0, 0, 5, 8, 0, 5, 5, 5, 0, 0, 0, 0], [0, 0, 0, 5, 5, 5, 0, 5, 0, 5, 0, 5, 5, 2, 5, 0, 5, 0, 0, 5, 5, 0], [0, 5, 5, 5, 0, 0, 0, 5, 5, 5, 5, 0, 0, 5, 0, 5, 5, 0, 0, 0, 5, 5], [5, 5, 0, 0, 5, 8, 5, 0, 0, 5, 5, 0, 5, 0, 5, 5, 0, 0, 5, 5, 0, 5], [0, 0, 5, 5, 5, 8, 5, 5, 5, 5, 0, 0, 5, 5, 5, 5, 5, 0, 0, 5, 0, 5], [5, 5, 0, 8, 8, 2, 2, 2, 5, 5, 5, 0, 5, 8, 5, 0, 5, 0, 0, 5, 5, 0], [5, 0, 0, 0, 5, 2, 5, 0, 5, 0, 5, 0, 5, 8, 5, 5, 0, 0, 0, 0, 5, 5], [5, 5, 5, 0, 0, 2, 0, 5, 5, 0, 0, 2, 2, 2, 2, 2, 5, 0, 5, 0, 5, 5], [5, 0, 5, 0, 0, 5, 0, 5, 0, 0, 0, 0, 0, 8, 0, 5, 5, 5, 0, 5, 5, 0], [5, 5, 5, 5, 5, 0, 5, 0, 5, 5, 5, 5, 0, 8, 0, 0, 5, 5, 0, 5, 0, 5]]]}) (input_keys={'test_inputs', 'training_examples'}): 'dict' object has no attribute 'input'
Traceback (most recent call last):
File "<string>", line 84, in _test_code
AttributeError: 'dict' object has no attribute 'input'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 109, in forward
File "<string>", line 90, in _test_code
AttributeError: 'dict' object has no attribute 'input'
2025/08/29 14:48:01 INFO dspy.evaluate.evaluate: Average Metric: 0.0 / 3 (0.0%)
GEPA Optimization: 64%|██████████████████████████████████████████████████████████████████████ | 2570/4000 [17:14:05<24:41:49, 62.17s/rollouts]Iteration 109: New subsample score is not better, skipping
Iteration 110: Selected program 8 score: 0.62
Average Metric: 0.00 / 3 (0.0%): : 4it [07:50, 117.70s/it] 2025/08/29 14:55:52 INFO dspy.evaluate.evaluate: Average Metric: 0.0 / 3 (0.0%)
Iteration 110: Proposed new text for program: import dspy
from typing import List
import pydantic
import copy
# Define type aliases and pydantic models for clarity and structure.
MATRIX = List[List[int]]
class TrainingExample(pydantic.BaseModel):
input: MATRIX
output: MATRIX
# --- Step 1: A Signature to deduce the transformation rule in natural language ---
# This signature is effective and remains unchanged.
class HypothesizeRule(dspy.Signature):
"""
Analyzes pairs of input/output matrices from the Abstraction and Reasoning Corpus (ARC)
and deduces the underlying transformation rule. Describe the rule in clear, step-by-step
natural language.
**Successful Strategies to Consider:**
- **Grid Properties:** Analyze changes in dimensions, colors, and object counts.
- **Object Transformations:** Identify objects/shapes and describe how they are moved, rotated, scaled, colored, or combined.
- **Pattern Recognition:** Look for patterns like symmetry, repetition, or subgrid extraction. For example, is the output a small subgrid from the input?
- **Conditional Logic:** The rule might depend on specific conditions, like the color of a neighboring cell or the number of objects present.
"""
training_examples: List[TrainingExample] = dspy.InputField(desc="A list of input/output pairs demonstrating the transformation rule.")
hypothesis: str = dspy.OutputField(desc="A clear, step-by-step natural language description of the transformation rule.")
# --- Step 2: Signatures for Code Generation and Correction ---
class ImplementRuleInPython(dspy.Signature):
"""
You are an expert programmer. Your task is to write a single, self-contained Python function based on a provided hypothesis about a matrix transformation rule. Use the training examples for context.
**Function Requirements:**
- The function must be named `transform_matrix`.
- It must accept one argument: `matrix`, which is a list of lists of integers.
- It must return a list of lists of integers (the transformed grid).
- The function should not use any external libraries except for `copy`.
- Your output must be ONLY the Python code for the function. Do not include explanations or markdown formatting.
**Pitfalls to Avoid:**
- **Hardcoding:** Do not hardcode values from the examples. The function must be general.
- **Index Errors:** Be extremely careful with grid boundaries and coordinates.
- **Incorrect Logic:** Ensure your code perfectly matches the logic described in the hypothesis.
- **Edge Cases:** Consider edge cases like empty grids or grids that don't contain the described patterns. Your function should gracefully handle these, typically by returning the original matrix unmodified.
"""
hypothesis: str = dspy.InputField(desc="The natural language description of the rule to implement.")
training_examples: List[TrainingExample] = dspy.InputField(desc="The original examples, for context and verification.")
python_function: str = dspy.OutputField(desc="A string containing a single Python function `transform_matrix` that implements the rule.")
class CorrectPythonCode(dspy.Signature):
"""
You are an expert programmer debugging a Python function. You will be given the original hypothesis, the buggy code, and the feedback from testing. Your task is to analyze the error and provide a corrected version of the function.
**Function Requirements:**
- The function must be named `transform_matrix`.
- It must accept one argument: `matrix`, which is a list of lists of integers.
- It must return a list of lists of integers.
- Do not use external libraries except for `copy`.
- Your output must be ONLY the Python code for the function. Do not include explanations or markdown formatting.
**Debugging Strategies:**
- **Compare Logic to Hypothesis:** Does the code's logic perfectly match the described steps in the hypothesis?
- **Analyze the Feedback:** The feedback will tell you if the code failed a test case or had a runtime error. Use this to pinpoint the logical flaw or bug.
- **Edge Cases:** The error might be due to an edge case the original code missed (e.g., empty grids, unexpected patterns).
"""
hypothesis: str = dspy.InputField(desc="The natural language description of the rule the function should implement.")
training_examples: List[TrainingExample] = dspy.InputField(desc="The original examples, for context and verification.")
previous_code: str = dspy.InputField(desc="The previous version of the Python code that failed.")
feedback: str = dspy.InputField(desc="The error message or description of why the previous code was incorrect.")
corrected_python_function: str = dspy.OutputField(desc="A string containing the corrected, single Python function `transform_matrix`.")
# --- The Improved Custom Module with a Self-Correction Loop ---
class ARCSolver(dspy.Module):
"""A module that solves ARC tasks by hypothesizing, generating, testing, and correcting code."""
def __init__(self, max_attempts=3):
super().__init__()
self.max_attempts = max_attempts
self.rule_hypothesizer = dspy.ChainOfThought(HypothesizeRule)
self.code_generator = dspy.Predict(ImplementRuleInPython)
self.code_corrector = dspy.ChainOfThought(CorrectPythonCode)
def forward(self, training_examples: List[TrainingExample], test_inputs: List[MATRIX]):
fallback_outputs = [copy.deepcopy(matrix) for matrix in test_inputs]
# Step 1: Generate a natural language hypothesis.
try:
prediction = self.rule_hypothesizer(training_examples=training_examples)
hypothesis = prediction.hypothesis
except Exception:
return dspy.Prediction(test_outputs=fallback_outputs)
# Step 2: Iteratively generate and test code.
python_code = ""
feedback = "No feedback yet. This is the first attempt."
for attempt in range(self.max_attempts):
try:
# Generate or correct code
if attempt == 0:
code_gen_pred = self.code_generator(hypothesis=hypothesis, training_examples=training_examples)
current_code = code_gen_pred.python_function
else:
code_corr_pred = self.code_corrector(
hypothesis=hypothesis,
training_examples=training_examples,
previous_code=python_code,
feedback=feedback
)
current_code = code_corr_pred.corrected_python_function
python_code = current_code
if not python_code or not isinstance(python_code, str):
feedback = "Code generation failed: output was not a valid string."
continue
# Test the generated code against training examples
local_scope = {}
exec(python_code, globals(), local_scope)
transform_func = local_scope.get('transform_matrix')
if not callable(transform_func):
feedback = "Code execution failed: 'transform_matrix' function not defined."
continue
all_passed = True
for i, example in enumerate(training_examples):
input_copy = copy.deepcopy(example.input)
try:
result = transform_func(input_copy)
if result != example.output:
all_passed = False
feedback = f"Validation failed on training example {i}. The output did not match the expected output."
break
except Exception as e:
all_passed = False
feedback = f"Validation failed on training example {i} with a runtime error: {e}"
break
if all_passed:
# Code is correct, proceed to solve test inputs
solved_outputs = []
for test_matrix in test_inputs:
try:
result = transform_func(copy.deepcopy(test_matrix))
solved_outputs.append(result)
except Exception:
solved_outputs.append(copy.deepcopy(test_matrix))
return dspy.Prediction(test_outputs=solved_outputs)
except Exception as e:
feedback = f"An unexpected error occurred during attempt {attempt + 1}: {e}"
# If loop finishes without success, return fallback
return dspy.Prediction(test_outputs=fallback_outputs)
# The overall task signature, defining the final inputs and outputs of the program.
class SolveTaskSignature(dspy.Signature):
"""
Given a set of training examples demonstrating a matrix transformation,
apply the same transformation to a new set of test matrices.
"""
training_examples: List[TrainingExample] = dspy.InputField(description="Input and output examples demonstrating the task to be performed.")
test_inputs: List[MATRIX] = dspy.InputField(description="Input matrices to be solved following the task described in the training examples.")
test_outputs: List[MATRIX] = dspy.OutputField(description="Output matrices corresponding to the test inputs.")
# The final program is an instance of our new, more robust module.
program = ARCSolver()
2025/08/29 15:04:24 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 15:04:58 INFO dspy.evaluate.evaluate: Average Metric: 0.0 / 3 (0.0%)
GEPA Optimization: 64%|██████████████████████████████████████████████████████████████████████▏ | 2576/4000 [17:31:02<30:34:39, 77.30s/rollouts]Iteration 110: New subsample score is not better, skipping
Iteration 111: Selected program 7 score: 0.675
Average Metric: 2.00 / 3 (66.7%): : 4it [03:10, 47.67s/it] 2025/08/29 15:08:09 INFO dspy.evaluate.evaluate: Average Metric: 2.0 / 3 (66.7%)
Iteration 111: Proposed new text for program: import dspy
from typing import List, Any, Optional
import pydantic
import traceback
# Define the type for a matrix for clarity
MATRIX = List[List[int]]
class TrainingExample(pydantic.BaseModel):
"""A Pydantic model for a single training example, containing an input and output matrix."""
input: MATRIX
output: MATRIX
class InferRuleSignature(dspy.Signature):
"""
Analyzes pairs of input/output matrices to deduce the transformation rule.
You are an expert analyst of abstract visual reasoning puzzles. Your task is to carefully examine the provided training examples. Each example consists of an 'input' matrix and a corresponding 'output' matrix.
Your goal is to deduce the single, consistent transformation rule that converts each input to its output. Describe this rule in clear, unambiguous, step-by-step natural language. The description must be precise enough for another AI or a programmer to follow it to solve a new, unseen test input.
Consider various possible transformation patterns, including but not limited to:
- Geometric operations: rotation, reflection, scaling, repetition, shifting.
- Color/value transformations: changing specific numbers to others, conditional changes.
- Object-based logic: identifying shapes or objects and applying rules based on their properties (size, color, position).
- Cropping or subgrid extraction: such as finding a bounding box or selecting a specific region.
- Fill/completion patterns: such as flood fills or completing a grid based on object boundaries.
- Bounding box or frame logic: creating structures based on the extents of objects.
- Sorting, ordering, or re-arranging objects/lines based on properties like size or color.
"""
training_examples: List[TrainingExample] = dspy.InputField(description="A list of input and output examples demonstrating the task.")
transformation_rule: str = dspy.OutputField(description="A step-by-step natural language description of the transformation rule.")
class GeneratePythonCodeSignature(dspy.Signature):
"""
Converts a natural language transformation rule into a Python function.
You are an expert Python programmer. Your task is to write a single Python function named `transform_matrix` that implements the given natural language `transformation_rule`.
**Function Requirements:**
- The function must be named exactly `transform_matrix`.
- It must accept one argument: `matrix: List[List[int]]`.
- It must return the transformed matrix as a `List[List[int]]`.
- You can use standard Python libraries. Do not use external libraries like numpy or pandas.
- The `training_examples` are provided for context. Mentally dry-run your code against them to ensure your logic is correct and handles dimensions and edge cases properly.
**Output Format:**
- Your output must be ONLY the Python code for the function.
- Do NOT include any explanations, comments outside the function, or markdown formatting like ```python ... ```.
"""
transformation_rule: str = dspy.InputField(description="The natural language rule to implement.")
training_examples: List[TrainingExample] = dspy.InputField(description="Example input/output pairs to validate the logic.")
python_code: str = dspy.OutputField(description="A string containing only the Python code for the `transform_matrix` function.")
class DebugPythonCodeSignature(dspy.Signature):
"""
Corrects a buggy Python function based on execution feedback.
You are an expert Python debugger. You will be given a `transformation_rule`, some `training_examples`, a `buggy_code` snippet, and `feedback` detailing the error. Your task is to fix the bug in the code.
**Analysis Steps:**
1. Read the `transformation_rule` to understand the intended logic.
2. Examine the `buggy_code` to see the current implementation.
3. Analyze the `feedback`, which could be a Python traceback or a description of an incorrect output.
4. Rewrite the function to fix the bug while preserving the correct parts of the logic. Pay close attention to issues like off-by-one errors, incorrect indexing, or dimension mismatches.
**Output Format:**
- Your output must be ONLY the corrected Python code for the `transform_matrix` function.
- Do NOT include any explanations or markdown formatting.
"""
transformation_rule: str = dspy.InputField(description="The intended natural language rule.")
training_examples: List[TrainingExample] = dspy.InputField(description="Example pairs to validate the logic.")
buggy_code: str = dspy.InputField(description="The Python code that failed.")
feedback: str = dspy.InputField(description="The error message or description of the incorrect output.")
corrected_code: str = dspy.OutputField(description="The corrected, complete Python code for the `transform_matrix` function.")
class ApplyRuleSignature(dspy.Signature):
"""
Applies a given transformation rule to a single test input matrix.
You are an expert in meticulously following instructions to transform matrices. You will be given a specific, detailed transformation rule and a single test input matrix.
Your task is to:
1. Carefully reason step-by-step, applying the rule to the test input matrix.
2. Produce the final output matrix.
**Crucially, your final output must be ONLY the resulting matrix, formatted as a valid JSON list of lists of integers. Do not include any extra text, explanations, or markdown formatting in the final answer field.**
"""
transformation_rule: str = dspy.InputField(description="The natural language rule to apply.")
test_input: MATRIX = dspy.InputField(description="The input matrix to be transformed.")
reasoning: str = dspy.OutputField(description="Think step-by-step about how to apply the rule to the input matrix.")
test_output: MATRIX = dspy.OutputField(description="The resulting matrix after applying the rule, as a list of lists of integers.")
class ARCProgram(dspy.Module):
"""A program that infers a rule, generates and debugs code, and falls back to direct application."""
def __init__(self, max_attempts=3):
super().__init__()
self.max_attempts = max_attempts
self.rule_inferrer = dspy.ChainOfThought(InferRuleSignature)
self.code_generator = dspy.Predict(GeneratePythonCodeSignature)
self.code_debugger = dspy.ChainOfThought(DebugPythonCodeSignature)
self.rule_applier_fallback = dspy.ChainOfThought(ApplyRuleSignature)
def _execute_code(self, code: str) -> callable:
"""Safely execute generated code and return the transform function."""
local_namespace = {}
exec(code, globals(), local_namespace)
return local_namespace['transform_matrix']
def forward(self, training_examples: List[TrainingExample], test_inputs: List[MATRIX]) -> dspy.Prediction:
# 1. Infer the transformation rule once.
inferred = self.rule_inferrer(training_examples=training_examples)
rule = inferred.transformation_rule
python_code = None
feedback = "No feedback yet. This is the first attempt."
# 2. Iterative Code Generation and Debugging Loop
for attempt in range(self.max_attempts):
try:
if attempt == 0:
generated = self.code_generator(transformation_rule=rule, training_examples=training_examples)
python_code = generated.python_code
else:
corrected = self.code_debugger(
transformation_rule=rule,
training_examples=training_examples,
buggy_code=python_code,
feedback=feedback
)
python_code = corrected.corrected_code
transform_func = self._execute_code(python_code)
# 2a. Verify code against all training examples.
all_examples_passed = True
for example in training_examples:
predicted_output = transform_func(example.input)
if predicted_output != example.output:
feedback = f"Verification failed. For input {example.input}, the code produced {predicted_output}, but the expected output was {example.output}."
all_examples_passed = False
break
if all_examples_passed:
# 2b. If verification passes, apply to test inputs.
all_test_outputs = [transform_func(matrix) for matrix in test_inputs]
return dspy.Prediction(test_outputs=all_test_outputs)
except Exception:
feedback = f"Execution failed with an exception: {traceback.format_exc()}"
# Continue to the next attempt in the loop.
# 3. Fallback Strategy: If all code generation/debugging attempts fail.
all_test_outputs = []
for test_matrix in test_inputs:
try:
result = self.rule_applier_fallback(transformation_rule=rule, test_input=test_matrix)
all_test_outputs.append(result.test_output)
except Exception:
# If the fallback also fails, append a default empty/zero matrix.
if test_matrix and isinstance(test_matrix[0], list):
all_test_outputs.append([[0] * len(test_matrix[0]) for _ in range(len(test_matrix))])
else:
all_test_outputs.append([])
return dspy.Prediction(test_outputs=all_test_outputs)
program = ARCProgram()
2025/08/29 15:15:42 INFO dspy.evaluate.evaluate: Average Metric: 3.0 / 3 (100.0%)
2025/08/29 15:18:51 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 15:19:23 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 15:19:27 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 15:19:35 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 15:22:03 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 15:25:08 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 15:26:03 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 15:26:34 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 15:27:29 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 15:28:23 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 15:28:42 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 15:30:39 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 15:32:17 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 15:34:23 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 15:34:56 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 15:39:45 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 15:41:21 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 15:41:21 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 15:41:21 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 15:43:21 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 15:43:21 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 15:45:22 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 15:45:22 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 15:46:20 INFO dspy.evaluate.evaluate: Average Metric: 130.0 / 200 (65.0%)
GEPA Optimization: 70%|████████████████████████████████████████████████████████████████████████████▌ | 2782/4000 [18:12:24<6:52:02, 20.30s/rollouts]Iteration 111: Full valset score for new program: 0.65
Iteration 111: Full train_val score for new program: 0.65
Iteration 111: Individual valset scores for new program: [True, True, False, True, True, False, True, True, True, True, True, True, True, False, True, True, False, True, True, True, False, True, True, False, True, True, False, False, True, True, False, False, False, True, True, True, False, True, False, True, False, True, True, False, False, True, False, False, True, True, False, True, True, True, True, False, True, True, False, True, False, False, False, True, False, False, True, True, False, False, True, True, True, False, True, True, True, False, True, True, False, True, True, True, True, True, True, False, False, False, True, True, True, True, True, True, True, False, True, True, True, True, False, False, True, False, True, True, True, True, False, False, True, True, True, False, True, True, True, True, False, True, False, False, False, True, False, True, True, True, False, False, True, True, True, False, False, False, True, True, True, True, True, False, True, False, True, False, True, True, False, False, True, False, True, True, True, True, True, False, False, True, True, True, False, True, True, True, True, False, False, True, False, True, False, True, False, True, False, True, True, True, True, False, False, True, True, True, True, True, True, True, True, True, True, False, True, True, False, True]
Iteration 111: New valset pareto front scores: [True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, False, True, True, True, True, True, True, True, True, True, False, True, True, True, True, False, True, True, True, True, False, True, True, True, False, True, True, True, False, True, False, True, True, True, True, True, True, True, True, 0, True, True, False, True, True, True, False, True, True, False, True, True, 0, False, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, False, True, True, True, True, True, True, True, True, True, True, True, True, True, False, True, True, True, True, True, True, False, True, True, True, True, True, True, True, True, True, True, True, True, False, False, True, False, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, False, True, True, True, False, True, True, True, True, True, True, True, True, True, True, True, False, True, True, True, True, False, True, True, True, True, False, True, True, True, True, True, True, False, True, False, True, True, True, True, False, True, True, True, True, True, True, True, True, True, True, True, True, True, True, False, True]
Iteration 111: Full valset pareto front score: 0.86
Iteration 111: Updated valset pareto front programs: [{1, 3, 5, 8, 9, 10}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10}, {0, 9}, {0, 2, 3, 4, 5, 6, 7, 8, 9, 10}, {0, 1, 2, 3, 4, 5, 6, 7, 9, 10}, {0, 4}, {0, 1, 2, 3, 4, 5, 6, 7, 9, 10}, {0, 1, 3, 4, 5, 6, 7, 8, 9, 10}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10}, {0, 2, 3, 4, 5, 6, 7, 8, 9, 10}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10}, {8}, {0, 1, 2, 3, 4, 6, 9, 10}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10}, {1, 4, 5, 7, 9, 10}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10}, {0, 2, 4, 6, 7, 10}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, {0, 2, 3, 4, 5, 6, 7, 8, 9, 10}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10}, {1, 5, 9, 7}, {0, 3, 4, 6, 8, 9, 10}, {0, 1, 3, 6, 7, 8, 9, 10}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10}, {0, 4, 7}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10}, {1}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10}, {2}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10}, {0, 1, 2, 3, 5, 6, 7, 8, 9, 10}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10}, {2, 5}, {0, 2, 3, 4, 5, 6, 7, 8, 9, 10}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10}, {1}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10}, {0, 1, 2, 3, 4, 6, 8, 9, 10}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10}, {8}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10}, {0, 1, 2, 3, 4, 6, 7, 8, 9, 10}, {0, 1, 2, 3, 4, 6, 7}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10}, {0, 1, 3, 4, 5, 6, 7, 8, 9, 10}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10}, {0, 1, 3, 4, 5, 6, 7, 8, 9, 10}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10}, {0, 1, 2, 3, 8}, {0, 1, 5}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10}, {8, 4, 6, 7}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10}, {1, 2, 3, 5, 6, 7, 8, 9, 10}, {0, 1, 3, 4, 5, 6, 7, 8, 9, 10}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10}, {2, 3, 4, 5, 6, 7, 8, 9}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10}, {1, 2, 3, 5, 6, 7, 8, 9, 10}, {0, 3, 4, 5, 6, 7, 9, 10}, {4}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10}, {0, 2, 3, 4, 5, 7, 8, 9, 10}, {0, 8, 2, 6}, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10}, {0, 1, 2, 4, 5, 6, 7, 8, 10}, {0, 1, 2, 3, 4, 5, 6, 7}, {0, 3, 4, 6, 7}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10}, {0, 1, 2, 4, 5, 6, 7, 8, 9, 10}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10}, {8, 1, 10, 3}, {7}, {0, 3, 4, 6, 7, 9, 10}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10}, {0, 1, 2, 3, 4, 5, 6, 8, 9, 10}, {0, 4, 6, 7, 9}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10}, {0, 1, 2, 3, 5, 7, 8}, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10}, {0, 1, 2, 3, 5, 6, 7, 8, 9, 10}, {1, 2, 10}, {0, 1, 4, 5, 6, 8, 10}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10}, {0, 2, 5}, {1, 10, 3}, {9, 2, 10, 4}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10}, {3, 4, 5, 7, 8}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10}, {2, 3, 4, 6, 7, 8, 9, 10}, {0, 1, 2, 3, 4, 5, 6, 8, 10}, {0, 1, 2, 3, 5, 6, 7, 9, 10}, {2, 3, 4, 5, 6, 7, 9}, {0, 1, 7, 9, 10}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10}, {0, 1, 2, 3, 4, 5, 6, 7, 9, 10}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10}, {0, 1, 2, 3, 5, 6, 7, 8, 9, 10}, {0, 1, 2, 3, 5, 6, 7, 8, 9, 10}, {2, 7}, {0, 1, 3, 9}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10}, {0, 1, 3, 5, 6, 7, 8, 9, 10}, {0, 1, 2, 3, 4, 6, 7, 8, 9, 10}, {8, 9, 7}, {0, 1, 3, 6, 7, 8, 9}, {3, 5, 6, 7}, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10}, {1, 2, 3, 4, 5, 6, 7, 9, 10}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10}, {0, 1, 2, 3, 4, 6, 7, 8, 9, 10}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10}, {9}, {0, 2, 3, 5, 6, 7, 9, 10}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10}, {4}, {8, 9, 7}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10}, {0, 1, 3, 4, 5, 6, 7, 8, 9}, {0, 1, 3, 4, 5, 6, 7, 8, 10}, {0, 1, 2, 3, 5, 6, 7, 8, 9, 10}, {0, 3, 5, 6, 8, 9, 10}, {0, 1, 2, 3, 4, 5, 6, 7, 9, 10}, {0, 1, 2, 3, 6, 7, 9, 10}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10}, {1}, {0, 1, 2, 3, 4, 5, 6, 8, 9, 10}, {0, 4, 5, 6, 7, 8, 9, 10}, {0, 10, 3, 5}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10}, {0, 1, 2, 4, 5, 6, 7, 9, 10}, {0, 1, 2, 4, 5, 6, 7, 9, 10}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10}, {0, 8, 9, 7}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10}, {9, 6}, {0, 1, 3, 5, 6, 7, 8, 9, 10}, {0, 1, 2, 3, 4, 5, 6, 8, 9}, {1, 2, 3, 5, 6, 8, 9, 10}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10}, {0, 1, 3, 4, 5, 6, 7, 8, 10}, {0, 2, 7, 8, 9, 10}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10}, {9}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10}, {0, 1, 2, 4, 5, 6, 7, 8, 9, 10}, {0, 5, 6, 7, 8, 9, 10}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10}, {0, 1, 2, 3, 4, 5, 6, 7, 10}, {1, 2, 4, 5, 6, 7, 8, 10}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10}, {6}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10}, {0, 10}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10}, {0, 2, 3, 4, 5, 6, 7, 8, 9, 10}]
Iteration 111: Best valset aggregate score so far: 0.675
Iteration 111: Best program as per aggregate score on train_val: 6
Iteration 111: Best program as per aggregate score on valset: 6
Iteration 111: Best score on valset: 0.675
Iteration 111: Best score on train_val: 0.675
Iteration 111: Linear pareto front program index: 6
Iteration 111: New program candidate index: 10
Iteration 112: Selected program 1 score: 0.63
Average Metric: 3.00 / 3 (100.0%): 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [01:01<00:00, 20.43s/it]2025/08/29 15:47:21 INFO dspy.evaluate.evaluate: Average Metric: 3.0 / 3 (100.0%)
GEPA Optimization: 70%|████████████████████████████████████████████████████████████████████████████▌ | 2785/4000 [18:13:25<6:51:04, 20.30s/rollouts]
Iteration 112: All subsample scores perfect. Skipping.
Iteration 112: Reflective mutation did not propose a new candidate
Iteration 113: Selected program 6 score: 0.675
Average Metric: 2.00 / 3 (66.7%): 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [03:00<00:00, 60.02s/it]2025/08/29 15:50:21 INFO dspy.evaluate.evaluate: Average Metric: 2.0 / 3 (66.7%)
Iteration 113: Proposed new text for program: import dspy
from typing import List, Tuple, Optional
import pydantic
import copy
import traceback
# Define type aliases and pydantic models for clarity and structure.
MATRIX = List[List[int]]
class TrainingExample(pydantic.BaseModel):
input: MATRIX
output: MATRIX
# --- Signatures ---
class HypothesizeRule(dspy.Signature):
"""
Analyze the provided input/output matrix pairs from the Abstraction and Reasoning Corpus (ARC).
Deduce the underlying transformation rule that converts each input matrix to its corresponding output matrix.
Describe this rule in clear, step-by-step, unambiguous English.
Focus on the logic and the sequence of operations, not on Python code implementation.
"""
training_examples: List[TrainingExample] = dspy.InputField(desc="A list of input/output pairs demonstrating the transformation rule.")
rule_description: str = dspy.OutputField(desc="A step-by-step English description of the transformation rule.")
class ImplementRule(dspy.Signature):
"""
You are an expert programmer. Your task is to write a single, self-contained Python function based on a provided rule description and example pairs.
**Function Requirements:**
- The function must be named `transform_matrix`.
- It must accept one argument: `matrix`, which is a list of lists of integers (representing the input grid).
- It must return a list of lists of integers (representing the transformed output grid).
- The function should not use any external libraries except for `copy` if needed (e.g., `import copy; new_matrix = copy.deepcopy(matrix)`).
- Your output must be ONLY the Python code for the function. Do not include any explanations, comments outside the function, or markdown formatting like ```python.
**Successful Strategies to Consider:**
- **Robust Parsing:** When rules involve sub-regions (e.g., quadrants, objects), write code that can robustly find their boundaries. Don't assume boundaries are perfectly aligned or can be found by checking just the first row/column. A good strategy is to iterate through the grid to find the first change in a background color to identify a split point.
- **Iterative Processes:** Some rules are applied repeatedly until the grid no longer changes. Consider using a `while` loop that continues as long as modifications are being made in a pass.
- **Neighborhoods:** Often, a cell's new value depends on its immediate neighbors (e.g., von Neumann or Moore neighborhoods).
- **Handling Edge Cases:** Ensure your code handles empty matrices or matrices with unexpected dimensions gracefully.
**Example of a Correctly Formatted Output:**
def transform_matrix(matrix: list[list[int]]) -> list[list[int]]:
# Your implementation here
# For example, find the most frequent color and fill the grid
from collections import Counter
import itertools
if not matrix or not matrix[0]:
return []
counts = Counter(itertools.chain.from_iterable(matrix))
if counts:
# Handle ties by picking the smaller number value
most_common_color = sorted(counts.items(), key=lambda item: (-item[1], item[0]))[0][0]
else:
return []
height = len(matrix)
width = len(matrix[0])
return [[most_common_color for _ in range(width)] for _ in range(height)]
"""
rule_description: str = dspy.InputField(desc="The English description of the rule to implement.")
training_examples: List[TrainingExample] = dspy.InputField(desc="A list of input/output pairs to use as a reference for implementation.")
python_function: str = dspy.OutputField(desc="A string containing a single Python function `transform_matrix` that implements the rule.")
class RefineCode(dspy.Signature):
"""
You are a senior programmer debugging a Python function. You will be given a rule description, training examples, the buggy Python code, and feedback on why it failed.
Your task is to fix the code so that it correctly implements the rule and passes the training examples.
The refined function must adhere to all the original requirements.
**Function Requirements:**
- The function must be named `transform_matrix`.
- It must accept one argument: `matrix`, which is a list of lists of integers.
- It must return a list of lists of integers.
- The function should not use any external libraries except for `copy`.
- Your output must be ONLY the Python code for the function. Do not include any explanations or markdown formatting.
"""
rule_description: str = dspy.InputField(desc="The English description of the rule to implement.")
training_examples: List[TrainingExample] = dspy.InputField(desc="A list of input/output pairs the function must correctly handle.")
buggy_code: str = dspy.InputField(desc="The Python code that failed verification.")
feedback: str = dspy.InputField(desc="A description of the error or the discrepancy between the function's output and the expected output for a training example.")
refined_python_function: str = dspy.OutputField(desc="A string containing the corrected, single Python function `transform_matrix`.")
# --- Custom Module with Self-Correction ---
class ARCSolver(dspy.Module):
"""A module that solves ARC tasks by hypothesizing a rule, generating code, and refining it through verification."""
def __init__(self, max_retries=2):
super().__init__()
self.max_retries = max_retries
self.rule_hypothesizer = dspy.ChainOfThought(HypothesizeRule)
self.code_implementer = dspy.Predict(ImplementRule)
self.code_refiner = dspy.Predict(RefineCode)
def _execute_and_verify(self, python_code: str, examples: List[TrainingExample]) -> Tuple[bool, Optional[str]]:
"""
Executes the generated Python code and verifies its correctness against training examples.
Returns a tuple: (is_correct, feedback_message).
"""
local_scope = {}
try:
exec(python_code, globals(), local_scope)
transform_func = local_scope.get('transform_matrix')
if not callable(transform_func):
return False, "The generated code does not define a callable function named 'transform_matrix'."
except Exception as e:
return False, f"Code compilation failed with an error: {e}\n{traceback.format_exc()}"
for i, example in enumerate(examples):
try:
input_copy = copy.deepcopy(example.input)
predicted_output = transform_func(input_copy)
if predicted_output != example.output:
feedback = (
f"Verification failed on training example {i}.\n"
f"Input:\n{example.input}\n"
f"Expected Output:\n{example.output}\n"
f"Actual Output:\n{predicted_output}"
)
return False, feedback
except Exception as e:
feedback = (
f"An exception occurred during verification on training example {i}: {e}\n"
f"{traceback.format_exc()}"
)
return False, feedback
return True, None
def forward(self, training_examples: List[TrainingExample], test_inputs: List[MATRIX]):
# Step 1: Generate an English description of the transformation rule.
hypothesis = self.rule_hypothesizer(training_examples=training_examples)
# Step 2: Generate the initial Python code.
prediction = self.code_implementer(
rule_description=hypothesis.rule_description,
training_examples=training_examples
)
python_code = prediction.python_function
# Step 3: Verify and refine the code in a loop.
for _ in range(self.max_retries):
is_correct, feedback = self._execute_and_verify(python_code, training_examples)
if is_correct:
break
refinement = self.code_refiner(
rule_description=hypothesis.rule_description,
training_examples=training_examples,
buggy_code=python_code,
feedback=feedback
)
python_code = refinement.refined_python_function
# Step 4: Execute the final code on test inputs with robust fallbacks.
fallback_outputs = [copy.deepcopy(matrix) for matrix in test_inputs]
local_scope = {}
try:
exec(python_code, globals(), local_scope)
transform_func = local_scope.get('transform_matrix')
if not callable(transform_func):
return dspy.Prediction(test_outputs=fallback_outputs)
solved_outputs = []
for test_matrix in test_inputs:
try:
input_copy = copy.deepcopy(test_matrix)
result = transform_func(input_copy)
solved_outputs.append(result)
except Exception:
solved_outputs.append(copy.deepcopy(test_matrix))
return dspy.Prediction(test_outputs=solved_outputs)
except Exception:
return dspy.Prediction(test_outputs=fallback_outputs)
# The overall task signature, defining the final inputs and outputs of the program.
class SolveTaskSignature(dspy.Signature):
"""
Given a set of training examples demonstrating a matrix transformation,
apply the same transformation to a new set of test matrices.
"""
training_examples: List[TrainingExample] = dspy.InputField(description="Input and output examples demonstrating the task to be performed.")
test_inputs: List[MATRIX] = dspy.InputField(description="Input matrices to be solved following the task described in the training examples.")
test_outputs: List[MATRIX] = dspy.OutputField(description="Output matrices corresponding to the test inputs.")
# The final program is an instance of our new, more robust module.
program = ARCSolver()
2025/08/29 15:58:46 INFO dspy.evaluate.evaluate: Average Metric: 3.0 / 3 (100.0%)
2025/08/29 15:58:46 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 15:58:47 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 16:01:58 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 16:02:33 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 16:03:08 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 16:05:13 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 16:05:13 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 16:05:26 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 16:06:05 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 16:06:05 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 16:07:02 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 16:07:12 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 16:07:34 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 16:19:12 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 16:30:29 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 16:35:27 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 16:38:35 INFO dspy.evaluate.evaluate: Average Metric: 133.0 / 200 (66.5%)
GEPA Optimization: 75%|██████████████████████████████████████████████████████████████████████████████████▎ | 2991/4000 [19:04:39<4:43:47, 16.88s/rollouts]Iteration 113: Full valset score for new program: 0.665
Iteration 113: Full train_val score for new program: 0.665
Iteration 113: Individual valset scores for new program: [True, False, False, True, True, False, True, True, True, True, True, True, True, False, True, True, False, False, True, True, True, True, True, False, True, True, False, False, True, True, False, False, False, True, True, True, False, True, False, True, False, True, True, False, False, True, False, False, True, True, True, True, True, True, True, False, True, False, False, True, False, False, False, True, True, False, True, True, False, True, True, True, True, True, True, True, True, False, True, True, True, True, True, True, True, True, True, False, True, False, True, True, True, True, True, True, False, False, True, True, True, True, True, False, True, True, True, True, False, False, False, False, False, True, True, False, True, True, True, True, True, False, True, False, False, True, False, True, True, True, False, False, True, True, True, False, True, True, True, True, True, False, True, False, True, False, True, False, True, True, False, False, True, True, True, True, True, True, True, False, False, True, False, True, False, True, True, False, False, False, False, True, True, True, True, True, False, True, False, True, False, True, True, False, True, True, True, True, True, True, True, True, True, True, False, True, True, False, False, True]
Iteration 113: New valset pareto front scores: [True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, False, True, True, True, True, True, True, True, True, True, False, True, True, True, True, False, True, True, True, True, False, True, True, True, False, True, True, True, False, True, False, True, True, True, True, True, True, True, True, 0, True, True, False, True, True, True, False, True, True, False, True, True, 0, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, False, True, True, True, True, True, True, True, True, True, True, True, True, True, False, True, True, True, True, True, True, False, True, True, True, True, True, True, True, True, True, True, True, True, False, False, True, False, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, False, True, True, True, False, True, True, True, True, True, True, True, True, True, True, True, False, True, True, True, True, False, True, True, True, True, False, True, True, True, True, True, True, False, True, False, True, True, True, True, False, True, True, True, True, True, True, True, True, True, True, True, True, True, True, False, True]
Iteration 113: Full valset pareto front score: 0.865
Iteration 113: Updated valset pareto front programs: [{1, 3, 5, 8, 9, 10, 11}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10}, {0, 9}, {0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}, {0, 1, 2, 3, 4, 5, 6, 7, 9, 10, 11}, {0, 4}, {0, 1, 2, 3, 4, 5, 6, 7, 9, 10, 11}, {0, 1, 3, 4, 5, 6, 7, 8, 9, 10, 11}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}, {0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}, {8}, {0, 1, 2, 3, 4, 6, 9, 10, 11}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}, {1, 4, 5, 7, 9, 10}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}, {0, 2, 4, 6, 7, 10, 11}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 11}, {0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}, {1, 5, 9, 7}, {0, 3, 4, 6, 8, 9, 10, 11}, {0, 1, 3, 6, 7, 8, 9, 10, 11}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}, {0, 4, 7}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}, {1}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}, {2}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}, {0, 1, 2, 3, 5, 6, 7, 8, 9, 10, 11}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}, {2, 5}, {0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}, {1}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}, {0, 1, 2, 3, 4, 6, 8, 9, 10, 11}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}, {8}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}, {0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 11}, {0, 1, 2, 3, 4, 6, 7, 11}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}, {0, 1, 3, 4, 5, 6, 7, 8, 9, 10, 11}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}, {0, 1, 3, 4, 5, 6, 7, 8, 9, 10, 11}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}, {0, 1, 2, 3, 8}, {0, 1, 5}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}, {4, 6, 7, 8, 11}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}, {1, 2, 3, 5, 6, 7, 8, 9, 10, 11}, {0, 1, 3, 4, 5, 6, 7, 8, 9, 10, 11}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}, {11}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}, {2, 3, 4, 5, 6, 7, 8, 9, 11}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}, {1, 2, 3, 5, 6, 7, 8, 9, 10, 11}, {0, 3, 4, 5, 6, 7, 9, 10, 11}, {4}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}, {0, 2, 3, 4, 5, 7, 8, 9, 10, 11}, {0, 2, 6, 8, 11}, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}, {0, 1, 2, 4, 5, 6, 7, 8, 10, 11}, {0, 1, 2, 3, 4, 5, 6, 7}, {0, 3, 4, 6, 7, 11}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}, {0, 1, 2, 4, 5, 6, 7, 8, 9, 10, 11}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}, {8, 1, 10, 3}, {7}, {0, 3, 4, 6, 7, 9, 10, 11}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}, {0, 1, 2, 3, 4, 5, 6, 8, 9, 10, 11}, {0, 4, 6, 7, 9, 11}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}, {0, 1, 2, 3, 5, 7, 8, 11}, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}, {0, 1, 2, 3, 5, 6, 7, 8, 9, 10, 11}, {1, 2, 10}, {0, 1, 4, 5, 6, 8, 10}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}, {0, 2, 5}, {1, 10, 3}, {2, 4, 9, 10, 11}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}, {3, 4, 5, 7, 8}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}, {2, 3, 4, 6, 7, 8, 9, 10, 11}, {0, 1, 2, 3, 4, 5, 6, 8, 10, 11}, {0, 1, 2, 3, 5, 6, 7, 9, 10, 11}, {2, 3, 4, 5, 6, 7, 9, 11}, {0, 1, 7, 9, 10}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 11}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}, {0, 1, 2, 3, 4, 5, 6, 7, 9, 10, 11}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}, {0, 1, 2, 3, 5, 6, 7, 8, 9, 10, 11}, {0, 1, 2, 3, 5, 6, 7, 8, 9, 10, 11}, {2, 7}, {0, 1, 3, 9}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}, {0, 1, 3, 5, 6, 7, 8, 9, 10, 11}, {0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 11}, {8, 9, 7}, {0, 1, 3, 6, 7, 8, 9, 11}, {3, 5, 6, 7, 11}, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}, {1, 2, 3, 4, 5, 6, 7, 9, 10, 11}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}, {0, 1, 2, 3, 4, 6, 7, 8, 9, 10}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}, {9}, {0, 2, 3, 5, 6, 7, 9, 10, 11}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}, {4}, {8, 9, 7}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}, {0, 1, 3, 4, 5, 6, 7, 8, 9, 11}, {0, 1, 3, 4, 5, 6, 7, 8, 10, 11}, {0, 1, 2, 3, 5, 6, 7, 8, 9, 10, 11}, {0, 3, 5, 6, 8, 9, 10, 11}, {0, 1, 2, 3, 4, 5, 6, 7, 9, 10, 11}, {0, 1, 2, 3, 6, 7, 9, 10, 11}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}, {1}, {0, 1, 2, 3, 4, 5, 6, 8, 9, 10, 11}, {0, 4, 5, 6, 7, 8, 9, 10}, {0, 3, 5, 10, 11}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}, {0, 1, 2, 4, 5, 6, 7, 9, 10}, {0, 1, 2, 4, 5, 6, 7, 9, 10}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}, {0, 8, 9, 7}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}, {9, 11, 6}, {0, 1, 3, 5, 6, 7, 8, 9, 10, 11}, {0, 1, 2, 3, 4, 5, 6, 8, 9, 11}, {1, 2, 3, 5, 6, 8, 9, 10, 11}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}, {0, 1, 3, 4, 5, 6, 7, 8, 10, 11}, {0, 2, 7, 8, 9, 10}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}, {9, 11}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}, {0, 1, 2, 4, 5, 6, 7, 8, 9, 10, 11}, {0, 5, 6, 7, 8, 9, 10, 11}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}, {0, 1, 2, 3, 4, 5, 6, 7, 10, 11}, {1, 2, 4, 5, 6, 7, 8, 10, 11}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10}, {11, 6}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}, {0, 10}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}, {0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}]
Iteration 113: Best valset aggregate score so far: 0.675
Iteration 113: Best program as per aggregate score on train_val: 6
Iteration 113: Best program as per aggregate score on valset: 6
Iteration 113: Best score on valset: 0.675
Iteration 113: Best score on train_val: 0.675
Iteration 113: Linear pareto front program index: 6
Iteration 113: New program candidate index: 11
Iteration 114: Selected program 7 score: 0.675
Average Metric: 2.00 / 3 (66.7%): : 5it [03:08, 37.68s/it] 2025/08/29 16:41:43 INFO dspy.evaluate.evaluate: Average Metric: 2.0 / 3 (66.7%)
Iteration 114: Proposed new text for program: import dspy
from typing import List, Any
import pydantic
import traceback
# Define the type for a matrix for clarity
MATRIX = List[List[int]]
class TrainingExample(pydantic.BaseModel):
"""A Pydantic model for a single training example, containing an input and output matrix."""
input: MATRIX
output: MATRIX
class InferRuleSignature(dspy.Signature):
"""
Analyzes pairs of input/output matrices to deduce the transformation rule.
You are an expert analyst of abstract visual reasoning puzzles. Your task is to carefully examine the provided training examples. Each example consists of an 'input' matrix and a corresponding 'output' matrix.
Your goal is to deduce a single, consistent transformation rule that is general enough to solve all examples. Describe this rule in clear, unambiguous, step-by-step natural language. The description must be precise enough for another AI or a programmer to implement it.
Consider various possible transformation patterns, including but not limited to:
- Geometric operations: rotation, reflection, scaling, repetition, shifting.
- Color/value transformations: changing specific numbers to others, conditional changes.
- Object-based logic: identifying shapes or objects and applying rules based on their properties (size, color, position).
- Cropping or subgrid extraction: such as finding a bounding box or selecting a specific region.
- Fill/completion patterns: such as flood fills or completing a grid based on object boundaries.
- Tiling or pattern repetition.
"""
training_examples: List[TrainingExample] = dspy.InputField(description="A list of input and output examples demonstrating the task.")
transformation_rule: str = dspy.OutputField(description="A step-by-step natural language description of the transformation rule.")
class GeneratePythonCodeSignature(dspy.Signature):
"""
Converts a natural language transformation rule into a Python function.
You are an expert Python programmer. Your task is to write a single Python function named `transform_matrix` that implements the given natural language `transformation_rule`.
**Function Requirements:**
- The function must be named exactly `transform_matrix`.
- It must accept one argument: `matrix: List[List[int]]`.
- It must return the transformed matrix as a `List[List[int]]`.
- You can use standard Python libraries (like `collections`). Do not use external libraries like numpy or pandas.
- The `training_examples` are provided for context. **Crucially, your generated code will be automatically tested against these examples. Ensure your function logic correctly transforms every training input into its corresponding training output.**
**Output Format:**
- Your output must be ONLY the Python code for the function.
- Do NOT include any explanations, comments outside the function, or markdown formatting like ```python ... ```.
"""
transformation_rule: str = dspy.InputField(description="The natural language rule to implement.")
training_examples: List[TrainingExample] = dspy.InputField(description="Example input/output pairs to validate the logic.")
python_code: str = dspy.OutputField(description="A string containing only the Python code for the `transform_matrix` function.")
class RefineCodeSignature(dspy.Signature):
"""
Fixes buggy Python code based on feedback from a failed test case.
You are an expert Python programmer specializing in debugging. You will be given a natural language `transformation_rule`, the `buggy_python_code` that failed to implement it correctly, and `error_feedback` detailing the failure.
Your task is to analyze the feedback, identify the bug in the code, and provide a corrected version. The refined code must still adhere to all original requirements (function name `transform_matrix`, no external libraries, etc.).
**Output Format:**
- Your output must be ONLY the corrected Python code for the function.
- Do NOT include any explanations or markdown formatting.
"""
transformation_rule: str = dspy.InputField(description="The original natural language rule.")
training_examples: List[TrainingExample] = dspy.InputField(description="The full set of examples for context.")
buggy_python_code: str = dspy.InputField(description="The Python code that failed verification.")
error_feedback: str = dspy.InputField(description="A description of the error or the failed test case (input, expected output, actual output).")
refined_python_code: str = dspy.OutputField(description="A string containing only the corrected Python code for the `transform_matrix` function.")
class ApplyRuleSignature(dspy.Signature):
"""
Applies a given transformation rule to a single test input matrix.
You are an expert in meticulously following instructions to transform matrices. You will be given a specific, detailed transformation rule and a single test input matrix.
Your task is to:
1. Carefully read and understand the provided rule.
2. Apply the rule step-by-step to the test input matrix.
3. Produce the final output matrix.
**Crucially, your output must be ONLY the resulting matrix, formatted as a valid JSON list of lists of integers. Do not include any extra text, explanations, or markdown formatting.**
"""
transformation_rule: str = dspy.InputField(description="The natural language rule to apply.")
test_input: MATRIX = dspy.InputField(description="The input matrix to be transformed.")
test_output: MATRIX = dspy.OutputField(description="The resulting matrix after applying the rule, as a list of lists of integers.")
class ARCProgram(dspy.Module):
"""A program that infers a rule, generates/refines code, and falls back to direct application."""
def __init__(self, max_retries=2):
super().__init__()
self.max_retries = max_retries
self.rule_inferrer = dspy.ChainOfThought(InferRuleSignature)
self.code_generator = dspy.Predict(GeneratePythonCodeSignature)
self.code_refiner = dspy.Predict(RefineCodeSignature)
self.rule_applier_fallback = dspy.Predict(ApplyRuleSignature)
def forward(self, training_examples: List[TrainingExample], test_inputs: List[MATRIX]) -> dspy.Prediction:
# 1. Infer the transformation rule once.
inferred = self.rule_inferrer(training_examples=training_examples)
rule = inferred.transformation_rule
# 2. Attempt to generate, verify, and refine Python code.
generated = self.code_generator(transformation_rule=rule, training_examples=training_examples)
current_code = generated.python_code
for attempt in range(self.max_retries):
try:
# Prepare a namespace for safe execution.
local_namespace = {}
exec(current_code, globals(), local_namespace)
transform_func = local_namespace['transform_matrix']
# Verification step against all training examples.
is_verified = True
error_feedback = ""
for example in training_examples:
# Use a deep copy of the input to prevent mutation issues
input_copy = [row[:] for row in example.input]
predicted_output = transform_func(input_copy)
if predicted_output != example.output:
is_verified = False
error_feedback = (
f"The code failed on a training example.\n"
f"Input: {example.input}\n"
f"Expected Output: {example.output}\n"
f"Actual Output from your code: {predicted_output}\n"
f"Please analyze the rule and the code to find the bug and fix it."
)
break
if is_verified:
# If code is verified, apply it to all test inputs and return.
all_test_outputs = [transform_func([row[:] for row in test_matrix]) for test_matrix in test_inputs]
return dspy.Prediction(test_outputs=all_test_outputs)
# If not verified and we have retries left, refine the code.
if attempt < self.max_retries - 1:
refined = self.code_refiner(
transformation_rule=rule,
training_examples=training_examples,
buggy_python_code=current_code,
error_feedback=error_feedback
)
current_code = refined.refined_python_code
continue # Go to the next attempt with the new code
else:
# Ran out of retries on logical error
break
except Exception:
# This catches exec errors or function runtime errors.
error_feedback = f"The code produced a runtime error: {traceback.format_exc()}"
if attempt < self.max_retries - 1:
refined = self.code_refiner(
transformation_rule=rule,
training_examples=training_examples,
buggy_python_code=current_code,
error_feedback=error_feedback
)
current_code = refined.refined_python_code
continue
else:
# Ran out of retries on runtime error
break
# 3. Fallback Strategy: If code path fails, revert to direct LM application.
all_test_outputs = []
for test_matrix in test_inputs:
try:
result = self.rule_applier_fallback(transformation_rule=rule, test_input=test_matrix)
all_test_outputs.append(result.test_output)
except Exception:
# If the fallback also fails, append a default empty/zero matrix.
if test_matrix and test_matrix[0]:
all_test_outputs.append([([0] * len(test_matrix[0])) for _ in range(len(test_matrix))])
else:
all_test_outputs.append([])
return dspy.Prediction(test_outputs=all_test_outputs)
# The final 'program' object is an instance of our robust, multi-step module.
program = ARCProgram()
2025/08/29 17:14:15 INFO dspy.evaluate.evaluate: Average Metric: 1.0 / 3 (33.3%)
GEPA Optimization: 75%|██████████████████████████████████████████████████████████████████████████████████▍ | 2997/4000 [19:40:19<7:08:41, 25.64s/rollouts]Iteration 114: New subsample score is not better, skipping
Iteration 115: Selected program 1 score: 0.63
Average Metric: 2.00 / 3 (66.7%): 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [01:20<00:00, 26.96s/it]2025/08/29 17:15:36 INFO dspy.evaluate.evaluate: Average Metric: 2.0 / 3 (66.7%)
Iteration 115: Proposed new text for program: import dspy
from typing import List
import pydantic
import re
# Define the type for a matrix for clarity
MATRIX = List[List[int]]
class TrainingExample(pydantic.BaseModel):
"""A Pydantic model for a single training example, containing an input and output matrix."""
input: MATRIX
output: MATRIX
class InferRuleSignature(dspy.Signature):
"""
Analyzes pairs of input/output matrices to deduce the transformation rule.
You are an expert analyst of abstract visual reasoning puzzles. Your task is to carefully examine the provided training examples. Each example consists of an 'input' matrix and a corresponding 'output' matrix.
Your goal is to deduce the single, consistent transformation rule that converts each input to its output. Describe this rule in clear, unambiguous, step-by-step natural language. The description must be precise enough for another AI to follow it to generate a Python function to solve a new, unseen test input.
Consider various possible transformation patterns, including but not limited to:
- Geometric operations: rotation, reflection, scaling, repetition, shifting.
- Color/value transformations: changing specific numbers to others, conditional changes.
- Object-based logic: identifying shapes or objects and applying rules based on their properties (size, color, position).
- Fill/completion patterns: such as flood fills or completing a grid based on object boundaries.
- Bounding box or frame logic: creating structures based on the extents of objects.
- Grid partitioning and regional analysis.
"""
training_examples: List[TrainingExample] = dspy.InputField(description="A list of input and output examples demonstrating the task.")
transformation_rule: str = dspy.OutputField(description="A step-by-step natural language description of the transformation rule.")
class GeneratePythonCodeSignature(dspy.Signature):
"""
Generates a Python function to apply a transformation rule to a matrix.
You are an expert Python programmer. You will be given a detailed, step-by-step transformation rule and an example input matrix. Your task is to write a single, self-contained Python function named `transform_matrix` that implements this rule.
**Function Requirements:**
- The function must be named `transform_matrix`.
- It must accept one argument: `matrix` (a list of lists of integers).
- It must return the transformed matrix (a list of lists of integers).
- The function must be entirely self-contained. Do NOT use any external libraries like numpy or pandas. Standard Python libraries are acceptable if needed.
- Your output must be ONLY the Python code, enclosed in a single markdown code block like this:
```python
# Your code here
def transform_matrix(matrix):
# ... implementation ...
return new_matrix
```
"""
transformation_rule: str = dspy.InputField(description="The natural language rule to implement.")
test_input: MATRIX = dspy.InputField(description="An example input matrix to guide the implementation.")
python_code: str = dspy.OutputField(description="A self-contained Python function `transform_matrix` that implements the rule.")
def extract_python_code(text: str) -> str:
"""Extracts Python code from a markdown block."""
match = re.search(r"```python\n(.*?)\n```", text, re.DOTALL)
if match:
return match.group(1)
return ""
class ARCProgram(dspy.Module):
"""A program that first infers a rule, generates code to apply it, and then executes the code."""
def __init__(self):
super().__init__()
# Use ChainOfThought for the complex reasoning task of rule inference.
self.rule_inferrer = dspy.ChainOfThought(InferRuleSignature)
# Use a simple Predict for the more direct task of generating code from the rule.
self.code_generator = dspy.Predict(GeneratePythonCodeSignature)
def forward(self, training_examples: List[TrainingExample], test_inputs: List[MATRIX]) -> dspy.Prediction:
"""
Infers a rule, generates a Python function, and applies it to each test input.
Args:
training_examples: A list of TrainingExample objects.
test_inputs: A list of input matrices to be solved.
Returns:
A dspy.Prediction object with the 'test_outputs' field populated.
"""
# 1. Infer the transformation rule once based on all training examples.
inferred = self.rule_inferrer(training_examples=training_examples)
rule = inferred.transformation_rule
all_test_outputs = []
# 2. Iterate through each test input, generate code, and execute it.
for test_matrix in test_inputs:
try:
# Generate a Python function that implements the rule.
# Providing the test_input helps the LM generate more context-aware code.
code_generation_result = self.code_generator(transformation_rule=rule, test_input=test_matrix)
code_str = extract_python_code(code_generation_result.python_code)
if not code_str:
raise ValueError("Failed to generate or extract Python code.")
# Execute the generated code in a restricted scope to define the function.
local_scope = {}
exec(code_str, globals(), local_scope)
transform_func = local_scope.get('transform_matrix')
if not callable(transform_func):
raise ValueError("`transform_matrix` function not found in generated code.")
# Apply the generated function to the current test matrix.
output_matrix = transform_func(test_matrix)
all_test_outputs.append(output_matrix)
except Exception:
# Fallback strategy: if any step fails, append a size-matched zero matrix.
if test_matrix and test_matrix[0]:
all_test_outputs.append([([0] * len(test_matrix[0])) for _ in range(len(test_matrix))])
else:
all_test_outputs.append([])
# 3. Return the collected outputs in a single Prediction object.
return dspy.Prediction(test_outputs=all_test_outputs)
# The final 'program' object is an instance of our robust, multi-step module.
program = ARCProgram()
2025/08/29 17:21:57 INFO dspy.evaluate.evaluate: Average Metric: 0.0 / 3 (0.0%)
GEPA Optimization: 75%|██████████████████████████████████████████████████████████████████████████████████▌ | 3003/4000 [19:48:00<7:36:22, 27.47s/rollouts]Iteration 115: New subsample score is not better, skipping
Iteration 116: Selected program 2 score: 0.605
Average Metric: 3.00 / 3 (100.0%): 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [04:12<00:00, 84.03s/it]2025/08/29 17:26:09 INFO dspy.evaluate.evaluate: Average Metric: 3.0 / 3 (100.0%)
GEPA Optimization: 75%|██████████████████████████████████████████████████████████████████████████████████▋ | 3006/4000 [19:52:12<7:58:12, 28.87s/rollouts]
Iteration 116: All subsample scores perfect. Skipping.
Iteration 116: Reflective mutation did not propose a new candidate
Iteration 117: Selected program 11 score: 0.665
Average Metric: 2.00 / 3 (66.7%): : 6it [14:17, 142.87s/it] 2025/08/29 17:40:26 INFO dspy.evaluate.evaluate: Average Metric: 2.0 / 3 (66.7%)
Iteration 117: Proposed new text for program: import dspy
from typing import List, Tuple, Optional
import pydantic
import copy
import traceback
# Define type aliases and pydantic models for clarity and structure.
MATRIX = List[List[int]]
class TrainingExample(pydantic.BaseModel):
input: MATRIX
output: MATRIX
# --- Signatures ---
class HypothesizeRule(dspy.Signature):
"""
Analyze the provided input/output matrix pairs from the Abstraction and Reasoning Corpus (ARC).
Deduce the underlying transformation rule that converts each input matrix to its corresponding output matrix.
Describe this rule in clear, step-by-step, unambiguous English.
**Strategy:**
- Deconstruct the transformation into independent steps. For example, is there a change in grid size? A change in colors? A spatial rearrangement of objects? A pattern repetition?
- Describe each step separately before combining them into a full, coherent rule.
- Focus on the logic and the sequence of operations, not on Python code implementation.
"""
training_examples: List[TrainingExample] = dspy.InputField(desc="A list of input/output pairs demonstrating the transformation rule.")
rule_description: str = dspy.OutputField(desc="A step-by-step English description of the transformation rule.")
class ImplementRule(dspy.Signature):
"""
You are an expert programmer. Your task is to write a single, self-contained Python function based on a provided rule description and example pairs.
**Function Requirements:**
- The function must be named `transform_matrix`.
- It must accept one argument: `matrix`, which is a list of lists of integers (representing the input grid).
- It must return a list of lists of integers (representing the transformed output grid).
- The function should not use any external libraries except for `copy` if needed (e.g., `import copy; new_matrix = copy.deepcopy(matrix)`).
- Your output must be ONLY the Python code for the function. Do not include any explanations, comments outside the function, or markdown formatting like ```python.
**Successful Strategies to Consider:**
- **Pattern Repetition:** For tasks involving sequences or patterns (e.g., extending a grid), first write code to robustly identify the smallest repeating unit (the period) of the pattern. Then, use this unit to construct the output.
- **Object-Based Logic:** Many tasks operate on "objects" (contiguous blocks of the same color). Write helper functions to find all objects, their colors, sizes, and bounding boxes. The core logic can then operate on these abstract objects.
- **Robust Parsing:** When rules involve sub-regions (e.g., quadrants, objects), write code that can robustly find their boundaries. Don't assume boundaries are perfectly aligned or can be found by checking just the first row/column.
- **Neighborhoods:** Often, a cell's new value depends on its immediate neighbors (e.g., von Neumann or Moore neighborhoods).
- **Handling Edge Cases:** Ensure your code handles empty matrices or matrices with unexpected dimensions gracefully.
**Example of a Correctly Formatted Output:**
def transform_matrix(matrix: list[list[int]]) -> list[list[int]]:
# Your implementation here
# For example, find the most frequent color and fill the grid
from collections import Counter
import itertools
if not matrix or not matrix[0]:
return []
counts = Counter(itertools.chain.from_iterable(matrix))
if counts:
# Handle ties by picking the smaller number value
most_common_color = sorted(counts.items(), key=lambda item: (-item[1], item[0]))[0][0]
else:
return []
height = len(matrix)
width = len(matrix[0])
return [[most_common_color for _ in range(width)] for _ in range(height)]
"""
rule_description: str = dspy.InputField(desc="The English description of the rule to implement.")
training_examples: List[TrainingExample] = dspy.InputField(desc="A list of input/output pairs to use as a reference for implementation.")
python_function: str = dspy.OutputField(desc="A string containing a single Python function `transform_matrix` that implements the rule.")
class RefineCode(dspy.Signature):
"""
You are a senior programmer debugging a Python function. You will be given a rule description, training examples, the buggy Python code, and feedback on why it failed.
Your task is to fix the code so that it correctly implements the rule and passes the training examples.
The refined function must adhere to all the original requirements.
**Debugging Strategy:**
- **Analyze the Feedback:** Pay close attention to the `feedback` field. It details the exact input that failed and the difference between your function's output and the correct output. The error is in the logic of your code, not syntax.
- **Re-read the Rule:** Carefully compare the logic in the `buggy_code` against the `rule_description`. The bug is a misinterpretation of the rule.
- **Trace with the Failing Example:** Mentally (or with comments) trace your code's execution using the failing input from the feedback to pinpoint the exact line or block where the logic goes wrong.
**Function Requirements:**
- The function must be named `transform_matrix`.
- It must accept one argument: `matrix`, which is a list of lists of integers.
- It must return a list of lists of integers.
- The function should not use any external libraries except for `copy`.
- Your output must be ONLY the Python code for the function. Do not include any explanations or markdown formatting.
"""
rule_description: str = dspy.InputField(desc="The English description of the rule to implement.")
training_examples: List[TrainingExample] = dspy.InputField(desc="A list of input/output pairs the function must correctly handle.")
buggy_code: str = dspy.InputField(desc="The Python code that failed verification.")
feedback: str = dspy.InputField(desc="A description of the error or the discrepancy between the function's output and the expected output for a training example.")
refined_python_function: str = dspy.OutputField(desc="A string containing the corrected, single Python function `transform_matrix`.")
# --- Custom Module with Self-Correction ---
class ARCSolver(dspy.Module):
"""A module that solves ARC tasks by hypothesizing a rule, generating code, and refining it through verification."""
def __init__(self, max_retries=2):
super().__init__()
self.max_retries = max_retries
self.rule_hypothesizer = dspy.ChainOfThought(HypothesizeRule)
self.code_implementer = dspy.Predict(ImplementRule)
self.code_refiner = dspy.Predict(RefineCode)
def _execute_and_verify(self, python_code: str, examples: List[TrainingExample]) -> Tuple[bool, Optional[str]]:
"""
Executes the generated Python code and verifies its correctness against training examples.
Returns a tuple: (is_correct, feedback_message).
"""
local_scope = {}
try:
exec(python_code, globals(), local_scope)
transform_func = local_scope.get('transform_matrix')
if not callable(transform_func):
return False, "The generated code does not define a callable function named 'transform_matrix'."
except Exception as e:
return False, f"Code compilation failed with an error: {e}\n{traceback.format_exc()}"
for i, example in enumerate(examples):
try:
# Use the Pydantic model's attributes directly.
input_copy = copy.deepcopy(example.input)
expected_output = example.output
predicted_output = transform_func(input_copy)
if predicted_output != expected_output:
feedback = (
f"Verification failed on training example {i}.\n"
f"Input:\n{example.input}\n"
f"Expected Output:\n{expected_output}\n"
f"Actual Output:\n{predicted_output}"
)
return False, feedback
except Exception as e:
feedback = (
f"An exception occurred during verification on training example {i} with input {example.input}: {e}\n"
f"{traceback.format_exc()}"
)
return False, feedback
return True, None
def forward(self, training_examples: List[TrainingExample], test_inputs: List[MATRIX]):
# Robustly convert input dicts to Pydantic models to ensure consistency.
validated_examples = [
TrainingExample.model_validate(ex) if isinstance(ex, dict) else ex
for ex in training_examples
]
# Step 1: Generate an English description of the transformation rule.
hypothesis = self.rule_hypothesizer(training_examples=validated_examples)
# Step 2: Generate the initial Python code.
prediction = self.code_implementer(
rule_description=hypothesis.rule_description,
training_examples=validated_examples
)
python_code = prediction.python_function
# Step 3: Verify and refine the code in a loop.
for i in range(self.max_retries):
is_correct, feedback = self._execute_and_verify(python_code, validated_examples)
if is_correct:
break
# If it's the last attempt and still incorrect, we'll proceed with the buggy code.
if i == self.max_retries - 1:
break
refinement = self.code_refiner(
rule_description=hypothesis.rule_description,
training_examples=validated_examples,
buggy_code=python_code,
feedback=feedback
)
python_code = refinement.refined_python_function
# Step 4: Execute the final code on test inputs with robust fallbacks.
fallback_outputs = [copy.deepcopy(matrix) for matrix in test_inputs]
local_scope = {}
try:
exec(python_code, globals(), local_scope)
transform_func = local_scope.get('transform_matrix')
if not callable(transform_func):
# Fallback if the final code is invalid
return dspy.Prediction(test_outputs=fallback_outputs)
solved_outputs = []
for test_matrix in test_inputs:
try:
input_copy = copy.deepcopy(test_matrix)
result = transform_func(input_copy)
solved_outputs.append(result)
except Exception:
# Fallback for individual test case failure
solved_outputs.append(copy.deepcopy(test_matrix))
return dspy.Prediction(test_outputs=solved_outputs)
except Exception:
# Fallback if the final code fails to execute at all
return dspy.Prediction(test_outputs=fallback_outputs)
# The overall task signature, defining the final inputs and outputs of the program.
class SolveTaskSignature(dspy.Signature):
"""
Given a set of training examples demonstrating a matrix transformation,
apply the same transformation to a new set of test matrices.
"""
training_examples: List[TrainingExample] = dspy.InputField(description="Input and output examples demonstrating the task to be performed.")
test_inputs: List[MATRIX] = dspy.InputField(description="Input matrices to be solved following the task described in the training examples.")
test_outputs: List[MATRIX] = dspy.OutputField(description="Output matrices corresponding to the test inputs.")
# The final program is an instance of our new, more robust module.
program = ARCSolver()
2025/08/29 17:49:51 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 17:50:57 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 17:51:46 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 17:52:39 INFO dspy.evaluate.evaluate: Average Metric: 2.0 / 3 (66.7%)
GEPA Optimization: 75%|██████████████████████████████████████████████████████████████████████████████████ | 3012/4000 [20:18:43<12:12:23, 44.48s/rollouts]Iteration 117: New subsample score is not better, skipping
Iteration 118: Selected program 9 score: 0.67
Average Metric: 3.00 / 3 (100.0%): 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [02:14<00:00, 44.99s/it]2025/08/29 17:54:55 INFO dspy.evaluate.evaluate: Average Metric: 3.0 / 3 (100.0%)
GEPA Optimization: 75%|██████████████████████████████████████████████████████████████████████████████████▏ | 3015/4000 [20:20:58<12:10:33, 44.50s/rollouts]
Iteration 118: All subsample scores perfect. Skipping.
Iteration 118: Reflective mutation did not propose a new candidate
Iteration 119: Selected program 7 score: 0.675
Average Metric: 2.00 / 3 (66.7%): : 6it [2:07:04, 1270.81s/it] 2025/08/29 20:01:59 INFO dspy.evaluate.evaluate: Average Metric: 2.0 / 3 (66.7%)
Iteration 119: Proposed new text for program: import dspy
from typing import List
import pydantic
# Define the type for a matrix for clarity
MATRIX = List[List[int]]
class TrainingExample(pydantic.BaseModel):
"""A Pydantic model for a single training example, containing an input and output matrix."""
input: MATRIX
output: MATRIX
class InferRuleSignature(dspy.Signature):
"""
Analyzes pairs of input/output matrices to deduce the transformation rule.
You are an expert analyst of abstract visual reasoning puzzles. Your task is to carefully examine the provided training examples. Each example consists of an 'input' matrix and a corresponding 'output' matrix.
Your goal is to deduce the single, consistent transformation rule that converts each input to its output. Describe this rule in clear, unambiguous, step-by-step natural language. The description must be precise enough for another AI or a programmer to follow it to solve a new, unseen test input.
Consider various possible transformation patterns, including but not limited to:
- Geometric operations: rotation, reflection, scaling, repetition, shifting.
- Color/value transformations: changing specific numbers to others, conditional changes.
- Object-based logic: identifying shapes or objects and applying rules based on their properties (size, color, position).
- Cropping or subgrid extraction: such as finding a bounding box or selecting a specific region.
- Fill/completion patterns: such as flood fills or completing a grid based on object boundaries.
- Bounding box or frame logic: creating structures based on the extents of objects.
**Pro Tips for Analysis:**
- **Symmetry and Reflection:** Look for patterns that are copied and reflected across a central axis. Sometimes a central "frame" is removed, and a pattern from one quadrant is used to generate the others.
- **Center of Mass:** For tasks involving movement or propagation, consider if the direction is related to the relative position of objects or their centers of mass.
- **Boundary Fill:** If areas of one color are being filled with another, it's likely a "flood fill" or "boundary fill" operation, where a boundary color encloses the area to be filled.
- **Look for the simplest explanation:** The underlying rule is usually an elegant combination of a few simple steps. Avoid overly complex or case-specific explanations.
"""
training_examples: List[TrainingExample] = dspy.InputField(description="A list of input and output examples demonstrating the task.")
transformation_rule: str = dspy.OutputField(description="A step-by-step natural language description of the transformation rule.")
class GeneratePythonCodeSignature(dspy.Signature):
"""
Converts a natural language transformation rule into a Python function.
You are an expert Python programmer. Your task is to write a single Python function named `transform_matrix` that implements the given natural language `transformation_rule`.
**Function Requirements:**
- The function must be named exactly `transform_matrix`.
- It must accept one argument: `matrix: List[List[int]]`.
- It must return the transformed matrix as a `List[List[int]]`.
- You can use standard Python libraries. Do not use external libraries like numpy or pandas.
- The `training_examples` are provided for context, allowing you to mentally check if your function logic would produce the correct output for the given inputs.
**Output Format:**
- Your output must be ONLY the Python code for the function.
- Do NOT include any explanations, comments outside the function, or markdown formatting like ```python ... ```.
"""
transformation_rule: str = dspy.InputField(description="The natural language rule to implement.")
training_examples: List[TrainingExample] = dspy.InputField(description="Example input/output pairs to validate the logic.")
python_code: str = dspy.OutputField(description="A string containing only the Python code for the `transform_matrix` function.")
class ApplyRuleSignature(dspy.Signature):
"""
Applies a given transformation rule to a single test input matrix.
You are an expert in meticulously following instructions to transform matrices. You will be given a specific, detailed transformation rule and a single test input matrix.
Your task is to:
1. Carefully read and understand the provided rule.
2. Apply the rule step-by-step to the test input matrix.
3. Produce the final output matrix.
**Crucially, your output must be ONLY the resulting matrix, formatted as a valid JSON list of lists of integers. Do not include any extra text, explanations, or markdown formatting.**
"""
transformation_rule: str = dspy.InputField(description="The natural language rule to apply.")
test_input: MATRIX = dspy.InputField(description="The input matrix to be transformed.")
test_output: MATRIX = dspy.OutputField(description="The resulting matrix after applying the rule, as a list of lists of integers.")
class ARCProgram(dspy.Module):
"""A program that infers a rule, generates code to apply it, and falls back to direct application if needed."""
def __init__(self):
super().__init__()
self.rule_inferrer = dspy.ChainOfThought(InferRuleSignature)
self.code_generator = dspy.Predict(GeneratePythonCodeSignature)
self.rule_applier_fallback = dspy.Predict(ApplyRuleSignature)
def forward(self, training_examples: List[TrainingExample], test_inputs: List[MATRIX]) -> dspy.Prediction:
"""
Infers a rule, generates, verifies, and executes code, falling back to direct application if the code is invalid.
Args:
training_examples: A list of TrainingExample objects.
test_inputs: A list of input matrices to be solved.
Returns:
A dspy.Prediction object with the 'test_outputs' field populated.
"""
# 1. Infer the transformation rule once from the training examples.
inferred = self.rule_inferrer(training_examples=training_examples)
rule = inferred.transformation_rule
# 2. Attempt to generate and verify Python code for the rule.
try:
generated = self.code_generator(transformation_rule=rule, training_examples=training_examples)
python_code = generated.python_code
# Prepare a namespace for safe execution of the generated code.
local_namespace = {}
exec(python_code, globals(), local_namespace)
transform_func = local_namespace['transform_matrix']
# 2a. Verify the generated function against all training examples.
is_verified = True
for example in training_examples:
try:
predicted_output = transform_func(example.input)
if predicted_output != example.output:
is_verified = False
break # Mismatch found, code is invalid.
except Exception:
is_verified = False
break # Code crashed during verification, it's invalid.
# 2b. If verification passed, use the trusted function on test inputs.
if is_verified:
all_test_outputs = [transform_func(test_matrix) for test_matrix in test_inputs]
return dspy.Prediction(test_outputs=all_test_outputs)
except Exception:
# This catches errors in code generation, exec(), or finding the function.
# If any of these fail, we will proceed to the fallback.
pass
# 3. Fallback Strategy: If code generation, verification, or execution fails.
all_test_outputs = []
for test_matrix in test_inputs:
try:
result = self.rule_applier_fallback(transformation_rule=rule, test_input=test_matrix)
all_test_outputs.append(result.test_output)
except Exception:
# If the fallback also fails, append a default empty/zero matrix as a last resort.
if test_matrix and isinstance(test_matrix[0], list):
all_test_outputs.append([([0] * len(test_matrix[0])) for _ in range(len(test_matrix))])
else:
all_test_outputs.append([])
return dspy.Prediction(test_outputs=all_test_outputs)
# The final 'program' object is an instance of our robust, multi-step module.
program = ARCProgram()
2025/08/29 20:07:02 INFO dspy.evaluate.evaluate: Average Metric: 2.0 / 3 (66.7%)
GEPA Optimization: 76%|█████████████████████████████████████████████████████████████████████████████████▌ | 3021/4000 [22:33:05<51:42:33, 190.15s/rollouts]Iteration 119: New subsample score is not better, skipping
Iteration 120: Selected program 7 score: 0.675
Average Metric: 3.00 / 3 (100.0%): 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [02:18<00:00, 46.25s/it]2025/08/29 20:09:21 INFO dspy.evaluate.evaluate: Average Metric: 3.0 / 3 (100.0%)
GEPA Optimization: 76%|█████████████████████████████████████████████████████████████████████████████████▋ | 3024/4000 [22:35:24<48:36:42, 179.31s/rollouts]
Iteration 120: All subsample scores perfect. Skipping.
Iteration 120: Reflective mutation did not propose a new candidate
Iteration 121: Selected program 2 score: 0.605
Average Metric: 2.00 / 3 (66.7%): : 4it [04:04, 61.21s/it] 2025/08/29 20:13:25 INFO dspy.evaluate.evaluate: Average Metric: 2.0 / 3 (66.7%)
Iteration 121: Proposed new text for program: import dspy
from typing import List
import pydantic
import copy
# Define type aliases and pydantic models for clarity and structure.
MATRIX = List[List[int]]
class TrainingExample(pydantic.BaseModel):
input: MATRIX
output: MATRIX
class HypothesizeRule(dspy.Signature):
"""
Analyze the provided input/output matrix pairs from the Abstraction and Reasoning Corpus (ARC).
Deduce the underlying transformation rule that maps each input to its corresponding output.
Describe this rule clearly and concisely in natural language. Your reasoning should consider patterns like color transformations, object manipulation (moving, rotating, scaling), shape detection, and spatial relationships.
"""
training_examples: List[TrainingExample] = dspy.InputField(desc="A list of input/output pairs demonstrating the transformation rule.")
hypothesis: str = dspy.OutputField(desc="A concise, natural language description of the transformation rule.")
class GeneratePythonFunction(dspy.Signature):
"""
You are an expert programmer. Your task is to write a single, self-contained Python function that implements a given transformation rule. You will be provided with a natural language hypothesis about the rule and a series of input-output examples that follow it.
**Hypothesis to Implement:**
{hypothesis}
**Function Requirements:**
- The function must be named `transform_matrix`.
- It must accept one argument: `matrix`, which is a list of lists of integers (representing the input grid).
- It must return a list of lists of integers (representing the transformed output grid).
- The function should not use any external libraries except for `copy` if needed (e.g., `import copy; new_matrix = copy.deepcopy(matrix)`).
- Your output must be ONLY the Python code for the function. Do not include any explanations, comments outside the function, or markdown formatting like ```python.
**Successful Strategies to Consider:**
- **Object Permanence:** Most transformations preserve the grid dimensions.
- **Color Transformations:** Look for rules that change colors based on their value or their neighbors.
- **Spatial Reasoning:** Analyze shapes, positions, and relationships. Common patterns include filling enclosed areas, drawing lines, moving objects, or detecting symmetry.
- **Iterative Processes:** Some rules are applied repeatedly until the grid no longer changes. Consider using a `while` loop that continues as long as modifications are being made in a pass.
- **Neighborhoods:** Often, a cell's new value depends on its immediate neighbors (e.g., von Neumann or Moore neighborhoods).
"""
hypothesis: str = dspy.InputField(desc="A natural language description of the transformation rule to implement.")
training_examples: List[TrainingExample] = dspy.InputField(desc="A list of input/output pairs to guide the implementation.")
python_function: str = dspy.OutputField(desc="A string containing a single Python function `transform_matrix` that implements the hypothesized rule.")
class ARCSolver(dspy.Module):
"""A module that solves ARC tasks by hypothesizing a rule and then generating code to implement it."""
def __init__(self):
super().__init__()
# Decompose the problem: first reason about the rule, then implement it.
self.rule_hypothesizer = dspy.ChainOfThought(HypothesizeRule)
self.code_generator = dspy.Predict(GeneratePythonFunction)
def forward(self, training_examples: List[TrainingExample], test_inputs: List[MATRIX]):
# Step 1: Generate a natural language hypothesis about the transformation rule.
# The ChainOfThought module will reason about the examples before producing the hypothesis.
prediction = self.rule_hypothesizer(training_examples=training_examples)
hypothesis = prediction.hypothesis
# Step 2: Generate Python code based on the explicit hypothesis.
# This focuses the LM on implementation rather than simultaneous reasoning and implementation.
code_prediction = self.code_generator(hypothesis=hypothesis, training_examples=training_examples)
python_code = code_prediction.python_function
# Prepare a dictionary to hold the executed function.
local_scope = {}
# Create fallback outputs in case of any failure.
fallback_outputs = [copy.deepcopy(matrix) for matrix in test_inputs]
try:
# Step 3: Execute the generated code string to define the function.
exec(python_code, globals(), local_scope)
transform_func = local_scope.get('transform_matrix')
if not callable(transform_func):
return dspy.Prediction(test_outputs=fallback_outputs)
# Step 4: Apply the generated function to each test input.
solved_outputs = []
for test_matrix in test_inputs:
try:
input_copy = copy.deepcopy(test_matrix)
result = transform_func(input_copy)
solved_outputs.append(result)
except Exception:
# If the function fails on a specific test case, append the original
# matrix as a fallback for that case and continue.
solved_outputs.append(copy.deepcopy(test_matrix))
return dspy.Prediction(test_outputs=solved_outputs)
except Exception:
# If code generation or `exec` fails entirely, return the original inputs.
return dspy.Prediction(test_outputs=fallback_outputs)
# The overall task signature remains the same, defining the final input/output of the program.
class SolveTaskSignature(dspy.Signature):
"""
Given a set of training examples demonstrating a matrix transformation,
apply the same transformation to a new set of test matrices.
"""
training_examples: List[TrainingExample] = dspy.InputField(description="Input and output examples demonstrating the task to be performed.")
test_inputs: List[MATRIX] = dspy.InputField(description="Input matrices to be solved following the task described in the training examples.")
test_outputs: List[MATRIX] = dspy.OutputField(description="Output matrices corresponding to the test inputs.")
# The final program is an instance of our new, more robust module.
program = ARCSolver()
2025/08/29 20:19:07 INFO dspy.evaluate.evaluate: Average Metric: 1.0 / 3 (33.3%)
GEPA Optimization: 76%|█████████████████████████████████████████████████████████████████████████████████▊ | 3030/4000 [22:45:11<44:25:17, 164.86s/rollouts]Iteration 121: New subsample score is not better, skipping
Iteration 122: Selected program 4 score: 0.61
Average Metric: 2.00 / 3 (66.7%): 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [02:09<00:00, 43.01s/it]2025/08/29 20:21:16 INFO dspy.evaluate.evaluate: Average Metric: 2.0 / 3 (66.7%)
Iteration 122: Proposed new text for program: import dspy
from typing import List, Optional
import pydantic
import traceback
import copy
# Define the type for a grid/matrix as a list of lists of integers.
MATRIX = List[List[int]]
class TrainingExample(pydantic.BaseModel):
"""A Pydantic model for a single training example, containing an input and output grid."""
input: MATRIX
output: MATRIX
# --- Signature for Initial Code Generation ---
class GenerateTransformationFunction(dspy.Signature):
"""
You are an expert programmer solving visual puzzles from the Abstraction and Reasoning Corpus (ARC).
Your goal is to analyze a set of training examples, each consisting of an input grid and an output grid.
Based on this analysis, you must write a single, self-contained Python function named `transform_grid` that implements the observed transformation.
**Function Requirements:**
- The function must be named exactly `transform_grid`.
- It must accept one argument: `grid`, which is a 2D list of integers (the input grid).
- It must return a new 2D list of integers representing the transformed grid.
- The function should not rely on any external libraries unless they are standard (e.g., `copy`). If you need a library like numpy, import it inside the function.
- Do not modify the input grid in place; create a copy if necessary.
**Analysis Strategy:**
1. **Observe Core Patterns:** Look for simple, recurring patterns like geometric transformations (rotation, reflection, scaling), color changes, object manipulation (copying, moving, recoloring), pattern propagation, or flood-fills.
2. **Decompose the Problem:** Break down the transformation into logical steps. For example, "first, find all objects of color blue, then for each object, reflect it horizontally."
3. **Generalize:** The logic must be general enough to work for all training examples and, by extension, the unseen test inputs.
4. **Code Implementation:** Translate your logic into a clear and correct Python function. Ensure your code is robust and handles edge cases observed in the examples.
**Common Pitfalls to Avoid:**
- **Off-by-One Errors:** Double-check all loop boundaries and grid indexing.
- **Overly Specific Logic:** Do not hardcode values or dimensions from a single example. The function must be general.
- **Complex Conditionals:** For problems that seem to have multiple rules, be extremely careful. The third failure case in the provided examples had two distinct rules. The generated code attempted to handle both but had a subtle bug. Be meticulous.
"""
training_examples: List[TrainingExample] = dspy.InputField(desc="A list of input/output grid pairs demonstrating the transformation rule.")
test_input_grid: MATRIX = dspy.InputField(desc="A single test input grid to which the transformation should be applicable. Use its properties (e.g., dimensions) to ensure your code is general.")
reasoning: str = dspy.OutputField(desc="Step-by-step thinking process to deduce the transformation rule and how to implement it in Python.")
python_code: str = dspy.OutputField(desc="A string containing the complete, self-contained Python function `transform_grid(grid)`.")
# --- Signature for Code Correction ---
class CorrectTransformationFunction(dspy.Signature):
"""
You are an expert Python debugger specializing in visual reasoning puzzles.
You are given a Python function that FAILED to correctly solve a puzzle.
You will receive the original training examples, the buggy code, and a description of the failure (which input it failed on, what the expected output was, and what the code actually produced).
Your task is to:
1. Analyze the buggy code and the failure description.
2. Identify the logical error or bug in the code.
3. Explain the bug and your correction strategy in your reasoning.
4. Provide a complete, corrected version of the `transform_grid` function.
The corrected function must still adhere to all original requirements (self-contained, named `transform_grid`, etc.).
"""
training_examples: List[TrainingExample] = dspy.InputField(desc="The original list of input/output grid pairs.")
buggy_code: str = dspy.InputField(desc="The Python code that failed verification.")
failure_description: str = dspy.InputField(desc="A detailed description of the failure, including the input that caused it, the expected output, and the actual (incorrect) output.")
reasoning: str = dspy.OutputField(desc="A step-by-step explanation of the bug and the plan to fix it.")
corrected_python_code: str = dspy.OutputField(desc="The complete, corrected, self-contained Python function `transform_grid(grid)`.")
# --- The Main Orchestrator Module ---
class ARCSolver(dspy.Module):
"""
A self-correcting DSPy module that solves ARC tasks.
It first generates a Python function, then tests it against the training examples.
If the function fails, it enters a correction loop to debug and refine the code.
"""
def __init__(self, max_attempts=3):
super().__init__()
self.max_attempts = max_attempts
self.code_generator = dspy.ChainOfThought(GenerateTransformationFunction)
self.code_corrector = dspy.ChainOfThought(CorrectTransformationFunction)
def _verify_code(self, code_str: str, examples: List[TrainingExample]) -> Optional[str]:
"""
Verifies the generated code against all training examples.
Returns None if successful, or a failure description string if not.
"""
try:
local_scope = {}
# The LM might wrap the code in markdown, so we extract it.
if "```python" in code_str:
code_str = code_str.split("```python")[1].split("```")[0].strip()
exec(code_str, globals(), local_scope)
transform_function = local_scope.get('transform_grid')
if not (transform_function and callable(transform_function)):
return "Code execution did not define a callable function named `transform_grid`."
for i, example in enumerate(examples):
input_grid = copy.deepcopy(example.input)
expected_output = example.output
try:
actual_output = transform_function(input_grid)
if actual_output != expected_output:
return (
f"Verification failed on training example {i}.\n"
f"Input:\n{example.input}\n"
f"Expected Output:\n{expected_output}\n"
f"Actual Output:\n{actual_output}"
)
except Exception as e:
return (
f"Verification failed on training example {i} with a runtime error.\n"
f"Input:\n{example.input}\n"
f"Error: {e}\n"
f"Traceback: {traceback.format_exc()}"
)
return None # All examples passed
except Exception as e:
return f"Failed to execute or parse the generated code. Error: {e}\nTraceback: {traceback.format_exc()}"
def forward(self, training_examples: List[TrainingExample], test_inputs: List[MATRIX]):
python_code = ""
transform_function = None
# Initial code generation
prediction = self.code_generator(training_examples=training_examples, test_input_grid=test_inputs[0])
python_code = prediction.python_code
for attempt in range(self.max_attempts):
failure_description = self._verify_code(python_code, training_examples)
if failure_description is None:
# Code is correct, we can proceed
print(f"Code verified successfully on attempt {attempt + 1}.")
break
print(f"Attempt {attempt + 1} failed verification: {failure_description}")
if attempt + 1 == self.max_attempts:
print("Max correction attempts reached. Proceeding with potentially buggy code.")
break
# If verification fails, call the corrector
correction = self.code_corrector(
training_examples=training_examples,
buggy_code=python_code,
failure_description=failure_description
)
python_code = correction.corrected_python_code
# --- Execution Phase ---
# Try to get the final callable function
try:
local_scope = {}
if "```python" in python_code:
python_code = python_code.split("```python")[1].split("```")[0].strip()
exec(python_code, globals(), local_scope)
transform_function = local_scope.get('transform_grid')
if not callable(transform_function):
transform_function = None
except Exception:
transform_function = None
if not transform_function:
print("Warning: Final `transform_grid` function is not available. Using fallback.")
# Apply the function to all test inputs
generated_outputs = []
for test_input in test_inputs:
if transform_function:
try:
output_grid = transform_function(copy.deepcopy(test_input))
generated_outputs.append(output_grid)
except Exception as e:
print(f"Error applying `transform_grid` to a test input: {e}. Using fallback.")
generated_outputs.append(copy.deepcopy(test_input))
else:
# Fallback: if code is invalid, return the original input.
generated_outputs.append(copy.deepcopy(test_input))
return dspy.Prediction(test_outputs=generated_outputs)
# The final program object is an instance of our new, self-correcting module.
program = ARCSolver()
Attempt 1 failed verification: Failed to execute or parse the generated code. Error: 'dict' object has no attribute 'input'
Traceback: Traceback (most recent call last):
File "<string>", line 97, in _verify_code
AttributeError: 'dict' object has no attribute 'input'
Attempt 1 failed verification: Failed to execute or parse the generated code. Error: 'dict' object has no attribute 'input'
Traceback: Traceback (most recent call last):
File "<string>", line 97, in _verify_code
AttributeError: 'dict' object has no attribute 'input'
Error applying `transform_grid` to a test input: name 'collections' is not defined
Attempt 1 failed verification: Failed to execute or parse the generated code. Error: 'dict' object has no attribute 'input'
Traceback: Traceback (most recent call last):
File "<string>", line 97, in _verify_code
AttributeError: 'dict' object has no attribute 'input'
Attempt 1 failed verification: Failed to execute or parse the generated code. Error: 'dict' object has no attribute 'input'
Traceback: Traceback (most recent call last):
File "<string>", line 97, in _verify_code
AttributeError: 'dict' object has no attribute 'input'
Attempt 2 failed verification: Failed to execute or parse the generated code. Error: 'dict' object has no attribute 'input'
Traceback: Traceback (most recent call last):
File "<string>", line 97, in _verify_code
AttributeError: 'dict' object has no attribute 'input'
Attempt 2 failed verification: Failed to execute or parse the generated code. Error: 'dict' object has no attribute 'input'
Traceback: Traceback (most recent call last):
File "<string>", line 97, in _verify_code
AttributeError: 'dict' object has no attribute 'input'
Attempt 3 failed verification: Failed to execute or parse the generated code. Error: 'dict' object has no attribute 'input'
Traceback: Traceback (most recent call last):
File "<string>", line 97, in _verify_code
AttributeError: 'dict' object has no attribute 'input'
Max correction attempts reached. Proceeding with potentially buggy code.
Attempt 2 failed verification: Failed to execute or parse the generated code. Error: 'dict' object has no attribute 'input'
Traceback: Traceback (most recent call last):
File "<string>", line 97, in _verify_code
AttributeError: 'dict' object has no attribute 'input'
Attempt 3 failed verification: Failed to execute or parse the generated code. Error: 'dict' object has no attribute 'input'
Traceback: Traceback (most recent call last):
File "<string>", line 97, in _verify_code
AttributeError: 'dict' object has no attribute 'input'
Max correction attempts reached. Proceeding with potentially buggy code.
Attempt 2 failed verification: Failed to execute or parse the generated code. Error: 'dict' object has no attribute 'input'
Traceback: Traceback (most recent call last):
File "<string>", line 97, in _verify_code
AttributeError: 'dict' object has no attribute 'input'
Attempt 1 failed verification: Failed to execute or parse the generated code. Error: 'dict' object has no attribute 'input'
Traceback: Traceback (most recent call last):
File "<string>", line 97, in _verify_code
AttributeError: 'dict' object has no attribute 'input'
Attempt 3 failed verification: Failed to execute or parse the generated code. Error: 'dict' object has no attribute 'input'
Traceback: Traceback (most recent call last):
File "<string>", line 97, in _verify_code
AttributeError: 'dict' object has no attribute 'input'
Max correction attempts reached. Proceeding with potentially buggy code.
Attempt 1 failed verification: Failed to execute or parse the generated code. Error: 'dict' object has no attribute 'input'
Traceback: Traceback (most recent call last):
File "<string>", line 97, in _verify_code
AttributeError: 'dict' object has no attribute 'input'
Attempt 1 failed verification: Failed to execute or parse the generated code. Error: 'dict' object has no attribute 'input'
Traceback: Traceback (most recent call last):
File "<string>", line 97, in _verify_code
AttributeError: 'dict' object has no attribute 'input'
Attempt 2 failed verification: Failed to execute or parse the generated code. Error: 'dict' object has no attribute 'input'
Traceback: Traceback (most recent call last):
File "<string>", line 97, in _verify_code
AttributeError: 'dict' object has no attribute 'input'
Attempt 2 failed verification: Failed to execute or parse the generated code. Error: 'dict' object has no attribute 'input'
Traceback: Traceback (most recent call last):
File "<string>", line 97, in _verify_code
AttributeError: 'dict' object has no attribute 'input'
Attempt 2 failed verification: Failed to execute or parse the generated code. Error: 'dict' object has no attribute 'input'
Traceback: Traceback (most recent call last):
File "<string>", line 97, in _verify_code
AttributeError: 'dict' object has no attribute 'input'
Attempt 1 failed verification: Failed to execute or parse the generated code. Error: 'dict' object has no attribute 'input'
Traceback: Traceback (most recent call last):
File "<string>", line 97, in _verify_code
AttributeError: 'dict' object has no attribute 'input'
Attempt 3 failed verification: Failed to execute or parse the generated code. Error: 'dict' object has no attribute 'input'
Traceback: Traceback (most recent call last):
File "<string>", line 97, in _verify_code
AttributeError: 'dict' object has no attribute 'input'
Max correction attempts reached. Proceeding with potentially buggy code.
Attempt 3 failed verification: Failed to execute or parse the generated code. Error: 'dict' object has no attribute 'input'
Traceback: Traceback (most recent call last):
File "<string>", line 97, in _verify_code
AttributeError: 'dict' object has no attribute 'input'
Max correction attempts reached. Proceeding with potentially buggy code.
Attempt 1 failed verification: Failed to execute or parse the generated code. Error: 'dict' object has no attribute 'input'
Traceback: Traceback (most recent call last):
File "<string>", line 97, in _verify_code
AttributeError: 'dict' object has no attribute 'input'
Attempt 2 failed verification: Failed to execute or parse the generated code. Error: 'dict' object has no attribute 'input'
Traceback: Traceback (most recent call last):
File "<string>", line 97, in _verify_code
AttributeError: 'dict' object has no attribute 'input'
2025/08/29 20:28:28 INFO dspy.evaluate.evaluate: Average Metric: 2.0 / 3 (66.7%)
GEPA Optimization: 76%|█████████████████████████████████████████████████████████████████████████████████▉ | 3036/4000 [22:54:32<40:17:10, 150.45s/rollouts]Attempt 3 failed verification: Failed to execute or parse the generated code. Error: 'dict' object has no attribute 'input'
Traceback: Traceback (most recent call last):
File "<string>", line 97, in _verify_code
AttributeError: 'dict' object has no attribute 'input'
Max correction attempts reached. Proceeding with potentially buggy code.
Iteration 122: New subsample score is not better, skipping
Iteration 123: Selected program 2 score: 0.605
0%| | 0/3 [00:00<?, ?it/s]Attempt 3 failed verification: Failed to execute or parse the generated code. Error: 'dict' object has no attribute 'input'
Traceback: Traceback (most recent call last):
File "<string>", line 97, in _verify_code
AttributeError: 'dict' object has no attribute 'input'
Max correction attempts reached. Proceeding with potentially buggy code.
Attempt 3 failed verification: Failed to execute or parse the generated code. Error: 'dict' object has no attribute 'input'
Traceback: Traceback (most recent call last):
File "<string>", line 97, in _verify_code
AttributeError: 'dict' object has no attribute 'input'
Max correction attempts reached. Proceeding with potentially buggy code.
Average Metric: 1.00 / 1 (100.0%): 33%|█████████████████████████████████████ | 1/3 [01:30<03:01, 90.59s/it]Attempt 2 failed verification: Failed to execute or parse the generated code. Error: 'dict' object has no attribute 'input'
Traceback: Traceback (most recent call last):
File "<string>", line 97, in _verify_code
AttributeError: 'dict' object has no attribute 'input'
Average Metric: 2.00 / 3 (66.7%): 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [02:19<00:00, 46.48s/it]2025/08/29 20:30:48 INFO dspy.evaluate.evaluate: Average Metric: 2.0 / 3 (66.7%)
Iteration 123: Proposed new text for program: import dspy
from typing import List
import pydantic
import copy
# Define type aliases and pydantic models for clarity and structure.
MATRIX = List[List[int]]
class TrainingExample(pydantic.BaseModel):
input: MATRIX
output: MATRIX
class HypothesizeRule(dspy.Signature):
"""
Analyze a series of input/output matrix pairs from the Abstraction and Reasoning Corpus (ARC).
Based on these examples, deduce the underlying transformation rule and describe it in clear, unambiguous, step-by-step English.
**Successful Strategies for Deducing Rules:**
- **Analyze Changes:** What colors are added, removed, or changed? Do shapes move, rotate, grow, or shrink?
- **Identify Patterns:** Look for symmetry, repetition, or sequences.
- **Consider Spatial Relationships:** How do objects relate to each other or to the grid boundaries? Look for concepts like "inside," "outside," "bordering," or "enclosing."
- **Count and Compare:** Are transformations based on the number of objects, the most frequent color, or the size of shapes?
- **Be Specific:** Instead of "fills a shape," describe how the shape is identified and what color is used to fill it (e.g., "Find the shape enclosed by blue pixels and fill its interior with red pixels.").
"""
training_examples: List[TrainingExample] = dspy.InputField(desc="A list of input/output pairs demonstrating the transformation rule.")
rule_description: str = dspy.OutputField(desc="A step-by-step English description of the transformation rule.")
class GeneratePythonFunctionWithHypothesis(dspy.Signature):
"""
You are an expert programmer. Your task is to write a single, self-contained Python function that implements a given transformation rule. You will be provided with the rule's description and the original training examples for context.
**Function Requirements:**
- The function must be named `transform_matrix`.
- It must accept one argument: `matrix`, which is a list of lists of integers.
- It must return a list of lists of integers (the transformed grid).
- The function should not use any external libraries except for `copy` if needed.
- Your output must be ONLY the Python code for the function. Do not include any explanations, comments outside the function, or markdown formatting like ```python.
**Successful Strategies to Consider:**
- **Object Permanence:** Most transformations preserve the grid dimensions.
- **Color Transformations:** Look for rules that change colors based on their value or their neighbors.
- **Spatial Reasoning:** Analyze shapes, positions, and relationships. Common patterns include filling enclosed areas, drawing lines, moving objects, or detecting symmetry.
- **Iterative Processes:** Some rules are applied repeatedly. Consider using a `while` loop.
- **Neighborhoods:** Often, a cell's new value depends on its immediate neighbors.
"""
training_examples: List[TrainingExample] = dspy.InputField(desc="A list of input/output pairs for context.")
rule_description: str = dspy.InputField(desc="The English description of the rule to implement.")
python_function: str = dspy.OutputField(desc="A string containing a single Python function `transform_matrix` that implements the rule.")
class ARCSolver(dspy.Module):
"""A module that solves ARC tasks by hypothesizing a rule and then generating code to implement it."""
def __init__(self):
super().__init__()
# Decompose the problem: 1) Reason about the rule, 2) Write code for the rule.
self.rule_hypothesizer = dspy.ChainOfThought(HypothesizeRule)
self.code_generator = dspy.Predict(GeneratePythonFunctionWithHypothesis)
def forward(self, training_examples: List[TrainingExample], test_inputs: List[MATRIX]):
# Step 1: Generate a high-level English description of the rule.
# Using ChainOfThought encourages the LM to reason more deeply.
prediction = self.rule_hypothesizer(training_examples=training_examples)
rule_description = prediction.rule_description
# Step 2: Generate Python code based on the hypothesized rule.
# This is a more constrained task than generating code from examples alone.
code_prediction = self.code_generator(
training_examples=training_examples,
rule_description=rule_description
)
python_code = code_prediction.python_function
# Prepare a dictionary to hold the executed function.
local_scope = {}
# Create fallback outputs in case of any failure.
fallback_outputs = [copy.deepcopy(matrix) for matrix in test_inputs]
try:
# Step 3: Execute the generated code string to define the function.
exec(python_code, globals(), local_scope)
transform_func = local_scope.get('transform_matrix')
if not callable(transform_func):
return dspy.Prediction(test_outputs=fallback_outputs)
# Step 4: Apply the generated function to each test input.
solved_outputs = []
for test_matrix in test_inputs:
try:
input_copy = copy.deepcopy(test_matrix)
result = transform_func(input_copy)
solved_outputs.append(result)
except Exception:
# If the function fails on a specific test case, append the original
# matrix as a fallback for that case and continue.
solved_outputs.append(copy.deepcopy(test_matrix))
return dspy.Prediction(test_outputs=solved_outputs)
except Exception:
# If code generation or `exec` fails entirely, return the original inputs.
return dspy.Prediction(test_outputs=fallback_outputs)
# The overall task signature, for clarity and potential use in evaluation.
class SolveTaskSignature(dspy.Signature):
"""
Given a set of training examples demonstrating a matrix transformation,
apply the same transformation to a new set of test matrices.
"""
training_examples: List[TrainingExample] = dspy.InputField(description="Input and output examples demonstrating the task to be performed.")
test_inputs: List[MATRIX] = dspy.InputField(description="Input matrices to be solved following the task described in the training examples.")
test_outputs: List[MATRIX] = dspy.OutputField(description="Output matrices corresponding to the test inputs.")
# The final program is an instance of our new, more robust module.
program = ARCSolver()
Attempt 3 failed verification: Failed to execute or parse the generated code. Error: 'dict' object has no attribute 'input'
Traceback: Traceback (most recent call last):
File "<string>", line 97, in _verify_code
AttributeError: 'dict' object has no attribute 'input'
Max correction attempts reached. Proceeding with potentially buggy code.
2025/08/29 20:33:27 INFO dspy.evaluate.evaluate: Average Metric: 2.0 / 3 (66.7%)
GEPA Optimization: 76%|██████████████████████████████████████████████████████████████████████████████████▏ | 3042/4000 [22:59:31<34:02:24, 127.92s/rollouts]Iteration 123: New subsample score is not better, skipping
Iteration 124: Selected program 7 score: 0.675
Average Metric: 3.00 / 3 (100.0%): : 4it [03:47, 56.89s/it] 2025/08/29 20:37:15 INFO dspy.evaluate.evaluate: Average Metric: 3.0 / 3 (100.0%)
GEPA Optimization: 76%|██████████████████████████████████████████████████████████████████████████████████▏ | 3045/4000 [23:03:18<32:01:45, 120.74s/rollouts]
Iteration 124: All subsample scores perfect. Skipping.
Iteration 124: Reflective mutation did not propose a new candidate
Iteration 125: Selected program 7 score: 0.675
Average Metric: 2.00 / 3 (66.7%): : 5it [05:26, 65.38s/it] 2025/08/29 20:42:42 INFO dspy.evaluate.evaluate: Average Metric: 2.0 / 3 (66.7%)
Iteration 125: Proposed new text for program: import dspy
from typing import List, Any
import pydantic
import json
# Define the type for a matrix for clarity
MATRIX = List[List[int]]
class TrainingExample(pydantic.BaseModel):
"""A Pydantic model for a single training example, containing an input and output matrix."""
input: MATRIX
output: MATRIX
class InferRuleSignature(dspy.Signature):
"""
Analyzes pairs of input/output matrices to deduce the transformation rule.
You are an expert analyst of abstract visual reasoning puzzles. Your task is to carefully examine the provided training examples. Each example consists of an 'input' matrix and a corresponding 'output' matrix.
Your goal is to deduce the single, consistent transformation rule that converts each input to its output. Describe this rule in clear, unambiguous, step-by-step natural language. The description must be precise enough for another AI or a programmer to follow it to solve a new, unseen test input.
Consider various possible transformation patterns, including but not limited to:
- Geometric operations: rotation, reflection, scaling, repetition, shifting.
- Color/value transformations: changing specific numbers to others, conditional changes.
- Object-based logic: identifying shapes or objects and applying rules based on their properties (size, color, position).
- Cropping or subgrid extraction: such as finding a bounding box or selecting a specific region.
- Fill/completion patterns: such as flood fills or completing a grid based on object boundaries.
- Bounding box or frame logic: creating structures based on the extents of objects.
"""
training_examples: List[TrainingExample] = dspy.InputField(description="A list of input and output examples demonstrating the task.")
transformation_rule: str = dspy.OutputField(description="A step-by-step natural language description of the transformation rule.")
class GeneratePythonCodeSignature(dspy.Signature):
"""
Converts a natural language transformation rule into a Python function.
You are an expert Python programmer. Your task is to write a single Python function named `transform_matrix` that implements the given natural language `transformation_rule`.
**Function Requirements:**
- The function must be named exactly `transform_matrix`.
- It must accept one argument: `matrix: List[List[int]]`.
- It must return the transformed matrix as a `List[List[int]]`.
- You can use standard Python libraries. Do not use external libraries like numpy or pandas.
- The `training_examples` are provided for context. Your generated code must be robust and work for all of them. Mentally verify your logic against these examples before outputting the code.
**Output Format:**
- Your output must be ONLY the Python code for the function.
- Do NOT include any explanations, comments outside the function, or markdown formatting like ```python ... ```.
"""
transformation_rule: str = dspy.InputField(description="The natural language rule to implement.")
training_examples: List[TrainingExample] = dspy.InputField(description="Example input/output pairs to validate the logic.")
python_code: str = dspy.OutputField(description="A string containing only the Python code for the `transform_matrix` function.")
class ApplyRuleSignature(dspy.Signature):
"""
Applies a given transformation rule to a single test input matrix.
You are an expert in meticulously following instructions to transform matrices. You will be given a specific, detailed transformation rule and a single test input matrix.
Your task is to:
1. Carefully read and understand the provided rule.
2. Apply the rule step-by-step to the test input matrix.
3. Produce the final output matrix.
**Crucially, your output must be ONLY the resulting matrix, formatted as a valid JSON list of lists of integers. Do not include any extra text, explanations, or markdown formatting.**
"""
transformation_rule: str = dspy.InputField(description="The natural language rule to apply.")
test_input: MATRIX = dspy.InputField(description="The input matrix to be transformed.")
test_output: MATRIX = dspy.OutputField(description="The resulting matrix after applying the rule, as a list of lists of integers.")
class ARCProgram(dspy.Module):
"""A program that infers a rule, generates and verifies code, and falls back to direct application."""
def __init__(self):
super().__init__()
self.rule_inferrer = dspy.ChainOfThought(InferRuleSignature)
self.code_generator = dspy.Predict(GeneratePythonCodeSignature)
# Use ChainOfThought for the fallback to encourage more careful, step-by-step reasoning.
self.rule_applier_fallback = dspy.ChainOfThought(ApplyRuleSignature)
def _verify_code(self, python_code: str, training_examples: List[TrainingExample]):
"""
Executes and verifies the generated Python code against all training examples.
Returns the transform function if it passes verification, otherwise None.
"""
try:
local_namespace = {}
exec(python_code, globals(), local_namespace)
transform_func = local_namespace.get('transform_matrix')
if not callable(transform_func):
return None
# Verification step
for example in training_examples:
# The Pydantic models need to be converted back to plain lists for the function
input_matrix = example.input
expected_output = example.output
# The generated function might have its own internal errors
try:
actual_output = transform_func(input_matrix)
if actual_output != expected_output:
# Logical error detected
return None
except Exception:
# Execution error during verification
return None
# If all examples pass, return the verified function
return transform_func
except Exception:
# Syntax error or other issue with the code itself
return None
def forward(self, training_examples: List[TrainingExample], test_inputs: List[MATRIX]) -> dspy.Prediction:
"""
Infers a rule, generates and verifies code, and applies it to each test input.
Args:
training_examples: A list of TrainingExample objects.
test_inputs: A list of input matrices to be solved.
Returns:
A dspy.Prediction object with the 'test_outputs' field populated.
"""
# 1. Infer the transformation rule once.
inferred = self.rule_inferrer(training_examples=training_examples)
rule = inferred.transformation_rule
# 2. Attempt to generate and verify Python code.
generated = self.code_generator(transformation_rule=rule, training_examples=training_examples)
python_code = generated.python_code
verified_func = self._verify_code(python_code, training_examples)
# 3. If code is verified, use it to solve the test cases.
if verified_func:
all_test_outputs = []
try:
for test_matrix in test_inputs:
output = verified_func(test_matrix)
all_test_outputs.append(output)
return dspy.Prediction(test_outputs=all_test_outputs)
except Exception:
# If the verified function fails on test data, fall through to the LM-based fallback.
pass
# 4. Fallback Strategy: If code is unverified or fails at test time, use direct LM application.
all_test_outputs = []
for test_matrix in test_inputs:
try:
# Use the more robust ChainOfThought fallback
result = self.rule_applier_fallback(transformation_rule=rule, test_input=test_matrix)
# The output might be a string representation of a list, so we parse it.
if isinstance(result.test_output, str):
parsed_output = json.loads(result.test_output)
all_test_outputs.append(parsed_output)
else:
all_test_outputs.append(result.test_output)
except Exception:
# If the fallback also fails, append a default empty/zero matrix.
if test_matrix and isinstance(test_matrix, list) and len(test_matrix) > 0:
width = len(test_matrix[0]) if isinstance(test_matrix[0], list) else 0
all_test_outputs.append([([0] * width) for _ in range(len(test_matrix))])
else:
all_test_outputs.append([])
return dspy.Prediction(test_outputs=all_test_outputs)
# The final 'program' object is an instance of our robust, multi-step module.
program = ARCProgram()
2025/08/29 20:47:57 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 20:47:57 INFO dspy.evaluate.evaluate: Average Metric: 2.0 / 3 (66.7%)
GEPA Optimization: 76%|██████████████████████████████████████████████████████████████████████████████████▍ | 3051/4000 [23:14:01<30:48:40, 116.88s/rollouts]Iteration 125: New subsample score is not better, skipping
Iteration 126: Selected program 9 score: 0.67
0%| | 0/3 [00:00<?, ?it/s]2025/08/29 20:48:43 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
Average Metric: 1.00 / 2 (50.0%): 67%|██████████████████████████████████████████████████████████████████████████▋ | 2/3 [01:54<00:54, 54.35s/it]2025/08/29 20:50:43 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
Average Metric: 2.00 / 3 (66.7%): 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [04:21<00:00, 87.15s/it]2025/08/29 20:52:19 INFO dspy.evaluate.evaluate: Average Metric: 2.0 / 3 (66.7%)
Iteration 126: Proposed new text for program: import dspy
from typing import List, Any, Optional
import pydantic
import traceback
# Define the type for a matrix for clarity
MATRIX = List[List[int]]
class TrainingExample(pydantic.BaseModel):
"""A Pydantic model for a single training example, containing an input and output matrix."""
input: MATRIX
output: MATRIX
class InferRuleSignature(dspy.Signature):
"""
Analyzes pairs of input/output matrices to deduce the transformation rule.
You are an expert analyst of abstract visual reasoning puzzles. Your task is to carefully examine the provided training examples. Each example consists of an 'input' matrix and a corresponding 'output' matrix.
Your goal is to deduce the single, consistent transformation rule that converts each input to its output. Describe this rule in clear, unambiguous, step-by-step natural language. The description must be precise enough for an expert Python programmer to implement it without further clarification.
Consider various possible transformation patterns, including but not limited to:
- Simple value substitutions (e.g., change all 6s to 2s).
- Geometric operations: rotation, reflection, scaling, repetition, shifting.
- Object-based logic: identifying shapes or objects and applying rules based on their properties (size, color, position, aspect ratio of bounding box).
- Cropping and framing: Cropping the input to the bounding box of the largest object, or the object of a specific color.
- Color logic: Swapping the most frequent color with the least frequent color.
- Fill/completion patterns: such as flood fills or completing a grid based on object boundaries.
Focus on creating a generalizable, algorithmic description.
"""
training_examples: List[TrainingExample] = dspy.InputField(description="A list of input and output examples demonstrating the task.")
transformation_rule: str = dspy.OutputField(description="A step-by-step natural language description of the transformation rule.")
class GenerateCodeSignature(dspy.Signature):
"""
Writes a Python function to implement a transformation rule for matrices.
You are an expert Python programmer specializing in algorithmic tasks. You will be given a natural language description of a rule that transforms a matrix (list of lists of integers) and the original training examples that the rule was derived from.
Your task is to write a single Python function named `transform_matrix` that implements this rule.
**Function Requirements:**
- The function must be named exactly `transform_matrix`.
- It must accept one argument: `matrix` (a list of lists of integers).
- It must return a new matrix (a list of lists of integers).
- You CANNOT use any external libraries like numpy. You must implement all logic using standard Python data structures and control flow.
- The function should be self-contained. Do not define helper functions outside of its scope unless absolutely necessary (e.g., via nesting).
**Output Format:**
- Your output must be ONLY the Python code for the function.
- Do not include any explanations, comments, or surrounding text.
- Do not include the function call, just the definition.
- Start with `def transform_matrix(matrix):` and end with the last line of the function's code.
**Correctional Feedback:**
If you are provided with 'previous_code' and 'feedback', it means your last attempt failed. Carefully analyze the feedback, which explains the error, and provide a corrected version of the code. Do not repeat the same mistake.
"""
transformation_rule: str = dspy.InputField(description="The natural language rule to implement.")
training_examples: List[TrainingExample] = dspy.InputField(description="The original examples used to derive the rule, for context and validation.")
previous_code: Optional[str] = dspy.InputField(description="The previous, incorrect code attempt. If this is the first attempt, this will be empty.", default=None)
feedback: Optional[str] = dspy.InputField(description="Detailed feedback on why the previous code was wrong. If this is the first attempt, this will be empty.", default=None)
python_code: str = dspy.OutputField(description="A string containing a single Python function `transform_matrix` that implements the rule.")
class ARCProgram(dspy.Module):
"""A program that infers a rule, generates code for it, validates and refines the code, and then executes it."""
def __init__(self, max_attempts=3):
super().__init__()
self.rule_inferrer = dspy.ChainOfThought(InferRuleSignature)
self.code_generator = dspy.Predict(GenerateCodeSignature)
self.max_attempts = max_attempts
def _validate_code(self, code_str: str, training_examples: List[TrainingExample]):
"""
Compiles and validates the generated code against training examples.
Returns a tuple of (callable_function, error_message_or_none).
"""
try:
# Clean up markdown formatting
if code_str.strip().startswith("```python"):
code_str = code_str.strip()[9:].strip()
if code_str.strip().startswith("```"):
code_str = code_str.strip()[3:].strip()
if code_str.strip().endswith("```"):
code_str = code_str.strip()[:-3].strip()
local_scope = {}
exec(code_str, globals(), local_scope)
transform_func = local_scope.get('transform_matrix')
if not callable(transform_func):
return None, "Validation failed: `transform_matrix` function not found or not callable."
# Test the function against all training examples
for i, example in enumerate(training_examples):
# Create a deep copy to prevent the function from modifying the original input
input_copy = [row[:] for row in example.input]
result = transform_func(input_copy)
if result != example.output:
feedback = (
f"Validation failed on training example #{i+1}.\n"
f"Input:\n{example.input}\n"
f"Expected Output:\n{example.output}\n"
f"Actual Output from your code:\n{result}\n"
"Please analyze the discrepancy and provide a corrected implementation."
)
return None, feedback
return transform_func, None # Success
except Exception as e:
error_trace = traceback.format_exc()
return None, f"An exception occurred during code compilation or execution: {e}\nTraceback:\n{error_trace}"
def forward(self, training_examples: List[TrainingExample], test_inputs: List[MATRIX]) -> dspy.Prediction:
"""
Infers a rule, generates, validates, and refines Python code, and applies it to each test input.
"""
# 1. Infer the transformation rule from the examples.
inferred = self.rule_inferrer(training_examples=training_examples)
rule = inferred.transformation_rule
validated_func = None
code = None
feedback = None
# 2. Iteratively generate and validate code.
for attempt in range(self.max_attempts):
generated = self.code_generator(
transformation_rule=rule,
training_examples=training_examples,
previous_code=code,
feedback=feedback
)
code = generated.python_code
validated_func, feedback = self._validate_code(code, training_examples)
if validated_func:
# print(f"Code validated successfully on attempt {attempt + 1}.")
break
else:
# print(f"Attempt {attempt + 1} failed. Feedback: {feedback}")
pass
# 3. Apply the validated function to test inputs.
all_test_outputs = []
if validated_func:
for test_matrix in test_inputs:
try:
# Create a deep copy for safety
input_copy = [row[:] for row in test_matrix]
result = validated_func(input_copy)
all_test_outputs.append(result)
except Exception as e:
# print(f"Execution of validated function failed on a test input: {e}")
# Fallback for this specific input
fallback_output = [[0] * len(test_matrix[0]) for _ in range(len(test_matrix))] if test_matrix and test_matrix[0] else []
all_test_outputs.append(fallback_output)
else:
# Fallback if no valid code could be generated after all attempts
# print("Failed to generate valid code after all attempts.")
for test_matrix in test_inputs:
fallback_output = [[0] * len(test_matrix[0]) for _ in range(len(test_matrix))] if test_matrix and test_matrix[0] else []
all_test_outputs.append(fallback_output)
return dspy.Prediction(test_outputs=all_test_outputs)
# The final 'program' object is an instance of our robust, multi-step module.
program = ARCProgram()
2025/08/29 20:59:34 INFO dspy.evaluate.evaluate: Average Metric: 0.0 / 3 (0.0%)
GEPA Optimization: 76%|██████████████████████████████████████████████████████████████████████████████████▌ | 3057/4000 [23:25:38<30:33:41, 116.67s/rollouts]Iteration 126: New subsample score is not better, skipping
Iteration 127: Selected program 8 score: 0.62
Average Metric: 3.00 / 3 (100.0%): : 4it [03:37, 54.27s/it] 2025/08/29 21:03:11 INFO dspy.evaluate.evaluate: Average Metric: 3.0 / 3 (100.0%)
GEPA Optimization: 76%|██████████████████████████████████████████████████████████████████████████████████▌ | 3060/4000 [23:29:15<28:29:34, 109.12s/rollouts]
Iteration 127: All subsample scores perfect. Skipping.
Iteration 127: Reflective mutation did not propose a new candidate
Iteration 128: Selected program 8 score: 0.62
0%| | 0/3 [00:00<?, ?it/s]2025/08/29 21:06:33 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 21:08:34 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
Average Metric: 1.00 / 2 (50.0%): 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [06:32<00:00, 94.40s/it]2025/08/29 21:09:44 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
Average Metric: 2.00 / 3 (66.7%): : 6it [07:03, 70.66s/it] 2025/08/29 21:10:15 INFO dspy.evaluate.evaluate: Average Metric: 2.0 / 3 (66.7%)
Iteration 128: Proposed new text for program: import dspy
from typing import List, Optional
import pydantic
import copy
import traceback
# Define type aliases and pydantic models for clarity and structure.
MATRIX = List[List[int]]
class TrainingExample(pydantic.BaseModel):
input: MATRIX
output: MATRIX
# --- Step 1: A Signature to deduce the transformation rule in natural language ---
class HypothesizeRule(dspy.Signature):
"""
Analyzes pairs of input/output matrices from the Abstraction and Reasoning Corpus (ARC)
and deduces the underlying transformation rule. Describe the rule in clear, step-by-step
natural language. The training_examples are provided as a list of JSON-like objects.
**Successful Strategies to Consider:**
- **Grid Properties:** Analyze changes in dimensions, colors, and object counts.
- **Object Transformations:** Identify objects/shapes and describe how they are moved, rotated, scaled, colored, or combined.
- **Pattern Recognition:** Look for patterns like symmetry, repetition, or subgrid extraction. For example, is the output a small subgrid from the input?
- **Conditional Logic:** The rule might depend on specific conditions, like the color of a neighboring cell or the number of objects present.
- **Decoding:** Sometimes the input represents a key or code that maps to a complex output pattern.
"""
training_examples: List[TrainingExample] = dspy.InputField(desc="A list of input/output pairs demonstrating the transformation rule.")
hypothesis: str = dspy.OutputField(desc="A clear, step-by-step natural language description of the transformation rule.")
# --- Step 2: A Signature to generate and refine Python code based on the rule ---
class ImplementRuleInPython(dspy.Signature):
"""
You are an expert programmer. Your task is to write a single, self-contained Python function `transform_matrix` based on a hypothesis. If feedback on a previous incorrect attempt is provided, you MUST use it to correct the code.
**Function Requirements:**
- The function must be named `transform_matrix`.
- It must accept one argument: `matrix` (a list of lists of integers).
- It must return a list of lists of integers (the transformed grid).
- The function should not use any external libraries except for `copy`.
- Your output must be ONLY the Python code for the function, inside a single markdown code block.
**Verification Strategy:**
- **Crucially, before finalizing your code, mentally trace its execution using the provided `training_examples` to ensure it correctly transforms the input to the expected output for all pairs.** This is the most important step to avoid errors.
**Pitfalls to Avoid:**
- **Hardcoding:** Do not hardcode values from the examples. The function must be general.
- **Index Errors:** Be extremely careful with grid boundaries and coordinates.
"""
hypothesis: str = dspy.InputField(desc="The natural language description of the rule to implement.")
training_examples: List[TrainingExample] = dspy.InputField(desc="The original examples, for context and verification.")
previous_code: Optional[str] = dspy.InputField(desc="The previous, incorrect code attempt. If this is the first attempt, this will be empty.", prefix="Previous incorrect code:")
feedback: Optional[str] = dspy.InputField(desc="Feedback on the previous attempt's code, detailing why it was incorrect. You MUST use this to fix the code.", prefix="Feedback on incorrect code:")
python_function: str = dspy.OutputField(desc="A string containing ONLY the Python function `transform_matrix` that implements the rule.")
# --- The Improved Custom Module with a Self-Correction Loop ---
class ARCSolver(dspy.Module):
"""A module that solves ARC tasks by hypothesizing a rule, then generating, validating, and refining Python code."""
def __init__(self, max_attempts=3):
super().__init__()
self.max_attempts = max_attempts
self.rule_hypothesizer = dspy.ChainOfThought(HypothesizeRule)
self.code_generator = dspy.Predict(ImplementRuleInPython)
def _validate_code(self, python_code: str, training_examples: List[TrainingExample]):
"""Executes the generated code against training examples to validate its correctness."""
if not python_code or "def transform_matrix" not in python_code:
return False, "Generated output was not a valid Python function."
# Prepare a sandboxed environment to execute the code
local_scope = {}
try:
# The generated code is often wrapped in markdown, so extract it.
if "```python" in python_code:
python_code = python_code.split("```python\n")[1].split("```")[0]
elif "```" in python_code:
python_code = python_code.split("```\n")[1].split("```")[0]
exec(python_code, globals(), local_scope)
transform_func = local_scope.get('transform_matrix')
if not callable(transform_func):
return False, "Could not find a callable function named 'transform_matrix' in the generated code."
except Exception as e:
return False, f"Code execution failed with error: {e}\n{traceback.format_exc()}"
# Test the function against each training example
for i, example in enumerate(training_examples):
input_matrix = copy.deepcopy(example.input)
expected_output = example.output
try:
actual_output = transform_func(input_matrix)
if actual_output != expected_output:
feedback = (f"Validation failed on training example #{i}.\n"
f"Input:\n{example.input}\n"
f"Expected Output:\n{expected_output}\n"
f"Actual Output:\n{actual_output}\n"
"Please analyze the discrepancy and correct the logic.")
return False, feedback
except Exception as e:
feedback = (f"Function failed during execution on training example #{i} with error: {e}\n"
f"{traceback.format_exc()}\n"
"Please fix the bug.")
return False, feedback
return True, "Code passed all validation tests."
def forward(self, training_examples: List[TrainingExample], test_inputs: List[MATRIX]):
fallback_outputs = [copy.deepcopy(matrix) for matrix in test_inputs]
try:
# Step 1: Generate a natural language hypothesis.
prediction = self.rule_hypothesizer(training_examples=training_examples)
hypothesis = prediction.hypothesis
if not hypothesis:
# Fallback if hypothesis generation fails
return dspy.Prediction(test_outputs=fallback_outputs)
# Step 2: Iteratively generate, validate, and refine code.
feedback = None
python_code = None
validated_code = None
for attempt in range(self.max_attempts):
code_gen_result = self.code_generator(
hypothesis=hypothesis,
training_examples=training_examples,
previous_code=python_code,
feedback=feedback
)
python_code = code_gen_result.python_function
is_correct, feedback_or_success_msg = self._validate_code(python_code, training_examples)
if is_correct:
validated_code = python_code
break
else:
# The validation message becomes feedback for the next attempt.
feedback = feedback_or_success_msg
# Step 3: If a validated function was found, execute it on test inputs.
if validated_code:
local_scope = {}
# The generated code is often wrapped in markdown, so extract it.
if "```python" in validated_code:
validated_code = validated_code.split("```python\n")[1].split("```")[0]
elif "```" in validated_code:
validated_code = validated_code.split("```\n")[1].split("```")[0]
exec(validated_code, globals(), local_scope)
transform_func = local_scope.get('transform_matrix')
solved_outputs = []
for test_matrix in test_inputs:
try:
input_copy = copy.deepcopy(test_matrix)
result = transform_func(input_copy)
solved_outputs.append(result)
except Exception:
solved_outputs.append(copy.deepcopy(test_matrix))
return dspy.Prediction(test_outputs=solved_outputs)
except Exception:
# If any step in the pipeline fails catastrophically, return the original inputs.
return dspy.Prediction(test_outputs=fallback_outputs)
# Fallback if all code generation attempts fail.
return dspy.Prediction(test_outputs=fallback_outputs)
# The overall task signature, defining the final inputs and outputs of the program.
class SolveTaskSignature(dspy.Signature):
"""
Given a set of training examples demonstrating a matrix transformation,
apply the same transformation to a new set of test matrices.
"""
training_examples: List[TrainingExample] = dspy.InputField(description="Input and output examples demonstrating the task to be performed.")
test_inputs: List[MATRIX] = dspy.InputField(description="Input matrices to be solved following the task described in the training examples.")
test_outputs: List[MATRIX] = dspy.OutputField(description="Output matrices corresponding to the test inputs.")
# The final program is an instance of our new, more robust module.
program = ARCSolver()
2025/08/29 21:20:04 INFO dspy.evaluate.evaluate: Average Metric: 0.0 / 3 (0.0%)
GEPA Optimization: 77%|██████████████████████████████████████████████████████████████████████████████████▊ | 3066/4000 [23:46:08<33:22:58, 128.67s/rollouts]Iteration 128: New subsample score is not better, skipping
Iteration 129: Selected program 11 score: 0.665
0%| | 0/3 [00:00<?, ?it/s]2025/08/29 21:20:23 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
Average Metric: 2.00 / 3 (66.7%): : 6it [14:41, 146.90s/it] 2025/08/29 21:34:45 INFO dspy.evaluate.evaluate: Average Metric: 2.0 / 3 (66.7%)
Iteration 129: Proposed new text for program: import dspy
from typing import List, Tuple, Optional
import pydantic
import copy
import traceback
# Define type aliases and pydantic models for clarity and structure.
MATRIX = List[List[int]]
class TrainingExample(pydantic.BaseModel):
input: MATRIX
output: MATRIX
# --- Signatures ---
class HypothesizeRule(dspy.Signature):
"""
Analyze the provided input/output matrix pairs from the Abstraction and Reasoning Corpus (ARC).
Deduce the underlying transformation rule that converts each input matrix to its corresponding output matrix.
Describe this rule in clear, step-by-step, unambiguous English.
**Crucial Instructions:**
- Your rule **must** be consistent across all provided training examples.
- If you find a rule that works for some but not all, you must state the inconsistency and attempt to find a more general or conditional rule that explains all cases.
- Focus on the logic and the sequence of operations, not on Python code implementation.
"""
training_examples: List[TrainingExample] = dspy.InputField(desc="A list of input/output pairs demonstrating the transformation rule.")
rule_description: str = dspy.OutputField(desc="A step-by-step English description of the transformation rule.")
class ImplementRule(dspy.Signature):
"""
You are an expert programmer. Your task is to write a single, self-contained Python function based on a provided rule description and example pairs.
**Function Requirements:**
- The function must be named `transform_matrix`.
- It must accept one argument: `matrix`, which is a list of lists of integers (representing the input grid).
- It must return a list of lists of integers (representing the transformed output grid).
- The function should not use any external libraries except for `copy` if needed (e.g., `import copy; new_matrix = copy.deepcopy(matrix)`).
- Your output must be ONLY the Python code for the function. Do not include any explanations, comments outside the function, or markdown formatting like ```python.
**Successful Strategies to Consider:**
- **Robust Parsing:** When rules involve sub-regions (e.g., quadrants, objects), write code that can robustly find their boundaries.
- **Iterative Processes:** Some rules are applied repeatedly until the grid no longer changes. Consider using a `while` loop.
- **Neighborhoods:** Often, a cell's new value depends on its immediate neighbors.
- **Complex Shapes/Fills:** For tasks involving finding enclosed areas or connected components, consider using algorithms like Breadth-First Search (BFS) or Depth-First Search (DFS) for flood-filling or component discovery.
- **Handling Edge Cases:** Ensure your code handles empty matrices or matrices with unexpected dimensions gracefully.
**Example of a Correctly Formatted Output:**
def transform_matrix(matrix: list[list[int]]) -> list[list[int]]:
# Your implementation here
from collections import Counter
import itertools
if not matrix or not matrix[0]: return []
counts = Counter(itertools.chain.from_iterable(matrix))
if not counts: return []
most_common_color = sorted(counts.items(), key=lambda item: (-item[1], item[0]))[0][0]
height, width = len(matrix), len(matrix[0])
return [[most_common_color for _ in range(width)] for _ in range(height)]
"""
rule_description: str = dspy.InputField(desc="The English description of the rule to implement.")
training_examples: List[TrainingExample] = dspy.InputField(desc="A list of input/output pairs to use as a reference for implementation.")
python_function: str = dspy.OutputField(desc="A string containing a single Python function `transform_matrix` that implements the rule.")
class RefineCode(dspy.Signature):
"""
You are a senior programmer debugging a Python function. You will be given a rule description, training examples, the buggy Python code, and feedback on why it failed.
Your task is to fix the code so that it correctly implements the rule and passes the training examples.
The refined function must adhere to all the original requirements.
**Function Requirements:**
- The function must be named `transform_matrix`.
- It must accept one argument: `matrix`, which is a list of lists of integers.
- It must return a list of lists of integers.
- The function should not use any external libraries except for `copy`.
- Your output must be ONLY the Python code for the function. Do not include any explanations or markdown formatting.
"""
rule_description: str = dspy.InputField(desc="The English description of the rule to implement.")
training_examples: List[TrainingExample] = dspy.InputField(desc="A list of input/output pairs the function must correctly handle.")
buggy_code: str = dspy.InputField(desc="The Python code that failed verification.")
feedback: str = dspy.InputField(desc="A description of the error or the discrepancy between the function's output and the expected output for a training example.")
refined_python_function: str = dspy.OutputField(desc="A string containing the corrected, single Python function `transform_matrix`.")
# --- Custom Module with Self-Correction ---
class ARCSolver(dspy.Module):
"""A module that solves ARC tasks by hypothesizing a rule, generating code, and refining it through verification."""
def __init__(self, max_retries=2):
super().__init__()
self.max_retries = max_retries
self.rule_hypothesizer = dspy.ChainOfThought(HypothesizeRule)
self.code_implementer = dspy.Predict(ImplementRule)
self.code_refiner = dspy.Predict(RefineCode)
def _execute_and_verify(self, python_code: str, examples: List[TrainingExample]) -> Tuple[bool, Optional[str]]:
"""
Executes the generated Python code and verifies its correctness against training examples.
Returns a tuple: (is_correct, feedback_message).
"""
local_scope = {}
try:
exec(python_code, globals(), local_scope)
transform_func = local_scope.get('transform_matrix')
if not callable(transform_func):
return False, "The generated code does not define a callable function named 'transform_matrix'."
except Exception as e:
return False, f"Code compilation failed with an error: {e}\n{traceback.format_exc()}"
for i, example_data in enumerate(examples):
try:
# CRITICAL FIX: Re-validate the dict into a Pydantic model to enable attribute access.
example = TrainingExample.model_validate(example_data)
input_copy = copy.deepcopy(example.input)
predicted_output = transform_func(input_copy)
if predicted_output != example.output:
feedback = (
f"Verification failed on training example {i}.\n"
f"Input:\n{example.input}\n"
f"Expected Output:\n{example.output}\n"
f"Actual Output:\n{predicted_output}"
)
return False, feedback
except Exception as e:
feedback = (
f"An exception occurred during verification on training example {i}: {e}\n"
f"{traceback.format_exc()}"
)
return False, feedback
return True, None
def forward(self, training_examples: List[TrainingExample], test_inputs: List[MATRIX]):
# Phase 1: Hypothesize, Implement, and Refine until code is correct.
hypothesis = self.rule_hypothesizer(training_examples=training_examples)
prediction = self.code_implementer(
rule_description=hypothesis.rule_description,
training_examples=training_examples
)
python_code = prediction.python_function
for i in range(self.max_retries):
is_correct, feedback = self._execute_and_verify(python_code, training_examples)
if is_correct:
break # Code is validated, proceed to execution.
dspy.logger.info(f"Code failed verification on attempt {i+1}. Feedback: {feedback}")
refinement = self.code_refiner(
rule_description=hypothesis.rule_description,
training_examples=training_examples,
buggy_code=python_code,
feedback=feedback
)
python_code = refinement.refined_python_function
# Phase 2: Execute the final, validated code on the test inputs.
fallback_outputs = [copy.deepcopy(matrix) for matrix in test_inputs]
local_scope = {}
try:
exec(python_code, globals(), local_scope)
transform_func = local_scope.get('transform_matrix')
if not callable(transform_func):
return dspy.Prediction(test_outputs=fallback_outputs)
solved_outputs = []
for test_matrix in test_inputs:
try:
input_copy = copy.deepcopy(test_matrix)
result = transform_func(input_copy)
solved_outputs.append(result)
except Exception:
# If a single test case fails, fallback for that case only.
solved_outputs.append(copy.deepcopy(test_matrix))
return dspy.Prediction(test_outputs=solved_outputs)
except Exception:
# If the final code fails to compile/run at all, return fallbacks for all test cases.
return dspy.Prediction(test_outputs=fallback_outputs)
# The overall task signature, defining the final inputs and outputs of the program.
class SolveTaskSignature(dspy.Signature):
"""
Given a set of training examples demonstrating a matrix transformation,
apply the same transformation to a new set of test matrices.
"""
training_examples: List[TrainingExample] = dspy.InputField(description="Input and output examples demonstrating the task to be performed.")
test_inputs: List[MATRIX] = dspy.InputField(description="Input matrices to be solved following the task described in the training examples.")
test_outputs: List[MATRIX] = dspy.OutputField(description="Output matrices corresponding to the test inputs.")
# The final program is an instance of our new, more robust module.
program = ARCSolver()
2025/08/29 21:39:09 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 1, 0, 1, 1, 0, 0, 0, 0], [0, 1, 0, 0, 0, 1, 0, 0, 0, 0], [0, 1, 0, 2, 0, 1, 0, 0, 0, 0], [0, 1, 0, 0, 0, 1, 0, 0, 0, 0], [0, 1, 1, 1, 1, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 2, 2, 2, 2, 2, 0, 0, 0, 0], [0, 1, 1, 2, 1, 1, 0, 0, 0, 0], [0, 1, 2, 2, 2, 1, 0, 0, 0, 0], [0, 1, 2, 2, 2, 1, 0, 0, 0, 0], [0, 1, 2, 2, 2, 1, 0, 0, 0, 0], [0, 1, 1, 1, 1, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}, {'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 1, 0, 1, 1, 0, 0, 0, 0], [0, 1, 0, 2, 0, 1, 0, 0, 0, 0], [0, 1, 0, 0, 0, 1, 0, 0, 0, 0], [0, 1, 1, 1, 1, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 1, 0, 1, 1, 0], [0, 0, 0, 0, 1, 0, 3, 0, 1, 0], [0, 0, 0, 0, 1, 1, 1, 1, 1, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 2, 2, 2, 2, 2, 0, 0, 0, 0], [0, 1, 1, 2, 1, 1, 0, 0, 0, 0], [0, 1, 2, 2, 2, 1, 0, 0, 0, 0], [0, 1, 2, 2, 2, 1, 0, 0, 0, 0], [0, 1, 1, 1, 1, 1, 0, 0, 0, 0], [0, 0, 0, 0, 3, 3, 3, 3, 3, 0], [0, 0, 0, 0, 1, 1, 3, 1, 1, 0], [0, 0, 0, 0, 1, 3, 3, 3, 1, 0], [0, 0, 0, 0, 1, 1, 1, 1, 1, 0]]}, {'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 1, 0, 1, 1, 0, 0, 0, 0], [0, 1, 0, 6, 0, 1, 0, 0, 0, 0], [0, 1, 0, 0, 0, 1, 0, 0, 0, 0], [0, 1, 1, 1, 1, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 1, 0, 1, 1, 0], [0, 0, 0, 0, 1, 0, 8, 0, 1, 0], [0, 0, 0, 0, 1, 1, 1, 1, 1, 0]], 'output': [[0, 6, 6, 6, 6, 6, 0, 0, 0, 0], [0, 1, 1, 6, 1, 1, 0, 0, 0, 0], [0, 1, 6, 6, 6, 1, 0, 0, 0, 0], [0, 1, 6, 6, 6, 1, 0, 0, 0, 0], [0, 1, 1, 1, 1, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 8, 8, 8, 8, 8, 0], [0, 0, 0, 0, 1, 1, 8, 1, 1, 0], [0, 0, 0, 0, 1, 8, 8, 8, 1, 0], [0, 0, 0, 0, 1, 1, 1, 1, 1, 0]]}], 'test_inputs': [[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 0, 1, 1, 0, 0, 0, 0, 0], [1, 0, 4, 0, 1, 0, 0, 0, 0, 0], [1, 0, 0, 0, 1, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 1, 0, 1, 1, 0], [0, 0, 0, 0, 1, 0, 7, 0, 1, 0], [0, 0, 0, 0, 1, 0, 0, 0, 1, 0], [0, 0, 0, 0, 1, 1, 1, 1, 1, 0]]], 'test_outputs': [[[4, 4, 4, 4, 4, 0, 0, 0, 0, 0], [1, 1, 4, 1, 1, 0, 0, 0, 0, 0], [1, 4, 4, 4, 1, 0, 0, 0, 0, 0], [1, 4, 4, 4, 1, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 7, 7, 7, 7, 7, 0], [0, 0, 0, 0, 1, 1, 7, 1, 1, 0], [0, 0, 0, 0, 1, 7, 7, 7, 1, 0], [0, 0, 0, 0, 1, 7, 7, 7, 1, 0], [0, 0, 0, 0, 1, 1, 1, 1, 1, 0]]]}) (input_keys={'test_inputs', 'training_examples'}): module 'dspy' has no attribute 'logger'
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 146, in forward
AttributeError: module 'dspy' has no attribute 'logger'
2025/08/29 21:41:08 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 3, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 3, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 3, 0, 0], [0, 0, 0, 0, 0, 0, 3, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 8, 0, 0, 0, 0, 0], [0, 0, 3, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 3, 0, 0, 0, 0, 0, 0], [0, 8, 0, 0, 0, 8, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 3, 0, 0], [0, 0, 0, 0, 0, 0, 3, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 8, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}, {'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 3, 3, 0, 0, 0, 0, 0], [0, 0, 0, 3, 3, 0, 0, 0, 0, 0], [0, 3, 3, 0, 0, 0, 0, 0, 0, 0], [0, 3, 3, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [8, 0, 0, 0, 0, 0, 0, 0, 0, 0], [8, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 3, 3, 0, 0, 0, 0, 0], [0, 0, 0, 3, 3, 0, 0, 0, 0, 0], [0, 3, 3, 0, 0, 0, 0, 0, 0, 0], [0, 3, 3, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 8, 8, 0, 0, 0], [0, 0, 0, 0, 0, 8, 8, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}, {'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 3, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 3, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 8, 0, 0, 0, 0], [0, 0, 0, 3, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 3, 0, 0, 0, 0, 0], [0, 0, 8, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}], 'test_inputs': [[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 3, 3, 3, 0], [0, 0, 0, 0, 0, 0, 3, 3, 3, 0], [0, 0, 0, 0, 0, 0, 3, 3, 3, 0], [0, 0, 0, 3, 3, 3, 0, 0, 0, 0], [0, 0, 0, 3, 3, 3, 0, 0, 0, 0], [0, 0, 0, 3, 3, 3, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]], 'test_outputs': [[[8, 8, 8, 0, 0, 0, 0, 0, 0, 0], [8, 8, 8, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 3, 3, 3, 0], [0, 0, 0, 0, 0, 0, 3, 3, 3, 0], [0, 0, 0, 0, 0, 0, 3, 3, 3, 0], [0, 0, 0, 3, 3, 3, 0, 0, 0, 0], [0, 0, 0, 3, 3, 3, 0, 0, 0, 0], [0, 0, 0, 3, 3, 3, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 8], [0, 0, 0, 0, 0, 0, 0, 0, 0, 8]]]}) (input_keys={'test_inputs', 'training_examples'}): module 'dspy' has no attribute 'logger'
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 146, in forward
AttributeError: module 'dspy' has no attribute 'logger'
2025/08/29 21:41:30 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 3, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 3, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 3, 0, 0], [0, 0, 0, 0, 0, 0, 3, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 8, 0, 0, 0, 0, 0], [0, 0, 3, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 3, 0, 0, 0, 0, 0, 0], [0, 8, 0, 0, 0, 8, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 3, 0, 0], [0, 0, 0, 0, 0, 0, 3, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 8, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}, {'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 3, 3, 0, 0, 0, 0, 0], [0, 0, 0, 3, 3, 0, 0, 0, 0, 0], [0, 3, 3, 0, 0, 0, 0, 0, 0, 0], [0, 3, 3, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [8, 0, 0, 0, 0, 0, 0, 0, 0, 0], [8, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 3, 3, 0, 0, 0, 0, 0], [0, 0, 0, 3, 3, 0, 0, 0, 0, 0], [0, 3, 3, 0, 0, 0, 0, 0, 0, 0], [0, 3, 3, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 8, 8, 0, 0, 0], [0, 0, 0, 0, 0, 8, 8, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}, {'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 3, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 3, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 8, 0, 0, 0, 0], [0, 0, 0, 3, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 3, 0, 0, 0, 0, 0], [0, 0, 8, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}], 'test_inputs': [[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 3, 3, 3, 0], [0, 0, 0, 0, 0, 0, 3, 3, 3, 0], [0, 0, 0, 0, 0, 0, 3, 3, 3, 0], [0, 0, 0, 3, 3, 3, 0, 0, 0, 0], [0, 0, 0, 3, 3, 3, 0, 0, 0, 0], [0, 0, 0, 3, 3, 3, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]], 'test_outputs': [[[8, 8, 8, 0, 0, 0, 0, 0, 0, 0], [8, 8, 8, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 3, 3, 3, 0], [0, 0, 0, 0, 0, 0, 3, 3, 3, 0], [0, 0, 0, 0, 0, 0, 3, 3, 3, 0], [0, 0, 0, 3, 3, 3, 0, 0, 0, 0], [0, 0, 0, 3, 3, 3, 0, 0, 0, 0], [0, 0, 0, 3, 3, 3, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 8], [0, 0, 0, 0, 0, 0, 0, 0, 0, 8]]]}) (input_keys={'test_inputs', 'training_examples'}): module 'dspy' has no attribute 'logger'
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 146, in forward
AttributeError: module 'dspy' has no attribute 'logger'
2025/08/29 21:41:49 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 3, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 3, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 3, 0, 0], [0, 0, 0, 0, 0, 0, 3, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 8, 0, 0, 0, 0, 0], [0, 0, 3, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 3, 0, 0, 0, 0, 0, 0], [0, 8, 0, 0, 0, 8, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 3, 0, 0], [0, 0, 0, 0, 0, 0, 3, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 8, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}, {'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 3, 3, 0, 0, 0, 0, 0], [0, 0, 0, 3, 3, 0, 0, 0, 0, 0], [0, 3, 3, 0, 0, 0, 0, 0, 0, 0], [0, 3, 3, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [8, 0, 0, 0, 0, 0, 0, 0, 0, 0], [8, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 3, 3, 0, 0, 0, 0, 0], [0, 0, 0, 3, 3, 0, 0, 0, 0, 0], [0, 3, 3, 0, 0, 0, 0, 0, 0, 0], [0, 3, 3, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 8, 8, 0, 0, 0], [0, 0, 0, 0, 0, 8, 8, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}, {'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 3, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 3, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 8, 0, 0, 0, 0], [0, 0, 0, 3, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 3, 0, 0, 0, 0, 0], [0, 0, 8, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}], 'test_inputs': [[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 3, 3, 3, 0], [0, 0, 0, 0, 0, 0, 3, 3, 3, 0], [0, 0, 0, 0, 0, 0, 3, 3, 3, 0], [0, 0, 0, 3, 3, 3, 0, 0, 0, 0], [0, 0, 0, 3, 3, 3, 0, 0, 0, 0], [0, 0, 0, 3, 3, 3, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]], 'test_outputs': [[[8, 8, 8, 0, 0, 0, 0, 0, 0, 0], [8, 8, 8, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 3, 3, 3, 0], [0, 0, 0, 0, 0, 0, 3, 3, 3, 0], [0, 0, 0, 0, 0, 0, 3, 3, 3, 0], [0, 0, 0, 3, 3, 3, 0, 0, 0, 0], [0, 0, 0, 3, 3, 3, 0, 0, 0, 0], [0, 0, 0, 3, 3, 3, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 8], [0, 0, 0, 0, 0, 0, 0, 0, 0, 8]]]}) (input_keys={'test_inputs', 'training_examples'}): module 'dspy' has no attribute 'logger'
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 146, in forward
AttributeError: module 'dspy' has no attribute 'logger'
2025/08/29 21:42:33 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 3, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 3, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 3, 0, 0], [0, 0, 0, 0, 0, 0, 3, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 8, 0, 0, 0, 0, 0], [0, 0, 3, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 3, 0, 0, 0, 0, 0, 0], [0, 8, 0, 0, 0, 8, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 3, 0, 0], [0, 0, 0, 0, 0, 0, 3, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 8, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}, {'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 3, 3, 0, 0, 0, 0, 0], [0, 0, 0, 3, 3, 0, 0, 0, 0, 0], [0, 3, 3, 0, 0, 0, 0, 0, 0, 0], [0, 3, 3, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [8, 0, 0, 0, 0, 0, 0, 0, 0, 0], [8, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 3, 3, 0, 0, 0, 0, 0], [0, 0, 0, 3, 3, 0, 0, 0, 0, 0], [0, 3, 3, 0, 0, 0, 0, 0, 0, 0], [0, 3, 3, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 8, 8, 0, 0, 0], [0, 0, 0, 0, 0, 8, 8, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}, {'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 3, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 3, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 8, 0, 0, 0, 0], [0, 0, 0, 3, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 3, 0, 0, 0, 0, 0], [0, 0, 8, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}], 'test_inputs': [[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 3, 3, 3, 0], [0, 0, 0, 0, 0, 0, 3, 3, 3, 0], [0, 0, 0, 0, 0, 0, 3, 3, 3, 0], [0, 0, 0, 3, 3, 3, 0, 0, 0, 0], [0, 0, 0, 3, 3, 3, 0, 0, 0, 0], [0, 0, 0, 3, 3, 3, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]], 'test_outputs': [[[8, 8, 8, 0, 0, 0, 0, 0, 0, 0], [8, 8, 8, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 3, 3, 3, 0], [0, 0, 0, 0, 0, 0, 3, 3, 3, 0], [0, 0, 0, 0, 0, 0, 3, 3, 3, 0], [0, 0, 0, 3, 3, 3, 0, 0, 0, 0], [0, 0, 0, 3, 3, 3, 0, 0, 0, 0], [0, 0, 0, 3, 3, 3, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 8], [0, 0, 0, 0, 0, 0, 0, 0, 0, 8]]]}) (input_keys={'test_inputs', 'training_examples'}): module 'dspy' has no attribute 'logger'
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 146, in forward
AttributeError: module 'dspy' has no attribute 'logger'
2025/08/29 21:42:33 INFO dspy.evaluate.evaluate: Average Metric: 2.0 / 3 (66.7%)
GEPA Optimization: 77%|██████████████████████████████████████████████████████████████████████████████████▉ | 3072/4000 [24:08:36<41:03:47, 159.30s/rollouts]Iteration 129: New subsample score is not better, skipping
Iteration 130: Selected program 9 score: 0.67
Average Metric: 2.00 / 3 (66.7%): 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [06:07<00:00, 122.40s/it]2025/08/29 21:48:40 INFO dspy.evaluate.evaluate: Average Metric: 2.0 / 3 (66.7%)
Iteration 130: Proposed new text for program: import dspy
from typing import List, Any, Tuple
import pydantic
import traceback
import copy
# Define the type for a matrix for clarity
MATRIX = List[List[int]]
class TrainingExample(pydantic.BaseModel):
"""A Pydantic model for a single training example, containing an input and output matrix."""
input: MATRIX
output: MATRIX
class InferRuleSignature(dspy.Signature):
"""
Analyzes pairs of input/output matrices to deduce the transformation rule.
You are an expert analyst of abstract visual reasoning puzzles. Your task is to carefully examine the provided training examples. Each example consists of an 'input' matrix and a corresponding 'output' matrix.
Your goal is to deduce the single, consistent transformation rule that converts each input to its output. Describe this rule in clear, unambiguous, step-by-step natural language. The description must be precise enough for an expert Python programmer to implement it without further clarification.
**Successful Strategies to Consider:**
- First, describe the high-level transformation (e.g., 'move an object and fill an area'), then detail the specific rules for each part.
- Consider properties of the grid itself, such as its dimensions (height, width, parity of dimensions), as potential conditions for the rule.
- Look for patterns related to object properties like color, size, shape, or position.
- Transformation types can include:
- Simple value substitutions (e.g., change all 6s to 2s).
- Geometric operations: rotation, reflection, scaling, repetition, shifting.
- Object-based logic: identifying shapes and applying rules based on their properties.
- Cropping, framing, or extending grids.
- Color logic: swapping most/least frequent colors.
- Fill/completion patterns: flood fills, drawing lines, or checkerboards.
**Pitfalls to Avoid:**
- Do not propose overly simple rules that fit the training data but are unlikely to generalize. Look for the most robust and comprehensive explanation.
- Ensure the rule is deterministic and can be applied algorithmically.
"""
training_examples: List[TrainingExample] = dspy.InputField(description="A list of input and output examples demonstrating the task.")
transformation_rule: str = dspy.OutputField(description="A step-by-step natural language description of the transformation rule.")
class GenerateCodeSignature(dspy.Signature):
"""
Writes a Python function to implement a transformation rule for matrices.
You are an expert Python programmer specializing in algorithmic tasks. You will be given a natural language description of a rule that transforms a matrix (list of lists of integers) and the original training examples that the rule was derived from.
Your task is to write a single Python function named `transform_matrix` that implements this rule.
**Function Requirements:**
- The function must be named exactly `transform_matrix`.
- It must accept one argument: `matrix` (a list of lists of integers).
- It must return a new matrix (a list of lists of integers).
- You CANNOT use any external libraries like numpy. You must implement all logic using standard Python data structures and control flow.
- The function should be self-contained. Do not define helper functions outside of its scope unless absolutely necessary (e.g., via nesting).
- Ensure your code handles edge cases like empty matrices.
**Output Format:**
- Your output must be ONLY the Python code for the function.
- Do not include any explanations, comments, or surrounding text.
- Do not include the function call, just the definition.
- Start with `def transform_matrix(matrix):` and end with the last line of the function's code.
"""
transformation_rule: str = dspy.InputField(description="The natural language rule to implement.")
training_examples: List[TrainingExample] = dspy.InputField(description="The original examples used to derive the rule, for context and validation.")
python_code: str = dspy.OutputField(description="A string containing a single Python function `transform_matrix` that implements the rule.")
class VerifyAndCorrectSignature(dspy.Signature):
"""
Analyzes a transformation rule, its generated Python code, and feedback on its failure to correctly handle the training examples.
Your task is to act as a debugger. Analyze the discrepancy between the expected output and the actual output described in the feedback.
Pinpoint the logical flaw in the original `transformation_rule`. Propose a `corrected_transformation_rule` that accounts for the failure cases.
"""
training_examples: List[TrainingExample] = dspy.InputField(desc="The original examples used to derive the rule.")
transformation_rule: str = dspy.InputField(desc="The flawed transformation rule that was proposed.")
python_code: str = dspy.InputField(desc="The Python code generated from the flawed rule.")
feedback: str = dspy.InputField(desc="Detailed feedback from executing the code on the training examples, highlighting the mismatches.")
corrected_transformation_rule: str = dspy.OutputField(desc="A refined, corrected version of the transformation rule that should fix the observed errors.")
class ARCProgram(dspy.Module):
"""A program that infers a rule, generates code, verifies, corrects, and executes."""
def __init__(self, max_retries: int = 2):
super().__init__()
self.rule_inferrer = dspy.ChainOfThought(InferRuleSignature)
self.code_generator = dspy.Predict(GenerateCodeSignature)
self.rule_corrector = dspy.ChainOfThought(VerifyAndCorrectSignature)
self.max_retries = max_retries
def _clean_code(self, code: str) -> str:
"""Strips markdown formatting from the generated code string."""
if code.strip().startswith("```python"):
code = code.strip()[9:].strip()
if code.strip().startswith("```"):
code = code.strip()[3:].strip()
if code.strip().endswith("```"):
code = code.strip()[:-3].strip()
return code
def _verify_on_training(self, code: str, training_examples: List[TrainingExample]) -> Tuple[bool, str, Any]:
"""
Executes the generated code on the training examples to verify its correctness.
Returns a tuple of (is_correct, feedback_string, transform_function).
"""
try:
local_scope = {}
exec(code, globals(), local_scope)
transform_func = local_scope.get('transform_matrix')
if not callable(transform_func):
return False, "Code did not define a callable function 'transform_matrix'.", None
except Exception as e:
return False, f"Code failed to compile. Error: {e}\n{traceback.format_exc()}", None
for i, example in enumerate(training_examples):
try:
# Use deepcopy to avoid accidental modification of inputs
input_copy = copy.deepcopy(example.input)
result = transform_func(input_copy)
if result != example.output:
feedback = (f"Verification failed on training example {i}.\n"
f"Input:\n{example.input}\n"
f"Expected Output:\n{example.output}\n"
f"Actual Output:\n{result}")
return False, feedback, None
except Exception as e:
feedback = (f"Code execution failed on training example {i} with error: {e}\n"
f"{traceback.format_exc()}")
return False, feedback, None
return True, "All training examples passed.", transform_func
def forward(self, training_examples: List[TrainingExample], test_inputs: List[MATRIX]) -> dspy.Prediction:
"""
Infers, generates, verifies, corrects, and applies code to solve the task.
"""
# 1. Initial rule inference.
inferred = self.rule_inferrer(training_examples=training_examples)
rule = inferred.transformation_rule
transform_func = None
# 2. Verification and Correction Loop.
for attempt in range(self.max_retries):
print(f"--- Attempt {attempt + 1}/{self.max_retries} ---")
print(f"Current Rule: {rule}")
# Generate code for the current rule.
generated = self.code_generator(transformation_rule=rule, training_examples=training_examples)
code = self._clean_code(generated.python_code)
# Verify the code against training examples.
is_correct, feedback, func = self._verify_on_training(code, training_examples)
if is_correct:
print("Rule and code successfully verified on training examples.")
transform_func = func
break
# If not correct, try to correct the rule.
print(f"Verification failed. Feedback: {feedback}")
if attempt < self.max_retries - 1:
print("Attempting to correct the rule...")
correction = self.rule_corrector(
training_examples=training_examples,
transformation_rule=rule,
python_code=code,
feedback=feedback
)
rule = correction.corrected_transformation_rule
else:
print("Max retries reached. Proceeding with last generated code if possible.")
# As a last resort, try to get the function from the last failed attempt
try:
local_scope = {}
exec(code, globals(), local_scope)
transform_func = local_scope.get('transform_matrix')
except:
transform_func = None
# 3. Apply the final function to each test input.
all_test_outputs = []
for test_matrix in test_inputs:
# Default fallback output is a zero-filled matrix of the same size.
if test_matrix and test_matrix[0]:
fallback_output = [[0] * len(test_matrix[0]) for _ in range(len(test_matrix))]
else:
fallback_output = []
if callable(transform_func):
try:
# Use deepcopy to ensure test inputs are not modified across calls
input_copy = copy.deepcopy(test_matrix)
result = transform_func(input_copy)
all_test_outputs.append(result)
except Exception as e:
print(f"Execution of final transform_matrix failed for a test input: {e}")
traceback.print_exc()
all_test_outputs.append(fallback_output)
else:
print("No valid transform_matrix function available. Using fallback.")
all_test_outputs.append(fallback_output)
return dspy.Prediction(test_outputs=all_test_outputs)
# The final 'program' object is an instance of our robust, multi-step module.
program = ARCProgram()
--- Attempt 1/2 ---
Current Rule: 1. Create a copy of the input grid to serve as the output grid.
2. Locate the coordinates of the single pixel with color 2. Let these be `(r2, c2)`.
3. Locate the coordinates of the single pixel with color 3. Let these be `(r3, c3)`.
4. Draw the horizontal segment of an 'L' shape with color 8. This segment lies on row `r2`. Iterate through all columns `c` from `min(c2, c3)` to `max(c2, c3)`. For each `c`, if `c` is not equal to `c2`, set the color of the cell at `(r2, c)` in the output grid to 8.
5. Draw the vertical segment of the 'L' shape with color 8. This segment lies on column `c3`. Iterate through all rows `r` from `min(r2, r3) + 1` to `max(r2, r3) - 1`. For each `r`, set the color of the cell at `(r, c3)` in the output grid to 8.
6. The modified grid is the final output.
--- Attempt 1/2 ---
Current Rule: 1. **Identify Candidate Rectangles:** Find all maximal rectangular regions within the input grid that are composed entirely of cells with the color `0`. A maximal rectangle is one that cannot be extended in any of the four cardinal directions without including a cell of a different color.
2. **Select the Target Rectangle:** Apply the following hierarchical criteria to select a single target rectangle from the candidates identified in step 1:
a. **Primary Criterion (Maximum Height):** Select the rectangle(s) with the greatest height.
b. **Secondary Criterion (Maximum Area):** If the primary criterion results in a tie (more than one rectangle has the maximum height), select from this subset the rectangle(s) with the largest area.
c. **Tertiary Criterion (Position):** If a tie still persists, select the rectangle whose top-left corner appears first in reading order (i.e., has the smallest row index, and then the smallest column index in case of a row tie).
3. **Apply the Transformation:** Create a copy of the input grid. In this copy, change the color of all cells within the bounds of the single target rectangle selected in step 2 to the color `6`. The resulting grid is the output.
--- Attempt 1/2 ---
Current Rule: 1. Identify the single non-zero cell in the input grid. Let its row index be `r`, its column index be `c`, and its color (value) be `C`.
2. Create a new output grid of the same dimensions as the input, initialized with the background color 0.
3. Place the object, moved one row down, into the output grid. Specifically, set the value of the cell at coordinates `(r+1, c)` to `C`.
4. Generate a vertical stripe pattern in the rectangular region of the output grid from row 0 to row `r` (inclusive), spanning all columns.
5. For each cell `(i, j)` within this region (where `0 <= i <= r`):
a. Determine if the cell's column index `j` has the same parity (both even or both odd) as the object's original column index `c`.
b. If they have the same parity, set the value of cell `(i, j)` to 4. Otherwise, its value remains 0.
Verification failed. Feedback: Code execution failed on training example 0 with error: 'dict' object has no attribute 'input'
Traceback (most recent call last):
File "<string>", line 117, in _verify_on_training
AttributeError: 'dict' object has no attribute 'input'
Attempting to correct the rule...
Verification failed. Feedback: Code execution failed on training example 0 with error: 'dict' object has no attribute 'input'
Traceback (most recent call last):
File "<string>", line 117, in _verify_on_training
AttributeError: 'dict' object has no attribute 'input'
Attempting to correct the rule...
--- Attempt 1/2 ---
Current Rule: 1. Identify the single non-zero cell in the input grid. Let its row index be `r`, its column index be `c`, and its color (value) be `C`.
2. Create a new output grid of the same dimensions as the input, initialized with the background color 0.
3. Place the object, moved one row down, into the output grid. Specifically, set the value of the cell at coordinates `(r+1, c)` to `C`.
4. Generate a vertical stripe pattern in the rectangular region of the output grid from row 0 to row `r` (inclusive), spanning all columns.
5. For each cell `(i, j)` within this region (where `0 <= i <= r`):
a. Determine if the cell's column index `j` has the same parity (both even or both odd) as the object's original column index `c`.
b. If they have the same parity, set the value of cell `(i, j)` to 4. Otherwise, its value remains 0.
--- Attempt 1/2 ---
Current Rule: 1. **Identify Candidate Rectangles:** Find all maximal rectangular regions within the input grid that are composed entirely of cells with the color `0`. A maximal rectangle is one that cannot be extended in any of the four cardinal directions without including a cell of a different color.
2. **Select the Target Rectangle:** Apply the following hierarchical criteria to select a single target rectangle from the candidates identified in step 1:
a. **Primary Criterion (Maximum Height):** Select the rectangle(s) with the greatest height.
b. **Secondary Criterion (Maximum Area):** If the primary criterion results in a tie (more than one rectangle has the maximum height), select from this subset the rectangle(s) with the largest area.
c. **Tertiary Criterion (Position):** If a tie still persists, select the rectangle whose top-left corner appears first in reading order (i.e., has the smallest row index, and then the smallest column index in case of a row tie).
3. **Apply the Transformation:** Create a copy of the input grid. In this copy, change the color of all cells within the bounds of the single target rectangle selected in step 2 to the color `6`. The resulting grid is the output.
--- Attempt 1/2 ---
Current Rule: 1. Create a copy of the input grid to serve as the output grid.
2. Locate the coordinates of the single pixel with color 2. Let these be `(r2, c2)`.
3. Locate the coordinates of the single pixel with color 3. Let these be `(r3, c3)`.
4. Draw the horizontal segment of an 'L' shape with color 8. This segment lies on row `r2`. Iterate through all columns `c` from `min(c2, c3)` to `max(c2, c3)`. For each `c`, if `c` is not equal to `c2`, set the color of the cell at `(r2, c)` in the output grid to 8.
5. Draw the vertical segment of the 'L' shape with color 8. This segment lies on column `c3`. Iterate through all rows `r` from `min(r2, r3) + 1` to `max(r2, r3) - 1`. For each `r`, set the color of the cell at `(r, c3)` in the output grid to 8.
6. The modified grid is the final output.
Verification failed. Feedback: Code execution failed on training example 0 with error: 'dict' object has no attribute 'input'
Traceback (most recent call last):
File "<string>", line 117, in _verify_on_training
AttributeError: 'dict' object has no attribute 'input'
Attempting to correct the rule...
Verification failed. Feedback: Code execution failed on training example 0 with error: 'dict' object has no attribute 'input'
Traceback (most recent call last):
File "<string>", line 117, in _verify_on_training
AttributeError: 'dict' object has no attribute 'input'
Attempting to correct the rule...
--- Attempt 2/2 ---
Current Rule: 1. Identify the single non-zero cell in the input grid. If no such cell exists, the output is an identical copy of the input grid. Otherwise, let its row index be `r`, its column index be `c`, and its value be `C`.
2. Create a new output grid of the same dimensions as the input, initialized with the background color 0.
3. Generate a vertical stripe pattern in the rectangular region of the output grid from row 0 to row `r` (inclusive), spanning all columns.
a. For each cell `(i, j)` within this region (where `0 <= i <= r`):
b. If the cell's column index `j` has the same parity (both even or both odd) as the object's original column index `c`, set the value of cell `(i, j)` to 4.
4. If the object's original row `r` is not the last row of the grid, place the object, moved one row down, into the output grid by setting the value of the cell at coordinates `(r+1, c)` to `C`.
--- Attempt 2/2 ---
Current Rule: 1. Identify the single non-zero cell in the input grid. Let its original location be `(r, c)` and its value be `C`.
2. Create a new output grid of the same dimensions as the input, initialized with the background color 0.
3. Move the object one row down to its new location at `(r+1, c)`. Set the value of the cell at `(r+1, c)` to `C` in the output grid.
4. In all rows above the object's new location (i.e., for each row `i` from 0 to `r` inclusive), generate a vertical stripe pattern.
5. This pattern is based on the parity of the object's original column `c`. For each cell `(i, j)` in this region, if the column index `j` has the same parity (both even or both odd) as `c`, set the cell's value to 4.
--- Attempt 2/2 ---
Current Rule: 1. Create a copy of the input grid to serve as the output grid.
2. Locate the coordinates of the single pixel with color 2. Let these be `(r2, c2)`.
3. Locate the coordinates of the single pixel with color 3. Let these be `(r3, c3)`.
4. Draw the horizontal segment of the 'L' shape on row `r2`. Iterate through all columns `c` from `min(c2, c3)` to `max(c2, c3)`. If `c` is not equal to `c2`, set the color of the cell at `(r2, c)` to 8.
5. Draw the vertical segment of the 'L' shape on column `c3`. Iterate through all rows `r` from `min(r2, r3)` to `max(r2, r3)`. If `r` is not equal to `r2` (to avoid overwriting the corner) and `r` is not equal to `r3` (to avoid overwriting the color 3 pixel), set the color of the cell at `(r, c3)` to 8.
6. The modified grid is the final output.
Verification failed. Feedback: Code execution failed on training example 0 with error: 'dict' object has no attribute 'input'
Traceback (most recent call last):
File "<string>", line 117, in _verify_on_training
AttributeError: 'dict' object has no attribute 'input'
Max retries reached. Proceeding with last generated code if possible.
Verification failed. Feedback: Code execution failed on training example 0 with error: 'dict' object has no attribute 'input'
Traceback (most recent call last):
File "<string>", line 117, in _verify_on_training
AttributeError: 'dict' object has no attribute 'input'
Max retries reached. Proceeding with last generated code if possible.
Verification failed. Feedback: Code execution failed on training example 0 with error: 'dict' object has no attribute 'input'
Traceback (most recent call last):
File "<string>", line 117, in _verify_on_training
AttributeError: 'dict' object has no attribute 'input'
Max retries reached. Proceeding with last generated code if possible.
--- Attempt 1/2 ---
Current Rule: 1. **Identify Candidate Rectangles:** Find all maximal rectangular regions within the input grid that are composed entirely of cells with the color `0`. A maximal rectangle is one that cannot be extended in any of the four cardinal directions without including a cell of a different color.
2. **Select the Target Rectangle:** Apply the following hierarchical criteria to select a single target rectangle from the candidates identified in step 1:
a. **Primary Criterion (Maximum Height):** Select the rectangle(s) with the greatest height.
b. **Secondary Criterion (Maximum Area):** If the primary criterion results in a tie (more than one rectangle has the maximum height), select from this subset the rectangle(s) with the largest area.
c. **Tertiary Criterion (Position):** If a tie still persists, select the rectangle whose top-left corner appears first in reading order (i.e., has the smallest row index, and then the smallest column index in case of a row tie).
3. **Apply the Transformation:** Create a copy of the input grid. In this copy, change the color of all cells within the bounds of the single target rectangle selected in step 2 to the color `6`. The resulting grid is the output.
--- Attempt 1/2 ---
Current Rule: 1. Create a copy of the input grid to serve as the output grid.
2. Locate the coordinates of the single pixel with color 2. Let these be `(r2, c2)`.
3. Locate the coordinates of the single pixel with color 3. Let these be `(r3, c3)`.
4. Draw the horizontal segment of an 'L' shape with color 8. This segment lies on row `r2`. Iterate through all columns `c` from `min(c2, c3)` to `max(c2, c3)`. For each `c`, if `c` is not equal to `c2`, set the color of the cell at `(r2, c)` in the output grid to 8.
5. Draw the vertical segment of the 'L' shape with color 8. This segment lies on column `c3`. Iterate through all rows `r` from `min(r2, r3) + 1` to `max(r2, r3) - 1`. For each `r`, set the color of the cell at `(r, c3)` in the output grid to 8.
6. The modified grid is the final output.
Verification failed. Feedback: Code execution failed on training example 0 with error: 'dict' object has no attribute 'input'
Traceback (most recent call last):
File "<string>", line 117, in _verify_on_training
AttributeError: 'dict' object has no attribute 'input'
Attempting to correct the rule...
--- Attempt 2/2 ---
Current Rule: 1. Create a copy of the input grid to serve as the output grid.
2. Locate the coordinates of the single pixel with color 2. Let these be `(r2, c2)`.
3. Locate the coordinates of the single pixel with color 3. Let these be `(r3, c3)`.
4. Draw the horizontal segment of the 'L' shape on row `r2`. Iterate through all columns `c` from `min(c2, c3)` to `max(c2, c3)`. If `c` is not equal to `c2`, set the color of the cell at `(r2, c)` to 8.
5. Draw the vertical segment of the 'L' shape on column `c3`. Iterate through all rows `r` from `min(r2, r3)` to `max(r2, r3)`. If `r` is not equal to `r2` (to avoid overwriting the corner) and `r` is not equal to `r3` (to avoid overwriting the color 3 pixel), set the color of the cell at `(r, c3)` to 8.
6. The modified grid is the final output.
Verification failed. Feedback: Code execution failed on training example 0 with error: 'dict' object has no attribute 'input'
Traceback (most recent call last):
File "<string>", line 117, in _verify_on_training
AttributeError: 'dict' object has no attribute 'input'
Max retries reached. Proceeding with last generated code if possible.
--- Attempt 2/2 ---
Current Rule: 1. Create a copy of the input grid to serve as the output grid.
2. Locate the coordinates of the single pixel with color 2. Let these be `(r2, c2)`.
3. Locate the coordinates of the single pixel with color 3. Let these be `(r3, c3)`.
4. Draw the horizontal segment of the 'L' shape, including its corner, with color 8. This segment lies on row `r2`. Iterate through all columns `c` from `min(c2, c3)` to `max(c2, c3)`. If `c` is not equal to `c2`, set the color of the cell at `(r2, c)` in the output grid to 8.
5. Complete the 'L' shape by drawing the rest of the vertical segment on column `c3`. This segment connects the corner at `(r2, c3)` to the endpoint at `(r3, c3)`. Iterate through all rows `r` strictly between `r2` and `r3` (i.e., from `min(r2, r3) + 1` to `max(r2, r3) - 1`, inclusive). For each such `r`, set the color of the cell at `(r, c3)` in the output grid to 8.
6. The modified grid is the final output.
Verification failed. Feedback: Code execution failed on training example 0 with error: 'dict' object has no attribute 'input'
Traceback (most recent call last):
File "<string>", line 117, in _verify_on_training
AttributeError: 'dict' object has no attribute 'input'
Attempting to correct the rule...
Verification failed. Feedback: Code execution failed on training example 0 with error: 'dict' object has no attribute 'input'
Traceback (most recent call last):
File "<string>", line 117, in _verify_on_training
AttributeError: 'dict' object has no attribute 'input'
Max retries reached. Proceeding with last generated code if possible.
Verification failed. Feedback: Code execution failed on training example 0 with error: 'dict' object has no attribute 'input'
Traceback (most recent call last):
File "<string>", line 117, in _verify_on_training
AttributeError: 'dict' object has no attribute 'input'
Attempting to correct the rule...
Verification failed. Feedback: Code execution failed on training example 0 with error: 'dict' object has no attribute 'input'
Traceback (most recent call last):
File "<string>", line 117, in _verify_on_training
AttributeError: 'dict' object has no attribute 'input'
Attempting to correct the rule...
--- Attempt 1/2 ---
Current Rule: 1. **Identify Candidate Rectangles:** Find all maximal rectangular regions within the input grid that are composed entirely of cells with the color `0`. A maximal rectangle is one that cannot be extended in any of the four cardinal directions without including a cell of a different color.
2. **Select the Target Rectangle:** Apply the following hierarchical criteria to select a single target rectangle from the candidates identified in step 1:
a. **Primary Criterion (Maximum Height):** Select the rectangle(s) with the greatest height.
b. **Secondary Criterion (Maximum Area):** If the primary criterion results in a tie (more than one rectangle has the maximum height), select from this subset the rectangle(s) with the largest area.
c. **Tertiary Criterion (Position):** If a tie still persists, select the rectangle whose top-left corner appears first in reading order (i.e., has the smallest row index, and then the smallest column index in case of a row tie).
3. **Apply the Transformation:** Create a copy of the input grid. In this copy, change the color of all cells within the bounds of the single target rectangle selected in step 2 to the color `6`. The resulting grid is the output.
Verification failed. Feedback: Code execution failed on training example 0 with error: 'dict' object has no attribute 'input'
Traceback (most recent call last):
File "<string>", line 117, in _verify_on_training
AttributeError: 'dict' object has no attribute 'input'
Attempting to correct the rule...
--- Attempt 2/2 ---
Current Rule: 1. **Identify Candidate Rectangles:** Find all maximal rectangular regions within the input grid that are composed entirely of cells with the color `0`. A maximal rectangle is one that cannot be extended in any of the four cardinal directions without including a cell of a different color.
2. **Select the Target Rectangle:** Apply the following hierarchical criteria to select a single target rectangle from the candidates identified in step 1:
a. **Primary Criterion (Maximum Height):** Select the rectangle(s) with the greatest height.
b. **Secondary Criterion (Maximum Width):** If the primary criterion results in a tie (more than one rectangle has the maximum height), select from this subset the rectangle(s) with the greatest width.
c. **Tertiary Criterion (Position):** If a tie still persists, select the rectangle whose top-left corner appears first in reading order (i.e., has the smallest row index, and then the smallest column index in case of a row tie).
3. **Apply the Transformation:** Create a copy of the input grid. In this copy, change the color of all cells within the bounds of the single target rectangle selected in step 2 to the color `6`. The resulting grid is the output.
--- Attempt 2/2 ---
Current Rule: 1. **Identify Candidate Rectangles:** Find all maximal rectangular regions within the input grid that are composed entirely of cells with the color `0`. A maximal rectangle is one that cannot be extended in any of the four cardinal directions without including a cell of a different color.
2. **Select the Target Rectangle:** Apply the following hierarchical criteria to select a single target rectangle from the candidates identified in step 1:
a. **Primary Criterion (Most Square-like):** Select the rectangle(s) with the minimum absolute difference between height and width.
b. **Secondary Criterion (Maximum Area):** If the primary criterion results in a tie, select from this subset the rectangle(s) with the largest area.
c. **Tertiary Criterion (Position):** If a tie still persists, select the rectangle whose top-left corner appears first in reading order (i.e., has the smallest row index, and then the smallest column index in case of a row tie).
3. **Apply the Transformation:** Create a copy of the input grid. In this copy, change the color of all cells within the bounds of the single target rectangle selected in step 2 to the color `6`. The resulting grid is the output.
--- Attempt 2/2 ---
Current Rule: 1. **Identify Candidate Rectangles:** Find all maximal rectangular regions within the input grid that are composed entirely of cells with the color `0`. A maximal rectangle is one that cannot be extended in any of the four cardinal directions without including a cell of a different color.
2. **Select the Target Rectangle:** Apply the following hierarchical criteria to select a single target rectangle from the candidates identified in step 1:
a. **Primary Criterion (Maximum Height):** Select the rectangle(s) with the greatest height.
b. **Secondary Criterion (Maximum Width):** If the primary criterion results in a tie (more than one rectangle has the maximum height), select from this subset the rectangle(s) with the greatest width.
c. **Tertiary Criterion (Position):** If a tie still persists, select the rectangle whose top-left corner appears first in reading order (i.e., has the smallest row index, and then the smallest column index in case of a row tie).
3. **Apply the Transformation:** Create a copy of the input grid. In this copy, change the color of all cells within the bounds of the single target rectangle selected in step 2 to the color `6`. The resulting grid is the output.
--- Attempt 2/2 ---
Current Rule: 1. **Determine Maximum Height:** First, analyze the input grid to determine the maximum possible height (`H_max`) for any rectangular region composed entirely of cells with the color `0`.
2. **Identify Candidate Rectangles:** Find all rectangular regions of `0`s within the grid that have the height `H_max`. Note that these rectangles do not need to be maximal in width, only that they are valid rectangles of `0`s with the maximum possible height.
3. **Select the Target Rectangle:** Apply the following hierarchical criteria to select a single target rectangle from the candidates identified in step 2:
a. **Primary Criterion (Maximum Area):** Select the rectangle(s) with the largest area.
b. **Secondary Criterion (Position):** If a tie still persists, select the rectangle whose top-left corner appears first in reading order (i.e., has the smallest row index, and then the smallest column index in case of a row tie).
4. **Apply the Transformation:** Create a copy of the input grid. In this copy, change the color of all cells within the bounds of the single target rectangle selected in step 3 to the color `6`. The resulting grid is the output.
2025/08/29 22:00:23 INFO dspy.evaluate.evaluate: Average Metric: 2.0 / 3 (66.7%)
GEPA Optimization: 77%|███████████████████████████████████████████████████████████████████████████████████ | 3078/4000 [24:26:26<42:19:26, 165.26s/rollouts]Verification failed. Feedback: Code execution failed on training example 0 with error: 'dict' object has no attribute 'input'
Traceback (most recent call last):
File "<string>", line 117, in _verify_on_training
AttributeError: 'dict' object has no attribute 'input'
Max retries reached. Proceeding with last generated code if possible.
Iteration 130: New subsample score is not better, skipping
Iteration 131: Selected program 4 score: 0.61
0%| | 0/3 [00:00<?, ?it/s]Verification failed. Feedback: Code execution failed on training example 0 with error: 'dict' object has no attribute 'input'
Traceback (most recent call last):
File "<string>", line 117, in _verify_on_training
AttributeError: 'dict' object has no attribute 'input'
Max retries reached. Proceeding with last generated code if possible.
Average Metric: 1.00 / 1 (100.0%): 33%|█████████████████████████████████████ | 1/3 [00:57<01:54, 57.05s/it]Verification failed. Feedback: Code execution failed on training example 0 with error: 'dict' object has no attribute 'input'
Traceback (most recent call last):
File "<string>", line 117, in _verify_on_training
AttributeError: 'dict' object has no attribute 'input'
Max retries reached. Proceeding with last generated code if possible.
Verification failed. Feedback: Code execution failed on training example 0 with error: 'dict' object has no attribute 'input'
Traceback (most recent call last):
File "<string>", line 117, in _verify_on_training
AttributeError: 'dict' object has no attribute 'input'
Max retries reached. Proceeding with last generated code if possible.
Average Metric: 1.00 / 2 (50.0%): 67%|██████████████████████████████████████████████████████████████████████████ | 2/3 [03:34<01:55, 115.93s/it]2025/08/29 22:04:09 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
Average Metric: 1.00 / 3 (33.3%): 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [03:46<00:00, 75.52s/it]2025/08/29 22:04:09 INFO dspy.evaluate.evaluate: Average Metric: 1.0 / 3 (33.3%)
Iteration 131: Proposed new text for program: import dspy
from typing import List, Optional
import pydantic
import traceback
import copy
# Define the type for a grid/matrix as a list of lists of integers.
MATRIX = List[List[int]]
class TrainingExample(pydantic.BaseModel):
"""A Pydantic model for a single training example, containing an input and output grid."""
input: MATRIX
output: MATRIX
class GenerateHypothesis(dspy.Signature):
"""
Analyze a set of training examples from the Abstraction and Reasoning Corpus (ARC).
Your goal is to deduce the underlying transformation rule and describe it clearly in English.
**Analysis Strategy:**
1. **Observe Core Patterns:** Look for recurring patterns. Consider these common ARC transformations:
- Geometric: Rotation, reflection, scaling, cropping, padding.
- Object-based: Identifying, counting, moving, or modifying objects (contiguous shapes of the same color).
- Pixel-wise Logic: Boolean operations (AND, OR, XOR) between grids or patterns.
- Tiling/Repetition: Repeating a core pattern to form a larger grid.
- Separators/Sub-grids: A specific color (e.g., gray) might divide the input into distinct sub-grids that are operated on.
- Color Manipulation: Swapping, changing, or mapping colors based on rules.
- Propagation/Filling: Rules that propagate from certain points, like flood-fills.
- Checkerboard patterns or rules based on coordinate parity (r+c is even/odd).
2. **Decompose the Problem:** Break the transformation into logical steps.
3. **Generalize:** Formulate a hypothesis that is general enough to work for all training examples and, by extension, unseen test inputs.
"""
training_examples: List[TrainingExample] = dspy.InputField(desc="A list of input/output grid pairs demonstrating the transformation rule.")
test_input_grid: MATRIX = dspy.InputField(desc="A single test input grid to which the transformation should be applicable.")
reasoning: str = dspy.OutputField(desc="Step-by-step thinking process to deduce the transformation rule.")
hypothesis: str = dspy.OutputField(desc="A clear, concise English description of the transformation rule.")
class GenerateCode(dspy.Signature):
"""
You are an expert programmer. Your task is to write a single, self-contained Python function named `transform_grid` that implements a given hypothesis.
**Function Requirements:**
- The function must be named exactly `transform_grid`.
- It must accept one argument: `grid` (a 2D list of integers).
- It must return a new 2D list of integers.
- Do not modify the input grid in place.
- Any necessary standard libraries (like `copy`) must be imported inside the function.
**Refinement Instructions:**
If `previous_code` and `feedback` are provided, it means the previous attempt failed. Analyze the feedback, identify the bug in the `previous_code`, and provide a corrected version.
"""
hypothesis: str = dspy.InputField(desc="The English description of the transformation rule to implement.")
training_examples: List[TrainingExample] = dspy.InputField(desc="The examples the code must work for.")
previous_code: Optional[str] = dspy.InputField(desc="The previous, incorrect code attempt.", prefix="Previous Code:")
feedback: Optional[str] = dspy.InputField(desc="Detailed feedback explaining why the previous code was wrong.", prefix="Feedback:")
python_code: str = dspy.OutputField(desc="A string containing the complete, self-contained Python function `transform_grid(grid)`.")
class ARCSolver(dspy.Module):
"""A DSPy module that solves ARC tasks via hypothesis, generation, and refinement."""
def __init__(self, max_attempts=3):
super().__init__()
self.max_attempts = max_attempts
self.generate_hypothesis = dspy.ChainOfThought(GenerateHypothesis)
self.code_generator = dspy.Predict(GenerateCode)
def _validate_and_get_feedback(self, code_str: str, training_examples: List[TrainingExample]):
"""
Executes the generated code and validates it against training examples.
Returns the compiled function if valid, otherwise returns feedback.
"""
local_scope = {}
try:
# The LM might wrap the code in markdown, so we extract it.
if "```python" in code_str:
code_str = code_str.split("```python")[1].split("```")[0].strip()
exec(code_str, globals(), local_scope)
transform_function = local_scope.get('transform_grid')
if not (transform_function and callable(transform_function)):
return None, "Error: `transform_grid` function not found or not callable."
for example in training_examples:
input_grid = copy.deepcopy(example.input)
expected_output = example.output
try:
actual_output = transform_function(input_grid)
if actual_output != expected_output:
feedback = (
f"Logic Error: The function produced an incorrect output for a training example.\n"
f"Input Grid:\n{example.input}\n"
f"Expected Output:\n{expected_output}\n"
f"Actual Output:\n{actual_output}"
)
return None, feedback
except Exception as e:
return None, f"Runtime Error: {e}\nTraceback: {traceback.format_exc()}"
return transform_function, None # Success
except Exception as e:
return None, f"Syntax Error or Execution Failed: {e}\nTraceback: {traceback.format_exc()}"
def forward(self, training_examples: List[TrainingExample], test_inputs: List[MATRIX]):
# 1. Generate a hypothesis based on the examples.
prediction = self.generate_hypothesis(training_examples=training_examples, test_input_grid=test_inputs[0])
hypothesis = prediction.hypothesis
# 2. Iteratively generate and refine code.
current_code = None
feedback = None
transform_function = None
for attempt in range(self.max_attempts):
# Generate code based on hypothesis and any feedback from previous attempts.
code_pred = self.code_generator(
hypothesis=hypothesis,
training_examples=training_examples,
previous_code=current_code,
feedback=feedback
)
current_code = code_pred.python_code
# Validate the new code.
transform_function, feedback = self._validate_and_get_feedback(current_code, training_examples)
if transform_function:
# print(f"Code validated successfully on attempt {attempt + 1}.")
break # Code is correct, exit the loop.
# else:
# print(f"Attempt {attempt + 1} failed. Feedback: {feedback}")
# 3. Apply the validated function or fallback.
generated_outputs = []
if transform_function:
for test_input in test_inputs:
try:
output_grid = transform_function(copy.deepcopy(test_input))
generated_outputs.append(output_grid)
except Exception:
# If the validated function fails on a test input, fallback for that input.
generated_outputs.append(copy.deepcopy(test_input))
else:
# Fallback: if no valid function was generated, return original inputs.
for test_input in test_inputs:
generated_outputs.append(copy.deepcopy(test_input))
return dspy.Prediction(test_outputs=generated_outputs)
# The final program object is an instance of our new, more robust custom module.
program = ARCSolver()
2025/08/29 22:06:05 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 22:06:10 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 22:16:54 INFO dspy.evaluate.evaluate: Average Metric: 0.0 / 3 (0.0%)
GEPA Optimization: 77%|███████████████████████████████████████████████████████████████████████████████████▎ | 3084/4000 [24:42:57<42:02:26, 165.23s/rollouts]Iteration 131: New subsample score is not better, skipping
Iteration 132: Selected program 7 score: 0.675
Average Metric: 0.00 / 3 (0.0%): : 5it [08:37, 103.51s/it] 2025/08/29 22:25:31 INFO dspy.evaluate.evaluate: Average Metric: 0.0 / 3 (0.0%)
Iteration 132: Proposed new text for program: import dspy
from typing import List, Any
import pydantic
import traceback
import copy
# Define the type for a matrix for clarity
MATRIX = List[List[int]]
class TrainingExample(pydantic.BaseModel):
"""A Pydantic model for a single training example, containing an input and output matrix."""
input: MATRIX
output: MATRIX
class InferRuleSignature(dspy.Signature):
"""
Analyzes pairs of input/output matrices to deduce the transformation rule.
You are an expert analyst of abstract visual reasoning puzzles. Your task is to carefully examine the provided training examples. Each example consists of an 'input' matrix and a corresponding 'output' matrix.
Your goal is to deduce the single, consistent transformation rule that converts each input to its output. Describe this rule in clear, unambiguous, step-by-step natural language. The description must be precise enough for another AI or a programmer to follow it to solve a new, unseen test input.
**Guiding Principles:**
- Strive for the simplest possible rule that explains the transformations. A simple rule that explains 95% of the cells is better than a complex, convoluted rule that is likely incorrect.
- If you identify multiple distinct steps (e.g., 'first, crop the grid, then second, apply gravity to the objects'), describe them sequentially.
Consider various possible transformation patterns, including but not limited to:
- Geometric operations: rotation, reflection, scaling, repetition, shifting.
- Color/value transformations: changing specific numbers to others, conditional changes.
- Object-based logic: identifying shapes or objects and applying rules based on their properties (size, color, position).
- Cropping or subgrid extraction: such as finding a bounding box or selecting a specific region.
- Fill/completion patterns: such as flood fills or completing a grid based on object boundaries.
- Bounding box or frame logic: creating structures based on the extents of objects.
"""
training_examples: List[TrainingExample] = dspy.InputField(description="A list of input and output examples demonstrating the task.")
transformation_rule: str = dspy.OutputField(description="A step-by-step natural language description of the transformation rule.")
class GeneratePythonCodeSignature(dspy.Signature):
"""
Converts a natural language transformation rule into a Python function.
You are an expert Python programmer. Your task is to write a single Python function named `transform_matrix` that implements the given natural language `transformation_rule`.
**Function Requirements:**
- The function must be named exactly `transform_matrix`.
- It must accept one argument: `matrix: List[List[int]]`.
- It must return the transformed matrix as a `List[List[int]]`.
- Your code must be self-contained. Import any necessary standard libraries (e.g., `math`, `copy`) *inside* the function or at the top of the code string you provide.
- Do NOT use external libraries like numpy or pandas.
- The `training_examples` are provided for context, allowing you to mentally check if your function logic would produce the correct output for the given inputs.
**Output Format:**
- Your output must be ONLY the Python code for the function.
- Do NOT include any explanations, comments outside the function, or markdown formatting like ```python ... ```.
"""
transformation_rule: str = dspy.InputField(description="The natural language rule to implement.")
training_examples: List[TrainingExample] = dspy.InputField(description="Example input/output pairs to validate the logic.")
python_code: str = dspy.OutputField(description="A string containing only the Python code for the `transform_matrix` function.")
class ApplyRuleSignature(dspy.Signature):
"""
Applies a given transformation rule to a single test input matrix.
You are an expert in meticulously following instructions to transform matrices. You will be given a specific, detailed transformation rule and a single test input matrix.
Your task is to:
1. Carefully read and understand the provided rule.
2. Think step-by-step about how to apply the rule to the test input matrix.
3. Produce the final output matrix that results from the transformation.
**Crucially, your final answer must be ONLY the resulting matrix, formatted as a valid JSON list of lists of integers. Do not include any extra text, explanations, or markdown formatting in the final output field.**
"""
transformation_rule: str = dspy.InputField(description="The natural language rule to apply.")
test_input: MATRIX = dspy.InputField(description="The input matrix to be transformed.")
test_output: MATRIX = dspy.OutputField(description="The resulting matrix after applying the rule, as a list of lists of integers.")
class ARCProgram(dspy.Module):
"""A program that infers a rule, generates code to apply it, and falls back to direct application if needed."""
def __init__(self):
super().__init__()
self.rule_inferrer = dspy.ChainOfThought(InferRuleSignature)
self.code_generator = dspy.Predict(GeneratePythonCodeSignature)
# IMPROVEMENT: Use ChainOfThought for the fallback to allow for more complex reasoning during application.
self.rule_applier_fallback = dspy.ChainOfThought(ApplyRuleSignature)
def _execute_and_validate_code(self, python_code: str, training_examples: List[TrainingExample]) -> Any:
"""
Safely executes the generated Python code and validates its correctness against training examples.
Returns the validated transform function if successful, otherwise returns None.
"""
try:
local_namespace = {}
# The generated code might have its own imports.
exec(python_code, globals(), local_namespace)
transform_func = local_namespace.get('transform_matrix')
if not callable(transform_func):
return None
# IMPROVEMENT: Validate the generated function against all training examples before using it on test data.
for example in training_examples:
# Use deepcopy to avoid modifying the original example input
input_matrix = copy.deepcopy(example.input)
predicted_output = transform_func(input_matrix)
if predicted_output != example.output:
# The function doesn't reproduce the training examples, so it's likely incorrect.
return None
return transform_func
except Exception:
# Any error during execution or validation means the code is not usable.
return None
def forward(self, training_examples: List[TrainingExample], test_inputs: List[MATRIX]) -> dspy.Prediction:
"""
Infers a rule, generates and executes code, and applies it to each test input.
Args:
training_examples: A list of TrainingExample objects.
test_inputs: A list of input matrices to be solved.
Returns:
A dspy.Prediction object with the 'test_outputs' field populated.
"""
# 1. Infer the transformation rule once.
inferred = self.rule_inferrer(training_examples=training_examples)
rule = inferred.transformation_rule
# 2. Attempt to generate, validate, and execute Python code for the rule.
generated = self.code_generator(transformation_rule=rule, training_examples=training_examples)
python_code = generated.python_code
transform_func = self._execute_and_validate_code(python_code, training_examples)
if transform_func:
all_test_outputs = []
for test_matrix in test_inputs:
try:
# Use deepcopy to protect the original test input
input_matrix = copy.deepcopy(test_matrix)
output = transform_func(input_matrix)
all_test_outputs.append(output)
except Exception:
# If the validated function fails on a test case, we must fall back for all cases.
transform_func = None
break
if transform_func:
# If we successfully processed all test cases with code, we are done.
return dspy.Prediction(test_outputs=all_test_outputs)
# 3. Fallback Strategy: If code path fails, revert to direct LM application for all test inputs.
all_test_outputs = []
for test_matrix in test_inputs:
try:
# The improved fallback uses ChainOfThought for better reasoning.
result = self.rule_applier_fallback(transformation_rule=rule, test_input=test_matrix)
all_test_outputs.append(result.test_output)
except Exception:
# If the fallback also fails, append a default empty/zero matrix as a last resort.
if test_matrix and isinstance(test_matrix, list) and len(test_matrix) > 0 and isinstance(test_matrix[0], list):
all_test_outputs.append([([0] * len(test_matrix[0])) for _ in range(len(test_matrix))])
else:
all_test_outputs.append([])
return dspy.Prediction(test_outputs=all_test_outputs)
# The final 'program' object is an instance of our robust, multi-step module.
program = ARCProgram()
2025/08/29 22:30:02 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 22:32:04 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 22:32:27 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 22:33:07 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 22:33:07 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 22:34:20 INFO dspy.evaluate.evaluate: Average Metric: 1.0 / 3 (33.3%)
2025/08/29 22:37:34 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 22:37:53 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 22:37:59 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 22:38:09 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 22:38:13 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 22:41:02 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 22:41:19 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 22:41:26 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 22:41:33 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 22:42:55 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 22:43:46 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 22:45:04 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/29 22:53:21 INFO dspy.evaluate.evaluate: Average Metric: 130.0 / 200 (65.0%)
GEPA Optimization: 82%|██████████████████████████████████████████████████████████████████████████████████████████▍ | 3290/4000 [25:19:25<3:58:55, 20.19s/rollouts]Iteration 132: Full valset score for new program: 0.65
Iteration 132: Full train_val score for new program: 0.65
Iteration 132: Individual valset scores for new program: [True, True, True, True, True, False, True, True, True, True, True, True, True, False, True, True, False, True, True, True, True, True, True, True, True, True, False, False, True, True, False, False, False, True, True, True, False, True, True, False, False, False, True, False, False, True, False, False, True, True, False, True, True, True, True, False, True, True, False, True, True, False, False, True, False, False, True, True, False, False, True, True, False, False, True, True, True, False, True, True, True, True, True, True, True, False, True, False, True, False, True, True, True, True, True, True, True, False, True, True, True, True, True, False, True, True, True, True, False, True, False, False, True, False, True, False, True, False, False, True, True, False, True, False, False, True, False, True, True, True, False, True, True, True, True, False, True, False, True, True, True, False, True, False, True, False, False, False, True, True, False, False, True, True, True, True, True, True, False, False, True, True, True, False, False, True, True, True, False, False, True, True, True, False, False, True, False, True, False, True, True, True, True, False, True, True, True, True, True, False, True, True, False, True, True, False, True, False, False, True]
Iteration 132: New valset pareto front scores: [True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, False, True, True, True, True, True, True, True, True, True, False, True, True, True, True, False, True, True, True, True, False, True, True, True, False, True, True, True, False, True, False, True, True, True, True, True, True, True, True, 0, True, True, False, True, True, True, False, True, True, False, True, True, 0, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, False, True, True, True, True, True, True, True, True, True, True, True, True, True, False, True, True, True, True, True, True, False, True, True, True, True, True, True, True, True, True, True, True, True, False, False, True, False, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, False, True, True, True, False, True, True, True, True, True, True, True, True, True, True, True, False, True, True, True, True, False, True, True, True, True, False, True, True, True, True, True, True, False, True, False, True, True, True, True, False, True, True, True, True, True, True, True, True, True, True, True, True, True, True, False, True]
Iteration 132: Full valset pareto front score: 0.865
Iteration 132: Updated valset pareto front programs: [{1, 3, 5, 8, 9, 10, 11, 12}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12}, {0, 9, 12}, {0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}, {0, 1, 2, 3, 4, 5, 6, 7, 9, 10, 11, 12}, {0, 4}, {0, 1, 2, 3, 4, 5, 6, 7, 9, 10, 11, 12}, {0, 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}, {0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}, {8}, {0, 1, 2, 3, 4, 6, 9, 10, 11, 12}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}, {1, 4, 5, 7, 9, 10, 12}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}, {0, 2, 4, 6, 7, 10, 11, 12}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12}, {0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}, {1, 5, 7, 9, 12}, {0, 3, 4, 6, 8, 9, 10, 11, 12}, {0, 1, 3, 6, 7, 8, 9, 10, 11, 12}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}, {0, 4, 7}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}, {1}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}, {2}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}, {0, 1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}, {2, 12, 5}, {0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}, {1}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}, {0, 1, 2, 3, 4, 6, 8, 9, 10, 11, 12}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}, {8}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}, {0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12}, {0, 1, 2, 3, 4, 6, 7, 11}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}, {0, 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}, {0, 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}, {0, 1, 2, 3, 8, 12}, {0, 1, 5}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}, {4, 6, 7, 8, 11}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}, {1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12}, {0, 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}, {11}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}, {2, 3, 4, 5, 6, 7, 8, 9, 11}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}, {1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12}, {0, 3, 4, 5, 6, 7, 9, 10, 11, 12}, {4}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}, {0, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12}, {0, 2, 6, 8, 11, 12}, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}, {0, 1, 2, 4, 5, 6, 7, 8, 10, 11, 12}, {0, 1, 2, 3, 4, 5, 6, 7}, {0, 3, 4, 6, 7, 11, 12}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}, {0, 1, 2, 4, 5, 6, 7, 8, 9, 10, 11, 12}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}, {1, 3, 8, 10, 12}, {7}, {0, 3, 4, 6, 7, 9, 10, 11, 12}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}, {0, 1, 2, 3, 4, 5, 6, 8, 9, 10, 11, 12}, {0, 4, 6, 7, 9, 11, 12}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}, {0, 1, 2, 3, 5, 7, 8, 11, 12}, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}, {0, 1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12}, {1, 2, 10}, {0, 1, 4, 5, 6, 8, 10, 12}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}, {0, 2, 5}, {1, 10, 3, 12}, {2, 4, 9, 10, 11}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}, {3, 4, 5, 7, 8}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}, {2, 3, 4, 6, 7, 8, 9, 10, 11}, {0, 1, 2, 3, 4, 5, 6, 8, 10, 11}, {0, 1, 2, 3, 5, 6, 7, 9, 10, 11, 12}, {2, 3, 4, 5, 6, 7, 9, 11, 12}, {0, 1, 7, 9, 10}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}, {0, 1, 2, 3, 4, 5, 6, 7, 9, 10, 11, 12}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}, {0, 1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12}, {0, 1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12}, {2, 7}, {0, 1, 3, 9, 12}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}, {0, 1, 3, 5, 6, 7, 8, 9, 10, 11, 12}, {0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12}, {8, 9, 7}, {0, 1, 3, 6, 7, 8, 9, 11, 12}, {3, 5, 6, 7, 11}, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}, {1, 2, 3, 4, 5, 6, 7, 9, 10, 11, 12}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}, {0, 1, 2, 3, 4, 6, 7, 8, 9, 10}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}, {9}, {0, 2, 3, 5, 6, 7, 9, 10, 11}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}, {4}, {8, 9, 7}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}, {0, 1, 3, 4, 5, 6, 7, 8, 9, 11, 12}, {0, 1, 3, 4, 5, 6, 7, 8, 10, 11, 12}, {0, 1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12}, {0, 3, 5, 6, 8, 9, 10, 11, 12}, {0, 1, 2, 3, 4, 5, 6, 7, 9, 10, 11, 12}, {0, 1, 2, 3, 6, 7, 9, 10, 11}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}, {1, 12}, {0, 1, 2, 3, 4, 5, 6, 8, 9, 10, 11, 12}, {0, 4, 5, 6, 7, 8, 9, 10, 12}, {0, 3, 5, 10, 11}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}, {0, 1, 2, 4, 5, 6, 7, 9, 10, 12}, {0, 1, 2, 4, 5, 6, 7, 9, 10}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}, {0, 7, 8, 9, 12}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}, {9, 11, 12, 6}, {0, 1, 3, 5, 6, 7, 8, 9, 10, 11}, {0, 1, 2, 3, 4, 5, 6, 8, 9, 11}, {1, 2, 3, 5, 6, 8, 9, 10, 11, 12}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}, {0, 1, 3, 4, 5, 6, 7, 8, 10, 11, 12}, {0, 2, 7, 8, 9, 10, 12}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}, {9, 11, 12}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}, {0, 1, 2, 4, 5, 6, 7, 8, 9, 10, 11, 12}, {0, 5, 6, 7, 8, 9, 10, 11}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}, {0, 1, 2, 3, 4, 5, 6, 7, 10, 11}, {1, 2, 4, 5, 6, 7, 8, 10, 11, 12}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12}, {11, 6}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}, {0, 10}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}, {0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}]
Iteration 132: Best valset aggregate score so far: 0.675
Iteration 132: Best program as per aggregate score on train_val: 6
Iteration 132: Best program as per aggregate score on valset: 6
Iteration 132: Best score on valset: 0.675
Iteration 132: Best score on train_val: 0.675
Iteration 132: Linear pareto front program index: 6
Iteration 132: New program candidate index: 12
Iteration 133: Selected program 9 score: 0.67
Average Metric: 1.00 / 3 (33.3%): : 4it [07:41, 115.26s/it] 2025/08/29 23:01:02 INFO dspy.evaluate.evaluate: Average Metric: 1.0 / 3 (33.3%)
Iteration 133: Proposed new text for program: import dspy
from typing import List, Any
import pydantic
import traceback
import copy
# Define the type for a matrix for clarity
MATRIX = List[List[int]]
class TrainingExample(pydantic.BaseModel):
"""A Pydantic model for a single training example, containing an input and output matrix."""
input: MATRIX
output: MATRIX
class InferRuleSignature(dspy.Signature):
"""
Analyzes pairs of input/output matrices to deduce the transformation rule.
You are an expert analyst of abstract visual reasoning puzzles. Your task is to carefully examine the provided training examples to deduce the single, consistent transformation rule that converts each input to its output.
**Goal:** Describe a single, generalizable, algorithmic rule in clear, unambiguous, step-by-step natural language. The description must be precise enough for an expert Python programmer to implement it without further clarification.
**Analytical Strategies:**
- **Hypothesize and Test:** Formulate a hypothesis about the rule and mentally check if it holds for ALL training examples. If it doesn't, discard it and form a new one.
- **Look for General Principles:** The rule is often based on abstract concepts. Do not just describe the direct transformation for each example. Find the underlying logic. Consider properties like:
- **Object Properties:** size (pixel count), shape, color, bounding box area, aspect ratio, symmetry.
- **Relationships:** relative positions of objects, containment, intersection, repetition.
- **Global Properties:** grid size changes, color mappings (e.g., most frequent to least frequent), cropping based on a specific criterion.
**Common Pitfalls to Avoid:**
- **Coincidental Patterns:** Be wary of rules that seem to work for the training set but are overly specific or based on a coincidence. For example, if the rule "select the shape with the median color" works for all training examples, also consider if a more robust rule like "select the shape with the largest bounding box area" might also work and be more general. The simplest, most general principle is usually the correct one.
- **Ambiguity:** Avoid vague descriptions. Be explicit about how to select objects, calculate positions, and apply transformations.
"""
training_examples: List[TrainingExample] = dspy.InputField(description="A list of input and output examples demonstrating the task.")
transformation_rule: str = dspy.OutputField(description="A step-by-step natural language description of the transformation rule.")
class GenerateCodeSignature(dspy.Signature):
"""
Writes a Python function to implement a transformation rule for matrices.
You are an expert Python programmer specializing in algorithmic tasks. You will be given a natural language description of a rule that transforms a matrix (list of lists of integers) and the original training examples that the rule was derived from.
Your task is to write a single Python function named `transform_matrix` that implements this rule.
**Function Requirements:**
- The function must be named exactly `transform_matrix`.
- It must accept one argument: `matrix` (a list of lists of integers).
- It must return a new matrix (a list of lists of integers).
- You CANNOT use any external libraries like numpy. You must implement all logic using standard Python data structures and control flow.
- The function should be self-contained. Do not define helper functions outside of its scope unless absolutely necessary (e.g., via nesting).
**Output Format:**
- Your output must be ONLY the Python code for the function.
- Do not include any explanations, comments, or surrounding text.
- Do not include the function call, just the definition.
- Start with `def transform_matrix(matrix):` and end with the last line of the function's code.
"""
transformation_rule: str = dspy.InputField(description="The natural language rule to implement.")
training_examples: List[TrainingExample] = dspy.InputField(description="The original examples used to derive the rule, for context and validation.")
python_code: str = dspy.OutputField(description="A string containing a single Python function `transform_matrix` that implements the rule.")
class ARCProgram(dspy.Module):
"""
A program that proposes multiple rule hypotheses, generates code for each,
validates the code against training examples, and executes the first valid
code on the test inputs.
"""
def __init__(self, num_hypotheses=3):
super().__init__()
# Use dspy.Predict with n > 1 to generate multiple rule hypotheses.
self.rule_proposer = dspy.Predict(InferRuleSignature, n=num_hypotheses)
self.code_generator = dspy.Predict(GenerateCodeSignature)
def _clean_code(self, code: str) -> str:
"""Strips markdown formatting from the generated code."""
if code.strip().startswith("```python"):
code = code.strip()[9:].strip()
if code.strip().startswith("```"):
code = code.strip()[3:].strip()
if code.strip().endswith("```"):
code = code.strip()[:-3].strip()
return code
def _compile_code(self, code: str) -> Any:
"""Compiles the generated code string into a callable function."""
try:
cleaned_code = self._clean_code(code)
local_scope = {}
exec(cleaned_code, globals(), local_scope)
return local_scope.get('transform_matrix')
except Exception as e:
# print(f"Failed to compile generated code: {e}\nCode:\n{code}")
return None
def _validate_code(self, func: Any, training_examples: List[TrainingExample]) -> bool:
"""Validates the function by checking if it correctly solves all training examples."""
for example in training_examples:
try:
# Use deepcopy to avoid modifying the original input matrix
input_matrix = copy.deepcopy(example.input)
predicted_output = func(input_matrix)
if predicted_output != example.output:
return False # Mismatch found
except Exception:
return False # Code failed to execute on a training example
return True # All examples passed
def _get_fallback_output(self, matrix: MATRIX) -> MATRIX:
"""Provides a default output if all strategies fail."""
if matrix and isinstance(matrix, list) and matrix[0] and isinstance(matrix[0], list):
return [[0] * len(matrix[0]) for _ in range(len(matrix))]
return []
def forward(self, training_examples: List[TrainingExample], test_inputs: List[MATRIX]) -> dspy.Prediction:
# 1. Propose multiple rule hypotheses.
# The 'n' in the module definition causes this to return a list of Predictions.
rule_hypotheses = self.rule_proposer(training_examples=training_examples).completions
validated_func = None
# 2. Iterate through hypotheses to find one that works.
for hypothesis in rule_hypotheses.transformation_rule:
# 3. Generate code for the current hypothesis.
generated = self.code_generator(transformation_rule=hypothesis, training_examples=training_examples)
code = generated.python_code
# 4. Compile the generated code.
transform_func = self._compile_code(code)
# 5. If compilation is successful, validate against training examples.
if transform_func and self._validate_code(transform_func, training_examples):
validated_func = transform_func
break # Found a working function, no need to check other hypotheses.
# 6. Apply the validated function to each test input.
all_test_outputs = []
for test_matrix in test_inputs:
if validated_func:
try:
# Use deepcopy to ensure test inputs are not modified across retries
input_matrix = copy.deepcopy(test_matrix)
result = validated_func(input_matrix)
all_test_outputs.append(result)
except Exception:
all_test_outputs.append(self._get_fallback_output(test_matrix))
else:
# If no function was validated, append fallback for all inputs.
all_test_outputs.append(self._get_fallback_output(test_matrix))
return dspy.Prediction(test_outputs=all_test_outputs)
# The final 'program' object is an instance of our robust, multi-step module.
program = ARCProgram()
2025/08/29 23:21:02 INFO dspy.evaluate.evaluate: Average Metric: 0.0 / 3 (0.0%)
GEPA Optimization: 82%|██████████████████████████████████████████████████████████████████████████████████████████▋ | 3296/4000 [25:47:05<5:49:59, 29.83s/rollouts]Iteration 133: New subsample score is not better, skipping
Iteration 134: Selected program 0 score: 0.67
Average Metric: 1.00 / 3 (33.3%): 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [02:12<00:00, 44.25s/it]2025/08/29 23:23:14 INFO dspy.evaluate.evaluate: Average Metric: 1.0 / 3 (33.3%)
2025/08/29 23:24:07 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 8, 8, 8, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 8, 0, 8, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 8, 8, 8, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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0, 0, 0, 0, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 3, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]]}) (input_keys={'test_inputs', 'training_examples'}): 'dict' object has no attribute 'model_dump'
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 79, in forward
File "<string>", line 79, in <listcomp>
AttributeError: 'dict' object has no attribute 'model_dump'
2025/08/29 23:24:07 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[1, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 6, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 3, 3, 0, 0, 0, 0, 1, 0], [0, 0, 3, 3, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 6, 0, 0], [0, 0, 0, 0, 0, 0, 6, 0, 0, 0], [0, 0, 6, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0, 0]], 'output': [[1, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 6, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 3, 3, 1, 1, 1, 1, 1, 0], [0, 0, 3, 3, 0, 0, 0, 0, 0, 0], [0, 0, 6, 0, 0, 0, 0, 0, 0, 0], [0, 0, 6, 0, 0, 0, 0, 6, 0, 0], [0, 0, 6, 0, 0, 0, 6, 0, 0, 0], [0, 0, 6, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0, 0]]}, {'input': [[0, 7, 0, 0, 0, 0, 8, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 7, 0, 3, 3, 0, 0, 8], [0, 0, 0, 0, 0, 3, 3, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 8, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 8, 0], [0, 0, 0, 8, 0, 0, 0, 0, 0, 0], [0, 7, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 7, 0, 0, 0, 7]], 'output': [[0, 7, 0, 0, 0, 0, 8, 0, 0, 0], [0, 0, 0, 0, 0, 0, 8, 0, 0, 0], [0, 0, 0, 7, 7, 3, 3, 8, 8, 8], [0, 0, 0, 0, 0, 3, 3, 0, 0, 0], [0, 0, 0, 0, 0, 7, 0, 0, 0, 0], [0, 8, 0, 0, 0, 7, 0, 0, 0, 0], [0, 0, 0, 0, 0, 7, 0, 0, 8, 0], [0, 0, 0, 8, 0, 7, 0, 0, 0, 0], [0, 7, 0, 0, 0, 7, 0, 0, 0, 0], [0, 0, 0, 0, 0, 7, 0, 0, 0, 7]]}, {'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 1], [0, 0, 0, 0, 3, 3, 0, 0, 0, 0], [0, 0, 0, 0, 3, 3, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 1, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0, 1], [0, 0, 0, 0, 3, 3, 0, 0, 0, 0], [0, 0, 0, 0, 3, 3, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0, 0, 0]]}], 'test_inputs': [[[0, 0, 0, 2, 0, 0, 0, 0, 0, 0], [2, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 2, 0, 0], [6, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 6, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [6, 0, 3, 3, 0, 0, 0, 0, 0, 0], [0, 0, 3, 3, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 2, 0, 0], [0, 0, 0, 6, 0, 6, 0, 0, 0, 0]]], 'test_outputs': [[[0, 0, 0, 2, 0, 0, 0, 0, 0, 0], [2, 0, 0, 2, 0, 0, 0, 0, 0, 0], [0, 0, 0, 2, 0, 0, 0, 2, 0, 0], [6, 0, 0, 2, 0, 0, 0, 0, 0, 0], [0, 0, 0, 2, 0, 0, 0, 6, 0, 0], [0, 0, 0, 2, 0, 0, 0, 0, 0, 0], [6, 6, 3, 3, 0, 0, 0, 0, 0, 0], [0, 0, 3, 3, 0, 0, 0, 0, 0, 0], [0, 0, 0, 6, 0, 0, 0, 2, 0, 0], [0, 0, 0, 6, 0, 6, 0, 0, 0, 0]]]}) (input_keys={'test_inputs', 'training_examples'}): 'dict' object has no attribute 'model_dump'
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 79, in forward
File "<string>", line 79, in <listcomp>
AttributeError: 'dict' object has no attribute 'model_dump'
2025/08/29 23:24:07 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[0, 0, 0, 0, 8, 8, 8, 8, 8, 0, 0, 8, 0, 8, 8, 8, 0, 8, 0, 8], [0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 8, 0, 0, 0, 0], [8, 8, 8, 8, 8, 0, 8, 0, 8, 0, 0, 0, 8, 8, 8, 0, 0, 2, 0, 0], [8, 0, 8, 8, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 0, 2, 0, 0], [8, 0, 0, 8, 8, 0, 0, 0, 0, 0, 0, 8, 0, 8, 0, 0, 0, 0, 0, 0], [0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0], [8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0], [0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8], [8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 8], [0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 8, 0, 0, 0, 0, 0, 8, 8], [0, 8, 0, 0, 0, 0, 8, 8, 8, 0, 8, 0, 0, 8, 0, 8, 8, 0, 0, 0], [8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [0, 0, 0, 0, 0, 0, 8, 8, 8, 0, 0, 8, 8, 8, 0, 8, 0, 0, 8, 8], [0, 0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 8], [0, 0, 0, 3, 0, 0, 0, 8, 0, 8, 0, 8, 0, 0, 8, 0, 0, 8, 0, 8], [0, 0, 0, 3, 0, 0, 8, 8, 8, 0, 0, 0, 8, 8, 8, 8, 0, 0, 0, 0], [0, 8, 0, 0, 0, 0, 0, 8, 0, 8, 8, 0, 8, 0, 8, 0, 8, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 8, 0], [0, 0, 0, 8, 0, 0, 0, 8, 0, 8, 0, 0, 8, 8, 8, 0, 0, 0, 0, 8], [0, 0, 0, 0, 8, 8, 8, 8, 0, 0, 8, 0, 0, 0, 0, 8, 8, 8, 0, 0]], 'output': [[0, 0, 0, 0, 8, 8, 8, 8, 8, 0, 0, 8, 0, 8, 8, 8, 0, 8, 0, 8], [0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 8, 0, 0, 0, 0], [8, 8, 8, 8, 8, 0, 8, 0, 8, 0, 0, 0, 8, 8, 8, 0, 0, 2, 0, 0], [8, 0, 8, 8, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 0, 2, 0, 0], [8, 0, 0, 8, 8, 0, 0, 0, 0, 0, 0, 8, 0, 8, 0, 0, 0, 3, 0, 0], [0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 3, 0, 0], [8, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 8, 0], [0, 0, 8, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8], [8, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 8], [0, 0, 0, 3, 0, 0, 0, 0, 0, 8, 0, 0, 8, 0, 0, 0, 0, 0, 8, 8], [0, 8, 0, 3, 0, 0, 8, 8, 8, 0, 8, 0, 0, 8, 0, 8, 8, 0, 0, 0], [8, 0, 0, 3, 0, 8, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [0, 0, 0, 3, 0, 0, 8, 8, 8, 0, 0, 8, 8, 8, 0, 8, 0, 0, 8, 8], [0, 0, 0, 3, 0, 0, 8, 8, 0, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 8], [0, 0, 0, 3, 0, 0, 0, 8, 0, 8, 0, 8, 0, 0, 8, 0, 0, 8, 0, 8], [0, 0, 0, 3, 0, 0, 8, 8, 8, 0, 0, 0, 8, 8, 8, 8, 0, 0, 0, 0], [0, 8, 0, 0, 0, 0, 0, 8, 0, 8, 8, 0, 8, 0, 8, 0, 8, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8, 8, 0], [0, 0, 0, 8, 0, 0, 0, 8, 0, 8, 0, 0, 8, 8, 8, 0, 0, 0, 0, 8], [0, 0, 0, 0, 8, 8, 8, 8, 0, 0, 8, 0, 0, 0, 0, 8, 8, 8, 0, 0]]}, {'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 8], [0, 3, 8, 0, 0, 0, 0, 0, 0, 0], [0, 3, 0, 0, 0, 0, 0, 8, 0, 0], [0, 0, 0, 0, 0, 0, 8, 0, 0, 8], [0, 8, 0, 8, 0, 0, 0, 0, 0, 0], [0, 0, 0, 8, 0, 0, 0, 0, 0, 0], [0, 8, 8, 0, 0, 2, 0, 0, 0, 0], [0, 0, 8, 0, 0, 2, 0, 0, 0, 0], [0, 0, 8, 0, 0, 0, 0, 0, 0, 0], [8, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 8], [0, 3, 8, 0, 0, 0, 0, 0, 0, 0], [0, 3, 0, 0, 0, 0, 0, 8, 0, 0], [0, 3, 3, 3, 3, 3, 8, 0, 0, 8], [0, 8, 0, 8, 0, 3, 0, 0, 0, 0], [0, 0, 0, 8, 0, 3, 0, 0, 0, 0], [0, 8, 8, 0, 0, 2, 0, 0, 0, 0], [0, 0, 8, 0, 0, 2, 0, 0, 0, 0], [0, 0, 8, 0, 0, 0, 0, 0, 0, 0], [8, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}, {'input': [[0, 0, 0, 0, 0, 8, 0, 8, 0, 0, 8, 0, 0, 8, 0], [0, 0, 0, 8, 0, 0, 8, 0, 0, 0, 0, 8, 0, 8, 8], [8, 0, 0, 0, 8, 8, 8, 0, 0, 0, 0, 8, 8, 8, 0], [0, 0, 0, 0, 0, 8, 0, 8, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 8, 0], [0, 3, 3, 0, 0, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 8, 0], [0, 8, 8, 0, 0, 8, 0, 0, 8, 0, 8, 8, 0, 0, 0], [0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [8, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0], [8, 0, 0, 0, 0, 0, 0, 8, 8, 8, 0, 0, 0, 0, 0], [0, 8, 0, 0, 8, 0, 8, 0, 0, 0, 8, 8, 8, 8, 0], [0, 0, 0, 0, 0, 8, 0, 8, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 8, 8, 0, 8, 0, 0, 8, 0, 0, 8], [0, 8, 0, 0, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 8, 0, 8, 0, 0, 8, 0, 0, 8, 0], [0, 0, 0, 8, 0, 0, 8, 0, 0, 0, 0, 8, 0, 8, 8], [8, 0, 0, 0, 8, 8, 8, 0, 0, 0, 0, 8, 8, 8, 0], [0, 0, 0, 0, 0, 8, 0, 8, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 8, 0], [0, 3, 3, 3, 3, 3, 3, 3, 8, 0, 0, 0, 8, 0, 0], [0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 8, 0, 8, 0], [0, 8, 8, 0, 0, 8, 0, 3, 8, 0, 8, 8, 0, 0, 0], [0, 8, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0], [8, 2, 2, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 8, 0], [8, 0, 0, 0, 0, 0, 0, 8, 8, 8, 0, 0, 0, 0, 0], [0, 8, 0, 0, 8, 0, 8, 0, 0, 0, 8, 8, 8, 8, 0], [0, 0, 0, 0, 0, 8, 0, 8, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 8, 8, 0, 8, 0, 0, 8, 0, 0, 8], [0, 8, 0, 0, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}], 'test_inputs': [[[8, 8, 8, 8, 0, 0, 0, 0, 0, 8, 8, 0, 0], [8, 0, 0, 0, 0, 8, 2, 2, 0, 0, 0, 0, 0], [0, 8, 0, 0, 8, 8, 0, 0, 0, 0, 0, 0, 0], [0, 0, 8, 0, 0, 0, 0, 0, 8, 0, 0, 0, 8], [0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 0, 0, 8], [0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 8, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0], [8, 0, 8, 3, 3, 0, 0, 0, 0, 0, 8, 0, 0], [0, 8, 8, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0], [0, 0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0], [0, 8, 8, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0], [0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0]]], 'test_outputs': [[[8, 8, 8, 8, 0, 0, 0, 0, 0, 8, 8, 0, 0], [8, 0, 0, 0, 0, 8, 2, 2, 3, 3, 0, 0, 0], [0, 8, 0, 0, 8, 8, 0, 0, 0, 3, 0, 0, 0], [0, 0, 8, 0, 0, 0, 0, 0, 8, 3, 0, 0, 8], [0, 0, 8, 0, 0, 0, 8, 0, 0, 3, 0, 0, 8], [0, 0, 0, 8, 0, 0, 0, 0, 8, 3, 8, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 8, 0, 0], [8, 0, 8, 3, 3, 3, 3, 3, 3, 3, 8, 0, 0], [0, 8, 8, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0], [0, 0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0], [0, 8, 8, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0], [0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0]]]}) (input_keys={'test_inputs', 'training_examples'}): 'dict' object has no attribute 'model_dump'
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 79, in forward
File "<string>", line 79, in <listcomp>
AttributeError: 'dict' object has no attribute 'model_dump'
2025/08/29 23:24:07 INFO dspy.evaluate.evaluate: Average Metric: 0.0 / 3 (0.0%)
GEPA Optimization: 83%|██████████████████████████████████████████████████████████████████████████████████████████▊ | 3302/4000 [25:50:11<5:47:40, 29.89s/rollouts]Iteration 134: Proposed new text for program: import dspy
from typing import List
import pydantic
import json
import traceback
import copy
# Define the type for a matrix, which is a list of lists of integers.
MATRIX = List[List[int]]
# Define a Pydantic model for a single training example, ensuring structured input.
class TrainingExample(pydantic.BaseModel):
input: MATRIX
output: MATRIX
# --- Signature for the Fallback Module (Original Signature) ---
class SolveTaskSignature(dspy.Signature):
"""Given input and output examples demonstrating a task, solve the same task for new test inputs."""
training_examples: List[TrainingExample] = dspy.InputField(description="Input and output examples demonstrating the task to be performed.")
test_inputs: List[MATRIX] = dspy.InputField(description="Input matrices to be solved following the task described in the training examples.")
test_outputs: List[MATRIX] = dspy.OutputField(description="Output matrices corresponding to the test inputs.")
# --- Signature for the Rule Induction Step ---
class InduceRuleSignature(dspy.Signature):
"""
Analyzes pairs of input and output matrices to deduce the transformation rule.
The task is to understand the underlying pattern or algorithm that transforms each input matrix into its corresponding output matrix. The rule should be described in clear, step-by-step, unambiguous English.
**Successful Strategies:**
- Look for geometric transformations: rotation, reflection, scaling, repetition.
- Identify object-based rules: copying, moving, deleting, or modifying objects (contiguous blocks of non-zero colors).
- Check for pathfinding or flood-fill algorithms between specific colors.
- Analyze color-specific rules: does a certain color always change to another?
- Consider pixel-level logic based on the state of neighboring cells.
- The rule must be general enough to apply to all provided examples.
**Pitfalls to Avoid:**
- Do not just describe the changes for one example. Find the common, generalizable rule.
- Avoid vague descriptions. Be specific about the conditions, actions, and order of operations.
"""
training_examples: str = dspy.InputField(desc="A JSON string representing a list of input/output matrix pairs.")
rule_description: str = dspy.OutputField(desc="A step-by-step, algorithmic description of the transformation rule in English.")
# --- Signature for the Code Generation Step ---
class RuleToPythonSignature(dspy.Signature):
"""
Writes a self-contained Python function based on an English description of a rule.
The function must be named `solve`, take one argument `matrix` (a list of lists of integers), and return the transformed matrix.
**Function Requirements:**
- Must be named `solve(matrix)`.
- Must be entirely self-contained.
- Must NOT use any external libraries (e.g., numpy, pandas). Only standard Python built-in functions and modules are allowed.
- The code should be robust and accurately implement the provided rule, considering the examples.
"""
rule_description: str = dspy.InputField(desc="The English description of the transformation rule.")
training_examples: str = dspy.InputField(desc="A JSON string of examples to validate the logic.")
python_code: str = dspy.OutputField(desc="A string containing only the Python code for the `solve` function.")
# --- The Main Custom Module for Solving ARC Tasks ---
class ARCProgram(dspy.Module):
"""
A program that solves Abstract Reasoning Corpus (ARC) style tasks by
first inducing a rule, then generating and executing Python code to apply it.
"""
def __init__(self):
super().__init__()
# A ChainOfThought module is better for the complex reasoning of rule induction.
self.induce_rule = dspy.ChainOfThought(InduceRuleSignature)
# A simple Predict module is sufficient for the more direct task of code generation.
self.rule_to_python = dspy.Predict(RuleToPythonSignature)
# A fallback module in case the primary code-generation strategy fails.
self.fallback = dspy.ChainOfThought(SolveTaskSignature)
def forward(self, training_examples: List[TrainingExample], test_inputs: List[MATRIX]):
# Serialize examples to a compact JSON string for the LM.
examples_str = json.dumps([ex.model_dump() for ex in training_examples])
try:
# 1. Induce the rule in English from the examples.
rule_pred = self.induce_rule(training_examples=examples_str)
rule_description = rule_pred.rule_description
# 2. Generate Python code from the English rule.
code_pred = self.rule_to_python(
rule_description=rule_description,
training_examples=examples_str
)
python_code = code_pred.python_code
# Clean up the python code in case it's wrapped in markdown backticks.
if python_code.startswith("```python"):
python_code = python_code[9:]
if python_code.startswith("```"):
python_code = python_code[3:]
if python_code.endswith("```"):
python_code = python_code[:-3]
python_code = python_code.strip()
# 3. Execute the generated code for each test input.
all_test_outputs = []
# Define the function in a local scope to make it available.
local_scope = {}
exec(python_code, {}, local_scope)
solve_func = local_scope.get("solve")
if not callable(solve_func):
raise ValueError("'solve' function not found or not callable in generated code.")
# Apply the generated function to all test inputs.
for test_matrix in test_inputs:
# Use deepcopy to prevent the function from modifying the original input list.
input_copy = copy.deepcopy(test_matrix)
result = solve_func(input_copy)
all_test_outputs.append(result)
if not all_test_outputs:
raise ValueError("Code executed but produced no outputs.")
return dspy.Prediction(test_outputs=all_test_outputs)
except Exception as e:
print(f"Primary strategy (code generation) failed: {e}")
print(f"Traceback: {traceback.format_exc()}")
print("--- Using fallback strategy ---")
# 4. If any step fails, use the simpler fallback strategy.
fallback_pred = self.fallback(training_examples=training_examples, test_inputs=test_inputs)
return dspy.Prediction(test_outputs=fallback_pred.test_outputs)
# The final 'program' is an instance of our advanced, multi-step module.
program = ARCProgram()
Iteration 134: New subsample score is not better, skipping
Iteration 135: Selected program 8 score: 0.62
Average Metric: 2.00 / 3 (66.7%): 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [05:18<00:00, 106.01s/it]2025/08/29 23:29:25 INFO dspy.evaluate.evaluate: Average Metric: 2.0 / 3 (66.7%)
Iteration 135: Proposed new text for program: import dspy
from typing import List, Optional
import pydantic
import copy
# Define type aliases and pydantic models for clarity and structure.
MATRIX = List[List[int]]
class TrainingExample(pydantic.BaseModel):
input: MATRIX
output: MATRIX
# --- Step 1: A Signature to deduce the transformation rule in natural language ---
# This signature remains effective for generating a high-level plan.
class HypothesizeRule(dspy.Signature):
"""
Analyzes pairs of input/output matrices from the Abstraction and Reasoning Corpus (ARC)
and deduces the underlying transformation rule. Describe the rule in clear, step-by-step
natural language. Focus on a generalizable rule that is not hardcoded to the examples.
**Successful Strategies to Consider:**
- **Grid Properties:** Analyze changes in dimensions, colors, and object counts.
- **Object Transformations:** Identify objects/shapes and describe how they are moved, rotated, scaled, colored, or combined.
- **Pattern Recognition:** Look for patterns like symmetry, repetition, or subgrid extraction.
- **Conditional Logic:** The rule might depend on specific conditions, like the color of a neighboring cell.
- **Pixel-level Rules:** Consider rules that apply to each pixel based on its neighbors (e.g., flood fills, boundary detection).
"""
training_examples: List[TrainingExample] = dspy.InputField(desc="A list of input/output pairs demonstrating the transformation rule.")
hypothesis: str = dspy.OutputField(desc="A clear, step-by-step natural language description of the transformation rule.")
# --- Step 2: An improved, self-correcting Signature to generate and refine Python code ---
# This signature is enhanced to accept feedback for iterative correction.
class ImplementAndRefineRule(dspy.Signature):
"""
You are an expert programmer. Your task is to write a single, self-contained Python function `transform_matrix` based on a hypothesis.
If you are provided with `feedback` on a previous attempt, you MUST analyze the error and provide a corrected version of the function.
**Function Requirements:**
- The function must be named `transform_matrix`.
- It must accept one argument: `matrix`, which is a list of lists of integers.
- It must return a list of lists of integers (the transformed grid).
- The function should not use any external libraries except for `copy`.
- Your output must be ONLY the Python code for the function, enclosed in ```python ... ```. Do not include explanations.
**Pitfalls to Avoid:**
- **Hardcoding:** Do not hardcode values from the examples. The function must be general.
- **Index Errors:** Be extremely careful with grid boundaries and coordinates.
- **Logic Mismatches:** Ensure your code perfectly matches the logic in the hypothesis. If you are correcting code, identify the specific logical flaw that caused the previous error.
"""
hypothesis: str = dspy.InputField(desc="The natural language description of the rule to implement.")
training_examples: List[TrainingExample] = dspy.InputField(desc="The original examples, for context and verification.")
feedback: Optional[str] = dspy.InputField(desc="Feedback on the previous code attempt, detailing which training example failed and why. Use this to correct your code.", prefix="Feedback on Previous Attempt:")
python_function: str = dspy.OutputField(desc="A string containing ONLY the Python code for the `transform_matrix` function.")
# --- The Improved Custom Module with a Self-Correction Loop ---
class ARCSolver(dspy.Module):
"""A module that solves ARC tasks by hypothesizing a rule, then generating, testing, and refining Python code."""
def __init__(self, max_attempts=3):
super().__init__()
self.max_attempts = max_attempts
# Decompose the problem: 1) Reason about the rule, 2) Implement and refine the rule.
# ChainOfThought encourages more detailed reasoning for both steps.
self.rule_hypothesizer = dspy.ChainOfThought(HypothesizeRule)
self.code_implementer = dspy.ChainOfThought(ImplementAndRefineRule)
def forward(self, training_examples: List[TrainingExample], test_inputs: List[MATRIX]):
# Create fallback outputs in case of any failure.
fallback_outputs = [copy.deepcopy(matrix) for matrix in test_inputs]
# Step 1: Generate a natural language hypothesis about the transformation rule.
try:
prediction = self.rule_hypothesizer(training_examples=training_examples)
hypothesis = prediction.hypothesis
except Exception as e:
# If hypothesis fails, we cannot proceed.
return dspy.Prediction(test_outputs=fallback_outputs)
feedback = None
final_transform_func = None
for attempt in range(self.max_attempts):
# Step 2: Generate or refine Python code based on the hypothesis and feedback.
try:
code_prediction = self.code_implementer(
hypothesis=hypothesis,
training_examples=training_examples,
feedback=feedback
)
python_code = code_prediction.python_function
# The LM sometimes wraps the code in markdown, so we extract it.
if "```python" in python_code:
python_code = python_code.split("```python\n")[1].split("```")[0]
# Step 3: Execute the generated code in a restricted scope.
local_scope = {}
exec(python_code, {"copy": copy}, local_scope)
transform_func = local_scope.get('transform_matrix')
if not callable(transform_func):
feedback = "The generated code did not define a callable function named 'transform_matrix'."
continue
# Step 4: Verify the function against all training examples.
is_correct = True
for i, example in enumerate(training_examples):
try:
input_copy = copy.deepcopy(example.input)
actual_output = transform_func(input_copy)
if actual_output != example.output:
feedback = (
f"Verification failed on training example {i+1}.\n"
f"Input:\n{example.input}\n"
f"Your function's output:\n{actual_output}\n"
f"The correct output should have been:\n{example.output}\n"
"Please analyze the error and fix the code."
)
is_correct = False
break
except Exception as e:
feedback = f"Verification failed on training example {i+1} with a runtime error: {e}. Please fix the code."
is_correct = False
break
if is_correct:
final_transform_func = transform_func
break # Success! Exit the refinement loop.
except Exception as e:
feedback = f"An error occurred during code generation or execution: {e}. Please try again."
# Step 5: If a correct function was found, apply it to the test inputs.
if callable(final_transform_func):
solved_outputs = []
for test_matrix in test_inputs:
try:
input_copy = copy.deepcopy(test_matrix)
result = final_transform_func(input_copy)
solved_outputs.append(result)
except Exception:
# If the verified function fails on a test case, fallback for that case.
solved_outputs.append(copy.deepcopy(test_matrix))
return dspy.Prediction(test_outputs=solved_outputs)
else:
# If the loop finishes without a correct function, return the fallback.
return dspy.Prediction(test_outputs=fallback_outputs)
# The overall task signature, defining the final inputs and outputs of the program.
class SolveTaskSignature(dspy.Signature):
"""
Given a set of training examples demonstrating a matrix transformation,
apply the same transformation to a new set of test matrices.
"""
training_examples: List[TrainingExample] = dspy.InputField(description="Input and output examples demonstrating the task to be performed.")
test_inputs: List[MATRIX] = dspy.InputField(description="Input matrices to be solved following the task described in the training examples.")
test_outputs: List[MATRIX] = dspy.OutputField(description="Output matrices corresponding to the test inputs.")
# The final program is an instance of our new, more robust module.
program = ARCSolver()
2025/08/29 23:38:10 INFO dspy.evaluate.evaluate: Average Metric: 0.0 / 3 (0.0%)
GEPA Optimization: 83%|██████████████████████████████████████████████████████████████████████████████████████████▉ | 3308/4000 [26:04:14<7:11:15, 37.39s/rollouts]Iteration 135: New subsample score is not better, skipping
Iteration 136: Selected program 0 score: 0.67
Average Metric: 2.00 / 3 (66.7%): 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [03:22<00:00, 67.61s/it]2025/08/29 23:41:33 INFO dspy.evaluate.evaluate: Average Metric: 2.0 / 3 (66.7%)
Iteration 136: Proposed new text for program: import dspy
from typing import List
import pydantic
import re
# Define type aliases for clarity
MATRIX = List[List[int]]
class TrainingExample(pydantic.BaseModel):
"""A Pydantic model for a single training example, containing an input and output matrix."""
input: MATRIX
output: MATRIX
class GeneratePythonFunction(dspy.Signature):
"""
Analyzes training examples of matrix transformations to infer the underlying rule.
Based on the inferred rule, it generates a Python function to perform the transformation.
**Instructions:**
1. **Analyze the Task:** Carefully examine the `training_examples`. Each example consists of an 'input' matrix and a corresponding 'output' matrix.
2. **Infer the Rule:** Identify the logical or geometric transformation rule that converts the input matrix to the output matrix. This could involve colors, shapes, positions, counts, or other patterns.
3. **Describe the Rule:** In the 'thought' field, provide a step-by-step, clear, and concise explanation of the transformation rule you have inferred.
4. **Generate a Python Function:** Write a single, self-contained Python function named `transform_matrix`.
- This function must accept one argument: `matrix` (a list of lists of integers).
- It must return the transformed matrix (a list of lists of integers).
- The function should not rely on any external libraries (like numpy or pandas). Standard Python libraries are acceptable.
- Ensure the function is robust and handles the logic derived from all training examples.
5. **Format the Output:** Place the complete Python function inside a markdown code block in the `python_function` output field.
"""
training_examples: List[TrainingExample] = dspy.InputField(desc="A list of input-output pairs demonstrating the transformation.")
thought: str = dspy.OutputField(desc="A detailed, step-by-step explanation of the inferred transformation rule.")
python_function: str = dspy.OutputField(desc="A self-contained Python function `transform_matrix` that implements the rule.")
class SolveTaskSignature(dspy.Signature):
"""Given training examples demonstrating a task, apply the inferred task to the test inputs."""
training_examples: List[TrainingExample] = dspy.InputField(description="Input and output examples demonstrating the task to be performed.")
test_inputs: List[MATRIX] = dspy.InputField(description="Input matrices to be solved following the task described in the training examples.")
test_outputs: List[MATRIX] = dspy.OutputField(description="Output matrices corresponding to the test inputs.")
class ARC_Solver(dspy.Module):
"""A module that solves ARC-like tasks by generating and executing Python code."""
def __init__(self):
super().__init__()
self.code_generator = dspy.ChainOfThought(GeneratePythonFunction)
self.direct_solver = dspy.ChainOfThought(SolveTaskSignature)
def forward(self, training_examples: List[TrainingExample], test_inputs: List[MATRIX]):
"""
Generates a Python function based on training examples and executes it on test inputs.
Falls back to a direct solving method if code generation or execution fails.
"""
try:
# Step 1: Generate the Python function
prediction = self.code_generator(training_examples=training_examples)
code_block = prediction.python_function
# Extract code from markdown block
match = re.search(r"```python\n(.*?)\n```", code_block, re.DOTALL)
if not match:
raise ValueError("Could not find a Python code block in the output.")
func_code = match.group(1)
# Step 2: Execute the generated function
# Prepare a local scope for exec
local_scope = {}
exec(func_code, globals(), local_scope)
transform_matrix_func = local_scope.get('transform_matrix')
if not callable(transform_matrix_func):
raise ValueError("`transform_matrix` function not found or is not callable.")
# Apply the function to all test inputs
solved_outputs = [transform_matrix_func(matrix) for matrix in test_inputs]
return dspy.Prediction(test_outputs=solved_outputs)
except Exception as e:
# Fallback strategy: Use the direct solver
print(f"Code generation/execution failed: {e}. Falling back to direct solver.")
fallback_prediction = self.direct_solver(
training_examples=training_examples,
test_inputs=test_inputs
)
return dspy.Prediction(test_outputs=fallback_prediction.test_outputs)
program = ARC_Solver()
Code generation/execution failed: name 'copy' is not defined. Falling back to direct solver.
2025/08/29 23:45:27 INFO dspy.evaluate.evaluate: Average Metric: 2.0 / 3 (66.7%)
GEPA Optimization: 83%|███████████████████████████████████████████████████████████████████████████████████████████▏ | 3314/4000 [26:11:31<7:43:16, 40.52s/rollouts]Iteration 136: New subsample score is not better, skipping
Iteration 137: Selected program 2 score: 0.605
Average Metric: 2.00 / 3 (66.7%): 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [06:43<00:00, 134.65s/it]2025/08/29 23:52:11 INFO dspy.evaluate.evaluate: Average Metric: 2.0 / 3 (66.7%)
Iteration 137: Proposed new text for program: import dspy
from typing import List, Optional, Callable
import pydantic
import copy
import traceback
# Define type aliases and pydantic models for clarity and structure.
MATRIX = List[List[int]]
class TrainingExample(pydantic.BaseModel):
input: MATRIX
output: MATRIX
# The signature is enhanced to accept optional feedback for refinement.
class GeneratePythonFunction(dspy.Signature):
"""
You are an expert programmer. Your task is to analyze a series of input and output matrix pairs from the Abstraction and Reasoning Corpus (ARC). Based on these examples, you must deduce the underlying transformation rule and write a single, self-contained Python function that implements this rule.
You may be given feedback on a previous attempt. Analyze the feedback carefully to correct your logic and generate an improved function.
**Function Requirements:**
- The function must be named `transform_matrix`.
- It must accept one argument: `matrix`, which is a list of lists of integers (representing the input grid).
- It must return a list of lists of integers (representing the transformed output grid).
- The function should not use any external libraries except for `copy` if needed (e.g., `import copy; new_matrix = copy.deepcopy(matrix)`).
- Your output must be ONLY the Python code for the function. Do not include any explanations, comments outside the function, or markdown formatting like ```python.
**Successful Strategies to Consider:**
- **Analyze the feedback:** If feedback is provided, it means your previous attempt was wrong. The feedback will tell you which training example failed and what the incorrect output was. This is the most important clue for improvement.
- **Object Permanence & Dimensions:** Check if the output dimensions change relative to the input. This is a common transformation.
- **Color Transformations:** Look for rules that change colors based on their value or their neighbors.
- **Spatial Reasoning:** Analyze shapes, positions, and relationships. Common patterns include filling enclosed areas, drawing lines, moving objects, or detecting symmetry.
- **Iterative Processes:** Some rules are applied repeatedly. Consider a `while` loop that continues as long as modifications are being made.
- **Neighborhoods:** A cell's new value often depends on its immediate neighbors.
**Example of a Correctly Formatted Output:**
def transform_matrix(matrix: list[list[int]]) -> list[list[int]]:
# Your implementation here
# For example, find the most frequent color and fill the grid
from collections import Counter
import itertools
if not matrix or not matrix[0]:
return []
counts = Counter(itertools.chain.from_iterable(matrix))
if counts:
# Handle ties by picking the smaller number value
most_common_color = sorted(counts.items(), key=lambda item: (-item[1], item[0]))[0][0]
else:
return []
height = len(matrix)
width = len(matrix[0])
return [[most_common_color for _ in range(width)] for _ in range(height)]
"""
training_examples: List[TrainingExample] = dspy.InputField(desc="A list of input/output pairs demonstrating the transformation rule.")
feedback: Optional[str] = dspy.InputField(desc="Feedback on the previous incorrect function. Analyze this to fix your logic.", prefix="Feedback on Previous Attempt:")
python_function: str = dspy.OutputField(desc="A string containing a single Python function `transform_matrix` that implements the rule.")
# The ARCSolver module is redesigned to include a generate-test-refine loop.
class ARCSolver(dspy.Module):
"""A module that solves ARC tasks by generating, testing, and refining Python code."""
def __init__(self, max_attempts: int = 3):
super().__init__()
self.max_attempts = max_attempts
# Using ChainOfThought to encourage the LM to reason about the feedback before generating code.
self.code_generator = dspy.ChainOfThought(GeneratePythonFunction)
def _validate_function(self, code_str: str, training_examples: List[TrainingExample]) -> tuple[Optional[Callable], Optional[str]]:
"""
Tries to execute the generated code and validates it against training examples.
Returns (function, None) on success, or (None, error_message) on failure.
"""
try:
local_scope = {}
exec(code_str, globals(), local_scope)
transform_func = local_scope.get('transform_matrix')
if not callable(transform_func):
return None, "Failed to define a callable function named `transform_matrix`."
# Test the function against each training example.
for i, example in enumerate(training_examples):
input_copy = copy.deepcopy(example.input)
try:
result = transform_func(input_copy)
if result != example.output:
feedback = (
f"Validation failed on training example {i+1}.\n"
f"Input:\n{example.input}\n"
f"Your function's output:\n{result}\n"
f"Correct output:\n{example.output}"
)
return None, feedback
except Exception:
feedback = (
f"Validation failed on training example {i+1} with a runtime error.\n"
f"Input:\n{example.input}\n"
f"Error: {traceback.format_exc()}"
)
return None, feedback
# If all examples pass, the function is valid.
return transform_func, None
except Exception:
return None, f"The generated code could not be executed. Error: {traceback.format_exc()}"
def forward(self, training_examples: List[TrainingExample], test_inputs: List[MATRIX]):
feedback = "No feedback yet. This is the first attempt. Analyze the examples carefully."
validated_func = None
# --- Refinement Loop ---
for attempt in range(self.max_attempts):
# Step 1: Generate Python function, potentially with feedback from previous attempt.
prediction = self.code_generator(training_examples=training_examples, feedback=feedback)
python_code = prediction.python_function
# Step 2: Validate the generated function against the training examples.
transform_func, validation_error = self._validate_function(python_code, training_examples)
if transform_func:
# If validation is successful, we have our function.
validated_func = transform_func
break
else:
# If validation fails, prepare feedback for the next attempt.
feedback = validation_error
# --- Execution on Test Inputs ---
fallback_outputs = [copy.deepcopy(matrix) for matrix in test_inputs]
if not callable(validated_func):
# If no function was successfully validated after all attempts, fall back.
return dspy.Prediction(test_outputs=fallback_outputs)
# Step 3: Apply the validated function to each test input.
solved_outputs = []
for test_matrix in test_inputs:
try:
input_copy = copy.deepcopy(test_matrix)
result = validated_func(input_copy)
solved_outputs.append(result)
except Exception:
# If the validated function fails on a test case, use fallback for that case.
solved_outputs.append(copy.deepcopy(test_matrix))
return dspy.Prediction(test_outputs=solved_outputs)
# The overall task signature remains the same, defining the high-level goal.
class SolveTaskSignature(dspy.Signature):
"""
Given a set of training examples demonstrating a matrix transformation,
apply the same transformation to a new set of test matrices.
"""
training_examples: List[TrainingExample] = dspy.InputField(description="Input and output examples demonstrating the task to be performed.")
test_inputs: List[MATRIX] = dspy.InputField(description="Input matrices to be solved following the task described in the training examples.")
test_outputs: List[MATRIX] = dspy.OutputField(description="Output matrices corresponding to the test inputs.")
# The final program is an instance of our new, more robust module.
program = ARCSolver()
2025/08/30 00:01:23 INFO dspy.evaluate.evaluate: Average Metric: 0.0 / 3 (0.0%)
GEPA Optimization: 83%|██████████████████████████████████████████████████████████████████████████████████████████▍ | 3320/4000 [26:27:27<10:10:05, 53.83s/rollouts]Iteration 137: New subsample score is not better, skipping
Iteration 138: Selected program 8 score: 0.62
Average Metric: 2.00 / 2 (100.0%): 67%|██████████████████████████████████████████████████████████████████████████ | 2/3 [01:24<00:40, 40.57s/it]2025/08/30 00:03:33 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/30 00:09:14 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
Average Metric: 2.00 / 3 (66.7%): 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [07:50<00:00, 156.87s/it]2025/08/30 00:09:14 INFO dspy.evaluate.evaluate: Average Metric: 2.0 / 3 (66.7%)
Iteration 138: Proposed new text for program: import dspy
from typing import List
import pydantic
import copy
import traceback
# Define type aliases and pydantic models for clarity and structure.
MATRIX = List[List[int]]
class TrainingExample(pydantic.BaseModel):
input: MATRIX
output: MATRIX
# --- Step 1: A Signature to deduce the transformation rule in natural language ---
# This signature is already well-designed and effective.
class HypothesizeRule(dspy.Signature):
"""
Analyzes pairs of input/output matrices from the Abstraction and Reasoning Corpus (ARC)
and deduces the underlying transformation rule. Describe the rule in clear, step-by-step
natural language.
**Successful Strategies to Consider:**
- **Grid Properties:** Analyze changes in dimensions, colors, and object counts.
- **Object Transformations:** Identify objects/shapes and describe how they are moved, rotated, scaled, colored, or combined.
- **Pattern Recognition:** Look for patterns like symmetry, repetition, or subgrid extraction. For example, is the output a small subgrid from the input?
- **Conditional Logic:** The rule might depend on specific conditions, like the color of a neighboring cell or the number of objects present.
"""
training_examples: List[TrainingExample] = dspy.InputField(desc="A list of input/output pairs demonstrating the transformation rule.")
hypothesis: str = dspy.OutputField(desc="A clear, step-by-step natural language description of the transformation rule.")
# --- Step 2: A Signature to generate Python code based on the deduced rule ---
# Upgraded to encourage a reasoning step before coding.
class ImplementRuleInPython(dspy.Signature):
"""
You are an expert programmer. Your task is to write a single, self-contained Python function based on a provided hypothesis about a matrix transformation rule. Use the training examples for context and to verify your logic.
**Function Requirements:**
- The function must be named `transform_matrix`.
- It must accept one argument: `matrix`, which is a list of lists of integers.
- It must return a list of lists of integers (the transformed grid).
- The function should not use any external libraries except for `copy`.
- Your output must be ONLY the Python code for the function. Do not include explanations or markdown formatting.
- Think step-by-step about how to translate the natural language hypothesis into code before writing the function.
**Pitfalls to Avoid:**
- **Hardcoding:** Do not hardcode values from the examples. The function must be general.
- **Index Errors:** Be extremely careful with grid boundaries and coordinates.
- **Incorrect Logic:** Ensure your code perfectly matches the logic described in the hypothesis.
"""
hypothesis: str = dspy.InputField(desc="The natural language description of the rule to implement.")
training_examples: List[TrainingExample] = dspy.InputField(desc="The original examples, for context and verification.")
python_function: str = dspy.OutputField(desc="A string containing a single Python function `transform_matrix` that implements the rule.")
# --- Step 3 (New): A Signature to debug faulty Python code ---
class DebugCode(dspy.Signature):
"""
You are an expert Python debugger. You are given a hypothesis, training examples, a buggy Python function, and the error it produced.
Your task is to fix the code so that it correctly implements the hypothesis and works for all training examples.
The corrected function must adhere to the original requirements (named `transform_matrix`, takes one `matrix` argument, uses no external libraries except `copy`).
Analyze the error, the buggy code, and the hypothesis to find the mistake. Then, provide the corrected, complete Python function.
"""
hypothesis: str = dspy.InputField(desc="The natural language description of the intended rule.")
training_examples: List[TrainingExample] = dspy.InputField(desc="The examples the code should work on.")
buggy_code: str = dspy.InputField(desc="The Python function that failed.")
feedback: str = dspy.InputField(desc="Detailed feedback on why the code is wrong (e.g., exception trace, or input/output mismatch).")
corrected_python_function: str = dspy.OutputField(desc="A string containing the single, corrected Python function `transform_matrix`.")
# --- The Improved Custom Module with a Generate-Test-Debug Loop ---
class ARCSolver(dspy.Module):
"""A module that solves ARC tasks by hypothesizing, generating, and debugging Python code."""
def __init__(self, max_attempts=3):
super().__init__()
self.max_attempts = max_attempts
# Decompose the problem: 1) Reason, 2) Implement, 3) Debug.
self.rule_hypothesizer = dspy.ChainOfThought(HypothesizeRule)
# Use ChainOfThought to encourage the LM to plan the code structure.
self.code_generator = dspy.ChainOfThought(ImplementRuleInPython)
# A dedicated module for self-correction.
self.code_debugger = dspy.ChainOfThought(DebugCode)
def _execute_and_verify(self, code_str: str, training_examples: List[TrainingExample]):
"""Executes the generated code and verifies it against all training examples."""
if not code_str or not isinstance(code_str, str):
return None, "Generated code is empty or not a string."
try:
local_scope = {}
exec(code_str, globals(), local_scope)
transform_func = local_scope.get('transform_matrix')
if not callable(transform_func):
return None, "Failed to define the `transform_matrix` function."
for example in training_examples:
input_matrix = copy.deepcopy(example.input)
expected_output = example.output
try:
actual_output = transform_func(input_matrix)
if actual_output != expected_output:
feedback = (
f"Verification failed on an example.\n"
f"Input:\n{example.input}\n"
f"Expected Output:\n{expected_output}\n"
f"Actual Output:\n{actual_output}"
)
return None, feedback
except Exception:
feedback = (
f"An exception occurred during execution with this example:\n"
f"Input:\n{example.input}\n"
f"Traceback:\n{traceback.format_exc()}"
)
return None, feedback
# All examples passed
return transform_func, None
except Exception:
return None, f"A syntax error or other exception occurred during `exec`:\n{traceback.format_exc()}"
def forward(self, training_examples: List[TrainingExample], test_inputs: List[MATRIX]):
fallback_outputs = [copy.deepcopy(matrix) for matrix in test_inputs]
# Step 1: Generate a natural language hypothesis.
prediction = self.rule_hypothesizer(training_examples=training_examples)
hypothesis = prediction.hypothesis
# Step 2: Initial code generation.
code_prediction = self.code_generator(hypothesis=hypothesis, training_examples=training_examples)
python_code = code_prediction.python_function
transform_func = None
# Step 3: Generate-Test-Debug Loop.
for attempt in range(self.max_attempts):
transform_func, feedback = self._execute_and_verify(python_code, training_examples)
if transform_func:
# Code is correct, break the loop.
break
if attempt < self.max_attempts - 1:
# Code is buggy, try to debug it.
debug_prediction = self.code_debugger(
hypothesis=hypothesis,
training_examples=training_examples,
buggy_code=python_code,
feedback=feedback
)
python_code = debug_prediction.corrected_python_function
else:
# Max attempts reached, exit loop with failure.
transform_func = None
# Step 4: Apply the validated function to test inputs.
if not callable(transform_func):
return dspy.Prediction(test_outputs=fallback_outputs)
try:
solved_outputs = []
for test_matrix in test_inputs:
input_copy = copy.deepcopy(test_matrix)
result = transform_func(input_copy)
solved_outputs.append(result)
return dspy.Prediction(test_outputs=solved_outputs)
except Exception:
# If the validated function fails on test data, fall back.
return dspy.Prediction(test_outputs=fallback_outputs)
# The overall task signature, defining the final inputs and outputs of the program.
class SolveTaskSignature(dspy.Signature):
"""
Given a set of training examples demonstrating a matrix transformation,
apply the same transformation to a new set of test matrices.
"""
training_examples: List[TrainingExample] = dspy.InputField(description="Input and output examples demonstrating the task to be performed.")
test_inputs: List[MATRIX] = dspy.InputField(description="Input matrices to be solved following the task described in the training examples.")
test_outputs: List[MATRIX] = dspy.OutputField(description="Output matrices corresponding to the test inputs.")
# The final program is an instance of our new, more robust module.
program = ARCSolver()
2025/08/30 00:10:36 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/30 00:12:11 INFO dspy.evaluate.evaluate: Average Metric: 0.0 / 3 (0.0%)
GEPA Optimization: 83%|██████████████████████████████████████████████████████████████████████████████████████████▋ | 3326/4000 [26:38:14<11:28:33, 61.30s/rollouts]Iteration 138: New subsample score is not better, skipping
Iteration 139: Selected program 2 score: 0.605
Average Metric: 2.00 / 3 (66.7%): 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [06:55<00:00, 138.64s/it]2025/08/30 00:19:07 INFO dspy.evaluate.evaluate: Average Metric: 2.0 / 3 (66.7%)
Iteration 139: Proposed new text for program: import dspy
from typing import List
import pydantic
import copy
# Define type aliases and pydantic models for clarity and structure.
MATRIX = List[List[int]]
class TrainingExample(pydantic.BaseModel):
input: MATRIX
output: MATRIX
# Signature to deduce the transformation rule in natural language.
class HypothesizeRule(dspy.Signature):
"""
Analyzes a series of input/output matrix pairs from the Abstraction and Reasoning Corpus (ARC).
Your goal is to deduce the underlying transformation rule and describe it clearly and precisely in natural language.
**Analysis Guidance:**
- **Be Specific:** Instead of "moves the shape," describe *which* shape (e.g., "the largest red object," "all blue squares") and *where* it moves (e.g., "to the center of the grid," "reflected across the vertical axis").
- **Consider Core Concepts:**
- **Object Permanence:** Do grid dimensions change? Are objects preserved?
- **Color Transformations:** Are colors changed, swapped, or filled based on patterns?
- **Spatial Reasoning:** Look for patterns like symmetry, rotation, filling enclosed areas, drawing lines, moving objects, or copying/repeating patterns.
- **Counting/Frequency:** Does the transformation depend on the number of objects or the most/least frequent color?
- **Neighborhoods:** Does a cell's new value depend on its immediate neighbors?
**Example Rule Description:**
"The transformation reverses the order of the rows in the matrix, effectively flipping it vertically. The dimensions and colors within the rows remain unchanged."
"""
training_examples: List[TrainingExample] = dspy.InputField(desc="A list of input/output pairs demonstrating the transformation rule.")
rule_description: str = dspy.OutputField(desc="A clear, step-by-step natural language description of the transformation rule.")
# Updated signature to generate a Python function from a rule description.
class GeneratePythonFunction(dspy.Signature):
"""
You are an expert programmer. You are given a natural language description of a transformation rule and several examples that follow this rule. Your task is to write a single, self-contained Python function that implements this rule.
**Function Requirements:**
- The function must be named `transform_matrix`.
- It must accept one argument: `matrix`, which is a list of lists of integers (representing the input grid).
- It must return a list of lists of integers (representing the transformed output grid).
- The function should not use any external libraries except for `copy` if needed (e.g., `import copy; new_matrix = copy.deepcopy(matrix)`).
- Your output must be ONLY the Python code for the function. Do not include any explanations, comments outside the function, or markdown formatting like ```python.
**Example of a Correctly Formatted Output:**
def transform_matrix(matrix: list[list[int]]) -> list[list[int]]:
# Your implementation here
# For example, to flip the matrix vertically based on the rule description
return matrix[::-1]
"""
rule_description: str = dspy.InputField(desc="The natural language description of the rule to implement.")
training_examples: List[TrainingExample] = dspy.InputField(desc="A list of input/output pairs to use as a reference for implementation details.")
python_function: str = dspy.OutputField(desc="A string containing a single Python function `transform_matrix` that implements the rule.")
class ARCSolver(dspy.Module):
"""
A module that solves ARC tasks by first hypothesizing a rule in natural language,
then generating Python code to implement that rule, and finally executing the code.
This two-step process improves reliability for complex tasks.
"""
def __init__(self):
super().__init__()
# Step 1: A ChainOfThought module to reason about the examples and describe the rule.
self.rule_hypothesizer = dspy.ChainOfThought(HypothesizeRule)
# Step 2: A Predict module to generate code based on the rule description.
self.code_generator = dspy.Predict(GeneratePythonFunction)
def forward(self, training_examples: List[TrainingExample], test_inputs: List[MATRIX]):
# Step 1: Generate a natural language description of the transformation rule.
rule_pred = self.rule_hypothesizer(training_examples=training_examples)
rule_description = rule_pred.rule_description
# Step 2: Generate the Python function from the rule and examples.
code_pred = self.code_generator(rule_description=rule_description, training_examples=training_examples)
python_code = code_pred.python_function
# Prepare a dictionary to hold the executed function.
local_scope = {}
# Create fallback outputs in case of any failure.
fallback_outputs = [copy.deepcopy(matrix) for matrix in test_inputs]
try:
# Step 3: Execute the generated code string to define the function.
exec(python_code, globals(), local_scope)
transform_func = local_scope.get('transform_matrix')
if not callable(transform_func):
return dspy.Prediction(test_outputs=fallback_outputs)
# Step 4: Apply the generated function to each test input.
solved_outputs = []
for test_matrix in test_inputs:
try:
input_copy = copy.deepcopy(test_matrix)
result = transform_func(input_copy)
solved_outputs.append(result)
except Exception:
# If the function fails on a specific test case, append the original
# matrix as a fallback for that case and continue.
solved_outputs.append(copy.deepcopy(test_matrix))
return dspy.Prediction(test_outputs=solved_outputs)
except Exception:
# If code generation or `exec` fails entirely, return the original inputs.
return dspy.Prediction(test_outputs=fallback_outputs)
# The overall task signature remains the same, defining the program's external interface.
class SolveTaskSignature(dspy.Signature):
"""
Given a set of training examples demonstrating a matrix transformation,
apply the same transformation to a new set of test matrices.
"""
training_examples: List[TrainingExample] = dspy.InputField(description="Input and output examples demonstrating the task to be performed.")
test_inputs: List[MATRIX] = dspy.InputField(description="Input matrices to be solved following the task described in the training examples.")
test_outputs: List[MATRIX] = dspy.OutputField(description="Output matrices corresponding to the test inputs.")
# The final program is an instance of our new, more robust module.
program = ARCSolver()
2025/08/30 00:28:22 INFO dspy.evaluate.evaluate: Average Metric: 1.0 / 3 (33.3%)
GEPA Optimization: 83%|██████████████████████████████████████████████████████████████████████████████████████████▊ | 3332/4000 [26:54:25<14:26:43, 77.85s/rollouts]Iteration 139: New subsample score is not better, skipping
Iteration 140: Selected program 0 score: 0.67
Average Metric: 0.00 / 2 (0.0%): 33%|█████████████████████████████████████▋ | 1/3 [00:00<00:00, 30.96it/s]2025/08/30 00:32:15 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/30 00:32:23 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
Average Metric: 1.00 / 3 (33.3%): 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [05:39<00:00, 113.20s/it]2025/08/30 00:34:01 INFO dspy.evaluate.evaluate: Average Metric: 1.0 / 3 (33.3%)
2025/08/30 00:34:20 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
Iteration 140: Proposed new text for program: import dspy
from typing import List, Dict, Any
import pydantic
import traceback
# Define the type for a grid/matrix
MATRIX = List[List[int]]
class TrainingExample(pydantic.BaseModel):
"""Represents a single input-output pair for the reasoning task."""
input: MATRIX
output: MATRIX
class GenerateRuleSignature(dspy.Signature):
"""
Analyzes a series of input-output grid pairs to deduce the transformation rule.
Then, it generates a Python function that implements this rule.
**Task Instructions:**
1. **Analyze:** Carefully examine the provided `training_examples`. Each example shows an input grid and its corresponding output grid. Identify the underlying pattern, transformation, or algorithm that connects the input to the output.
2. **Describe the Rule:** In the `rule_description` field, write a clear, step-by-step explanation of the transformation rule you discovered. This should be understandable by a human.
3. **Generate Python Function:** In the `python_function` field, write a complete, standalone Python function named `transform_matrix`.
- This function MUST have the exact signature: `def transform_matrix(matrix: List[List[int]]) -> List[List[int]]:`
- The function should take one argument, an input grid (`matrix`), and return the transformed output grid.
- The code should be self-contained. Do NOT include any import statements. You can assume standard libraries like `copy` are available if needed.
- Focus on creating a correct and robust implementation of the rule. Pay close attention to details like grid dimensions, coordinates, and slicing logic.
"""
training_examples: List[TrainingExample] = dspy.InputField(
desc="A list of input-output pairs demonstrating the task."
)
rule_description: str = dspy.OutputField(
desc="A step-by-step natural language description of the transformation rule."
)
python_function: str = dspy.OutputField(
desc="A standalone Python function `transform_matrix` that implements the rule."
)
class SolveTaskSignature(dspy.Signature):
"""
Given training examples demonstrating a task and new test inputs, this signature's
purpose is to produce the corresponding outputs for the test inputs by following
the demonstrated task. This is used as a fallback method.
"""
training_examples: List[TrainingExample] = dspy.InputField(
description="Input and output examples demonstrating the task to be performed."
)
test_inputs: List[MATRIX] = dspy.InputField(
description="Input matrices to be solved following the task described in the training examples."
)
test_outputs: List[MATRIX] = dspy.OutputField(
description="Output matrices corresponding to the test inputs."
)
class ARC_Solver(dspy.Module):
"""A module that solves ARC-like tasks by generating and executing code."""
def __init__(self):
super().__init__()
# Module to generate the transformation logic as a Python function.
self.rule_generator = dspy.ChainOfThought(GenerateRuleSignature)
# Fallback module that attempts to solve the task end-to-end.
self.fallback_solver = dspy.ChainOfThought(SolveTaskSignature)
def forward(self, training_examples: List[TrainingExample], test_inputs: List[MATRIX]) -> dspy.Prediction:
# Step 1: Ask the LM to generate the rule and a Python function.
prediction = self.rule_generator(training_examples=training_examples)
python_code = prediction.python_function
# Step 2: Try to execute the generated Python function.
try:
# Prepare a local namespace for safe execution of the generated code.
local_namespace: Dict[str, Any] = {}
# The generated code is expected to contain the 'transform_matrix' function.
exec(python_code, globals(), local_namespace)
transform_func = local_namespace['transform_matrix']
# Apply the executed function to each test input.
test_outputs = [transform_func(test_input) for test_input in test_inputs]
# Return the successful results.
return dspy.Prediction(test_outputs=test_outputs)
except Exception as e:
# Step 3: If code generation or execution fails, use the fallback.
print(f"Code execution failed: {e}")
print(f"Traceback: {traceback.format_exc()}")
print("Falling back to the end-to-end solver.")
fallback_prediction = self.fallback_solver(
training_examples=training_examples,
test_inputs=test_inputs
)
return dspy.Prediction(test_outputs=fallback_prediction.test_outputs)
# The final program is an instance of our robust, code-generating module.
program = ARC_Solver()
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
2025/08/30 00:40:33 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
Code execution failed: exec() arg 1 must be a string, bytes or code object
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
TypeError: exec() arg 1 must be a string, bytes or code object
Falling back to the end-to-end solver.
2025/08/30 00:40:40 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
Code execution failed: exec() arg 1 must be a string, bytes or code object
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
TypeError: exec() arg 1 must be a string, bytes or code object
Falling back to the end-to-end solver.
2025/08/30 00:42:06 INFO dspy.evaluate.evaluate: Average Metric: 2.0 / 3 (66.7%)
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
2025/08/30 00:45:33 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
Code execution failed: exec() arg 1 must be a string, bytes or code object
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
TypeError: exec() arg 1 must be a string, bytes or code object
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
2025/08/30 00:45:44 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
Code execution failed: exec() arg 1 must be a string, bytes or code object
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
TypeError: exec() arg 1 must be a string, bytes or code object
Falling back to the end-to-end solver.
2025/08/30 00:45:46 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
Code execution failed: exec() arg 1 must be a string, bytes or code object
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
TypeError: exec() arg 1 must be a string, bytes or code object
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
2025/08/30 00:46:05 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
Code execution failed: exec() arg 1 must be a string, bytes or code object
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
TypeError: exec() arg 1 must be a string, bytes or code object
Falling back to the end-to-end solver.
2025/08/30 00:46:06 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
2025/08/30 00:48:54 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
2025/08/30 00:49:05 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/30 00:49:09 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
2025/08/30 00:49:21 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
2025/08/30 00:49:39 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
2025/08/30 00:49:51 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
2025/08/30 00:49:58 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
2025/08/30 00:51:32 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
2025/08/30 00:51:58 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/30 00:53:11 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
2025/08/30 00:57:08 INFO dspy.evaluate.evaluate: Average Metric: 133.0 / 200 (66.5%)
GEPA Optimization: 88%|█████████████████████████████████████████████████████████████████████████████████████████████████▎ | 3538/4000 [27:23:12<2:03:28, 16.03s/rollouts]Iteration 140: Full valset score for new program: 0.665
Iteration 140: Full train_val score for new program: 0.665
Iteration 140: Individual valset scores for new program: [False, True, False, False, True, False, True, True, True, True, True, True, True, False, True, True, False, True, True, False, True, True, True, True, True, True, False, 0, True, True, False, False, False, True, True, True, False, True, False, False, False, True, True, False, False, True, False, False, True, True, True, True, True, True, True, 0, True, True, False, True, True, False, False, True, False, False, False, True, 0, False, True, True, True, False, True, True, True, False, True, True, True, True, True, True, True, True, True, True, True, False, True, True, True, True, True, False, True, False, True, True, True, True, False, False, True, False, True, True, False, True, True, True, True, True, True, False, True, 0, False, True, False, True, True, False, False, True, False, True, True, True, False, True, True, True, True, False, True, True, True, True, True, True, True, False, True, False, False, False, True, True, False, False, True, True, False, True, True, True, True, False, False, True, True, True, False, True, True, True, True, False, True, True, True, False, True, False, False, True, False, True, True, True, True, False, False, True, True, True, True, True, True, True, True, True, True, False, True, False, False, True]
Iteration 140: New valset pareto front scores: [True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, False, True, True, True, True, True, True, True, True, True, False, True, True, True, True, False, True, True, True, True, False, True, True, True, False, True, True, True, False, True, False, True, True, True, True, True, True, True, True, 0, True, True, False, True, True, True, False, True, True, False, True, True, 0, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, False, True, True, True, True, True, True, True, True, True, True, True, True, True, False, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, False, False, True, False, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, False, True, True, True, False, True, True, True, True, True, True, True, True, True, True, True, False, True, True, True, True, False, True, True, True, True, False, True, True, True, True, True, True, False, True, False, True, True, True, True, False, True, True, True, True, True, True, True, True, True, True, True, True, True, True, False, True]
Iteration 140: Full valset pareto front score: 0.87
Iteration 140: Updated valset pareto front programs: [{1, 3, 5, 8, 9, 10, 11, 12}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 13}, {0, 9, 12}, {0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}, {0, 1, 2, 3, 4, 5, 6, 7, 9, 10, 11, 12, 13}, {0, 4}, {0, 1, 2, 3, 4, 5, 6, 7, 9, 10, 11, 12, 13}, {0, 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13}, {0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13}, {8}, {0, 1, 2, 3, 4, 6, 9, 10, 11, 12, 13}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13}, {1, 4, 5, 7, 9, 10, 12, 13}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13}, {0, 2, 4, 6, 7, 10, 11, 12}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13}, {0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13}, {1, 5, 7, 9, 12, 13}, {0, 3, 4, 6, 8, 9, 10, 11, 12, 13}, {0, 1, 3, 6, 7, 8, 9, 10, 11, 12, 13}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13}, {0, 4, 7}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13}, {1}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13}, {2}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13}, {0, 1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13}, {2, 12, 5}, {0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13}, {1}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13}, {0, 1, 2, 3, 4, 6, 8, 9, 10, 11, 12, 13}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13}, {8}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13}, {0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13}, {0, 1, 2, 3, 4, 6, 7, 11, 13}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13}, {0, 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13}, {0, 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 13}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13}, {0, 1, 2, 3, 8, 12, 13}, {0, 1, 5}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13}, {4, 6, 7, 8, 11}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13}, {1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12}, {0, 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13}, {11}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13}, {2, 3, 4, 5, 6, 7, 8, 9, 11}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13}, {1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13}, {0, 3, 4, 5, 6, 7, 9, 10, 11, 12, 13}, {4}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13}, {0, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13}, {0, 2, 6, 8, 11, 12, 13}, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13}, {0, 1, 2, 4, 5, 6, 7, 8, 10, 11, 12, 13}, {0, 1, 2, 3, 4, 5, 6, 7, 13}, {0, 3, 4, 6, 7, 11, 12, 13}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13}, {0, 1, 2, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}, {1, 3, 8, 10, 12, 13}, {7}, {0, 3, 4, 6, 7, 9, 10, 11, 12, 13}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13}, {0, 1, 2, 3, 4, 5, 6, 8, 9, 10, 11, 12, 13}, {0, 4, 6, 7, 9, 11, 12}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13}, {0, 1, 2, 3, 5, 7, 8, 11, 12}, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13}, {0, 1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13}, {1, 2, 10}, {0, 1, 4, 5, 6, 8, 10, 12, 13}, {13}, {0, 2, 13, 5}, {1, 3, 10, 12, 13}, {2, 4, 9, 10, 11, 13}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13}, {3, 4, 5, 7, 8}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13}, {2, 3, 4, 6, 7, 8, 9, 10, 11}, {0, 1, 2, 3, 4, 5, 6, 8, 10, 11}, {0, 1, 2, 3, 5, 6, 7, 9, 10, 11, 12, 13}, {2, 3, 4, 5, 6, 7, 9, 11, 12}, {0, 1, 7, 9, 10, 13}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13}, {0, 1, 2, 3, 4, 5, 6, 7, 9, 10, 11, 12, 13}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13}, {0, 1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13}, {0, 1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13}, {2, 7}, {0, 1, 3, 9, 12, 13}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13}, {0, 1, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13}, {0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13}, {8, 9, 7}, {0, 1, 3, 6, 7, 8, 9, 11, 12, 13}, {3, 5, 6, 7, 11, 13}, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13}, {1, 2, 3, 4, 5, 6, 7, 9, 10, 11, 12, 13}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13}, {0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 13}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13}, {9}, {0, 2, 3, 5, 6, 7, 9, 10, 11}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13}, {4}, {8, 9, 7}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13}, {0, 1, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13}, {0, 1, 3, 4, 5, 6, 7, 8, 10, 11, 12}, {0, 1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13}, {0, 3, 5, 6, 8, 9, 10, 11, 12, 13}, {0, 1, 2, 3, 4, 5, 6, 7, 9, 10, 11, 12, 13}, {0, 1, 2, 3, 6, 7, 9, 10, 11, 13}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13}, {1, 12}, {0, 1, 2, 3, 4, 5, 6, 8, 9, 10, 11, 12, 13}, {0, 4, 5, 6, 7, 8, 9, 10, 12, 13}, {0, 3, 5, 10, 11, 13}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13}, {0, 1, 2, 4, 5, 6, 7, 9, 10, 12, 13}, {0, 1, 2, 4, 5, 6, 7, 9, 10, 13}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13}, {0, 7, 8, 9, 12, 13}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13}, {6, 9, 11, 12, 13}, {0, 1, 3, 5, 6, 7, 8, 9, 10, 11}, {0, 1, 2, 3, 4, 5, 6, 8, 9, 11, 13}, {1, 2, 3, 5, 6, 8, 9, 10, 11, 12}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13}, {0, 1, 3, 4, 5, 6, 7, 8, 10, 11, 12, 13}, {0, 2, 7, 8, 9, 10, 12, 13}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13}, {9, 11, 12}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13}, {0, 1, 2, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13}, {0, 5, 6, 7, 8, 9, 10, 11, 13}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13}, {0, 1, 2, 3, 4, 5, 6, 7, 10, 11, 13}, {1, 2, 4, 5, 6, 7, 8, 10, 11, 12, 13}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 13}, {11, 6}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13}, {0, 10}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13}, {0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13}]
Iteration 140: Best valset aggregate score so far: 0.675
Iteration 140: Best program as per aggregate score on train_val: 6
Iteration 140: Best program as per aggregate score on valset: 6
Iteration 140: Best score on valset: 0.675
Iteration 140: Best score on train_val: 0.675
Iteration 140: Linear pareto front program index: 6
Iteration 140: New program candidate index: 13
Iteration 141: Selected program 2 score: 0.605
0%| | 0/3 [00:00<?, ?it/s]2025/08/30 00:57:08 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
Average Metric: 0.00 / 1 (0.0%): 0%| | 0/3 [00:00<?, ?it/s]2025/08/30 00:57:35 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/30 00:57:35 WARNING dspy.adapters.json_adapter: Failed to use structured output format, falling back to JSON mode.
Average Metric: 2.00 / 3 (66.7%): 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [02:27<00:00, 49.16s/it]2025/08/30 00:59:36 INFO dspy.evaluate.evaluate: Average Metric: 2.0 / 3 (66.7%)
Iteration 141: Proposed new text for program: import dspy
from typing import List
import pydantic
import copy
# Define type aliases and pydantic models for clarity and structure.
MATRIX = List[List[int]]
class TrainingExample(pydantic.BaseModel):
input: MATRIX
output: MATRIX
class HypothesizeRule(dspy.Signature):
"""
Analyze a series of ARC input/output matrix pairs and describe the transformation rule in plain English.
Focus on identifying the core logic. Consider transformations like:
- Finding and extracting a sub-grid or pattern from a noisy background (especially if output dimensions are smaller than input).
- Tiling or repeating a pattern.
- Drawing lines or shapes based on the position of colored cells.
- Color substitution or mapping.
- Resizing, rotating, or flipping the entire grid or objects within it.
"""
training_examples: List[TrainingExample] = dspy.InputField(desc="A list of input/output pairs demonstrating the transformation rule.")
hypothesis: str = dspy.OutputField(desc="A clear, step-by-step description of the transformation rule.")
class GeneratePythonFunctionWithHypothesis(dspy.Signature):
"""
You are an expert programmer. Your task is to write a single, self-contained Python function based on a provided hypothesis and a series of examples from the Abstraction and Reasoning Corpus (ARC).
**Hypothesis:** You will be given a natural language description of the transformation rule. Your primary goal is to implement this hypothesis faithfully.
**Examples:** You will also be given the input/output pairs that the hypothesis is based on. Use them to clarify any ambiguities in the hypothesis.
**Function Requirements:**
- The function must be named `transform_matrix`.
- It must accept one argument: `matrix`, which is a list of lists of integers (representing the input grid).
- It must return a list of lists of integers (representing the transformed output grid).
- The function should not use any external libraries except for `copy` if needed (e.g., `import copy; new_matrix = copy.deepcopy(matrix)`).
- Your output must be ONLY the Python code for the function. Do not include any explanations, comments outside the function, or markdown formatting like ```python.
**Successful Strategies to Consider:**
- **Pattern Extraction:** For tasks where the output is smaller than the input, the rule often involves finding the smallest sub-grid that contains all non-background (non-zero) pixels.
- **Object Permanence:** Most transformations preserve the grid dimensions or have a clear rule for changing them (e.g., tiling, extraction).
- **Color Transformations:** Look for rules that change colors based on their value or their neighbors.
- **Spatial Reasoning:** Analyze shapes, positions, and relationships. Common patterns include filling enclosed areas, drawing lines, moving objects, or detecting symmetry.
- **Iterative Processes:** Some rules are applied repeatedly. Consider using a `while` loop.
**Example of a Correctly Formatted Output:**
def transform_matrix(matrix: list[list[int]]) -> list[list[int]]:
# Your implementation here
# For example, find the most frequent color and fill the grid
from collections import Counter
import itertools
if not matrix or not matrix[0]:
return []
counts = Counter(itertools.chain.from_iterable(matrix))
if counts:
most_common_color = sorted(counts.items(), key=lambda item: (-item[1], item[0]))[0][0]
else:
return []
height = len(matrix)
width = len(matrix[0])
return [[most_common_color for _ in range(width)] for _ in range(height)]
"""
training_examples: List[TrainingExample] = dspy.InputField(desc="A list of input/output pairs demonstrating the transformation rule.")
hypothesis: str = dspy.InputField(desc="The natural language hypothesis describing the transformation rule.")
python_function: str = dspy.OutputField(desc="A string containing a single Python function `transform_matrix` that implements the rule based on the hypothesis.")
class ARCSolver(dspy.Module):
"""A module that solves ARC tasks by hypothesizing a rule and then generating and executing Python code."""
def __init__(self):
super().__init__()
# Decompose the problem: 1) Hypothesize the rule, 2) Generate code based on the hypothesis.
# Using ChainOfThought for hypothesizing encourages more detailed reasoning.
self.rule_hypothesizer = dspy.ChainOfThought(HypothesizeRule)
self.code_generator = dspy.Predict(GeneratePythonFunctionWithHypothesis)
def forward(self, training_examples: List[TrainingExample], test_inputs: List[MATRIX]):
# Create fallback outputs in case of any failure.
fallback_outputs = [copy.deepcopy(matrix) for matrix in test_inputs]
try:
# Step 1: Generate a natural language hypothesis about the transformation rule.
prediction = self.rule_hypothesizer(training_examples=training_examples)
hypothesis = prediction.hypothesis
# Step 2: Generate Python code based on the hypothesis and examples.
code_prediction = self.code_generator(training_examples=training_examples, hypothesis=hypothesis)
python_code = code_prediction.python_function
if not python_code:
# If code generation fails, return the fallback.
return dspy.Prediction(test_outputs=fallback_outputs)
# Prepare a dictionary to hold the executed function.
local_scope = {}
# Step 3: Execute the generated code string to define the function.
exec(python_code, globals(), local_scope)
transform_func = local_scope.get('transform_matrix')
if not callable(transform_func):
# If function definition failed or the name is wrong, use the fallback.
return dspy.Prediction(test_outputs=fallback_outputs)
# Step 4: Apply the generated function to each test input.
solved_outputs = []
for test_matrix in test_inputs:
try:
# Use a deepcopy to prevent the function from modifying the original input list.
input_copy = copy.deepcopy(test_matrix)
result = transform_func(input_copy)
solved_outputs.append(result)
except Exception:
# If the function fails on a specific test case, append the original
# matrix as a fallback for that case and continue.
solved_outputs.append(copy.deepcopy(test_matrix))
return dspy.Prediction(test_outputs=solved_outputs)
except Exception:
# If any step in the chain fails, return the original inputs.
return dspy.Prediction(test_outputs=fallback_outputs)
# The overall task signature, defining the final inputs and outputs of the program.
class SolveTaskSignature(dspy.Signature):
"""
Given a set of training examples demonstrating a matrix transformation,
apply the same transformation to a new set of test matrices.
"""
training_examples: List[TrainingExample] = dspy.InputField(description="Input and output examples demonstrating the task to be performed.")
test_inputs: List[MATRIX] = dspy.InputField(description="Input matrices to be solved following the task described in the training examples.")
test_outputs: List[MATRIX] = dspy.OutputField(description="Output matrices corresponding to the test inputs.")
# The final program is an instance of our new, more robust module.
program = ARCSolver()
2025/08/30 01:04:20 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/30 01:04:24 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/30 01:06:02 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/30 01:06:25 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/30 01:11:15 INFO dspy.evaluate.evaluate: Average Metric: 2.0 / 3 (66.7%)
GEPA Optimization: 89%|█████████████████████████████████████████████████████████████████████████████████████████████████▍ | 3544/4000 [27:37:18<2:35:47, 20.50s/rollouts]Iteration 141: New subsample score is not better, skipping
Iteration 142: Selected program 11 score: 0.665
0%| | 0/3 [00:00<?, ?it/s]2025/08/30 01:11:22 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/30 01:13:23 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
Average Metric: 3.00 / 3 (100.0%): : 5it [08:12, 98.56s/it] 2025/08/30 01:19:28 INFO dspy.evaluate.evaluate: Average Metric: 3.0 / 3 (100.0%)
GEPA Optimization: 89%|█████████████████████████████████████████████████████████████████████████████████████████████████▌ | 3547/4000 [27:45:31<3:01:46, 24.08s/rollouts]
Iteration 142: All subsample scores perfect. Skipping.
Iteration 142: Reflective mutation did not propose a new candidate
Iteration 143: Selected program 0 score: 0.67
Average Metric: 2.00 / 3 (66.7%): 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 67.17it/s]2025/08/30 01:19:28 INFO dspy.evaluate.evaluate: Average Metric: 2.0 / 3 (66.7%)
2025/08/30 01:20:04 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[0, 7, 7, 7, 1, 0, 4, 0, 4], [7, 7, 7, 0, 1, 4, 4, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 4], [7, 0, 0, 0, 1, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 0, 0, 0, 1, 6, 6, 6, 0], [0, 0, 8, 8, 1, 0, 0, 0, 0], [8, 0, 8, 0, 1, 6, 0, 0, 6], [0, 0, 0, 8, 1, 0, 0, 0, 0]], 'output': [[6, 7, 7, 7], [7, 7, 7, 8], [8, 0, 8, 4], [7, 0, 0, 8]]}, {'input': [[7, 7, 7, 0, 1, 0, 4, 0, 0], [7, 0, 7, 0, 1, 4, 0, 4, 4], [0, 7, 0, 7, 1, 4, 0, 4, 4], [0, 0, 0, 7, 1, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 0, 8, 0, 1, 6, 0, 0, 6], [0, 0, 0, 0, 1, 6, 0, 0, 0], [0, 0, 0, 0, 1, 6, 6, 0, 6], [8, 8, 8, 0, 1, 6, 0, 6, 6]], 'output': [[7, 7, 7, 6], [7, 0, 7, 4], [4, 7, 4, 7], [8, 8, 8, 7]]}, {'input': [[0, 0, 7, 7, 1, 0, 4, 4, 0], [0, 0, 0, 7, 1, 0, 0, 4, 4], [7, 7, 7, 7, 1, 0, 0, 0, 4], [0, 7, 0, 0, 1, 0, 4, 4, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 0, 8, 8, 1, 0, 6, 6, 6], [0, 0, 0, 0, 1, 0, 0, 6, 0], [0, 0, 0, 8, 1, 6, 0, 6, 0], [8, 0, 0, 0, 1, 6, 6, 0, 0]], 'output': [[0, 4, 7, 7], [0, 0, 4, 7], [7, 7, 7, 7], [8, 7, 4, 0]]}, {'input': [[7, 7, 0, 0, 1, 4, 4, 0, 4], [7, 0, 7, 0, 1, 4, 0, 0, 0], [7, 0, 0, 7, 1, 4, 4, 4, 0], [7, 0, 7, 7, 1, 4, 0, 4, 4], [1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 0, 8, 0, 1, 0, 0, 0, 0], [0, 0, 8, 0, 1, 6, 6, 0, 0], [0, 0, 8, 0, 1, 0, 6, 6, 6], [0, 8, 0, 8, 1, 0, 6, 6, 0]], 'output': [[7, 7, 8, 4], [7, 6, 7, 0], [7, 4, 4, 7], [7, 8, 7, 7]]}, {'input': [[7, 7, 0, 0, 1, 0, 0, 0, 4], [7, 0, 0, 0, 1, 4, 4, 4, 4], [7, 0, 7, 0, 1, 4, 0, 0, 0], [0, 7, 7, 0, 1, 4, 4, 4, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1], [8, 0, 8, 0, 1, 6, 6, 6, 6], [0, 0, 8, 8, 1, 0, 0, 6, 0], [0, 0, 0, 0, 1, 0, 6, 0, 6], [8, 8, 8, 8, 1, 0, 0, 0, 6]], 'output': [[7, 7, 8, 4], [7, 4, 4, 4], [7, 6, 7, 6], [4, 7, 7, 8]]}, {'input': [[7, 0, 0, 7, 1, 4, 4, 4, 0], [0, 7, 7, 7, 1, 4, 4, 0, 4], [7, 7, 7, 0, 1, 4, 4, 0, 4], [7, 7, 7, 0, 1, 0, 4, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1], [8, 8, 0, 8, 1, 6, 6, 6, 6], [0, 8, 8, 8, 1, 0, 0, 0, 6], [0, 8, 0, 8, 1, 0, 0, 6, 0], [8, 8, 0, 8, 1, 0, 6, 0, 0]], 'output': [[7, 4, 4, 7], [4, 7, 7, 7], [7, 7, 7, 4], [7, 7, 7, 8]]}], 'test_inputs': [[[7, 7, 7, 0, 1, 0, 0, 4, 0], [0, 7, 7, 0, 1, 4, 4, 0, 4], [7, 7, 7, 7, 1, 0, 4, 0, 4], [7, 0, 0, 0, 1, 4, 0, 4, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 0, 0, 8, 1, 0, 6, 0, 6], [8, 0, 0, 8, 1, 6, 0, 0, 6], [8, 0, 8, 0, 1, 6, 6, 6, 6], [0, 8, 0, 8, 1, 0, 6, 0, 0]]], 'test_outputs': [[[7, 7, 7, 8], [4, 7, 7, 4], [7, 7, 7, 7], [7, 8, 4, 8]]]}) (input_keys={'test_inputs', 'training_examples'}): 'dict' object has no attribute 'input'
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 69, in forward
File "<string>", line 69, in <listcomp>
AttributeError: 'dict' object has no attribute 'input'
2025/08/30 01:20:04 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[4, 4, 8], [6, 4, 3], [6, 3, 0]], 'output': [[4, 4, 4], [4, 4, 4], [4, 4, 4]]}, {'input': [[6, 8, 9], [1, 8, 1], [9, 4, 9]], 'output': [[9, 9, 9], [9, 9, 9], [9, 9, 9]]}, {'input': [[4, 6, 9], [6, 4, 1], [8, 8, 6]], 'output': [[6, 6, 6], [6, 6, 6], [6, 6, 6]]}], 'test_inputs': [[[8, 8, 6], [4, 6, 9], [8, 3, 0]]], 'test_outputs': [[[8, 8, 8], [8, 8, 8], [8, 8, 8]]]}) (input_keys={'test_inputs', 'training_examples'}): 'dict' object has no attribute 'input'
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 69, in forward
File "<string>", line 69, in <listcomp>
AttributeError: 'dict' object has no attribute 'input'
2025/08/30 01:20:04 ERROR dspy.utils.parallelizer: Error for Example({'training_examples': [{'input': [[0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0], [0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0], [0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0], [0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 8, 8, 8, 8, 8, 8], [0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0], [0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0], [0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0], [8, 8, 8, 8, 8, 8, 8, 8, 0, 8, 8, 8, 8, 0, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0], [0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0], [0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 8, 8, 0], [0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0], [0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0], [0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0], [0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0], [0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0], [0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0], [0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0, 8, 0, 0, 0, 0]], 'output': [[3, 3, 3, 3, 8, 3, 3, 3, 3, 8, 3, 3, 3, 3, 8, 4, 4, 4, 4, 8, 3, 3, 3, 3], [3, 3, 3, 3, 8, 3, 3, 3, 3, 8, 3, 3, 3, 3, 8, 4, 4, 4, 4, 8, 3, 3, 3, 3], [3, 3, 3, 3, 8, 3, 3, 3, 3, 8, 3, 3, 3, 3, 8, 4, 4, 4, 4, 8, 3, 3, 3, 3], [3, 3, 3, 3, 8, 3, 3, 3, 3, 8, 3, 3, 3, 3, 8, 4, 4, 4, 4, 8, 3, 3, 3, 3], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 4, 8, 8, 8, 8, 8, 8], [4, 4, 4, 4, 8, 4, 4, 4, 4, 8, 4, 4, 4, 4, 8, 4, 4, 4, 4, 8, 4, 4, 4, 4], [4, 4, 4, 4, 8, 4, 4, 4, 4, 8, 4, 4, 4, 4, 8, 4, 4, 4, 4, 8, 4, 4, 4, 4], [4, 4, 4, 4, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 8, 4, 4, 4, 4, 8, 4, 4, 4, 4], [4, 4, 4, 4, 8, 4, 4, 4, 4, 8, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 4, 4, 4, 4], [8, 8, 8, 8, 8, 8, 8, 8, 4, 8, 8, 8, 8, 4, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [4, 4, 4, 4, 8, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4], [4, 4, 4, 4, 8, 4, 4, 4, 4, 8, 4, 4, 4, 4, 8, 4, 4, 4, 4, 8, 4, 4, 4, 4], [4, 4, 4, 4, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 8, 4, 4, 4, 4, 8, 4, 4, 4, 4], [4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 8, 4, 4, 4, 4], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 4, 8, 8, 4], [3, 3, 3, 3, 8, 4, 4, 4, 4, 8, 4, 4, 4, 4, 8, 3, 3, 3, 3, 8, 4, 4, 4, 4], [3, 3, 3, 3, 8, 4, 4, 4, 4, 8, 4, 4, 4, 4, 8, 3, 3, 3, 3, 8, 4, 4, 4, 4], [3, 3, 3, 3, 8, 4, 4, 4, 4, 4, 4, 4, 4, 4, 8, 3, 3, 3, 3, 8, 4, 4, 4, 4], [3, 3, 3, 3, 8, 4, 4, 4, 4, 8, 4, 4, 4, 4, 8, 3, 3, 3, 3, 8, 4, 4, 4, 4], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [3, 3, 3, 3, 8, 3, 3, 3, 3, 8, 3, 3, 3, 3, 8, 3, 3, 3, 3, 8, 3, 3, 3, 3], [3, 3, 3, 3, 8, 3, 3, 3, 3, 8, 3, 3, 3, 3, 8, 3, 3, 3, 3, 8, 3, 3, 3, 3], [3, 3, 3, 3, 8, 3, 3, 3, 3, 8, 3, 3, 3, 3, 8, 3, 3, 3, 3, 8, 3, 3, 3, 3], [3, 3, 3, 3, 8, 3, 3, 3, 3, 8, 3, 3, 3, 3, 8, 3, 3, 3, 3, 8, 3, 3, 3, 3]]}, {'input': [[0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1], [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1], [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0]], 'output': [[3, 3, 3, 3, 3, 1, 3, 3, 3, 3, 3, 1, 3, 3, 3, 3, 3, 1, 3, 3, 3, 3, 3, 1, 3, 3, 3, 3, 3], [3, 3, 3, 3, 3, 1, 3, 3, 3, 3, 3, 1, 3, 3, 3, 3, 3, 1, 3, 3, 3, 3, 3, 1, 3, 3, 3, 3, 3], [3, 3, 3, 3, 3, 1, 3, 3, 3, 3, 3, 1, 3, 3, 3, 3, 3, 1, 3, 3, 3, 3, 3, 1, 3, 3, 3, 3, 3], [3, 3, 3, 3, 3, 1, 3, 3, 3, 3, 3, 1, 3, 3, 3, 3, 3, 1, 3, 3, 3, 3, 3, 1, 3, 3, 3, 3, 3], [3, 3, 3, 3, 3, 1, 3, 3, 3, 3, 3, 1, 3, 3, 3, 3, 3, 1, 3, 3, 3, 3, 3, 1, 3, 3, 3, 3, 3], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [4, 4, 4, 4, 4, 1, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 1, 4, 4, 4, 4, 4, 1, 4, 4, 4, 4, 4], [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 1, 4, 4, 4, 4, 4, 1, 4, 4, 4, 4, 4, 1, 4, 4, 4, 4, 4], [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 1, 4, 4, 4, 4, 4, 1, 4, 4, 4, 4, 4, 1, 4, 4, 4, 4, 4], [4, 4, 4, 4, 4, 1, 4, 4, 4, 4, 4, 1, 4, 4, 4, 4, 4, 1, 4, 4, 4, 4, 4, 1, 4, 4, 4, 4, 4], [4, 4, 4, 4, 4, 1, 4, 4, 4, 4, 4, 1, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 1, 4, 4, 4, 4, 4], [1, 1, 1, 1, 1, 1, 1, 1, 4, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 4, 1, 1, 1, 1], [4, 4, 4, 4, 4, 1, 4, 4, 4, 4, 4, 1, 4, 4, 4, 4, 4, 1, 4, 4, 4, 4, 4, 1, 4, 4, 4, 4, 4], [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 1, 4, 4, 4, 4, 4, 1, 4, 4, 4, 4, 4], [4, 4, 4, 4, 4, 1, 4, 4, 4, 4, 4, 1, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 1, 4, 4, 4, 4, 4], [4, 4, 4, 4, 4, 1, 4, 4, 4, 4, 4, 1, 4, 4, 4, 4, 4, 1, 4, 4, 4, 4, 4, 1, 4, 4, 4, 4, 4], [4, 4, 4, 4, 4, 1, 4, 4, 4, 4, 4, 1, 4, 4, 4, 4, 4, 1, 4, 4, 4, 4, 4, 1, 4, 4, 4, 4, 4], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 4, 1, 1, 1, 1, 1, 4, 1, 1, 1, 1, 4, 1, 1, 1], [4, 4, 4, 4, 4, 1, 3, 3, 3, 3, 3, 1, 4, 4, 4, 4, 4, 1, 4, 4, 4, 4, 4, 1, 4, 4, 4, 4, 4], [4, 4, 4, 4, 4, 1, 3, 3, 3, 3, 3, 1, 4, 4, 4, 4, 4, 1, 4, 4, 4, 4, 4, 1, 4, 4, 4, 4, 4], [4, 4, 4, 4, 4, 1, 3, 3, 3, 3, 3, 1, 4, 4, 4, 4, 4, 1, 4, 4, 4, 4, 4, 1, 4, 4, 4, 4, 4], [4, 4, 4, 4, 4, 1, 3, 3, 3, 3, 3, 1, 4, 4, 4, 4, 4, 1, 4, 4, 4, 4, 4, 1, 4, 4, 4, 4, 4], [4, 4, 4, 4, 4, 1, 3, 3, 3, 3, 3, 1, 4, 4, 4, 4, 4, 1, 4, 4, 4, 4, 4, 1, 4, 4, 4, 4, 4], [4, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [4, 4, 4, 4, 4, 1, 3, 3, 3, 3, 3, 1, 3, 3, 3, 3, 3, 1, 3, 3, 3, 3, 3, 1, 3, 3, 3, 3, 3], [4, 4, 4, 4, 4, 1, 3, 3, 3, 3, 3, 1, 3, 3, 3, 3, 3, 1, 3, 3, 3, 3, 3, 1, 3, 3, 3, 3, 3], [4, 4, 4, 4, 4, 1, 3, 3, 3, 3, 3, 1, 3, 3, 3, 3, 3, 1, 3, 3, 3, 3, 3, 1, 3, 3, 3, 3, 3], [4, 4, 4, 4, 4, 1, 3, 3, 3, 3, 3, 1, 3, 3, 3, 3, 3, 1, 3, 3, 3, 3, 3, 1, 3, 3, 3, 3, 3], [4, 4, 4, 4, 4, 1, 3, 3, 3, 3, 3, 1, 3, 3, 3, 3, 3, 1, 3, 3, 3, 3, 3, 1, 3, 3, 3, 3, 3]]}, {'input': [[0, 0, 0, 0, 9, 0, 0, 0, 0, 9, 0, 0, 0, 0, 9, 0, 0, 0, 0], [0, 0, 0, 0, 9, 0, 0, 0, 0, 9, 0, 0, 0, 0, 9, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 9, 0, 0, 0, 0, 9, 0, 0, 0, 0], [9, 9, 9, 9, 9, 9, 9, 0, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9], [0, 0, 0, 0, 9, 0, 0, 0, 0, 9, 0, 0, 0, 0, 9, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 9, 0, 0, 0, 0, 9, 0, 0, 0, 0], [0, 0, 0, 0, 9, 0, 0, 0, 0, 9, 0, 0, 0, 0, 9, 0, 0, 0, 0], [0, 0, 0, 0, 9, 0, 0, 0, 0, 9, 0, 0, 0, 0, 9, 0, 0, 0, 0], [9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 0, 9, 9, 9, 9, 9, 9, 9, 9], [0, 0, 0, 0, 9, 0, 0, 0, 0, 9, 0, 0, 0, 0, 9, 0, 0, 0, 0], [0, 0, 0, 0, 9, 0, 0, 0, 0, 9, 0, 0, 0, 0, 9, 0, 0, 0, 0], [0, 0, 0, 0, 9, 0, 0, 0, 0, 9, 0, 0, 0, 0, 9, 0, 0, 0, 0], [0, 0, 0, 0, 9, 0, 0, 0, 0, 9, 0, 0, 0, 0, 9, 0, 0, 0, 0], [9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 0], [0, 0, 0, 0, 9, 0, 0, 0, 0, 9, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 9, 0, 0, 0, 0, 9, 0, 0, 0, 0, 9, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 9, 0, 0, 0, 0, 9, 0, 0, 0, 0], [0, 0, 0, 0, 9, 0, 0, 0, 0, 9, 0, 0, 0, 0, 9, 0, 0, 0, 0]], 'output': [[4, 4, 4, 4, 9, 4, 4, 4, 4, 9, 4, 4, 4, 4, 9, 3, 3, 3, 3], [4, 4, 4, 4, 9, 4, 4, 4, 4, 9, 4, 4, 4, 4, 9, 3, 3, 3, 3], [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 9, 3, 3, 3, 3], [4, 4, 4, 4, 4, 4, 4, 4, 4, 9, 4, 4, 4, 4, 9, 3, 3, 3, 3], [9, 9, 9, 9, 9, 9, 9, 4, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9], [4, 4, 4, 4, 9, 4, 4, 4, 4, 9, 4, 4, 4, 4, 9, 3, 3, 3, 3], [4, 4, 4, 4, 4, 4, 4, 4, 4, 9, 4, 4, 4, 4, 9, 3, 3, 3, 3], [4, 4, 4, 4, 9, 4, 4, 4, 4, 9, 4, 4, 4, 4, 9, 3, 3, 3, 3], [4, 4, 4, 4, 9, 4, 4, 4, 4, 9, 4, 4, 4, 4, 9, 3, 3, 3, 3], [9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 4, 9, 9, 9, 9, 9, 9, 9, 9], [3, 3, 3, 3, 9, 3, 3, 3, 3, 9, 4, 4, 4, 4, 9, 4, 4, 4, 4], [3, 3, 3, 3, 9, 3, 3, 3, 3, 9, 4, 4, 4, 4, 9, 4, 4, 4, 4], [3, 3, 3, 3, 9, 3, 3, 3, 3, 9, 4, 4, 4, 4, 9, 4, 4, 4, 4], [3, 3, 3, 3, 9, 3, 3, 3, 3, 9, 4, 4, 4, 4, 9, 4, 4, 4, 4], [9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 4], [4, 4, 4, 4, 9, 4, 4, 4, 4, 9, 4, 4, 4, 4, 4, 4, 4, 4, 4], [4, 4, 4, 4, 9, 4, 4, 4, 4, 9, 4, 4, 4, 4, 9, 4, 4, 4, 4], [4, 4, 4, 4, 4, 4, 4, 4, 4, 9, 4, 4, 4, 4, 9, 4, 4, 4, 4], [4, 4, 4, 4, 9, 4, 4, 4, 4, 9, 4, 4, 4, 4, 9, 4, 4, 4, 4]]}], 'test_inputs': [[[0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0], [0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0], [0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0], [5, 5, 5, 5, 5, 5, 0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0], [0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0], [0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0], [0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0], [0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0], [0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0], [5, 5, 5, 5, 5, 0, 0, 5, 5, 5, 0, 5, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0], [0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0], [5, 5, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 5, 5, 5, 5, 5], [0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0], [0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0], [0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 5, 5, 5, 5], [0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0], [0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0], [5, 5, 5, 5, 5, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 5, 5, 5, 5, 5, 5, 5, 5], [0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0], [0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0]]], 'test_outputs': [[[3, 3, 3, 5, 4, 4, 4, 5, 4, 4, 4, 5, 3, 3, 3, 5, 3, 3, 3, 5, 3, 3, 3, 5, 4, 4, 4], [3, 3, 3, 5, 4, 4, 4, 5, 4, 4, 4, 5, 3, 3, 3, 5, 3, 3, 3, 5, 3, 3, 3, 5, 4, 4, 4], [3, 3, 3, 5, 4, 4, 4, 4, 4, 4, 4, 5, 3, 3, 3, 5, 3, 3, 3, 5, 3, 3, 3, 5, 4, 4, 4], [5, 5, 5, 5, 5, 5, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 4], [4, 4, 4, 4, 4, 4, 4, 5, 4, 4, 4, 5, 4, 4, 4, 5, 4, 4, 4, 5, 3, 3, 3, 5, 4, 4, 4], [4, 4, 4, 5, 4, 4, 4, 5, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 3, 3, 3, 5, 4, 4, 4], [4, 4, 4, 5, 4, 4, 4, 5, 4, 4, 4, 5, 4, 4, 4, 5, 4, 4, 4, 5, 3, 3, 3, 5, 4, 4, 4], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [3, 3, 3, 5, 4, 4, 4, 5, 4, 4, 4, 5, 4, 4, 4, 5, 3, 3, 3, 5, 3, 3, 3, 5, 3, 3, 3], [3, 3, 3, 5, 4, 4, 4, 4, 4, 4, 4, 5, 4, 4, 4, 5, 3, 3, 3, 5, 3, 3, 3, 5, 3, 3, 3], [3, 3, 3, 5, 4, 4, 4, 5, 4, 4, 4, 5, 4, 4, 4, 5, 3, 3, 3, 5, 3, 3, 3, 5, 3, 3, 3], [5, 5, 5, 5, 5, 4, 4, 5, 5, 5, 4, 5, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], [4, 4, 4, 4, 4, 4, 4, 5, 4, 4, 4, 4, 4, 4, 4, 5, 4, 4, 4, 4, 4, 4, 4, 5, 3, 3, 3], [4, 4, 4, 4, 4, 4, 4, 5, 4, 4, 4, 5, 4, 4, 4, 5, 4, 4, 4, 5, 4, 4, 4, 5, 3, 3, 3], [4, 4, 4, 5, 4, 4, 4, 5, 4, 4, 4, 5, 4, 4, 4, 5, 4, 4, 4, 5, 4, 4, 4, 5, 3, 3, 3], [5, 5, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 4, 5, 5, 5, 5, 5], [4, 4, 4, 5, 3, 3, 3, 5, 4, 4, 4, 5, 4, 4, 4, 5, 3, 3, 3, 5, 4, 4, 4, 5, 3, 3, 3], [4, 4, 4, 5, 3, 3, 3, 5, 4, 4, 4, 5, 4, 4, 4, 5, 3, 3, 3, 5, 4, 4, 4, 5, 3, 3, 3], [4, 4, 4, 5, 3, 3, 3, 5, 4, 4, 4, 4, 4, 4, 4, 5, 3, 3, 3, 5, 4, 4, 4, 5, 3, 3, 3], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 4, 5, 5, 5, 5], [3, 3, 3, 5, 4, 4, 4, 5, 4, 4, 4, 5, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 4, 4, 4], [3, 3, 3, 5, 4, 4, 4, 5, 4, 4, 4, 5, 4, 4, 4, 4, 4, 4, 4, 5, 4, 4, 4, 4, 4, 4, 4], [3, 3, 3, 5, 4, 4, 4, 5, 4, 4, 4, 4, 4, 4, 4, 5, 4, 4, 4, 5, 4, 4, 4, 4, 4, 4, 4], [5, 5, 5, 5, 5, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 4, 5, 5, 5, 5, 5, 5, 5, 5], [4, 4, 4, 5, 4, 4, 4, 5, 4, 4, 4, 4, 4, 4, 4, 5, 4, 4, 4, 5, 3, 3, 3, 5, 3, 3, 3], [4, 4, 4, 4, 4, 4, 4, 5, 4, 4, 4, 5, 4, 4, 4, 5, 4, 4, 4, 5, 3, 3, 3, 5, 3, 3, 3], [4, 4, 4, 5, 4, 4, 4, 4, 4, 4, 4, 5, 4, 4, 4, 4, 4, 4, 4, 5, 3, 3, 3, 5, 3, 3, 3]]]}) (input_keys={'test_inputs', 'training_examples'}): 'dict' object has no attribute 'input'
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/parallelizer.py", line 55, in safe_func
return user_function(item)
^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/evaluate/evaluate.py", line 158, in process_item
prediction = program(**example.inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<string>", line 69, in forward
File "<string>", line 69, in <listcomp>
AttributeError: 'dict' object has no attribute 'input'
2025/08/30 01:20:04 INFO dspy.evaluate.evaluate: Average Metric: 0.0 / 3 (0.0%)
GEPA Optimization: 89%|█████████████████████████████████████████████████████████████████████████████████████████████████▋ | 3553/4000 [27:46:08<2:50:28, 22.88s/rollouts]Iteration 143: Proposed new text for program: import dspy
from typing import List
import pydantic
import traceback
# Define the type for a matrix for clarity.
MATRIX = List[List[int]]
# Pydantic model for structuring training examples.
class TrainingExample(pydantic.BaseModel):
input: MATRIX
output: MATRIX
# --- Main Signature for the Overall Task ---
# This signature defines the high-level goal. The docstring is crucial for guiding the LM.
class SolveTaskSignature(dspy.Signature):
"""
You are an expert at solving abstract reasoning challenges.
Given a set of 'training_examples', each containing an 'input' matrix and a corresponding 'output' matrix, your goal is to deduce the transformation rule.
Then, you must apply this rule to every matrix in the 'test_inputs' list to generate the corresponding 'test_outputs'.
The transformation rules can involve concepts like:
- Object counting, movement, and color changes.
- Geometric operations like rotation, reflection, and scaling.
- Pattern repetition and completion.
- Flood fills or connectivity-based coloring.
- Overlaying or combining objects based on priority rules.
Analyze the examples carefully to identify the core logic before applying it.
"""
training_examples: List[TrainingExample] = dspy.InputField(description="A list of input/output pairs demonstrating the transformation rule.")
test_inputs: List[MATRIX] = dspy.InputField(description="A list of input matrices to be transformed.")
test_outputs: List[MATRIX] = dspy.OutputField(description="The corresponding list of transformed output matrices.")
# --- Sub-Signatures for the Decomposed Pipeline ---
class GenerateAlgorithmSignature(dspy.Signature):
"""
Analyze the provided training examples (input -> output pairs) and describe the transformation rule in clear, step-by-step natural language.
Focus on creating a generalizable algorithm that can be applied to new test inputs.
"""
training_examples: str = dspy.InputField(desc="String representation of the training examples.")
algorithm_description: str = dspy.OutputField(desc="A step-by-step description of the transformation logic.")
class GeneratePythonCodeSignature(dspy.Signature):
"""
Based on the provided algorithm description, write a single Python function named `solve`.
This function must accept one argument, `test_input` (a list of lists of integers), and return the transformed matrix (a list of lists of integers).
The function should be self-contained. Do not include any import statements, example usage, or explanations outside the function body.
"""
algorithm_description: str = dspy.InputField(desc="The natural language description of the algorithm.")
test_input_example: str = dspy.InputField(desc="An example of a test input matrix to inform the function signature.")
python_code: str = dspy.OutputField(desc="A string containing only the Python function `solve`.")
# --- Custom Module Implementing the Code Generation Strategy ---
class CodeGeneratingProgram(dspy.Module):
def __init__(self):
super().__init__()
# Module to deduce the algorithm from examples.
self.generate_algorithm = dspy.ChainOfThought(GenerateAlgorithmSignature)
# Module to generate Python code from the algorithm description.
self.generate_code = dspy.Predict(GeneratePythonCodeSignature)
# Fallback module in case the code generation/execution fails.
self.fallback_solver = dspy.ChainOfThought(SolveTaskSignature)
def forward(self, training_examples: List[TrainingExample], test_inputs: List[MATRIX]):
# Convert complex objects to strings for the LM prompts.
training_examples_str = "\n".join([f"Input: {ex.input}\nOutput: {ex.output}" for ex in training_examples])
test_input_example_str = str(test_inputs[0]) if test_inputs else "[]"
try:
# Step 1: Generate the algorithm description.
algo_result = self.generate_algorithm(training_examples=training_examples_str)
# Step 2: Generate Python code based on the algorithm.
code_result = self.generate_code(
algorithm_description=algo_result.algorithm_description,
test_input_example=test_input_example_str
)
# Step 3: Execute the generated code.
local_scope = {}
# The generated code is a string containing the 'solve' function.
# We execute it to define the function in our local scope.
exec(code_result.python_code, globals(), local_scope)
solve_func = local_scope.get('solve')
if not callable(solve_func):
raise ValueError("`solve` function not found in the generated code.")
# Apply the generated function to each test input.
predicted_outputs = [solve_func(test_input) for test_input in test_inputs]
return dspy.Prediction(test_outputs=predicted_outputs)
except Exception as e:
print(f"Code generation or execution failed: {e}")
print(traceback.format_exc())
# Fallback: If the advanced strategy fails, revert to a direct ChainOfThought approach.
print("Falling back to direct prediction.")
fallback_result = self.fallback_solver(training_examples=training_examples, test_inputs=test_inputs)
return dspy.Prediction(test_outputs=fallback_result.test_outputs)
# The final 'program' object is an instance of our new, improved module.
program = CodeGeneratingProgram()
Iteration 143: New subsample score is not better, skipping
Iteration 144: Selected program 9 score: 0.67
Average Metric: 3.00 / 3 (100.0%): 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [02:13<00:00, 44.42s/it]2025/08/30 01:22:17 INFO dspy.evaluate.evaluate: Average Metric: 3.0 / 3 (100.0%)
GEPA Optimization: 89%|█████████████████████████████████████████████████████████████████████████████████████████████████▊ | 3556/4000 [27:48:21<2:56:32, 23.86s/rollouts]
Iteration 144: All subsample scores perfect. Skipping.
Iteration 144: Reflective mutation did not propose a new candidate
Iteration 145: Selected program 4 score: 0.61
Average Metric: 1.00 / 1 (100.0%): 0%| | 0/3 [00:00<?, ?it/s]Error applying `transform_grid` to a test input: name 'collections' is not defined
Average Metric: 2.00 / 3 (66.7%): 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [01:44<00:00, 34.83s/it]2025/08/30 01:24:02 INFO dspy.evaluate.evaluate: Average Metric: 2.0 / 3 (66.7%)
Iteration 145: Proposed new text for program: import dspy
from typing import List, Optional
import pydantic
import traceback
import copy
# Define the type for a grid/matrix as a list of lists of integers.
MATRIX = List[List[int]]
class TrainingExample(pydantic.BaseModel):
"""A Pydantic model for a single training example, containing an input and output grid."""
input: MATRIX
output: MATRIX
class GenerateTransformationFunction(dspy.Signature):
"""
You are an expert programmer solving visual puzzles from the Abstraction and Reasoning Corpus (ARC).
Your goal is to analyze a set of training examples, each consisting of an input grid and an output grid.
Based on this analysis, you must write a single, self-contained Python function named `transform_grid` that implements the observed transformation.
**Function Requirements:**
- The function must be named exactly `transform_grid`.
- It must accept one argument: `grid`, which is a 2D list of integers (the input grid).
- It must return a new 2D list of integers representing the transformed grid.
- The function should not rely on any external libraries unless they are standard (e.g., `copy`). If you need a library like numpy, import it inside the function.
- Do not modify the input grid in place; create a copy if necessary.
**Analysis Strategy:**
1. **Observe Core Patterns:** Look for simple, recurring patterns like geometric transformations (rotation, reflection, scaling), color changes, object manipulation (copying, moving, recoloring), pattern propagation, or flood-fills.
2. **Decompose the Problem:** Break down the transformation into logical steps. For example, "first, find all objects of color blue, then for each object, reflect it horizontally."
3. **Generalize:** The logic must be general enough to work for all training examples and, by extension, the unseen test inputs.
4. **Code Implementation:** Translate your logic into a clear and correct Python function. Ensure your code is robust and handles edge cases observed in the examples.
"""
training_examples: List[TrainingExample] = dspy.InputField(desc="A list of input/output grid pairs demonstrating the transformation rule.")
test_input_grid: MATRIX = dspy.InputField(desc="A single test input grid to which the transformation should be applicable. Use its properties (e.g., dimensions) to ensure your code is general.")
reasoning: str = dspy.OutputField(desc="Step-by-step thinking process to deduce the transformation rule and how to implement it in Python.")
python_code: str = dspy.OutputField(desc="A string containing the complete, self-contained Python function `transform_grid(grid)`.")
class CorrectTransformationFunction(dspy.Signature):
"""
You are an expert Python programmer and debugger. You were given a task to write a `transform_grid` function for an ARC puzzle, but your previous attempt failed.
Analyze the provided training examples, the faulty code, and the error traceback.
Your goal is to rewrite the `transform_grid` function to fix the error and correctly implement the transformation logic.
**Debugging Strategy:**
1. **Understand the Goal:** Re-examine the `training_examples` to ensure you understand the required transformation.
2. **Analyze the Error:** Read the `error_traceback` carefully to understand why the `faulty_code` failed. Was it a syntax error, a runtime error (e.g., index out of bounds), or a logical error?
3. **Identify the Flaw:** Pinpoint the specific lines or logic in the `faulty_code` that caused the error.
4. **Rewrite the Function:** Provide a new, complete, and self-contained Python function named `transform_grid` that corrects the error and works for all examples. Do not just explain the fix; provide the full, runnable code.
"""
training_examples: List[TrainingExample] = dspy.InputField(desc="The original list of input/output grid pairs.")
test_input_grid: MATRIX = dspy.InputField(desc="A representative test input grid.")
faulty_code: str = dspy.InputField(desc="The Python code that produced an error.")
error_traceback: str = dspy.InputField(desc="The traceback from the error that occurred when running the faulty code.")
corrected_reasoning: str = dspy.OutputField(desc="A brief explanation of the error and the logic behind your correction.")
corrected_python_code: str = dspy.OutputField(desc="The complete, corrected, self-contained Python function `transform_grid(grid)`.")
class ARCSolver(dspy.Module):
"""A DSPy module that solves ARC tasks by generating, testing, and refining Python code."""
def __init__(self, max_attempts=3):
super().__init__()
self.max_attempts = max_attempts
self.code_generator = dspy.ChainOfThought(GenerateTransformationFunction)
self.code_corrector = dspy.ChainOfThought(CorrectTransformationFunction)
def forward(self, training_examples: List[TrainingExample], test_inputs: List[MATRIX]):
"""
Generates, executes, and corrects a transformation function, applying it to all test inputs.
"""
python_code = None
error_traceback = None
transform_function = None
# We pass the first test input as a representative example for the LM.
representative_test_input = test_inputs[0]
for attempt in range(self.max_attempts):
try:
if attempt == 0:
# First attempt: generate initial code
prediction = self.code_generator(
training_examples=training_examples,
test_input_grid=representative_test_input
)
python_code = prediction.python_code
else:
# Subsequent attempts: correct faulty code
print(f"\n--- Attempt {attempt + 1}: Correcting failed code ---")
correction = self.code_corrector(
training_examples=training_examples,
test_input_grid=representative_test_input,
faulty_code=python_code,
error_traceback=error_traceback
)
python_code = correction.corrected_python_code
# The LM might wrap the code in markdown, so we extract it.
if "```python" in python_code:
python_code = python_code.split("```python")[1].split("```")[0].strip()
# Prepare a local scope for executing the generated code safely.
local_scope = {}
exec(python_code, globals(), local_scope)
current_transform_function = local_scope.get('transform_grid')
if not (current_transform_function and callable(current_transform_function)):
raise ValueError("`transform_grid` function not found or not callable in the generated code.")
# Test the function on the first test input to see if it runs.
_ = current_transform_function(copy.deepcopy(representative_test_input))
# If execution and the test call succeed, we have a valid function.
transform_function = current_transform_function
print(f"--- Successfully generated and validated code on attempt {attempt + 1} ---")
break # Exit the loop on success
except Exception as e:
print(f"--- Code execution failed on attempt {attempt + 1} ---")
error_traceback = f"Error: {e}\nTraceback:\n{traceback.format_exc()}"
print(error_traceback)
# The loop will continue to the next attempt.
# Apply the final, validated function to all test inputs.
generated_outputs = []
if transform_function:
for test_input in test_inputs:
try:
output_grid = transform_function(copy.deepcopy(test_input))
generated_outputs.append(output_grid)
except Exception as e:
print(f"Error applying validated `transform_grid` to a test input: {e}")
# Fallback for this specific input if the function is not robust enough.
generated_outputs.append(copy.deepcopy(test_input))
else:
# Fallback if all attempts to generate working code failed.
print("--- All code generation attempts failed. Using fallback. ---")
for test_input in test_inputs:
generated_outputs.append(copy.deepcopy(test_input))
return dspy.Prediction(test_outputs=generated_outputs)
# The final program object is an instance of our new, more robust custom module.
program = ARCSolver()
--- Successfully generated and validated code on attempt 1 ---
--- Successfully generated and validated code on attempt 1 ---
--- Code execution failed on attempt 1 ---
Error: name 'collections' is not defined
Traceback:
Traceback (most recent call last):
File "<string>", line 111, in forward
File "<string>", line 10, in transform_grid
NameError: name 'collections' is not defined
--- Attempt 2: Correcting failed code ---
2025/08/30 01:25:12 INFO dspy.evaluate.evaluate: Average Metric: 3.0 / 3 (100.0%)
--- Successfully generated and validated code on attempt 2 ---
--- Successfully generated and validated code on attempt 1 ---
--- Successfully generated and validated code on attempt 1 ---
--- Successfully generated and validated code on attempt 1 ---
--- Successfully generated and validated code on attempt 1 ---
--- Successfully generated and validated code on attempt 1 ---
--- Successfully generated and validated code on attempt 1 ---
--- Successfully generated and validated code on attempt 1 ---
--- Successfully generated and validated code on attempt 1 ---
--- Successfully generated and validated code on attempt 1 ---
--- Successfully generated and validated code on attempt 1 ---
--- Successfully generated and validated code on attempt 1 ---
--- Successfully generated and validated code on attempt 1 ---
--- Successfully generated and validated code on attempt 1 ---
--- Successfully generated and validated code on attempt 1 ---
--- Successfully generated and validated code on attempt 1 ---
2025/08/30 01:25:12 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/30 01:25:12 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/30 01:25:12 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
--- Successfully generated and validated code on attempt 1 ---
--- Code execution failed on attempt 1 ---
Error: name 'collections' is not defined
Traceback:
Traceback (most recent call last):
File "<string>", line 111, in forward
File "<string>", line 18, in transform_grid
NameError: name 'collections' is not defined
--- Attempt 2: Correcting failed code ---
--- Successfully generated and validated code on attempt 1 ---
--- Code execution failed on attempt 1 ---
Error: name 'collections' is not defined
Traceback:
Traceback (most recent call last):
File "<string>", line 111, in forward
File "<string>", line 30, in transform_grid
NameError: name 'collections' is not defined
--- Attempt 2: Correcting failed code ---
--- Successfully generated and validated code on attempt 1 ---
--- Successfully generated and validated code on attempt 1 ---
--- Successfully generated and validated code on attempt 1 ---
--- Successfully generated and validated code on attempt 1 ---
--- Successfully generated and validated code on attempt 1 ---
--- Successfully generated and validated code on attempt 1 ---
--- Successfully generated and validated code on attempt 1 ---
--- Successfully generated and validated code on attempt 1 ---
--- Successfully generated and validated code on attempt 1 ---
--- Successfully generated and validated code on attempt 1 ---
--- Successfully generated and validated code on attempt 1 ---
--- Code execution failed on attempt 1 ---
Error: name 'collections' is not defined
Traceback:
Traceback (most recent call last):
File "<string>", line 111, in forward
File "<string>", line 63, in transform_grid
File "<string>", line 24, in find_shapes
NameError: name 'collections' is not defined
--- Attempt 2: Correcting failed code ---
--- Code execution failed on attempt 1 ---
Error: name 'collections' is not defined
Traceback:
Traceback (most recent call last):
File "<string>", line 111, in forward
File "<string>", line 19, in transform_grid
NameError: name 'collections' is not defined
--- Attempt 2: Correcting failed code ---
--- Successfully generated and validated code on attempt 1 ---
--- Successfully generated and validated code on attempt 1 ---
--- Successfully generated and validated code on attempt 1 ---
--- Successfully generated and validated code on attempt 1 ---
--- Code execution failed on attempt 1 ---
Error: name 'collections' is not defined
Traceback:
Traceback (most recent call last):
File "<string>", line 111, in forward
File "<string>", line 20, in transform_grid
NameError: name 'collections' is not defined
--- Attempt 2: Correcting failed code ---
--- Successfully generated and validated code on attempt 1 ---
--- Successfully generated and validated code on attempt 1 ---
--- Successfully generated and validated code on attempt 1 ---
--- Successfully generated and validated code on attempt 1 ---
--- Successfully generated and validated code on attempt 1 ---
--- Successfully generated and validated code on attempt 1 ---
--- Successfully generated and validated code on attempt 1 ---
2025/08/30 01:25:12 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/30 01:25:12 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/30 01:25:12 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
--- Successfully generated and validated code on attempt 1 ------ Successfully generated and validated code on attempt 1 ---
--- Successfully generated and validated code on attempt 1 ---
--- Successfully generated and validated code on attempt 1 ---
--- Successfully generated and validated code on attempt 1 ---
--- Successfully generated and validated code on attempt 1 ---
--- Successfully generated and validated code on attempt 1 ---
--- Successfully generated and validated code on attempt 1 ---
--- Code execution failed on attempt 1 ---
Error: name 'collections' is not defined
Traceback:
Traceback (most recent call last):
File "<string>", line 111, in forward
File "<string>", line 12, in transform_grid
NameError: name 'collections' is not defined
--- Attempt 2: Correcting failed code ---
--- Code execution failed on attempt 1 ---
Error: name 'collections' is not defined
Traceback:
Traceback (most recent call last):
File "<string>", line 111, in forward
File "<string>", line 22, in transform_grid
NameError: name 'collections' is not defined
--- Attempt 2: Correcting failed code ---
--- Successfully generated and validated code on attempt 1 ---
--- Successfully generated and validated code on attempt 1 ---
--- Code execution failed on attempt 1 ---
Error: name 'collections' is not defined
Traceback:
Traceback (most recent call last):
File "<string>", line 111, in forward
File "<string>", line 14, in transform_grid
NameError: name 'collections' is not defined
--- Attempt 2: Correcting failed code ---
--- Successfully generated and validated code on attempt 1 ---
--- Successfully generated and validated code on attempt 1 ---
--- Successfully generated and validated code on attempt 1 ---
--- Successfully generated and validated code on attempt 1 ---
--- Successfully generated and validated code on attempt 1 ---
--- Code execution failed on attempt 1 ---
Error: argument of type 'NoneType' is not iterable
Traceback:
Traceback (most recent call last):
File "<string>", line 99, in forward
TypeError: argument of type 'NoneType' is not iterable
--- Attempt 2: Correcting failed code ---
--- Successfully generated and validated code on attempt 1 ---
--- Successfully generated and validated code on attempt 1 ---
--- Successfully generated and validated code on attempt 1 ---
--- Successfully generated and validated code on attempt 1 ---
2025/08/30 01:25:12 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
--- Successfully generated and validated code on attempt 1 ---
--- Successfully generated and validated code on attempt 1 ---
--- Successfully generated and validated code on attempt 1 ---
--- Successfully generated and validated code on attempt 1 ---
--- Successfully generated and validated code on attempt 1 ---
--- Successfully generated and validated code on attempt 1 ---
--- Successfully generated and validated code on attempt 1 ---
--- Successfully generated and validated code on attempt 1 ---
--- Successfully generated and validated code on attempt 1 ---
--- Successfully generated and validated code on attempt 1 ---
--- Successfully generated and validated code on attempt 1 ---
--- Successfully generated and validated code on attempt 1 ---
--- Successfully generated and validated code on attempt 1 ---
--- Successfully generated and validated code on attempt 1 ---
--- Successfully generated and validated code on attempt 1 ---
--- Successfully generated and validated code on attempt 1 ---
--- Successfully generated and validated code on attempt 1 ---
--- Successfully generated and validated code on attempt 1 ---
--- Successfully generated and validated code on attempt 1 ---
--- Successfully generated and validated code on attempt 1 ---
--- Successfully generated and validated code on attempt 1 ---
--- Successfully generated and validated code on attempt 1 ---
--- Successfully generated and validated code on attempt 1 ---
--- Code execution failed on attempt 1 ---
Error: name 'collections' is not defined
Traceback:
Traceback (most recent call last):
File "<string>", line 111, in forward
File "<string>", line 24, in transform_grid
NameError: name 'collections' is not defined
--- Attempt 2: Correcting failed code ---
--- Successfully generated and validated code on attempt 1 ---
--- Successfully generated and validated code on attempt 1 ---
--- Successfully generated and validated code on attempt 1 ---
--- Successfully generated and validated code on attempt 1 ---
--- Successfully generated and validated code on attempt 1 ---
--- Code execution failed on attempt 1 ---
Error: name 'collections' is not defined
Traceback:
Traceback (most recent call last):
File "<string>", line 111, in forward
File "<string>", line 49, in transform_grid
File "<string>", line 23, in get_final_color
NameError: name 'collections' is not defined
--- Attempt 2: Correcting failed code ---
--- Successfully generated and validated code on attempt 1 ---
--- Successfully generated and validated code on attempt 1 ---
--- Successfully generated and validated code on attempt 1 ---
--- Code execution failed on attempt 1 ---
Error: name 'collections' is not defined
Traceback:
Traceback (most recent call last):
File "<string>", line 111, in forward
File "<string>", line 16, in transform_grid
NameError: name 'collections' is not defined
--- Attempt 2: Correcting failed code ---
--- Successfully generated and validated code on attempt 1 ---
--- Successfully generated and validated code on attempt 1 ---
--- Successfully generated and validated code on attempt 1 ---
--- Successfully generated and validated code on attempt 1 ---
--- Code execution failed on attempt 1 ---
Error: name 'collections' is not defined
Traceback:
Traceback (most recent call last):
File "<string>", line 111, in forward
File "<string>", line 8, in transform_grid
NameError: name 'collections' is not defined
--- Attempt 2: Correcting failed code ---
--- Successfully generated and validated code on attempt 1 ---
--- Code execution failed on attempt 1 ---
Error: name 'collections' is not defined
Traceback:
Traceback (most recent call last):
File "<string>", line 111, in forward
File "<string>", line 35, in transform_grid
NameError: name 'collections' is not defined
--- Attempt 2: Correcting failed code ---
--- Code execution failed on attempt 1 ---
Error: name 'collections' is not defined
Traceback:
Traceback (most recent call last):
File "<string>", line 111, in forward
File "<string>", line 60, in transform_grid
NameError: name 'collections' is not defined
--- Attempt 2: Correcting failed code ---
--- Successfully generated and validated code on attempt 1 ---
--- Successfully generated and validated code on attempt 1 ---
--- Successfully generated and validated code on attempt 1 ---
--- Successfully generated and validated code on attempt 1 ---
--- Successfully generated and validated code on attempt 1 ---
--- Successfully generated and validated code on attempt 1 ---
2025/08/30 01:25:13 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/30 01:25:13 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
--- Successfully generated and validated code on attempt 1 ---
--- Code execution failed on attempt 1 ---
Error: name 'collections' is not defined
Traceback:
Traceback (most recent call last):
File "<string>", line 111, in forward
File "<string>", line 72, in transform_grid
File "<string>", line 57, in count_fill_area
NameError: name 'collections' is not defined
--- Attempt 2: Correcting failed code ---
--- Successfully generated and validated code on attempt 1 ---
--- Successfully generated and validated code on attempt 1 ---
--- Successfully generated and validated code on attempt 1 ---
--- Code execution failed on attempt 1 ---
Error: name 'collections' is not defined
Traceback:
Traceback (most recent call last):
File "<string>", line 111, in forward
File "<string>", line 17, in transform_grid
NameError: name 'collections' is not defined
--- Attempt 2: Correcting failed code ---
--- Successfully generated and validated code on attempt 1 ---
--- Successfully generated and validated code on attempt 1 ---
--- Code execution failed on attempt 1 ---
Error: name 'collections' is not defined
Traceback:
Traceback (most recent call last):
File "<string>", line 111, in forward
File "<string>", line 33, in transform_grid
NameError: name 'collections' is not defined
--- Attempt 2: Correcting failed code ---
--- Successfully generated and validated code on attempt 1 ---
--- Successfully generated and validated code on attempt 1 ---
--- Code execution failed on attempt 1 ---
Error: name 'collections' is not defined
Traceback:
Traceback (most recent call last):
File "<string>", line 111, in forward
File "<string>", line 24, in transform_grid
NameError: name 'collections' is not defined
--- Attempt 2: Correcting failed code ---
--- Successfully generated and validated code on attempt 1 ---
--- Code execution failed on attempt 1 ---
Error: name 'collections' is not defined
Traceback:
Traceback (most recent call last):
File "<string>", line 111, in forward
File "<string>", line 67, in transform_grid
File "<string>", line 15, in find_objects
NameError: name 'collections' is not defined
--- Attempt 2: Correcting failed code ---
--- Code execution failed on attempt 1 ---
Error: name 'collections' is not defined
Traceback:
Traceback (most recent call last):
File "<string>", line 111, in forward
File "<string>", line 18, in transform_grid
NameError: name 'collections' is not defined
--- Attempt 2: Correcting failed code ---
--- Code execution failed on attempt 1 ---
Error: name 'collections' is not defined
Traceback:
Traceback (most recent call last):
File "<string>", line 111, in forward
File "<string>", line 12, in transform_grid
NameError: name 'collections' is not defined
--- Attempt 2: Correcting failed code ---
--- Code execution failed on attempt 1 ---
Error: argument of type 'NoneType' is not iterable
Traceback:
Traceback (most recent call last):
File "<string>", line 99, in forward
TypeError: argument of type 'NoneType' is not iterable
--- Attempt 2: Correcting failed code ---
--- Successfully generated and validated code on attempt 1 ---
--- Successfully generated and validated code on attempt 1 ---
--- Successfully generated and validated code on attempt 1 ---
--- Successfully generated and validated code on attempt 1 ---
--- Successfully generated and validated code on attempt 1 ---
2025/08/30 01:25:13 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/30 01:25:13 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/30 01:25:13 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/30 01:25:13 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
--- Successfully generated and validated code on attempt 1 ---
--- Successfully generated and validated code on attempt 1 ---
--- Successfully generated and validated code on attempt 1 ---
--- Code execution failed on attempt 1 ---
Error: name 'collections' is not defined
Traceback:
Traceback (most recent call last):
File "<string>", line 111, in forward
File "<string>", line 14, in transform_grid
NameError: name 'collections' is not defined
--- Attempt 2: Correcting failed code ---
--- Successfully generated and validated code on attempt 1 ---
--- Successfully generated and validated code on attempt 1 ---
--- Successfully generated and validated code on attempt 1 ---
--- Code execution failed on attempt 1 ---
Error: name 'collections' is not defined
Traceback:
Traceback (most recent call last):
File "<string>", line 111, in forward
File "<string>", line 21, in transform_grid
NameError: name 'collections' is not defined
--- Attempt 2: Correcting failed code ---
--- Successfully generated and validated code on attempt 1 ---
--- Successfully generated and validated code on attempt 1 ---
--- Successfully generated and validated code on attempt 1 ---
--- Successfully generated and validated code on attempt 1 ---
--- Successfully generated and validated code on attempt 1 ---
--- Successfully generated and validated code on attempt 1 ---
--- Successfully generated and validated code on attempt 1 ---
--- Successfully generated and validated code on attempt 1 ---
--- Successfully generated and validated code on attempt 1 ---
--- Successfully generated and validated code on attempt 1 ---
--- Successfully generated and validated code on attempt 1 ---
--- Successfully generated and validated code on attempt 1 ---
--- Successfully generated and validated code on attempt 1 ---
--- Successfully generated and validated code on attempt 1 ---
--- Successfully generated and validated code on attempt 1 ---
--- Code execution failed on attempt 1 ---
Error: name 'collections' is not defined
Traceback:
Traceback (most recent call last):
File "<string>", line 111, in forward
File "<string>", line 13, in transform_grid
NameError: name 'collections' is not defined
--- Attempt 2: Correcting failed code ---
--- Successfully generated and validated code on attempt 1 ---
--- Successfully generated and validated code on attempt 1 ---
--- Successfully generated and validated code on attempt 1 ---
--- Code execution failed on attempt 1 ---
Error: name 'collections' is not defined
Traceback:
Traceback (most recent call last):
File "<string>", line 111, in forward
File "<string>", line 17, in transform_grid
NameError: name 'collections' is not defined
--- Attempt 2: Correcting failed code ---
--- Code execution failed on attempt 1 ---
Error: name 'collections' is not defined
Traceback:
Traceback (most recent call last):
File "<string>", line 111, in forward
File "<string>", line 12, in transform_grid
NameError: name 'collections' is not defined
--- Attempt 2: Correcting failed code ---
--- Code execution failed on attempt 1 ---
Error: name 'collections' is not defined
Traceback:
Traceback (most recent call last):
File "<string>", line 111, in forward
File "<string>", line 41, in transform_grid
NameError: name 'collections' is not defined
--- Attempt 2: Correcting failed code ---
--- Successfully generated and validated code on attempt 1 ---
--- Successfully generated and validated code on attempt 1 ---
--- Successfully generated and validated code on attempt 1 ---
--- Code execution failed on attempt 1 ---
Error: name 'collections' is not defined
Traceback:
Traceback (most recent call last):
File "<string>", line 111, in forward
File "<string>", line 53, in transform_grid
File "<string>", line 31, in find_objects
NameError: name 'collections' is not defined
--- Attempt 2: Correcting failed code ---
--- Successfully generated and validated code on attempt 1 ---
--- Successfully generated and validated code on attempt 1 ---
--- Successfully generated and validated code on attempt 1 ---
--- Successfully generated and validated code on attempt 1 ---
--- Successfully generated and validated code on attempt 1 ---
--- Successfully generated and validated code on attempt 1 ---
--- Code execution failed on attempt 1 ---
Error: name 'collections' is not defined
Traceback:
Traceback (most recent call last):
File "<string>", line 111, in forward
File "<string>", line 26, in transform_grid
NameError: name 'collections' is not defined
--- Attempt 2: Correcting failed code ---
2025/08/30 01:25:14 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/30 01:25:14 WARNING dspy.adapters.json_adapter: Failed to use structured output format, falling back to JSON mode.
2025/08/30 01:25:14 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
--- Successfully generated and validated code on attempt 1 ---
--- Successfully generated and validated code on attempt 1 ---
--- Code execution failed on attempt 1 ---
Error: cannot access local variable 'cell' where it is not associated with a value
Traceback:
Traceback (most recent call last):
File "<string>", line 111, in forward
File "<string>", line 34, in transform_grid
File "<string>", line 34, in <listcomp>
UnboundLocalError: cannot access local variable 'cell' where it is not associated with a value
--- Attempt 2: Correcting failed code ---
--- Successfully generated and validated code on attempt 1 ---
--- Successfully generated and validated code on attempt 1 ---
--- Successfully generated and validated code on attempt 1 ---
--- Successfully generated and validated code on attempt 1 ---
--- Successfully generated and validated code on attempt 1 ---
--- Successfully generated and validated code on attempt 1 ---
--- Code execution failed on attempt 1 ---
Error: name 'collections' is not defined
Traceback:
Traceback (most recent call last):
File "<string>", line 111, in forward
File "<string>", line 29, in transform_grid
NameError: name 'collections' is not defined
--- Attempt 2: Correcting failed code ---
--- Successfully generated and validated code on attempt 1 ---
--- Code execution failed on attempt 1 ---
Error: name 'collections' is not defined
Traceback:
Traceback (most recent call last):
File "<string>", line 111, in forward
File "<string>", line 45, in transform_grid
File "<string>", line 20, in find_component
NameError: name 'collections' is not defined
--- Attempt 2: Correcting failed code ---
--- Successfully generated and validated code on attempt 1 ---
--- Code execution failed on attempt 1 ---
Error: argument of type 'NoneType' is not iterable
Traceback:
Traceback (most recent call last):
File "<string>", line 99, in forward
TypeError: argument of type 'NoneType' is not iterable
--- Attempt 2: Correcting failed code ---
--- Successfully generated and validated code on attempt 1 ---
--- Code execution failed on attempt 1 ---
Error: name 'collections' is not defined
Traceback:
Traceback (most recent call last):
File "<string>", line 111, in forward
File "<string>", line 8, in transform_grid
NameError: name 'collections' is not defined
--- Attempt 2: Correcting failed code ---
--- Successfully generated and validated code on attempt 1 ---
--- Code execution failed on attempt 1 ---
Error: name 'collections' is not defined
Traceback:
Traceback (most recent call last):
File "<string>", line 111, in forward
File "<string>", line 27, in transform_grid
NameError: name 'collections' is not defined
--- Attempt 2: Correcting failed code ---
--- Code execution failed on attempt 1 ---
Error: name 'collections' is not defined
Traceback:
Traceback (most recent call last):
File "<string>", line 111, in forward
File "<string>", line 30, in transform_grid
NameError: name 'collections' is not defined
--- Attempt 2: Correcting failed code ---
2025/08/30 01:25:15 WARNING dspy.adapters.json_adapter: Failed to use structured output format, falling back to JSON mode.
--- Code execution failed on attempt 1 ---
Error: Adapter JSONAdapter failed to parse the LM response.
LM Response: {
"reasoning": "1. **Initial Observation:** The input grids are 9x9 and the output grids are 3x3. This strongly suggests that the transformation works by dividing the 9x9 input grid into a 3x3 grid of 3x3 subgrids, and each subgrid is mapped to a single pixel in the output grid.\n\n2. **Color Analysis:** The input grids contain colors 0, 1, and 8. The output grids only contain colors 1 and 8. Color 0 appears to be a background color that is ignored in the main calculation.\n\n3. **Formulating a Hypothesis (Majority Vote):** A simple first hypothesis is that the output pixel's color is determined by the majority color (between 1 and 8) within the corresponding 3x3 subgrid. Let's denote the count of color 1 as `count1` and color 8 as `count8`.\n - If `count1 > count8`, output is 1.\n - If `count8 > count1`, output is 8.\n - If `count1 == count8`, a tie-breaking rule is needed.\n Testing this hypothesis across the examples shows it fails frequently. In many cases, the output color is the *minority* color. This indicates the rule is more complex.\n\n4. **Searching for a Pattern:** The rule seems to switch between majority and minority vote. The trigger for this switch must be a property of the subgrid or its position. I explored several properties:\n - The parity of the subgrid's coordinates `(i, j)`.\n - The parity of the total count of non-zero cells (`count1 + count8`).\n - The parity of `count1` or `count8` individually.\n\n5. **Discovering the Core Rule (Parity of `count1`):** After extensive testing, a promising pattern emerged related to the parity of `count1`. The rule `output = 1 if count1 is odd else 8` works for a significant number of cases (5 out of 9 in the first example).\n\n6. **Refining the Rule (Positional Inversion):** The cases where the `count1` parity rule failed in the first example were `(0,0)`, `(1,0)`, `(1,1)`, and `(2,2)`. Notice that three of these, `(0,0)`, `(1,1)`, and `(2,2)`, lie on the main diagonal of the 3x3 grid of subgrids. This suggests a positional modification to the rule.\n - Let's hypothesize: The base rule is `output = 1 if count1 is odd else 8`.\n - However, if the subgrid is on the main diagonal (`i == j`), the rule is inverted: `output = 8 if count1 is odd else 1`.\n\n7. **Final Verification:** Let's test this refined hypothesis on the first training example:\n - `(0,0)`: Diagonal. `count1=1` (odd). Inverted rule -> `8`. Correct.\n - `(0,1)`: Off-diagonal. `count1=3` (odd). Base rule -> `1`. Correct.\n - `(0,2)`: Off-diagonal. `count1=0` (even). Base rule -> `8`. Correct.\n - `(1,0)`: Off-diagonal. `count1=0` (even). Base rule -> `8`. Actual is `1`. This case fails.\n - `(1,1)`: Diagonal. `count1=1` (odd). Inverted rule -> `8`. Correct.\n - `(1,2)`: Off-diagonal. `count1=1` (odd). Base rule -> `1`. Correct.\n - `(2,0)`: Off-diagonal. `count1=1` (odd). Base rule -> `1`. Correct.\n - `(2,1)`: Off-diagonal. `count1=0` (even). Base rule -> `8`. Correct.\n - `(2,2)`: Diagonal. `count1=4` (even). Inverted rule -> `1`. Correct.\n\n This rule works for 8 out of 9 subgrids in the first example. While not perfect, it is the most consistent and simplest rule found after analyzing all examples. The single failure might indicate an additional, more subtle condition, but this rule captures the primary logic. Applying this rule to the other examples shows it performs better than other simple hypotheses. I will proceed with implementing this rule as the most likely solution.\n\n8. **Implementation Plan:**\n - Create an empty 3x3 output grid.\n - Iterate through the 3x3 grid of subgrids using indices `i` (for rows) and `j` (for columns).\n - For each subgrid at `(i, j)`:\n - Extract the 3x3 subgrid from the input.\n - Count the occurrences of the number 1 (`count1`).\n - Check if the subgrid is on the main diagonal (`i == j`).\n - Apply the base rule or the inverted rule based on the diagonal check and the parity of `count1`.\n - Assign the
Expected to find output fields in the LM response: [reasoning, python_code]
Actual output fields parsed from the LM response: [reasoning]
Traceback:
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/adapters/chat_adapter.py", line 38, in __call__
return super().__call__(lm, lm_kwargs, signature, demos, inputs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/adapters/base.py", line 128, in __call__
return self._call_postprocess(processed_signature, signature, outputs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/adapters/base.py", line 89, in _call_postprocess
value = self.parse(processed_signature, text)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/adapters/chat_adapter.py", line 197, in parse
raise AdapterParseError(
dspy.utils.exceptions.AdapterParseError: Adapter ChatAdapter failed to parse the LM response.
LM Response: [[ ## reasoning ## ]]
1. **Analyze Input and Output:** The input grids are 9x9, and the output grids are 3x3. This strongly suggests that the transformation operates on 3x3 sub-grids (or blocks) of the input to produce a single cell in the output. The output grid's cell at `(r, c)` corresponds to the input grid's 3x3 block starting at `(r*3, c*3)`.
2. **Formulate Initial Hypothesis (Majority Color):** A simple first guess is that the output color is the majority color (ignoring the background color 0) within the corresponding 3x3 sub-grid. Let's count the number of blue cells (color 1), `c1`, and red cells (color 8), `c8`, in each sub-grid.
* Hypothesis: If `c1 > c8`, output is 1. If `c8 > c1`, output is 8. If `c1 == c8`, a tie-breaker is needed.
3. **Test the Majority Color Hypothesis:**
* Let's test this on the first training example.
* Sub-grid (0,0): `c1=1, c8=8`. `c8 > c1`, so output should be 8. The actual output is 8. Correct.
* Sub-grid (0,1): `c1=2, c8=1`. `c1 > c8`, so output should be 1. The actual output is 1. Correct.
* Sub-grid (1,0): `c1=0, c8=4`. `c8 > c1`, so output should be 8. The actual output is 1. **Incorrect.**
* This hypothesis is wrong. The counts `c1` and `c8` alone are not sufficient to determine the output color.
4. **Look for a Deeper Pattern (Parity and Position):** Since the content of the sub-grid is not enough, let's consider other properties. The fact that two all-zero sub-grids in example 3 produce different outputs (sub-grid (1,1) -> 1, sub-grid (2,1) -> 8) is a crucial clue. This implies the position of the sub-grid, `(r, c)`, must be part of the rule.
5. **Investigate Parity:** Let's explore rules based on parity (even/odd).
* Let's check the parity of the difference in counts: `(c1 - c8) % 2`.
* Let's check the parity of the sub-grid's position, for which `(r + c) % 2` (a checkerboard pattern) is a common feature in ARC puzzles.
6. **Develop a Combined Hypothesis:** Let's try to combine these parity observations into a single rule.
* **Case 1: `(c1 - c8)` is odd.**
* Let's re-examine the examples where `c1 - c8` is odd.
* Ex1 (0,0): `c1-c8=-7` (odd). `
Expected to find output fields in the LM response: [reasoning, python_code]
Actual output fields parsed from the LM response: [reasoning]
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/adapters/json_adapter.py", line 78, in __call__
return super().__call__(lm, lm_kwargs, signature, demos, inputs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/adapters/chat_adapter.py", line 46, in __call__
raise e
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/adapters/chat_adapter.py", line 38, in __call__
return super().__call__(lm, lm_kwargs, signature, demos, inputs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/adapters/base.py", line 128, in __call__
return self._call_postprocess(processed_signature, signature, outputs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/adapters/base.py", line 89, in _call_postprocess
value = self.parse(processed_signature, text)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/adapters/json_adapter.py", line 172, in parse
raise AdapterParseError(
dspy.utils.exceptions.AdapterParseError: Adapter JSONAdapter failed to parse the LM response.
LM Response: {
"reasoning": "1. **Analyze Input/Output Structure:** The input grids are 9x9, and the output grids are 3x3. This strongly suggests that the transformation operates on 3x3 subgrids of the input to produce a single pixel in the output. The 9x9 input can be seen as a 3x3 grid of these subgrids.\n\n2. **Identify Key Features:** The grids contain three colors: 0 (black), 1 (blue), and 8 (red). The output grids only contain colors 1 and 8. This implies the rule for each subgrid is to decide between outputting a 1 or an 8.\n\n3. **Formulate a Hypothesis (Subgrid to Pixel Rule):** The decision for each output pixel likely depends on the properties of the corresponding 3x3 input subgrid. Let's analyze the properties. A simple property is the count of each non-black color. Let `C1` be the count of color 1 and `C8` be the count of color 8 within a subgrid.\n\n4. **Test Initial Hypotheses:**\n * **Majority Rule:** Does the color with the higher count win? This fails; in several cases, the color with the lower count determines the output.\n * **Parity of Counts:** Let's consider the parity (even or odd) of `C1` and `C8`. Let `p1 = C1 % 2` and `p8 = C8 % 2`. A rule based solely on `p1` and `p8` (e.g., `output = 1 if p1 == p8 else 8`) is inconsistent across different examples and even within the same example. This indicates another factor is involved.\n\n5. **Incorporate Positional Information:** The rule must also depend on the position of the subgrid. Let the subgrid's position be `(r, c)` where `r` and `c` range from 0 to 2. A common positional feature in ARC is the parity of the sum of coordinates, `pc = (r + c) % 2`, which creates a chessboard pattern.\n\n6. **Synthesize the Final Rule:** The most likely rule combines these three parity features: `p1`, `p8`, and `pc`. Through trial and error, a consistent logical expression is sought. Let's test the boolean expression `(p1 XOR p8) == pc`. We can map this boolean result to the output colors. Let's say if the expression is true, the output is 1, and if false, the output is 8.\n\n * Let's verify this rule: `output = 1 if (p1 ^ p8) == pc else 8`.\n * **Example 1, Subgrid (0,0):** `r=0, c=0`. `pc=0`. `C1=1, C8=8`. `p1=1, p8=0`. `p1^p8 = 1`. `1 == 0` is false. Output should be 8. **Correct.**\n * **Example 1, Subgrid (0,1):** `r=0, c=1`. `pc=1`. `C1=3, C8=1`. `p1=1, p8=1`. `p1^p8 = 0`. `0 == 1` is false. Output should be 8. Actual output is 1. **This rule fails.**\n\n7. **Refine the Final Rule:** Let's try another combination: `output = 1 if p1 == (p8 ^ pc) else 8`.\n * **Example 1, Subgrid (0,0):** `r=0, c=0`. `pc=0`. `C1=1, C8=8`. `p1=1, p8=0`. `p8^pc = 0`. `p1 == 0` is false. Output should be 8. **Correct.**\n * **Example 1, Subgrid (0,1):** `r=0, c=1`. `pc=1`. `C1=3, C8=1`. `p1=1, p8=1`. `p8^pc = 0`. `p1 == 0` is false. Output should be 8. Actual output is 1. **This rule also fails.**\n\n8. **Discover the Correct Rule:** After extensive testing of boolean combinations, the correct rule is found to be: **The output color is 8 if the parity of the count of color 8 is equal to the parity of the subgrid's column index `c`. Otherwise, the output is 1.** This can be expressed as `output = 8 if (C8 % 2) == (c % 2) else 1`. Let's verify this.\n * **Example 1, Subgrid (0,0):** `c=0`. `pc_col=0`. `C8=8`. `p8=0`. `p8 == pc_col` is true. Output 8. **Correct.**\n * **Example 1, Subgrid (0,1):** `c=1`. `pc_col=1`. `C8=1`. `p8=1`. `p8 == pc_col` is true. Output 8. Actual is 1. **This rule also fails.**\n\n9. **Final Re-evaluation and Correct Rule Discovery:** The interaction is more subtle. Let's re-examine the simplest cases (all-black subgrids). The output follows a chessboard pattern based on `r+c`. This suggests a base pattern which is then modified. The modification rule seems to be a flip if `p1 != p8`. Let's formalize this: `base_color = 1 if (r+c)%2 == 0 else 8`. `flip = (p1 != p8)`. If `flip` is true, the output is the other color. If `flip` is false, the output is the `base_color`. Let's test this.\n * **Example 1, (0,0):** `r+c=0`(E). Base=1. `p1=1, p8=0`. `p1!=p8` is true (flip). Output is 8. **Correct.**\n * **Example 1, (0,1):** `r+c=1`(O). Base=8. `p1=1, p8=1`. `p1!=p8` is false (no flip). Output is 8. Actual is 1. **Fails.**\n\n10. **The Actual Correct Rule:** The rule is a specific boolean function of the three parities. The output is 1 if `(p1 == p8) == ((r+c)%2 == 0)`. Otherwise, the output is 8. Let's verify:\n * **Example 1, (0,0):** `p1=1, p8=0`. `p1==p8` is F. `r+c=0`. `(r+c)%2==0` is T. `F == T` is false. Output 8. **Correct.**\n * **Example 1, (0,1):** `p1=1, p8=1`. `p1==p8` is T. `r+c=1`. `(r+c)%2==0` is F. `T == F` is false. Output 8. Actual is 1. **Fails.**\n\n11. **Final Attempt at a Simple Rule:** Let's try `output = 1 if (p1 + p8 + c) % 2 == 1 else 8`.\n * **Example 1, (0,0):** `p1=1, p8=0, c=0`. `(1+0+0)%2=1`. Output 1. Actual 8. **Fails.**\n * Let's flip it: `output = 8 if (p1 + p8 + c) % 2 == 1 else 1`.\n * **Example 1, (0,0):** `(1+0+0)%2=1`. Output 8. **Correct.**\n * **Example 1, (0,1):** `p1=1, p8=1, c=1`. `(1+1+1)%2=1`. Output 8. Actual 1. **Fails.**\n\n12. **The Correct Rule (Re-discovered):** The output color is 1 if `(C1 % 2) == (r % 2)`. Otherwise, the output is 8. Let's test this simple rule.\n * **Example 1, (0,0):** `r=0`. `pr=0`. `C1=1`. `p1=1`. `1==0` is F. Output 8. **Correct.**\n * **Example 1, (0,1):** `r=0`. `pr=0`. `C1=3`. `p1=1`. `1==0` is F. Output 8. Actual 1. **Fails.**\n\nLet's try `output = 1 if (C8 % 2) == (r % 2) else 8`.\n * **Example 1, (0,0):** `r=0`. `pr=0`. `C8=8`. `p8=0`. `0==0` is T. Output 1. Actual 8. **Fails.**\n\nAfter exhausting simple rules, a more complex but correct one is found: The output is 1 if `(C1 % 2) XOR ((r+c) % 2) == 0`. Otherwise, the output is 8. This is equivalent to `output = 1 if p1 == pc else 8`.\n * **Example 1, (0,0):** `p1=1, pc=0`. `1==0` is F. Output 8. **Correct.**\n * **Example 1, (0,1):** `p1=1, pc=1`. `1==1` is T. Output 1. **Correct.**\n * **Example 1, (0,2):** `p1=0, pc=0`. `0==0` is T. Output 1. Actual 8. **Fails.**\n\nLet's try `output = 1 if (C8 % 2) == ((r+c) % 2) else 8`.\n * **Example 1, (0,0):** `p8=0, pc=0`. `0==0` is T. Output 1. Actual 8. **Fails.**\n\nLet's try `output = 1 if (C1%2 + C8%2)%2 == ((r+c)%2) else 8`.\n * **Example 1, (0,0):** `p1^p8=1, pc=0`. `1==0` is F. Output 8. **Correct.**\n * **Example 1, (0,1):** `p1^p8=0, pc=1`. `0==1` is F. Output 8. Actual 1. **Fails.**\n\nLet's try `output = 1 if (C1%2 + C8%2)%2 != ((r+c)%2) else 8`.\n * **Example 1, (0,0):** `p1^p8=1, pc=0`. `1!=0` is T. Output 1. Actual 8. **Fails.**\n\nLet's try `output = 1 if (C1%2) != ((r+c)%2) else 8`.\n * **Example 1, (0,0):** `p1=1, pc=0`. `1!=0` is T. Output 1. Actual 8. **Fails.**\n\nLet's try `output = 1 if (C8%2) != ((r+c)%2) else 8`.\n * **Example 1, (0,0):** `p8=0, pc=0`. `0!=0` is F. Output 8. **Correct.**\n * **Example 1, (0,1):** `p8=1, pc=1`. `1!=1` is F. Output 8. Actual 1. **Fails.**\n\nLet's try `output = 1 if (C1%2 + C8%2 + r + c)%2 == 0 else 8`.\n * **Example 1, (0,0):** `1+0+0+0=1`. `1%2=1`. Output 8. **Correct.**\n * **Example 1, (0,1):** `1+1+0+1=3`. `3%2=1`. Output 8. Actual 1. **Fails.**\n\nLet's try `output = 1 if (C1%2 + C8%2 + r + c)%2 == 1 else 8`.\n * **Example 1, (0,0):** `1+0+0+0=1`. `1%2=1`. Output 1. Actual 8. **Fails.**\n\nLet's try `output = 1 if (C1%2 + r)%2 == (C8%2 + c)%2 else 8`.\n * **Example 1, (0,0):** `(1+0)%2=1`. `(0+0)%2=0`. `1==0` is F. Output 8. **Correct.**\n * **Example 1, (0,1):** `(1+0)%2=1`. `(1+1)%2=0`. `1==0` is F. Output 8. Actual 1. **Fails.**\n\nLet's try `output = 1 if (C1%2 + c)%2 == (C8%2 + r)%2 else 8`.\n * **Example 1, (0,0):** `(1+0)%2=1`. `(0+0)%2=0`. `1==0` is F. Output 8. **Correct.**\n * **Example 1, (0,1):** `(1+1)%2=0`. `(1+0)%2=1`. `0==1` is F. Output 8. Actual 1. **Fails.**\n\nLet's try `output = 1 if (C1%2 + C8%2)%2 == (r%2) else 8`.\n * **Example 1, (0,0):** `(1+0)%2=1`. `0%2=0`. `1==0` is F. Output 8. **Correct.**\n * **Example 1, (0,1):** `(1+1)%2=0`. `0%2=0`. `0==0` is T. Output 1. **Correct.**\n * **Example 1, (0,2):** `(0+0)%2=0`. `0%2=0`. `0==0` is T. Output 1. Actual 8. **Fails.**\n\nLet's try `output = 1 if (C1%2 + C8%2)%2 == (c%2) else 8`.\n * **Example 1, (0,0):** `(1+0)%2=1`. `0%2=0`. `1==0` is F. Output 8. **Correct.**\n * **Example 1, (0,1):** `(1+1)%2=0`. `1%2=1`. `0==1` is F. Output 8. Actual 1. **Fails.**\n\nLet's try `output = 1 if (C1%2) == (C8%2) else 8`.\n * **Example 1, (0,0):** `1==0` is F. Output 8. **Correct.**\n * **Example 1, (0,1):** `1==1` is T. Output 1. **Correct.**\n * **Example 1, (0,2):** `0==0` is T. Output 1. Actual 8. **Fails.**\n\nLet's try `output = 1 if (C1%2) != (C8%2) else 8`.\n * **Example 1, (0,0):** `1!=0` is T. Output 1. Actual 8. **Fails.**\n\nLet's try `output = 1 if (C1%2 + C8%2 + r)%2 == 0 else 8`.\n * **Example 1, (0,0):** `1+0+0=1`. `1%2=1`. Output 8. **Correct.**\n * **Example 1, (0,1):** `1+1+0=2`. `2%2=0`. Output 1. **Correct.**\n * **Example 1, (0,2):** `0+0+0=0`. `0%2=0`. Output 1. Actual 8. **Fails.**\n\nLet's try `output = 1 if (C1%2 + C8%2 + c)%2 == 0 else 8`.\n * **Example 1, (0,0):** `1+0+0=1`. `1%2=1`. Output 8. **Correct.**\
Expected to find output fields in the LM response: [reasoning, python_code]
Actual output fields parsed from the LM response: [reasoning]
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "<string>", line 82, in forward
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/predict/chain_of_thought.py", line 37, in forward
return self.predict(**kwargs)
^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/predict/predict.py", line 86, in __call__
return super().__call__(**kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/predict/predict.py", line 175, in forward
completions = adapter(lm, lm_kwargs=config, signature=signature, demos=demos, inputs=kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/adapters/chat_adapter.py", line 47, in __call__
return JSONAdapter()(lm, lm_kwargs, signature, demos, inputs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/adapters/json_adapter.py", line 82, in __call__
return super().__call__(lm, lm_kwargs, signature, demos, inputs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/adapters/chat_adapter.py", line 46, in __call__
raise e
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/adapters/chat_adapter.py", line 38, in __call__
return super().__call__(lm, lm_kwargs, signature, demos, inputs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/adapters/base.py", line 128, in __call__
return self._call_postprocess(processed_signature, signature, outputs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/adapters/base.py", line 89, in _call_postprocess
value = self.parse(processed_signature, text)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/adapters/json_adapter.py", line 172, in parse
raise AdapterParseError(
dspy.utils.exceptions.AdapterParseError: Adapter JSONAdapter failed to parse the LM response.
LM Response: {
"reasoning": "1. **Initial Observation:** The input grids are 9x9 and the output grids are 3x3. This strongly suggests that the transformation works by dividing the 9x9 input grid into a 3x3 grid of 3x3 subgrids, and each subgrid is mapped to a single pixel in the output grid.\n\n2. **Color Analysis:** The input grids contain colors 0, 1, and 8. The output grids only contain colors 1 and 8. Color 0 appears to be a background color that is ignored in the main calculation.\n\n3. **Formulating a Hypothesis (Majority Vote):** A simple first hypothesis is that the output pixel's color is determined by the majority color (between 1 and 8) within the corresponding 3x3 subgrid. Let's denote the count of color 1 as `count1` and color 8 as `count8`.\n - If `count1 > count8`, output is 1.\n - If `count8 > count1`, output is 8.\n - If `count1 == count8`, a tie-breaking rule is needed.\n Testing this hypothesis across the examples shows it fails frequently. In many cases, the output color is the *minority* color. This indicates the rule is more complex.\n\n4. **Searching for a Pattern:** The rule seems to switch between majority and minority vote. The trigger for this switch must be a property of the subgrid or its position. I explored several properties:\n - The parity of the subgrid's coordinates `(i, j)`.\n - The parity of the total count of non-zero cells (`count1 + count8`).\n - The parity of `count1` or `count8` individually.\n\n5. **Discovering the Core Rule (Parity of `count1`):** After extensive testing, a promising pattern emerged related to the parity of `count1`. The rule `output = 1 if count1 is odd else 8` works for a significant number of cases (5 out of 9 in the first example).\n\n6. **Refining the Rule (Positional Inversion):** The cases where the `count1` parity rule failed in the first example were `(0,0)`, `(1,0)`, `(1,1)`, and `(2,2)`. Notice that three of these, `(0,0)`, `(1,1)`, and `(2,2)`, lie on the main diagonal of the 3x3 grid of subgrids. This suggests a positional modification to the rule.\n - Let's hypothesize: The base rule is `output = 1 if count1 is odd else 8`.\n - However, if the subgrid is on the main diagonal (`i == j`), the rule is inverted: `output = 8 if count1 is odd else 1`.\n\n7. **Final Verification:** Let's test this refined hypothesis on the first training example:\n - `(0,0)`: Diagonal. `count1=1` (odd). Inverted rule -> `8`. Correct.\n - `(0,1)`: Off-diagonal. `count1=3` (odd). Base rule -> `1`. Correct.\n - `(0,2)`: Off-diagonal. `count1=0` (even). Base rule -> `8`. Correct.\n - `(1,0)`: Off-diagonal. `count1=0` (even). Base rule -> `8`. Actual is `1`. This case fails.\n - `(1,1)`: Diagonal. `count1=1` (odd). Inverted rule -> `8`. Correct.\n - `(1,2)`: Off-diagonal. `count1=1` (odd). Base rule -> `1`. Correct.\n - `(2,0)`: Off-diagonal. `count1=1` (odd). Base rule -> `1`. Correct.\n - `(2,1)`: Off-diagonal. `count1=0` (even). Base rule -> `8`. Correct.\n - `(2,2)`: Diagonal. `count1=4` (even). Inverted rule -> `1`. Correct.\n\n This rule works for 8 out of 9 subgrids in the first example. While not perfect, it is the most consistent and simplest rule found after analyzing all examples. The single failure might indicate an additional, more subtle condition, but this rule captures the primary logic. Applying this rule to the other examples shows it performs better than other simple hypotheses. I will proceed with implementing this rule as the most likely solution.\n\n8. **Implementation Plan:**\n - Create an empty 3x3 output grid.\n - Iterate through the 3x3 grid of subgrids using indices `i` (for rows) and `j` (for columns).\n - For each subgrid at `(i, j)`:\n - Extract the 3x3 subgrid from the input.\n - Count the occurrences of the number 1 (`count1`).\n - Check if the subgrid is on the main diagonal (`i == j`).\n - Apply the base rule or the inverted rule based on the diagonal check and the parity of `count1`.\n - Assign the
Expected to find output fields in the LM response: [reasoning, python_code]
Actual output fields parsed from the LM response: [reasoning]
--- Attempt 2: Correcting failed code ---
--- Code execution failed on attempt 1 ---
Error: name 'collections' is not defined
Traceback:
Traceback (most recent call last):
File "<string>", line 111, in forward
File "<string>", line 38, in transform_grid
File "<string>", line 33, in get_bg_and_density
NameError: name 'collections' is not defined
--- Attempt 2: Correcting failed code ---
--- Successfully generated and validated code on attempt 2 ---
--- Successfully generated and validated code on attempt 2 ---
--- Code execution failed on attempt 2 ---
Error: name 'collections' is not defined
Traceback:
Traceback (most recent call last):
File "<string>", line 111, in forward
File "<string>", line 22, in transform_grid
NameError: name 'collections' is not defined
--- Attempt 3: Correcting failed code ---
--- Code execution failed on attempt 3 ---
Error: name 'collections' is not defined
Traceback:
Traceback (most recent call last):
File "<string>", line 111, in forward
File "<string>", line 22, in transform_grid
NameError: name 'collections' is not defined
--- All code generation attempts failed. Using fallback. ---
--- Successfully generated and validated code on attempt 2 ---
--- Code execution failed on attempt 2 ---
Error: name 'collections' is not defined
Traceback:
Traceback (most recent call last):
File "<string>", line 111, in forward
File "<string>", line 75, in transform_grid
File "<string>", line 58, in count_fill_area
NameError: name 'collections' is not defined
--- Attempt 3: Correcting failed code ---
--- Code execution failed on attempt 2 ---
Error: name 'collections' is not defined
Traceback:
Traceback (most recent call last):
File "<string>", line 111, in forward
File "<string>", line 29, in transform_grid
NameError: name 'collections' is not defined
--- Attempt 3: Correcting failed code ---
--- Code execution failed on attempt 3 ---
Error: name 'collections' is not defined
Traceback:
Traceback (most recent call last):
File "<string>", line 111, in forward
File "<string>", line 29, in transform_grid
NameError: name 'collections' is not defined
--- All code generation attempts failed. Using fallback. ---
--- Code execution failed on attempt 2 ---
Error: name 'collections' is not defined
Traceback:
Traceback (most recent call last):
File "<string>", line 111, in forward
File "<string>", line 20, in transform_grid
NameError: name 'collections' is not defined
--- Attempt 3: Correcting failed code ---
--- Code execution failed on attempt 3 ---
Error: name 'collections' is not defined
Traceback:
Traceback (most recent call last):
File "<string>", line 111, in forward
File "<string>", line 20, in transform_grid
NameError: name 'collections' is not defined
--- All code generation attempts failed. Using fallback. ---
--- Code execution failed on attempt 2 ---
Error: name 'collections' is not defined
Traceback:
Traceback (most recent call last):
File "<string>", line 111, in forward
File "<string>", line 27, in transform_grid
NameError: name 'collections' is not defined
--- Attempt 3: Correcting failed code ---
--- Successfully generated and validated code on attempt 2 ---
--- Successfully generated and validated code on attempt 2 ---
--- Successfully generated and validated code on attempt 2 ---
--- Successfully generated and validated code on attempt 2 ---
--- Code execution failed on attempt 2 ---
Error: name 'collections' is not defined
Traceback:
Traceback (most recent call last):
File "<string>", line 111, in forward
File "<string>", line 12, in transform_grid
NameError: name 'collections' is not defined
--- Attempt 3: Correcting failed code ---
--- Successfully generated and validated code on attempt 2 ---
--- Successfully generated and validated code on attempt 2 ---
--- Code execution failed on attempt 2 ---
Error: name 'collections' is not defined
Traceback:
Traceback (most recent call last):
File "<string>", line 111, in forward
File "<string>", line 21, in transform_grid
NameError: name 'collections' is not defined
--- Attempt 3: Correcting failed code ---
--- Code execution failed on attempt 3 ---
Error: name 'collections' is not defined
Traceback:
Traceback (most recent call last):
File "<string>", line 111, in forward
File "<string>", line 21, in transform_grid
NameError: name 'collections' is not defined
--- All code generation attempts failed. Using fallback. ---
--- Code execution failed on attempt 2 ---
Error: name 'collections' is not defined
Traceback:
Traceback (most recent call last):
File "<string>", line 111, in forward
File "<string>", line 45, in transform_grid
File "<string>", line 20, in find_component
NameError: name 'collections' is not defined
--- Attempt 3: Correcting failed code ---
--- Code execution failed on attempt 3 ---
Error: name 'collections' is not defined
Traceback:
Traceback (most recent call last):
File "<string>", line 111, in forward
File "<string>", line 45, in transform_grid
File "<string>", line 20, in find_component
NameError: name 'collections' is not defined
--- All code generation attempts failed. Using fallback. ---
--- Successfully generated and validated code on attempt 2 ---
--- Successfully generated and validated code on attempt 2 ---
--- Code execution failed on attempt 2 ---
Error: name 'collections' is not defined
Traceback:
Traceback (most recent call last):
File "<string>", line 111, in forward
File "<string>", line 18, in transform_grid
NameError: name 'collections' is not defined
--- Attempt 3: Correcting failed code ---
--- Successfully generated and validated code on attempt 2 ---
--- Code execution failed on attempt 3 ---
Error: name 'collections' is not defined
Traceback:
Traceback (most recent call last):
File "<string>", line 111, in forward
File "<string>", line 27, in transform_grid
NameError: name 'collections' is not defined
--- All code generation attempts failed. Using fallback. ---
--- Successfully generated and validated code on attempt 3 ---
--- Code execution failed on attempt 2 ---
Error: name 'collections' is not defined
Traceback:
Traceback (most recent call last):
File "<string>", line 111, in forward
File "<string>", line 21, in transform_grid
NameError: name 'collections' is not defined
--- Attempt 3: Correcting failed code ---
--- Code execution failed on attempt 2 ---
Error: name 'collections' is not defined
Traceback:
Traceback (most recent call last):
File "<string>", line 111, in forward
File "<string>", line 62, in transform_grid
File "<string>", line 24, in find_shapes
NameError: name 'collections' is not defined
--- Attempt 3: Correcting failed code ---
--- Code execution failed on attempt 3 ---
Error: name 'collections' is not defined
Traceback:
Traceback (most recent call last):
File "<string>", line 111, in forward
File "<string>", line 12, in transform_grid
NameError: name 'collections' is not defined
--- All code generation attempts failed. Using fallback. ---
--- Code execution failed on attempt 2 ---
Error: name 'collections' is not defined
Traceback:
Traceback (most recent call last):
File "<string>", line 111, in forward
File "<string>", line 14, in transform_grid
NameError: name 'collections' is not defined
--- Attempt 3: Correcting failed code ---
--- Successfully generated and validated code on attempt 1 ---
--- Code execution failed on attempt 2 ---
Error: name 'collections' is not defined
Traceback:
Traceback (most recent call last):
File "<string>", line 111, in forward
File "<string>", line 33, in transform_grid
NameError: name 'collections' is not defined
--- Attempt 3: Correcting failed code ---
--- Successfully generated and validated code on attempt 2 ---
--- Successfully generated and validated code on attempt 2 ---
--- Code execution failed on attempt 2 ---
Error: name 'collections' is not defined
Traceback:
Traceback (most recent call last):
File "<string>", line 111, in forward
File "<string>", line 41, in transform_grid
NameError: name 'collections' is not defined
--- Attempt 3: Correcting failed code ---
--- Successfully generated and validated code on attempt 3 ---
--- Code execution failed on attempt 3 ---
Error: name 'collections' is not defined
Traceback:
Traceback (most recent call last):
File "<string>", line 111, in forward
File "<string>", line 18, in transform_grid
NameError: name 'collections' is not defined
--- All code generation attempts failed. Using fallback. ---
--- Successfully generated and validated code on attempt 3 ---
--- Code execution failed on attempt 2 ---
Error: name 'collections' is not defined
Traceback:
Traceback (most recent call last):
File "<string>", line 111, in forward
File "<string>", line 8, in transform_grid
NameError: name 'collections' is not defined
--- Attempt 3: Correcting failed code ---
--- Code execution failed on attempt 2 ---
Error: name 'collections' is not defined
Traceback:
Traceback (most recent call last):
File "<string>", line 111, in forward
File "<string>", line 22, in transform_grid
NameError: name 'collections' is not defined
--- Attempt 3: Correcting failed code ---
--- Successfully generated and validated code on attempt 3 ---
--- Code execution failed on attempt 2 ---
Error: name 'collections' is not defined
Traceback:
Traceback (most recent call last):
File "<string>", line 111, in forward
File "<string>", line 25, in transform_grid
NameError: name 'collections' is not defined
--- Attempt 3: Correcting failed code ---
--- Code execution failed on attempt 3 ---
Error: name 'collections' is not defined
Traceback:
Traceback (most recent call last):
File "<string>", line 111, in forward
File "<string>", line 17, in transform_grid
NameError: name 'collections' is not defined
--- All code generation attempts failed. Using fallback. ---
--- Code execution failed on attempt 2 ---
Error: name 'collections' is not defined
Traceback:
Traceback (most recent call last):
File "<string>", line 111, in forward
File "<string>", line 13, in transform_grid
NameError: name 'collections' is not defined
--- Attempt 3: Correcting failed code ---
--- Successfully generated and validated code on attempt 3 ---
--- Code execution failed on attempt 2 ---
Error: name 'collections' is not defined
Traceback:
Traceback (most recent call last):
File "<string>", line 111, in forward
File "<string>", line 42, in transform_grid
File "<string>", line 37, in get_bg_and_density
NameError: name 'collections' is not defined
--- Attempt 3: Correcting failed code ---
--- Code execution failed on attempt 2 ---
Error: name 'np' is not defined
Traceback:
Traceback (most recent call last):
File "<string>", line 111, in forward
File "<string>", line 8, in transform_grid
NameError: name 'np' is not defined
--- Attempt 3: Correcting failed code ---
--- Successfully generated and validated code on attempt 2 ---
--- Successfully generated and validated code on attempt 2 ---
--- Code execution failed on attempt 2 ---
Error: name 'collections' is not defined
Traceback:
Traceback (most recent call last):
File "<string>", line 111, in forward
File "<string>", line 19, in transform_grid
NameError: name 'collections' is not defined
--- Attempt 3: Correcting failed code ---
--- Successfully generated and validated code on attempt 3 ---
--- Code execution failed on attempt 3 ---
Error: name 'np' is not defined
Traceback:
Traceback (most recent call last):
File "<string>", line 111, in forward
File "<string>", line 8, in transform_grid
NameError: name 'np' is not defined
--- All code generation attempts failed. Using fallback. ---
--- Code execution failed on attempt 3 ---
Error: name 'collections' is not defined
Traceback:
Traceback (most recent call last):
File "<string>", line 111, in forward
File "<string>", line 8, in transform_grid
NameError: name 'collections' is not defined
--- All code generation attempts failed. Using fallback. ---
--- Successfully generated and validated code on attempt 2 ---
2025/08/30 01:29:03 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
--- Code execution failed on attempt 2 ---
Error: argument of type 'NoneType' is not iterable
Traceback:
Traceback (most recent call last):
File "<string>", line 99, in forward
TypeError: argument of type 'NoneType' is not iterable
--- Attempt 3: Correcting failed code ---
--- Successfully generated and validated code on attempt 3 ---
2025/08/30 01:29:22 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/30 01:29:22 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
--- Code execution failed on attempt 2 ---
Error: argument of type 'NoneType' is not iterable
Traceback:
Traceback (most recent call last):
File "<string>", line 99, in forward
TypeError: argument of type 'NoneType' is not iterable
--- Attempt 3: Correcting failed code ---
--- Code execution failed on attempt 3 ---
Error: argument of type 'NoneType' is not iterable
Traceback:
Traceback (most recent call last):
File "<string>", line 99, in forward
TypeError: argument of type 'NoneType' is not iterable
--- All code generation attempts failed. Using fallback. ---
2025/08/30 01:29:31 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/30 01:29:31 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/30 01:29:31 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/30 01:29:31 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/30 01:29:31 WARNING dspy.adapters.json_adapter: Failed to use structured output format, falling back to JSON mode.
2025/08/30 01:29:31 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/30 01:29:31 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/30 01:29:31 WARNING dspy.adapters.json_adapter: Failed to use structured output format, falling back to JSON mode.
--- Code execution failed on attempt 3 ---
Error: name 'collections' is not defined
Traceback:
Traceback (most recent call last):
File "<string>", line 111, in forward
File "<string>", line 19, in transform_grid
NameError: name 'collections' is not defined
--- All code generation attempts failed. Using fallback. ---
--- Code execution failed on attempt 1 ---
Error: name 'collections' is not defined
Traceback:
Traceback (most recent call last):
File "<string>", line 111, in forward
File "<string>", line 35, in transform_grid
NameError: name 'collections' is not defined
--- Attempt 2: Correcting failed code ---
--- Code execution failed on attempt 2 ---
Error: name 'collections' is not defined
Traceback:
Traceback (most recent call last):
File "<string>", line 111, in forward
File "<string>", line 25, in transform_grid
NameError: name 'collections' is not defined
--- Attempt 3: Correcting failed code ---
--- Code execution failed on attempt 1 ---
Error: Adapter JSONAdapter failed to parse the LM response.
LM Response: {
"reasoning": "1. **Initial Observation:** The input grids are 9x9 and the output grids are 3x3. This strongly suggests that the transformation works by dividing the 9x9 input grid into a 3x3 grid of 3x3 subgrids, and each subgrid is mapped to a single pixel in the output grid.\n\n2. **Color Analysis:** The input grids contain colors 0, 1, and 8. The output grids only contain colors 1 and 8. Color 0 appears to be a background color that is ignored in the main calculation.\n\n3. **Formulating a Hypothesis (Majority Vote):** A simple first hypothesis is that the output pixel's color is determined by the majority color (between 1 and 8) within the corresponding 3x3 subgrid. Let's denote the count of color 1 as `count1` and color 8 as `count8`.\n - If `count1 > count8`, output is 1.\n - If `count8 > count1`, output is 8.\n - If `count1 == count8`, a tie-breaking rule is needed.\n Testing this hypothesis across the examples shows it fails frequently. In many cases, the output color is the *minority* color. This indicates the rule is more complex.\n\n4. **Searching for a Pattern:** The rule seems to switch between majority and minority vote. The trigger for this switch must be a property of the subgrid or its position. I explored several properties:\n - The parity of the subgrid's coordinates `(i, j)`.\n - The parity of the total count of non-zero cells (`count1 + count8`).\n - The parity of `count1` or `count8` individually.\n\n5. **Discovering the Core Rule (Parity of `count1`):** After extensive testing, a promising pattern emerged related to the parity of `count1`. The rule `output = 1 if count1 is odd else 8` works for a significant number of cases (5 out of 9 in the first example).\n\n6. **Refining the Rule (Positional Inversion):** The cases where the `count1` parity rule failed in the first example were `(0,0)`, `(1,0)`, `(1,1)`, and `(2,2)`. Notice that three of these, `(0,0)`, `(1,1)`, and `(2,2)`, lie on the main diagonal of the 3x3 grid of subgrids. This suggests a positional modification to the rule.\n - Let's hypothesize: The base rule is `output = 1 if count1 is odd else 8`.\n - However, if the subgrid is on the main diagonal (`i == j`), the rule is inverted: `output = 8 if count1 is odd else 1`.\n\n7. **Final Verification:** Let's test this refined hypothesis on the first training example:\n - `(0,0)`: Diagonal. `count1=1` (odd). Inverted rule -> `8`. Correct.\n - `(0,1)`: Off-diagonal. `count1=3` (odd). Base rule -> `1`. Correct.\n - `(0,2)`: Off-diagonal. `count1=0` (even). Base rule -> `8`. Correct.\n - `(1,0)`: Off-diagonal. `count1=0` (even). Base rule -> `8`. Actual is `1`. This case fails.\n - `(1,1)`: Diagonal. `count1=1` (odd). Inverted rule -> `8`. Correct.\n - `(1,2)`: Off-diagonal. `count1=1` (odd). Base rule -> `1`. Correct.\n - `(2,0)`: Off-diagonal. `count1=1` (odd). Base rule -> `1`. Correct.\n - `(2,1)`: Off-diagonal. `count1=0` (even). Base rule -> `8`. Correct.\n - `(2,2)`: Diagonal. `count1=4` (even). Inverted rule -> `1`. Correct.\n\n This rule works for 8 out of 9 subgrids in the first example. While not perfect, it is the most consistent and simplest rule found after analyzing all examples. The single failure might indicate an additional, more subtle condition, but this rule captures the primary logic. Applying this rule to the other examples shows it performs better than other simple hypotheses. I will proceed with implementing this rule as the most likely solution.\n\n8. **Implementation Plan:**\n - Create an empty 3x3 output grid.\n - Iterate through the 3x3 grid of subgrids using indices `i` (for rows) and `j` (for columns).\n - For each subgrid at `(i, j)`:\n - Extract the 3x3 subgrid from the input.\n - Count the occurrences of the number 1 (`count1`).\n - Check if the subgrid is on the main diagonal (`i == j`).\n - Apply the base rule or the inverted rule based on the diagonal check and the parity of `count1`.\n - Assign the
Expected to find output fields in the LM response: [reasoning, python_code]
Actual output fields parsed from the LM response: [reasoning]
Traceback:
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/adapters/chat_adapter.py", line 38, in __call__
return super().__call__(lm, lm_kwargs, signature, demos, inputs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/adapters/base.py", line 128, in __call__
return self._call_postprocess(processed_signature, signature, outputs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/adapters/base.py", line 89, in _call_postprocess
value = self.parse(processed_signature, text)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/adapters/chat_adapter.py", line 197, in parse
raise AdapterParseError(
dspy.utils.exceptions.AdapterParseError: Adapter ChatAdapter failed to parse the LM response.
LM Response: [[ ## reasoning ## ]]
1. **Analyze Input and Output:** The input grids are 9x9, and the output grids are 3x3. This strongly suggests that the transformation operates on 3x3 sub-grids (or blocks) of the input to produce a single cell in the output. The output grid's cell at `(r, c)` corresponds to the input grid's 3x3 block starting at `(r*3, c*3)`.
2. **Formulate Initial Hypothesis (Majority Color):** A simple first guess is that the output color is the majority color (ignoring the background color 0) within the corresponding 3x3 sub-grid. Let's count the number of blue cells (color 1), `c1`, and red cells (color 8), `c8`, in each sub-grid.
* Hypothesis: If `c1 > c8`, output is 1. If `c8 > c1`, output is 8. If `c1 == c8`, a tie-breaker is needed.
3. **Test the Majority Color Hypothesis:**
* Let's test this on the first training example.
* Sub-grid (0,0): `c1=1, c8=8`. `c8 > c1`, so output should be 8. The actual output is 8. Correct.
* Sub-grid (0,1): `c1=2, c8=1`. `c1 > c8`, so output should be 1. The actual output is 1. Correct.
* Sub-grid (1,0): `c1=0, c8=4`. `c8 > c1`, so output should be 8. The actual output is 1. **Incorrect.**
* This hypothesis is wrong. The counts `c1` and `c8` alone are not sufficient to determine the output color.
4. **Look for a Deeper Pattern (Parity and Position):** Since the content of the sub-grid is not enough, let's consider other properties. The fact that two all-zero sub-grids in example 3 produce different outputs (sub-grid (1,1) -> 1, sub-grid (2,1) -> 8) is a crucial clue. This implies the position of the sub-grid, `(r, c)`, must be part of the rule.
5. **Investigate Parity:** Let's explore rules based on parity (even/odd).
* Let's check the parity of the difference in counts: `(c1 - c8) % 2`.
* Let's check the parity of the sub-grid's position, for which `(r + c) % 2` (a checkerboard pattern) is a common feature in ARC puzzles.
6. **Develop a Combined Hypothesis:** Let's try to combine these parity observations into a single rule.
* **Case 1: `(c1 - c8)` is odd.**
* Let's re-examine the examples where `c1 - c8` is odd.
* Ex1 (0,0): `c1-c8=-7` (odd). `
Expected to find output fields in the LM response: [reasoning, python_code]
Actual output fields parsed from the LM response: [reasoning]
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/adapters/json_adapter.py", line 78, in __call__
return super().__call__(lm, lm_kwargs, signature, demos, inputs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/adapters/chat_adapter.py", line 46, in __call__
raise e
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/adapters/chat_adapter.py", line 38, in __call__
return super().__call__(lm, lm_kwargs, signature, demos, inputs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/adapters/base.py", line 128, in __call__
return self._call_postprocess(processed_signature, signature, outputs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/adapters/base.py", line 89, in _call_postprocess
value = self.parse(processed_signature, text)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/adapters/json_adapter.py", line 172, in parse
raise AdapterParseError(
dspy.utils.exceptions.AdapterParseError: Adapter JSONAdapter failed to parse the LM response.
LM Response: {
"reasoning": "1. **Analyze Input/Output Structure:** The input grids are 9x9, and the output grids are 3x3. This strongly suggests that the transformation operates on 3x3 subgrids of the input to produce a single pixel in the output. The 9x9 input can be seen as a 3x3 grid of these subgrids.\n\n2. **Identify Key Features:** The grids contain three colors: 0 (black), 1 (blue), and 8 (red). The output grids only contain colors 1 and 8. This implies the rule for each subgrid is to decide between outputting a 1 or an 8.\n\n3. **Formulate a Hypothesis (Subgrid to Pixel Rule):** The decision for each output pixel likely depends on the properties of the corresponding 3x3 input subgrid. Let's analyze the properties. A simple property is the count of each non-black color. Let `C1` be the count of color 1 and `C8` be the count of color 8 within a subgrid.\n\n4. **Test Initial Hypotheses:**\n * **Majority Rule:** Does the color with the higher count win? This fails; in several cases, the color with the lower count determines the output.\n * **Parity of Counts:** Let's consider the parity (even or odd) of `C1` and `C8`. Let `p1 = C1 % 2` and `p8 = C8 % 2`. A rule based solely on `p1` and `p8` (e.g., `output = 1 if p1 == p8 else 8`) is inconsistent across different examples and even within the same example. This indicates another factor is involved.\n\n5. **Incorporate Positional Information:** The rule must also depend on the position of the subgrid. Let the subgrid's position be `(r, c)` where `r` and `c` range from 0 to 2. A common positional feature in ARC is the parity of the sum of coordinates, `pc = (r + c) % 2`, which creates a chessboard pattern.\n\n6. **Synthesize the Final Rule:** The most likely rule combines these three parity features: `p1`, `p8`, and `pc`. Through trial and error, a consistent logical expression is sought. Let's test the boolean expression `(p1 XOR p8) == pc`. We can map this boolean result to the output colors. Let's say if the expression is true, the output is 1, and if false, the output is 8.\n\n * Let's verify this rule: `output = 1 if (p1 ^ p8) == pc else 8`.\n * **Example 1, Subgrid (0,0):** `r=0, c=0`. `pc=0`. `C1=1, C8=8`. `p1=1, p8=0`. `p1^p8 = 1`. `1 == 0` is false. Output should be 8. **Correct.**\n * **Example 1, Subgrid (0,1):** `r=0, c=1`. `pc=1`. `C1=3, C8=1`. `p1=1, p8=1`. `p1^p8 = 0`. `0 == 1` is false. Output should be 8. Actual output is 1. **This rule fails.**\n\n7. **Refine the Final Rule:** Let's try another combination: `output = 1 if p1 == (p8 ^ pc) else 8`.\n * **Example 1, Subgrid (0,0):** `r=0, c=0`. `pc=0`. `C1=1, C8=8`. `p1=1, p8=0`. `p8^pc = 0`. `p1 == 0` is false. Output should be 8. **Correct.**\n * **Example 1, Subgrid (0,1):** `r=0, c=1`. `pc=1`. `C1=3, C8=1`. `p1=1, p8=1`. `p8^pc = 0`. `p1 == 0` is false. Output should be 8. Actual output is 1. **This rule also fails.**\n\n8. **Discover the Correct Rule:** After extensive testing of boolean combinations, the correct rule is found to be: **The output color is 8 if the parity of the count of color 8 is equal to the parity of the subgrid's column index `c`. Otherwise, the output is 1.** This can be expressed as `output = 8 if (C8 % 2) == (c % 2) else 1`. Let's verify this.\n * **Example 1, Subgrid (0,0):** `c=0`. `pc_col=0`. `C8=8`. `p8=0`. `p8 == pc_col` is true. Output 8. **Correct.**\n * **Example 1, Subgrid (0,1):** `c=1`. `pc_col=1`. `C8=1`. `p8=1`. `p8 == pc_col` is true. Output 8. Actual is 1. **This rule also fails.**\n\n9. **Final Re-evaluation and Correct Rule Discovery:** The interaction is more subtle. Let's re-examine the simplest cases (all-black subgrids). The output follows a chessboard pattern based on `r+c`. This suggests a base pattern which is then modified. The modification rule seems to be a flip if `p1 != p8`. Let's formalize this: `base_color = 1 if (r+c)%2 == 0 else 8`. `flip = (p1 != p8)`. If `flip` is true, the output is the other color. If `flip` is false, the output is the `base_color`. Let's test this.\n * **Example 1, (0,0):** `r+c=0`(E). Base=1. `p1=1, p8=0`. `p1!=p8` is true (flip). Output is 8. **Correct.**\n * **Example 1, (0,1):** `r+c=1`(O). Base=8. `p1=1, p8=1`. `p1!=p8` is false (no flip). Output is 8. Actual is 1. **Fails.**\n\n10. **The Actual Correct Rule:** The rule is a specific boolean function of the three parities. The output is 1 if `(p1 == p8) == ((r+c)%2 == 0)`. Otherwise, the output is 8. Let's verify:\n * **Example 1, (0,0):** `p1=1, p8=0`. `p1==p8` is F. `r+c=0`. `(r+c)%2==0` is T. `F == T` is false. Output 8. **Correct.**\n * **Example 1, (0,1):** `p1=1, p8=1`. `p1==p8` is T. `r+c=1`. `(r+c)%2==0` is F. `T == F` is false. Output 8. Actual is 1. **Fails.**\n\n11. **Final Attempt at a Simple Rule:** Let's try `output = 1 if (p1 + p8 + c) % 2 == 1 else 8`.\n * **Example 1, (0,0):** `p1=1, p8=0, c=0`. `(1+0+0)%2=1`. Output 1. Actual 8. **Fails.**\n * Let's flip it: `output = 8 if (p1 + p8 + c) % 2 == 1 else 1`.\n * **Example 1, (0,0):** `(1+0+0)%2=1`. Output 8. **Correct.**\n * **Example 1, (0,1):** `p1=1, p8=1, c=1`. `(1+1+1)%2=1`. Output 8. Actual 1. **Fails.**\n\n12. **The Correct Rule (Re-discovered):** The output color is 1 if `(C1 % 2) == (r % 2)`. Otherwise, the output is 8. Let's test this simple rule.\n * **Example 1, (0,0):** `r=0`. `pr=0`. `C1=1`. `p1=1`. `1==0` is F. Output 8. **Correct.**\n * **Example 1, (0,1):** `r=0`. `pr=0`. `C1=3`. `p1=1`. `1==0` is F. Output 8. Actual 1. **Fails.**\n\nLet's try `output = 1 if (C8 % 2) == (r % 2) else 8`.\n * **Example 1, (0,0):** `r=0`. `pr=0`. `C8=8`. `p8=0`. `0==0` is T. Output 1. Actual 8. **Fails.**\n\nAfter exhausting simple rules, a more complex but correct one is found: The output is 1 if `(C1 % 2) XOR ((r+c) % 2) == 0`. Otherwise, the output is 8. This is equivalent to `output = 1 if p1 == pc else 8`.\n * **Example 1, (0,0):** `p1=1, pc=0`. `1==0` is F. Output 8. **Correct.**\n * **Example 1, (0,1):** `p1=1, pc=1`. `1==1` is T. Output 1. **Correct.**\n * **Example 1, (0,2):** `p1=0, pc=0`. `0==0` is T. Output 1. Actual 8. **Fails.**\n\nLet's try `output = 1 if (C8 % 2) == ((r+c) % 2) else 8`.\n * **Example 1, (0,0):** `p8=0, pc=0`. `0==0` is T. Output 1. Actual 8. **Fails.**\n\nLet's try `output = 1 if (C1%2 + C8%2)%2 == ((r+c)%2) else 8`.\n * **Example 1, (0,0):** `p1^p8=1, pc=0`. `1==0` is F. Output 8. **Correct.**\n * **Example 1, (0,1):** `p1^p8=0, pc=1`. `0==1` is F. Output 8. Actual 1. **Fails.**\n\nLet's try `output = 1 if (C1%2 + C8%2)%2 != ((r+c)%2) else 8`.\n * **Example 1, (0,0):** `p1^p8=1, pc=0`. `1!=0` is T. Output 1. Actual 8. **Fails.**\n\nLet's try `output = 1 if (C1%2) != ((r+c)%2) else 8`.\n * **Example 1, (0,0):** `p1=1, pc=0`. `1!=0` is T. Output 1. Actual 8. **Fails.**\n\nLet's try `output = 1 if (C8%2) != ((r+c)%2) else 8`.\n * **Example 1, (0,0):** `p8=0, pc=0`. `0!=0` is F. Output 8. **Correct.**\n * **Example 1, (0,1):** `p8=1, pc=1`. `1!=1` is F. Output 8. Actual 1. **Fails.**\n\nLet's try `output = 1 if (C1%2 + C8%2 + r + c)%2 == 0 else 8`.\n * **Example 1, (0,0):** `1+0+0+0=1`. `1%2=1`. Output 8. **Correct.**\n * **Example 1, (0,1):** `1+1+0+1=3`. `3%2=1`. Output 8. Actual 1. **Fails.**\n\nLet's try `output = 1 if (C1%2 + C8%2 + r + c)%2 == 1 else 8`.\n * **Example 1, (0,0):** `1+0+0+0=1`. `1%2=1`. Output 1. Actual 8. **Fails.**\n\nLet's try `output = 1 if (C1%2 + r)%2 == (C8%2 + c)%2 else 8`.\n * **Example 1, (0,0):** `(1+0)%2=1`. `(0+0)%2=0`. `1==0` is F. Output 8. **Correct.**\n * **Example 1, (0,1):** `(1+0)%2=1`. `(1+1)%2=0`. `1==0` is F. Output 8. Actual 1. **Fails.**\n\nLet's try `output = 1 if (C1%2 + c)%2 == (C8%2 + r)%2 else 8`.\n * **Example 1, (0,0):** `(1+0)%2=1`. `(0+0)%2=0`. `1==0` is F. Output 8. **Correct.**\n * **Example 1, (0,1):** `(1+1)%2=0`. `(1+0)%2=1`. `0==1` is F. Output 8. Actual 1. **Fails.**\n\nLet's try `output = 1 if (C1%2 + C8%2)%2 == (r%2) else 8`.\n * **Example 1, (0,0):** `(1+0)%2=1`. `0%2=0`. `1==0` is F. Output 8. **Correct.**\n * **Example 1, (0,1):** `(1+1)%2=0`. `0%2=0`. `0==0` is T. Output 1. **Correct.**\n * **Example 1, (0,2):** `(0+0)%2=0`. `0%2=0`. `0==0` is T. Output 1. Actual 8. **Fails.**\n\nLet's try `output = 1 if (C1%2 + C8%2)%2 == (c%2) else 8`.\n * **Example 1, (0,0):** `(1+0)%2=1`. `0%2=0`. `1==0` is F. Output 8. **Correct.**\n * **Example 1, (0,1):** `(1+1)%2=0`. `1%2=1`. `0==1` is F. Output 8. Actual 1. **Fails.**\n\nLet's try `output = 1 if (C1%2) == (C8%2) else 8`.\n * **Example 1, (0,0):** `1==0` is F. Output 8. **Correct.**\n * **Example 1, (0,1):** `1==1` is T. Output 1. **Correct.**\n * **Example 1, (0,2):** `0==0` is T. Output 1. Actual 8. **Fails.**\n\nLet's try `output = 1 if (C1%2) != (C8%2) else 8`.\n * **Example 1, (0,0):** `1!=0` is T. Output 1. Actual 8. **Fails.**\n\nLet's try `output = 1 if (C1%2 + C8%2 + r)%2 == 0 else 8`.\n * **Example 1, (0,0):** `1+0+0=1`. `1%2=1`. Output 8. **Correct.**\n * **Example 1, (0,1):** `1+1+0=2`. `2%2=0`. Output 1. **Correct.**\n * **Example 1, (0,2):** `0+0+0=0`. `0%2=0`. Output 1. Actual 8. **Fails.**\n\nLet's try `output = 1 if (C1%2 + C8%2 + c)%2 == 0 else 8`.\n * **Example 1, (0,0):** `1+0+0=1`. `1%2=1`. Output 8. **Correct.**\
Expected to find output fields in the LM response: [reasoning, python_code]
Actual output fields parsed from the LM response: [reasoning]
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "<string>", line 82, in forward
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/predict/chain_of_thought.py", line 37, in forward
return self.predict(**kwargs)
^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/predict/predict.py", line 86, in __call__
return super().__call__(**kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/predict/predict.py", line 175, in forward
completions = adapter(lm, lm_kwargs=config, signature=signature, demos=demos, inputs=kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/adapters/chat_adapter.py", line 47, in __call__
return JSONAdapter()(lm, lm_kwargs, signature, demos, inputs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/adapters/json_adapter.py", line 82, in __call__
return super().__call__(lm, lm_kwargs, signature, demos, inputs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/adapters/chat_adapter.py", line 46, in __call__
raise e
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/adapters/chat_adapter.py", line 38, in __call__
return super().__call__(lm, lm_kwargs, signature, demos, inputs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/adapters/base.py", line 128, in __call__
return self._call_postprocess(processed_signature, signature, outputs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/adapters/base.py", line 89, in _call_postprocess
value = self.parse(processed_signature, text)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/adapters/json_adapter.py", line 172, in parse
raise AdapterParseError(
dspy.utils.exceptions.AdapterParseError: Adapter JSONAdapter failed to parse the LM response.
LM Response: {
"reasoning": "1. **Initial Observation:** The input grids are 9x9 and the output grids are 3x3. This strongly suggests that the transformation works by dividing the 9x9 input grid into a 3x3 grid of 3x3 subgrids, and each subgrid is mapped to a single pixel in the output grid.\n\n2. **Color Analysis:** The input grids contain colors 0, 1, and 8. The output grids only contain colors 1 and 8. Color 0 appears to be a background color that is ignored in the main calculation.\n\n3. **Formulating a Hypothesis (Majority Vote):** A simple first hypothesis is that the output pixel's color is determined by the majority color (between 1 and 8) within the corresponding 3x3 subgrid. Let's denote the count of color 1 as `count1` and color 8 as `count8`.\n - If `count1 > count8`, output is 1.\n - If `count8 > count1`, output is 8.\n - If `count1 == count8`, a tie-breaking rule is needed.\n Testing this hypothesis across the examples shows it fails frequently. In many cases, the output color is the *minority* color. This indicates the rule is more complex.\n\n4. **Searching for a Pattern:** The rule seems to switch between majority and minority vote. The trigger for this switch must be a property of the subgrid or its position. I explored several properties:\n - The parity of the subgrid's coordinates `(i, j)`.\n - The parity of the total count of non-zero cells (`count1 + count8`).\n - The parity of `count1` or `count8` individually.\n\n5. **Discovering the Core Rule (Parity of `count1`):** After extensive testing, a promising pattern emerged related to the parity of `count1`. The rule `output = 1 if count1 is odd else 8` works for a significant number of cases (5 out of 9 in the first example).\n\n6. **Refining the Rule (Positional Inversion):** The cases where the `count1` parity rule failed in the first example were `(0,0)`, `(1,0)`, `(1,1)`, and `(2,2)`. Notice that three of these, `(0,0)`, `(1,1)`, and `(2,2)`, lie on the main diagonal of the 3x3 grid of subgrids. This suggests a positional modification to the rule.\n - Let's hypothesize: The base rule is `output = 1 if count1 is odd else 8`.\n - However, if the subgrid is on the main diagonal (`i == j`), the rule is inverted: `output = 8 if count1 is odd else 1`.\n\n7. **Final Verification:** Let's test this refined hypothesis on the first training example:\n - `(0,0)`: Diagonal. `count1=1` (odd). Inverted rule -> `8`. Correct.\n - `(0,1)`: Off-diagonal. `count1=3` (odd). Base rule -> `1`. Correct.\n - `(0,2)`: Off-diagonal. `count1=0` (even). Base rule -> `8`. Correct.\n - `(1,0)`: Off-diagonal. `count1=0` (even). Base rule -> `8`. Actual is `1`. This case fails.\n - `(1,1)`: Diagonal. `count1=1` (odd). Inverted rule -> `8`. Correct.\n - `(1,2)`: Off-diagonal. `count1=1` (odd). Base rule -> `1`. Correct.\n - `(2,0)`: Off-diagonal. `count1=1` (odd). Base rule -> `1`. Correct.\n - `(2,1)`: Off-diagonal. `count1=0` (even). Base rule -> `8`. Correct.\n - `(2,2)`: Diagonal. `count1=4` (even). Inverted rule -> `1`. Correct.\n\n This rule works for 8 out of 9 subgrids in the first example. While not perfect, it is the most consistent and simplest rule found after analyzing all examples. The single failure might indicate an additional, more subtle condition, but this rule captures the primary logic. Applying this rule to the other examples shows it performs better than other simple hypotheses. I will proceed with implementing this rule as the most likely solution.\n\n8. **Implementation Plan:**\n - Create an empty 3x3 output grid.\n - Iterate through the 3x3 grid of subgrids using indices `i` (for rows) and `j` (for columns).\n - For each subgrid at `(i, j)`:\n - Extract the 3x3 subgrid from the input.\n - Count the occurrences of the number 1 (`count1`).\n - Check if the subgrid is on the main diagonal (`i == j`).\n - Apply the base rule or the inverted rule based on the diagonal check and the parity of `count1`.\n - Assign the
Expected to find output fields in the LM response: [reasoning, python_code]
Actual output fields parsed from the LM response: [reasoning]
--- Attempt 2: Correcting failed code ---
--- Code execution failed on attempt 2 ---
Error: argument of type 'NoneType' is not iterable
Traceback:
Traceback (most recent call last):
File "<string>", line 99, in forward
TypeError: argument of type 'NoneType' is not iterable
--- Attempt 3: Correcting failed code ---
--- Code execution failed on attempt 1 ---
Error: name 'collections' is not defined
Traceback:
Traceback (most recent call last):
File "<string>", line 111, in forward
File "<string>", line 38, in transform_grid
File "<string>", line 33, in get_bg_and_density
NameError: name 'collections' is not defined
--- Attempt 2: Correcting failed code ---
--- Code execution failed on attempt 2 ---
Error: name 'collections' is not defined
Traceback:
Traceback (most recent call last):
File "<string>", line 111, in forward
File "<string>", line 42, in transform_grid
File "<string>", line 37, in get_bg_and_density
NameError: name 'collections' is not defined
--- Attempt 3: Correcting failed code ---
--- Code execution failed on attempt 3 ---
Error: name 'collections' is not defined
Traceback:
Traceback (most recent call last):
File "<string>", line 111, in forward
File "<string>", line 42, in transform_grid
File "<string>", line 37, in get_bg_and_density
NameError: name 'collections' is not defined
--- All code generation attempts failed. Using fallback. ---
--- Successfully generated and validated code on attempt 3 ---
2025/08/30 01:31:46 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/30 01:31:46 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/30 01:31:46 WARNING dspy.adapters.json_adapter: Failed to use structured output format, falling back to JSON mode.
2025/08/30 01:31:46 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/30 01:31:47 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
--- Code execution failed on attempt 3 ---
Error: name 'collections' is not defined
Traceback:
Traceback (most recent call last):
File "<string>", line 111, in forward
File "<string>", line 43, in transform_grid
File "<string>", line 38, in get_bg_and_density
NameError: name 'collections' is not defined
--- All code generation attempts failed. Using fallback. ---
--- Code execution failed on attempt 1 ---
Error: name 'collections' is not defined
Traceback:
Traceback (most recent call last):
File "<string>", line 111, in forward
File "<string>", line 35, in transform_grid
NameError: name 'collections' is not defined
--- Attempt 2: Correcting failed code ---
--- Code execution failed on attempt 2 ---
Error: name 'collections' is not defined
Traceback:
Traceback (most recent call last):
File "<string>", line 111, in forward
File "<string>", line 25, in transform_grid
NameError: name 'collections' is not defined
--- Attempt 3: Correcting failed code ---
--- Successfully generated and validated code on attempt 3 ---
--- Code execution failed on attempt 1 ---
Error: Adapter JSONAdapter failed to parse the LM response.
LM Response: {
"reasoning": "1. **Initial Observation:** The input grids are 9x9 and the output grids are 3x3. This strongly suggests that the transformation works by dividing the 9x9 input grid into a 3x3 grid of 3x3 subgrids, and each subgrid is mapped to a single pixel in the output grid.\n\n2. **Color Analysis:** The input grids contain colors 0, 1, and 8. The output grids only contain colors 1 and 8. Color 0 appears to be a background color that is ignored in the main calculation.\n\n3. **Formulating a Hypothesis (Majority Vote):** A simple first hypothesis is that the output pixel's color is determined by the majority color (between 1 and 8) within the corresponding 3x3 subgrid. Let's denote the count of color 1 as `count1` and color 8 as `count8`.\n - If `count1 > count8`, output is 1.\n - If `count8 > count1`, output is 8.\n - If `count1 == count8`, a tie-breaking rule is needed.\n Testing this hypothesis across the examples shows it fails frequently. In many cases, the output color is the *minority* color. This indicates the rule is more complex.\n\n4. **Searching for a Pattern:** The rule seems to switch between majority and minority vote. The trigger for this switch must be a property of the subgrid or its position. I explored several properties:\n - The parity of the subgrid's coordinates `(i, j)`.\n - The parity of the total count of non-zero cells (`count1 + count8`).\n - The parity of `count1` or `count8` individually.\n\n5. **Discovering the Core Rule (Parity of `count1`):** After extensive testing, a promising pattern emerged related to the parity of `count1`. The rule `output = 1 if count1 is odd else 8` works for a significant number of cases (5 out of 9 in the first example).\n\n6. **Refining the Rule (Positional Inversion):** The cases where the `count1` parity rule failed in the first example were `(0,0)`, `(1,0)`, `(1,1)`, and `(2,2)`. Notice that three of these, `(0,0)`, `(1,1)`, and `(2,2)`, lie on the main diagonal of the 3x3 grid of subgrids. This suggests a positional modification to the rule.\n - Let's hypothesize: The base rule is `output = 1 if count1 is odd else 8`.\n - However, if the subgrid is on the main diagonal (`i == j`), the rule is inverted: `output = 8 if count1 is odd else 1`.\n\n7. **Final Verification:** Let's test this refined hypothesis on the first training example:\n - `(0,0)`: Diagonal. `count1=1` (odd). Inverted rule -> `8`. Correct.\n - `(0,1)`: Off-diagonal. `count1=3` (odd). Base rule -> `1`. Correct.\n - `(0,2)`: Off-diagonal. `count1=0` (even). Base rule -> `8`. Correct.\n - `(1,0)`: Off-diagonal. `count1=0` (even). Base rule -> `8`. Actual is `1`. This case fails.\n - `(1,1)`: Diagonal. `count1=1` (odd). Inverted rule -> `8`. Correct.\n - `(1,2)`: Off-diagonal. `count1=1` (odd). Base rule -> `1`. Correct.\n - `(2,0)`: Off-diagonal. `count1=1` (odd). Base rule -> `1`. Correct.\n - `(2,1)`: Off-diagonal. `count1=0` (even). Base rule -> `8`. Correct.\n - `(2,2)`: Diagonal. `count1=4` (even). Inverted rule -> `1`. Correct.\n\n This rule works for 8 out of 9 subgrids in the first example. While not perfect, it is the most consistent and simplest rule found after analyzing all examples. The single failure might indicate an additional, more subtle condition, but this rule captures the primary logic. Applying this rule to the other examples shows it performs better than other simple hypotheses. I will proceed with implementing this rule as the most likely solution.\n\n8. **Implementation Plan:**\n - Create an empty 3x3 output grid.\n - Iterate through the 3x3 grid of subgrids using indices `i` (for rows) and `j` (for columns).\n - For each subgrid at `(i, j)`:\n - Extract the 3x3 subgrid from the input.\n - Count the occurrences of the number 1 (`count1`).\n - Check if the subgrid is on the main diagonal (`i == j`).\n - Apply the base rule or the inverted rule based on the diagonal check and the parity of `count1`.\n - Assign the
Expected to find output fields in the LM response: [reasoning, python_code]
Actual output fields parsed from the LM response: [reasoning]
Traceback:
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/adapters/chat_adapter.py", line 38, in __call__
return super().__call__(lm, lm_kwargs, signature, demos, inputs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/adapters/base.py", line 128, in __call__
return self._call_postprocess(processed_signature, signature, outputs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/adapters/base.py", line 89, in _call_postprocess
value = self.parse(processed_signature, text)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/adapters/chat_adapter.py", line 197, in parse
raise AdapterParseError(
dspy.utils.exceptions.AdapterParseError: Adapter ChatAdapter failed to parse the LM response.
LM Response: [[ ## reasoning ## ]]
1. **Analyze Input and Output:** The input grids are 9x9, and the output grids are 3x3. This strongly suggests that the transformation operates on 3x3 sub-grids (or blocks) of the input to produce a single cell in the output. The output grid's cell at `(r, c)` corresponds to the input grid's 3x3 block starting at `(r*3, c*3)`.
2. **Formulate Initial Hypothesis (Majority Color):** A simple first guess is that the output color is the majority color (ignoring the background color 0) within the corresponding 3x3 sub-grid. Let's count the number of blue cells (color 1), `c1`, and red cells (color 8), `c8`, in each sub-grid.
* Hypothesis: If `c1 > c8`, output is 1. If `c8 > c1`, output is 8. If `c1 == c8`, a tie-breaker is needed.
3. **Test the Majority Color Hypothesis:**
* Let's test this on the first training example.
* Sub-grid (0,0): `c1=1, c8=8`. `c8 > c1`, so output should be 8. The actual output is 8. Correct.
* Sub-grid (0,1): `c1=2, c8=1`. `c1 > c8`, so output should be 1. The actual output is 1. Correct.
* Sub-grid (1,0): `c1=0, c8=4`. `c8 > c1`, so output should be 8. The actual output is 1. **Incorrect.**
* This hypothesis is wrong. The counts `c1` and `c8` alone are not sufficient to determine the output color.
4. **Look for a Deeper Pattern (Parity and Position):** Since the content of the sub-grid is not enough, let's consider other properties. The fact that two all-zero sub-grids in example 3 produce different outputs (sub-grid (1,1) -> 1, sub-grid (2,1) -> 8) is a crucial clue. This implies the position of the sub-grid, `(r, c)`, must be part of the rule.
5. **Investigate Parity:** Let's explore rules based on parity (even/odd).
* Let's check the parity of the difference in counts: `(c1 - c8) % 2`.
* Let's check the parity of the sub-grid's position, for which `(r + c) % 2` (a checkerboard pattern) is a common feature in ARC puzzles.
6. **Develop a Combined Hypothesis:** Let's try to combine these parity observations into a single rule.
* **Case 1: `(c1 - c8)` is odd.**
* Let's re-examine the examples where `c1 - c8` is odd.
* Ex1 (0,0): `c1-c8=-7` (odd). `
Expected to find output fields in the LM response: [reasoning, python_code]
Actual output fields parsed from the LM response: [reasoning]
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/adapters/json_adapter.py", line 78, in __call__
return super().__call__(lm, lm_kwargs, signature, demos, inputs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/adapters/chat_adapter.py", line 46, in __call__
raise e
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/adapters/chat_adapter.py", line 38, in __call__
return super().__call__(lm, lm_kwargs, signature, demos, inputs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/adapters/base.py", line 128, in __call__
return self._call_postprocess(processed_signature, signature, outputs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/adapters/base.py", line 89, in _call_postprocess
value = self.parse(processed_signature, text)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/adapters/json_adapter.py", line 172, in parse
raise AdapterParseError(
dspy.utils.exceptions.AdapterParseError: Adapter JSONAdapter failed to parse the LM response.
LM Response: {
"reasoning": "1. **Analyze Input/Output Structure:** The input grids are 9x9, and the output grids are 3x3. This strongly suggests that the transformation operates on 3x3 subgrids of the input to produce a single pixel in the output. The 9x9 input can be seen as a 3x3 grid of these subgrids.\n\n2. **Identify Key Features:** The grids contain three colors: 0 (black), 1 (blue), and 8 (red). The output grids only contain colors 1 and 8. This implies the rule for each subgrid is to decide between outputting a 1 or an 8.\n\n3. **Formulate a Hypothesis (Subgrid to Pixel Rule):** The decision for each output pixel likely depends on the properties of the corresponding 3x3 input subgrid. Let's analyze the properties. A simple property is the count of each non-black color. Let `C1` be the count of color 1 and `C8` be the count of color 8 within a subgrid.\n\n4. **Test Initial Hypotheses:**\n * **Majority Rule:** Does the color with the higher count win? This fails; in several cases, the color with the lower count determines the output.\n * **Parity of Counts:** Let's consider the parity (even or odd) of `C1` and `C8`. Let `p1 = C1 % 2` and `p8 = C8 % 2`. A rule based solely on `p1` and `p8` (e.g., `output = 1 if p1 == p8 else 8`) is inconsistent across different examples and even within the same example. This indicates another factor is involved.\n\n5. **Incorporate Positional Information:** The rule must also depend on the position of the subgrid. Let the subgrid's position be `(r, c)` where `r` and `c` range from 0 to 2. A common positional feature in ARC is the parity of the sum of coordinates, `pc = (r + c) % 2`, which creates a chessboard pattern.\n\n6. **Synthesize the Final Rule:** The most likely rule combines these three parity features: `p1`, `p8`, and `pc`. Through trial and error, a consistent logical expression is sought. Let's test the boolean expression `(p1 XOR p8) == pc`. We can map this boolean result to the output colors. Let's say if the expression is true, the output is 1, and if false, the output is 8.\n\n * Let's verify this rule: `output = 1 if (p1 ^ p8) == pc else 8`.\n * **Example 1, Subgrid (0,0):** `r=0, c=0`. `pc=0`. `C1=1, C8=8`. `p1=1, p8=0`. `p1^p8 = 1`. `1 == 0` is false. Output should be 8. **Correct.**\n * **Example 1, Subgrid (0,1):** `r=0, c=1`. `pc=1`. `C1=3, C8=1`. `p1=1, p8=1`. `p1^p8 = 0`. `0 == 1` is false. Output should be 8. Actual output is 1. **This rule fails.**\n\n7. **Refine the Final Rule:** Let's try another combination: `output = 1 if p1 == (p8 ^ pc) else 8`.\n * **Example 1, Subgrid (0,0):** `r=0, c=0`. `pc=0`. `C1=1, C8=8`. `p1=1, p8=0`. `p8^pc = 0`. `p1 == 0` is false. Output should be 8. **Correct.**\n * **Example 1, Subgrid (0,1):** `r=0, c=1`. `pc=1`. `C1=3, C8=1`. `p1=1, p8=1`. `p8^pc = 0`. `p1 == 0` is false. Output should be 8. Actual output is 1. **This rule also fails.**\n\n8. **Discover the Correct Rule:** After extensive testing of boolean combinations, the correct rule is found to be: **The output color is 8 if the parity of the count of color 8 is equal to the parity of the subgrid's column index `c`. Otherwise, the output is 1.** This can be expressed as `output = 8 if (C8 % 2) == (c % 2) else 1`. Let's verify this.\n * **Example 1, Subgrid (0,0):** `c=0`. `pc_col=0`. `C8=8`. `p8=0`. `p8 == pc_col` is true. Output 8. **Correct.**\n * **Example 1, Subgrid (0,1):** `c=1`. `pc_col=1`. `C8=1`. `p8=1`. `p8 == pc_col` is true. Output 8. Actual is 1. **This rule also fails.**\n\n9. **Final Re-evaluation and Correct Rule Discovery:** The interaction is more subtle. Let's re-examine the simplest cases (all-black subgrids). The output follows a chessboard pattern based on `r+c`. This suggests a base pattern which is then modified. The modification rule seems to be a flip if `p1 != p8`. Let's formalize this: `base_color = 1 if (r+c)%2 == 0 else 8`. `flip = (p1 != p8)`. If `flip` is true, the output is the other color. If `flip` is false, the output is the `base_color`. Let's test this.\n * **Example 1, (0,0):** `r+c=0`(E). Base=1. `p1=1, p8=0`. `p1!=p8` is true (flip). Output is 8. **Correct.**\n * **Example 1, (0,1):** `r+c=1`(O). Base=8. `p1=1, p8=1`. `p1!=p8` is false (no flip). Output is 8. Actual is 1. **Fails.**\n\n10. **The Actual Correct Rule:** The rule is a specific boolean function of the three parities. The output is 1 if `(p1 == p8) == ((r+c)%2 == 0)`. Otherwise, the output is 8. Let's verify:\n * **Example 1, (0,0):** `p1=1, p8=0`. `p1==p8` is F. `r+c=0`. `(r+c)%2==0` is T. `F == T` is false. Output 8. **Correct.**\n * **Example 1, (0,1):** `p1=1, p8=1`. `p1==p8` is T. `r+c=1`. `(r+c)%2==0` is F. `T == F` is false. Output 8. Actual is 1. **Fails.**\n\n11. **Final Attempt at a Simple Rule:** Let's try `output = 1 if (p1 + p8 + c) % 2 == 1 else 8`.\n * **Example 1, (0,0):** `p1=1, p8=0, c=0`. `(1+0+0)%2=1`. Output 1. Actual 8. **Fails.**\n * Let's flip it: `output = 8 if (p1 + p8 + c) % 2 == 1 else 1`.\n * **Example 1, (0,0):** `(1+0+0)%2=1`. Output 8. **Correct.**\n * **Example 1, (0,1):** `p1=1, p8=1, c=1`. `(1+1+1)%2=1`. Output 8. Actual 1. **Fails.**\n\n12. **The Correct Rule (Re-discovered):** The output color is 1 if `(C1 % 2) == (r % 2)`. Otherwise, the output is 8. Let's test this simple rule.\n * **Example 1, (0,0):** `r=0`. `pr=0`. `C1=1`. `p1=1`. `1==0` is F. Output 8. **Correct.**\n * **Example 1, (0,1):** `r=0`. `pr=0`. `C1=3`. `p1=1`. `1==0` is F. Output 8. Actual 1. **Fails.**\n\nLet's try `output = 1 if (C8 % 2) == (r % 2) else 8`.\n * **Example 1, (0,0):** `r=0`. `pr=0`. `C8=8`. `p8=0`. `0==0` is T. Output 1. Actual 8. **Fails.**\n\nAfter exhausting simple rules, a more complex but correct one is found: The output is 1 if `(C1 % 2) XOR ((r+c) % 2) == 0`. Otherwise, the output is 8. This is equivalent to `output = 1 if p1 == pc else 8`.\n * **Example 1, (0,0):** `p1=1, pc=0`. `1==0` is F. Output 8. **Correct.**\n * **Example 1, (0,1):** `p1=1, pc=1`. `1==1` is T. Output 1. **Correct.**\n * **Example 1, (0,2):** `p1=0, pc=0`. `0==0` is T. Output 1. Actual 8. **Fails.**\n\nLet's try `output = 1 if (C8 % 2) == ((r+c) % 2) else 8`.\n * **Example 1, (0,0):** `p8=0, pc=0`. `0==0` is T. Output 1. Actual 8. **Fails.**\n\nLet's try `output = 1 if (C1%2 + C8%2)%2 == ((r+c)%2) else 8`.\n * **Example 1, (0,0):** `p1^p8=1, pc=0`. `1==0` is F. Output 8. **Correct.**\n * **Example 1, (0,1):** `p1^p8=0, pc=1`. `0==1` is F. Output 8. Actual 1. **Fails.**\n\nLet's try `output = 1 if (C1%2 + C8%2)%2 != ((r+c)%2) else 8`.\n * **Example 1, (0,0):** `p1^p8=1, pc=0`. `1!=0` is T. Output 1. Actual 8. **Fails.**\n\nLet's try `output = 1 if (C1%2) != ((r+c)%2) else 8`.\n * **Example 1, (0,0):** `p1=1, pc=0`. `1!=0` is T. Output 1. Actual 8. **Fails.**\n\nLet's try `output = 1 if (C8%2) != ((r+c)%2) else 8`.\n * **Example 1, (0,0):** `p8=0, pc=0`. `0!=0` is F. Output 8. **Correct.**\n * **Example 1, (0,1):** `p8=1, pc=1`. `1!=1` is F. Output 8. Actual 1. **Fails.**\n\nLet's try `output = 1 if (C1%2 + C8%2 + r + c)%2 == 0 else 8`.\n * **Example 1, (0,0):** `1+0+0+0=1`. `1%2=1`. Output 8. **Correct.**\n * **Example 1, (0,1):** `1+1+0+1=3`. `3%2=1`. Output 8. Actual 1. **Fails.**\n\nLet's try `output = 1 if (C1%2 + C8%2 + r + c)%2 == 1 else 8`.\n * **Example 1, (0,0):** `1+0+0+0=1`. `1%2=1`. Output 1. Actual 8. **Fails.**\n\nLet's try `output = 1 if (C1%2 + r)%2 == (C8%2 + c)%2 else 8`.\n * **Example 1, (0,0):** `(1+0)%2=1`. `(0+0)%2=0`. `1==0` is F. Output 8. **Correct.**\n * **Example 1, (0,1):** `(1+0)%2=1`. `(1+1)%2=0`. `1==0` is F. Output 8. Actual 1. **Fails.**\n\nLet's try `output = 1 if (C1%2 + c)%2 == (C8%2 + r)%2 else 8`.\n * **Example 1, (0,0):** `(1+0)%2=1`. `(0+0)%2=0`. `1==0` is F. Output 8. **Correct.**\n * **Example 1, (0,1):** `(1+1)%2=0`. `(1+0)%2=1`. `0==1` is F. Output 8. Actual 1. **Fails.**\n\nLet's try `output = 1 if (C1%2 + C8%2)%2 == (r%2) else 8`.\n * **Example 1, (0,0):** `(1+0)%2=1`. `0%2=0`. `1==0` is F. Output 8. **Correct.**\n * **Example 1, (0,1):** `(1+1)%2=0`. `0%2=0`. `0==0` is T. Output 1. **Correct.**\n * **Example 1, (0,2):** `(0+0)%2=0`. `0%2=0`. `0==0` is T. Output 1. Actual 8. **Fails.**\n\nLet's try `output = 1 if (C1%2 + C8%2)%2 == (c%2) else 8`.\n * **Example 1, (0,0):** `(1+0)%2=1`. `0%2=0`. `1==0` is F. Output 8. **Correct.**\n * **Example 1, (0,1):** `(1+1)%2=0`. `1%2=1`. `0==1` is F. Output 8. Actual 1. **Fails.**\n\nLet's try `output = 1 if (C1%2) == (C8%2) else 8`.\n * **Example 1, (0,0):** `1==0` is F. Output 8. **Correct.**\n * **Example 1, (0,1):** `1==1` is T. Output 1. **Correct.**\n * **Example 1, (0,2):** `0==0` is T. Output 1. Actual 8. **Fails.**\n\nLet's try `output = 1 if (C1%2) != (C8%2) else 8`.\n * **Example 1, (0,0):** `1!=0` is T. Output 1. Actual 8. **Fails.**\n\nLet's try `output = 1 if (C1%2 + C8%2 + r)%2 == 0 else 8`.\n * **Example 1, (0,0):** `1+0+0=1`. `1%2=1`. Output 8. **Correct.**\n * **Example 1, (0,1):** `1+1+0=2`. `2%2=0`. Output 1. **Correct.**\n * **Example 1, (0,2):** `0+0+0=0`. `0%2=0`. Output 1. Actual 8. **Fails.**\n\nLet's try `output = 1 if (C1%2 + C8%2 + c)%2 == 0 else 8`.\n * **Example 1, (0,0):** `1+0+0=1`. `1%2=1`. Output 8. **Correct.**\
Expected to find output fields in the LM response: [reasoning, python_code]
Actual output fields parsed from the LM response: [reasoning]
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "<string>", line 82, in forward
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/predict/chain_of_thought.py", line 37, in forward
return self.predict(**kwargs)
^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/predict/predict.py", line 86, in __call__
return super().__call__(**kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/primitives/module.py", line 78, in __call__
return self.forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/predict/predict.py", line 175, in forward
completions = adapter(lm, lm_kwargs=config, signature=signature, demos=demos, inputs=kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/adapters/chat_adapter.py", line 47, in __call__
return JSONAdapter()(lm, lm_kwargs, signature, demos, inputs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/adapters/json_adapter.py", line 82, in __call__
return super().__call__(lm, lm_kwargs, signature, demos, inputs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/adapters/chat_adapter.py", line 46, in __call__
raise e
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/adapters/chat_adapter.py", line 38, in __call__
return super().__call__(lm, lm_kwargs, signature, demos, inputs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/adapters/base.py", line 128, in __call__
return self._call_postprocess(processed_signature, signature, outputs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/adapters/base.py", line 89, in _call_postprocess
value = self.parse(processed_signature, text)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/utils/callback.py", line 326, in sync_wrapper
return fn(instance, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/lakshyaaagrawal/.cache/uv/archive-v0/dkVgzzPSvERsvAw20pkDb/lib/python3.11/site-packages/dspy/adapters/json_adapter.py", line 172, in parse
raise AdapterParseError(
dspy.utils.exceptions.AdapterParseError: Adapter JSONAdapter failed to parse the LM response.
LM Response: {
"reasoning": "1. **Initial Observation:** The input grids are 9x9 and the output grids are 3x3. This strongly suggests that the transformation works by dividing the 9x9 input grid into a 3x3 grid of 3x3 subgrids, and each subgrid is mapped to a single pixel in the output grid.\n\n2. **Color Analysis:** The input grids contain colors 0, 1, and 8. The output grids only contain colors 1 and 8. Color 0 appears to be a background color that is ignored in the main calculation.\n\n3. **Formulating a Hypothesis (Majority Vote):** A simple first hypothesis is that the output pixel's color is determined by the majority color (between 1 and 8) within the corresponding 3x3 subgrid. Let's denote the count of color 1 as `count1` and color 8 as `count8`.\n - If `count1 > count8`, output is 1.\n - If `count8 > count1`, output is 8.\n - If `count1 == count8`, a tie-breaking rule is needed.\n Testing this hypothesis across the examples shows it fails frequently. In many cases, the output color is the *minority* color. This indicates the rule is more complex.\n\n4. **Searching for a Pattern:** The rule seems to switch between majority and minority vote. The trigger for this switch must be a property of the subgrid or its position. I explored several properties:\n - The parity of the subgrid's coordinates `(i, j)`.\n - The parity of the total count of non-zero cells (`count1 + count8`).\n - The parity of `count1` or `count8` individually.\n\n5. **Discovering the Core Rule (Parity of `count1`):** After extensive testing, a promising pattern emerged related to the parity of `count1`. The rule `output = 1 if count1 is odd else 8` works for a significant number of cases (5 out of 9 in the first example).\n\n6. **Refining the Rule (Positional Inversion):** The cases where the `count1` parity rule failed in the first example were `(0,0)`, `(1,0)`, `(1,1)`, and `(2,2)`. Notice that three of these, `(0,0)`, `(1,1)`, and `(2,2)`, lie on the main diagonal of the 3x3 grid of subgrids. This suggests a positional modification to the rule.\n - Let's hypothesize: The base rule is `output = 1 if count1 is odd else 8`.\n - However, if the subgrid is on the main diagonal (`i == j`), the rule is inverted: `output = 8 if count1 is odd else 1`.\n\n7. **Final Verification:** Let's test this refined hypothesis on the first training example:\n - `(0,0)`: Diagonal. `count1=1` (odd). Inverted rule -> `8`. Correct.\n - `(0,1)`: Off-diagonal. `count1=3` (odd). Base rule -> `1`. Correct.\n - `(0,2)`: Off-diagonal. `count1=0` (even). Base rule -> `8`. Correct.\n - `(1,0)`: Off-diagonal. `count1=0` (even). Base rule -> `8`. Actual is `1`. This case fails.\n - `(1,1)`: Diagonal. `count1=1` (odd). Inverted rule -> `8`. Correct.\n - `(1,2)`: Off-diagonal. `count1=1` (odd). Base rule -> `1`. Correct.\n - `(2,0)`: Off-diagonal. `count1=1` (odd). Base rule -> `1`. Correct.\n - `(2,1)`: Off-diagonal. `count1=0` (even). Base rule -> `8`. Correct.\n - `(2,2)`: Diagonal. `count1=4` (even). Inverted rule -> `1`. Correct.\n\n This rule works for 8 out of 9 subgrids in the first example. While not perfect, it is the most consistent and simplest rule found after analyzing all examples. The single failure might indicate an additional, more subtle condition, but this rule captures the primary logic. Applying this rule to the other examples shows it performs better than other simple hypotheses. I will proceed with implementing this rule as the most likely solution.\n\n8. **Implementation Plan:**\n - Create an empty 3x3 output grid.\n - Iterate through the 3x3 grid of subgrids using indices `i` (for rows) and `j` (for columns).\n - For each subgrid at `(i, j)`:\n - Extract the 3x3 subgrid from the input.\n - Count the occurrences of the number 1 (`count1`).\n - Check if the subgrid is on the main diagonal (`i == j`).\n - Apply the base rule or the inverted rule based on the diagonal check and the parity of `count1`.\n - Assign the
Expected to find output fields in the LM response: [reasoning, python_code]
Actual output fields parsed from the LM response: [reasoning]
--- Attempt 2: Correcting failed code ---
--- Code execution failed on attempt 2 ---
Error: argument of type 'NoneType' is not iterable
Traceback:
Traceback (most recent call last):
File "<string>", line 99, in forward
TypeError: argument of type 'NoneType' is not iterable
--- Attempt 3: Correcting failed code ---
--- Code execution failed on attempt 3 ---
Error: name 'collections' is not defined
Traceback:
Traceback (most recent call last):
File "<string>", line 111, in forward
File "<string>", line 17, in transform_grid
NameError: name 'collections' is not defined
--- All code generation attempts failed. Using fallback. ---
2025/08/30 01:33:06 INFO dspy.evaluate.evaluate: Average Metric: 131.0 / 200 (65.5%)
GEPA Optimization: 94%|█████████████████████████████████████████████████████████████████████████████████████████████████████████▎ | 3762/4000 [27:59:09<27:35, 6.96s/rollouts]--- Successfully generated and validated code on attempt 3 ---
Iteration 145: Full valset score for new program: 0.655
Iteration 145: Full train_val score for new program: 0.655
Iteration 145: Individual valset scores for new program: [False, True, False, True, True, True, True, True, True, True, True, True, True, False, True, True, False, True, True, True, True, True, True, False, True, False, False, True, True, True, False, False, False, True, True, True, False, True, False, True, False, True, True, False, False, True, False, False, True, True, True, True, True, True, True, False, True, True, False, True, False, False, False, True, True, False, False, True, False, False, True, True, True, True, True, False, True, True, True, True, False, True, True, True, True, True, True, True, True, False, True, True, True, True, True, True, False, False, True, True, True, True, True, False, True, False, True, False, False, True, False, False, True, True, True, True, True, True, True, False, True, False, True, False, False, True, False, True, True, True, False, False, True, False, True, False, False, False, True, True, True, True, True, False, True, False, True, False, True, True, True, False, True, True, True, False, True, True, False, False, False, True, True, False, False, True, True, True, True, False, False, True, True, False, True, True, False, True, False, True, False, True, True, True, False, True, True, True, True, False, True, True, True, True, True, False, True, False, False, True]
Iteration 145: New valset pareto front scores: [True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, False, True, True, True, True, True, True, True, True, True, False, True, True, True, True, False, True, True, True, True, False, True, True, True, False, True, True, True, False, True, False, True, True, True, True, True, True, True, True, 0, True, True, False, True, True, True, False, True, True, False, True, True, 0, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, False, True, True, True, True, True, True, True, True, True, True, True, True, True, False, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, False, False, True, False, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, False, True, True, True, False, True, True, True, True, True, True, True, True, True, True, True, False, True, True, True, True, False, True, True, True, True, False, True, True, True, True, True, True, False, True, False, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, False, True]
Iteration 145: Full valset pareto front score: 0.875
Iteration 145: Updated valset pareto front programs: [{1, 3, 5, 8, 9, 10, 11, 12}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 13, 14}, {0, 9, 12}, {0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14}, {0, 1, 2, 3, 4, 5, 6, 7, 9, 10, 11, 12, 13, 14}, {0, 4, 14}, {0, 1, 2, 3, 4, 5, 6, 7, 9, 10, 11, 12, 13, 14}, {0, 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14}, {0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14}, {8}, {0, 1, 2, 3, 4, 6, 9, 10, 11, 12, 13, 14}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14}, {1, 4, 5, 7, 9, 10, 12, 13, 14}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14}, {0, 2, 4, 6, 7, 10, 11, 12, 14}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14}, {0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14}, {1, 5, 7, 9, 12, 13}, {0, 3, 4, 6, 8, 9, 10, 11, 12, 13, 14}, {0, 1, 3, 6, 7, 8, 9, 10, 11, 12, 13}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14}, {0, 4, 14, 7}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14}, {1}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14}, {2}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14}, {0, 1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14}, {2, 12, 5}, {0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 14}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14}, {1}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14}, {0, 1, 2, 3, 4, 6, 8, 9, 10, 11, 12, 13, 14}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14}, {8}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14}, {0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14}, {0, 1, 2, 3, 4, 6, 7, 11, 13, 14}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14}, {0, 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14}, {0, 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 13, 14}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14}, {0, 1, 2, 3, 8, 12, 13}, {0, 1, 5}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14}, {4, 6, 7, 8, 11, 14}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14}, {1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12}, {0, 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14}, {11}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14}, {2, 3, 4, 5, 6, 7, 8, 9, 11, 14}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14}, {1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13}, {0, 3, 4, 5, 6, 7, 9, 10, 11, 12, 13, 14}, {4, 14}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14}, {0, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 14}, {0, 2, 6, 8, 11, 12, 13}, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14}, {0, 1, 2, 4, 5, 6, 7, 8, 10, 11, 12, 13, 14}, {0, 1, 2, 3, 4, 5, 6, 7, 13, 14}, {0, 3, 4, 6, 7, 11, 12, 13, 14}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14}, {0, 1, 2, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14}, {1, 3, 8, 10, 12, 13}, {7}, {0, 3, 4, 6, 7, 9, 10, 11, 12, 13, 14}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14}, {0, 1, 2, 3, 4, 5, 6, 8, 9, 10, 11, 12, 13, 14}, {0, 4, 6, 7, 9, 11, 12, 14}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14}, {0, 1, 2, 3, 5, 7, 8, 11, 12}, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14}, {0, 1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13}, {1, 2, 10}, {0, 1, 4, 5, 6, 8, 10, 12, 13, 14}, {13}, {0, 2, 13, 5}, {1, 3, 10, 12, 13, 14}, {2, 4, 9, 10, 11, 13, 14}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14}, {3, 4, 5, 7, 8, 14}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14}, {2, 3, 4, 6, 7, 8, 9, 10, 11, 14}, {0, 1, 2, 3, 4, 5, 6, 8, 10, 11, 14}, {0, 1, 2, 3, 5, 6, 7, 9, 10, 11, 12, 13}, {2, 3, 4, 5, 6, 7, 9, 11, 12, 14}, {0, 1, 7, 9, 10, 13}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14}, {0, 1, 2, 3, 4, 5, 6, 7, 9, 10, 11, 12, 13, 14}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14}, {0, 1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14}, {0, 1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14}, {2, 7}, {0, 1, 3, 9, 12, 13}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14}, {0, 1, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13}, {0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14}, {8, 9, 7}, {0, 1, 3, 6, 7, 8, 9, 11, 12, 13}, {3, 5, 6, 7, 11, 13}, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14}, {1, 2, 3, 4, 5, 6, 7, 9, 10, 11, 12, 13, 14}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14}, {0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 13, 14}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14}, {9}, {0, 2, 3, 5, 6, 7, 9, 10, 11, 14}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14}, {4, 14}, {8, 9, 7}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14}, {0, 1, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14}, {0, 1, 3, 4, 5, 6, 7, 8, 10, 11, 12, 14}, {0, 1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13}, {0, 3, 5, 6, 8, 9, 10, 11, 12, 13, 14}, {0, 1, 2, 3, 4, 5, 6, 7, 9, 10, 11, 12, 13, 14}, {0, 1, 2, 3, 6, 7, 9, 10, 11, 13}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14}, {1, 12}, {0, 1, 2, 3, 4, 5, 6, 8, 9, 10, 11, 12, 13, 14}, {0, 4, 5, 6, 7, 8, 9, 10, 12, 13, 14}, {0, 3, 5, 10, 11, 13}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14}, {0, 1, 2, 4, 5, 6, 7, 9, 10, 12, 13, 14}, {0, 1, 2, 4, 5, 6, 7, 9, 10, 13, 14}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14}, {0, 7, 8, 9, 12, 13}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14}, {6, 9, 11, 12, 13, 14}, {0, 1, 3, 5, 6, 7, 8, 9, 10, 11}, {0, 1, 2, 3, 4, 5, 6, 8, 9, 11, 13, 14}, {1, 2, 3, 5, 6, 8, 9, 10, 11, 12, 14}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14}, {0, 1, 3, 4, 5, 6, 7, 8, 10, 11, 12, 13, 14}, {0, 2, 7, 8, 9, 10, 12, 13}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14}, {14}, {9, 11, 12}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14}, {0, 1, 2, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14}, {0, 5, 6, 7, 8, 9, 10, 11, 13}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14}, {0, 1, 2, 3, 4, 5, 6, 7, 10, 11, 13, 14}, {1, 2, 4, 5, 6, 7, 8, 10, 11, 12, 13, 14}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 13, 14}, {11, 6}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14}, {0, 10}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14}, {0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14}]
Iteration 145: Best valset aggregate score so far: 0.675
Iteration 145: Best program as per aggregate score on train_val: 6
Iteration 145: Best program as per aggregate score on valset: 6
Iteration 145: Best score on valset: 0.675
Iteration 145: Best score on train_val: 0.675
Iteration 145: Linear pareto front program index: 6
Iteration 145: New program candidate index: 14
Iteration 146: Selected program 1 score: 0.63
Average Metric: 2.00 / 2 (100.0%): 33%|█████████████████████████████████████ | 1/3 [00:00<00:00, 12.26it/s]--- Successfully generated and validated code on attempt 3 ---
--- Code execution failed on attempt 3 ---
Error: name 'np' is not defined
Traceback:
Traceback (most recent call last):
File "<string>", line 111, in forward
File "<string>", line 9, in transform_grid
NameError: name 'np' is not defined
--- All code generation attempts failed. Using fallback. ---
Average Metric: 2.00 / 3 (66.7%): 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [02:08<00:00, 42.91s/it]2025/08/30 01:35:15 INFO dspy.evaluate.evaluate: Average Metric: 2.0 / 3 (66.7%)
GEPA Optimization: 94%|█████████████████████████████████████████████████████████████████████████████████████████████████████████▌ | 3768/4000 [28:02:11<29:51, 7.72s/rollouts]Iteration 146: Proposed new text for program: import dspy
from typing import List
import pydantic
import traceback
import numpy as np
# Define the type for a matrix for clarity
MATRIX = List[List[int]]
class TrainingExample(pydantic.BaseModel):
"""A Pydantic model for a single training example, containing an input and output matrix."""
input: MATRIX
output: MATRIX
class InferCodeSignature(dspy.Signature):
"""
Analyzes pairs of input/output matrices to deduce the transformation rule and write a Python function to implement it.
You are an expert programmer and analyst of abstract visual reasoning puzzles. Your task is to carefully examine the provided training examples. Each example consists of an 'input' matrix and a corresponding 'output' matrix.
Your goal is to write a single, self-contained Python function named `transform_matrix` that implements the transformation rule. This function will take one argument, `matrix` (a list of lists of integers), and must return the transformed matrix (a list of lists of integers).
**Function Requirements:**
1. The function must be named exactly `transform_matrix`.
2. It must accept one argument: `matrix: list[list[int]]`.
3. It must return a `list[list[int]]`.
4. The function must be entirely self-contained. Do not call any functions defined outside its scope.
5. You may use standard Python libraries. The `numpy` library is available and recommended for complex array manipulations (use `import numpy as np`).
6. Your output should be ONLY the Python code for the function. Do not include any other text or explanation.
**Example of a valid output for a vertical reflection task:**
```python
import numpy as np
def transform_matrix(matrix: list[list[int]]) -> list[list[int]]:
"""
Vertically reflects the input matrix and appends it to the original.
"""
arr = np.array(matrix, dtype=int)
flipped_arr = np.flipud(arr)
result_arr = np.concatenate((arr, flipped_arr), axis=0)
return result_arr.tolist()
```
"""
training_examples: List[TrainingExample] = dspy.InputField(description="A list of input and output examples demonstrating the task.")
python_code: str = dspy.OutputField(description="A self-contained Python function `transform_matrix` that implements the transformation.")
class ARCProgram(dspy.Module):
"""A program that first infers a Python function for the rule and then executes it to solve test inputs."""
def __init__(self):
super().__init__()
# Use ChainOfThought for the complex reasoning task of code inference.
self.code_inferrer = dspy.ChainOfThought(InferCodeSignature)
def _execute_generated_code(self, code: str, test_matrix: MATRIX) -> MATRIX:
"""Safely executes the generated Python code."""
# Prepare a dedicated namespace for the execution to capture the defined function.
# We provide 'numpy' as 'np' in the execution scope.
local_namespace = {}
global_scope = {'np': np}
# The code from the LM might be wrapped in markdown, so we clean it.
if code.strip().startswith("```python"):
code = code.strip()[9:]
if code.strip().endswith("```"):
code = code.strip()[:-3]
code = code.strip()
# Execute the code to define the function in our namespace
exec(code, global_scope, local_namespace)
transform_func = local_namespace.get('transform_matrix')
if not callable(transform_func):
raise ValueError("Generated code did not define a callable function named 'transform_matrix'.")
# Call the function with the test matrix
return transform_func(test_matrix)
def forward(self, training_examples: List[TrainingExample], test_inputs: List[MATRIX]) -> dspy.Prediction:
"""
Infers a transformation function from training examples and applies it to each test input.
Args:
training_examples: A list of TrainingExample objects.
test_inputs: A list of input matrices to be solved.
Returns:
A dspy.Prediction object with the 'test_outputs' field populated.
"""
# 1. Infer the transformation function once based on all training examples.
inferred = self.code_inferrer(training_examples=training_examples)
generated_code = inferred.python_code
all_test_outputs = []
# 2. Iterate through each test input and apply the inferred function.
for test_matrix in test_inputs:
try:
# Execute the generated code for the current test matrix.
result_matrix = self._execute_generated_code(generated_code, test_matrix)
all_test_outputs.append(result_matrix)
except Exception:
# Fallback strategy: if code execution fails, append a default matrix.
# This could be due to syntax errors, runtime errors, or missing function.
if test_matrix and len(test_matrix) > 0 and len(test_matrix[0]) > 0:
all_test_outputs.append([([0] * len(test_matrix[0])) for _ in range(len(test_matrix))])
else:
all_test_outputs.append([])
# 3. Return the collected outputs in a single Prediction object.
return dspy.Prediction(test_outputs=all_test_outputs)
# The final 'program' object is an instance of our robust, code-generating module.
program = ARCProgram()
Iteration 146: New subsample score is not better, skipping
Iteration 147: Selected program 11 score: 0.665
Average Metric: 3.00 / 3 (100.0%): 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [06:57<00:00, 139.20s/it]2025/08/30 01:43:05 INFO dspy.evaluate.evaluate: Average Metric: 3.0 / 3 (100.0%)
GEPA Optimization: 94%|█████████████████████████████████████████████████████████████████████████████████████████████████████████▌ | 3771/4000 [28:09:09<40:58, 10.74s/rollouts]
Iteration 147: All subsample scores perfect. Skipping.
Iteration 147: Reflective mutation did not propose a new candidate
Iteration 148: Selected program 1 score: 0.63
Average Metric: 1.00 / 3 (33.3%): 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [01:29<00:00, 29.86s/it]2025/08/30 01:44:35 INFO dspy.evaluate.evaluate: Average Metric: 1.0 / 3 (33.3%)
Iteration 148: Proposed new text for program: import dspy
from typing import List
import pydantic
import re
import copy
# Define the type for a matrix for clarity
MATRIX = List[List[int]]
class TrainingExample(pydantic.BaseModel):
"""A Pydantic model for a single training example, containing an input and output matrix."""
input: MATRIX
output: MATRIX
class InferRuleSignature(dspy.Signature):
"""
Analyzes pairs of input/output matrices to deduce the transformation rule.
You are an expert analyst of abstract visual reasoning puzzles. Your task is to carefully examine the provided training examples. Each example consists of an 'input' matrix and a corresponding 'output' matrix.
Your goal is to deduce the single, consistent transformation rule that converts each input to its output. Describe this rule in clear, unambiguous, step-by-step natural language. The description must be precise enough for another AI to follow it to generate a correct Python function to solve a new, unseen test input.
Consider various possible transformation patterns, including but not limited to:
- Geometric operations: rotation, reflection, scaling, repetition, shifting.
- Color/value transformations: changing specific numbers to others, conditional changes.
- Object-based logic: identifying shapes or objects and applying rules based on their properties (size, color, position).
- Fill/completion patterns: such as flood fills or completing a grid based on object boundaries.
- Bounding box or frame logic: creating structures based on the extents of objects.
"""
training_examples: List[TrainingExample] = dspy.InputField(description="A list of input and output examples demonstrating the task.")
transformation_rule: str = dspy.OutputField(description="A step-by-step natural language description of the transformation rule.")
class GenerateCodeSignature(dspy.Signature):
"""
Given a natural language transformation rule and training examples, write a Python function to implement it.
You are an expert programmer specializing in matrix transformations. Your task is to write a single, self-contained Python function named `transform_matrix`.
This function must:
1. Accept one argument: `matrix`, which is a list of lists of integers.
2. Implement the logic described in the provided `transformation_rule`.
3. Return the transformed matrix as a list of lists of integers.
4. Contain all necessary logic inside it. Do not use any external libraries besides standard Python ones like `copy`.
5. Handle edge cases gracefully. The input matrix might be empty or have rows of varying lengths.
**Crucially, your output must be ONLY the Python code, enclosed in a single markdown code block.**
Example format:
```python
import copy
def transform_matrix(matrix):
# Your implementation here.
# It's often a good idea to work on a deep copy to avoid modifying the original.
new_matrix = copy.deepcopy(matrix)
# ... implementation ...
return new_matrix
```
"""
transformation_rule: str = dspy.InputField(description="The natural language rule to implement.")
training_examples: List[TrainingExample] = dspy.InputField(description="Examples to understand the context and expected behavior of the rule.")
python_code: str = dspy.OutputField(description="A string containing the complete Python function `transform_matrix` enclosed in a markdown block.")
class ARCProgram(dspy.Module):
"""A program that infers a rule, generates code to implement it, and then executes the code."""
def __init__(self):
super().__init__()
self.rule_inferrer = dspy.ChainOfThought(InferRuleSignature)
self.code_generator = dspy.Predict(GenerateCodeSignature)
def _get_fallback_matrix(self, matrix: MATRIX) -> MATRIX:
"""Provides a default zero-filled matrix of the same dimensions."""
if not matrix or not matrix[0]:
return []
return [[0] * len(matrix[0]) for _ in range(len(matrix))]
def forward(self, training_examples: List[TrainingExample], test_inputs: List[MATRIX]) -> dspy.Prediction:
"""
Infers a rule, generates a Python function, and applies it to each test input.
"""
# 1. Infer the transformation rule from the training examples.
inferred = self.rule_inferrer(training_examples=training_examples)
rule = inferred.transformation_rule
# 2. Generate a Python function based on the inferred rule.
generated = self.code_generator(transformation_rule=rule, training_examples=training_examples)
code_str = generated.python_code
transform_func = None
try:
# 3. Extract the Python code from the markdown block.
match = re.search(r"```python\n(.*?)\n```", code_str, re.DOTALL)
if not match:
# If no markdown block is found, try to execute the whole string,
# assuming it might be raw code.
code_to_exec = code_str
else:
code_to_exec = match.group(1)
# 4. Execute the code in a restricted global scope to define the function.
exec_globals = {'copy': copy}
exec(code_to_exec, exec_globals)
transform_func = exec_globals.get('transform_matrix')
if not callable(transform_func):
# Handle cases where the function wasn't defined correctly.
transform_func = None
except Exception as e:
print(f"Failed to parse or execute generated code: {e}")
transform_func = None
# 5. Apply the generated function to each test input.
all_test_outputs = []
for test_matrix in test_inputs:
if transform_func:
try:
# Create a deep copy to prevent the function from modifying the input list.
matrix_copy = copy.deepcopy(test_matrix)
output_matrix = transform_func(matrix_copy)
all_test_outputs.append(output_matrix)
except Exception as e:
# If the function fails on a specific test case, use the fallback.
print(f"Generated function failed during execution on a test case: {e}")
all_test_outputs.append(self._get_fallback_matrix(test_matrix))
else:
# If the function was never created, use the fallback for all test cases.
all_test_outputs.append(self._get_fallback_matrix(test_matrix))
return dspy.Prediction(test_outputs=all_test_outputs)
# The final 'program' object is an instance of our robust, code-generating module.
program = ARCProgram()
2025/08/30 01:52:39 INFO dspy.evaluate.evaluate: Average Metric: 2.0 / 3 (66.7%)
2025/08/30 01:56:36 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/30 01:56:49 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/30 01:57:04 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
Generated function failed during execution on a test case: name 'min_rr' is not defined
2025/08/30 02:01:59 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
Failed to parse or execute generated code: expected string or bytes-like object, got 'NoneType'
Generated function failed during execution on a test case: list index out of range
Generated function failed during execution on a test case: list index out of range
2025/08/30 02:09:49 INFO dspy.evaluate.evaluate: Average Metric: 128.0 / 200 (64.0%)
GEPA Optimization: 99%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████▎| 3977/4000 [28:35:52<03:19, 8.69s/rollouts]Iteration 148: Full valset score for new program: 0.64
Iteration 148: Full train_val score for new program: 0.64
Iteration 148: Individual valset scores for new program: [False, True, False, True, True, False, True, True, True, True, True, True, True, False, False, True, False, True, True, False, True, True, True, False, True, True, False, True, True, True, False, False, False, True, True, True, False, True, False, True, False, True, True, False, False, True, False, False, True, True, True, True, True, True, True, False, True, True, False, True, False, False, False, True, False, False, True, True, False, False, True, True, True, True, True, True, False, False, True, True, True, True, True, True, True, True, False, False, True, False, True, True, True, True, True, True, True, False, True, True, True, True, False, False, True, False, True, True, False, True, False, False, False, False, True, True, True, False, True, True, True, False, True, False, False, True, False, True, True, True, False, False, True, True, False, False, True, False, True, True, True, True, True, False, False, False, True, False, True, True, False, False, True, True, True, True, True, True, True, False, True, True, False, False, False, True, True, True, True, False, True, True, False, True, True, True, False, True, False, True, True, True, True, False, False, True, True, True, True, False, True, True, True, True, True, False, True, False, False, True]
Iteration 148: New valset pareto front scores: [True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, False, True, True, True, True, True, True, True, True, True, False, True, True, True, True, False, True, True, True, True, False, True, True, True, False, True, True, True, False, True, False, True, True, True, True, True, True, True, True, 0, True, True, False, True, True, True, False, True, True, False, True, True, 0, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, False, True, True, True, True, True, True, True, True, True, True, True, True, True, False, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, False, False, True, False, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, False, True, True, True, False, True, True, True, True, True, True, True, True, True, True, True, False, True, True, True, True, False, True, True, True, True, False, True, True, True, True, True, True, False, True, False, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, False, True]
Iteration 148: Full valset pareto front score: 0.875
Iteration 148: Updated valset pareto front programs: [{1, 3, 5, 8, 9, 10, 11, 12}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 13, 14, 15}, {0, 9, 12}, {0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15}, {0, 1, 2, 3, 4, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15}, {0, 4, 14}, {0, 1, 2, 3, 4, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15}, {0, 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15}, {0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15}, {8}, {0, 1, 2, 3, 4, 6, 9, 10, 11, 12, 13, 14}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15}, {1, 4, 5, 7, 9, 10, 12, 13, 14, 15}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15}, {0, 2, 4, 6, 7, 10, 11, 12, 14}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15}, {0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15}, {1, 5, 7, 9, 12, 13}, {0, 3, 4, 6, 8, 9, 10, 11, 12, 13, 14, 15}, {0, 1, 3, 6, 7, 8, 9, 10, 11, 12, 13, 15}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15}, {0, 4, 7, 14, 15}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15}, {1}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15}, {2}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15}, {0, 1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15}, {2, 12, 5}, {0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 14, 15}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15}, {1}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15}, {0, 1, 2, 3, 4, 6, 8, 9, 10, 11, 12, 13, 14, 15}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15}, {8}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15}, {0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15}, {0, 1, 2, 3, 4, 6, 7, 11, 13, 14, 15}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15}, {0, 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15}, {0, 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 13, 14, 15}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15}, {0, 1, 2, 3, 8, 12, 13}, {0, 1, 5}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15}, {4, 6, 7, 8, 11, 14}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15}, {1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 15}, {0, 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15}, {11}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15}, {2, 3, 4, 5, 6, 7, 8, 9, 11, 14, 15}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15}, {1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 15}, {0, 3, 4, 5, 6, 7, 9, 10, 11, 12, 13, 14}, {4, 14}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15}, {0, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15}, {0, 2, 6, 8, 11, 12, 13, 15}, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15}, {0, 1, 2, 4, 5, 6, 7, 8, 10, 11, 12, 13, 14}, {0, 1, 2, 3, 4, 5, 6, 7, 13, 14}, {0, 3, 4, 6, 7, 11, 12, 13, 14, 15}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15}, {0, 1, 2, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15}, {1, 3, 8, 10, 12, 13, 15}, {7}, {0, 3, 4, 6, 7, 9, 10, 11, 12, 13, 14, 15}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15}, {0, 1, 2, 3, 4, 5, 6, 8, 9, 10, 11, 12, 13, 14, 15}, {0, 4, 6, 7, 9, 11, 12, 14}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15}, {0, 1, 2, 3, 5, 7, 8, 11, 12}, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15}, {0, 1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 15}, {1, 2, 10}, {0, 1, 4, 5, 6, 8, 10, 12, 13, 14, 15}, {13}, {0, 2, 13, 5}, {1, 3, 10, 12, 13, 14}, {2, 4, 9, 10, 11, 13, 14}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15}, {3, 4, 5, 7, 8, 14, 15}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15}, {2, 3, 4, 6, 7, 8, 9, 10, 11, 14}, {0, 1, 2, 3, 4, 5, 6, 8, 10, 11, 14, 15}, {0, 1, 2, 3, 5, 6, 7, 9, 10, 11, 12, 13, 15}, {2, 3, 4, 5, 6, 7, 9, 11, 12, 14, 15}, {0, 1, 7, 9, 10, 13}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15}, {0, 1, 2, 3, 4, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15}, {0, 1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15}, {0, 1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15}, {2, 7}, {0, 1, 3, 9, 12, 13}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15}, {0, 1, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 15}, {0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14}, {8, 9, 7}, {0, 1, 3, 6, 7, 8, 9, 11, 12, 13, 15}, {3, 5, 6, 7, 11, 13}, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15}, {1, 2, 3, 4, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15}, {0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 13, 14, 15}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14}, {9}, {0, 2, 3, 5, 6, 7, 9, 10, 11, 14, 15}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15}, {4, 14}, {8, 9, 7}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15}, {0, 1, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15}, {0, 1, 3, 4, 5, 6, 7, 8, 10, 11, 12, 14, 15}, {0, 1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 15}, {0, 3, 5, 6, 8, 9, 10, 11, 12, 13, 14, 15}, {0, 1, 2, 3, 4, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15}, {0, 1, 2, 3, 6, 7, 9, 10, 11, 13, 15}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15}, {1, 12, 15}, {0, 1, 2, 3, 4, 5, 6, 8, 9, 10, 11, 12, 13, 14, 15}, {0, 4, 5, 6, 7, 8, 9, 10, 12, 13, 14}, {0, 3, 5, 10, 11, 13}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15}, {0, 1, 2, 4, 5, 6, 7, 9, 10, 12, 13, 14, 15}, {0, 1, 2, 4, 5, 6, 7, 9, 10, 13, 14, 15}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15}, {0, 7, 8, 9, 12, 13, 15}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15}, {6, 9, 11, 12, 13, 14}, {0, 1, 3, 5, 6, 7, 8, 9, 10, 11, 15}, {0, 1, 2, 3, 4, 5, 6, 8, 9, 11, 13, 14, 15}, {1, 2, 3, 5, 6, 8, 9, 10, 11, 12, 14, 15}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15}, {0, 1, 3, 4, 5, 6, 7, 8, 10, 11, 12, 13, 14, 15}, {0, 2, 7, 8, 9, 10, 12, 13, 15}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15}, {14}, {9, 11, 12}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15}, {0, 1, 2, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15}, {0, 5, 6, 7, 8, 9, 10, 11, 13}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15}, {0, 1, 2, 3, 4, 5, 6, 7, 10, 11, 13, 14, 15}, {1, 2, 4, 5, 6, 7, 8, 10, 11, 12, 13, 14, 15}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 13, 14, 15}, {11, 6}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15}, {0, 10}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15}, {0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15}]
Iteration 148: Best valset aggregate score so far: 0.675
Iteration 148: Best program as per aggregate score on train_val: 6
Iteration 148: Best program as per aggregate score on valset: 6
Iteration 148: Best score on valset: 0.675
Iteration 148: Best score on train_val: 0.675
Iteration 148: Linear pareto front program index: 6
Iteration 148: New program candidate index: 15
Iteration 149: Selected program 9 score: 0.67
Average Metric: 3.00 / 3 (100.0%): : 4it [05:56, 89.15s/it] 2025/08/30 02:15:45 INFO dspy.evaluate.evaluate: Average Metric: 3.0 / 3 (100.0%)
GEPA Optimization: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████▍| 3980/4000 [28:41:49<03:25, 10.26s/rollouts]
Iteration 149: All subsample scores perfect. Skipping.
Iteration 149: Reflective mutation did not propose a new candidate
Iteration 150: Selected program 1 score: 0.63
Average Metric: 2.00 / 3 (66.7%): 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [01:08<00:00, 22.69s/it]2025/08/30 02:16:53 INFO dspy.evaluate.evaluate: Average Metric: 2.0 / 3 (66.7%)
Iteration 150: Proposed new text for program: import dspy
from typing import List
import pydantic
import copy
# Define the type for a matrix for clarity
MATRIX = List[List[int]]
class TrainingExample(pydantic.BaseModel):
"""A Pydantic model for a single training example, containing an input and output matrix."""
input: MATRIX
output: MATRIX
class InferRuleSignature(dspy.Signature):
"""
Analyzes pairs of input/output matrices to deduce the transformation rule.
You are an expert analyst of abstract visual reasoning puzzles. Your task is to carefully examine the provided training examples. Each example consists of an 'input' matrix and a corresponding 'output' matrix.
Your goal is to deduce the single, consistent transformation rule that converts each input to its output. Describe this rule in clear, unambiguous, step-by-step natural language. The description must be precise enough for an expert Python programmer to implement it.
Consider various possible transformation patterns, including but not limited to:
- Geometric operations: rotation, reflection, scaling, repetition, shifting.
- Color/value transformations: changing specific numbers to others, conditional changes based on properties.
- Object-based logic: identifying shapes or objects and applying rules based on their properties (size, color, position, connectivity).
- Cropping, filtering, and extraction: finding the most significant object and extracting it based on its bounding box.
- Fill/completion patterns: such as flood fills or completing a grid based on object boundaries.
- Bounding box or frame logic: creating structures based on the extents of objects.
Focus on creating a generalizable rule that holds true for all provided examples.
"""
training_examples: List[TrainingExample] = dspy.InputField(description="A list of input and output examples demonstrating the task.")
transformation_rule: str = dspy.OutputField(description="A step-by-step natural language description of the transformation rule.")
class GenerateCodeSignature(dspy.Signature):
"""
Translates a natural language transformation rule into a Python function.
You are an expert programmer specializing in writing Python code for matrix manipulations based on high-level descriptions.
Your task is to write a single Python function named `transform_matrix` that accepts one argument, `matrix` (a list of lists of integers). This function must implement the logic described in the provided `transformation_rule`.
The function should:
1. Process the input `matrix` according to the rule. It is good practice to work on a deep copy of the matrix to avoid modifying the original.
2. Return the fully transformed matrix as a list of lists of integers.
**IMPORTANT CONSTRAINTS:**
- The code you generate must be a single, complete Python function definition.
- Do not include any code outside the function definition (e.g., no example usage or print statements).
- The function must be named `transform_matrix`.
- You can use standard Python libraries like `copy` for `copy.deepcopy`.
- The function must be self-contained.
"""
transformation_rule: str = dspy.InputField(description="The natural language rule to implement.")
test_input_example: MATRIX = dspy.InputField(description="A sample test input matrix to provide context on data structure and dimensions.")
python_code: str = dspy.OutputField(description="A string containing ONLY the Python function `transform_matrix(matrix)`.")
class ARCProgram(dspy.Module):
"""A program that first infers a rule, generates code for it, and then executes the code."""
def __init__(self):
super().__init__()
self.rule_inferrer = dspy.ChainOfThought(InferRuleSignature)
self.code_generator = dspy.Predict(GenerateCodeSignature)
def _fallback_output(self, matrix: MATRIX) -> MATRIX:
"""Generates a zero-filled matrix of the same dimensions as the input."""
if not matrix or not matrix[0]:
return []
return [[0] * len(matrix[0]) for _ in range(len(matrix))]
def forward(self, training_examples: List[TrainingExample], test_inputs: List[MATRIX]) -> dspy.Prediction:
"""
Infers a rule, generates code, and applies it to each test input.
Args:
training_examples: A list of TrainingExample objects.
test_inputs: A list of input matrices to be solved.
Returns:
A dspy.Prediction object with the 'test_outputs' field populated.
"""
# 1. Infer the transformation rule from the training examples.
inferred = self.rule_inferrer(training_examples=training_examples)
rule = inferred.transformation_rule
if not test_inputs:
return dspy.Prediction(test_outputs=[])
# 2. Generate a Python function that implements the rule, using the first test input as context.
generated = self.code_generator(transformation_rule=rule, test_input_example=test_inputs[0])
code_str = generated.python_code
all_test_outputs = []
transform_func = None
# 3. Prepare a scope and execute the generated code to define the function.
try:
# Clean up potential markdown fences that LMs sometimes add.
if code_str.strip().startswith("```python"):
code_str = code_str.strip()[9:].strip()
if code_str.strip().endswith("```"):
code_str = code_str.strip()[:-3].strip()
local_scope = {}
exec(code_str, globals(), local_scope)
transform_func = local_scope.get('transform_matrix')
except Exception as e:
print(f"Failed to generate or execute valid Python code: {e}\nCode:\n{code_str}")
# If code is invalid, we can't proceed. Fallback for all inputs.
for test_matrix in test_inputs:
all_test_outputs.append(self._fallback_output(test_matrix))
return dspy.Prediction(test_outputs=all_test_outputs)
if not callable(transform_func):
print(f"`transform_matrix` function not found or not callable in generated code.\nCode:\n{code_str}")
for test_matrix in test_inputs:
all_test_outputs.append(self._fallback_output(test_matrix))
return dspy.Prediction(test_outputs=all_test_outputs)
# 4. Apply the successfully generated function to each test input.
for test_matrix in test_inputs:
try:
# Pass a deep copy to prevent side effects within the generated function.
matrix_copy = copy.deepcopy(test_matrix)
result_matrix = transform_func(matrix_copy)
all_test_outputs.append(result_matrix)
except Exception as e:
print(f"Generated function failed on a specific test case: {e}")
# Fallback for this specific matrix if the function fails at runtime.
all_test_outputs.append(self._fallback_output(test_matrix))
return dspy.Prediction(test_outputs=all_test_outputs)
# The final 'program' object is an instance of our robust, multi-step module.
program = ARCProgram()
Generated function failed on a specific test case: name 'math' is not defined
Generated function failed on a specific test case: name 'collections' is not defined
2025/08/30 02:22:13 INFO dspy.evaluate.evaluate: Average Metric: 0.0 / 3 (0.0%)
GEPA Optimization: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████▌| 3986/4000 [28:48:17<02:53, 12.37s/rollouts]Iteration 150: New subsample score is not better, skipping
Iteration 151: Selected program 13 score: 0.665
0%| | 0/3 [00:00<?, ?it/s]Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Average Metric: 1.00 / 1 (100.0%): 33%|████████████████████████████████████▋ | 1/3 [01:59<03:58, 119.09s/it]Code execution failed: invalid syntax (<string>, line 1)Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Average Metric: 2.00 / 2 (100.0%): 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [03:58<00:00, 72.38s/it]Code execution failed: invalid syntax (<string>, line 1)
Traceback: Traceback (most recent call last):
File "<string>", line 73, in forward
File "<string>", line 1
```python
^
SyntaxError: invalid syntax
Falling back to the end-to-end solver.
Average Metric: 3.00 / 3 (100.0%): : 4it [04:03, 60.75s/it] 2025/08/30 02:26:16 INFO dspy.evaluate.evaluate: Average Metric: 3.0 / 3 (100.0%)
GEPA Optimization: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████▋| 3989/4000 [28:52:20<02:36, 14.23s/rollouts]
Iteration 151: All subsample scores perfect. Skipping.
Iteration 151: Reflective mutation did not propose a new candidate
Iteration 152: Selected program 14 score: 0.655
0%| | 0/3 [00:00<?, ?it/s]--- Successfully generated and validated code on attempt 1 ---
Average Metric: 1.00 / 1 (100.0%): 33%|█████████████████████████████████████ | 1/3 [01:10<02:20, 70.47s/it]--- Successfully generated and validated code on attempt 1 ---
Average Metric: 2.00 / 2 (100.0%): 67%|█████████████████████████████████████████████████████████████████████████▎ | 2/3 [03:17<01:43, 103.59s/it]2025/08/30 02:29:49 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
--- Successfully generated and validated code on attempt 1 ---
Average Metric: 2.00 / 2 (100.0%): 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [05:17<00:00, 111.15s/it]--- Code execution failed on attempt 1 ---
Error: name 'collections' is not defined
Traceback:
Traceback (most recent call last):
File "<string>", line 111, in forward
File "<string>", line 37, in transform_grid
File "<string>", line 15, in find_objects
NameError: name 'collections' is not defined
--- Attempt 2: Correcting failed code ---
--- Code execution failed on attempt 1 ---
Error: name 'collections' is not defined
Traceback:
Traceback (most recent call last):
File "<string>", line 111, in forward
File "<string>", line 37, in transform_grid
File "<string>", line 15, in find_objects
NameError: name 'collections' is not defined
--- Attempt 2: Correcting failed code ---
--- Code execution failed on attempt 1 ---
Error: name 'collections' is not defined
Traceback:
Traceback (most recent call last):
File "<string>", line 111, in forward
File "<string>", line 14, in transform_grid
NameError: name 'collections' is not defined
--- Attempt 2: Correcting failed code ---
--- Code execution failed on attempt 2 ---
Error: name 'collections' is not defined
Traceback:
Traceback (most recent call last):
File "<string>", line 111, in forward
File "<string>", line 43, in transform_grid
File "<string>", line 20, in find_objects
NameError: name 'collections' is not defined
--- Attempt 3: Correcting failed code ---
--- Code execution failed on attempt 1 ---
Error: name 'collections' is not defined
Traceback:
Traceback (most recent call last):
File "<string>", line 111, in forward
File "<string>", line 46, in transform_grid
File "<string>", line 32, in find_objects
NameError: name 'collections' is not defined
--- Attempt 2: Correcting failed code ---
--- Code execution failed on attempt 2 ---
Error: name 'collections' is not defined
Traceback:
Traceback (most recent call last):
File "<string>", line 111, in forward
File "<string>", line 43, in transform_grid
File "<string>", line 20, in find_objects
NameError: name 'collections' is not defined
--- Attempt 3: Correcting failed code ---
--- Code execution failed on attempt 2 ---
Error: name 'collections' is not defined
Traceback:
Traceback (most recent call last):
File "<string>", line 111, in forward
File "<string>", line 21, in transform_grid
NameError: name 'collections' is not defined
--- Attempt 3: Correcting failed code ---
--- Successfully generated and validated code on attempt 3 ---
Average Metric: 2.00 / 3 (66.7%): : 4it [08:41, 130.44s/it] 2025/08/30 02:34:58 INFO dspy.evaluate.evaluate: Average Metric: 2.0 / 3 (66.7%)
--- Successfully generated and validated code on attempt 3 ---
Iteration 152: Proposed new text for program: import dspy
from typing import List, Optional
import pydantic
import traceback
import copy
# Define the type for a grid/matrix as a list of lists of integers.
MATRIX = List[List[int]]
class TrainingExample(pydantic.BaseModel):
"""A Pydantic model for a single training example, containing an input and output grid."""
input: MATRIX
output: MATRIX
class GenerateTransformationFunction(dspy.Signature):
"""
You are an expert programmer solving visual puzzles from the Abstraction and Reasoning Corpus (ARC).
Your goal is to analyze a set of training examples, each consisting of an input grid and an output grid.
Based on this analysis, you must write a single, self-contained Python function named `transform_grid` that implements the observed transformation.
**Function Requirements:**
- The function must be named exactly `transform_grid`.
- It must accept one argument: `grid`, which is a 2D list of integers (the input grid).
- It must return a new 2D list of integers representing the transformed grid.
- The function should not rely on any external libraries unless they are standard (e.g., `copy`). If you need a library like numpy or collections, import it inside the function to ensure it is self-contained.
- Do not modify the input grid in place; create a copy if necessary.
**Analysis Strategy:**
1. **Observe Core Patterns:** Look for simple, recurring patterns like geometric transformations (rotation, reflection, scaling), color changes, object manipulation (copying, moving, recoloring), pattern propagation, or flood-fills.
2. **Decompose the Problem:** Break down the transformation into logical steps. For example, "first, find all objects of color blue, then for each object, reflect it horizontally."
3. **Generalize:** The logic must be general enough to work for all training examples and, by extension, the unseen test inputs.
4. **Code Implementation:** Translate your logic into a clear and correct Python function. Ensure your code is robust and handles edge cases observed in the examples.
"""
training_examples: List[TrainingExample] = dspy.InputField(desc="A list of input/output grid pairs demonstrating the transformation rule.")
test_input_grid: MATRIX = dspy.InputField(desc="A single test input grid to which the transformation should be applicable. Use its properties (e.g., dimensions) to ensure your code is general.")
reasoning: str = dspy.OutputField(desc="Step-by-step thinking process to deduce the transformation rule and how to implement it in Python.")
python_code: str = dspy.OutputField(desc="A string containing the complete, self-contained Python function `transform_grid(grid)`.")
class CorrectTransformationFunction(dspy.Signature):
"""
You are an expert Python programmer and debugger. You were given a task to write a `transform_grid` function for an ARC puzzle, but your previous attempt failed with a runtime error.
Analyze the provided training examples, the faulty code, and the error traceback.
Your goal is to rewrite the `transform_grid` function to fix the error and correctly implement the transformation logic.
**Debugging Strategy:**
1. **Understand the Goal:** Re-examine the `training_examples` to ensure you understand the required transformation.
2. **Analyze the Error:** Read the `error_traceback` carefully to understand why the `faulty_code` failed. Was it a syntax error, a runtime error (e.g., index out of bounds, NameError), or a logical error?
3. **Identify the Flaw:** Pinpoint the specific lines or logic in the `faulty_code` that caused the error.
4. **Rewrite the Function:** Provide a new, complete, and self-contained Python function named `transform_grid` that corrects the error and works for all examples. Do not just explain the fix; provide the full, runnable code.
"""
training_examples: List[TrainingExample] = dspy.InputField(desc="The original list of input/output grid pairs.")
test_input_grid: MATRIX = dspy.InputField(desc="A representative test input grid.")
faulty_code: str = dspy.InputField(desc="The Python code that produced an error.")
error_traceback: str = dspy.InputField(desc="The traceback from the error that occurred when running the faulty code.")
corrected_reasoning: str = dspy.OutputField(desc="A brief explanation of the error and the logic behind your correction.")
corrected_python_code: str = dspy.OutputField(desc="The complete, corrected, self-contained Python function `transform_grid(grid)`.")
class RefineTransformationFunction(dspy.Signature):
"""
You are an expert Python programmer and debugger. You were given a task to write a `transform_grid` function for an ARC puzzle.
Your previous attempt produced a function that runs without errors, but it fails to correctly transform one or more of the training examples.
Analyze the provided training examples, your faulty code, and a specific example that your code failed on.
Your goal is to rewrite the `transform_grid` function to fix the logical error and correctly implement the transformation for ALL examples.
**Debugging Strategy:**
1. **Understand the Goal:** Re-examine the complete set of `training_examples` to understand the required transformation.
2. **Analyze the Failure:** Carefully study the `failed_example`. Compare the `input`, the `expected_output`, and the `actual_output` produced by your faulty code. This comparison is the key to finding the logical flaw.
3. **Identify the Flaw:** Pinpoint the specific logic in the `faulty_code` that leads to the incorrect output. Did you misinterpret a pattern? Is your object identification wrong? Is a calculation off?
4. **Rewrite the Function:** Provide a new, complete, and self-contained Python function named `transform_grid` that corrects the logical error and works for all examples. Do not just explain the fix; provide the full, runnable code.
"""
training_examples: List[TrainingExample] = dspy.InputField(desc="The original list of input/output grid pairs.")
test_input_grid: MATRIX = dspy.InputField(desc="A representative test input grid.")
faulty_code: str = dspy.InputField(desc="The Python code that produced logically incorrect outputs.")
failed_example: str = dspy.InputField(desc="A summary of a specific training example where the faulty code failed, showing the input, expected output, and the incorrect actual output.")
refined_reasoning: str = dspy.OutputField(desc="A brief explanation of the logical error and the reasoning behind your correction.")
refined_python_code: str = dspy.OutputField(desc="The complete, corrected, self-contained Python function `transform_grid(grid)`.")
class ARCSolver(dspy.Module):
"""A DSPy module that solves ARC tasks by generating, testing, and refining Python code."""
def __init__(self, max_attempts=3):
super().__init__()
self.max_attempts = max_attempts
self.code_generator = dspy.ChainOfThought(GenerateTransformationFunction)
self.code_corrector = dspy.ChainOfThought(CorrectTransformationFunction)
self.code_refiner = dspy.ChainOfThought(RefineTransformationFunction)
def forward(self, training_examples: List[TrainingExample], test_inputs: List[MATRIX]):
python_code = None
error_traceback = None
failed_example_summary = None
transform_function = None
representative_test_input = test_inputs[0]
for attempt in range(self.max_attempts):
current_state = "generation"
if error_traceback:
current_state = "correction"
elif failed_example_summary:
current_state = "refinement"
try:
# Step 1: Generate or correct/refine code based on the current state
if current_state == "generation":
prediction = self.code_generator(
training_examples=training_examples,
test_input_grid=representative_test_input
)
python_code = prediction.python_code
elif current_state == "correction":
print(f"\n--- Attempt {attempt + 1}: Correcting runtime error ---")
correction = self.code_corrector(
training_examples=training_examples,
test_input_grid=representative_test_input,
faulty_code=python_code,
error_traceback=error_traceback
)
python_code = correction.corrected_python_code
elif current_state == "refinement":
print(f"\n--- Attempt {attempt + 1}: Refining logical error ---")
refinement = self.code_refiner(
training_examples=training_examples,
test_input_grid=representative_test_input,
faulty_code=python_code,
failed_example=failed_example_summary
)
python_code = refinement.refined_python_code
# Reset error states after using them
error_traceback = None
failed_example_summary = None
# Step 2: Extract and execute the code
if "```python" in python_code:
python_code = python_code.split("```python")[1].split("```")[0].strip()
local_scope = {}
exec(python_code, globals(), local_scope)
current_transform_function = local_scope.get('transform_grid')
if not (current_transform_function and callable(current_transform_function)):
raise ValueError("`transform_grid` function not found or not callable.")
# Step 3: Verify the function's logic against all training examples
failures = []
is_correct = True
for i, example in enumerate(training_examples):
input_grid = copy.deepcopy(example.input)
expected_output = example.output
actual_output = current_transform_function(input_grid)
if actual_output != expected_output:
is_correct = False
failures.append({
"example_index": i,
"input": example.input,
"expected": expected_output,
"actual": actual_output
})
break # Stop at the first failure to provide focused feedback
if is_correct:
transform_function = current_transform_function
print(f"--- Successfully generated and validated code on attempt {attempt + 1} ---")
break
else:
print(f"--- Code validation failed on attempt {attempt + 1} ---")
failure = failures[0]
failed_example_summary = (
f"The function failed on training example {failure['example_index']}.\n"
f"Input Grid:\n{failure['input']}\n"
f"Expected Output:\n{failure['expected']}\n"
f"Actual Output (from your code):\n{failure['actual']}\n"
)
print(failed_example_summary)
except Exception as e:
print(f"--- Code execution failed on attempt {attempt + 1} ---")
error_traceback = f"Error: {e}\nTraceback:\n{traceback.format_exc()}"
print(error_traceback)
# Step 4: Apply the final, validated function to all test inputs
generated_outputs = []
if transform_function:
for test_input in test_inputs:
try:
output_grid = transform_function(copy.deepcopy(test_input))
generated_outputs.append(output_grid)
except Exception as e:
print(f"Error applying validated `transform_grid` to a test input: {e}")
generated_outputs.append(copy.deepcopy(test_input))
else:
print("--- All code generation attempts failed. Using fallback. ---")
for test_input in test_inputs:
generated_outputs.append(copy.deepcopy(test_input))
return dspy.Prediction(test_outputs=generated_outputs)
# The final program object is an instance of our new, more robust custom module.
program = ARCSolver()
--- Code execution failed on attempt 1 ---
Error: 'dict' object has no attribute 'input'
Traceback:
Traceback (most recent call last):
File "<string>", line 150, in forward
AttributeError: 'dict' object has no attribute 'input'
--- Attempt 2: Correcting runtime error ---
--- Code execution failed on attempt 2 ---
Error: name 'collections' is not defined
Traceback:
Traceback (most recent call last):
File "<string>", line 111, in forward
File "<string>", line 63, in transform_grid
File "<string>", line 27, in find_objects
NameError: name 'collections' is not defined
--- Attempt 3: Correcting failed code ---
--- Code execution failed on attempt 1 ---
Error: 'dict' object has no attribute 'input'
Traceback:
Traceback (most recent call last):
File "<string>", line 150, in forward
AttributeError: 'dict' object has no attribute 'input'
--- Attempt 2: Correcting runtime error ---
--- Code execution failed on attempt 1 ---
Error: 'dict' object has no attribute 'input'
Traceback:
Traceback (most recent call last):
File "<string>", line 150, in forward
AttributeError: 'dict' object has no attribute 'input'
--- Attempt 2: Correcting runtime error ---
--- Code execution failed on attempt 1 ---
Error: 'dict' object has no attribute 'input'
Traceback:
Traceback (most recent call last):
File "<string>", line 150, in forward
AttributeError: 'dict' object has no attribute 'input'
--- Attempt 2: Correcting runtime error ---
--- Code execution failed on attempt 3 ---
Error: name 'collections' is not defined
Traceback:
Traceback (most recent call last):
File "<string>", line 111, in forward
File "<string>", line 43, in transform_grid
File "<string>", line 20, in find_objects
NameError: name 'collections' is not defined
--- All code generation attempts failed. Using fallback. ---
--- Code execution failed on attempt 2 ---
Error: 'dict' object has no attribute 'input'
Traceback:
Traceback (most recent call last):
File "<string>", line 150, in forward
AttributeError: 'dict' object has no attribute 'input'
--- Attempt 3: Correcting runtime error ---
--- Code execution failed on attempt 2 ---
Error: 'dict' object has no attribute 'input'
Traceback:
Traceback (most recent call last):
File "<string>", line 150, in forward
AttributeError: 'dict' object has no attribute 'input'
--- Attempt 3: Correcting runtime error ---
--- Code execution failed on attempt 2 ---
Error: 'dict' object has no attribute 'input'
Traceback:
Traceback (most recent call last):
File "<string>", line 150, in forward
AttributeError: 'dict' object has no attribute 'input'
--- Attempt 3: Correcting runtime error ---
--- Code execution failed on attempt 3 ---
Error: name 'collections' is not defined
Traceback:
Traceback (most recent call last):
File "<string>", line 111, in forward
File "<string>", line 64, in transform_grid
File "<string>", line 29, in find_objects
NameError: name 'collections' is not defined
--- All code generation attempts failed. Using fallback. ---
--- Code execution failed on attempt 2 ---
Error: 'dict' object has no attribute 'input'
Traceback:
Traceback (most recent call last):
File "<string>", line 150, in forward
AttributeError: 'dict' object has no attribute 'input'
--- Attempt 3: Correcting runtime error ---
--- Code execution failed on attempt 3 ---
Error: 'dict' object has no attribute 'input'
Traceback:
Traceback (most recent call last):
File "<string>", line 150, in forward
AttributeError: 'dict' object has no attribute 'input'
--- All code generation attempts failed. Using fallback. ---
--- Code execution failed on attempt 3 ---
Error: 'dict' object has no attribute 'input'
Traceback:
Traceback (most recent call last):
File "<string>", line 150, in forward
AttributeError: 'dict' object has no attribute 'input'
--- All code generation attempts failed. Using fallback. ---
--- Code execution failed on attempt 3 ---
Error: 'dict' object has no attribute 'input'
Traceback:
Traceback (most recent call last):
File "<string>", line 150, in forward
AttributeError: 'dict' object has no attribute 'input'
--- All code generation attempts failed. Using fallback. ---
--- Code execution failed on attempt 1 ---
Error: 'dict' object has no attribute 'input'
Traceback:
Traceback (most recent call last):
File "<string>", line 150, in forward
AttributeError: 'dict' object has no attribute 'input'
--- Attempt 2: Correcting runtime error ---
--- Code execution failed on attempt 2 ---
Error: 'dict' object has no attribute 'input'
Traceback:
Traceback (most recent call last):
File "<string>", line 150, in forward
AttributeError: 'dict' object has no attribute 'input'
--- Attempt 3: Correcting runtime error ---
--- Code execution failed on attempt 3 ---
Error: 'dict' object has no attribute 'input'
Traceback:
Traceback (most recent call last):
File "<string>", line 150, in forward
AttributeError: 'dict' object has no attribute 'input'
--- All code generation attempts failed. Using fallback. ---
--- Code execution failed on attempt 3 ---
Error: 'dict' object has no attribute 'input'
Traceback:
Traceback (most recent call last):
File "<string>", line 150, in forward
AttributeError: 'dict' object has no attribute 'input'
--- All code generation attempts failed. Using fallback. ---
--- Code execution failed on attempt 1 ---
Error: 'dict' object has no attribute 'input'
Traceback:
Traceback (most recent call last):
File "<string>", line 150, in forward
AttributeError: 'dict' object has no attribute 'input'
--- Attempt 2: Correcting runtime error ---
--- Code execution failed on attempt 1 ---
Error: 'dict' object has no attribute 'input'
Traceback:
Traceback (most recent call last):
File "<string>", line 150, in forward
AttributeError: 'dict' object has no attribute 'input'
--- Attempt 2: Correcting runtime error ---
--- Code execution failed on attempt 1 ---
Error: 'dict' object has no attribute 'input'
Traceback:
Traceback (most recent call last):
File "<string>", line 150, in forward
AttributeError: 'dict' object has no attribute 'input'
--- Attempt 2: Correcting runtime error ---
--- Code execution failed on attempt 1 ---
Error: 'dict' object has no attribute 'input'
Traceback:
Traceback (most recent call last):
File "<string>", line 150, in forward
AttributeError: 'dict' object has no attribute 'input'
--- Attempt 2: Correcting runtime error ---
--- Code execution failed on attempt 2 ---
Error: 'dict' object has no attribute 'input'
Traceback:
Traceback (most recent call last):
File "<string>", line 150, in forward
AttributeError: 'dict' object has no attribute 'input'
--- Attempt 3: Correcting runtime error ---
--- Code execution failed on attempt 2 ---
Error: 'dict' object has no attribute 'input'
Traceback:
Traceback (most recent call last):
File "<string>", line 150, in forward
AttributeError: 'dict' object has no attribute 'input'
--- Attempt 3: Correcting runtime error ---
--- Code execution failed on attempt 2 ---
Error: 'dict' object has no attribute 'input'
Traceback:
Traceback (most recent call last):
File "<string>", line 150, in forward
AttributeError: 'dict' object has no attribute 'input'
--- Attempt 3: Correcting runtime error ---
--- Code execution failed on attempt 2 ---
Error: 'dict' object has no attribute 'input'
Traceback:
Traceback (most recent call last):
File "<string>", line 150, in forward
AttributeError: 'dict' object has no attribute 'input'
--- Attempt 3: Correcting runtime error ---
2025/08/30 02:46:31 INFO dspy.evaluate.evaluate: Average Metric: 0.0 / 3 (0.0%)
GEPA Optimization: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████▊| 3995/4000 [29:12:35<02:18, 27.76s/rollouts]--- Code execution failed on attempt 3 ---
Error: 'dict' object has no attribute 'input'
Traceback:
Traceback (most recent call last):
File "<string>", line 150, in forward
AttributeError: 'dict' object has no attribute 'input'
--- All code generation attempts failed. Using fallback. ---
Iteration 152: New subsample score is not better, skipping
Iteration 153: Selected program 11 score: 0.665
0%| | 0/3 [00:00<?, ?it/s]--- Code execution failed on attempt 3 ---
Error: 'dict' object has no attribute 'input'
Traceback:
Traceback (most recent call last):
File "<string>", line 150, in forward
AttributeError: 'dict' object has no attribute 'input'
--- All code generation attempts failed. Using fallback. ---
--- Code execution failed on attempt 3 ---
Error: 'dict' object has no attribute 'input'
Traceback:
Traceback (most recent call last):
File "<string>", line 150, in forward
AttributeError: 'dict' object has no attribute 'input'
--- All code generation attempts failed. Using fallback. ---
--- Code execution failed on attempt 3 ---
Error: 'dict' object has no attribute 'input'
Traceback:
Traceback (most recent call last):
File "<string>", line 150, in forward
AttributeError: 'dict' object has no attribute 'input'
--- All code generation attempts failed. Using fallback. ---
Average Metric: 1.00 / 3 (33.3%): : 6it [11:12, 112.08s/it] 2025/08/30 02:57:44 INFO dspy.evaluate.evaluate: Average Metric: 1.0 / 3 (33.3%)
Iteration 153: Proposed new text for program: import dspy
from typing import List, Tuple, Optional
import pydantic
import copy
import traceback
# Define type aliases and pydantic models for clarity and structure.
MATRIX = List[List[int]]
class TrainingExample(pydantic.BaseModel):
input: MATRIX
output: MATRIX
# --- Signatures ---
class HypothesizeRule(dspy.Signature):
"""
Analyze the provided input/output matrix pairs from the Abstraction and Reasoning Corpus (ARC).
Deduce the single, underlying transformation rule that converts each input matrix to its corresponding output matrix.
Describe this rule in clear, step-by-step, unambiguous English. Focus on the logic, not Python code.
**Successful Strategies to Consider:**
- **Start Simple:** First, check for simple rules. Is there a global transformation (e.g., rotation, reflection)? Is the output a subgrid of the input? Is a single color being replaced?
- **Look for Separators:** Check if the grid is partitioned by separator lines (e.g., rows or columns that are all one color, usually black). The transformation might be applied to each section independently.
- **Identify Objects:** Group contiguous non-background pixels into objects. Analyze how these objects are created, destroyed, or modified. Consider their properties: color, shape, size, position.
- **Find Marker Points:** Look for special 'marker' pixels (e.g., a uniquely colored pixel) that might define the geometry of an operation, like the corners of a shape to be drawn.
- **Relate Input to Output:** How do the properties of the input grid (dimensions, colors, object counts) relate to the output grid?
- **Avoid Over-complication:** Propose the simplest rule that explains ALL training examples. Do not suggest overly complex mathematical or recursive patterns unless absolutely necessary and supported by every example.
"""
training_examples: List[TrainingExample] = dspy.InputField(desc="A list of input/output pairs demonstrating the transformation rule.")
rule_description: str = dspy.OutputField(desc="A step-by-step English description of the transformation rule.")
class ImplementRule(dspy.Signature):
"""
You are an expert programmer. Your task is to write a single, self-contained Python function based on a provided rule description and example pairs.
**Function Requirements:**
- The function must be named `transform_matrix`.
- It must accept one argument: `matrix`, which is a list of lists of integers (representing the input grid).
- It must return a list of lists of integers (representing the transformed output grid).
- The function should not use any external libraries except for `copy` if needed (e.g., `import copy; new_matrix = copy.deepcopy(matrix)`).
- Your output must be ONLY the Python code for the function. Do not include any explanations, comments outside the function, or markdown formatting like ```python.
**Successful Strategies to Consider:**
- **Robust Parsing:** When rules involve sub-regions (e.g., quadrants, objects), write code that can robustly find their boundaries. Don't assume boundaries are perfectly aligned or can be found by checking just the first row/column.
- **Iterative Processes:** Some rules are applied repeatedly until the grid no longer changes. Consider using a `while` loop that continues as long as modifications are being made in a pass.
- **Consistent Output Shape:** When constructing the output grid row by row (e.g., from sections), ensure each generated row has the same length to avoid creating a staggered/jagged matrix.
- **Handling Edge Cases:** Ensure your code handles empty matrices or matrices with unexpected dimensions gracefully.
**Example of a Correctly Formatted Output:**
def transform_matrix(matrix: list[list[int]]) -> list[list[int]]:
# Your implementation here
# For example, find the most frequent color and fill the grid
from collections import Counter
import itertools
if not matrix or not matrix[0]:
return []
counts = Counter(itertools.chain.from_iterable(matrix))
if counts:
# Handle ties by picking the smaller number value
most_common_color = sorted(counts.items(), key=lambda item: (-item[1], item[0]))[0][0]
else:
return []
height = len(matrix)
width = len(matrix[0])
return [[most_common_color for _ in range(width)] for _ in range(height)]
"""
rule_description: str = dspy.InputField(desc="The English description of the rule to implement.")
training_examples: List[TrainingExample] = dspy.InputField(desc="A list of input/output pairs to use as a reference for implementation.")
python_function: str = dspy.OutputField(desc="A string containing a single Python function `transform_matrix` that implements the rule.")
class RefineCode(dspy.Signature):
"""
You are a senior programmer debugging a Python function. You will be given a rule description, training examples, the buggy Python code, and feedback on why it failed.
Your task is to fix the code so that it correctly implements the rule and passes the training examples.
The refined function must adhere to all the original requirements.
**Function Requirements:**
- The function must be named `transform_matrix`.
- It must accept one argument: `matrix`, which is a list of lists of integers.
- It must return a list of lists of integers.
- The function should not use any external libraries except for `copy`.
- Your output must be ONLY the Python code for the function. Do not include any explanations or markdown formatting.
"""
rule_description: str = dspy.InputField(desc="The English description of the rule to implement.")
training_examples: List[TrainingExample] = dspy.InputField(desc="A list of input/output pairs the function must correctly handle.")
buggy_code: str = dspy.InputField(desc="The Python code that failed verification.")
feedback: str = dspy.InputField(desc="A description of the error or the discrepancy between the function's output and the expected output for a training example.")
refined_python_function: str = dspy.OutputField(desc="A string containing the corrected, single Python function `transform_matrix`.")
# --- Custom Module with Self-Correction ---
class ARCSolver(dspy.Module):
"""A module that solves ARC tasks by hypothesizing a rule, generating code, and refining it through verification."""
def __init__(self, max_retries=2):
super().__init__()
self.max_retries = max_retries
self.rule_hypothesizer = dspy.ChainOfThought(HypothesizeRule)
self.code_implementer = dspy.Predict(ImplementRule)
self.code_refiner = dspy.Predict(RefineCode)
def _execute_and_verify(self, python_code: str, examples: List[TrainingExample]) -> Tuple[bool, Optional[str]]:
"""
Executes the generated Python code and verifies its correctness against training examples.
Returns a tuple: (is_correct, feedback_message).
"""
local_scope = {}
try:
exec(python_code, globals(), local_scope)
transform_func = local_scope.get('transform_matrix')
if not callable(transform_func):
return False, "The generated code does not define a callable function named 'transform_matrix'."
except Exception as e:
return False, f"Code compilation failed with an error: {e}\n{traceback.format_exc()}"
for i, example_obj in enumerate(examples):
# Robustly handle both Pydantic models and dicts, as DSPy can convert them.
if isinstance(example_obj, dict):
try:
example = TrainingExample(**example_obj)
except pydantic.ValidationError as e:
return False, f"Failed to parse training example {i} from dict: {e}"
else:
example = example_obj
try:
input_copy = copy.deepcopy(example.input)
predicted_output = transform_func(input_copy)
if predicted_output != example.output:
feedback = (
f"Verification failed on training example {i}.\n"
f"Input:\n{example.input}\n"
f"Expected Output:\n{example.output}\n"
f"Actual Output:\n{predicted_output}"
)
return False, feedback
except Exception as e:
feedback = (
f"An exception occurred during execution on training example {i}: {e}\n"
f"Input was:\n{example.input}\n"
f"{traceback.format_exc()}"
)
return False, feedback
return True, None
def forward(self, training_examples: List[TrainingExample], test_inputs: List[MATRIX]):
# Step 1: Generate an English description of the transformation rule.
hypothesis = self.rule_hypothesizer(training_examples=training_examples)
# Step 2: Generate the initial Python code.
prediction = self.code_implementer(
rule_description=hypothesis.rule_description,
training_examples=training_examples
)
python_code = prediction.python_function
# Step 3: Verify and refine the code in a loop.
for _ in range(self.max_retries):
is_correct, feedback = self._execute_and_verify(python_code, training_examples)
if is_correct:
break
refinement = self.code_refiner(
rule_description=hypothesis.rule_description,
training_examples=training_examples,
buggy_code=python_code,
feedback=feedback
)
python_code = refinement.refined_python_function
# Step 4: Execute the final code on test inputs with robust fallbacks.
fallback_outputs = [copy.deepcopy(matrix) for matrix in test_inputs]
local_scope = {}
try:
exec(python_code, globals(), local_scope)
transform_func = local_scope.get('transform_matrix')
if not callable(transform_func):
return dspy.Prediction(test_outputs=fallback_outputs)
solved_outputs = []
for test_matrix in test_inputs:
try:
input_copy = copy.deepcopy(test_matrix)
result = transform_func(input_copy)
solved_outputs.append(result)
except Exception:
solved_outputs.append(copy.deepcopy(test_matrix))
return dspy.Prediction(test_outputs=solved_outputs)
except Exception:
return dspy.Prediction(test_outputs=fallback_outputs)
# The overall task signature, defining the final inputs and outputs of the program.
class SolveTaskSignature(dspy.Signature):
"""
Given a set of training examples demonstrating a matrix transformation,
apply the same transformation to a new set of test matrices.
"""
training_examples: List[TrainingExample] = dspy.InputField(description="Input and output examples demonstrating the task to be performed.")
test_inputs: List[MATRIX] = dspy.InputField(description="Input matrices to be solved following the task described in the training examples.")
test_outputs: List[MATRIX] = dspy.OutputField(description="Output matrices corresponding to the test inputs.")
# The final program is an instance of our new, more robust module.
program = ARCSolver()
2025/08/30 03:02:15 INFO dspy.evaluate.evaluate: Average Metric: 2.0 / 3 (66.7%)
2025/08/30 03:05:58 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/30 03:05:59 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/30 03:08:06 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/30 03:08:40 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/30 03:09:03 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/30 03:09:51 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/30 03:10:14 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/30 03:12:27 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/30 03:13:02 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/30 03:13:27 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/30 03:13:37 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/30 03:15:20 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/30 03:16:27 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/30 03:16:27 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/30 03:18:35 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/30 03:20:57 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/30 03:20:57 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/30 03:21:49 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/30 03:28:31 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/30 03:29:20 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/30 03:30:42 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
2025/08/30 03:31:12 INFO dspy.evaluate.evaluate: Average Metric: 141.0 / 200 (70.5%)
GEPA Optimization: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████▊| 3995/4000 [29:57:16<02:14, 26.99s/rollouts]Iteration 153: New program is on the linear pareto front
Iteration 153: Full valset score for new program: 0.705
Iteration 153: Full train_val score for new program: 0.705
Iteration 153: Individual valset scores for new program: [True, True, True, True, True, False, True, True, True, True, True, True, True, False, True, True, False, True, True, True, False, True, True, False, True, True, False, True, True, True, False, False, False, True, True, True, False, True, True, False, False, True, True, False, False, True, False, False, True, True, True, True, True, True, True, False, True, True, False, True, True, True, False, True, False, False, True, True, False, False, True, True, True, True, True, True, True, False, True, True, True, True, True, True, True, True, True, False, True, False, False, True, True, True, True, True, True, False, True, True, True, True, False, False, True, True, True, True, False, True, False, False, True, True, True, False, True, True, True, True, True, False, True, False, False, True, False, True, True, True, True, False, True, True, True, False, True, False, True, True, True, True, True, False, True, False, False, True, True, True, False, False, True, False, True, True, True, True, True, False, False, True, True, True, False, True, True, True, True, False, True, True, True, True, True, True, False, True, False, True, False, True, True, True, False, True, True, True, True, True, True, True, False, True, True, False, True, False, False, True]
Iteration 153: New valset pareto front scores: [True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, False, True, True, True, True, True, True, True, True, True, False, True, True, True, True, False, True, True, True, True, False, True, True, True, False, True, True, True, False, True, False, True, True, True, True, True, True, True, True, 0, True, True, False, True, True, True, False, True, True, False, True, True, 0, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, False, True, True, True, True, True, True, True, True, True, True, True, True, True, False, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, False, False, True, False, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, False, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, False, True, True, True, True, False, True, True, True, True, False, True, True, True, True, True, True, False, True, False, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, False, True]
Iteration 153: Full valset pareto front score: 0.88
Iteration 153: Updated valset pareto front programs: [{1, 3, 5, 8, 9, 10, 11, 12, 16}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 13, 14, 15, 16}, {0, 9, 12, 16}, {0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 16}, {0, 1, 2, 3, 4, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16}, {0, 4, 14}, {0, 1, 2, 3, 4, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16}, {0, 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}, {0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}, {8}, {0, 1, 2, 3, 4, 6, 9, 10, 11, 12, 13, 14, 16}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}, {1, 4, 5, 7, 9, 10, 12, 13, 14, 15, 16}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}, {0, 2, 4, 6, 7, 10, 11, 12, 14, 16}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15}, {0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}, {1, 5, 7, 9, 12, 13}, {0, 3, 4, 6, 8, 9, 10, 11, 12, 13, 14, 15, 16}, {0, 1, 3, 6, 7, 8, 9, 10, 11, 12, 13, 15, 16}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}, {0, 4, 7, 14, 15, 16}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}, {1}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}, {2}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}, {0, 1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}, {16, 2, 12, 5}, {0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 14, 15}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}, {1}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}, {0, 1, 2, 3, 4, 6, 8, 9, 10, 11, 12, 13, 14, 15, 16}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}, {8}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}, {0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}, {0, 1, 2, 3, 4, 6, 7, 11, 13, 14, 15, 16}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}, {0, 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}, {0, 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 13, 14, 15, 16}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}, {0, 1, 2, 3, 8, 12, 13, 16}, {0, 1, 5, 16}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}, {4, 6, 7, 8, 11, 14}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}, {1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 15, 16}, {0, 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}, {11}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16}, {2, 3, 4, 5, 6, 7, 8, 9, 11, 14, 15, 16}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}, {1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 15, 16}, {0, 3, 4, 5, 6, 7, 9, 10, 11, 12, 13, 14, 16}, {4, 14}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}, {0, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}, {0, 2, 6, 8, 11, 12, 13, 15, 16}, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16}, {0, 1, 2, 4, 5, 6, 7, 8, 10, 11, 12, 13, 14, 16}, {0, 1, 2, 3, 4, 5, 6, 7, 13, 14}, {0, 3, 4, 6, 7, 11, 12, 13, 14, 15, 16}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}, {0, 1, 2, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 16}, {1, 3, 8, 10, 12, 13, 15, 16}, {7}, {0, 3, 4, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}, {0, 1, 2, 3, 4, 5, 6, 8, 9, 10, 11, 12, 13, 14, 15, 16}, {0, 4, 6, 7, 9, 11, 12, 14}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}, {0, 1, 2, 3, 5, 7, 8, 11, 12, 16}, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}, {0, 1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 15, 16}, {1, 2, 10}, {0, 1, 4, 5, 6, 8, 10, 12, 13, 14, 15, 16}, {13}, {0, 2, 13, 5}, {1, 3, 10, 12, 13, 14, 16}, {2, 4, 9, 10, 11, 13, 14, 16}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}, {3, 4, 5, 7, 8, 14, 15}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}, {2, 3, 4, 6, 7, 8, 9, 10, 11, 14, 16}, {0, 1, 2, 3, 4, 5, 6, 8, 10, 11, 14, 15, 16}, {0, 1, 2, 3, 5, 6, 7, 9, 10, 11, 12, 13, 15, 16}, {2, 3, 4, 5, 6, 7, 9, 11, 12, 14, 15, 16}, {0, 1, 7, 9, 10, 13}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}, {0, 1, 2, 3, 4, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}, {0, 1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}, {0, 1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}, {16, 2, 7}, {0, 1, 3, 9, 12, 13}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}, {0, 1, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 15, 16}, {0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16}, {8, 9, 7}, {0, 1, 3, 6, 7, 8, 9, 11, 12, 13, 15, 16}, {3, 5, 6, 7, 11, 13}, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}, {1, 2, 3, 4, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}, {0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 13, 14, 15, 16}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16}, {9}, {0, 2, 3, 5, 6, 7, 9, 10, 11, 14, 15}, {16}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}, {4, 14}, {8, 9, 7}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}, {0, 1, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15}, {0, 1, 3, 4, 5, 6, 7, 8, 10, 11, 12, 14, 15, 16}, {0, 1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 15, 16}, {0, 3, 5, 6, 8, 9, 10, 11, 12, 13, 14, 15, 16}, {0, 1, 2, 3, 4, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16}, {0, 1, 2, 3, 6, 7, 9, 10, 11, 13, 15, 16}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}, {1, 12, 15}, {0, 1, 2, 3, 4, 5, 6, 8, 9, 10, 11, 12, 13, 14, 15, 16}, {0, 4, 5, 6, 7, 8, 9, 10, 12, 13, 14, 16}, {0, 3, 5, 10, 11, 13, 16}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}, {0, 1, 2, 4, 5, 6, 7, 9, 10, 12, 13, 14, 15, 16}, {0, 1, 2, 4, 5, 6, 7, 9, 10, 13, 14, 15, 16}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}, {0, 7, 8, 9, 12, 13, 15, 16}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}, {6, 9, 11, 12, 13, 14, 16}, {0, 1, 3, 5, 6, 7, 8, 9, 10, 11, 15, 16}, {0, 1, 2, 3, 4, 5, 6, 8, 9, 11, 13, 14, 15, 16}, {1, 2, 3, 5, 6, 8, 9, 10, 11, 12, 14, 15, 16}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}, {0, 1, 3, 4, 5, 6, 7, 8, 10, 11, 12, 13, 14, 15, 16}, {0, 2, 7, 8, 9, 10, 12, 13, 15}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}, {16, 14}, {9, 11, 12}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}, {0, 1, 2, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}, {0, 5, 6, 7, 8, 9, 10, 11, 13, 16}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}, {0, 1, 2, 3, 4, 5, 6, 7, 10, 11, 13, 14, 15}, {1, 2, 4, 5, 6, 7, 8, 10, 11, 12, 13, 14, 15, 16}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 13, 14, 15, 16}, {11, 6}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}, {0, 10}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}, {0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}]
Iteration 153: Best valset aggregate score so far: 0.705
Iteration 153: Best program as per aggregate score on train_val: 16
Iteration 153: Best program as per aggregate score on valset: 16
Iteration 153: Best score on valset: 0.705
Iteration 153: Best score on train_val: 0.705
Iteration 153: Linear pareto front program index: 16
Iteration 153: New program candidate index: 16
2025/08/30 03:33:58 WARNING dspy.clients.lm: LM response was truncated due to exceeding max_tokens=32000. You can inspect the latest LM interactions with `dspy.inspect_history()`. To avoid truncation, consider passing a larger max_tokens when setting up dspy.LM. You may also consider increasing the temperature (currently 0.0) if the reason for truncation is repetition.
GEPA Optimization Results¶
View the GEPA optimized DSPy program¶
In [30]:
Copied!
print(o.best_candidate["program"])
print(o.best_candidate["program"])
import dspy
from typing import List, Tuple, Optional
import pydantic
import copy
import traceback
# Define type aliases and pydantic models for clarity and structure.
MATRIX = List[List[int]]
class TrainingExample(pydantic.BaseModel):
input: MATRIX
output: MATRIX
# --- Signatures ---
class HypothesizeRule(dspy.Signature):
"""
Analyze the provided input/output matrix pairs from the Abstraction and Reasoning Corpus (ARC).
Deduce the single, underlying transformation rule that converts each input matrix to its corresponding output matrix.
Describe this rule in clear, step-by-step, unambiguous English. Focus on the logic, not Python code.
**Successful Strategies to Consider:**
- **Start Simple:** First, check for simple rules. Is there a global transformation (e.g., rotation, reflection)? Is the output a subgrid of the input? Is a single color being replaced?
- **Look for Separators:** Check if the grid is partitioned by separator lines (e.g., rows or columns that are all one color, usually black). The transformation might be applied to each section independently.
- **Identify Objects:** Group contiguous non-background pixels into objects. Analyze how these objects are created, destroyed, or modified. Consider their properties: color, shape, size, position.
- **Find Marker Points:** Look for special 'marker' pixels (e.g., a uniquely colored pixel) that might define the geometry of an operation, like the corners of a shape to be drawn.
- **Relate Input to Output:** How do the properties of the input grid (dimensions, colors, object counts) relate to the output grid?
- **Avoid Over-complication:** Propose the simplest rule that explains ALL training examples. Do not suggest overly complex mathematical or recursive patterns unless absolutely necessary and supported by every example.
"""
training_examples: List[TrainingExample] = dspy.InputField(desc="A list of input/output pairs demonstrating the transformation rule.")
rule_description: str = dspy.OutputField(desc="A step-by-step English description of the transformation rule.")
class ImplementRule(dspy.Signature):
"""
You are an expert programmer. Your task is to write a single, self-contained Python function based on a provided rule description and example pairs.
**Function Requirements:**
- The function must be named `transform_matrix`.
- It must accept one argument: `matrix`, which is a list of lists of integers (representing the input grid).
- It must return a list of lists of integers (representing the transformed output grid).
- The function should not use any external libraries except for `copy` if needed (e.g., `import copy; new_matrix = copy.deepcopy(matrix)`).
- Your output must be ONLY the Python code for the function. Do not include any explanations, comments outside the function, or markdown formatting like ```python.
**Successful Strategies to Consider:**
- **Robust Parsing:** When rules involve sub-regions (e.g., quadrants, objects), write code that can robustly find their boundaries. Don't assume boundaries are perfectly aligned or can be found by checking just the first row/column.
- **Iterative Processes:** Some rules are applied repeatedly until the grid no longer changes. Consider using a `while` loop that continues as long as modifications are being made in a pass.
- **Consistent Output Shape:** When constructing the output grid row by row (e.g., from sections), ensure each generated row has the same length to avoid creating a staggered/jagged matrix.
- **Handling Edge Cases:** Ensure your code handles empty matrices or matrices with unexpected dimensions gracefully.
**Example of a Correctly Formatted Output:**
def transform_matrix(matrix: list[list[int]]) -> list[list[int]]:
# Your implementation here
# For example, find the most frequent color and fill the grid
from collections import Counter
import itertools
if not matrix or not matrix[0]:
return []
counts = Counter(itertools.chain.from_iterable(matrix))
if counts:
# Handle ties by picking the smaller number value
most_common_color = sorted(counts.items(), key=lambda item: (-item[1], item[0]))[0][0]
else:
return []
height = len(matrix)
width = len(matrix[0])
return [[most_common_color for _ in range(width)] for _ in range(height)]
"""
rule_description: str = dspy.InputField(desc="The English description of the rule to implement.")
training_examples: List[TrainingExample] = dspy.InputField(desc="A list of input/output pairs to use as a reference for implementation.")
python_function: str = dspy.OutputField(desc="A string containing a single Python function `transform_matrix` that implements the rule.")
class RefineCode(dspy.Signature):
"""
You are a senior programmer debugging a Python function. You will be given a rule description, training examples, the buggy Python code, and feedback on why it failed.
Your task is to fix the code so that it correctly implements the rule and passes the training examples.
The refined function must adhere to all the original requirements.
**Function Requirements:**
- The function must be named `transform_matrix`.
- It must accept one argument: `matrix`, which is a list of lists of integers.
- It must return a list of lists of integers.
- The function should not use any external libraries except for `copy`.
- Your output must be ONLY the Python code for the function. Do not include any explanations or markdown formatting.
"""
rule_description: str = dspy.InputField(desc="The English description of the rule to implement.")
training_examples: List[TrainingExample] = dspy.InputField(desc="A list of input/output pairs the function must correctly handle.")
buggy_code: str = dspy.InputField(desc="The Python code that failed verification.")
feedback: str = dspy.InputField(desc="A description of the error or the discrepancy between the function's output and the expected output for a training example.")
refined_python_function: str = dspy.OutputField(desc="A string containing the corrected, single Python function `transform_matrix`.")
# --- Custom Module with Self-Correction ---
class ARCSolver(dspy.Module):
"""A module that solves ARC tasks by hypothesizing a rule, generating code, and refining it through verification."""
def __init__(self, max_retries=2):
super().__init__()
self.max_retries = max_retries
self.rule_hypothesizer = dspy.ChainOfThought(HypothesizeRule)
self.code_implementer = dspy.Predict(ImplementRule)
self.code_refiner = dspy.Predict(RefineCode)
def _execute_and_verify(self, python_code: str, examples: List[TrainingExample]) -> Tuple[bool, Optional[str]]:
"""
Executes the generated Python code and verifies its correctness against training examples.
Returns a tuple: (is_correct, feedback_message).
"""
local_scope = {}
try:
exec(python_code, globals(), local_scope)
transform_func = local_scope.get('transform_matrix')
if not callable(transform_func):
return False, "The generated code does not define a callable function named 'transform_matrix'."
except Exception as e:
return False, f"Code compilation failed with an error: {e}\n{traceback.format_exc()}"
for i, example_obj in enumerate(examples):
# Robustly handle both Pydantic models and dicts, as DSPy can convert them.
if isinstance(example_obj, dict):
try:
example = TrainingExample(**example_obj)
except pydantic.ValidationError as e:
return False, f"Failed to parse training example {i} from dict: {e}"
else:
example = example_obj
try:
input_copy = copy.deepcopy(example.input)
predicted_output = transform_func(input_copy)
if predicted_output != example.output:
feedback = (
f"Verification failed on training example {i}.\n"
f"Input:\n{example.input}\n"
f"Expected Output:\n{example.output}\n"
f"Actual Output:\n{predicted_output}"
)
return False, feedback
except Exception as e:
feedback = (
f"An exception occurred during execution on training example {i}: {e}\n"
f"Input was:\n{example.input}\n"
f"{traceback.format_exc()}"
)
return False, feedback
return True, None
def forward(self, training_examples: List[TrainingExample], test_inputs: List[MATRIX]):
# Step 1: Generate an English description of the transformation rule.
hypothesis = self.rule_hypothesizer(training_examples=training_examples)
# Step 2: Generate the initial Python code.
prediction = self.code_implementer(
rule_description=hypothesis.rule_description,
training_examples=training_examples
)
python_code = prediction.python_function
# Step 3: Verify and refine the code in a loop.
for _ in range(self.max_retries):
is_correct, feedback = self._execute_and_verify(python_code, training_examples)
if is_correct:
break
refinement = self.code_refiner(
rule_description=hypothesis.rule_description,
training_examples=training_examples,
buggy_code=python_code,
feedback=feedback
)
python_code = refinement.refined_python_function
# Step 4: Execute the final code on test inputs with robust fallbacks.
fallback_outputs = [copy.deepcopy(matrix) for matrix in test_inputs]
local_scope = {}
try:
exec(python_code, globals(), local_scope)
transform_func = local_scope.get('transform_matrix')
if not callable(transform_func):
return dspy.Prediction(test_outputs=fallback_outputs)
solved_outputs = []
for test_matrix in test_inputs:
try:
input_copy = copy.deepcopy(test_matrix)
result = transform_func(input_copy)
solved_outputs.append(result)
except Exception:
solved_outputs.append(copy.deepcopy(test_matrix))
return dspy.Prediction(test_outputs=solved_outputs)
except Exception:
return dspy.Prediction(test_outputs=fallback_outputs)
# The overall task signature, defining the final inputs and outputs of the program.
class SolveTaskSignature(dspy.Signature):
"""
Given a set of training examples demonstrating a matrix transformation,
apply the same transformation to a new set of test matrices.
"""
training_examples: List[TrainingExample] = dspy.InputField(description="Input and output examples demonstrating the task to be performed.")
test_inputs: List[MATRIX] = dspy.InputField(description="Input matrices to be solved following the task described in the training examples.")
test_outputs: List[MATRIX] = dspy.OutputField(description="Output matrices corresponding to the test inputs.")
# The final program is an instance of our new, more robust module.
program = ARCSolver()
As can be seen above, GEPA discovered an elaborate, 5-step program that:
- Ask LLM to hypothesize a natural language rule given training examples
- Ask LLM to generate a python program that executes the natural language rule
- Run the generated python program on all training examples, gathering feedback on how/when they fail to run, or identifying if it succeeds in all training examples.
- If
- succeed in all training examples: then proceed as-is
- otherwise, ask LLM to improve the program with gathered feedback
- Finally execute the improved program on all test-inputs, and return outputs.
Notably, GEPA with Gemini-2.5-Pro is able to discover reflective refinement!
Evaluating the optimized agent¶
In [29]:
Copied!
o_opt = adapter.evaluate(test_set, o.best_candidate)
o_opt = adapter.evaluate(test_set, o.best_candidate)
2025/08/30 04:55:24 INFO dspy.evaluate.evaluate: Average Metric: 198.0 / 400 (49.5%)
We see it going from 44% to 49.5%!