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from typing import List, Dict
from numpy import random
import pandas as pd
from transformers import AutoTokenizer
from absolute_zero_reasoner.data_construction.prompts import get_code_problem_generator_prompt, get_code_problem_predictor_prompt
from absolute_zero_reasoner.data_construction.process_data import boxed_instruction, instruction_following
from absolute_zero_reasoner.utils.code_utils.parsers import replace_main_function_name
def get_gen_code_io_data(
io_data: List[Dict],
target_data_len: int,
problem_type: str,
instruction_type: str,
content_max_length: int,
io_n: int,
output_path: str,
split: str,
tokenizer: AutoTokenizer,
banned_keywords: List[str],
banned_assertion_keywords: List[str],
weights: List[float] = None,
enable_composite_function: bool = False,
composite_function_n_min: int = -1,
composite_function_n_max: int = -1,
composite_chance: float = 0.5,
remove_after_return: bool = False,
num_inputs: int = 10,
remove_input_from_snippet: bool = False,
include_references: bool = True,
):
return_io_data = []
if instruction_type.startswith('boxed'):
instruction_template = boxed_instruction
elif instruction_type.startswith('answer'):
instruction_template = instruction_following
elif instruction_type.startswith('none'):
instruction_template = '{}'
else:
raise ValueError(f"Invalid instruction type: {instruction_type}")
if weights is None:
probabilities = [1.0 / len(io_data)] * len(io_data)
else:
# Normalize weights to form a probability distribution
probabilities = [float(w)/sum(weights) for w in weights]
idx = 0
while len(return_io_data) < target_data_len:
if not include_references and problem_type != 'code_f':
chosen_references = []
else:
chosen_references = random.choice(io_data, size=min(io_n, len(io_data)), replace=False, p=probabilities)
# composite functions is not used for code_f problem type
if problem_type != 'code_f' and composite_function_n_max > 0 and enable_composite_function and random.random() <= composite_chance and len(chosen_references) > composite_function_n_max:
# TODO: we only allow composite to sample from code snippets without composite functions
io_without_composite_function_indices = [i for i in range(len(io_data)) if not io_data[i]['composite_functions']]
io_without_composite_function_data = [io_data[i] for i in io_without_composite_function_indices]
io_without_composite_function_weights = [probabilities[i] for i in io_without_composite_function_indices]
# normalize the weights
io_without_composite_function_probabilities = [w / sum(io_without_composite_function_weights) for w in io_without_composite_function_weights]
# number of composite functions to sample is either fixed or random
composite_function_n = composite_function_n_min if composite_function_n_min == composite_function_n_max else random.randint(composite_function_n_min, composite_function_n_max)
composite_functions = random.choice(io_without_composite_function_data, size=composite_function_n, replace=False, p=io_without_composite_function_probabilities)
for i, composite_function in enumerate(composite_functions):
# TODO: need to also replace recursively called composite functions, ignore functions that have f as the last letter, only for function call f()
composite_functions[i]['snippet'] = replace_main_function_name(composite_function['snippet'], 'f', f'g_{i}')
imports = []
else:
composite_functions = []
if include_references:
imports = chosen_references[0]['imports']
else:
imports = []
io_prompt = instruction_template.format(
get_code_problem_generator_prompt(
problem_type=problem_type,
reference_snippets=chosen_references,
banned_keywords=banned_keywords,
banned_assertion_keywords=banned_assertion_keywords,
composite_functions=composite_functions,
remove_after_return=remove_after_return,
num_inputs=num_inputs,
remove_input_from_snippet=remove_input_from_snippet,
)
)
if len(tokenizer(io_prompt)['input_ids']) <= content_max_length:
io_item = {
"data_source": 'gen_' + problem_type,
"prompt": [{
"role": "user",
"content": io_prompt,
}],
"problem": '',
"ability": "code",
"reward_model": {
"style": "rule",
"ground_truth": '',
},
"extra_info": {
'split': split,
'index': idx,
'metric': 'gen_' + problem_type,
'chosen_references': chosen_references,
'composite_functions': composite_functions,
'imports': imports,
}
}
return_io_data.append(io_item)
idx += 1
if len(return_io_data) >= target_data_len:
break
# if io_data is not full, we sample upsample random data
while len(return_io_data) < target_data_len:
io_item = io_data[random.randint(0, len(io_data))]
return_io_data.append(io_item)
# output to parquet
df = pd.DataFrame(return_io_data)
df.to_parquet(output_path)
def get_pred_code_io_data(
io_data: List[Dict],
target_data_len: int,
problem_type: str,
instruction_type: str,
content_max_length: int,
output_path: str,
split: str,
tokenizer: AutoTokenizer,
):
return_io_data = []
if instruction_type.startswith('boxed'):
instruction_template = boxed_instruction
elif instruction_type.startswith('answer'):
instruction_template = instruction_following
elif instruction_type.startswith('none'):
instruction_template = '{}'
else:
raise ValueError(f"Invalid instruction type: {instruction_type}")
for idx, io_item in enumerate(io_data):
if problem_type == 'code_i':
ground_truth = io_item['input']
elif problem_type == 'code_o':
ground_truth = io_item['output']
elif problem_type == 'code_e':
ground_truth = io_item['output']
elif problem_type == 'code_f':
ground_truth = io_item['snippet']
else:
raise ValueError(f"Invalid problem type: {problem_type}")
if problem_type == 'code_f':
num_given_inputs = len(io_item['inputs']) // 2
num_given_outputs = len(io_item['outputs']) // 2
given_inputs = list(io_item['inputs'][:num_given_inputs])
given_outputs = list(io_item['outputs'][:num_given_outputs])
hidden_inputs = list(io_item['inputs'][num_given_inputs:])
hidden_outputs = list(io_item['outputs'][num_given_outputs:])
io_prompt = instruction_template.format(
get_code_problem_predictor_prompt(
problem_type=problem_type,
snippet=io_item['snippet'],
message=io_item['message'],
input_output_pairs=zip(given_inputs, given_outputs),
)
)
else:
io_prompt = instruction_template.format(
get_code_problem_predictor_prompt(
problem_type=problem_type,
snippet=io_item['snippet'],
input_args=io_item['input'],
output=io_item['output'],
)
)
if len(tokenizer(io_prompt)['input_ids']) <= content_max_length:
output_io_item = {
"data_source": 'pred_' + problem_type,
"prompt": [{
"role": "user",
"content": io_prompt,
}],
"problem": io_item['snippet'],
"ability": "code",
"reward_model": {
"style": "rule",
"ground_truth": ground_truth,
},
"extra_info": {
'split': split,
'index': idx,
'metric': 'pred_' + problem_type,
'imports': io_item['imports'],
}
}
if problem_type == 'code_f': # for code_f, we need to split the inputs and outputs into given and hidden, only show part of the inputs and outputs to the model
output_io_item['extra_info']['given_inputs'] = given_inputs
output_io_item['extra_info']['given_outputs'] = given_outputs
output_io_item['extra_info']['hidden_inputs'] = hidden_inputs
output_io_item['extra_info']['hidden_outputs'] = hidden_outputs
output_io_item['extra_info']['message'] = io_item['message']
else:
output_io_item['extra_info']['input'] = io_item['input']
output_io_item['extra_info']['output'] = io_item['output']
return_io_data.append(output_io_item)
if len(return_io_data) >= target_data_len:
break
# if io_data is not full, we sample upsample random data
while len(return_io_data) < target_data_len:
io_item = return_io_data[random.randint(0, len(return_io_data))]
return_io_data.append(io_item)
# output to parquet
df = pd.DataFrame(return_io_data)
df.to_parquet(output_path)
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