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import os
from OpenCodeEval.benchmark.base import Benchmark, PYTHON_STOP, PYTHON_IMPORTS
from OpenCodeEval.utils import program_extract, stream_jsonl
from OpenCodeEval.eval.func_eval import check_correctness
from OpenCodeEval.eval.sanitize import sanitize
from typing import List, Literal
from typing import *
from functools import *
from collections import *
from itertools import *
from heapq import *
from bisect import *
from string import *
from operator import *
from math import *
import numpy as np
from numpy import *
import datetime
import copy
inf = float('inf')
if os.environ.get("MAX_LINES"):
MAX_LINES = int(os.environ.get("MAX_LINES"))
else:
MAX_LINES = 200
def base_prompt(data):
prompt = 'You are an expert Python programmer, and here is your task:\n'
prompt = prompt + f'# Task: {data["title"]}\n'
prompt = prompt + f'# Description:\n{data["description"]}\n'
# prompt = prompt + f'# Examples:\n'
# for example_idx, (example, reasoning) in enumerate(zip(data["examples"], data["reasoning"])):
# prompt = prompt + f'## Example {example_idx + 1}:\n'
# prompt = prompt + f'### Input:\n{example["input"]}\n'
# prompt = prompt + f'### Output:\n{example["output"]}\n'
# prompt = prompt + f'### Reasoning:\n{reasoning}\n'
input_code = (data["import_code"] + "\n" + data["starter_code"]).strip()
prompt = prompt + f'# Your code should start with:\n```python\n{input_code}\n```\n'
if data['output_constrains'].strip():
prompt = prompt + f'# Output Constraints:\n{data["output_constrains"].strip()}\n'
return prompt
class understandml(Benchmark):
name: str = "understandml"
imports_code = PYTHON_IMPORTS
chat_stop = PYTHON_STOP
base_stop = ['\n"""', "\nassert"]
def __init__(
self,
split: Literal["human", "model"] = "model",
time_out: float = 3.0,
prompt_type: str = "Instruction"
):
super().__init__()
self.split = split
self.time_out = time_out
self.prompt_type = prompt_type
self.path = os.path.join(self.path, f"{self.name}/{self.split}_benchmark.jsonl")
self.tasks = self.get_task()
def get_task(self):
"""
Get the task data from the jsonl file into a dictionary.
"""
tasks = {}
for task_data in stream_jsonl(filename = self.path):
task_id = int(task_data["id"])
tasks[task_id] = task_data
return tasks
def get_prompt(self):
"""
Builds the prompt for the LM to generate from.
"""
assert self.prompt_type == "Instruction", "Prompt type must be Instruction for mbpp"
prompts = []
for task_id, task_data in self.tasks.items():
prompt = base_prompt(task_data)
prompts.append({
'task_id': task_id,
'prompt': prompt
})
return prompts
def postprocess_generation(self, generation):
"""
Postprocess the generations.
"""
entry_point = self.tasks[generation['task_id']]["entry_point"]
try:
completion = '\n'.join(generation['completion'].splitlines()[-MAX_LINES:])
if '</think>' in completion:
completion = completion.split('</think>')[1]
solution = sanitize(completion, entry_point)
except Exception:
solution = program_extract(generation['completion'], program="python", mode="all")
result = dict(
task_id = generation['task_id'],
completion_id = generation['completion_id'],
solution = solution
)
return result
def process_results(self, solution):
"""
Takes the list of LM generations and evaluates them against the test cases
"""
task_data = self.tasks[solution['task_id']]
code = (
task_data['import_code'] + "\n"
+ solution['solution'] + "\n"
+ "\n".join(task_data['test_cases'])
)
result = check_correctness(solution['task_id'],
solution['completion_id'],
code,
self.time_out)
return result
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