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import os
from typing import Literal
ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from OpenCodeEval.benchmark.base import Benchmark, PYTHON_STOP, PYTHON_IMPORTS
from OpenCodeEval.utils import refine_text, stream_jsonl
from OpenCodeEval.eval.func_eval import check_correctness
from OpenCodeEval.eval.sanitize import sanitize
class BigCodeBench(Benchmark):
name: str = "BigCodeBench"
path: str = None
fullset_path = os.path.abspath(os.path.join(ROOT, "../data/BigCodeBench.jsonl"))
subset_path = os.path.abspath(os.path.join(ROOT, "../data/BigCodeBench_Hard.jsonl"))
imports_code = PYTHON_IMPORTS
chat_stop = PYTHON_STOP
base_stop = ['\n"""', "\nassert"]
def __init__(self,
name: str = "BigCodeBench",
timeout:float = 10.0,
prompt_type: Literal["Completion", "Instruction"] = "Completion"
):
super().__init__()
self.name = name
self.timeout = timeout
self.prompt_type = prompt_type
if self.name == "BigCodeHard":
self.path = self.subset_path
elif self.name == "BigCodeBench":
self.path = self.fullset_path
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["task_id"].split("/")[-1])
tasks[task_id] = task_data
return tasks
def get_prompt(self):
"""
Builds the prompt for the LM to generate from.
"""
prompts = []
for task_id, task_data in self.tasks.items():
if self.prompt_type == "Completion":
prompt = task_data['complete_prompt']
elif self.prompt_type == "Instruction":
prompt = task_data['instruct_prompt']
prompts.append(
dict(
task_id = task_id,
prompt = refine_text(prompt)
)
)
return prompts
def postprocess_generation(self, generation):
"""
Postprocess the generations.
"""
entry_point = self.tasks[generation['task_id']]["entry_point"]
result = dict(
task_id = generation['task_id'],
completion_id = generation['completion_id'],
solution = sanitize(generation['completion'], entry_point)
)
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["code_prompt"] + "\n"
+ " pass\n" + "\n"
+ solution['solution'] + "\n"
)
result = check_correctness(solution['task_id'],
solution['completion_id'],
code,
task_data["test"],
self.timeout)
return result
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