






"""simple docstring"""
import itertools
import os
from collections import Counter, defaultdict
from concurrent.futures import ThreadPoolExecutor, as_completed

import numpy as np

import datasets

from .execute import check_correctness


lowerCAmelCase__    						=			'\\n@misc{chen2021evaluating,\n      title={Evaluating Large Language Models Trained on Code},\n      author={Mark Chen and Jerry Tworek and Heewoo Jun and Qiming Yuan \\nand Henrique Ponde de Oliveira Pinto and Jared Kaplan and Harri Edwards \\nand Yuri Burda and Nicholas Joseph and Greg Brockman and Alex Ray \\nand Raul Puri and Gretchen Krueger and Michael Petrov and Heidy Khlaaf \\nand Girish Sastry and Pamela Mishkin and Brooke Chan and Scott Gray \\nand Nick Ryder and Mikhail Pavlov and Alethea Power and Lukasz Kaiser \\nand Mohammad Bavarian and Clemens Winter and Philippe Tillet \\nand Felipe Petroski Such and Dave Cummings and Matthias Plappert \\nand Fotios Chantzis and Elizabeth Barnes and Ariel Herbert-Voss \\nand William Hebgen Guss and Alex Nichol and Alex Paino and Nikolas Tezak \\nand Jie Tang and Igor Babuschkin and Suchir Balaji and Shantanu Jain \\nand William Saunders and Christopher Hesse and Andrew N. Carr \\nand Jan Leike and Josh Achiam and Vedant Misra and Evan Morikawa \\nand Alec Radford and Matthew Knight and Miles Brundage and Mira Murati \\nand Katie Mayer and Peter Welinder and Bob McGrew and Dario Amodei \\nand Sam McCandlish and Ilya Sutskever and Wojciech Zaremba},\n      year={2021},\n      eprint={2107.03374},\n      archivePrefix={arXiv},\n      primaryClass={cs.LG}\n}\n'

lowerCAmelCase__    						=			'\\nThis metric implements the evaluation harness for the HumanEval problem solving dataset\ndescribed in the paper "Evaluating Large Language Models Trained on Code"\n(https://arxiv.org/abs/2107.03374).\n'


lowerCAmelCase__    						=			'\nCalculates how good are predictions given some references, using certain scores\nArgs:\n    predictions: list of candidates to evaluate. Each candidates should be a list\n        of strings with several code candidates to solve the problem.\n    references: a list with a test for each prediction. Each test should evaluate the\n        correctness of a code candidate.\n    k: number of code candidates to consider in the evaluation (Default: [1, 10, 100])\n    num_workers: number of workers used to evaluate the canidate programs (Default: 4).\n    timeout:\nReturns:\n    pass_at_k: dict with pass rates for each k\n    results: dict with granular results of each unittest\nExamples:\n    >>> code_eval = datasets.load_metric("code_eval")\n    >>> test_cases = ["assert add(2,3)==5"]\n    >>> candidates = [["def add(a,b): return a*b", "def add(a, b): return a+b"]]\n    >>> pass_at_k, results = code_eval.compute(references=test_cases, predictions=candidates, k=[1, 2])\n    >>> print(pass_at_k)\n    {\'pass@1\': 0.5, \'pass@2\': 1.0}\n'


lowerCAmelCase__    						=			'\n################################################################################\n                                  !!!WARNING!!!\n################################################################################\nThe "code_eval" metric executes untrusted model-generated code in Python.\nAlthough it is highly unlikely that model-generated code will do something\novertly malicious in response to this test suite, model-generated code may act\ndestructively due to a lack of model capability or alignment.\nUsers are strongly encouraged to sandbox this evaluation suite so that it\ndoes not perform destructive actions on their host or network. For more\ninformation on how OpenAI sandboxes its code, see the paper "Evaluating Large\nLanguage Models Trained on Code" (https://arxiv.org/abs/2107.03374).\n\nOnce you have read this disclaimer and taken appropriate precautions,\nset the environment variable HF_ALLOW_CODE_EVAL="1". Within Python you can to this\nwith:\n\n>>> import os\n>>> os.environ["HF_ALLOW_CODE_EVAL"] = "1"\n\n################################################################################\\n'

lowerCAmelCase__    						=			'The MIT License\n\nCopyright (c) OpenAI (https://openai.com)\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the "Software"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in\nall copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\nTHE SOFTWARE.'


@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION   ,  _KWARGS_DESCRIPTION       )
class _lowerCAmelCase						(    datasets.Metric       ):







						def 			A      (    self							)		->							Optional[int]:
										return datasets.MetricInfo(
										    # This is the description that will appear on the metrics page.
										    description=_DESCRIPTION   ,				citation=_CITATION   ,				inputs_description=_KWARGS_DESCRIPTION   ,				features=datasets.Features(
										        {
										            'predictions': datasets.Sequence(datasets.Value('string'							)							),
										            'references': datasets.Value('string'							),
										        }							)   ,				homepage='https://github.com/openai/human-eval'   ,				codebase_urls=['https://github.com/openai/human-eval']   ,				reference_urls=['https://github.com/openai/human-eval']   ,				license=_LICENSE   ,				)







						def 			A      (    self   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_=[1, 1_0, 1_0_0]   ,				lowerCAmelCase_=4   ,				lowerCAmelCase_=3.0							)		->							Optional[int]:

										if os.getenv('HF_ALLOW_CODE_EVAL'   ,				0							) != "1":
														raise ValueError(_WARNING							)

										if os.name == "nt":
														raise NotImplementedError('This metric is currently not supported on Windows.'							)

										with ThreadPoolExecutor(max_workers=lowerCAmelCase_							) as executor:
														_SCREAMING_SNAKE_CASE   :		List[Any]       		=    []
														_SCREAMING_SNAKE_CASE   :		int       		=    Counter()
														_SCREAMING_SNAKE_CASE   :		Any       		=    0
														_SCREAMING_SNAKE_CASE   :		Any       		=    defaultdict(lowerCAmelCase_							)

														for task_id, (candidates, test_case) in enumerate(zip(lowerCAmelCase_   ,				lowerCAmelCase_							)							):
																		for candidate in candidates:
																						_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    candidate + '\n' + test_case
																						_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    (test_program, timeout, task_id, completion_id[task_id])
																						_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    executor.submit(lowerCAmelCase_   ,				*lowerCAmelCase_							)
																						futures.append(lowerCAmelCase_							)
																						completion_id[task_id] += 1
																						n_samples += 1

														for future in as_completed(lowerCAmelCase_							):
																		_SCREAMING_SNAKE_CASE   :		Any       		=    future.result()
																		results[result["task_id"]].append((result['completion_id'], result)							)

										_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE   :		List[Any]       		=    [], []
										for result in results.values():
														result.sort()
														_SCREAMING_SNAKE_CASE   :		Any       		=    [r[1]['passed'] for r in result]
														total.append(len(lowerCAmelCase_							)							)
														correct.append(sum(lowerCAmelCase_							)							)
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    np.array(lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		Any       		=    np.array(lowerCAmelCase_							)

										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    k
										_SCREAMING_SNAKE_CASE   :		int       		=    {F"""pass@{k}""": estimate_pass_at_k(lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_							).mean() for k in ks if (total >= k).all()}

										return pass_at_k, results







def 				lowercase__		(					lowerCamelCase,						lowerCamelCase,						lowerCamelCase       ):

				def estimator(lowerCamelCase,						lowerCamelCase,						lowerCamelCase       ) -> float:
								if n - c < k:
												return 1.0
								return 1.0 - np.prod(1.0 - k / np.arange(n - c + 1,						n + 1       )       )

				if isinstance(lowerCamelCase,						lowerCamelCase       ):
								_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    itertools.repeat(lowerCamelCase,						len(lowerCamelCase       )       )
				else:
								assert len(lowerCamelCase       ) == len(lowerCamelCase       )
								_SCREAMING_SNAKE_CASE   :		str       		=    iter(lowerCamelCase       )

				return np.array([estimator(int(lowerCamelCase       ),						int(lowerCamelCase       ),						lowerCamelCase       ) for n, c in zip(lowerCamelCase,						lowerCamelCase       )]       )









"""simple docstring"""
class _lowerCAmelCase						:







						def __init__(    self   ,				lowerCAmelCase_   ,				lowerCAmelCase_=None   ,				lowerCAmelCase_=None							)		->							int:
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    data
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    previous
										_SCREAMING_SNAKE_CASE   :		Any       		=    next_node







						def __str__(    self							)		->							str:
										return F"""{self.data}"""







						def 			A      (    self							)		->							int:
										return self.data







						def 			A      (    self							)		->							Dict:
										return self.next







						def 			A      (    self							)		->							Union[str, Any]:
										return self.previous







class _lowerCAmelCase						:







						def __init__(    self   ,				lowerCAmelCase_							)		->							List[str]:
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    head







						def __iter__(    self							)		->							Any:
										return self







						def 			A      (    self							)		->							Dict:
										if not self.current:
														raise StopIteration
										else:
														_SCREAMING_SNAKE_CASE   :		Any       		=    self.current.get_data()
														_SCREAMING_SNAKE_CASE   :		Dict       		=    self.current.get_next()
														return value







class _lowerCAmelCase						:







						def __init__(    self							)		->							Dict:
										_SCREAMING_SNAKE_CASE   :		Dict       		=    None  # First node in list
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    None  # Last node in list







						def __str__(    self							)		->							Any:
										_SCREAMING_SNAKE_CASE   :		str       		=    self.head
										_SCREAMING_SNAKE_CASE   :		int       		=    []
										while current is not None:
														nodes.append(current.get_data()							)
														_SCREAMING_SNAKE_CASE   :		List[Any]       		=    current.get_next()
										return " ".join(str(lowerCAmelCase_							) for node in nodes							)







						def __contains__(    self   ,				lowerCAmelCase_							)		->							Any:
										_SCREAMING_SNAKE_CASE   :		Any       		=    self.head
										while current:
														if current.get_data() == value:
																		return True
														_SCREAMING_SNAKE_CASE   :		int       		=    current.get_next()
										return False







						def __iter__(    self							)		->							str:
										return LinkedListIterator(self.head							)







						def 			A      (    self							)		->							Dict:
										if self.head:
														return self.head.get_data()
										return None







						def 			A      (    self							)		->							List[Any]:
										if self.tail:
														return self.tail.get_data()
										return None







						def 			A      (    self   ,				lowerCAmelCase_							)		->							None:
										if self.head is None:
														_SCREAMING_SNAKE_CASE   :		List[Any]       		=    node
														_SCREAMING_SNAKE_CASE   :		Dict       		=    node
										else:
														self.insert_before_node(self.head   ,				lowerCAmelCase_							)







						def 			A      (    self   ,				lowerCAmelCase_							)		->							None:
										if self.head is None:
														self.set_head(lowerCAmelCase_							)
										else:
														self.insert_after_node(self.tail   ,				lowerCAmelCase_							)







						def 			A      (    self   ,				lowerCAmelCase_							)		->							None:
										_SCREAMING_SNAKE_CASE   :		Any       		=    Node(lowerCAmelCase_							)
										if self.head is None:
														self.set_head(lowerCAmelCase_							)
										else:
														self.set_tail(lowerCAmelCase_							)







						def 			A      (    self   ,				lowerCAmelCase_   ,				lowerCAmelCase_							)		->							None:
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    node
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    node.previous

										if node.get_previous() is None:
														_SCREAMING_SNAKE_CASE   :		List[Any]       		=    node_to_insert
										else:
														_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    node_to_insert

										_SCREAMING_SNAKE_CASE   :		Any       		=    node_to_insert







						def 			A      (    self   ,				lowerCAmelCase_   ,				lowerCAmelCase_							)		->							None:
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    node
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    node.next

										if node.get_next() is None:
														_SCREAMING_SNAKE_CASE   :		Tuple       		=    node_to_insert
										else:
														_SCREAMING_SNAKE_CASE   :		Any       		=    node_to_insert

										_SCREAMING_SNAKE_CASE   :		List[str]       		=    node_to_insert







						def 			A      (    self   ,				lowerCAmelCase_   ,				lowerCAmelCase_							)		->							None:
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    1
										_SCREAMING_SNAKE_CASE   :		str       		=    Node(lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    self.head
										while node:
														if current_position == position:
																		self.insert_before_node(lowerCAmelCase_   ,				lowerCAmelCase_							)
																		return
														current_position += 1
														_SCREAMING_SNAKE_CASE   :		List[str]       		=    node.next
										self.insert_after_node(self.tail   ,				lowerCAmelCase_							)







						def 			A      (    self   ,				lowerCAmelCase_							)		->							Node:
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    self.head
										while node:
														if node.get_data() == item:
																		return node
														_SCREAMING_SNAKE_CASE   :		Dict       		=    node.get_next()
										raise Exception('Node not found'							)







						def 			A      (    self   ,				lowerCAmelCase_							)		->							int:
										if (node := self.get_node(lowerCAmelCase_							)) is not None:
														if node == self.head:
																		_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    self.head.get_next()

														if node == self.tail:
																		_SCREAMING_SNAKE_CASE   :		int       		=    self.tail.get_previous()

														self.remove_node_pointers(lowerCAmelCase_							)







						@staticmethod
						def 			A      (    lowerCAmelCase_							)		->							None:
										if node.get_next():
														_SCREAMING_SNAKE_CASE   :		List[Any]       		=    node.previous

										if node.get_previous():
														_SCREAMING_SNAKE_CASE   :		str       		=    node.next

										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    None
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    None







						def 			A      (    self							)		->							List[str]:
										return self.head is None







def 				lowercase__		(					):
				pass


if __name__ == "__main__":
			import doctest

			doctest.testmod()

