






from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
	import PIL

_UpperCamelCase			:	Union[str, Any]								=logging.get_logger(__name__)



def  a__ (__lowercase							:str   )		-> List[List[ImageInput]]:
				if isinstance(__lowercase					,  (list, tuple)   ) and isinstance(videos[0]					,  (list, tuple)   ) and is_valid_image(videos[0][0]   ):
								return videos

				elif isinstance(__lowercase					,  (list, tuple)   ) and is_valid_image(videos[0]   ):
								return [videos]

				elif is_valid_image(__lowercase   ):
								return [[videos]]

				raise ValueError(f"""Could not make batched video from {videos}"""   )






class 						UpperCAmelCase__ (							__snake_case							):
		__snake_case      :       Optional[int]     =   ["pixel_values"]







		def __init__(					self       ,A__ = True       ,A__ = None       ,A__ = PILImageResampling.BILINEAR       ,A__ = True       ,A__ = None       ,A__ = True       ,A__ = 1 / 255       ,A__ = True       ,A__ = True       ,A__ = None       ,A__ = None       ,**A__       ,):
						super().__init__(**A__		)
						_A	:				Tuple   =      size if size is not None else {'''shortest_edge''': 256}
						_A	:				str   =      get_size_dict(A__       ,default_to_square=A__		)
						_A	:				str   =      crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
						_A	:				str   =      get_size_dict(A__       ,param_name='''crop_size'''		)

						_A	:				Tuple   =      do_resize
						_A	:				Optional[Any]   =      size
						_A	:				Optional[Any]   =      do_center_crop
						_A	:				List[str]   =      crop_size
						_A	:				Dict   =      resample
						_A	:				Tuple   =      do_rescale
						_A	:				int   =      rescale_factor
						_A	:				List[str]   =      offset
						_A	:				List[Any]   =      do_normalize
						_A	:				List[Any]   =      image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
						_A	:				List[str]   =      image_std if image_std is not None else IMAGENET_STANDARD_STD







		def  A__      (					self       ,A__       ,A__       ,A__ = PILImageResampling.BILINEAR       ,A__ = None       ,**A__       ,):
						_A	:				str   =      get_size_dict(A__       ,default_to_square=A__		)
						if "shortest_edge" in size:
										_A	:				Optional[Any]   =      get_resize_output_image_size(A__       ,size['''shortest_edge''']       ,default_to_square=A__		)
						elif "height" in size and "width" in size:
										_A	:				str   =      (size['''height'''], size['''width'''])
						else:
										raise ValueError(f"""Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"""		)
						return resize(A__       ,size=A__       ,resample=A__       ,data_format=A__       ,**A__		)







		def  A__      (					self       ,A__       ,A__       ,A__ = None       ,**A__       ,):
						_A	:				Optional[int]   =      get_size_dict(A__		)
						if "height" not in size or "width" not in size:
										raise ValueError(f"""Size must have 'height' and 'width' as keys. Got {size.keys()}"""		)
						return center_crop(A__       ,size=(size['''height'''], size['''width'''])       ,data_format=A__       ,**A__		)







		def  A__      (					self       ,A__       ,A__       ,A__ = True       ,A__ = None       ,**A__       ,):
						_A	:				Any   =      image.astype(np.floataa		)
						if offset:
										_A	:				List[str]   =      image - (scale / 2)
						return rescale(A__       ,scale=A__       ,data_format=A__       ,**A__		)







		def  A__      (					self       ,A__       ,A__       ,A__       ,A__ = None       ,**A__       ,):
						return normalize(A__       ,mean=A__       ,std=A__       ,data_format=A__       ,**A__		)







		def  A__      (					self       ,A__       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = ChannelDimension.FIRST       ,):
						if do_resize and size is None or resample is None:
										raise ValueError('''Size and resample must be specified if do_resize is True.'''		)

						if do_center_crop and crop_size is None:
										raise ValueError('''Crop size must be specified if do_center_crop is True.'''		)

						if do_rescale and rescale_factor is None:
										raise ValueError('''Rescale factor must be specified if do_rescale is True.'''		)

						if do_normalize and (image_mean is None or image_std is None):
										raise ValueError('''Image mean and std must be specified if do_normalize is True.'''		)

						if offset and not do_rescale:
										raise ValueError('''For offset, do_rescale must also be set to True.'''		)

						# All transformations expect numpy arrays.
						_A	:				Dict   =      to_numpy_array(A__		)

						if do_resize:
										_A	:				Any   =      self.resize(image=A__       ,size=A__       ,resample=A__		)

						if do_center_crop:
										_A	:				str   =      self.center_crop(A__       ,size=A__		)

						if do_rescale:
										_A	:				Optional[int]   =      self.rescale(image=A__       ,scale=A__       ,offset=A__		)

						if do_normalize:
										_A	:				Dict   =      self.normalize(image=A__       ,mean=A__       ,std=A__		)

						_A	:				Union[str, Any]   =      to_channel_dimension_format(A__       ,A__		)
						return image







		def  A__      (					self       ,A__       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = ChannelDimension.FIRST       ,**A__       ,):
						_A	:				Optional[int]   =      do_resize if do_resize is not None else self.do_resize
						_A	:				List[str]   =      resample if resample is not None else self.resample
						_A	:				Union[str, Any]   =      do_center_crop if do_center_crop is not None else self.do_center_crop
						_A	:				Tuple   =      do_rescale if do_rescale is not None else self.do_rescale
						_A	:				List[str]   =      rescale_factor if rescale_factor is not None else self.rescale_factor
						_A	:				List[str]   =      offset if offset is not None else self.offset
						_A	:				Optional[int]   =      do_normalize if do_normalize is not None else self.do_normalize
						_A	:				Optional[Any]   =      image_mean if image_mean is not None else self.image_mean
						_A	:				Dict   =      image_std if image_std is not None else self.image_std

						_A	:				str   =      size if size is not None else self.size
						_A	:				int   =      get_size_dict(A__       ,default_to_square=A__		)
						_A	:				Any   =      crop_size if crop_size is not None else self.crop_size
						_A	:				Optional[int]   =      get_size_dict(A__       ,param_name='''crop_size'''		)

						if not valid_images(A__		):
										raise ValueError(
										    '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
										    '''torch.Tensor, tf.Tensor or jax.ndarray.'''		)

						_A	:				List[str]   =      make_batched(A__		)

						_A	:				Tuple   =      [
						    [
						        self._preprocess_image(
						            image=A__       ,do_resize=A__       ,size=A__       ,resample=A__       ,do_center_crop=A__       ,crop_size=A__       ,do_rescale=A__       ,rescale_factor=A__       ,offset=A__       ,do_normalize=A__       ,image_mean=A__       ,image_std=A__       ,data_format=A__       ,)
						        for img in video
						    ]
						    for video in videos
						]

						_A	:				Optional[int]   =      {'''pixel_values''': videos}
						return BatchFeature(data=A__       ,tensor_type=A__		)











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


_UpperCamelCase			:	Tuple								='\\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'

_UpperCamelCase			:	Tuple								='\\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'


_UpperCamelCase			:	Any								='\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'


_UpperCamelCase			:	Optional[Any]								='\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'

_UpperCamelCase			:	Tuple								='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 						UpperCAmelCase__ (							datasets.Metric							):







		def  A__      (					self		):
						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       ,A__       ,A__       ,A__=[1, 10, 100]       ,A__=4       ,A__=3.0		):

						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=A__		) as executor:
										_A	:				Any   =      []
										_A	:				List[Any]   =      Counter()
										_A	:				Optional[Any]   =      0
										_A	:				Any   =      defaultdict(A__		)

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

										for future in as_completed(A__		):
														_A	:				List[str]   =      future.result()
														results[result["task_id"]].append((result['''completion_id'''], result)		)

						_A  ,				_A	:				str   =      [], []
						for result in results.values():
										result.sort()
										_A	:				Optional[Any]   =      [r[1]['''passed'''] for r in result]
										total.append(len(A__		)		)
										correct.append(sum(A__		)		)
						_A	:				int   =      np.array(A__		)
						_A	:				Dict   =      np.array(A__		)

						_A	:				Optional[Any]   =      k
						_A	:				Union[str, Any]   =      {f"""pass@{k}""": estimate_pass_at_k(A__       ,A__       ,A__		).mean() for k in ks if (total >= k).all()}

						return pass_at_k, results



def  a__ (__lowercase							:List[Any]					,  __lowercase							:Union[str, Any]					,  __lowercase							:Any   )		-> List[Any]:

				def estimator(__lowercase							:int					,  __lowercase							:int					,  __lowercase							:int   ) -> 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(__lowercase					,  __lowercase   ):
								_A	:				Optional[Any]   =      itertools.repeat(__lowercase					,  len(__lowercase   )   )
				else:
								assert len(__lowercase   ) == len(__lowercase   )
								_A	:				Dict   =      iter(__lowercase   )

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



