
"""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


SCREAMING_SNAKE_CASE_												= '''\\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'''

SCREAMING_SNAKE_CASE_												= '''\\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'''


SCREAMING_SNAKE_CASE_												= '''\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'''


SCREAMING_SNAKE_CASE_												= '''\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'''

SCREAMING_SNAKE_CASE_												= '''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 				a ( datasets.Metric							):





       """simple docstring"""







       def 				__A						(			self			)    ->				Optional[Any]:
          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							,	snake_case_							,	snake_case_							,	snake_case_=[1, 10, 100]							,	snake_case_=4							,	snake_case_=3.0			)    ->				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=_SCREAMING_SNAKE_CASE			) as executor:
             _UpperCAmelCase         =      []
             _UpperCAmelCase         =      Counter()
             _UpperCAmelCase         =      0
             _UpperCAmelCase         =      defaultdict(_SCREAMING_SNAKE_CASE			)

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

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

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

          _UpperCAmelCase         =      k
          _UpperCAmelCase         =      {F"""pass@{k}""": estimate_pass_at_k(_SCREAMING_SNAKE_CASE							,	_SCREAMING_SNAKE_CASE							,	_SCREAMING_SNAKE_CASE			).mean() for k in ks if (total >= k).all()}

          return pass_at_k, results







def   A__ ( A__      ,      A__      ,      A__		)		->			int:





   '''simple docstring'''




   def estimator(A__      ,      A__      ,      A__		) -> 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		):
      _UpperCAmelCase         =      itertools.repeat(_lowercase      ,      len(_lowercase		)		)
   else:
      assert len(_lowercase		) == len(_lowercase		)
      _UpperCAmelCase         =      iter(_lowercase		)

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







import os
import sys
import warnings
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union

import numpy as np
import pyarrow as pa

from .. import config
from ..download.streaming_download_manager import xopen
from ..table import array_cast
from ..utils.file_utils import is_local_path
from ..utils.py_utils import first_non_null_value, no_op_if_value_is_null, string_to_dict


if TYPE_CHECKING:
     import PIL.Image

     from .features import FeatureType


__a							     =					None
__a							     =					'<' if sys.byteorder == 'little' else '>'
# Origin: https://github.com/python-pillow/Pillow/blob/698951e19e19972aeed56df686868f1329981c12/src/PIL/Image.py#L3126 minus "|i1" which values are not preserved correctly when saving and loading an image
__a							     =					[
    np.dtype('|b1'),
    np.dtype('|u1'),
    np.dtype('<u2'),
    np.dtype('>u2'),
    np.dtype('<i2'),
    np.dtype('>i2'),
    np.dtype('<u4'),
    np.dtype('>u4'),
    np.dtype('<i4'),
    np.dtype('>i4'),
    np.dtype('<f4'),
    np.dtype('>f4'),
    np.dtype('<f8'),
    np.dtype('>f8'),
]

@dataclass
class     __a:






    """simple docstring"""





    lowerCAmelCase         = True
    lowerCAmelCase         = None
    # Automatically constructed
    lowerCAmelCase         = "PIL.Image.Image"
    lowerCAmelCase         = pa.struct({'''bytes''': pa.binary(), '''path''': pa.string()}  )
    lowerCAmelCase         = field(default='''Image'''							,		init=_a							,		repr=_a  )





    def __call__( self    )					->						Tuple:
         return self.pa_type





    def 				a__ ( self  ,_SCREAMING_SNAKE_CASE    )					->						dict:
         if config.PIL_AVAILABLE:
              import PIL.Image
         else:
              raise ImportError('''To support encoding images, please install \'Pillow\'.'''    )

         if isinstance(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    ):
              UpperCAmelCase_     :				List[str]  =     np.array(_SCREAMING_SNAKE_CASE    )

         if isinstance(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    ):
              return {"path": value, "bytes": None}
         elif isinstance(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    ):
              return {"path": None, "bytes": value}
         elif isinstance(_SCREAMING_SNAKE_CASE  ,np.ndarray    ):
              # convert the image array to PNG/TIFF bytes
              return encode_np_array(_SCREAMING_SNAKE_CASE    )
         elif isinstance(_SCREAMING_SNAKE_CASE  ,PIL.Image.Image    ):
              # convert the PIL image to bytes (default format is PNG/TIFF)
              return encode_pil_image(_SCREAMING_SNAKE_CASE    )
         elif value.get('''path'''    ) is not None and os.path.isfile(value['''path''']    ):
              # we set "bytes": None to not duplicate the data if they're already available locally
              return {"bytes": None, "path": value.get('''path'''    )}
         elif value.get('''bytes'''    ) is not None or value.get('''path'''    ) is not None:
              # store the image bytes, and path is used to infer the image format using the file extension
              return {"bytes": value.get('''bytes'''    ), "path": value.get('''path'''    )}
         else:
              raise ValueError(
                  f'''An image sample should have one of \'path\' or \'bytes\' but they are missing or None in {value}.'''    )





    def 				a__ ( self  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE=None    )					->						"PIL.Image.Image":
         if not self.decode:
              raise RuntimeError('''Decoding is disabled for this feature. Please use Image(decode=True) instead.'''    )

         if config.PIL_AVAILABLE:
              import PIL.Image
         else:
              raise ImportError('''To support decoding images, please install \'Pillow\'.'''    )

         if token_per_repo_id is None:
              UpperCAmelCase_     :				Dict  =     {}

         UpperCAmelCase_,       UpperCAmelCase_     :				Union[str, Any]  =     value['''path'''], value['''bytes''']
         if bytes_ is None:
              if path is None:
                   raise ValueError(f'''An image should have one of \'path\' or \'bytes\' but both are None in {value}.'''    )
              else:
                   if is_local_path(_SCREAMING_SNAKE_CASE    ):
                        UpperCAmelCase_     :				Tuple  =     PIL.Image.open(_SCREAMING_SNAKE_CASE    )
                   else:
                        UpperCAmelCase_     :				Dict  =     path.split('''::'''    )[-1]
                        try:
                             UpperCAmelCase_     :				Optional[int]  =     string_to_dict(_SCREAMING_SNAKE_CASE  ,config.HUB_DATASETS_URL    )['''repo_id''']
                             UpperCAmelCase_     :				Tuple  =     token_per_repo_id.get(_SCREAMING_SNAKE_CASE    )
                        except ValueError:
                             UpperCAmelCase_     :				Optional[Any]  =     None
                        with xopen(_SCREAMING_SNAKE_CASE  ,'''rb'''  ,use_auth_token=_SCREAMING_SNAKE_CASE    ) as f:
                             UpperCAmelCase_     :				List[str]  =     BytesIO(f.read()    )
                        UpperCAmelCase_     :				Optional[Any]  =     PIL.Image.open(bytes_    )
         else:
              UpperCAmelCase_     :				List[Any]  =     PIL.Image.open(BytesIO(bytes_    )    )
         image.load()  # to avoid "Too many open files" errors
         return image





    def 				a__ ( self    )					->						Union["FeatureType", Dict[str, "FeatureType"]]:
         from .features import Value

         return (
             self
             if self.decode
             else {
                 "bytes": Value('''binary'''    ),
                 "path": Value('''string'''    ),
             }
         )





    def 				a__ ( self  ,_SCREAMING_SNAKE_CASE    )					->						pa.StructArray:
         if pa.types.is_string(storage.type    ):
              UpperCAmelCase_     :				Dict  =     pa.array([None] * len(_SCREAMING_SNAKE_CASE    )  ,type=pa.binary()    )
              UpperCAmelCase_     :				Dict  =     pa.StructArray.from_arrays([bytes_array, storage]  ,['''bytes''', '''path''']  ,mask=storage.is_null()    )
         elif pa.types.is_binary(storage.type    ):
              UpperCAmelCase_     :				List[str]  =     pa.array([None] * len(_SCREAMING_SNAKE_CASE    )  ,type=pa.string()    )
              UpperCAmelCase_     :				Tuple  =     pa.StructArray.from_arrays([storage, path_array]  ,['''bytes''', '''path''']  ,mask=storage.is_null()    )
         elif pa.types.is_struct(storage.type    ):
              if storage.type.get_field_index('''bytes'''    ) >= 0:
                   UpperCAmelCase_     :				Dict  =     storage.field('''bytes'''    )
              else:
                   UpperCAmelCase_     :				Any  =     pa.array([None] * len(_SCREAMING_SNAKE_CASE    )  ,type=pa.binary()    )
              if storage.type.get_field_index('''path'''    ) >= 0:
                   UpperCAmelCase_     :				int  =     storage.field('''path'''    )
              else:
                   UpperCAmelCase_     :				List[str]  =     pa.array([None] * len(_SCREAMING_SNAKE_CASE    )  ,type=pa.string()    )
              UpperCAmelCase_     :				Optional[Any]  =     pa.StructArray.from_arrays([bytes_array, path_array]  ,['''bytes''', '''path''']  ,mask=storage.is_null()    )
         elif pa.types.is_list(storage.type    ):
              UpperCAmelCase_     :				Optional[Any]  =     pa.array(
                  [encode_np_array(np.array(_SCREAMING_SNAKE_CASE    )    )['''bytes'''] if arr is not None else None for arr in storage.to_pylist()]  ,type=pa.binary()  ,)
              UpperCAmelCase_     :				Any  =     pa.array([None] * len(_SCREAMING_SNAKE_CASE    )  ,type=pa.string()    )
              UpperCAmelCase_     :				Dict  =     pa.StructArray.from_arrays(
                  [bytes_array, path_array]  ,['''bytes''', '''path''']  ,mask=bytes_array.is_null()    )
         return array_cast(_SCREAMING_SNAKE_CASE  ,self.pa_type    )





    def 				a__ ( self  ,_SCREAMING_SNAKE_CASE    )					->						pa.StructArray:

         @no_op_if_value_is_null
         def path_to_bytes(_SCREAMING_SNAKE_CASE    ):
              with xopen(_SCREAMING_SNAKE_CASE  ,'''rb'''    ) as f:
                   UpperCAmelCase_     :				Any  =     f.read()
              return bytes_

         UpperCAmelCase_     :				Union[str, Any]  =     pa.array(
             [
                 (path_to_bytes(x['''path''']    ) if x['''bytes'''] is None else x['''bytes''']) if x is not None else None
                 for x in storage.to_pylist()
             ]  ,type=pa.binary()  ,)
         UpperCAmelCase_     :				List[str]  =     pa.array(
             [os.path.basename(_SCREAMING_SNAKE_CASE    ) if path is not None else None for path in storage.field('''path'''    ).to_pylist()]  ,type=pa.string()  ,)
         UpperCAmelCase_     :				Union[str, Any]  =     pa.StructArray.from_arrays([bytes_array, path_array]  ,['''bytes''', '''path''']  ,mask=bytes_array.is_null()    )
         return array_cast(_SCREAMING_SNAKE_CASE  ,self.pa_type    )







def lowerCamelCase__  (     ):

     '''simple docstring'''


     if config.PIL_AVAILABLE:
          import PIL.Image
     else:
          raise ImportError('''To support encoding images, please install \'Pillow\'.''' )

     global _IMAGE_COMPRESSION_FORMATS
     if _IMAGE_COMPRESSION_FORMATS is None:
          PIL.Image.init()
          UpperCAmelCase_     :				Optional[int]  =     list(set(PIL.Image.OPEN.keys() ) & set(PIL.Image.SAVE.keys() ) )
     return _IMAGE_COMPRESSION_FORMATS







def lowerCamelCase__  (     _lowercase ):

     '''simple docstring'''


     UpperCAmelCase_     :				Optional[int]  =     BytesIO()
     if image.format in list_image_compression_formats():
          UpperCAmelCase_     :				int  =     image.format
     else:
          UpperCAmelCase_     :				List[Any]  =     '''PNG''' if image.mode in ['''1''', '''L''', '''LA''', '''RGB''', '''RGBA'''] else '''TIFF'''
     image.save(_lowercase					,		format=_lowercase )
     return buffer.getvalue()







def lowerCamelCase__  (     _lowercase ):

     '''simple docstring'''


     if hasattr(_lowercase					,		'''filename''' ) and image.filename != "":
          return {"path": image.filename, "bytes": None}
     else:
          return {"path": None, "bytes": image_to_bytes(_lowercase )}







def lowerCamelCase__  (     _lowercase ):

     '''simple docstring'''


     if config.PIL_AVAILABLE:
          import PIL.Image
     else:
          raise ImportError('''To support encoding images, please install \'Pillow\'.''' )

     UpperCAmelCase_     :				Tuple  =     array.dtype
     UpperCAmelCase_     :				List[str]  =     dtype.byteorder if dtype.byteorder != '''=''' else _NATIVE_BYTEORDER
     UpperCAmelCase_     :				Dict  =     dtype.kind
     UpperCAmelCase_     :				Union[str, Any]  =     dtype.itemsize

     UpperCAmelCase_     :				Optional[Any]  =     None

     # Multi-channel array case (only np.dtype("|u1") is allowed)
     if array.shape[2:]:
          UpperCAmelCase_     :				Tuple  =     np.dtype('''|u1''' )
          if dtype_kind not in ["u", "i"]:
               raise TypeError(
                   f'''Unsupported array dtype {dtype} for image encoding. Only {dest_dtype} is supported for multi-channel arrays.''' )
          if dtype is not dest_dtype:
               warnings.warn(f'''Downcasting array dtype {dtype} to {dest_dtype} to be compatible with \'Pillow\'''' )
    # Exact match
     elif dtype in _VALID_IMAGE_ARRAY_DTPYES:
          UpperCAmelCase_     :				Union[str, Any]  =     dtype
     else:  # Downcast the type within the kind (np.can_cast(from_type, to_type, casting="same_kind") doesn't behave as expected, so do it manually)
          while dtype_itemsize >= 1:
               UpperCAmelCase_     :				Union[str, Any]  =     dtype_byteorder + dtype_kind + str(_lowercase )
               UpperCAmelCase_     :				str  =     np.dtype(_lowercase )
               if dest_dtype in _VALID_IMAGE_ARRAY_DTPYES:
                    warnings.warn(f'''Downcasting array dtype {dtype} to {dest_dtype} to be compatible with \'Pillow\'''' )
                    break
               else:
                    dtype_itemsize //= 2

     if dest_dtype is None:
          raise TypeError(
              f'''Cannot convert dtype {dtype} to a valid image dtype. Valid image dtypes: {_VALID_IMAGE_ARRAY_DTPYES}''' )
     UpperCAmelCase_     :				Any  =     PIL.Image.fromarray(array.astype(_lowercase ) )
     return {"path": None, "bytes": image_to_bytes(_lowercase )}







def lowerCamelCase__  (     _lowercase ):

     '''simple docstring'''


     if config.PIL_AVAILABLE:
          import PIL.Image
     else:
          raise ImportError('''To support encoding images, please install \'Pillow\'.''' )

     if objs:
          UpperCAmelCase_,       UpperCAmelCase_     :				Tuple  =     first_non_null_value(_lowercase )
          if isinstance(_lowercase					,		_lowercase ):
               return [{"path": obj, "bytes": None} if obj is not None else None for obj in objs]
          if isinstance(_lowercase					,		np.ndarray ):
               UpperCAmelCase_     :				Any  =     no_op_if_value_is_null(_lowercase )
               return [obj_to_image_dict_func(_lowercase ) for obj in objs]
          elif isinstance(_lowercase					,		PIL.Image.Image ):
               UpperCAmelCase_     :				Union[str, Any]  =     no_op_if_value_is_null(_lowercase )
               return [obj_to_image_dict_func(_lowercase ) for obj in objs]
          else:
               return objs
     else:
          return objs