





import argparse
import importlib
from pathlib import Path


# Test all the extensions added in the setup
_UpperCAmelCase        =				[
    'kernels/rwkv/wkv_cuda.cu',
    'kernels/rwkv/wkv_op.cpp',
    'kernels/deformable_detr/ms_deform_attn.h',
    'kernels/deformable_detr/cuda/ms_deform_im2col_cuda.cuh',
    'models/graphormer/algos_graphormer.pyx',
]




def  lowerCAmelCase_ (							UpperCamelCase_  )     ->		List[str]:
							# Test all the extensions added in the setup
							for file in FILES_TO_FIND:
														if not (transformers_path / file).exists():
																					return False
							return True


if __name__ == "__main__":
					_UpperCAmelCase        =				argparse.ArgumentParser()
					parser.add_argument('--check_lib', action='store_true', help='Whether to check the build or the actual package.')
					_UpperCAmelCase        =				parser.parse_args()
					if args.check_lib:
										_UpperCAmelCase        =				importlib.import_module('transformers')
										_UpperCAmelCase        =				Path(transformers_module.__file__).parent
					else:
										_UpperCAmelCase        =				Path.cwd() / 'build/lib/transformers'
					if not test_custom_files_are_present(transformers_path):
										raise ValueError('The built release does not contain the custom files. Fix this before going further!')








import re
import string

import numpy as np

import datasets


_UpperCAmelCase        =				'\nReturns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.\n'

_UpperCAmelCase        =				'\nArgs:\n    predictions: List of predicted texts.\n    references: List of reference texts.\n    regexes_to_ignore: List, defaults to None. Regex expressions of characters to\n        ignore when calculating the exact matches. Note: these regexes are removed\n        from the input data before the changes based on the options below (e.g. ignore_case,\n        ignore_punctuation, ignore_numbers) are applied.\n    ignore_case: Boolean, defaults to False. If true, turns everything\n        to lowercase so that capitalization differences are ignored.\n    ignore_punctuation: Boolean, defaults to False. If true, removes all punctuation before\n        comparing predictions and references.\n    ignore_numbers: Boolean, defaults to False. If true, removes all punctuation before\n        comparing predictions and references.\nReturns:\n    exact_match: Dictionary containing exact_match rate. Possible values are between 0.0 and 100.0, inclusive.\nExamples:\n    >>> exact_match = datasets.load_metric("exact_match")\n    >>> refs = ["the cat", "theater", "YELLING", "agent007"]\n    >>> preds = ["cat?", "theater", "yelling", "agent"]\n    >>> results = exact_match.compute(references=refs, predictions=preds)\n    >>> print(round(results["exact_match"], 1))\n    25.0\n\n    >>> exact_match = datasets.load_metric("exact_match")\n    >>> refs = ["the cat", "theater", "YELLING", "agent007"]\n    >>> preds = ["cat?", "theater", "yelling", "agent"]\n    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=["the ", "yell"], ignore_case=True, ignore_punctuation=True)\n    >>> print(round(results["exact_match"], 1))\n    50.0\n\n\n    >>> exact_match = datasets.load_metric("exact_match")\n    >>> refs = ["the cat", "theater", "YELLING", "agent007"]\n    >>> preds = ["cat?", "theater", "yelling", "agent"]\n    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=["the ", "yell", "YELL"], ignore_case=True, ignore_punctuation=True)\n    >>> print(round(results["exact_match"], 1))\n    75.0\n\n    >>> exact_match = datasets.load_metric("exact_match")\n    >>> refs = ["the cat", "theater", "YELLING", "agent007"]\n    >>> preds = ["cat?", "theater", "yelling", "agent"]\n    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=["the ", "yell", "YELL"], ignore_case=True, ignore_punctuation=True, ignore_numbers=True)\n    >>> print(round(results["exact_match"], 1))\n    100.0\n\n    >>> exact_match = datasets.load_metric("exact_match")\n    >>> refs = ["The cat sat on the mat.", "Theaters are great.", "It\'s like comparing oranges and apples."]\n    >>> preds = ["The cat sat on the mat?", "Theaters are great.", "It\'s like comparing apples and oranges."]\n    >>> results = exact_match.compute(references=refs, predictions=preds)\n    >>> print(round(results["exact_match"], 1))\n    33.3\n\n'

_UpperCAmelCase        =				'\n'



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

       def lowercase (		self: Any		)       -> List[str]:




              """simple docstring"""


              return datasets.MetricInfo(
                  description=_DESCRIPTION					,   citation=_CITATION					,   inputs_description=_KWARGS_DESCRIPTION					,   features=datasets.Features(
                      {
                          "predictions": datasets.Value("string"					,   id="sequence"		),
                          "references": datasets.Value("string"					,   id="sequence"		),
                      }		)					,   reference_urls=[]					,   )



       def lowercase (		self: Any					,   _SCREAMING_SNAKE_CASE: Any					,   _SCREAMING_SNAKE_CASE: Tuple					,   _SCREAMING_SNAKE_CASE: List[Any]=None					,   _SCREAMING_SNAKE_CASE: Tuple=False					,   _SCREAMING_SNAKE_CASE: Optional[int]=False					,   _SCREAMING_SNAKE_CASE: Any=False					,   )       -> Optional[Any]:




              """simple docstring"""


              if regexes_to_ignore is not None:
                     for s in regexes_to_ignore:
                            UpperCamelCase_          =  np.array([re.sub(_SCREAMING_SNAKE_CASE					,   ""					,   _SCREAMING_SNAKE_CASE		) for x in predictions]		)
                            UpperCamelCase_          =  np.array([re.sub(_SCREAMING_SNAKE_CASE					,   ""					,   _SCREAMING_SNAKE_CASE		) for x in references]		)
              else:
                     UpperCamelCase_          =  np.asarray(_SCREAMING_SNAKE_CASE		)
                     UpperCamelCase_          =  np.asarray(_SCREAMING_SNAKE_CASE		)

              if ignore_case:
                     UpperCamelCase_          =  np.char.lower(_SCREAMING_SNAKE_CASE		)
                     UpperCamelCase_          =  np.char.lower(_SCREAMING_SNAKE_CASE		)

              if ignore_punctuation:
                     UpperCamelCase_          =  string.punctuation.maketrans(""					,   ""					,   string.punctuation		)
                     UpperCamelCase_          =  np.char.translate(_SCREAMING_SNAKE_CASE					,   table=_SCREAMING_SNAKE_CASE		)
                     UpperCamelCase_          =  np.char.translate(_SCREAMING_SNAKE_CASE					,   table=_SCREAMING_SNAKE_CASE		)

              if ignore_numbers:
                     UpperCamelCase_          =  string.digits.maketrans(""					,   ""					,   string.digits		)
                     UpperCamelCase_          =  np.char.translate(_SCREAMING_SNAKE_CASE					,   table=_SCREAMING_SNAKE_CASE		)
                     UpperCamelCase_          =  np.char.translate(_SCREAMING_SNAKE_CASE					,   table=_SCREAMING_SNAKE_CASE		)

              UpperCamelCase_          =  predictions == references

              return {"exact_match": np.mean(_SCREAMING_SNAKE_CASE		) * 100}

