






"""simple docstring"""





import re
import string

import numpy as np

import datasets


__lowerCamelCase							=  '\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'

__lowerCamelCase							=  '\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'

__lowerCamelCase							=  '\n'



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

					def   lowerCamelCase__   (			self  :      Union[str, Any]					)			->							Union[str, Any]:
											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   lowerCamelCase__   (			self  :      Any     ,		__snake_case  :      Optional[Any]     ,		__snake_case  :      Union[str, Any]     ,		__snake_case  :      int=None     ,		__snake_case  :      Dict=False     ,		__snake_case  :      int=False     ,		__snake_case  :      Dict=False     ,		)			->							Optional[int]:
											if regexes_to_ignore is not None:
																	for s in regexes_to_ignore:
																							__magic_name__:	str							=       np.array([re.sub(__snake_case     ,		""""""     ,		__snake_case					) for x in predictions]					)
																							__magic_name__:	Union[str, Any]							=       np.array([re.sub(__snake_case     ,		""""""     ,		__snake_case					) for x in references]					)
											else:
																	__magic_name__:	Tuple							=       np.asarray(__snake_case					)
																	__magic_name__:	str							=       np.asarray(__snake_case					)

											if ignore_case:
																	__magic_name__:	List[Any]							=       np.char.lower(__snake_case					)
																	__magic_name__:	Any							=       np.char.lower(__snake_case					)

											if ignore_punctuation:
																	__magic_name__:	Dict							=       string.punctuation.maketrans(""""""     ,		""""""     ,		string.punctuation					)
																	__magic_name__:	Optional[int]							=       np.char.translate(__snake_case     ,		table=__snake_case					)
																	__magic_name__:	str							=       np.char.translate(__snake_case     ,		table=__snake_case					)

											if ignore_numbers:
																	__magic_name__:	Optional[Any]							=       string.digits.maketrans(""""""     ,		""""""     ,		string.digits					)
																	__magic_name__:	Any							=       np.char.translate(__snake_case     ,		table=__snake_case					)
																	__magic_name__:	Tuple							=       np.char.translate(__snake_case     ,		table=__snake_case					)

											__magic_name__:	List[str]							=       predictions == references

											return {"exact_match": np.mean(__snake_case					) * 1_0_0}





"""simple docstring"""




def 							_lowerCamelCase			(			__a     ):
		if divisor % 5 == 0 or divisor % 2 == 0:
				return 0
		SCREAMING_SNAKE_CASE_    			=			1
		SCREAMING_SNAKE_CASE_    			=			1
		while repunit:
				SCREAMING_SNAKE_CASE_    			=			(10 * repunit + 1) % divisor
				repunit_index += 1
		return repunit_index



def 							_lowerCamelCase			(			__a = 1_000_000     ):
		SCREAMING_SNAKE_CASE_    			=			limit - 1
		if divisor % 2 == 0:
				divisor += 1
		while least_divisible_repunit(__a     ) <= limit:
				divisor += 2
		return divisor


if __name__ == "__main__":
						print(f'''{solution() = }''')