"""simple docstring"""



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     ):







			'''simple docstring'''





			def 		lowerCamelCase					(			self							):







										'''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 		lowerCamelCase					(			self   ,      snake_case_   ,      snake_case_   ,      snake_case_=None   ,      snake_case_=False   ,      snake_case_=False   ,      snake_case_=False   ,      ):







										'''simple docstring'''


										if regexes_to_ignore is not None:
																	for s in regexes_to_ignore:
																								A__       :     Dict								=			np.array([re.sub(snake_case_   ,      """"""   ,      snake_case_							) for x in predictions]							)
																								A__       :     Tuple								=			np.array([re.sub(snake_case_   ,      """"""   ,      snake_case_							) for x in references]							)
										else:
																	A__       :     List[Any]								=			np.asarray(snake_case_							)
																	A__       :     Optional[Any]								=			np.asarray(snake_case_							)

										if ignore_case:
																	A__       :     Tuple								=			np.char.lower(snake_case_							)
																	A__       :     Dict								=			np.char.lower(snake_case_							)

										if ignore_punctuation:
																	A__       :     Optional[Any]								=			string.punctuation.maketrans(""""""   ,      """"""   ,      string.punctuation							)
																	A__       :     Any								=			np.char.translate(snake_case_   ,      table=snake_case_							)
																	A__       :     Optional[Any]								=			np.char.translate(snake_case_   ,      table=snake_case_							)

										if ignore_numbers:
																	A__       :     Dict								=			string.digits.maketrans(""""""   ,      """"""   ,      string.digits							)
																	A__       :     List[str]								=			np.char.translate(snake_case_   ,      table=snake_case_							)
																	A__       :     str								=			np.char.translate(snake_case_   ,      table=snake_case_							)

										A__       :     Any								=			predictions == references

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






"""simple docstring"""



def  _A(		lowerCAmelCase			,  lowerCAmelCase							):
							A__       :     List[Any]								=			[1]
							for i in range(2			,  lowerCAmelCase							):
														factorials.append(factorials[-1] * i							)
							assert 0 <= k < factorials[-1] * n, "k out of bounds"

							A__       :     Union[str, Any]								=			[]
							A__       :     int								=			list(range(lowerCAmelCase							)							)

							# Find permutation
							while factorials:
														A__       :     Optional[int]								=			factorials.pop()
														A__				,							A__       :     Union[str, Any]								=			divmod(lowerCAmelCase			,  lowerCAmelCase							)
														permutation.append(elements[number]							)
														elements.remove(elements[number]							)
							permutation.append(elements[0]							)

							return permutation


if __name__ == "__main__":
				import doctest

				doctest.testmod()





