





import re
import string

import numpy as np

import datasets


lowerCamelCase__          =    """
Returns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.
"""

lowerCamelCase__          =    """
Args:
    predictions: List of predicted texts.
    references: List of reference texts.
    regexes_to_ignore: List, defaults to None. Regex expressions of characters to
        ignore when calculating the exact matches. Note: these regexes are removed
        from the input data before the changes based on the options below (e.g. ignore_case,
        ignore_punctuation, ignore_numbers) are applied.
    ignore_case: Boolean, defaults to False. If true, turns everything
        to lowercase so that capitalization differences are ignored.
    ignore_punctuation: Boolean, defaults to False. If true, removes all punctuation before
        comparing predictions and references.
    ignore_numbers: Boolean, defaults to False. If true, removes all punctuation before
        comparing predictions and references.
Returns:
    exact_match: Dictionary containing exact_match rate. Possible values are between 0.0 and 100.0, inclusive.
Examples:
    >>> exact_match = datasets.load_metric(\"exact_match\")
    >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]
    >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]
    >>> results = exact_match.compute(references=refs, predictions=preds)
    >>> print(round(results[\"exact_match\"], 1))
    25.0

    >>> exact_match = datasets.load_metric(\"exact_match\")
    >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]
    >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]
    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=[\"the \", \"yell\"], ignore_case=True, ignore_punctuation=True)
    >>> print(round(results[\"exact_match\"], 1))
    50.0


    >>> exact_match = datasets.load_metric(\"exact_match\")
    >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]
    >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]
    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=[\"the \", \"yell\", \"YELL\"], ignore_case=True, ignore_punctuation=True)
    >>> print(round(results[\"exact_match\"], 1))
    75.0

    >>> exact_match = datasets.load_metric(\"exact_match\")
    >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]
    >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]
    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=[\"the \", \"yell\", \"YELL\"], ignore_case=True, ignore_punctuation=True, ignore_numbers=True)
    >>> print(round(results[\"exact_match\"], 1))
    100.0

    >>> exact_match = datasets.load_metric(\"exact_match\")
    >>> refs = [\"The cat sat on the mat.\", \"Theaters are great.\", \"It's like comparing oranges and apples.\"]
    >>> preds = [\"The cat sat on the mat?\", \"Theaters are great.\", \"It's like comparing apples and oranges.\"]
    >>> results = exact_match.compute(references=refs, predictions=preds)
    >>> print(round(results[\"exact_match\"], 1))
    33.3

"""

lowerCamelCase__          =    """
"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION					,		_KWARGS_DESCRIPTION )
class 	__magic_name__			(datasets.Metric ):




					def 					__a     (       self						)			->					List[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 					__a     (       self	,   _a	,   _a	,   _a=None	,   _a=False	,   _a=False	,   _a=False	,   )			->					List[Any]:


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

							if ignore_case:
									lowerCAmelCase_												=				np.char.lower(_a						)
									lowerCAmelCase_												=				np.char.lower(_a						)

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

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

							lowerCAmelCase_												=				predictions == references

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






def        __a			(   __lowerCAmelCase      ,	__lowerCAmelCase = 0   )     ->     list:
					SCREAMING_SNAKE_CASE							:     int						=				length or len(__lowerCAmelCase   )
					SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				False
					for i in range(length - 1   ):
										if list_data[i] > list_data[i + 1]:
															SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE							:     List[Any]						=				list_data[i + 1], list_data[i]
															SCREAMING_SNAKE_CASE							:     Any						=				True

					return list_data if not swapped else bubble_sort(__lowerCAmelCase      ,	length - 1   )


if __name__ == "__main__":
			import doctest

			doctest.testmod()