





import re
import string

import numpy as np

import datasets


__A									=				"\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"

__A									=				"\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"

__A									=				"\n"



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







 '''simple docstring'''

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



   '''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 							SCREAMING_SNAKE_CASE_					(self				:   Any       , UpperCAmelCase_				:   str       , UpperCAmelCase_				:   Optional[Any]       , UpperCAmelCase_				:   Optional[Any]=None       , UpperCAmelCase_				:   Union[str, Any]=False       , UpperCAmelCase_				:   Optional[Any]=False       , UpperCAmelCase_				:   int=False       , )       ->Union[str, Any]:



   '''simple docstring'''



   if regexes_to_ignore is not None:
     for s in regexes_to_ignore:
       lowerCamelCase__:      List[str]			  =np.array([re.sub(UpperCAmelCase_       , ""       , UpperCAmelCase_) for x in predictions])
       lowerCamelCase__:      Tuple			  =np.array([re.sub(UpperCAmelCase_       , ""       , UpperCAmelCase_) for x in references])
   else:
     lowerCamelCase__:      Union[str, Any]			  =np.asarray(UpperCAmelCase_)
     lowerCamelCase__:      int			  =np.asarray(UpperCAmelCase_)

   if ignore_case:
     lowerCamelCase__:      Union[str, Any]			  =np.char.lower(UpperCAmelCase_)
     lowerCamelCase__:      Dict			  =np.char.lower(UpperCAmelCase_)

   if ignore_punctuation:
     lowerCamelCase__:      Any			  =string.punctuation.maketrans(""       , ""       , string.punctuation)
     lowerCamelCase__:      Any			  =np.char.translate(UpperCAmelCase_       , table=UpperCAmelCase_)
     lowerCamelCase__:      Tuple			  =np.char.translate(UpperCAmelCase_       , table=UpperCAmelCase_)

   if ignore_numbers:
     lowerCamelCase__:      int			  =string.digits.maketrans(""       , ""       , string.digits)
     lowerCamelCase__:      Optional[Any]			  =np.char.translate(UpperCAmelCase_       , table=UpperCAmelCase_)
     lowerCamelCase__:      Dict			  =np.char.translate(UpperCAmelCase_       , table=UpperCAmelCase_)

   lowerCamelCase__:      str			  =predictions == references

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





"""simple docstring"""





import numpy as np


def    _snake_case  (  _snake_case :	np.array	):
      return 1 / (1 + np.exp(-vector	))


if __name__ == "__main__":
 import doctest

 doctest.testmod()


