


from __future__ import annotations


def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE       ):
  return [ord(SCREAMING_SNAKE_CASE       ) - 96 for elem in plain]


def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE       ):
  return "".join(chr(elem + 96       ) for elem in encoded       )


def   _SCREAMING_SNAKE_CASE	( ):
  A_      :				Tuple		     =					encode(input('''-> '''       ).strip().lower()       )
  print('''Encoded: ''' ,			SCREAMING_SNAKE_CASE       )
  print('''Decoded:''' ,			decode(SCREAMING_SNAKE_CASE       )       )


if __name__ == "__main__":
     main()











"""simple docstring"""





import re
import string

import numpy as np

import datasets


SCREAMING_SNAKE_CASE__					=   "\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"

SCREAMING_SNAKE_CASE__					=   "\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"

SCREAMING_SNAKE_CASE__					=   "\n"

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





      def     _snake_case			(       self )		->     Optional[int]:
            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     _snake_case			(       self      ,						lowercase      ,						lowercase      ,						lowercase=None      ,						lowercase=False      ,						lowercase=False      ,						lowercase=False      ,						)		->     Optional[Any]:
            if regexes_to_ignore is not None:
                  for s in regexes_to_ignore:
                        lowerCAmelCase  						=       np.array([re.sub(lowercase      ,						""""""      ,						lowercase ) for x in predictions] )
                        lowerCAmelCase  						=       np.array([re.sub(lowercase      ,						""""""      ,						lowercase ) for x in references] )
            else:
                  lowerCAmelCase  						=       np.asarray(lowercase )
                  lowerCAmelCase  						=       np.asarray(lowercase )

            if ignore_case:
                  lowerCAmelCase  						=       np.char.lower(lowercase )
                  lowerCAmelCase  						=       np.char.lower(lowercase )

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

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

            lowerCAmelCase  						=       predictions == references

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






