

'''simple docstring'''




import re
import string

import numpy as np

import datasets


_a					:			Optional[int]							=    '\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					:			Union[str, Any]							=    '\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					:			Union[str, Any]							=    '\n'



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

  '''simple docstring'''





  def     snake_case_      (  self  )				->					Union[str, 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     snake_case_      (  self ,			a_ ,			a_ ,			a_=None ,			a_=False ,			a_=False ,			a_=False ,			)				->					str:


    """simple docstring"""
    if regexes_to_ignore is not None:
      for s in regexes_to_ignore:
        UpperCAmelCase    				=						np.array([re.sub(a_ ,			'' ,			a_  ) for x in predictions]  )
        UpperCAmelCase    				=						np.array([re.sub(a_ ,			'' ,			a_  ) for x in references]  )
    else:
      UpperCAmelCase    				=						np.asarray(a_  )
      UpperCAmelCase    				=						np.asarray(a_  )

    if ignore_case:
      UpperCAmelCase    				=						np.char.lower(a_  )
      UpperCAmelCase    				=						np.char.lower(a_  )

    if ignore_punctuation:
      UpperCAmelCase    				=						string.punctuation.maketrans('' ,			'' ,			string.punctuation  )
      UpperCAmelCase    				=						np.char.translate(a_ ,			table=a_  )
      UpperCAmelCase    				=						np.char.translate(a_ ,			table=a_  )

    if ignore_numbers:
      UpperCAmelCase    				=						string.digits.maketrans('' ,			'' ,			string.digits  )
      UpperCAmelCase    				=						np.char.translate(a_ ,			table=a_  )
      UpperCAmelCase    				=						np.char.translate(a_ ,			table=a_  )

    UpperCAmelCase    				=						predictions == references

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





'''simple docstring'''




from ..utils import DummyObject, requires_backends



class 							lowercase_						(		metaclass=a			):

  '''simple docstring'''





  __lowerCAmelCase       :			str     	= ["keras_nlp"]
  def __init__(  self ,			*a_ ,			**a_  )				->					Union[str, Any]:


    """simple docstring"""
    requires_backends(self ,			['keras_nlp']  )


