"""simple docstring"""





import re
import string

import numpy as np

import datasets


_UpperCamelCase   :						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"

_UpperCamelCase   :						Optional[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"

_UpperCamelCase   :						List[str]      =    "\n"


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


      def 		_UpperCAmelCase       (   self							)       ->					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 		_UpperCAmelCase       (   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:
                           lowercase__			:		int       =	np.array([re.sub(snake_case_		,			''		,			snake_case_							) for x in predictions]							)
                           lowercase__			:		Any       =	np.array([re.sub(snake_case_		,			''		,			snake_case_							) for x in references]							)
             else:
                    lowercase__			:		int       =	np.asarray(snake_case_							)
                    lowercase__			:		Optional[int]       =	np.asarray(snake_case_							)

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

             if ignore_punctuation:
                    lowercase__			:		str       =	string.punctuation.maketrans(''		,			''		,			string.punctuation							)
                    lowercase__			:		Tuple       =	np.char.translate(snake_case_		,			table=snake_case_							)
                    lowercase__			:		Dict       =	np.char.translate(snake_case_		,			table=snake_case_							)

             if ignore_numbers:
                    lowercase__			:		Any       =	string.digits.maketrans(''		,			''		,			string.digits							)
                    lowercase__			:		Union[str, Any]       =	np.char.translate(snake_case_		,			table=snake_case_							)
                    lowercase__			:		Union[str, Any]       =	np.char.translate(snake_case_		,			table=snake_case_							)

             lowercase__			:		Tuple       =	predictions == references

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






'''simple docstring'''





import numpy as np

from transformers import Pipeline





def 						__snake_case(				_lowerCAmelCase		)		-> Optional[int]:
      snake_case__  :    Optional[Any]    						=      np.max(_lowerCAmelCase     ,   axis=-1     ,   keepdims=_lowerCAmelCase		)
      snake_case__  :    List[str]    						=      np.exp(outputs - maxes		)
      return shifted_exp / shifted_exp.sum(axis=-1     ,   keepdims=_lowerCAmelCase		)







class     UpperCAmelCase_       (       _a	):
    """simple docstring"""







    def lowerCamelCase       (    self					:     Optional[Any] ,     **snake_case_					:     int						):
          snake_case__  :    Optional[int]    						=      {}
          if "second_text" in kwargs:
                snake_case__  :    Union[str, Any]    						=      kwargs["""second_text"""]
          return preprocess_kwargs, {}, {}




    def lowerCamelCase       (    self					:     str ,     snake_case_					:     Tuple ,     snake_case_					:     Union[str, Any]=None						):
          return self.tokenizer(snake_case_ ,     text_pair=snake_case_ ,     return_tensors=self.framework						)




    def lowerCamelCase       (    self					:     List[Any] ,     snake_case_					:     Dict						):
          return self.model(**snake_case_						)




    def lowerCamelCase       (    self					:     int ,     snake_case_					:     List[Any]						):
          snake_case__  :    Union[str, Any]    						=      model_outputs.logits[0].numpy()
          snake_case__  :    List[str]    						=      softmax(snake_case_						)

          snake_case__  :    List[str]    						=      np.argmax(snake_case_						)
          snake_case__  :    List[str]    						=      self.model.config.idalabel[best_class]
          snake_case__  :    Optional[int]    						=      probabilities[best_class].item()
          snake_case__  :    str    						=      logits.tolist()
          return {"label": label, "score": score, "logits": logits}
