




from typing import List

import jiwer
import jiwer.transforms as tr
from packaging import version

import datasets
from datasets.config import PY_VERSION


if PY_VERSION < version.parse("3.8"):
 import importlib_metadata
else:
 import importlib.metadata as importlib_metadata


lowerCamelCase							:						Any					       =  ""


if version.parse(importlib_metadata.version("jiwer")) < version.parse("2.3.0"):







 class     A(		tr.AbstractTransform  ):



  '''simple docstring'''


  def __init__(							self   :    List[Any]		,				A_   :    str = " "    )  ->      str:

        """simple docstring"""






        lowerCamelCase_							=       sentence_delimiter

  def 							a__					(							self   :    int		,				A_   :    str    )  ->      Union[str, Any]:

        """simple docstring"""






        return list(A_    )





  def 							a__					(							self   :    List[str]		,				A_   :    List[str]    )  ->      Dict:

        """simple docstring"""






        lowerCamelCase_							=       []
        for sent_idx, sentence in enumerate(A_    ):
              chars.extend(self.process_string(A_    )    )
              if self.sentence_delimiter is not None and self.sentence_delimiter != "" and sent_idx < len(A_    ) - 1:
                    chars.append(self.sentence_delimiter    )
        return chars

 lowerCamelCase							:						str					       =  tr.Compose(
     [tr.RemoveMultipleSpaces(), tr.Strip(), SentencesToListOfCharacters(SENTENCE_DELIMITER)]
 )
else:
 lowerCamelCase							:						Dict					       =  tr.Compose(
     [
         tr.RemoveMultipleSpaces(),
         tr.Strip(),
         tr.ReduceToSingleSentence(SENTENCE_DELIMITER),
         tr.ReduceToListOfListOfChars(),
     ]
 )


lowerCamelCase							:						int					       =  "\\n@inproceedings{inproceedings,\n    author = {Morris, Andrew and Maier, Viktoria and Green, Phil},\n    year = {2004},\n    month = {01},\n    pages = {},\n    title = {From WER and RIL to MER and WIL: improved evaluation measures for connected speech recognition.}\n}\n"

lowerCamelCase							:						Optional[Any]					       =  "\\nCharacter error rate (CER) is a common metric of the performance of an automatic speech recognition system.\n\nCER is similar to Word Error Rate (WER), but operates on character instead of word. Please refer to docs of WER for further information.\n\nCharacter error rate can be computed as:\n\nCER = (S + D + I) / N = (S + D + I) / (S + D + C)\n\nwhere\n\nS is the number of substitutions,\nD is the number of deletions,\nI is the number of insertions,\nC is the number of correct characters,\nN is the number of characters in the reference (N=S+D+C).\n\nCER's output is not always a number between 0 and 1, in particular when there is a high number of insertions. This value is often associated to the percentage of characters that were incorrectly predicted. The lower the value, the better the\nperformance of the ASR system with a CER of 0 being a perfect score.\n"

lowerCamelCase							:						List[Any]					       =  "\nComputes CER score of transcribed segments against references.\nArgs:\n    references: list of references for each speech input.\n    predictions: list of transcribtions to score.\n    concatenate_texts: Whether or not to concatenate sentences before evaluation, set to True for more accurate result.\nReturns:\n    (float): the character error rate\n\nExamples:\n\n    >>> predictions = [\"this is the prediction\", \"there is an other sample\"]\n    >>> references = [\"this is the reference\", \"there is another one\"]\n    >>> cer = datasets.load_metric(\"cer\")\n    >>> cer_score = cer.compute(predictions=predictions, references=references)\n    >>> print(cer_score)\n    0.34146341463414637\n"







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



 '''simple docstring'''


 def 							a__					(							self   :    int    )  ->      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'    ),
               }    )		,				codebase_urls=['https://github.com/jitsi/jiwer/']		,				reference_urls=[
               'https://en.wikipedia.org/wiki/Word_error_rate',
               'https://sites.google.com/site/textdigitisation/qualitymeasures/computingerrorrates',
           ]		,				)





 def 							a__					(							self   :    Optional[Any]		,				A_   :    str		,				A_   :    str		,				A_   :    Optional[Any]=False    )  ->      Tuple:

       """simple docstring"""






       if concatenate_texts:
             return jiwer.compute_measures(
                 A_		,				A_		,				truth_transform=A_		,				hypothesis_transform=A_		,				)["wer"]

       lowerCamelCase_							=       0
       lowerCamelCase_							=       0
       for prediction, reference in zip(A_		,				A_    ):
             lowerCamelCase_							=       jiwer.compute_measures(
                 A_		,				A_		,				truth_transform=A_		,				hypothesis_transform=A_		,				)
             incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
             total += measures["substitutions"] + measures["deletions"] + measures["hits"]

       return incorrect / total









'''simple docstring'''



import inspect
import warnings
from typing import Any, Dict, Optional, Union

from packaging import version




def   lowercase__(			*__UpperCamelCase:					Union[str, Any]      ,__UpperCamelCase:					Optional[Union[Dict, Any]] = None      ,__UpperCamelCase:					Dict=True      ,__UpperCamelCase:					List[Any]=2    ):

      """simple docstring"""






      from .. import __version__

      SCREAMING_SNAKE_CASE   :      int    					=						take_from
      SCREAMING_SNAKE_CASE   :      Optional[int]    					=						()
      if not isinstance(args[0]      ,__UpperCamelCase    ):
            SCREAMING_SNAKE_CASE   :      List[str]    					=						(args,)

      for attribute, version_name, message in args:
            if version.parse(version.parse(__UpperCamelCase    ).base_version    ) >= version.parse(__UpperCamelCase    ):
                  raise ValueError(
                      f"The deprecation tuple {(attribute, version_name, message)} should be removed since diffusers'"
                      f" version {__version__} is >= {version_name}"    )

            SCREAMING_SNAKE_CASE   :      Tuple    					=						None
            if isinstance(__UpperCamelCase      ,__UpperCamelCase    ) and attribute in deprecated_kwargs:
                  values += (deprecated_kwargs.pop(__UpperCamelCase    ),)
                  SCREAMING_SNAKE_CASE   :      Dict    					=						f"The `{attribute}` argument is deprecated and will be removed in version {version_name}."
            elif hasattr(__UpperCamelCase      ,__UpperCamelCase    ):
                  values += (getattr(__UpperCamelCase      ,__UpperCamelCase    ),)
                  SCREAMING_SNAKE_CASE   :      Optional[int]    					=						f"The `{attribute}` attribute is deprecated and will be removed in version {version_name}."
            elif deprecated_kwargs is None:
                  SCREAMING_SNAKE_CASE   :      Dict    					=						f"`{attribute}` is deprecated and will be removed in version {version_name}."

            if warning is not None:
                  SCREAMING_SNAKE_CASE   :      Dict    					=						warning + ' ' if standard_warn else ''
                  warnings.warn(warning + message      ,__UpperCamelCase      ,stacklevel=__UpperCamelCase    )

      if isinstance(__UpperCamelCase      ,__UpperCamelCase    ) and len(__UpperCamelCase    ) > 0:
            SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						inspect.getouterframes(inspect.currentframe()    )[1]
            SCREAMING_SNAKE_CASE   :      Any    					=						call_frame.filename
            SCREAMING_SNAKE_CASE   :      Tuple    					=						call_frame.lineno
            SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						call_frame.function
            SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      Tuple    					=						next(iter(deprecated_kwargs.items()    )    )
            raise TypeError(f"{function} in {filename} line {line_number-1} got an unexpected keyword argument `{key}`"    )

      if len(__UpperCamelCase    ) == 0:
            return
      elif len(__UpperCamelCase    ) == 1:
            return values[0]
      return values


