


"""simple docstring"""

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


UpperCAmelCase__  							=""


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





       class    lowerCamelCase__						(		tr.AbstractTransform    ):
             def __init__(		self	:	Tuple      ,       A_	:	str = " "							):






              '''simple docstring'''


              __lowercase									=	sentence_delimiter
             def SCREAMING_SNAKE_CASE_	(		self	:	Optional[Any]      ,       A_	:	str							):






              '''simple docstring'''


              return list(A_							)






             def SCREAMING_SNAKE_CASE_	(		self	:	Dict      ,       A_	:	List[str]							):






              '''simple docstring'''


              __lowercase									=	[]
              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






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


UpperCAmelCase__  							="\\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"

UpperCAmelCase__  							="\\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"

UpperCAmelCase__  							="\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    lowerCamelCase__						(		datasets.Metric    ):
      def SCREAMING_SNAKE_CASE_	(		self	:	List[str]							):






       '''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 SCREAMING_SNAKE_CASE_	(		self	:	List[str]      ,       A_	:	Union[str, Any]      ,       A_	:	Union[str, Any]      ,       A_	:	List[str]=False							):






       '''simple docstring'''


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

       __lowercase									=	0
       __lowercase									=	0
       for prediction, reference in zip(A_      ,       A_							):
        __lowercase									=	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"""

# Algorithm for the pigeonhole sorting




def lowerCAmelCase_      (				UpperCamelCase__	: Dict ):






 """simple docstring"""







 __lowercase									=	min(UpperCamelCase__ )  # min() finds the minimum value
 __lowercase									=	max(UpperCamelCase__ )  # max() finds the maximum value

 __lowercase									=	max_val - min_val + 1  # size is difference of max and min values plus one

 # list of pigeonholes of size equal to the variable size
 __lowercase									=	[0] * size

 # Populate the pigeonholes.
 for x in a:
  assert isinstance(UpperCamelCase__				,   UpperCamelCase__ ), "integers only please"
  holes[x - min_val] += 1

 # Putting the elements back into the array in an order.
 __lowercase									=	0
 for count in range(UpperCamelCase__ ):
  while holes[count] > 0:
   holes[count] -= 1
   __lowercase									=	count + min_val
   i += 1


def lowerCAmelCase_      (				):






 """simple docstring"""







 __lowercase									=	[8, 3, 2, 7, 4, 6, 8]
 pigeonhole_sort(UpperCamelCase__ )
 print("""Sorted order is:"""				,   """ """.join(UpperCamelCase__ ) )


if __name__ == "__main__":
       main()
