




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 = " "  )   ->	List[str]:

        """simple docstring"""






        lowerCamelCase_		     =    sentence_delimiter

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

        """simple docstring"""






        return list(A_  )





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

        """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				:		Tuple     =   tr.Compose(
     [tr.RemoveMultipleSpaces(), tr.Strip(), SentencesToListOfCharacters(SENTENCE_DELIMITER)]
 )
else:
 lowerCamelCase				:		Optional[int]     =   tr.Compose(
     [
         tr.RemoveMultipleSpaces(),
         tr.Strip(),
         tr.ReduceToSingleSentence(SENTENCE_DELIMITER),
         tr.ReduceToListOfListOfChars(),
     ]
 )


lowerCamelCase				:		Dict     =   "\\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				:		Dict     =   "\\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				:		Optional[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					:  str  )   ->	Optional[int]:

       """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					:  int						,							A_					:  Any						,							A_					:  Union[str, Any]						,							A_					:  Union[str, Any]=False  )   ->	Dict:

       """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









from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase				:		Optional[Any]     =   {
    "configuration_whisper": ["WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP", "WhisperConfig", "WhisperOnnxConfig"],
    "feature_extraction_whisper": ["WhisperFeatureExtractor"],
    "processing_whisper": ["WhisperProcessor"],
    "tokenization_whisper": ["WhisperTokenizer"],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase				:		Tuple     =   ["WhisperTokenizerFast"]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase				:		str     =   [
     "WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST",
     "WhisperForConditionalGeneration",
     "WhisperModel",
     "WhisperPreTrainedModel",
     "WhisperForAudioClassification",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase				:		List[str]     =   [
     "TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST",
     "TFWhisperForConditionalGeneration",
     "TFWhisperModel",
     "TFWhisperPreTrainedModel",
 ]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase				:		str     =   [
     "FlaxWhisperForConditionalGeneration",
     "FlaxWhisperModel",
     "FlaxWhisperPreTrainedModel",
     "FlaxWhisperForAudioClassification",
 ]


if TYPE_CHECKING:
 from .configuration_whisper import WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP, WhisperConfig, WhisperOnnxConfig
 from .feature_extraction_whisper import WhisperFeatureExtractor
 from .processing_whisper import WhisperProcessor
 from .tokenization_whisper import WhisperTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_whisper_fast import WhisperTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_whisper import (
      WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST,
      WhisperForAudioClassification,
      WhisperForConditionalGeneration,
      WhisperModel,
      WhisperPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_whisper import (
      TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFWhisperForConditionalGeneration,
      TFWhisperModel,
      TFWhisperPreTrainedModel,
  )

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_whisper import (
      FlaxWhisperForAudioClassification,
      FlaxWhisperForConditionalGeneration,
      FlaxWhisperModel,
      FlaxWhisperPreTrainedModel,
  )

else:
 import sys

 lowerCamelCase				:		Tuple     =   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



