


def  a   (lowerCAmelCase__		,				lowerCAmelCase__   ):
   __a					=  word.split()

   def justify(lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__   ) -> str:
      __a					=  max_width - width
      __a					=  len(lowerCAmelCase__   )
      if len(lowerCAmelCase__   ) == 1:
         # if there is only word in line
         # just insert overall_spaces_count for the remainder of line
         return line[0] + " " * overall_spaces_count
      else:
         __a					=  words_count - 1
         # num_spaces_between_words_list[i] : tells you to insert
         # num_spaces_between_words_list[i] spaces
         # after word on line[i]
         __a					=  spaces_to_insert_between_words * [
             overall_spaces_count // spaces_to_insert_between_words
         ]
         __a					=  (
             overall_spaces_count % spaces_to_insert_between_words
         )
         # distribute spaces via round robin to the left words
         for i in range(lowerCAmelCase__   ):
            num_spaces_between_words_list[i] += 1
         __a					=  []
         for i in range(lowerCAmelCase__   ):
            # add the word
            aligned_words_list.append(line[i]   )
            # add the spaces to insert
            aligned_words_list.append(num_spaces_between_words_list[i] * """ """   )
         # just add the last word to the sentence
         aligned_words_list.append(line[-1]   )
         # join the aligned words list to form a justified line
         return "".join(lowerCAmelCase__   )

   __a					=  []
   __a					=  []
   __a					=  0
   for word in words:
      if width + len(lowerCAmelCase__   ) + len(lowerCAmelCase__   ) <= max_width:
         # keep adding words until we can fill out max_width
         # width = sum of length of all words (without overall_spaces_count)
         # len(word) = length of current word
         # len(line) = number of overall_spaces_count to insert between words
         line.append(lowerCAmelCase__   )
         width += len(lowerCAmelCase__   )
      else:
         # justify the line and add it to result
         answer.append(justify(lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__   )   )
         # reset new line and new width
         __a	,    __a					=  [word], len(lowerCAmelCase__   )
   __a					=  max_width - width - len(lowerCAmelCase__   )
   answer.append(""" """.join(lowerCAmelCase__   ) + (remaining_spaces + 1) * """ """   )
   return answer


if __name__ == "__main__":
  from doctest import testmod

  testmod()










'''simple docstring'''






from typing import TYPE_CHECKING

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


A__							:  int    ={
    '''configuration_wav2vec2''': ['''WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Wav2Vec2Config'''],
    '''feature_extraction_wav2vec2''': ['''Wav2Vec2FeatureExtractor'''],
    '''processing_wav2vec2''': ['''Wav2Vec2Processor'''],
    '''tokenization_wav2vec2''': ['''Wav2Vec2CTCTokenizer''', '''Wav2Vec2Tokenizer'''],
}


try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__							:  str    =[
          '''WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''Wav2Vec2ForAudioFrameClassification''',
          '''Wav2Vec2ForCTC''',
          '''Wav2Vec2ForMaskedLM''',
          '''Wav2Vec2ForPreTraining''',
          '''Wav2Vec2ForSequenceClassification''',
          '''Wav2Vec2ForXVector''',
          '''Wav2Vec2Model''',
          '''Wav2Vec2PreTrainedModel''',
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__							:  Union[str, Any]    =[
          '''TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''TFWav2Vec2ForCTC''',
          '''TFWav2Vec2Model''',
          '''TFWav2Vec2PreTrainedModel''',
          '''TFWav2Vec2ForSequenceClassification''',
      ]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__							:  List[str]    =[
          '''FlaxWav2Vec2ForCTC''',
          '''FlaxWav2Vec2ForPreTraining''',
          '''FlaxWav2Vec2Model''',
          '''FlaxWav2Vec2PreTrainedModel''',
      ]


if TYPE_CHECKING:
      from .configuration_wavaveca import WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP, WavaVecaConfig
      from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
      from .processing_wavaveca import WavaVecaProcessor
      from .tokenization_wavaveca import WavaVecaCTCTokenizer, WavaVecaTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_wavaveca import (
                WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
                WavaVecaForAudioFrameClassification,
                WavaVecaForCTC,
                WavaVecaForMaskedLM,
                WavaVecaForPreTraining,
                WavaVecaForSequenceClassification,
                WavaVecaForXVector,
                WavaVecaModel,
                WavaVecaPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_wavaveca import (
                TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFWavaVecaForCTC,
                TFWavaVecaForSequenceClassification,
                TFWavaVecaModel,
                TFWavaVecaPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_wavaveca import (
                FlaxWavaVecaForCTC,
                FlaxWavaVecaForPreTraining,
                FlaxWavaVecaModel,
                FlaxWavaVecaPreTrainedModel,
            )


else:
      import sys

      A__							:  Any    =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






