





from typing import TYPE_CHECKING

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


_A :   Optional[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 :   List[Any]  			=  [
           '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 :   str  			=  [
           '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 :   Tuple  			=  [
           '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 :   int  			=  _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)










def 	_a						(   UpperCAmelCase					)    ->    bool:







   """simple docstring"""
   return str(UpperCAmelCase					) == str(UpperCAmelCase					)[::-1]



def 	_a						(   UpperCAmelCase					)    ->    int:







   """simple docstring"""
   return int(UpperCAmelCase					) + int(str(UpperCAmelCase					)[::-1]					)



def 	_a						(   UpperCAmelCase = 10000					)    ->    int:







   """simple docstring"""
   lowerCamelCase__       :						Tuple        =							[]
   for num in range(1      ,  UpperCAmelCase					):
      lowerCamelCase__       :						List[str]        =							0
      lowerCamelCase__       :						Union[str, Any]        =							num
      while iterations < 50:
         lowerCamelCase__       :						Dict        =							sum_reverse(UpperCAmelCase					)
         iterations += 1
         if is_palindrome(UpperCAmelCase					):
            break
      else:
         lychrel_nums.append(UpperCAmelCase					)
   return len(UpperCAmelCase					)


if __name__ == "__main__":
       print(F'''{solution() = }''')



