




"""simple docstring"""

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_tf_available,
    is_torch_available,
)


a							      =     {
    '''configuration_speech_to_text''': ['''SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Speech2TextConfig'''],
    '''processing_speech_to_text''': ['''Speech2TextProcessor'''],
}

try:
       if not is_sentencepiece_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a							      =     ['''Speech2TextTokenizer''']

try:
       if not is_speech_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a							      =     ['''Speech2TextFeatureExtractor''']

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a							      =     [
           '''TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''TFSpeech2TextForConditionalGeneration''',
           '''TFSpeech2TextModel''',
           '''TFSpeech2TextPreTrainedModel''',
       ]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a							      =     [
           '''SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''Speech2TextForConditionalGeneration''',
           '''Speech2TextModel''',
           '''Speech2TextPreTrainedModel''',
       ]


if TYPE_CHECKING:
       from .configuration_speech_to_text import SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextConfig
       from .processing_speech_to_text import SpeechaTextProcessor

       try:
              if not is_sentencepiece_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_speech_to_text import SpeechaTextTokenizer

       try:
              if not is_speech_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .feature_extraction_speech_to_text import SpeechaTextFeatureExtractor

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_speech_to_text import (
                  TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFSpeechaTextForConditionalGeneration,
                  TFSpeechaTextModel,
                  TFSpeechaTextPreTrainedModel,
              )

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_speech_to_text import (
                  SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  SpeechaTextForConditionalGeneration,
                  SpeechaTextModel,
                  SpeechaTextPreTrainedModel,
              )

else:
       import sys

       a							      =     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





"""simple docstring"""


def    _lowerCamelCase   (     lowerCamelCase__	:							str  ):
 assert column_title.isupper()
 lowercase__       :       int				=						0
 lowercase__       :       Optional[int]				=						len(lowerCamelCase__  ) - 1
 lowercase__       :       int				=						0

 while index >= 0:
  lowercase__       :       List[str]				=						(ord(column_title[index]  ) - 64) * pow(26       ,		lowerCamelCase__  )
  answer += value
  power += 1
  index -= 1

 return answer


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

   testmod()