'''simple docstring'''




from typing import TYPE_CHECKING

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


snake_case	=      {
    '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:
 snake_case	=      ['Speech2TextTokenizer']

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

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

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 snake_case	=      [
     '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

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









'''simple docstring'''

def _SCREAMING_SNAKE_CASE (A			)			->			bool:






       """simple docstring"""







       if not isinstance(A    ,					A			):
              raise ValueError('''Input series is not valid, valid series - [2, 4, 6]'''			)
       if len(A			) == 0:
              raise ValueError('''Input list must be a non empty list'''			)
       if len(A			) == 1:
              return True
       lowercase__      =					series[1] - series[0]
       for index in range(len(A			) - 1			):
              if series[index + 1] - series[index] != common_diff:
                     return False
       return True

def _SCREAMING_SNAKE_CASE (A			)			->			float:






       """simple docstring"""







       if not isinstance(A    ,					A			):
              raise ValueError('''Input series is not valid, valid series - [2, 4, 6]'''			)
       if len(A			) == 0:
              raise ValueError('''Input list must be a non empty list'''			)
       lowercase__      =					0
       for val in series:
              answer += val
       return answer / len(A			)


if __name__ == "__main__":
 import doctest

 doctest.testmod()




