



"""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							:     Union[str, Any]   =					{
    '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							:     Union[str, Any]   =					['Speech2TextTokenizer']

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

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __snake_case							:     Optional[int]   =					[
     '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							:     int   =					[
     '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							:     List[Any]   =					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




"""simple docstring"""






from ..utils import DummyObject, requires_backends



class     A__     (						metaclass=__SCREAMING_SNAKE_CASE							):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE					   =			['torch', 'scipy']

	def __init__(  self:	Tuple ,    *_SCREAMING_SNAKE_CASE:	Dict ,    **_SCREAMING_SNAKE_CASE:	str)       -> Union[str, Any]:

				"""simple docstring"""

				requires_backends(self ,    ["torch", "scipy"])

	@classmethod
	def 							_SCREAMING_SNAKE_CASE     (  cls:	Optional[Any] ,    *_SCREAMING_SNAKE_CASE:	Tuple ,    **_SCREAMING_SNAKE_CASE:	Optional[Any])       -> Optional[Any]:

				"""simple docstring"""

				requires_backends(cls ,    ["torch", "scipy"])







	@classmethod
	def 							_SCREAMING_SNAKE_CASE     (  cls:	int ,    *_SCREAMING_SNAKE_CASE:	List[Any] ,    **_SCREAMING_SNAKE_CASE:	Dict)       -> Union[str, Any]:

				"""simple docstring"""

				requires_backends(cls ,    ["torch", "scipy"])