



"""simple docstring"""

from ..utils import DummyObject, requires_backends
class     lowerCAmelCase__						(	metaclass=_lowerCamelCase ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE	:		Any  						= ['''torch''', '''torchsde''']





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





					"""simple docstring"""


					requires_backends(self     ,       ['torch', 'torchsde']    )





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





					"""simple docstring"""


					requires_backends(cls     ,       ['torch', 'torchsde']    )







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





					"""simple docstring"""


					requires_backends(cls     ,       ['torch', 'torchsde']    )










"""simple docstring"""

from typing import TYPE_CHECKING

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


A_						:    str				=      {'configuration_unispeech': ['UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP', 'UniSpeechConfig']}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A_						:    Optional[int]				=      [
							    'UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'UniSpeechForCTC',
							    'UniSpeechForPreTraining',
							    'UniSpeechForSequenceClassification',
							    'UniSpeechModel',
							    'UniSpeechPreTrainedModel',
							]

if TYPE_CHECKING:
							from .configuration_unispeech import UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_unispeech import (
														    UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST,
														    UniSpeechForCTC,
														    UniSpeechForPreTraining,
														    UniSpeechForSequenceClassification,
														    UniSpeechModel,
														    UniSpeechPreTrainedModel,
														)

else:
							import sys

							A_						:    int				=      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





