




'''simple docstring'''

from ..utils import DummyObject, requires_backends





class __magic_name__(						metaclass=__lowerCAmelCase      ):
						UpperCAmelCase_		:					str    =  ["""torch""", """librosa"""]






						def __init__(   self						:						Dict					,   *__UpperCamelCase						:						Tuple					,   **__UpperCamelCase						:						Optional[int]       ):



									'''simple docstring'''






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






						@classmethod
						def       __lowerCAmelCase(   cls						:						Dict					,   *__UpperCamelCase						:						Any					,   **__UpperCamelCase						:						Tuple       ):



									'''simple docstring'''






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







						@classmethod
						def       __lowerCAmelCase(   cls						:						Union[str, Any]					,   *__UpperCamelCase						:						Any					,   **__UpperCamelCase						:						int       ):



									'''simple docstring'''






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







class __magic_name__(						metaclass=__lowerCAmelCase      ):
						UpperCAmelCase_		:					List[Any]    =  ["""torch""", """librosa"""]






						def __init__(   self						:						Union[str, Any]					,   *__UpperCamelCase						:						Union[str, Any]					,   **__UpperCamelCase						:						Optional[int]       ):



									'''simple docstring'''






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






						@classmethod
						def       __lowerCAmelCase(   cls						:						List[str]					,   *__UpperCamelCase						:						Dict					,   **__UpperCamelCase						:						Union[str, Any]       ):



									'''simple docstring'''






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







						@classmethod
						def       __lowerCAmelCase(   cls						:						Dict					,   *__UpperCamelCase						:						Optional[int]					,   **__UpperCamelCase						:						Union[str, Any]       ):



									'''simple docstring'''






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






'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


UpperCAmelCase       :		Dict		=				{'configuration_hubert': ['HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'HubertConfig']}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase       :		Union[str, Any]		=				[
       'HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'HubertForCTC',
       'HubertForSequenceClassification',
       'HubertModel',
       'HubertPreTrainedModel',
   ]


try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase       :		Optional[int]		=				[
       'TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'TFHubertForCTC',
       'TFHubertModel',
       'TFHubertPreTrainedModel',
   ]

if TYPE_CHECKING:
   from .configuration_hubert import HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, HubertConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_hubert import (
          HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          HubertForCTC,
          HubertForSequenceClassification,
          HubertModel,
          HubertPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_hubert import (
          TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFHubertForCTC,
          TFHubertModel,
          TFHubertPreTrainedModel,
      )


else:
   import sys

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


