"""simple docstring"""







from typing import TYPE_CHECKING

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


_SCREAMING_SNAKE_CASE	:		Optional[Any]   =   {
    '''configuration_big_bird''': ['''BIG_BIRD_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BigBirdConfig''', '''BigBirdOnnxConfig'''],
}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _SCREAMING_SNAKE_CASE	:		Dict   =   ['''BigBirdTokenizer''']

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _SCREAMING_SNAKE_CASE	:		List[str]   =   ['''BigBirdTokenizerFast''']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _SCREAMING_SNAKE_CASE	:		Union[str, Any]   =   [
         '''BIG_BIRD_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''BigBirdForCausalLM''',
         '''BigBirdForMaskedLM''',
         '''BigBirdForMultipleChoice''',
         '''BigBirdForPreTraining''',
         '''BigBirdForQuestionAnswering''',
         '''BigBirdForSequenceClassification''',
         '''BigBirdForTokenClassification''',
         '''BigBirdLayer''',
         '''BigBirdModel''',
         '''BigBirdPreTrainedModel''',
         '''load_tf_weights_in_big_bird''',
     ]

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _SCREAMING_SNAKE_CASE	:		Any   =   [
         '''FlaxBigBirdForCausalLM''',
         '''FlaxBigBirdForMaskedLM''',
         '''FlaxBigBirdForMultipleChoice''',
         '''FlaxBigBirdForPreTraining''',
         '''FlaxBigBirdForQuestionAnswering''',
         '''FlaxBigBirdForSequenceClassification''',
         '''FlaxBigBirdForTokenClassification''',
         '''FlaxBigBirdModel''',
         '''FlaxBigBirdPreTrainedModel''',
     ]

if TYPE_CHECKING:
     from .configuration_big_bird import BIG_BIRD_PRETRAINED_CONFIG_ARCHIVE_MAP, BigBirdConfig, BigBirdOnnxConfig

     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_big_bird import BigBirdTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_big_bird_fast import BigBirdTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_big_bird import (
              BIG_BIRD_PRETRAINED_MODEL_ARCHIVE_LIST,
              BigBirdForCausalLM,
              BigBirdForMaskedLM,
              BigBirdForMultipleChoice,
              BigBirdForPreTraining,
              BigBirdForQuestionAnswering,
              BigBirdForSequenceClassification,
              BigBirdForTokenClassification,
              BigBirdLayer,
              BigBirdModel,
              BigBirdPreTrainedModel,
              load_tf_weights_in_big_bird,
          )

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_big_bird import (
              FlaxBigBirdForCausalLM,
              FlaxBigBirdForMaskedLM,
              FlaxBigBirdForMultipleChoice,
              FlaxBigBirdForPreTraining,
              FlaxBigBirdForQuestionAnswering,
              FlaxBigBirdForSequenceClassification,
              FlaxBigBirdForTokenClassification,
              FlaxBigBirdModel,
              FlaxBigBirdPreTrainedModel,
          )

else:
     import sys

     _SCREAMING_SNAKE_CASE	:		str   =   _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)







"""simple docstring"""







from ..utils import DummyObject, requires_backends






class      _SCREAMING_SNAKE_CASE							(			metaclass=UpperCAmelCase				):



   '''simple docstring'''



   __UpperCAmelCase	:							Union[str, Any]    =    ["torch", "librosa"]







   def __init__(	self ,				*_SCREAMING_SNAKE_CASE ,				**_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

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







   @classmethod
   def    _A				(	cls ,				*_SCREAMING_SNAKE_CASE ,				**_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

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







   @classmethod
   def    _A				(	cls ,				*_SCREAMING_SNAKE_CASE ,				**_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

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






class      _SCREAMING_SNAKE_CASE							(			metaclass=UpperCAmelCase				):



   '''simple docstring'''



   __UpperCAmelCase	:							Dict    =    ["torch", "librosa"]







   def __init__(	self ,				*_SCREAMING_SNAKE_CASE ,				**_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

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







   @classmethod
   def    _A				(	cls ,				*_SCREAMING_SNAKE_CASE ,				**_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

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







   @classmethod
   def    _A				(	cls ,				*_SCREAMING_SNAKE_CASE ,				**_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

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






