

"""simple docstring"""


from typing import TYPE_CHECKING

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


UpperCAmelCase													={
    """configuration_longformer""": [
        """LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """LongformerConfig""",
        """LongformerOnnxConfig""",
    ],
    """tokenization_longformer""": ["""LongformerTokenizer"""],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase													=["""LongformerTokenizerFast"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase													=[
      """LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """LongformerForMaskedLM""",
      """LongformerForMultipleChoice""",
      """LongformerForQuestionAnswering""",
      """LongformerForSequenceClassification""",
      """LongformerForTokenClassification""",
      """LongformerModel""",
      """LongformerPreTrainedModel""",
      """LongformerSelfAttention""",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase													=[
      """TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """TFLongformerForMaskedLM""",
      """TFLongformerForMultipleChoice""",
      """TFLongformerForQuestionAnswering""",
      """TFLongformerForSequenceClassification""",
      """TFLongformerForTokenClassification""",
      """TFLongformerModel""",
      """TFLongformerPreTrainedModel""",
      """TFLongformerSelfAttention""",
  ]


if TYPE_CHECKING:
  from .configuration_longformer import (
      LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
      LongformerConfig,
      LongformerOnnxConfig,
  )
  from .tokenization_longformer import LongformerTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_longformer_fast import LongformerTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_longformer import (
        LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        LongformerForMaskedLM,
        LongformerForMultipleChoice,
        LongformerForQuestionAnswering,
        LongformerForSequenceClassification,
        LongformerForTokenClassification,
        LongformerModel,
        LongformerPreTrainedModel,
        LongformerSelfAttention,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_longformer import (
        TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFLongformerForMaskedLM,
        TFLongformerForMultipleChoice,
        TFLongformerForQuestionAnswering,
        TFLongformerForSequenceClassification,
        TFLongformerForTokenClassification,
        TFLongformerModel,
        TFLongformerPreTrainedModel,
        TFLongformerSelfAttention,
    )

else:
  import sys

  UpperCAmelCase													=_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)








'''simple docstring'''







from ..utils import DummyObject, requires_backends
class 						UpperCamelCase__       (			metaclass=SCREAMING_SNAKE_CASE_						):



     """simple docstring"""






     __magic_name__           =     ["torch", "torchsde"]







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

            '''simple docstring'''

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







     @classmethod
     def a						(			cls   ,						*snake_case__   ,						**snake_case__ ):

            '''simple docstring'''

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







     @classmethod
     def a						(			cls   ,						*snake_case__   ,						**snake_case__ ):

            '''simple docstring'''

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

