






from typing import TYPE_CHECKING

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


snake_case__   :						str													=   {"""configuration_fnet""": ["""FNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FNetConfig"""]}

try:
 if not is_sentencepiece_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 snake_case__   :						List[Any]													=   ["""FNetTokenizer"""]

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 snake_case__   :						Optional[int]													=   ["""FNetTokenizerFast"""]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 snake_case__   :						Dict													=   [
     """FNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """FNetForMaskedLM""",
     """FNetForMultipleChoice""",
     """FNetForNextSentencePrediction""",
     """FNetForPreTraining""",
     """FNetForQuestionAnswering""",
     """FNetForSequenceClassification""",
     """FNetForTokenClassification""",
     """FNetLayer""",
     """FNetModel""",
     """FNetPreTrainedModel""",
 ]


if TYPE_CHECKING:
 from .configuration_fnet import FNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FNetConfig

 try:
  if not is_sentencepiece_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_fnet import FNetTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_fnet_fast import FNetTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_fnet import (
      FNET_PRETRAINED_MODEL_ARCHIVE_LIST,
      FNetForMaskedLM,
      FNetForMultipleChoice,
      FNetForNextSentencePrediction,
      FNetForPreTraining,
      FNetForQuestionAnswering,
      FNetForSequenceClassification,
      FNetForTokenClassification,
      FNetLayer,
      FNetModel,
      FNetPreTrainedModel,
  )


else:
 import sys

 snake_case__   :						List[str]													=   _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)











'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCamelCase_		:     List[Any]          =		{
    '''configuration_git''': ['''GIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GitConfig''', '''GitVisionConfig'''],
    '''processing_git''': ['''GitProcessor'''],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCamelCase_		:     Optional[Any]          =		[
       '''GIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''GitForCausalLM''',
       '''GitModel''',
       '''GitPreTrainedModel''',
       '''GitVisionModel''',
   ]

if TYPE_CHECKING:
   from .configuration_git import GIT_PRETRAINED_CONFIG_ARCHIVE_MAP, GitConfig, GitVisionConfig
   from .processing_git import GitProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_git import (
          GIT_PRETRAINED_MODEL_ARCHIVE_LIST,
          GitForCausalLM,
          GitModel,
          GitPreTrainedModel,
          GitVisionModel,
      )

else:
   import sys

   UpperCamelCase_		:     List[Any]          =		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


