



'''simple docstring'''





from typing import TYPE_CHECKING

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


_UpperCamelCase											=						{'configuration_xlnet': ['XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'XLNetConfig']}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase											=						['XLNetTokenizer']

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase											=						['XLNetTokenizerFast']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase											=						[
       'XLNET_PRETRAINED_MODEL_ARCHIVE_LIST',
       'XLNetForMultipleChoice',
       'XLNetForQuestionAnswering',
       'XLNetForQuestionAnsweringSimple',
       'XLNetForSequenceClassification',
       'XLNetForTokenClassification',
       'XLNetLMHeadModel',
       'XLNetModel',
       'XLNetPreTrainedModel',
       'load_tf_weights_in_xlnet',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase											=						[
       'TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST',
       'TFXLNetForMultipleChoice',
       'TFXLNetForQuestionAnsweringSimple',
       'TFXLNetForSequenceClassification',
       'TFXLNetForTokenClassification',
       'TFXLNetLMHeadModel',
       'TFXLNetMainLayer',
       'TFXLNetModel',
       'TFXLNetPreTrainedModel',
   ]


if TYPE_CHECKING:
   from .configuration_xlnet import XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLNetConfig

   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_xlnet import XLNetTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_xlnet_fast import XLNetTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_xlnet import (
          XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
          XLNetForMultipleChoice,
          XLNetForQuestionAnswering,
          XLNetForQuestionAnsweringSimple,
          XLNetForSequenceClassification,
          XLNetForTokenClassification,
          XLNetLMHeadModel,
          XLNetModel,
          XLNetPreTrainedModel,
          load_tf_weights_in_xlnet,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_xlnet import (
          TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFXLNetForMultipleChoice,
          TFXLNetForQuestionAnsweringSimple,
          TFXLNetForSequenceClassification,
          TFXLNetForTokenClassification,
          TFXLNetLMHeadModel,
          TFXLNetMainLayer,
          TFXLNetModel,
          TFXLNetPreTrainedModel,
      )

else:
   import sys

   _UpperCamelCase											=						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)











'''simple docstring'''





from typing import TYPE_CHECKING

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


_UpperCamelCase											=						{
    'configuration_biogpt': ['BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BioGptConfig'],
    'tokenization_biogpt': ['BioGptTokenizer'],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase											=						[
       'BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'BioGptForCausalLM',
       'BioGptForTokenClassification',
       'BioGptForSequenceClassification',
       'BioGptModel',
       'BioGptPreTrainedModel',
   ]


if TYPE_CHECKING:
   from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
   from .tokenization_biogpt import BioGptTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_biogpt import (
          BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
          BioGptForCausalLM,
          BioGptForSequenceClassification,
          BioGptForTokenClassification,
          BioGptModel,
          BioGptPreTrainedModel,
      )


else:
   import sys

   _UpperCamelCase											=						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






