



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,
)


UpperCamelCase_	   =			{
    'configuration_albert': ['ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'AlbertConfig', 'AlbertOnnxConfig'],
}

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

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

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCamelCase_	   =			[
           'ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
           'AlbertForMaskedLM',
           'AlbertForMultipleChoice',
           'AlbertForPreTraining',
           'AlbertForQuestionAnswering',
           'AlbertForSequenceClassification',
           'AlbertForTokenClassification',
           'AlbertModel',
           'AlbertPreTrainedModel',
           'load_tf_weights_in_albert',
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCamelCase_	   =			[
           'TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
           'TFAlbertForMaskedLM',
           'TFAlbertForMultipleChoice',
           'TFAlbertForPreTraining',
           'TFAlbertForQuestionAnswering',
           'TFAlbertForSequenceClassification',
           'TFAlbertForTokenClassification',
           'TFAlbertMainLayer',
           'TFAlbertModel',
           'TFAlbertPreTrainedModel',
       ]

try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCamelCase_	   =			[
           'FlaxAlbertForMaskedLM',
           'FlaxAlbertForMultipleChoice',
           'FlaxAlbertForPreTraining',
           'FlaxAlbertForQuestionAnswering',
           'FlaxAlbertForSequenceClassification',
           'FlaxAlbertForTokenClassification',
           'FlaxAlbertModel',
           'FlaxAlbertPreTrainedModel',
       ]

if TYPE_CHECKING:
       from .configuration_albert import ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, AlbertConfig, AlbertOnnxConfig

       try:
              if not is_sentencepiece_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_albert import AlbertTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_albert_fast import AlbertTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_albert import (
                  ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  AlbertForMaskedLM,
                  AlbertForMultipleChoice,
                  AlbertForPreTraining,
                  AlbertForQuestionAnswering,
                  AlbertForSequenceClassification,
                  AlbertForTokenClassification,
                  AlbertModel,
                  AlbertPreTrainedModel,
                  load_tf_weights_in_albert,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_albert import (
                  TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFAlbertForMaskedLM,
                  TFAlbertForMultipleChoice,
                  TFAlbertForPreTraining,
                  TFAlbertForQuestionAnswering,
                  TFAlbertForSequenceClassification,
                  TFAlbertForTokenClassification,
                  TFAlbertMainLayer,
                  TFAlbertModel,
                  TFAlbertPreTrainedModel,
              )

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_albert import (
                  FlaxAlbertForMaskedLM,
                  FlaxAlbertForMultipleChoice,
                  FlaxAlbertForPreTraining,
                  FlaxAlbertForQuestionAnswering,
                  FlaxAlbertForSequenceClassification,
                  FlaxAlbertForTokenClassification,
                  FlaxAlbertModel,
                  FlaxAlbertPreTrainedModel,
              )
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_sentencepiece_available


SCREAMING_SNAKE_CASE     =       {}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE     =       ["""GPTSw3Tokenizer"""]


if TYPE_CHECKING:
      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_gpt_swa import GPTSwaTokenizer

else:
      import sys

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



