






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

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

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

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     SCREAMING_SNAKE_CASE__											=     [
         '''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:
     SCREAMING_SNAKE_CASE__											=     [
         '''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:
     SCREAMING_SNAKE_CASE__											=     [
         '''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

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












'''simple docstring'''

from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE__											=     {
    'configuration_swiftformer': [
        'SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'SwiftFormerConfig',
        'SwiftFormerOnnxConfig',
    ]
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     SCREAMING_SNAKE_CASE__											=     [
         'SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
         'SwiftFormerForImageClassification',
         'SwiftFormerModel',
         'SwiftFormerPreTrainedModel',
     ]

if TYPE_CHECKING:
     from .configuration_swiftformer import (
         SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
         SwiftFormerConfig,
         SwiftFormerOnnxConfig,
     )

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_swiftformer import (
              SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
              SwiftFormerForImageClassification,
              SwiftFormerModel,
              SwiftFormerPreTrainedModel,
          )

else:
     import sys

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