


"""simple docstring"""

from typing import TYPE_CHECKING

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


UpperCAmelCase__					={
    '''configuration_bert''': ['''BERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BertConfig''', '''BertOnnxConfig'''],
    '''tokenization_bert''': ['''BasicTokenizer''', '''BertTokenizer''', '''WordpieceTokenizer'''],
}

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

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase__					=[
           '''BERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''BertForMaskedLM''',
           '''BertForMultipleChoice''',
           '''BertForNextSentencePrediction''',
           '''BertForPreTraining''',
           '''BertForQuestionAnswering''',
           '''BertForSequenceClassification''',
           '''BertForTokenClassification''',
           '''BertLayer''',
           '''BertLMHeadModel''',
           '''BertModel''',
           '''BertPreTrainedModel''',
           '''load_tf_weights_in_bert''',
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase__					=[
           '''TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''TFBertEmbeddings''',
           '''TFBertForMaskedLM''',
           '''TFBertForMultipleChoice''',
           '''TFBertForNextSentencePrediction''',
           '''TFBertForPreTraining''',
           '''TFBertForQuestionAnswering''',
           '''TFBertForSequenceClassification''',
           '''TFBertForTokenClassification''',
           '''TFBertLMHeadModel''',
           '''TFBertMainLayer''',
           '''TFBertModel''',
           '''TFBertPreTrainedModel''',
       ]
try:
       if not is_tensorflow_text_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase__					=['''TFBertTokenizer''']

try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase__					=[
           '''FlaxBertForCausalLM''',
           '''FlaxBertForMaskedLM''',
           '''FlaxBertForMultipleChoice''',
           '''FlaxBertForNextSentencePrediction''',
           '''FlaxBertForPreTraining''',
           '''FlaxBertForQuestionAnswering''',
           '''FlaxBertForSequenceClassification''',
           '''FlaxBertForTokenClassification''',
           '''FlaxBertModel''',
           '''FlaxBertPreTrainedModel''',
       ]

if TYPE_CHECKING:
       from .configuration_bert import BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, BertConfig, BertOnnxConfig
       from .tokenization_bert import BasicTokenizer, BertTokenizer, WordpieceTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_bert_fast import BertTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_bert import (
                  BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  BertForMaskedLM,
                  BertForMultipleChoice,
                  BertForNextSentencePrediction,
                  BertForPreTraining,
                  BertForQuestionAnswering,
                  BertForSequenceClassification,
                  BertForTokenClassification,
                  BertLayer,
                  BertLMHeadModel,
                  BertModel,
                  BertPreTrainedModel,
                  load_tf_weights_in_bert,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_bert import (
                  TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFBertEmbeddings,
                  TFBertForMaskedLM,
                  TFBertForMultipleChoice,
                  TFBertForNextSentencePrediction,
                  TFBertForPreTraining,
                  TFBertForQuestionAnswering,
                  TFBertForSequenceClassification,
                  TFBertForTokenClassification,
                  TFBertLMHeadModel,
                  TFBertMainLayer,
                  TFBertModel,
                  TFBertPreTrainedModel,
              )

       try:
              if not is_tensorflow_text_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_bert_tf import TFBertTokenizer

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_bert import (
                  FlaxBertForCausalLM,
                  FlaxBertForMaskedLM,
                  FlaxBertForMultipleChoice,
                  FlaxBertForNextSentencePrediction,
                  FlaxBertForPreTraining,
                  FlaxBertForQuestionAnswering,
                  FlaxBertForSequenceClassification,
                  FlaxBertForTokenClassification,
                  FlaxBertModel,
                  FlaxBertPreTrainedModel,
              )

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


__SCREAMING_SNAKE_CASE					:				List[str]									=		{
    '''configuration_graphormer''': ['''GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GraphormerConfig'''],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__SCREAMING_SNAKE_CASE					:				Optional[Any]									=		[
							    '''GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''GraphormerForGraphClassification''',
							    '''GraphormerModel''',
							    '''GraphormerPreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_graphormer import GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, GraphormerConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_graphormer import (
														    GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    GraphormerForGraphClassification,
														    GraphormerModel,
														    GraphormerPreTrainedModel,
														)


else:
							import sys

							__SCREAMING_SNAKE_CASE					:				Optional[int]									=		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





