

'''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  :		Union[str, Any]  =		{
    '''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  :		List[str]  =		['''BertTokenizerFast''']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCAmelCase  :		List[Any]  =		[
         '''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  :		Union[str, Any]  =		[
         '''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  :		Any  =		['''TFBertTokenizer''']

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCAmelCase  :		Dict  =		[
         '''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  :		List[str]  =		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)







import baseaa
def      A__       (    SCREAMING_SNAKE_CASE_   :      str						)  ->  bytes:






   """simple docstring"""







   return baseaa.baaencode(string.encode('''utf-8'''						)						)
def      A__       (    SCREAMING_SNAKE_CASE_   :      bytes						)  ->  str:






   """simple docstring"""







   return baseaa.baadecode(SCREAMING_SNAKE_CASE_						).decode('''utf-8'''						)


if __name__ == "__main__":
   UpperCAmelCase_           =	"Hello World!"
   UpperCAmelCase_           =	baseaa_encode(test)
   print(encoded)

   UpperCAmelCase_           =	baseaa_decode(encoded)
   print(decoded)