"""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,
)


__lowerCAmelCase    :       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:
       __lowerCAmelCase    :       Union[str, Any]					     =["""BertTokenizerFast"""]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowerCAmelCase    :       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:
       __lowerCAmelCase    :       str					     =[
           """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:
       __lowerCAmelCase    :       Optional[int]					     =["""TFBertTokenizer"""]

try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowerCAmelCase    :       int					     =[
           """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

       __lowerCAmelCase    :       Optional[Any]					     =_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




def    __lowercase     (						lowerCamelCase					:     str	):
 UpperCamelCase_					:						Dict =					0
 for ch in input_str:
  UpperCamelCase_					:						Tuple =					ord(lowerCamelCase	)
  UpperCamelCase_					:						str =					pow(2  ,  lowerCamelCase	)

  # If we already turned on bit for current character's unicode
  if bitmap >> ch_unicode & 1 == 1:
   return False
  bitmap |= ch_bit_index_on
 return True


if __name__ == "__main__":
   import doctest

   doctest.testmod()



