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,
)


a_    					=       {
    '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:
   a_    					=       ['BertTokenizerFast']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   a_    					=       [
       '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:
   a_    					=       [
       '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:
   a_    					=       ['TFBertTokenizer']

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   a_    					=       [
       '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

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




from __future__ import annotations





def    __lowercase     (						lowerCamelCase					:     Optional[Any]  ,  lowerCamelCase					:     Dict  ,  lowerCamelCase					:     Union[str, Any]  ,  lowerCamelCase					:     List[str]	):  # noqa: E741
 while r - l > 1:
  UpperCamelCase_					:						Union[str, Any] =					(l + r) // 2
  if v[m] >= key:
   UpperCamelCase_					:						str =					m
  else:
   UpperCamelCase_					:						List[Any] =					m  # noqa: E741
 return r





def    __lowercase     (						lowerCamelCase					:     list[int]	):
 if len(lowerCamelCase	) == 0:
  return 0

 UpperCamelCase_					:						Tuple =					[0] * len(lowerCamelCase	)
 UpperCamelCase_					:						int =					1

 UpperCamelCase_					:						Dict =					v[0]

 for i in range(1  ,  len(lowerCamelCase	)	):
  if v[i] < tail[0]:
   UpperCamelCase_					:						Any =					v[i]
  elif v[i] > tail[length - 1]:
   UpperCamelCase_					:						Dict =					v[i]
   length += 1
  else:
   UpperCamelCase_					:						List[str] =					v[i]

 return length


if __name__ == "__main__":
   import doctest

   doctest.testmod()



