'''simple docstring'''






import random





def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			int   ,		__snake_case							:			float   ,		__snake_case							:			bool = False				):
     _A    		=  {i: [] for i in range(__snake_case				)}

     # if probability is greater or equal than 1, then generate a complete graph
     if probability >= 1:
          return complete_graph(__snake_case				)
     # if probability is lower or equal than 0, then return a graph without edges
     if probability <= 0:
          return graph

     # for each couple of nodes, add an edge from u to v
     # if the number randomly generated is greater than probability probability
     for i in range(__snake_case				):
          for j in range(i + 1   ,		__snake_case				):
               if random.random() < probability:
                    graph[i].append(__snake_case				)
                    if not directed:
                         # if the graph is undirected, add an edge in from j to i, either
                         graph[j].append(__snake_case				)
     return graph





def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			int				):
     return {
         i: [j for j in range(__snake_case				) if i != j] for i in range(__snake_case				)
     }


if __name__ == "__main__":
   import doctest

   doctest.testmod()




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,
)


SCREAMING_SNAKE_CASE			:						Optional[int]				  =		{
    """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:
  SCREAMING_SNAKE_CASE			:						Optional[int]				  =		["""BertTokenizerFast"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE			:						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:
  SCREAMING_SNAKE_CASE			:						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:
  SCREAMING_SNAKE_CASE			:						Optional[Any]				  =		["""TFBertTokenizer"""]

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE			:						Any				  =		[
      """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

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


