


"""simple docstring"""

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


UpperCAmelCase__  							={
    "configuration_xlm": ["XLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMConfig", "XLMOnnxConfig"],
    "tokenization_xlm": ["XLMTokenizer"],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase__  							=[
           "XLM_PRETRAINED_MODEL_ARCHIVE_LIST",
           "XLMForMultipleChoice",
           "XLMForQuestionAnswering",
           "XLMForQuestionAnsweringSimple",
           "XLMForSequenceClassification",
           "XLMForTokenClassification",
           "XLMModel",
           "XLMPreTrainedModel",
           "XLMWithLMHeadModel",
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase__  							=[
           "TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST",
           "TFXLMForMultipleChoice",
           "TFXLMForQuestionAnsweringSimple",
           "TFXLMForSequenceClassification",
           "TFXLMForTokenClassification",
           "TFXLMMainLayer",
           "TFXLMModel",
           "TFXLMPreTrainedModel",
           "TFXLMWithLMHeadModel",
       ]


if TYPE_CHECKING:
       from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
       from .tokenization_xlm import XLMTokenizer

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_xlm import (
                  XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
                  XLMForMultipleChoice,
                  XLMForQuestionAnswering,
                  XLMForQuestionAnsweringSimple,
                  XLMForSequenceClassification,
                  XLMForTokenClassification,
                  XLMModel,
                  XLMPreTrainedModel,
                  XLMWithLMHeadModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_xlm import (
                  TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFXLMForMultipleChoice,
                  TFXLMForQuestionAnsweringSimple,
                  TFXLMForSequenceClassification,
                  TFXLMForTokenClassification,
                  TFXLMMainLayer,
                  TFXLMModel,
                  TFXLMPreTrainedModel,
                  TFXLMWithLMHeadModel,
              )

else:
       import sys

       UpperCAmelCase__  							=_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




"""simple docstring"""

import random


def lowerCAmelCase_      (				UpperCamelCase__	: int				,   UpperCamelCase__	: float				,   UpperCamelCase__	: bool = False ):






 """simple docstring"""







 __lowercase									=	{i: [] for i in range(UpperCamelCase__ )}

 # if probability is greater or equal than 1, then generate a complete graph
 if probability >= 1:
  return complete_graph(UpperCamelCase__ )
 # 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(UpperCamelCase__ ):
  for j in range(i + 1				,   UpperCamelCase__ ):
   if random.random() < probability:
    graph[i].append(UpperCamelCase__ )
    if not directed:
     # if the graph is undirected, add an edge in from j to i, either
     graph[j].append(UpperCamelCase__ )
 return graph


def lowerCAmelCase_      (				UpperCamelCase__	: int ):






 """simple docstring"""







 return {
     i: [j for j in range(UpperCamelCase__ ) if i != j] for i in range(UpperCamelCase__ )
 }


if __name__ == "__main__":
       import doctest

       doctest.testmod()
