


'''simple docstring'''







import random







def _UpperCamelCase       (     __A      ,			__A      ,			__A = False    )							->		dict:


      '''simple docstring'''



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

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







def _UpperCamelCase       (     __A    )							->		dict:


      '''simple docstring'''



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


if __name__ == "__main__":
  import doctest

  doctest.testmod()









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase__   	=							{
    '''configuration_timesformer''': ['''TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TimesformerConfig'''],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase__   	=							[
      '''TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''TimesformerModel''',
      '''TimesformerForVideoClassification''',
      '''TimesformerPreTrainedModel''',
  ]

if TYPE_CHECKING:
  from .configuration_timesformer import TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, TimesformerConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_timesformer import (
        TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        TimesformerForVideoClassification,
        TimesformerModel,
        TimesformerPreTrainedModel,
    )

else:
  import sys

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

