




'''simple docstring'''



from typing import TYPE_CHECKING

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


lowerCamelCase_			=						{
    'configuration_trajectory_transformer': [
        'TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'TrajectoryTransformerConfig',
    ],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase_			=						[
     'TRAJECTORY_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
     'TrajectoryTransformerModel',
     'TrajectoryTransformerPreTrainedModel',
     'load_tf_weights_in_trajectory_transformer',
 ]


if TYPE_CHECKING:
 from .configuration_trajectory_transformer import (
     TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
     TrajectoryTransformerConfig,
 )

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_trajectory_transformer import (
      TRAJECTORY_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      TrajectoryTransformerModel,
      TrajectoryTransformerPreTrainedModel,
      load_tf_weights_in_trajectory_transformer,
  )


else:
 import sys

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







'''simple docstring'''



import random
def 			SCREAMING_SNAKE_CASE_			(							__A    :			int	,					__A    :			float	,					__A    :			bool = False  )			->			dict:
   _SCREAMING_SNAKE_CASE	=				{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 			SCREAMING_SNAKE_CASE_			(							__A    :			int  )			->			dict:
   return {
       i: [j for j in range(__A  ) if i != j] for i in range(__A  )
   }


if __name__ == "__main__":
 import doctest

 doctest.testmod()

