






def   lowerCamelCase__			(     lowercase				):

       """simple docstring"""






       SCREAMING_SNAKE_CASE       :      Dict							=    [0] * len(lowercase				)
       SCREAMING_SNAKE_CASE       :      List[str]							=    []
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    []
       SCREAMING_SNAKE_CASE       :      Any							=    0

       for values in graph.values():
              for i in values:
                     indegree[i] += 1

       for i in range(len(lowercase				)				):
              if indegree[i] == 0:
                     queue.append(lowercase				)

       while queue:
              SCREAMING_SNAKE_CASE       :      Optional[Any]							=    queue.pop(0				)
              cnt += 1
              topo.append(lowercase				)
              for x in graph[vertex]:
                     indegree[x] -= 1
                     if indegree[x] == 0:
                            queue.append(lowercase				)

       if cnt != len(lowercase				):
              print("Cycle exists"				)
       else:
              print(lowercase				)


# Adjacency List of Graph
snake_case    				=		{0: [1, 2], 1: [3], 2: [3], 3: [4, 5], 4: [], 5: []}

topological_sort(graph)














from typing import TYPE_CHECKING

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


snake_case    				=		{
    """configuration_trajectory_transformer""": [
        """TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """TrajectoryTransformerConfig""",
    ],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   snake_case    				=		[
       """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

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






