def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase		)						->    bool:



      '''simple docstring'''

      return not any(
          neighbour == 1 and colored_vertices[i] == color
          for i, neighbour in enumerate(__UpperCAmelCase		)		)
def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase		)						->    bool:



      '''simple docstring'''

      if index == len(__UpperCAmelCase		):
            return True

      # Recursive Step
      for i in range(__UpperCAmelCase		):
            if valid_coloring(graph[index] ,						__UpperCAmelCase ,						__UpperCAmelCase		):
                  # Color current vertex
                  _lowerCamelCase			    =				i
                  # Validate coloring
                  if util_color(__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase ,						index + 1		):
                        return True
                  # Backtrack
                  _lowerCamelCase			    =				-1
      return False




def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase		)						->    list[int]:



      '''simple docstring'''

      _lowerCamelCase			    =				[-1] * len(__UpperCAmelCase		)

      if util_color(__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase ,						0		):
            return colored_vertices

      return []
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__)