
"""simple docstring"""







def       A_      (       lowercase    ,			lowercase    ,			lowercase   )  ->				bool:


     """simple docstring"""







     return not any(
         neighbour == 1 and colored_vertices[i] == color
         for i, neighbour in enumerate(lowercase   )   )




def       A_      (       lowercase    ,			lowercase    ,			lowercase    ,			lowercase   )  ->				bool:


     """simple docstring"""







     if index == len(lowercase   ):
          return True

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




def       A_      (       lowercase    ,			lowercase   )  ->				list[int]:


     """simple docstring"""







     UpperCAmelCase_    :     Dict            =	[-1] * len(lowercase   )

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

     return []



"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase_         =       {
    "configuration_timesformer": ["TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "TimesformerConfig"],
}

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

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

