





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


def       UpperCamelCase__(			UpperCamelCase__     :			list[list[int]]					,	UpperCamelCase__     :			int					,	UpperCamelCase__     :			list[int]					,	UpperCamelCase__     :			int    )->bool:

       # Base Case
       if index == len(UpperCamelCase__    ):
              return True

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


def       UpperCamelCase__(			UpperCamelCase__     :			list[list[int]]					,	UpperCamelCase__     :			int    )->list[int]:
       A__		   =		[-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_tokenizers_available, is_torch_available


a__: int							   =						{
    'configuration_m2m_100': ['M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP', 'M2M100Config', 'M2M100OnnxConfig'],
    'tokenization_m2m_100': ['M2M100Tokenizer'],
}


try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a__: List[Any]							   =						[
           'M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST',
           'M2M100ForConditionalGeneration',
           'M2M100Model',
           'M2M100PreTrainedModel',
       ]


if TYPE_CHECKING:
       from .configuration_mam_aaa import M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP, MaMaaaConfig, MaMaaaOnnxConfig
       from .tokenization_mam_aaa import MaMaaaTokenizer

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_mam_aaa import (
                  M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST,
                  MaMaaaForConditionalGeneration,
                  MaMaaaModel,
                  MaMaaaPreTrainedModel,
              )


else:
       import sys

       a__: List[str]							   =						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





