# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that there is no edge that connects
# vertices of same set.
from queue import Queue

def 	lowercase_       (    _lowerCamelCase							: Optional[int]):
    lowercase__				:     str					      =       Queue()
    lowercase__				:     str					      =       [False] * len(_lowerCamelCase)
    lowercase__				:     Tuple					      =       [-1] * len(_lowerCamelCase)

    def bfs():
        while not queue.empty():
            lowercase__				:     Union[str, Any]					      =       queue.get()
            lowercase__				:     Dict					      =       True

            for neighbour in graph[u]:
                if neighbour == u:
                    return False

                if color[neighbour] == -1:
                    lowercase__				:     Optional[Any]					      =       1 - color[u]
                    queue.put(_lowerCamelCase)

                elif color[neighbour] == color[u]:
                    return False

        return True

    for i in range(len(_lowerCamelCase)):
        if not visited[i]:
            queue.put(_lowerCamelCase)
            lowercase__				:     str					      =       0
            if bfs() is False:
                return False

    return True


if __name__ == "__main__":
      # Adjacency List of graph
      print(check_bipartite({0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2]}))




from typing import TYPE_CHECKING

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


UpperCamelCase													=		{
    '''configuration_flava''': [
        '''FLAVA_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''FlavaConfig''',
        '''FlavaImageCodebookConfig''',
        '''FlavaImageConfig''',
        '''FlavaMultimodalConfig''',
        '''FlavaTextConfig''',
    ],
}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase													=		['''FlavaFeatureExtractor''']
      UpperCamelCase													=		['''FlavaImageProcessor''']
      UpperCamelCase													=		['''FlavaProcessor''']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase													=		[
          '''FLAVA_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''FlavaForPreTraining''',
          '''FlavaImageCodebook''',
          '''FlavaImageModel''',
          '''FlavaModel''',
          '''FlavaMultimodalModel''',
          '''FlavaPreTrainedModel''',
          '''FlavaTextModel''',
      ]

if TYPE_CHECKING:
      from .configuration_flava import (
          FLAVA_PRETRAINED_CONFIG_ARCHIVE_MAP,
          FlavaConfig,
          FlavaImageCodebookConfig,
          FlavaImageConfig,
          FlavaMultimodalConfig,
          FlavaTextConfig,
      )

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_flava import FlavaFeatureExtractor
            from .image_processing_flava import FlavaImageProcessor
            from .processing_flava import FlavaProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flava import (
                FLAVA_PRETRAINED_MODEL_ARCHIVE_LIST,
                FlavaForPreTraining,
                FlavaImageCodebook,
                FlavaImageModel,
                FlavaModel,
                FlavaMultimodalModel,
                FlavaPreTrainedModel,
                FlavaTextModel,
            )

else:
      import sys

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



