

"""simple docstring"""




# 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.



def 	UpperCAmelCase__   (				A__   )      ->      str:




   """simple docstring"""
   lowerCamelCase__						=			[False] * len(A__   )
   lowerCamelCase__						=			[-1] * len(A__   )

   def dfs(A__	,    A__   ):
      lowerCamelCase__						=			True
      lowerCamelCase__						=			c
      for u in graph[v]:
         if not visited[u]:
            dfs(A__	,    1 - c   )

   for i in range(len(A__   )   ):
      if not visited[i]:
         dfs(A__	,    0   )

   for i in range(len(A__   )   ):
      for j in graph[i]:
         if color[i] == color[j]:
            return False

   return True


# Adjacency list of graph
SCREAMING_SNAKE_CASE_     :			List[Any]	  =			{0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2], 4: []}
print(check_bipartite_dfs(graph))







"""simple docstring"""




import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE_     :			Dict	  =			logging.get_logger(__name__)


class 		_A       (			__a					):
      __a								=	'encoder-decoder'
      __a								=	True





      def __init__(  self    ,						**SCREAMING_SNAKE_CASE__		)   ->					Optional[int]:
         super().__init__(**SCREAMING_SNAKE_CASE__		)
         assert (
             "encoder" in kwargs and "decoder" in kwargs
         ), "Config has to be initialized with encoder and decoder config"
         lowerCamelCase__						=			kwargs.pop("encoder"		)
         lowerCamelCase__						=			encoder_config.pop("model_type"		)
         lowerCamelCase__						=			kwargs.pop("decoder"		)
         lowerCamelCase__						=			decoder_config.pop("model_type"		)

         from ..auto.configuration_auto import AutoConfig

         lowerCamelCase__						=			AutoConfig.for_model(SCREAMING_SNAKE_CASE__    ,						**SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			AutoConfig.for_model(SCREAMING_SNAKE_CASE__    ,						**SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			True





      @classmethod
      def 					_lowerCamelCase  (  cls    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						**SCREAMING_SNAKE_CASE__		)   ->					PretrainedConfig:
         logger.info("Set `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config"		)
         lowerCamelCase__						=			True
         lowerCamelCase__						=			True

         return cls(encoder=encoder_config.to_dict()    ,						decoder=decoder_config.to_dict()    ,						**SCREAMING_SNAKE_CASE__		)





      def 					_lowerCamelCase  (  self		)   ->					int:
         lowerCamelCase__						=			copy.deepcopy(self.__dict__		)
         lowerCamelCase__						=			self.encoder.to_dict()
         lowerCamelCase__						=			self.decoder.to_dict()
         lowerCamelCase__						=			self.__class__.model_type
         return output




