

"""simple docstring"""

import copy

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


_UpperCamelCase		    =						logging.get_logger(__name__)

_UpperCamelCase		    =						{}






class  __a				(	__magic_name__					):





       """simple docstring"""


       __UpperCamelCase				:			Tuple          =	'fsmt_decoder'






       def __init__(      self ,				snake_case=0 ,				snake_case=0      ):





        """simple docstring"""

        super().__init__()
        lowerCAmelCase__      :							str    =							vocab_size
        lowerCAmelCase__      :							Any    =							bos_token_id






class  __a				(	__magic_name__					):





       """simple docstring"""


       __UpperCamelCase				:			str          =	'fsmt'
       __UpperCamelCase				:			Union[str, Any]          =	{'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}






       def __init__(      self ,				snake_case=["en", "de"] ,				snake_case=42_024 ,				snake_case=42_024 ,				snake_case="relu" ,				snake_case=1_024 ,				snake_case=200 ,				snake_case=1_024 ,				snake_case=4_096 ,				snake_case=12 ,				snake_case=16 ,				snake_case=0.0 ,				snake_case=4_096 ,				snake_case=12 ,				snake_case=16 ,				snake_case=0.0 ,				snake_case=0.0 ,				snake_case=0.1 ,				snake_case=0.0 ,				snake_case=0.02 ,				snake_case=2 ,				snake_case=True ,				snake_case=True ,				snake_case=False ,				snake_case=5 ,				snake_case=1.0 ,				snake_case=False ,				snake_case=True ,				snake_case=1 ,				snake_case=0 ,				snake_case=2 ,				snake_case=2 ,				**snake_case ,				):





        """simple docstring"""

        lowerCAmelCase__      :							List[Any]    =							langs
        lowerCAmelCase__      :							Optional[int]    =							src_vocab_size
        lowerCAmelCase__      :							Optional[Any]    =							tgt_vocab_size
        lowerCAmelCase__      :							Any    =							d_model  # encoder_embed_dim and decoder_embed_dim

        lowerCAmelCase__      :							Optional[Any]    =							encoder_ffn_dim
        lowerCAmelCase__      :							Dict    =							encoder_layers
        lowerCAmelCase__      :							Dict    =							encoder_attention_heads
        lowerCAmelCase__      :							Dict    =							encoder_layerdrop
        lowerCAmelCase__      :							Optional[Any]    =							decoder_layerdrop
        lowerCAmelCase__      :							int    =							decoder_ffn_dim
        lowerCAmelCase__      :							Tuple    =							decoder_layers
        lowerCAmelCase__      :							str    =							decoder_attention_heads
        lowerCAmelCase__      :							int    =							max_position_embeddings
        lowerCAmelCase__      :							Union[str, Any]    =							init_std  # Normal(0, this parameter)
        lowerCAmelCase__      :							Optional[int]    =							activation_function

        lowerCAmelCase__      :							Any    =							DecoderConfig(vocab_size=snake_case ,				bos_token_id=snake_case      )
        if "decoder" in common_kwargs:
         del common_kwargs["decoder"]

        lowerCAmelCase__      :							str    =							scale_embedding  # scale factor will be sqrt(d_model) if True

        # 3 Types of Dropout
        lowerCAmelCase__      :							Any    =							attention_dropout
        lowerCAmelCase__      :							List[Any]    =							activation_dropout
        lowerCAmelCase__      :							Union[str, Any]    =							dropout

        lowerCAmelCase__      :							Optional[Any]    =							use_cache
        super().__init__(
            pad_token_id=snake_case ,				bos_token_id=snake_case ,				eos_token_id=snake_case ,				decoder_start_token_id=snake_case ,				is_encoder_decoder=snake_case ,				tie_word_embeddings=snake_case ,				forced_eos_token_id=snake_case ,				max_length=snake_case ,				num_beams=snake_case ,				length_penalty=snake_case ,				early_stopping=snake_case ,				**snake_case ,				)






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        lowerCAmelCase__      :							Union[str, Any]    =							copy.deepcopy(self.__dict__      )
        lowerCAmelCase__      :							Any    =							self.decoder.to_dict()
        lowerCAmelCase__      :							Optional[Any]    =							self.__class__.model_type
        return output









"""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.
from queue import Queue



def 			SCREAMING_SNAKE_CASE       (   lowercase__      ) ->	List[str]:
 lowerCAmelCase__      :							Tuple    =							Queue()
 lowerCAmelCase__      :							Optional[int]    =							[False] * len(lowercase__      )
 lowerCAmelCase__      :							Dict    =							[-1] * len(lowercase__      )

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

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

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

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

  return True

 for i in range(len(lowercase__      )      ):
  if not visited[i]:
   queue.put(lowercase__      )
   lowerCAmelCase__      :							int    =							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]}))






