code: 
  
  
  
  
  
  '''simple docstring'''
  
  
  
  
  import copy
  
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
  
  
  a__				: List[Any]									=    logging.get_logger(__name__)
  
  a__				: Union[str, Any]									=    {}
  
  
  
  
  
  
  class 							__snake_case    (				__magic_name__       ):
  	__lowerCAmelCase						=     '''fsmt_decoder'''
  
  
  
  
  
  	def __init__(		self			,					UpperCamelCase_=0			,					UpperCamelCase_=0		)			->		int:
  		super().__init__()
  		snake_case__  					=      vocab_size
  		snake_case__  					=      bos_token_id
  
  
  
  
  
  
  class 							__snake_case    (				__magic_name__       ):
  	__lowerCAmelCase						=     '''fsmt'''
  	__lowerCAmelCase						=     {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}
  
  
  
  
  
  	def __init__(		self			,					UpperCamelCase_=["en", "de"]			,					UpperCamelCase_=4_2024			,					UpperCamelCase_=4_2024			,					UpperCamelCase_="relu"			,					UpperCamelCase_=1024			,					UpperCamelCase_=200			,					UpperCamelCase_=1024			,					UpperCamelCase_=4096			,					UpperCamelCase_=12			,					UpperCamelCase_=16			,					UpperCamelCase_=0.0			,					UpperCamelCase_=4096			,					UpperCamelCase_=12			,					UpperCamelCase_=16			,					UpperCamelCase_=0.0			,					UpperCamelCase_=0.0			,					UpperCamelCase_=0.1			,					UpperCamelCase_=0.0			,					UpperCamelCase_=0.0_2			,					UpperCamelCase_=2			,					UpperCamelCase_=True			,					UpperCamelCase_=True			,					UpperCamelCase_=False			,					UpperCamelCase_=5			,					UpperCamelCase_=1.0			,					UpperCamelCase_=False			,					UpperCamelCase_=True			,					UpperCamelCase_=1			,					UpperCamelCase_=0			,					UpperCamelCase_=2			,					UpperCamelCase_=2			,					**UpperCamelCase_			,					)			->		str:
  		snake_case__  					=      langs
  		snake_case__  					=      src_vocab_size
  		snake_case__  					=      tgt_vocab_size
  		snake_case__  					=      d_model  # encoder_embed_dim and decoder_embed_dim
  
  		snake_case__  					=      encoder_ffn_dim
  		snake_case__  					=      snake_case__  					=      encoder_layers
  		snake_case__  					=      encoder_attention_heads
  		snake_case__  					=      encoder_layerdrop
  		snake_case__  					=      decoder_layerdrop
  		snake_case__  					=      decoder_ffn_dim
  		snake_case__  					=      decoder_layers
  		snake_case__  					=      decoder_attention_heads
  		snake_case__  					=      max_position_embeddings
  		snake_case__  					=      init_std  # Normal(0, this parameter)
  		snake_case__  					=      activation_function
  
  		snake_case__  					=      DecoderConfig(vocab_size=UpperCamelCase_			,					bos_token_id=UpperCamelCase_		)
  		if "decoder" in common_kwargs:
  			del common_kwargs["decoder"]
  
  		snake_case__  					=      scale_embedding  # scale factor will be sqrt(d_model) if True
  
  		# 3 Types of Dropout
  		snake_case__  					=      attention_dropout
  		snake_case__  					=      activation_dropout
  		snake_case__  					=      dropout
  
  		snake_case__  					=      use_cache
  		super().__init__(
  		    pad_token_id=UpperCamelCase_			,					bos_token_id=UpperCamelCase_			,					eos_token_id=UpperCamelCase_			,					decoder_start_token_id=UpperCamelCase_			,					is_encoder_decoder=UpperCamelCase_			,					tie_word_embeddings=UpperCamelCase_			,					forced_eos_token_id=UpperCamelCase_			,					max_length=UpperCamelCase_			,					num_beams=UpperCamelCase_			,					length_penalty=UpperCamelCase_			,					early_stopping=UpperCamelCase_			,					**UpperCamelCase_			,					)
  
  
  
  
  
  	def      _snake_case      (		self		)			->		int:
  		snake_case__  					=      copy.deepcopy(self.__dict__		)
  		snake_case__  					=      self.decoder.to_dict()
  		snake_case__  					=      self.__class__.model_type
  		return output
  
  
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  
  
  
  a__				: Any									=    {
      0: '''underwear''',
      1: '''pants''',
      2: '''belt''',
      3: '''suit''',
      4: '''shoe''',
      5: '''socks''',
      6: '''shirt''',
      7: '''tie''',
      8: '''watch''',
  }
  
  a__				: Optional[Any]									=    [[1, 4], [2, 4], [3], [], [], [4], [2, 7], [3], []]
  
  a__				: List[str]									=    [0 for x in range(len(graph))]
  a__				: List[Any]									=    []
  
  
  
  
  def 	__lowerCamelCase (  UpperCAmelCase_				,							UpperCAmelCase_       )      ->Dict:
  	snake_case__  					=      1
  	while stack:
  		snake_case__  					=      stack.pop()
  		print(UpperCAmelCase_				,							clothes[current_clothing]       )
  		order += 1
  
  
  
  
  def 	__lowerCamelCase (  UpperCAmelCase_				,							UpperCAmelCase_				,							UpperCAmelCase_       )      ->Dict:
  	snake_case__  					=      1
  	for v in graph[u]:
  		if not visited[v]:
  			depth_first_search(UpperCAmelCase_				,							UpperCAmelCase_				,							UpperCAmelCase_       )
  
  	stack.append(UpperCAmelCase_       )
  
  
  
  
  def 	__lowerCamelCase (  UpperCAmelCase_				,							UpperCAmelCase_       )      ->Union[str, Any]:
  	for v in range(len(UpperCAmelCase_       )       ):
  		if not visited[v]:
  			depth_first_search(UpperCAmelCase_				,							UpperCAmelCase_				,							UpperCAmelCase_       )
  
  
  if __name__ == "__main__":
  			topological_sort(graph, visited)
  			print(stack)
  			print_stack(stack, clothes)
  
  
  
  