





import copy

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


_SCREAMING_SNAKE_CASE   						=		logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE   						=		{}
class SCREAMING_SNAKE_CASE_    (       snake_case_       ):
						__magic_name__:      Optional[Any]			   =							"fsmt_decoder"






						def __init__(   self   :   Any		, _A   :   List[str]=0		, _A   :   Any=0						)				->     Dict:







							"""simple docstring"""


							super().__init__()
							snake_case_		:      Optional[int]           =						vocab_size
							snake_case_		:      List[Any]           =						bos_token_id
class SCREAMING_SNAKE_CASE_    (       snake_case_       ):
						__magic_name__:      Tuple			   =							"fsmt"
						__magic_name__:      int			   =							{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}






						def __init__(   self   :   List[str]		, _A   :   int=["en", "de"]		, _A   :   List[Any]=42024		, _A   :   List[Any]=42024		, _A   :   str="relu"		, _A   :   List[str]=1024		, _A   :   str=200		, _A   :   Optional[Any]=1024		, _A   :   Any=4096		, _A   :   List[Any]=12		, _A   :   Optional[int]=16		, _A   :   Tuple=0.0		, _A   :   List[Any]=4096		, _A   :   List[Any]=12		, _A   :   str=16		, _A   :   int=0.0		, _A   :   List[str]=0.0		, _A   :   List[str]=0.1		, _A   :   Union[str, Any]=0.0		, _A   :   Optional[int]=0.0_2		, _A   :   Tuple=2		, _A   :   int=True		, _A   :   Optional[int]=True		, _A   :   Dict=False		, _A   :   List[Any]=5		, _A   :   List[str]=1.0		, _A   :   Dict=False		, _A   :   Optional[int]=True		, _A   :   List[Any]=1		, _A   :   List[str]=0		, _A   :   Optional[int]=2		, _A   :   Optional[int]=2		, **_A   :   List[Any]		, )				->     Optional[Any]:







							"""simple docstring"""


							snake_case_		:      str           =						langs
							snake_case_		:      List[str]           =						src_vocab_size
							snake_case_		:      Dict           =						tgt_vocab_size
							snake_case_		:      Union[str, Any]           =						d_model  # encoder_embed_dim and decoder_embed_dim

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

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

							snake_case_		:      Optional[Any]           =						scale_embedding  # scale factor will be sqrt(d_model) if True

							# 3 Types of Dropout
							snake_case_		:      str           =						attention_dropout
							snake_case_		:      List[str]           =						activation_dropout
							snake_case_		:      List[str]           =						dropout

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






						def UpperCAmelCase_					(   self   :   Optional[int]						)				->     str:







							"""simple docstring"""


							snake_case_		:      List[Any]           =						copy.deepcopy(self.__dict__						)
							snake_case_		:      Optional[int]           =						self.decoder.to_dict()
							snake_case_		:      Any           =						self.__class__.model_type
							return output








_SCREAMING_SNAKE_CASE   						=		{
    0: """underwear""",
    1: """pants""",
    2: """belt""",
    3: """suit""",
    4: """shoe""",
    5: """socks""",
    6: """shirt""",
    7: """tie""",
    8: """watch""",
}

_SCREAMING_SNAKE_CASE   						=		[[1, 4], [2, 4], [3], [], [], [4], [2, 7], [3], []]

_SCREAMING_SNAKE_CASE   						=		[0 for x in range(len(graph))]
_SCREAMING_SNAKE_CASE   						=		[]


def 				SCREAMING_SNAKE_CASE__    (     __a ,			__a ):
	snake_case_		:      Any           =						1
	while stack:
		snake_case_		:      List[Any]           =						stack.pop()
		print(__a ,			clothes[current_clothing] )
		order += 1


def 				SCREAMING_SNAKE_CASE__    (     __a ,			__a ,			__a ):
	snake_case_		:      int           =						1
	for v in graph[u]:
		if not visited[v]:
			depth_first_search(__a ,			__a ,			__a )

	stack.append(__a )


def 				SCREAMING_SNAKE_CASE__    (     __a ,			__a ):
	for v in range(len(__a ) ):
		if not visited[v]:
			depth_first_search(__a ,			__a ,			__a )


if __name__ == "__main__":
						topological_sort(graph, visited)
						print(stack)
						print_stack(stack, clothes)

