



'''simple docstring'''



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

_lowercase       :						Tuple						=[[1, 4], [2, 4], [3], [], [], [4], [2, 7], [3], []]

_lowercase       :						Dict						=[0 for x in range(len(graph))]
_lowercase       :						Any						=[]


def 							__UpperCAmelCase				(				UpperCamelCase__				:int   ,				UpperCamelCase__				:Dict			)		->						Any:
				snake_case__      :   Union[str, Any]      =    1
				while stack:
								snake_case__      :   int      =    stack.pop()
								print(UpperCamelCase__   ,				clothes[current_clothing]			)
								order += 1


def 							__UpperCAmelCase				(				UpperCamelCase__				:List[Any]   ,				UpperCamelCase__				:Dict   ,				UpperCamelCase__				:Optional[int]			)		->						str:
				snake_case__      :   Dict      =    1
				for v in graph[u]:
								if not visited[v]:
												depth_first_search(UpperCamelCase__   ,				UpperCamelCase__   ,				UpperCamelCase__			)

				stack.append(UpperCamelCase__			)


def 							__UpperCAmelCase				(				UpperCamelCase__				:Optional[Any]   ,				UpperCamelCase__				:Union[str, Any]			)		->						int:
				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)








"""simple docstring"""





from typing import Callable, Optional, Union

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


snake_case__		:  Optional[Any]     					=					logging.get_logger(__name__)

snake_case__		:  str     					=					{
    '''microsoft/prophetnet-large-uncased''': (
        '''https://huggingface.co/microsoft/prophetnet-large-uncased/resolve/main/config.json'''
    ),
}



class 		snake_case_(							a__      ):
     __UpperCamelCase	=       '''prophetnet'''
     __UpperCamelCase	=       ['''past_key_values''']
     __UpperCamelCase	=       {
         '''num_attention_heads''': '''num_encoder_attention_heads''',
     }






     def __init__(						self	:       Union[str, Any]       ,					UpperCamelCase_	:       Optional[float] = 0.1       ,					UpperCamelCase_	:       Optional[Union[str, Callable]] = "gelu"       ,					UpperCamelCase_	:       Optional[int] = 3_0_5_2_2       ,					UpperCamelCase_	:       Optional[int] = 1_0_2_4       ,					UpperCamelCase_	:       Optional[int] = 4_0_9_6       ,					UpperCamelCase_	:       Optional[int] = 1_2       ,					UpperCamelCase_	:       Optional[int] = 1_6       ,					UpperCamelCase_	:       Optional[int] = 4_0_9_6       ,					UpperCamelCase_	:       Optional[int] = 1_2       ,					UpperCamelCase_	:       Optional[int] = 1_6       ,					UpperCamelCase_	:       Optional[float] = 0.1       ,					UpperCamelCase_	:       Optional[float] = 0.1       ,					UpperCamelCase_	:       Optional[int] = 5_1_2       ,					UpperCamelCase_	:       Optional[float] = 0.02       ,					UpperCamelCase_	:       Optional[bool] = True       ,					UpperCamelCase_	:       Optional[bool] = True       ,					UpperCamelCase_	:       Optional[int] = 0       ,					UpperCamelCase_	:       Optional[int] = 2       ,					UpperCamelCase_	:       Optional[int] = 3_2       ,					UpperCamelCase_	:       Optional[int] = 1_2_8       ,					UpperCamelCase_	:       Optional[bool] = False       ,					UpperCamelCase_	:       Optional[float] = 0.0       ,					UpperCamelCase_	:       Optional[bool] = True       ,					UpperCamelCase_	:       Optional[int] = 0       ,					UpperCamelCase_	:       Optional[int] = 1       ,					UpperCamelCase_	:       Optional[int] = 2       ,					**UpperCamelCase_	:       List[Any]       ,					):
           lowerCAmelCase       :						Union[str, Any]							= vocab_size
           lowerCAmelCase       :						Any							= hidden_size
           lowerCAmelCase       :						Tuple							= encoder_ffn_dim
           lowerCAmelCase       :						str							= num_encoder_layers
           lowerCAmelCase       :						int							= num_encoder_attention_heads
           lowerCAmelCase       :						Optional[Any]							= decoder_ffn_dim
           lowerCAmelCase       :						Union[str, Any]							= num_decoder_layers
           lowerCAmelCase       :						Union[str, Any]							= num_decoder_attention_heads
           lowerCAmelCase       :						List[Any]							= max_position_embeddings
           lowerCAmelCase       :						Dict							= init_std  # Normal(0, this parameter)
           lowerCAmelCase       :						Optional[int]							= activation_function

           # parameters for prophetnet
           lowerCAmelCase       :						Tuple							= ngram
           lowerCAmelCase       :						List[Any]							= num_buckets
           lowerCAmelCase       :						Optional[Any]							= relative_max_distance
           lowerCAmelCase       :						Dict							= disable_ngram_loss
           lowerCAmelCase       :						Optional[int]							= eps

           # 3 Types of Dropout
           lowerCAmelCase       :						Any							= attention_dropout
           lowerCAmelCase       :						List[Any]							= activation_dropout
           lowerCAmelCase       :						Tuple							= dropout

           lowerCAmelCase       :						Optional[int]							= use_cache

           super().__init__(
               pad_token_id=UpperCamelCase_       ,					bos_token_id=UpperCamelCase_       ,					eos_token_id=UpperCamelCase_       ,					is_encoder_decoder=UpperCamelCase_       ,					add_cross_attention=UpperCamelCase_       ,					decoder_start_token_id=UpperCamelCase_       ,					**UpperCamelCase_       ,					)






     @property
     def   lowerCamelCase__  (						self	:       str					):
           return self.num_encoder_layers + self.num_decoder_layers






     @num_hidden_layers.setter
     def   lowerCamelCase__  (						self	:       Optional[Any]       ,					UpperCamelCase_	:       int					):
           raise NotImplementedError(
               '''This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and'''
               ''' `num_decoder_layers`.'''					)


