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


SCREAMING_SNAKE_CASE							:List[str]							 =				logging.get_logger(__name__)

SCREAMING_SNAKE_CASE							:Tuple							 =				{
    'transfo-xl-wt103': 'https://huggingface.co/transfo-xl-wt103/resolve/main/config.json',
}





class 							UpperCAmelCase      (  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_					    =     "transfo-xl"
	snake_case_					    =     ["mems"]
	snake_case_					    =     {
	    "n_token": "vocab_size",
	    "hidden_size": "d_model",
	    "num_attention_heads": "n_head",
	    "num_hidden_layers": "n_layer",
	}






	def __init__(       self					: int							,A					: List[str]=26_77_35							,A					: Tuple=[2_00_00, 4_00_00, 20_00_00]							,A					: int=10_24							,A					: Any=10_24							,A					: Dict=16							,A					: Union[str, Any]=64							,A					: Union[str, Any]=40_96							,A					: Union[str, Any]=4							,A					: Optional[Any]=False							,A					: Any=18							,A					: Optional[int]=16_00							,A					: str=10_00							,A					: Optional[int]=True							,A					: Dict=True							,A					: Any=0							,A					: int=-1							,A					: str=True							,A					: str=0.1							,A					: Dict=0.0							,A					: str=True							,A					: Any="normal"							,A					: List[str]=0.01							,A					: int=0.01							,A					: Optional[int]=0.02							,A					: int=1E-5							,A					: Optional[Any]=0							,**A					: Optional[int]							,):
					__A   			= vocab_size
					__A   			= []
					self.cutoffs.extend(A )
					if proj_share_all_but_first:
									__A   			= [False] + [True] * len(self.cutoffs )
					else:
									__A   			= [False] + [False] * len(self.cutoffs )
					__A   			= d_model
					__A   			= d_embed
					__A   			= d_head
					__A   			= d_inner
					__A   			= div_val
					__A   			= pre_lnorm
					__A   			= n_layer
					__A   			= n_head
					__A   			= mem_len
					__A   			= same_length
					__A   			= attn_type
					__A   			= clamp_len
					__A   			= sample_softmax
					__A   			= adaptive
					__A   			= dropout
					__A   			= dropatt
					__A   			= untie_r
					__A   			= init
					__A   			= init_range
					__A   			= proj_init_std
					__A   			= init_std
					__A   			= layer_norm_epsilon
					super().__init__(eos_token_id=A							,**A )






	@property
	def 				UpperCamelCase_  (       self					: List[str] ):
					# Message copied from Transformer-XL documentation
					logger.info(f'''The model {self.model_type} is one of the few models that has no sequence length limit.''' )
					return -1







	@max_position_embeddings.setter
	def 				UpperCamelCase_  (       self					: Any							,A					: Optional[Any] ):
					# Message copied from Transformer-XL documentation
					raise NotImplementedError(
					    f'''The model {self.model_type} is one of the few models that has no sequence length limit.''' )


def 						UpperCAmelCase			(	a_						)		->	Optional[int]:







				"""simple docstring"""



				__A   			= [0] * len(a_						)
				__A   			= []
				__A   			= [1] * len(a_						)

				for values in graph.values():
								for i in values:
												indegree[i] += 1

				for i in range(len(a_						)						):
								if indegree[i] == 0:
												queue.append(a_						)

				while queue:
								__A   			= queue.pop(0						)
								for x in graph[vertex]:
												indegree[x] -= 1

												if long_dist[vertex] + 1 > long_dist[x]:
																__A   			= long_dist[vertex] + 1

												if indegree[x] == 0:
																queue.append(a_						)

				print(max(a_						)						)


# Adjacency list of Graph
SCREAMING_SNAKE_CASE							:List[Any]							 =				{0: [2, 3, 4], 1: [2, 7], 2: [5], 3: [5, 7], 4: [7], 5: [6], 6: [7], 7: []}
longest_distance(graph)
