code: 
  
  
  lowercase_								=    float('inf')
  
  
  
  
  class 					_UpperCamelCase :
  
  
  
  
  
  
  
  							'''simple docstring'''
  
  
  
  
  
  
  
  							def __init__(		self  :				List[Any]		,				SCREAMING_SNAKE_CASE_  :				List[str]		):
  													_a     =			[0] * n
  													_a     =			[0] * n
  													_a     =			[0] * n
  													_a     =			[[] for _ in range(SCREAMING_SNAKE_CASE_		)]
  
  
  
  
  
  
  
  							def 		_UpperCAmelCase	(		self  :				str		,				SCREAMING_SNAKE_CASE_  :				Any		,				SCREAMING_SNAKE_CASE_  :				Tuple		,				SCREAMING_SNAKE_CASE_  :				Dict		,				SCREAMING_SNAKE_CASE_  :				int=0		):
  													self.adj[a].append([b, len(self.adj[b]		), c, 0]		)
  													self.adj[b].append([a, len(self.adj[a]		) - 1, rcap, 0]		)
  
  
  
  
  
  
  
  							def 		_UpperCAmelCase	(		self  :				Optional[Any]		,				SCREAMING_SNAKE_CASE_  :				List[Any]		,				SCREAMING_SNAKE_CASE_  :				Union[str, Any]		,				SCREAMING_SNAKE_CASE_  :				Optional[int]		):
  													if vertex == sink or not flow:
  																			return flow
  
  													for i in range(self.ptr[vertex]		,				len(self.adj[vertex]		)		):
  																			_a     =			self.adj[vertex][i]
  																			if self.lvl[e[0]] == self.lvl[vertex] + 1:
  																									_a     =			self.depth_first_search(e[0]		,				SCREAMING_SNAKE_CASE_		,				min(SCREAMING_SNAKE_CASE_		,				e[2] - e[3]		)		)
  																									if p:
  																															self.adj[vertex][i][3] += p
  																															self.adj[e[0]][e[1]][3] -= p
  																															return p
  																			_a     =			self.ptr[vertex] + 1
  													return 0
  
  
  
  
  
  
  
  							def 		_UpperCAmelCase	(		self  :				Optional[int]		,				SCREAMING_SNAKE_CASE_  :				str		,				SCREAMING_SNAKE_CASE_  :				str		):
  													_a ,	_a     =			0, source
  													for l in range(3_1		):  # noqa: E741  l = 30 maybe faster for random data
  																			while True:
  																									_a ,	_a     =			[0] * len(self.q		), [0] * len(self.q		)
  																									_a ,	_a ,	_a     =			0, 1, 1
  																									while qi < qe and not self.lvl[sink]:
  																															_a     =			self.q[qi]
  																															qi += 1
  																															for e in self.adj[v]:
  																																					if not self.lvl[e[0]] and (e[2] - e[3]) >> (3_0 - l):
  																																											_a     =			e[0]
  																																											qe += 1
  																																											_a     =			self.lvl[v] + 1
  
  																									_a     =			self.depth_first_search(SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		)
  																									while p:
  																															flow += p
  																															_a     =			self.depth_first_search(SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		)
  
  																									if not self.lvl[sink]:
  																															break
  
  													return flow
  lowercase_								=    Dinic(10)
  lowercase_								=    0
  lowercase_								=    9
  for vertex in range(1, 5):
  		graph.add_edge(source, vertex, 1)
  for vertex in range(5, 9):
  		graph.add_edge(vertex, sink, 1)
  for vertex in range(1, 5):
  		graph.add_edge(vertex, vertex + 4, 1)
  
  # Now we can know that is the maximum flow(source -> sink)
  print(graph.max_flow(source, sink))
  
  
  
style_context: 
  
  
  from typing import List, Union
  
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
  
  
  lowercase_								=    logging.get_logger(__name__)
  
  lowercase_								=    {
      'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/config.json',
      # See all LED models at https://huggingface.co/models?filter=led
  }
  
  
  
  
  class 					_UpperCamelCase (      lowerCamelCase__	):
  
  
  
  
  
  
  
  							'''simple docstring'''
  							_A  				=  "led"
  							_A  				=  {
  							    "num_attention_heads": "encoder_attention_heads",
  							    "hidden_size": "d_model",
  							    "attention_probs_dropout_prob": "attention_dropout",
  							    "initializer_range": "init_std",
  							}
  
  
  
  
  
  
  
  							def __init__(		self  :				List[str]		,				SCREAMING_SNAKE_CASE_  :				List[str]=5_0_2_6_5		,				SCREAMING_SNAKE_CASE_  :				str=1_6_3_8_4		,				SCREAMING_SNAKE_CASE_  :				List[Any]=1_0_2_4		,				SCREAMING_SNAKE_CASE_  :				List[str]=1_2		,				SCREAMING_SNAKE_CASE_  :				Any=4_0_9_6		,				SCREAMING_SNAKE_CASE_  :				Optional[Any]=1_6		,				SCREAMING_SNAKE_CASE_  :				str=1_2		,				SCREAMING_SNAKE_CASE_  :				List[Any]=4_0_9_6		,				SCREAMING_SNAKE_CASE_  :				List[str]=1_6		,				SCREAMING_SNAKE_CASE_  :				Optional[int]=0.0		,				SCREAMING_SNAKE_CASE_  :				Union[str, Any]=0.0		,				SCREAMING_SNAKE_CASE_  :				List[Any]=True		,				SCREAMING_SNAKE_CASE_  :				str=True		,				SCREAMING_SNAKE_CASE_  :				List[str]="gelu"		,				SCREAMING_SNAKE_CASE_  :				List[Any]=1_0_2_4		,				SCREAMING_SNAKE_CASE_  :				Tuple=0.1		,				SCREAMING_SNAKE_CASE_  :				Dict=0.0		,				SCREAMING_SNAKE_CASE_  :				Optional[Any]=0.0		,				SCREAMING_SNAKE_CASE_  :				Tuple=0.02		,				SCREAMING_SNAKE_CASE_  :				str=2		,				SCREAMING_SNAKE_CASE_  :				Tuple=0.0		,				SCREAMING_SNAKE_CASE_  :				Optional[int]=1		,				SCREAMING_SNAKE_CASE_  :				str=0		,				SCREAMING_SNAKE_CASE_  :				Union[str, Any]=2		,				SCREAMING_SNAKE_CASE_  :				Union[List[int], int] = 5_1_2		,				**SCREAMING_SNAKE_CASE_  :				Dict		,				):
  													_a     =			vocab_size
  													_a     =			max_encoder_position_embeddings
  													_a     =			max_decoder_position_embeddings
  													_a     =			d_model
  													_a     =			encoder_ffn_dim
  													_a     =			encoder_layers
  													_a     =			encoder_attention_heads
  													_a     =			decoder_ffn_dim
  													_a     =			decoder_layers
  													_a     =			decoder_attention_heads
  													_a     =			dropout
  													_a     =			attention_dropout
  													_a     =			activation_dropout
  													_a     =			activation_function
  													_a     =			init_std
  													_a     =			encoder_layerdrop
  													_a     =			decoder_layerdrop
  													_a     =			classifier_dropout
  													_a     =			use_cache
  													_a     =			encoder_layers
  													_a     =			attention_window
  
  													super().__init__(
  													    pad_token_id=SCREAMING_SNAKE_CASE_		,				bos_token_id=SCREAMING_SNAKE_CASE_		,				eos_token_id=SCREAMING_SNAKE_CASE_		,				is_encoder_decoder=SCREAMING_SNAKE_CASE_		,				decoder_start_token_id=SCREAMING_SNAKE_CASE_		,				**SCREAMING_SNAKE_CASE_		,				)
  
  
  