


snake_case__     							= float('inf')



class     snake_case__  :
				def __init__(  self     ,      _UpperCAmelCase      ):




										"""simple docstring"""




										__magic_name__				:							str				=      [0] * n
										__magic_name__				:							Optional[Any]				=      [0] * n
										__magic_name__				:							List[Any]				=      [0] * n
										__magic_name__				:							int				=      [[] for _ in range(_UpperCAmelCase      )]
				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase=0      ):




										"""simple docstring"""




										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     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase      ):




										"""simple docstring"""




										if vertex == sink or not flow:
																return flow

										for i in range(self.ptr[vertex]     ,      len(self.adj[vertex]      )      ):
																__magic_name__				:							List[str]				=      self.adj[vertex][i]
																if self.lvl[e[0]] == self.lvl[vertex] + 1:
																						__magic_name__				:							Union[str, Any]				=      self.depth_first_search(e[0]     ,      _UpperCAmelCase     ,      min(_UpperCAmelCase     ,      e[2] - e[3]      )      )
																						if p:
																												self.adj[vertex][i][3] += p
																												self.adj[e[0]][e[1]][3] -= p
																												return p
																__magic_name__				:							Optional[Any]				=      self.ptr[vertex] + 1
										return 0





				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase     ,      _UpperCAmelCase      ):




										"""simple docstring"""




										__magic_name__		, __magic_name__				:							str				=      0, source
										for l in range(31      ):  # noqa: E741  l = 30 maybe faster for random data
																while True:
																						__magic_name__		, __magic_name__				:							List[str]				=      [0] * len(self.q      ), [0] * len(self.q      )
																						__magic_name__		, __magic_name__		, __magic_name__				:							List[Any]				=      0, 1, 1
																						while qi < qe and not self.lvl[sink]:
																												__magic_name__				:							List[Any]				=      self.q[qi]
																												qi += 1
																												for e in self.adj[v]:
																																		if not self.lvl[e[0]] and (e[2] - e[3]) >> (30 - l):
																																								__magic_name__				:							List[Any]				=      e[0]
																																								qe += 1
																																								__magic_name__				:							Optional[int]				=      self.lvl[v] + 1

																						__magic_name__				:							Union[str, Any]				=      self.depth_first_search(_UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase      )
																						while p:
																												flow += p
																												__magic_name__				:							int				=      self.depth_first_search(_UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase      )

																						if not self.lvl[sink]:
																												break

										return flow






snake_case__     							= Dinic(10)
snake_case__     							= 0
snake_case__     							= 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))




import argparse

from . import (
    BertConfig,
    BertGenerationConfig,
    BertGenerationDecoder,
    BertGenerationEncoder,
    load_tf_weights_in_bert_generation,
    logging,
)


logging.set_verbosity_info()
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :Optional[int]      ,						lowercase__  :List[Any]      ,						lowercase__  :str      ,						lowercase__  :List[str]      ,						lowercase__  :List[Any]				)       ->  List[Any]:
						# Initialise PyTorch model
						__magic_name__				:							Any				=      BertConfig.from_pretrained(
						    'bert-large-cased'      ,						vocab_size=lowercase__      ,						max_position_embeddings=5_1_2      ,						is_decoder=lowercase__      ,						add_cross_attention=lowercase__      ,						)
						__magic_name__				:							Optional[int]				=      bert_config.to_dict()
						del bert_config_dict["type_vocab_size"]
						__magic_name__				:							Optional[int]				=      BertGenerationConfig(**lowercase__				)
						if is_encoder:
												__magic_name__				:							Dict				=      BertGenerationEncoder(lowercase__				)
						else:
												__magic_name__				:							str				=      BertGenerationDecoder(lowercase__				)
						print(f"Building PyTorch model from configuration: {config}"				)

						# Load weights from tf checkpoint
						load_tf_weights_in_bert_generation(
						    lowercase__      ,						lowercase__      ,						model_class='bert'      ,						is_encoder_named_decoder=lowercase__      ,						is_encoder=lowercase__      ,						)

						# Save pytorch-model
						print(f"Save PyTorch model and config to {pytorch_dump_path}"				)
						model.save_pretrained(lowercase__				)


if __name__ == "__main__":
			snake_case__     							= argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '--tf_hub_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
			)
			parser.add_argument(
			    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
			)
			parser.add_argument(
			    '--is_encoder_named_decoder',
			    action='store_true',
			    help='If decoder has to be renamed to encoder in PyTorch model.',
			)
			parser.add_argument('--is_encoder', action='store_true', help='If model is an encoder.')
			parser.add_argument('--vocab_size', default=5_0358, type=int, help='Vocab size of model')
			snake_case__     							= parser.parse_args()
			convert_tf_checkpoint_to_pytorch(
			    args.tf_hub_path,
			    args.pytorch_dump_path,
			    args.is_encoder_named_decoder,
			    args.vocab_size,
			    is_encoder=args.is_encoder,
			)


