
class     _SCREAMING_SNAKE_CASE     :



	def __init__(    self						,  lowercase						,  lowercase						,  lowercase )				->       Union[str, Any]:
					lowerCamelCase_						=			None
					lowerCamelCase_						=			None
					lowerCamelCase_						=			graph

					self._normalize_graph(lowercase						,  lowercase )
					lowerCamelCase_						=			len(lowercase )
					lowerCamelCase_						=			None



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase )				->       List[str]:
					if sources is int:
									lowerCamelCase_						=			[sources]
					if sinks is int:
									lowerCamelCase_						=			[sinks]

					if len(lowercase ) == 0 or len(lowercase ) == 0:
									return

					lowerCamelCase_						=			sources[0]
					lowerCamelCase_						=			sinks[0]

					# make fake vertex if there are more
					# than one source or sink
					if len(lowercase ) > 1 or len(lowercase ) > 1:
									lowerCamelCase_						=			0
									for i in sources:
													max_input_flow += sum(self.graph[i] )

									lowerCamelCase_						=			len(self.graph ) + 1
									for room in self.graph:
													room.insert(0						,  0 )
									self.graph.insert(0						,  [0] * size )
									for i in sources:
													lowerCamelCase_						=			max_input_flow
									lowerCamelCase_						=			0

									lowerCamelCase_						=			len(self.graph ) + 1
									for room in self.graph:
													room.append(0 )
									self.graph.append([0] * size )
									for i in sinks:
													lowerCamelCase_						=			max_input_flow
									lowerCamelCase_						=			size - 1



	def     SCREAMING_SNAKE_CASE_(    self )				->       Optional[Any]:
					if self.maximum_flow_algorithm is None:
									raise Exception("You need to set maximum flow algorithm before." )
					if self.source_index is None or self.sink_index is None:
									return 0

					self.maximum_flow_algorithm.execute()
					return self.maximum_flow_algorithm.getMaximumFlow()



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase )				->       Optional[Any]:
					lowerCamelCase_						=			algorithm(self )




class     _SCREAMING_SNAKE_CASE     :



	def __init__(    self						,  lowercase )				->       List[Any]:
					lowerCamelCase_						=			flow_network
					lowerCamelCase_						=			flow_network.verticesCount
					lowerCamelCase_						=			flow_network.sourceIndex
					lowerCamelCase_						=			flow_network.sinkIndex
					# it's just a reference, so you shouldn't change
					# it in your algorithms, use deep copy before doing that
					lowerCamelCase_						=			flow_network.graph
					lowerCamelCase_						=			False



	def     SCREAMING_SNAKE_CASE_(    self )				->       Tuple:
					if not self.executed:
									self._algorithm()
									lowerCamelCase_						=			True



	def     SCREAMING_SNAKE_CASE_(    self )				->       List[str]:
					pass




class     _SCREAMING_SNAKE_CASE     (      snake_case_						):



	def __init__(    self						,  lowercase )				->       Any:
					super().__init__(lowercase )
					# use this to save your result
					lowerCamelCase_						=			-1



	def     SCREAMING_SNAKE_CASE_(    self )				->       Any:
					if not self.executed:
									raise Exception("You should execute algorithm before using its result!" )

					return self.maximum_flow




class     _SCREAMING_SNAKE_CASE     (      snake_case_						):



	def __init__(    self						,  lowercase )				->       str:
					super().__init__(lowercase )

					lowerCamelCase_						=			[[0] * self.verticies_count for i in range(self.verticies_count )]

					lowerCamelCase_						=			[0] * self.verticies_count
					lowerCamelCase_						=			[0] * self.verticies_count



	def     SCREAMING_SNAKE_CASE_(    self )				->       str:
					lowerCamelCase_						=			self.verticies_count

					# push some substance to graph
					for nextvertex_index, bandwidth in enumerate(self.graph[self.source_index] ):
									self.preflow[self.source_index][nextvertex_index] += bandwidth
									self.preflow[nextvertex_index][self.source_index] -= bandwidth
									self.excesses[nextvertex_index] += bandwidth

					# Relabel-to-front selection rule
					lowerCamelCase_						=			[
					    i
					    for i in range(self.verticies_count )
					    if i != self.source_index and i != self.sink_index
					]

					# move through list
					lowerCamelCase_						=			0
					while i < len(lowercase ):
									lowerCamelCase_						=			vertices_list[i]
									lowerCamelCase_						=			self.heights[vertex_index]
									self.process_vertex(lowercase )
									if self.heights[vertex_index] > previous_height:
													# if it was relabeled, swap elements
													# and start from 0 index
													vertices_list.insert(0						,  vertices_list.pop(lowercase ) )
													lowerCamelCase_						=			0
									else:
													i += 1

					lowerCamelCase_						=			sum(self.preflow[self.source_index] )



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase )				->       List[Any]:
					while self.excesses[vertex_index] > 0:
									for neighbour_index in range(self.verticies_count ):
													# if it's neighbour and current vertex is higher
													if (
													    self.graph[vertex_index][neighbour_index]
													    - self.preflow[vertex_index][neighbour_index]
													    > 0
													    and self.heights[vertex_index] > self.heights[neighbour_index]
													):
																	self.push(lowercase						,  lowercase )

									self.relabel(lowercase )



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase )				->       Any:
					lowerCamelCase_						=			min(
					    self.excesses[from_index]						,  self.graph[from_index][to_index] - self.preflow[from_index][to_index]						,  )
					self.preflow[from_index][to_index] += preflow_delta
					self.preflow[to_index][from_index] -= preflow_delta
					self.excesses[from_index] -= preflow_delta
					self.excesses[to_index] += preflow_delta



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase )				->       Dict:
					lowerCamelCase_						=			None
					for to_index in range(self.verticies_count ):
									if (
									    self.graph[vertex_index][to_index]
									    - self.preflow[vertex_index][to_index]
									    > 0
									) and (min_height is None or self.heights[to_index] < min_height):
													lowerCamelCase_						=			self.heights[to_index]

					if min_height is not None:
									lowerCamelCase_						=			min_height + 1


if __name__ == "__main__":
			__A				=[0]
			__A				=[3]
			# graph = [
			#     [0, 0, 4, 6, 0, 0],
			#     [0, 0, 5, 2, 0, 0],
			#     [0, 0, 0, 0, 4, 4],
			#     [0, 0, 0, 0, 6, 6],
			#     [0, 0, 0, 0, 0, 0],
			#     [0, 0, 0, 0, 0, 0],
			# ]
			__A				=[[0, 7, 0, 0], [0, 0, 6, 0], [0, 0, 0, 8], [9, 0, 0, 0]]

			# prepare our network
			__A				=FlowNetwork(graph, entrances, exits)
			# set algorithm
			flow_network.set_maximum_flow_algorithm(PushRelabelExecutor)
			# and calculate
			__A				=flow_network.find_maximum_flow()

			print(F"""maximum flow is {maximum_flow}""")







'''simple docstring'''




import fire
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save



def 	a			( lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__=10_24						,      lowerCamelCase__=10_24						,      lowerCamelCase__=False						,      **lowerCamelCase__    ):
			'''simple docstring'''





			A_	:						Any	=     AutoTokenizer.from_pretrained(lowerCamelCase__    )
			A_	:						Union[str, Any]	=     SeqaSeqDataset(lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      type_path="""train"""						,      **lowerCamelCase__    )
			A_	:						Optional[Any]	=     tok.pad_token_id

			def get_lens(lowerCamelCase__    ):
						A_	:						int	=     tqdm(
						    DataLoader(lowerCamelCase__						,      batch_size=5_12						,      num_workers=8						,      shuffle=lowerCamelCase__						,      collate_fn=ds.collate_fn    )						,      desc=str(ds.len_file    )						,      )
						A_	:						int	=     []
						for batch in dl:
									A_	:						str	=     batch["""input_ids"""].ne(lowerCamelCase__    ).sum(1    ).tolist()
									A_	:						Tuple	=     batch["""labels"""].ne(lowerCamelCase__    ).sum(1    ).tolist()
									if consider_target:
												for src, tgt in zip(lowerCamelCase__						,      lowerCamelCase__    ):
															max_lens.append(max(lowerCamelCase__						,      lowerCamelCase__    )    )
									else:
												max_lens.extend(lowerCamelCase__    )
						return max_lens

			A_	:						str	=     get_lens(lowerCamelCase__    )
			A_	:						Dict	=     SeqaSeqDataset(lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      type_path="""val"""						,      **lowerCamelCase__    )
			A_	:						List[Any]	=     get_lens(lowerCamelCase__    )
			pickle_save(lowerCamelCase__						,      train_ds.len_file    )
			pickle_save(lowerCamelCase__						,      val_ds.len_file    )


if __name__ == "__main__":
						fire.Fire(save_len_file)