




'''simple docstring'''

def 		__magic_name__    (	__UpperCAmelCase	)		->					set:

					'''simple docstring'''

					snake_case_									=				set()
					# edges = list of graph's edges
					snake_case_									=				get_edges(__UpperCAmelCase	)

					# While there are still elements in edges list, take an arbitrary edge
					# (from_node, to_node) and add his extremity to chosen_vertices and then
					# remove all arcs adjacent to the from_node and to_node
					while edges:
										snake_case_			,snake_case_									=				edges.pop()
										chosen_vertices.add(__UpperCAmelCase	)
										chosen_vertices.add(__UpperCAmelCase	)
										for edge in edges.copy():
															if from_node in edge or to_node in edge:
																				edges.discard(__UpperCAmelCase	)
					return chosen_vertices


def 		__magic_name__    (	__UpperCAmelCase	)		->					set:

					'''simple docstring'''

					snake_case_									=				set()
					for from_node, to_nodes in graph.items():
										for to_node in to_nodes:
															edges.add((from_node, to_node)	)
					return edges


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							# graph = {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
							# print(f"Matching vertex cover:\n{matching_min_vertex_cover(graph)}")






'''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      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_=1024						,			lowerCAmelCase_=1024						,			lowerCAmelCase_=False						,			**lowerCAmelCase_  )			->      List[Any]:
		_a     :      str        =							AutoTokenizer.from_pretrained(lowerCAmelCase_  )
		_a     :      List[Any]        =							SeqaSeqDataset(lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			type_path='train'						,			**lowerCAmelCase_  )
		_a     :      List[str]        =							tok.pad_token_id

		def get_lens(lowerCAmelCase_  ):
				_a     :      Dict        =							tqdm(
				    DataLoader(lowerCAmelCase_						,			batch_size=512						,			num_workers=8						,			shuffle=lowerCAmelCase_						,			collate_fn=ds.collate_fn  )						,			desc=str(ds.len_file  )						,			)
				_a     :      Union[str, Any]        =							[]
				for batch in dl:
						_a     :      Optional[Any]        =							batch['input_ids'].ne(lowerCAmelCase_  ).sum(1  ).tolist()
						_a     :      Optional[Any]        =							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     :      Optional[int]        =							SeqaSeqDataset(lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			type_path='val'						,			**lowerCAmelCase_  )
		_a     :      Dict        =							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)




