





'''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 			lowercase__(		_UpperCamelCase	:			Dict			, _UpperCamelCase	:			Any			, _UpperCamelCase	:			Optional[int]=1024			, _UpperCamelCase	:			Any=1024			, _UpperCamelCase	:			Tuple=False			, **_UpperCamelCase	:			List[Any]					)-> Tuple:






					"""simple docstring"""

					_UpperCamelCase									= AutoTokenizer.from_pretrained(__lowercase					)
					_UpperCamelCase									= SeqaSeqDataset(__lowercase			, __lowercase			, __lowercase			, __lowercase			, type_path="train"			, **__lowercase					)
					_UpperCamelCase									= tok.pad_token_id

					def get_lens(_UpperCamelCase	:			int					):
										_UpperCamelCase									= tqdm(
										    DataLoader(__lowercase			, batch_size=512			, num_workers=8			, shuffle=__lowercase			, collate_fn=ds.collate_fn					)			, desc=str(ds.len_file					)			, )
										_UpperCamelCase									= []
										for batch in dl:
															_UpperCamelCase									= batch['''input_ids'''].ne(__lowercase					).sum(1					).tolist()
															_UpperCamelCase									= batch['''labels'''].ne(__lowercase					).sum(1					).tolist()
															if consider_target:
																				for src, tgt in zip(__lowercase			, __lowercase					):
																									max_lens.append(max(__lowercase			, __lowercase					)					)
															else:
																				max_lens.extend(__lowercase					)
										return max_lens

					_UpperCamelCase									= get_lens(__lowercase					)
					_UpperCamelCase									= SeqaSeqDataset(__lowercase			, __lowercase			, __lowercase			, __lowercase			, type_path="val"			, **__lowercase					)
					_UpperCamelCase									= get_lens(__lowercase					)
					pickle_save(__lowercase			, train_ds.len_file					)
					pickle_save(__lowercase			, val_ds.len_file					)


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











def  a__ (__lowercase							:str					,  __lowercase							:str   )		-> float:

				def get_matched_characters(__lowercase							:str					,  __lowercase							:str   ) -> str:
								_A	:				Union[str, Any]   =      []
								_A	:				Dict   =      min(len(_stra   )					,  len(_stra   )   ) // 2
								for i, l in enumerate(_stra   ):
												_A	:				Tuple   =      int(max(0					,  i - limit   )   )
												_A	:				Union[str, Any]   =      int(min(i + limit + 1					,  len(_stra   )   )   )
												if l in _stra[left:right]:
																matched.append(__lowercase   )
																_A	:				List[Any]   =      f"""{_stra[0:_stra.index(__lowercase   )]} {_stra[_stra.index(__lowercase   ) + 1:]}"""

								return "".join(__lowercase   )

				# matching characters
				_A	:				str   =      get_matched_characters(__lowercase					,  __lowercase   )
				_A	:				Optional[Any]   =      get_matched_characters(__lowercase					,  __lowercase   )
				_A	:				Any   =      len(__lowercase   )

				# transposition
				_A	:				str   =      (
				    len([(ca, ca) for ca, ca in zip(__lowercase					,  __lowercase   ) if ca != ca]   ) // 2
				)

				if not match_count:
								_A	:				Optional[int]   =      0.0
				else:
								_A	:				str   =      (
								    1
								    / 3
								    * (
								        match_count / len(__lowercase   )
								        + match_count / len(__lowercase   )
								        + (match_count - transpositions) / match_count
								    )
								)

				# common prefix up to 4 characters
				_A	:				Any   =      0
				for ca, ca in zip(stra[:4]					,  stra[:4]   ):
								if ca == ca:
												prefix_len += 1
								else:
												break

				return jaro + 0.1 * prefix_len * (1 - jaro)


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	print(jaro_winkler('hello', 'world'))



