






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

from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save




def 					lowercase__				(					__snake_case   :							int					,	__snake_case   :							Dict					,	__snake_case   :							Optional[int]=1_024					,	__snake_case   :							Any=1_024					,	__snake_case   :							str=False					,	**__snake_case   :							str  ):


					'''simple docstring'''


					UpperCAmelCase_   :				Tuple						=						AutoTokenizer.from_pretrained(__snake_case  )
					UpperCAmelCase_   :				List[Any]						=						SeqaSeqDataset(__snake_case					,	__snake_case					,	__snake_case					,	__snake_case					,	type_path='train'					,	**__snake_case  )
					UpperCAmelCase_   :				str						=						tok.pad_token_id

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

					UpperCAmelCase_   :				Tuple						=						get_lens(__snake_case  )
					UpperCAmelCase_   :				Optional[int]						=						SeqaSeqDataset(__snake_case					,	__snake_case					,	__snake_case					,	__snake_case					,	type_path='val'					,	**__snake_case  )
					UpperCAmelCase_   :				List[str]						=						get_lens(__snake_case  )
					pickle_save(__snake_case					,	train_ds.len_file  )
					pickle_save(__snake_case					,	val_ds.len_file  )


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














from __future__ import annotations




def 					lowercase__				(					__snake_case   :							list[int]					,	__snake_case   :							int  ):


					'''simple docstring'''


					if len(__snake_case  ) == 0:
										return False
					UpperCAmelCase_   :				Optional[int]						=						len(__snake_case  ) // 2
					if a_list[midpoint] == item:
										return True
					if item < a_list[midpoint]:
										return binary_search(a_list[:midpoint]					,	__snake_case  )
					else:
										return binary_search(a_list[midpoint + 1 :]					,	__snake_case  )


if __name__ == "__main__":
		__UpperCAmelCase     							=						input('Enter numbers separated by comma:\n').strip()
		__UpperCAmelCase     							=						[int(item.strip()) for item in user_input.split(',')]
		__UpperCAmelCase     							=						int(input('Enter the number to be found in the list:\n').strip())
		__UpperCAmelCase     							=						'' if binary_search(sequence, target) else 'not '
		print(F'{target} was {not_str}found in {sequence}')






