



def      lowerCAmelCase__		(   lowerCamelCase_		:		Optional[Any]	,lowerCamelCase_		:		Union[str, Any]):





				'''simple docstring'''

				_validate_point(__snake_case)
				_validate_point(__snake_case)
				if len(__snake_case) != len(__snake_case):
								raise ValueError('''Both points must be in the same n-dimensional space''')

				return float(sum(abs(a - b) for a, b in zip(__snake_case	,__snake_case)))






def      lowerCAmelCase__		(   lowerCamelCase_		:		Tuple):





				'''simple docstring'''

				if point:
								if isinstance(__snake_case	,__snake_case):
												for item in point:
																if not isinstance(__snake_case	,(int, float)):
																				lowerCAmelCase__					:    int												=					(
																				    '''Expected a list of numbers as input, found '''
																				    f"""{type(__snake_case).__name__}"""
																				)
																				raise TypeError(__snake_case)
								else:
												lowerCAmelCase__					:    Union[str, Any]												=					f"""Expected a list of numbers as input, found {type(__snake_case).__name__}"""
												raise TypeError(__snake_case)
				else:
								raise ValueError('''Missing an input''')






def      lowerCAmelCase__		(   lowerCamelCase_		:		str	,lowerCamelCase_		:		Union[str, Any]):





				'''simple docstring'''

				_validate_point(__snake_case)
				_validate_point(__snake_case)
				if len(__snake_case) != len(__snake_case):
								raise ValueError('''Both points must be in the same n-dimensional space''')

				return float(sum(abs(x - y) for x, y in zip(__snake_case	,__snake_case)))


if __name__ == "__main__":
							import doctest

							doctest.testmod()






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_		:		Dict	,lowerCamelCase_		:		Optional[int]	,lowerCamelCase_		:		List[Any]=1024	,lowerCamelCase_		:		int=1024	,lowerCamelCase_		:		Dict=False	,**lowerCamelCase_		:		Tuple):





				'''simple docstring'''

				lowerCAmelCase__					:    Optional[int]												=					AutoTokenizer.from_pretrained(lowerCamelCase_)
				lowerCAmelCase__					:    Optional[Any]												=					SeqaSeqDataset(lowerCamelCase_	,lowerCamelCase_	,lowerCamelCase_	,lowerCamelCase_	,type_path='''train'''	,**lowerCamelCase_)
				lowerCAmelCase__					:    int												=					tok.pad_token_id

				def get_lens(lowerCamelCase_		:		Tuple):
								lowerCAmelCase__					:    Tuple												=					tqdm(
								    DataLoader(lowerCamelCase_	,batch_size=512	,num_workers=8	,shuffle=lowerCamelCase_	,collate_fn=ds.collate_fn)	,desc=str(ds.len_file)	,)
								lowerCAmelCase__					:    Tuple												=					[]
								for batch in dl:
												lowerCAmelCase__					:    Dict												=					batch['''input_ids'''].ne(lowerCamelCase_).sum(1).tolist()
												lowerCAmelCase__					:    Dict												=					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

				lowerCAmelCase__					:    str												=					get_lens(lowerCamelCase_)
				lowerCAmelCase__					:    Tuple												=					SeqaSeqDataset(lowerCamelCase_	,lowerCamelCase_	,lowerCamelCase_	,lowerCamelCase_	,type_path='''val'''	,**lowerCamelCase_)
				lowerCAmelCase__					:    Optional[int]												=					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)

