



from __future__ import annotations






def      lowerCAmelCase__		(   lowerCamelCase_		:		float	,lowerCamelCase_		:		float	,lowerCamelCase_		:		float	,):





				'''simple docstring'''

				if (electron_conc, hole_conc, intrinsic_conc).count(0) != 1:
								raise ValueError('''You cannot supply more or less than 2 values''')
				elif electron_conc < 0:
								raise ValueError('''Electron concentration cannot be negative in a semiconductor''')
				elif hole_conc < 0:
								raise ValueError('''Hole concentration cannot be negative in a semiconductor''')
				elif intrinsic_conc < 0:
								raise ValueError(
								    '''Intrinsic concentration cannot be negative in a semiconductor''')
				elif electron_conc == 0:
								return (
								    "electron_conc",
								    intrinsic_conc**2 / hole_conc,
								)
				elif hole_conc == 0:
								return (
								    "hole_conc",
								    intrinsic_conc**2 / electron_conc,
								)
				elif intrinsic_conc == 0:
								return (
								    "intrinsic_conc",
								    (electron_conc * hole_conc) ** 0.5,
								)
				else:
								return (-1, -1)


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)

