



"""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      snake_case__   (		__lowerCamelCase				:    Optional[Any]							,		__lowerCamelCase				:    List[str]							,		__lowerCamelCase				:    Optional[Any]=1024							,		__lowerCamelCase				:    str=1024							,		__lowerCamelCase				:    Union[str, Any]=False							,		**__lowerCamelCase				:    str	):
			"""simple docstring"""
			lowerCamelCase__		:		Union[str, Any]			=AutoTokenizer.from_pretrained(__lowerCamelCase	)
			lowerCamelCase__		:		str			=SeqaSeqDataset(__lowerCamelCase							,		__lowerCamelCase							,		__lowerCamelCase							,		__lowerCamelCase							,		type_path='''train'''							,		**__lowerCamelCase	)
			lowerCamelCase__		:		Dict			=tok.pad_token_id

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









"""simple docstring"""






from __future__ import annotations

_lowercase : Dict      						=						1.6_021E-19  # units = C






def      snake_case__   (		__lowerCamelCase				:    float							,		__lowerCamelCase				:    float							,		__lowerCamelCase				:    float							,		):
			"""simple docstring"""
			if (conductivity, electron_conc, mobility).count(0	) != 1:
						raise ValueError('''You cannot supply more or less than 2 values'''	)
			elif conductivity < 0:
						raise ValueError('''Conductivity cannot be negative'''	)
			elif electron_conc < 0:
						raise ValueError('''Electron concentration cannot be negative'''	)
			elif mobility < 0:
						raise ValueError('''mobility cannot be negative'''	)
			elif conductivity == 0:
						return (
						    "conductivity",
						    mobility * electron_conc * ELECTRON_CHARGE,
						)
			elif electron_conc == 0:
						return (
						    "electron_conc",
						    conductivity / (mobility * ELECTRON_CHARGE),
						)
			else:
						return (
						    "mobility",
						    conductivity / (electron_conc * ELECTRON_CHARGE),
						)


if __name__ == "__main__":
		import doctest

		doctest.testmod()




