





'''simple docstring'''







from math import pi, sqrt






def  _lowerCamelCase				( lowercase						:      float						)	->	float:
				if num <= 0:
								raise ValueError("math domain error"						)
				if num > 1_71.5:
								raise OverflowError("math range error"						)
				elif num - int(lowercase						) not in (0, 0.5):
								raise NotImplementedError("num must be an integer or a half-integer"						)
				elif num == 0.5:
								return sqrt(lowercase						)
				else:
								return 1.0 if num == 1 else (num - 1) * gamma(num - 1						)






def  _lowerCamelCase				( )	->	None:
				assert gamma(0.5						) == sqrt(lowercase						)
				assert gamma(1						) == 1.0
				assert gamma(2						) == 1.0


if __name__ == "__main__":
					from doctest import testmod

					testmod()
					lowerCAmelCase_					:    int      	=			1.0
					while num:
										lowerCAmelCase_					:    Optional[Any]      	=			float(input('Gamma of: '))
										print(f"""gamma({num}) = {gamma(num)}""")
										print('\nEnter 0 to exit...')







'''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  _lowerCamelCase				( lowercase						:      Union[str, Any]    ,							lowercase						:      int    ,							lowercase						:      int=1024    ,							lowercase						:      int=1024    ,							lowercase						:      Tuple=False    ,							**lowercase						:      Optional[int]						)	->	Union[str, Any]:
				_a								= AutoTokenizer.from_pretrained(lowercase						)
				_a								= SeqaSeqDataset(lowercase    ,							lowercase    ,							lowercase    ,							lowercase    ,							type_path="train"    ,							**lowercase						)
				_a								= tok.pad_token_id

				def get_lens(lowercase						:      Optional[int]						):
								_a								= tqdm(
								    DataLoader(lowercase    ,							batch_size=512    ,							num_workers=8    ,							shuffle=lowercase    ,							collate_fn=ds.collate_fn						)    ,							desc=str(ds.len_file						)    ,							)
								_a								= []
								for batch in dl:
												_a								= batch["input_ids"].ne(lowercase						).sum(1						).tolist()
												_a								= 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

				_a								= get_lens(lowercase						)
				_a								= SeqaSeqDataset(lowercase    ,							lowercase    ,							lowercase    ,							lowercase    ,							type_path="val"    ,							**lowercase						)
				_a								= 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)
