



'''simple docstring'''

import math



def      __lowerCamelCase     (      lowerCAmelCase_  )			->      int:

		if not isinstance(lowerCAmelCase_						,			lowerCAmelCase_  ):
				_a     :      Tuple        =							f"""Input value of [number={number}] must be an integer"""
				raise TypeError(lowerCAmelCase_  )

		if number < 1:
				_a     :      List[Any]        =							f"""Input value of [number={number}] must be > 0"""
				raise ValueError(lowerCAmelCase_  )
		elif number == 1:
				return 3
		elif number == 2:
				return 5
		else:
				_a     :      List[Any]        =							int(math.log(number // 3						,			2  )  ) + 2

				_a     :      str        =							[3, 5]
				_a     :      Optional[int]        =							2
				_a     :      Union[str, Any]        =							3
				for block in range(1						,			lowerCAmelCase_  ):
						for _ in range(lowerCAmelCase_  ):
								proth_list.append(2 ** (block + 1) + proth_list[proth_index - 1]  )
								proth_index += 1
						increment *= 2

		return proth_list[number - 1]


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							for number in range(11):
														__lowerCAmelCase									=   0
														try:
																					__lowerCAmelCase									=   proth(number)
														except ValueError:
																					print(f"""ValueError: there is no {number}th Proth number""")
																					continue

														print(f"""The {number}th Proth number: {value}""")









'''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     (      lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_=1024						,			lowerCAmelCase_=1024						,			lowerCAmelCase_=False						,			**lowerCAmelCase_  )			->      List[Any]:
		_a     :      str        =							AutoTokenizer.from_pretrained(lowerCAmelCase_  )
		_a     :      List[Any]        =							SeqaSeqDataset(lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			type_path='train'						,			**lowerCAmelCase_  )
		_a     :      List[str]        =							tok.pad_token_id

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

		_a     :      str        =							get_lens(lowerCAmelCase_  )
		_a     :      Optional[int]        =							SeqaSeqDataset(lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			type_path='val'						,			**lowerCAmelCase_  )
		_a     :      Dict        =							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)




