

'''simple docstring'''







import functools
def 					UpperCAmelCase__			(					UpperCAmelCase__,				UpperCAmelCase__ )	->	int:

       # Validation
       if not isinstance(UpperCAmelCase__,				UpperCAmelCase__ ) or not all(isinstance(UpperCAmelCase__,				UpperCAmelCase__ ) for day in days ):
              raise ValueError("""The parameter days should be a list of integers""" )

       if len(UpperCAmelCase__ ) != 3 or not all(isinstance(UpperCAmelCase__,				UpperCAmelCase__ ) for cost in costs ):
              raise ValueError("""The parameter costs should be a list of three integers""" )

       if len(UpperCAmelCase__ ) == 0:
              return 0

       if min(UpperCAmelCase__ ) <= 0:
              raise ValueError("""All days elements should be greater than 0""" )

       if max(UpperCAmelCase__ ) >= 3_66:
              raise ValueError("""All days elements should be less than 366""" )

       A_		    =					set(UpperCAmelCase__ )

       @functools.cache
       def dynamic_programming(UpperCAmelCase__ ) -> int:
              if index > 3_65:
                     return 0

              if index not in days_set:
                     return dynamic_programming(index + 1 )

              return min(
                  costs[0] + dynamic_programming(index + 1 ),				costs[1] + dynamic_programming(index + 7 ),				costs[2] + dynamic_programming(index + 30 ),				)

       return dynamic_programming(1 )


if __name__ == "__main__":
      import doctest

      doctest.testmod()







'''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)
