





import functools
from typing import Any

def   a_    ( lowerCAmelCase_      :			str,	lowerCAmelCase_      :			list[str]	):

   # Validation
   if not isinstance(lowerCAmelCase_,	lowerCAmelCase_	) or len(lowerCAmelCase_	) == 0:
      raise ValueError('the string should be not empty string'	)

   if not isinstance(lowerCAmelCase_,	lowerCAmelCase_	) or not all(
       isinstance(lowerCAmelCase_,	lowerCAmelCase_	) and len(lowerCAmelCase_	) > 0 for item in words	):
      raise ValueError('the words should be a list of non-empty strings'	)

   # Build trie
   __lowerCAmelCase								=	{}
   __lowerCAmelCase								=	'WORD_KEEPER'

   for word in words:
      __lowerCAmelCase								=	trie
      for c in word:
         if c not in trie_node:
            __lowerCAmelCase								=	{}

         __lowerCAmelCase								=	trie_node[c]

      __lowerCAmelCase								=	True

   __lowerCAmelCase								=	len(lowerCAmelCase_	)

   # Dynamic programming method
   @functools.cache
   def is_breakable(lowerCAmelCase_      :			int	) -> bool:
      if index == len_string:
         return True

      __lowerCAmelCase								=	trie
      for i in range(lowerCAmelCase_,	lowerCAmelCase_	):
         __lowerCAmelCase								=	trie_node.get(string[i],	lowerCAmelCase_	)

         if trie_node is None:
            return False

         if trie_node.get(lowerCAmelCase_,	lowerCAmelCase_	) and is_breakable(i + 1	):
            return True

      return False

   return is_breakable(0	)


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   a_    ( lowerCAmelCase_      :			List[str],	lowerCAmelCase_      :			Dict,	lowerCAmelCase_      :			Tuple=1024,	lowerCAmelCase_      :			Optional[Any]=1024,	lowerCAmelCase_      :			Tuple=False,	**lowerCAmelCase_      :			Union[str, Any]	):
   __lowerCAmelCase								=	AutoTokenizer.from_pretrained(lowerCAmelCase_	)
   __lowerCAmelCase								=	SeqaSeqDataset(lowerCAmelCase_,	lowerCAmelCase_,	lowerCAmelCase_,	lowerCAmelCase_,	type_path='train',	**lowerCAmelCase_	)
   __lowerCAmelCase								=	tok.pad_token_id

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

