"""simple docstring"""





import functools
from typing import Any





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





       '''simple docstring'''

       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
       lowercase__    :	dict[str, Any]     					=			{}
       lowercase__    :	str     					=			'WORD_KEEPER'

       for word in words:
              lowercase__    :	int     					=			trie
              for c in word:
                     if c not in trie_node:
                            lowercase__    :	Optional[Any]     					=			{}

                     lowercase__    :	Tuple     					=			trie_node[c]

              lowercase__    :	Optional[int]     					=			True

       lowercase__    :	Union[str, Any]     					=			len(_lowerCAmelCase  )

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

              lowercase__    :	Union[str, Any]     					=			trie
              for i in range(_lowerCAmelCase     ,				_lowerCAmelCase  ):
                     lowercase__    :	Any     					=			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()


"""simple docstring"""





import unittest

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device


if is_torch_available():
       import torch

       from transformers import AutoModelForImageClassification

if is_vision_available():
       from transformers import AutoImageProcessor


@require_torch
@require_vision
class   UpperCAmelCase_			(					unittest.TestCase):


      @slow
      def 	_UpperCAmelCase (     self							) ->      str:
             lowercase__    :	Optional[Any]     					=			AutoImageProcessor.from_pretrained('microsoft/dit-base-finetuned-rvlcdip'							)
             lowercase__    :	Union[str, Any]     					=			AutoModelForImageClassification.from_pretrained('microsoft/dit-base-finetuned-rvlcdip'							)
             model.to(a							)

             from datasets import load_dataset

             lowercase__    :	str     					=			load_dataset('nielsr/rvlcdip-demo'							)

             lowercase__    :	Tuple     					=			dataset['train'][0]['image'].convert('RGB'							)

             lowercase__    :	int     					=			image_processor(a	,    return_tensors='pt'							).to(a							)

             # forward pass
             with torch.no_grad():
                    lowercase__    :	List[str]     					=			model(**a							)
                    lowercase__    :	List[Any]     					=			outputs.logits

             lowercase__    :	Union[str, Any]     					=			torch.Size((1, 1_6)							)
             self.assertEqual(logits.shape	,    a							)

             lowercase__    :	Tuple     					=			torch.tensor(
                 [-0.4_158, -0.4_092, -0.4_347]	,    device=a	,    dtype=torch.float	,    )
             self.assertTrue(torch.allclose(logits[0, :3]	,    a	,    atol=1e-4							)							)

