




import sys







def lowerCamelCase__  (     _lowercase ):

     '''simple docstring'''


     UpperCAmelCase_     :				List[Any]  =     len(_lowercase )
     UpperCAmelCase_     :				Tuple  =     [[0 for x in range(_lowercase )] for x in range(_lowercase )]
     UpperCAmelCase_     :				Union[str, Any]  =     [[0 for x in range(_lowercase )] for x in range(_lowercase )]

     for chain_length in range(2					,		_lowercase ):
          for a in range(1					,		n - chain_length + 1 ):
               UpperCAmelCase_     :				Union[str, Any]  =     a + chain_length - 1

               UpperCAmelCase_     :				Optional[Any]  =     sys.maxsize
               for c in range(_lowercase					,		_lowercase ):
                    UpperCAmelCase_     :				Tuple  =     (
                        matrix[a][c] + matrix[c + 1][b] + array[a - 1] * array[c] * array[b]
                    )
                    if cost < matrix[a][b]:
                         UpperCAmelCase_     :				Tuple  =     cost
                         UpperCAmelCase_     :				Union[str, Any]  =     c
     return matrix, sol







def lowerCamelCase__  (     _lowercase					,		_lowercase					,		_lowercase ):

     '''simple docstring'''


     if i == j:
          print('''A''' + str(_lowercase )					,		end=''' ''' )
     else:
          print('''('''					,		end=''' ''' )
          print_optiomal_solution(_lowercase					,		_lowercase					,		optimal_solution[i][j] )
          print_optiomal_solution(_lowercase					,		optimal_solution[i][j] + 1					,		_lowercase )
          print(''')'''					,		end=''' ''' )







def lowerCamelCase__  (     ):

     '''simple docstring'''


     UpperCAmelCase_     :				str  =     [30, 35, 15, 5, 10, 20, 25]
     UpperCAmelCase_     :				int  =     len(_lowercase )
     # Size of matrix created from above array will be
     # 30*35 35*15 15*5 5*10 10*20 20*25
     UpperCAmelCase_,       UpperCAmelCase_     :				int  =     matrix_chain_order(_lowercase )

     print('''No. of Operation required: ''' + str(matrix[1][n - 1] ) )
     print_optiomal_solution(_lowercase					,		1					,		n - 1 )


if __name__ == "__main__":
     main()





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     __a(	unittest.TestCase  ):






    """simple docstring"""





    @slow
    def 				a__ ( self    )					->						List[str]:
         UpperCAmelCase_     :				Tuple  =     AutoImageProcessor.from_pretrained('''microsoft/dit-base-finetuned-rvlcdip'''    )
         UpperCAmelCase_     :				Union[str, Any]  =     AutoModelForImageClassification.from_pretrained('''microsoft/dit-base-finetuned-rvlcdip'''    )
         model.to(_SCREAMING_SNAKE_CASE    )

         from datasets import load_dataset

         UpperCAmelCase_     :				Optional[int]  =     load_dataset('''nielsr/rvlcdip-demo'''    )

         UpperCAmelCase_     :				Optional[Any]  =     dataset['''train'''][0]['''image'''].convert('''RGB'''    )

         UpperCAmelCase_     :				str  =     image_processor(_SCREAMING_SNAKE_CASE  ,return_tensors='''pt'''    ).to(_SCREAMING_SNAKE_CASE    )

         # forward pass
         with torch.no_grad():
              UpperCAmelCase_     :				Optional[int]  =     model(**_SCREAMING_SNAKE_CASE    )
              UpperCAmelCase_     :				List[Any]  =     outputs.logits

         UpperCAmelCase_     :				Tuple  =     torch.Size((1, 16)    )
         self.assertEqual(logits.shape  ,_SCREAMING_SNAKE_CASE    )

         UpperCAmelCase_     :				int  =     torch.tensor(
             [-0.41_58, -0.40_92, -0.43_47]  ,device=_SCREAMING_SNAKE_CASE  ,dtype=torch.float  ,)
         self.assertTrue(torch.allclose(logits[0, :3]  ,_SCREAMING_SNAKE_CASE  ,atol=1e-4    )    )