






'''simple docstring'''



def    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	list    )  ->			int:

   """simple docstring"""

   if not grid or not grid[0]:
      raise TypeError('The grid does not contain the appropriate information'    )

   for cell_n in range(1,					len(grid[0]    )    ):
      grid[0][cell_n] += grid[0][cell_n - 1]
   __a				     =    grid[0]

   for row_n in range(1,					len(SCREAMING_SNAKE_CASE__    )    ):
      __a				     =    grid[row_n]
      __a				     =    fill_row(SCREAMING_SNAKE_CASE__,					SCREAMING_SNAKE_CASE__    )
      __a				     =    grid[row_n]

   return grid[-1][-1]



def    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	list,					SCREAMING_SNAKE_CASE__:	list    )  ->			list:

   """simple docstring"""
   current_row[0] += row_above[0]
   for cell_n in range(1,					len(SCREAMING_SNAKE_CASE__    )    ):
      current_row[cell_n] += min(current_row[cell_n - 1],					row_above[cell_n]    )

   return current_row


if __name__ == "__main__":
 import doctest

 doctest.testmod()







'''simple docstring'''



from ...configuration_utils import PretrainedConfig
from ...utils import logging


__UpperCamelCase      :       List[str]      			=      logging.get_logger(__name__)

__UpperCamelCase      :       Tuple      			=      {
    """sayakpaul/vit-msn-base""": """https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json""",
    # See all ViT MSN models at https://huggingface.co/models?filter=vit_msn
}
class 							__SCREAMING_SNAKE_CASE       (							_lowerCAmelCase     ):
      __a  					="vit_msn"




      def __init__(  self ,   lowerCamelCase=768 ,   lowerCamelCase=12 ,   lowerCamelCase=12 ,   lowerCamelCase=3072 ,   lowerCamelCase="gelu" ,   lowerCamelCase=0.0 ,   lowerCamelCase=0.0 ,   lowerCamelCase=0.02 ,   lowerCamelCase=1e-06 ,   lowerCamelCase=224 ,   lowerCamelCase=16 ,   lowerCamelCase=3 ,   lowerCamelCase=True ,   **lowerCamelCase ,   )							->Tuple:



         '''simple docstring'''
         super().__init__(**lowerCamelCase      )

         __a				     =    hidden_size
         __a				     =    num_hidden_layers
         __a				     =    num_attention_heads
         __a				     =    intermediate_size
         __a				     =    hidden_act
         __a				     =    hidden_dropout_prob
         __a				     =    attention_probs_dropout_prob
         __a				     =    initializer_range
         __a				     =    layer_norm_eps
         __a				     =    image_size
         __a				     =    patch_size
         __a				     =    num_channels
         __a				     =    qkv_bias