





def lowerCamelCase__    (							snake_case_	:  str       ,      snake_case_	:  str       )      ->				bool:
   __snake_case							       =   len(snake_case_       ) + 1
   __snake_case							       =   len(snake_case_       ) + 1

   # dp is a 2d matrix where dp[i][j] denotes whether prefix string of
   # length i of input_string matches with prefix string of length j of
   # given pattern.
   # "dp" stands for dynamic programming.
   __snake_case							       =   [[0 for i in range(snake_case_       )] for j in range(snake_case_       )]

   # since string of zero length match pattern of zero length
   __snake_case							       =   1

   # since pattern of zero length will never match with string of non-zero length
   for i in range(1       ,      snake_case_       ):
      __snake_case							       =   0

   # since string of zero length will match with pattern where there
   # is at least one * alternatively
   for j in range(1       ,      snake_case_       ):
      __snake_case							       =   dp[0][j - 2] if pattern[j - 1] == '''*''' else 0

   # now using bottom-up approach to find for all remaining lengths
   for i in range(1       ,      snake_case_       ):
      for j in range(1       ,      snake_case_       ):
         if input_string[i - 1] == pattern[j - 1] or pattern[j - 1] == ".":
            __snake_case							       =   dp[i - 1][j - 1]

         elif pattern[j - 1] == "*":
            if dp[i][j - 2] == 1:
               __snake_case							       =   1
            elif pattern[j - 2] in (input_string[i - 1], "."):
               __snake_case							       =   dp[i - 1][j]
            else:
               __snake_case							       =   0
         else:
            __snake_case							       =   0

   return bool(dp[-1][-1]       )


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    # inputing the strings
    # input_string = input("input a string :")
    # pattern = input("input a pattern :")

    snake_case_				=			'aab'
    snake_case_				=			'c*a*b'

    # using function to check whether given string matches the given pattern
    if match_pattern(input_string, pattern):
        print(F'{input_string} matches the given pattern {pattern}')
    else:
        print(F'{input_string} does not match with the given pattern {pattern}')








from typing import List, Union

import numpy as np

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
    from PIL import Image

    from ..image_utils import load_image

if is_torch_available():
    import torch

    from ..models.auto.modeling_auto import MODEL_FOR_DEPTH_ESTIMATION_MAPPING

snake_case_				=			logging.get_logger(__name__)

@add_end_docstrings(_UpperCAmelCase  )
class 		SCREAMING_SNAKE_CASE__				(       _UpperCAmelCase  ):


      def __init__(self       :      Optional[int]				,			*a__       :      Any				,			**a__       :      Dict      ):





         """simple docstring"""



         super().__init__(*a__				,			**a__      )
         requires_backends(self				,			'''vision'''      )
         self.check_model_type(a__      )


      def __call__(self       :      Optional[int]				,			a__       :      Union[str, List[str], "Image.Image", List["Image.Image"]]				,			**a__       :      Tuple      ):





         """simple docstring"""



         return super().__call__(a__				,			**a__      )


      def    a (self       :      Dict				,			**a__       :      Any      ):





         """simple docstring"""



         return {}, {}, {}


      def    a (self       :      List[str]				,			a__       :      Any      ):





         """simple docstring"""



         __snake_case							       =   load_image(a__      )
         __snake_case							       =   image.size
         __snake_case							       =   self.image_processor(images=a__				,			return_tensors=self.framework      )
         return model_inputs


      def    a (self       :      int				,			a__       :      List[Any]      ):





         """simple docstring"""



         __snake_case							       =   self.model(**a__      )
         return model_outputs


      def    a (self       :      int				,			a__       :      str      ):





         """simple docstring"""



         __snake_case							       =   model_outputs.predicted_depth
         __snake_case							       =   torch.nn.functional.interpolate(
             predicted_depth.unsqueeze(1      )				,			size=self.image_size[::-1]				,			mode='''bicubic'''				,			align_corners=a__      )
         __snake_case							       =   prediction.squeeze().cpu().numpy()
         __snake_case							       =   (output * 255 / np.max(a__      )).astype('''uint8'''      )
         __snake_case							       =   Image.fromarray(a__      )
         __snake_case							       =   {}
         __snake_case							       =   predicted_depth
         __snake_case							       =   depth
         return output_dict

