


'''simple docstring'''






from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


a_					:    str          =    logging.get_logger(__name__)




class  a     (     _SCREAMING_SNAKE_CASE       ):
       _lowerCAmelCase 					=	["""pixel_values"""]
       def __init__( self      ,				__magic_name__ = True      ,				__magic_name__ = 32      ,				__magic_name__=PILImageResampling.BILINEAR      ,				__magic_name__ = True      ,				**__magic_name__      ,				)					->							None:
            _a			      =							do_resize
            _a			      =							do_rescale
            _a			      =							size_divisor
            _a			      =							resample
            super().__init__(**__magic_name__						)
       def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__ = None      ,				**__magic_name__						)					->							np.ndarray:
            _a      ,   _a			      =							get_image_size(__magic_name__						)
            # Rounds the height and width down to the closest multiple of size_divisor
            _a			      =							height // size_divisor * size_divisor
            _a			      =							width // size_divisor * size_divisor
            _a			      =							resize(__magic_name__      ,				(new_h, new_w)      ,				resample=__magic_name__      ,				data_format=__magic_name__      ,				**__magic_name__						)
            return image
       def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__ = None      ,				**__magic_name__						)					->							np.ndarray:
            return rescale(image=__magic_name__      ,				scale=__magic_name__      ,				data_format=__magic_name__      ,				**__magic_name__						)




       def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__ = None      ,				__magic_name__ = None      ,				__magic_name__=None      ,				__magic_name__ = None      ,				__magic_name__ = None      ,				__magic_name__ = ChannelDimension.FIRST      ,				**__magic_name__      ,				)					->							BatchFeature:
            _a			      =							do_resize if do_resize is not None else self.do_resize
            _a			      =							do_rescale if do_rescale is not None else self.do_rescale
            _a			      =							size_divisor if size_divisor is not None else self.size_divisor
            _a			      =							resample if resample is not None else self.resample

            if do_resize and size_divisor is None:
                 raise ValueError('size_divisor is required for resizing'						)

            _a			      =							make_list_of_images(__magic_name__						)

            if not valid_images(__magic_name__						):
                 raise ValueError('Invalid image(s)'						)

            # All transformations expect numpy arrays.
            _a			      =							[to_numpy_array(__magic_name__						) for img in images]

            if do_resize:
                 _a			      =							[self.resize(__magic_name__      ,				size_divisor=__magic_name__      ,				resample=__magic_name__						) for image in images]

            if do_rescale:
                 _a			      =							[self.rescale(__magic_name__      ,				scale=1 / 2_55						) for image in images]

            _a			      =							[to_channel_dimension_format(__magic_name__      ,				__magic_name__						) for image in images]

            _a			      =							{'pixel_values': images}
            return BatchFeature(data=__magic_name__      ,				tensor_type=__magic_name__						)





'''simple docstring'''






class  a     :
 def __init__( self						)					->							List[Any]:
      _a			      =							''
      _a			      =							''
      _a			      =							[]
 def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__						)					->							int:
      if m == -1:
           return n + 1
      elif n == -1:
           return m + 1
      elif self.dp[m][n] > -1:
           return self.dp[m][n]
      else:
           if self.worda[m] == self.worda[n]:
                _a			      =							self.__min_dist_top_down_dp(m - 1      ,				n - 1						)
           else:
                _a			      =							self.__min_dist_top_down_dp(__magic_name__      ,				n - 1						)
                _a			      =							self.__min_dist_top_down_dp(m - 1      ,				__magic_name__						)
                _a			      =							self.__min_dist_top_down_dp(m - 1      ,				n - 1						)
                _a			      =							1 + min(__magic_name__      ,				__magic_name__      ,				__magic_name__						)

           return self.dp[m][n]
 def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__						)					->							int:
      _a			      =							worda
      _a			      =							worda
      _a			      =							[[-1 for _ in range(len(__magic_name__						)						)] for _ in range(len(__magic_name__						)						)]

      return self.__min_dist_top_down_dp(len(__magic_name__						) - 1      ,				len(__magic_name__						) - 1						)




 def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__						)					->							int:
      _a			      =							worda
      _a			      =							worda
      _a			      =							len(__magic_name__						)
      _a			      =							len(__magic_name__						)
      _a			      =							[[0 for _ in range(n + 1						)] for _ in range(m + 1						)]

      for i in range(m + 1						):
           for j in range(n + 1						):
                if i == 0:  # first string is empty
                     _a			      =							j
                elif j == 0:  # second string is empty
                     _a			      =							i
                elif worda[i - 1] == worda[j - 1]:  # last characters are equal
                     _a			      =							self.dp[i - 1][j - 1]
                else:
                     _a			      =							self.dp[i][j - 1]
                     _a			      =							self.dp[i - 1][j]
                     _a			      =							self.dp[i - 1][j - 1]
                     _a			      =							1 + min(__magic_name__      ,				__magic_name__      ,				__magic_name__						)
      return self.dp[m][n]


if __name__ == "__main__":
 a_					:    Dict          =    EditDistance()

 print("****************** Testing Edit Distance DP Algorithm ******************")
 print()

 a_					:    Tuple          =    input("Enter the first string: ").strip()
 a_					:    Dict          =    input("Enter the second string: ").strip()

 print()
 print(f'''The minimum edit distance is: {solver.min_dist_top_down(Sa, Sa)}''')
 print(f'''The minimum edit distance is: {solver.min_dist_bottom_up(Sa, Sa)}''')
 print()
 print("*************** End of Testing Edit Distance DP Algorithm ***************")

