
from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


snake_case__      :		Tuple				=       logging.get_logger(__name__)


if is_vision_available():
       import PIL





class 						_a   (						a__       ):

       """simple docstring"""





       snake_case				=["""pixel_values"""]
       def __init__(					self       ,					_snake_case = True       ,					_snake_case = None       ,					_snake_case = PILImageResampling.BICUBIC       ,					_snake_case = True       ,					_snake_case = None       ,					_snake_case = True       ,					_snake_case = 1 / 255       ,					_snake_case = True       ,					_snake_case = None       ,					_snake_case = None       ,					_snake_case = True       ,					**_snake_case       ,					):
        super().__init__(**__a	)
        _UpperCAmelCase 				=size if size is not None else {"""shortest_edge""": 224}
        _UpperCAmelCase 				=get_size_dict(__a       ,					default_to_square=__a	)
        _UpperCAmelCase 				=crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
        _UpperCAmelCase 				=get_size_dict(__a       ,					default_to_square=__a       ,					param_name="crop_size"	)

        _UpperCAmelCase 				=do_resize
        _UpperCAmelCase 				=size
        _UpperCAmelCase 				=resample
        _UpperCAmelCase 				=do_center_crop
        _UpperCAmelCase 				=crop_size
        _UpperCAmelCase 				=do_rescale
        _UpperCAmelCase 				=rescale_factor
        _UpperCAmelCase 				=do_normalize
        _UpperCAmelCase 				=image_mean if image_mean is not None else OPENAI_CLIP_MEAN
        _UpperCAmelCase 				=image_std if image_std is not None else OPENAI_CLIP_STD
        _UpperCAmelCase 				=do_convert_rgb
       def     SCREAMING_SNAKE_CASE  (					self       ,					_snake_case       ,					_snake_case       ,					_snake_case = PILImageResampling.BICUBIC       ,					_snake_case = None       ,					**_snake_case       ,					):
        _UpperCAmelCase 				=get_size_dict(__a       ,					default_to_square=__a	)
        if "shortest_edge" not in size:
         raise ValueError(F"The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"	)
        _UpperCAmelCase 				=get_resize_output_image_size(__a       ,					size=size["shortest_edge"]       ,					default_to_square=__a	)
        return resize(__a       ,					size=__a       ,					resample=__a       ,					data_format=__a       ,					**__a	)
       def     SCREAMING_SNAKE_CASE  (					self       ,					_snake_case       ,					_snake_case       ,					_snake_case = None       ,					**_snake_case       ,					):
        _UpperCAmelCase 				=get_size_dict(__a	)
        if "height" not in size or "width" not in size:
         raise ValueError(F"The `size` parameter must contain the keys (height, width). Got {size.keys()}"	)
        return center_crop(__a       ,					size=(size["height"], size["width"])       ,					data_format=__a       ,					**__a	)
       def     SCREAMING_SNAKE_CASE  (					self       ,					_snake_case       ,					_snake_case       ,					_snake_case = None       ,					**_snake_case       ,					):
        return rescale(__a       ,					scale=__a       ,					data_format=__a       ,					**__a	)
       def     SCREAMING_SNAKE_CASE  (					self       ,					_snake_case       ,					_snake_case       ,					_snake_case       ,					_snake_case = None       ,					**_snake_case       ,					):
        return normalize(__a       ,					mean=__a       ,					std=__a       ,					data_format=__a       ,					**__a	)

       def     SCREAMING_SNAKE_CASE  (					self       ,					_snake_case       ,					_snake_case = None       ,					_snake_case = None       ,					_snake_case = None       ,					_snake_case = None       ,					_snake_case = None       ,					_snake_case = None       ,					_snake_case = None       ,					_snake_case = None       ,					_snake_case = None       ,					_snake_case = None       ,					_snake_case = None       ,					_snake_case = None       ,					_snake_case = ChannelDimension.FIRST       ,					**_snake_case       ,					):
        _UpperCAmelCase 				=do_resize if do_resize is not None else self.do_resize
        _UpperCAmelCase 				=size if size is not None else self.size
        _UpperCAmelCase 				=get_size_dict(__a       ,					param_name="size"       ,					default_to_square=__a	)
        _UpperCAmelCase 				=resample if resample is not None else self.resample
        _UpperCAmelCase 				=do_center_crop if do_center_crop is not None else self.do_center_crop
        _UpperCAmelCase 				=crop_size if crop_size is not None else self.crop_size
        _UpperCAmelCase 				=get_size_dict(__a       ,					param_name="crop_size"       ,					default_to_square=__a	)
        _UpperCAmelCase 				=do_rescale if do_rescale is not None else self.do_rescale
        _UpperCAmelCase 				=rescale_factor if rescale_factor is not None else self.rescale_factor
        _UpperCAmelCase 				=do_normalize if do_normalize is not None else self.do_normalize
        _UpperCAmelCase 				=image_mean if image_mean is not None else self.image_mean
        _UpperCAmelCase 				=image_std if image_std is not None else self.image_std
        _UpperCAmelCase 				=do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

        _UpperCAmelCase 				=make_list_of_images(__a	)

        if not valid_images(__a	):
         raise ValueError(
             "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
             "torch.Tensor, tf.Tensor or jax.ndarray."	)

        if do_resize and size is None:
         raise ValueError("Size must be specified if do_resize is True."	)

        if do_center_crop and crop_size is None:
         raise ValueError("Crop size must be specified if do_center_crop is True."	)

        if do_rescale and rescale_factor is None:
         raise ValueError("Rescale factor must be specified if do_rescale is True."	)

        if do_normalize and (image_mean is None or image_std is None):
         raise ValueError("Image mean and std must be specified if do_normalize is True."	)

        # PIL RGBA images are converted to RGB
        if do_convert_rgb:
         _UpperCAmelCase 				=[convert_to_rgb(__a	) for image in images]

        # All transformations expect numpy arrays.
        _UpperCAmelCase 				=[to_numpy_array(__a	) for image in images]

        if do_resize:
         _UpperCAmelCase 				=[self.resize(image=__a       ,					size=__a       ,					resample=__a	) for image in images]

        if do_center_crop:
         _UpperCAmelCase 				=[self.center_crop(image=__a       ,					size=__a	) for image in images]

        if do_rescale:
         _UpperCAmelCase 				=[self.rescale(image=__a       ,					scale=__a	) for image in images]

        if do_normalize:
         _UpperCAmelCase 				=[self.normalize(image=__a       ,					mean=__a       ,					std=__a	) for image in images]

        _UpperCAmelCase 				=[to_channel_dimension_format(__a       ,					__a	) for image in images]

        _UpperCAmelCase 				={"""pixel_values""": images}
        return BatchFeature(data=__a       ,					tensor_type=__a	)









class   SCREAMING_SNAKE_CASE_			:




       '''simple docstring'''



       def __init__(						self				:				List[Any]							)					->Tuple:
          lowerCamelCase_						:   Optional[Any]		= """"""
          lowerCamelCase_						:   Dict		= """"""
          lowerCamelCase_						:   Optional[Any]		= []



       def 				_lowerCAmelCase							(						self				:				Optional[Any]		,				__a				:				int		,				__a				:				int							)					->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]:
                lowerCamelCase_						:   Optional[Any]		= self.__min_dist_top_down_dp(m - 1		,				n - 1							)
             else:
                lowerCamelCase_						:   List[Any]		= self.__min_dist_top_down_dp(__a		,				n - 1							)
                lowerCamelCase_						:   Union[str, Any]		= self.__min_dist_top_down_dp(m - 1		,				__a							)
                lowerCamelCase_						:   Any		= self.__min_dist_top_down_dp(m - 1		,				n - 1							)
                lowerCamelCase_						:   Tuple		= 1 + min(__a		,				__a		,				__a							)

             return self.dp[m][n]



       def 				_lowerCAmelCase							(						self				:				Any		,				__a				:				str		,				__a				:				str							)					->int:
          lowerCamelCase_						:   Dict		= worda
          lowerCamelCase_						:   str		= worda
          lowerCamelCase_						:   Union[str, Any]		= [[-1 for _ in range(len(__a							)							)] for _ in range(len(__a							)							)]

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




       def 				_lowerCAmelCase							(						self				:				int		,				__a				:				str		,				__a				:				str							)					->int:
          lowerCamelCase_						:   Tuple		= worda
          lowerCamelCase_						:   Optional[Any]		= worda
          lowerCamelCase_						:   Tuple		= len(__a							)
          lowerCamelCase_						:   Optional[Any]		= len(__a							)
          lowerCamelCase_						:   List[Any]		= [[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
                   lowerCamelCase_						:   Optional[Any]		= j
                elif j == 0:  # second string is empty
                   lowerCamelCase_						:   Any		= i
                elif worda[i - 1] == worda[j - 1]:  # last characters are equal
                   lowerCamelCase_						:   Tuple		= self.dp[i - 1][j - 1]
                else:
                   lowerCamelCase_						:   Dict		= self.dp[i][j - 1]
                   lowerCamelCase_						:   Union[str, Any]		= self.dp[i - 1][j]
                   lowerCamelCase_						:   Any		= self.dp[i - 1][j - 1]
                   lowerCamelCase_						:   Optional[int]		= 1 + min(__a		,				__a		,				__a							)
          return self.dp[m][n]


if __name__ == "__main__":
      snake_case__				:	Optional[Any]					       =					EditDistance()

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

      snake_case__				:	Union[str, Any]					       =					input('Enter the first string: ').strip()
      snake_case__				:	List[Any]					       =					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 ***************')





