'''simple docstring'''




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,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__SCREAMING_SNAKE_CASE       :     str     =  logging.get_logger(__name__)

class 	lowerCamelCase_	(snake_case__    ):
      '''simple docstring'''

      __UpperCamelCase:  int						=						["pixel_values"]




      def __init__(       self						:				int						,					A						:				bool = True						,					A						:				Optional[Dict[str, int]] = None						,					A						:				PILImageResampling = PILImageResampling.BILINEAR						,					A						:				bool = True						,					A						:				Dict[str, int] = None						,					A						:				bool = True						,					A						:				Union[int, float] = 1 / 255						,					A						:				bool = True						,					A						:				Optional[Union[float, List[float]]] = None						,					A						:				Optional[Union[float, List[float]]] = None						,					**A						:				Any						,					):
             super().__init__(**A       )
             _UpperCAmelCase     :  Dict							      =    size if size is not None else {"shortest_edge": 256}
             _UpperCAmelCase     :  Optional[int]							      =    get_size_dict(A						,					default_to_square=A       )
             _UpperCAmelCase     :  Optional[Any]							      =    crop_size if crop_size is not None else {"height": 224, "width": 224}
             _UpperCAmelCase     :  List[str]							      =    get_size_dict(A       )
             _UpperCAmelCase     :  str							      =    do_resize
             _UpperCAmelCase     :  Union[str, Any]							      =    size
             _UpperCAmelCase     :  Any							      =    resample
             _UpperCAmelCase     :  int							      =    do_center_crop
             _UpperCAmelCase     :  Union[str, Any]							      =    crop_size
             _UpperCAmelCase     :  int							      =    do_rescale
             _UpperCAmelCase     :  List[Any]							      =    rescale_factor
             _UpperCAmelCase     :  List[Any]							      =    do_normalize
             _UpperCAmelCase     :  Any							      =    image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
             _UpperCAmelCase     :  Tuple							      =    image_std if image_std is not None else IMAGENET_STANDARD_STD




      def      _A	(       self						:				Optional[int]						,					A						:				np.ndarray						,					A						:				Dict[str, int]						,					A						:				PILImageResampling = PILImageResampling.BICUBIC						,					A						:				Optional[Union[str, ChannelDimension]] = None						,					**A						:				Tuple						,					):
             _UpperCAmelCase     :  List[Any]							      =    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     :  int							      =    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      _A	(       self						:				Optional[int]						,					A						:				np.ndarray						,					A						:				Dict[str, int]						,					A						:				Optional[Union[str, ChannelDimension]] = None						,					**A						:				str						,					):
             _UpperCAmelCase     :  int							      =    get_size_dict(A       )
             return center_crop(A						,					size=(size["height"], size["width"])						,					data_format=A						,					**A       )




      def      _A	(       self						:				Any						,					A						:				np.ndarray						,					A						:				float						,					A						:				Optional[Union[str, ChannelDimension]] = None						,					**A						:				Any       ):
             return rescale(A						,					scale=A						,					data_format=A						,					**A       )




      def      _A	(       self						:				List[Any]						,					A						:				np.ndarray						,					A						:				Union[float, List[float]]						,					A						:				Union[float, List[float]]						,					A						:				Optional[Union[str, ChannelDimension]] = None						,					**A						:				int						,					):
             return normalize(A						,					mean=A						,					std=A						,					data_format=A						,					**A       )





      def      _A	(       self						:				Optional[int]						,					A						:				ImageInput						,					A						:				Optional[bool] = None						,					A						:				Dict[str, int] = None						,					A						:				PILImageResampling = None						,					A						:				bool = None						,					A						:				Dict[str, int] = None						,					A						:				Optional[bool] = None						,					A						:				Optional[float] = None						,					A						:				Optional[bool] = None						,					A						:				Optional[Union[float, List[float]]] = None						,					A						:				Optional[Union[float, List[float]]] = None						,					A						:				Optional[Union[str, TensorType]] = None						,					A						:				Union[str, ChannelDimension] = ChannelDimension.FIRST						,					**A						:				Dict						,					):
             _UpperCAmelCase     :  Any							      =    do_resize if do_resize is not None else self.do_resize
             _UpperCAmelCase     :  Any							      =    size if size is not None else self.size
             _UpperCAmelCase     :  List[str]							      =    get_size_dict(A						,					default_to_square=A       )
             _UpperCAmelCase     :  Optional[int]							      =    resample if resample is not None else self.resample
             _UpperCAmelCase     :  Dict							      =    do_center_crop if do_center_crop is not None else self.do_center_crop
             _UpperCAmelCase     :  List[Any]							      =    crop_size if crop_size is not None else self.crop_size
             _UpperCAmelCase     :  Optional[int]							      =    get_size_dict(A       )
             _UpperCAmelCase     :  Dict							      =    do_rescale if do_rescale is not None else self.do_rescale
             _UpperCAmelCase     :  Any							      =    rescale_factor if rescale_factor is not None else self.rescale_factor
             _UpperCAmelCase     :  Any							      =    do_normalize if do_normalize is not None else self.do_normalize
             _UpperCAmelCase     :  Union[str, Any]							      =    image_mean if image_mean is not None else self.image_mean
             _UpperCAmelCase     :  Optional[Any]							      =    image_std if image_std is not None else self.image_std

             _UpperCAmelCase     :  List[Any]							      =    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."       )

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

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

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

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

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

             _UpperCAmelCase     :  List[str]							      =    [to_channel_dimension_format(A						,					A       ) for image in images]

             _UpperCAmelCase     :  Optional[Any]							      =    {"pixel_values": images}
             return BatchFeature(data=A						,					tensor_type=A       )



import functools






def    lowerCamelCase__				(				__lowerCAmelCase  :  str    ,							__lowerCAmelCase  :  str				):




			"""simple docstring"""


			lowerCAmelCase_						=	len(__lowerCAmelCase				)
			lowerCAmelCase_						=	len(__lowerCAmelCase				)

			@functools.cache
			def min_distance(__lowerCAmelCase  :  int    ,							__lowerCAmelCase  :  int				) -> int:
						# if first word index is overflow - delete all from the second word
						if indexa >= len_worda:
									return len_worda - indexa
						# if second word index is overflow - delete all from the first word
						if indexa >= len_worda:
									return len_worda - indexa
						lowerCAmelCase_						=	int(worda[indexa] != worda[indexa]				)  # current letters not identical
						return min(
						    1 + min_distance(indexa + 1    ,							__lowerCAmelCase				)    ,							1 + min_distance(__lowerCAmelCase    ,							indexa + 1				)    ,							diff + min_distance(indexa + 1    ,							indexa + 1				)    ,							)

			return min_distance(0    ,							0				)


if __name__ == "__main__":
			import doctest

			doctest.testmod()



