



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 rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
			import PIL


_snake_case				=					logging.get_logger(__name__)
def     _a							(     __lowercase				, __lowercase		)     ->						Tuple:

							"""simple docstring"""





							__UpperCamelCase          =  b.T
							__UpperCamelCase          =  np.sum(np.square(__lowercase		)				, axis=1		)
							__UpperCamelCase          =  np.sum(np.square(__lowercase		)				, axis=0		)
							__UpperCamelCase          =  np.matmul(__lowercase				, __lowercase		)
							__UpperCamelCase          =  aa[:, None] - 2 * ab + ba[None, :]
							return d
def     _a							(     __lowercase				, __lowercase		)     ->						Union[str, Any]:

							"""simple docstring"""





							__UpperCamelCase          =  x.reshape(-1				, 3		)
							__UpperCamelCase          =  squared_euclidean_distance(__lowercase				, __lowercase		)
							return np.argmin(__lowercase				, axis=1		)






class 		lowerCAmelCase_ (			_lowercase ):







		"""simple docstring"""
		UpperCAmelCase__													=       ["pixel_values"]
		def __init__(		self     ,    _SCREAMING_SNAKE_CASE = None     ,    _SCREAMING_SNAKE_CASE = True     ,    _SCREAMING_SNAKE_CASE = None     ,    _SCREAMING_SNAKE_CASE = PILImageResampling.BILINEAR     ,    _SCREAMING_SNAKE_CASE = True     ,    _SCREAMING_SNAKE_CASE = True     ,    **_SCREAMING_SNAKE_CASE     ,    )     ->  Dict:
									super().__init__(**_SCREAMING_SNAKE_CASE			)
									__UpperCamelCase          =  size if size is not None else {"height": 256, "width": 256}
									__UpperCamelCase          =  get_size_dict(_SCREAMING_SNAKE_CASE			)
									__UpperCamelCase          =  np.array(_SCREAMING_SNAKE_CASE			) if clusters is not None else None
									__UpperCamelCase          =  do_resize
									__UpperCamelCase          =  size
									__UpperCamelCase          =  resample
									__UpperCamelCase          =  do_normalize
									__UpperCamelCase          =  do_color_quantize
		def 						__lowercase(		self     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE = PILImageResampling.BILINEAR     ,    _SCREAMING_SNAKE_CASE = None     ,    **_SCREAMING_SNAKE_CASE     ,    )     ->  Any:
									__UpperCamelCase          =  get_size_dict(_SCREAMING_SNAKE_CASE			)
									if "height" not in size or "width" not in size:
																raise ValueError(f"""Size dictionary must contain both height and width keys. Got {size.keys()}"""			)
									return resize(
									    _SCREAMING_SNAKE_CASE     ,    size=(size['height'], size['width'])     ,    resample=_SCREAMING_SNAKE_CASE     ,    data_format=_SCREAMING_SNAKE_CASE     ,    **_SCREAMING_SNAKE_CASE			)
		def 						__lowercase(		self     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE = None     ,    )     ->  Dict:
									__UpperCamelCase          =  rescale(image=_SCREAMING_SNAKE_CASE     ,    scale=1 / 1_2_7.5     ,    data_format=_SCREAMING_SNAKE_CASE			)
									__UpperCamelCase          =  image - 1
									return image



		def 						__lowercase(		self     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE = None     ,    _SCREAMING_SNAKE_CASE = None     ,    _SCREAMING_SNAKE_CASE = None     ,    _SCREAMING_SNAKE_CASE = None     ,    _SCREAMING_SNAKE_CASE = None     ,    _SCREAMING_SNAKE_CASE = None     ,    _SCREAMING_SNAKE_CASE = None     ,    _SCREAMING_SNAKE_CASE = ChannelDimension.FIRST     ,    **_SCREAMING_SNAKE_CASE     ,    )     ->  str:
									__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(_SCREAMING_SNAKE_CASE			)
									__UpperCamelCase          =  resample if resample is not None else self.resample
									__UpperCamelCase          =  do_normalize if do_normalize is not None else self.do_normalize
									__UpperCamelCase          =  do_color_quantize if do_color_quantize is not None else self.do_color_quantize
									__UpperCamelCase          =  clusters if clusters is not None else self.clusters
									__UpperCamelCase          =  np.array(_SCREAMING_SNAKE_CASE			)

									__UpperCamelCase          =  make_list_of_images(_SCREAMING_SNAKE_CASE			)

									if not valid_images(_SCREAMING_SNAKE_CASE			):
																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 or resample is None:
																raise ValueError('Size and resample must be specified if do_resize is True.'			)

									if do_color_quantize and clusters is None:
																raise ValueError('Clusters must be specified if do_color_quantize is True.'			)

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

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

									if do_normalize:
																__UpperCamelCase          =  [self.normalize(image=_SCREAMING_SNAKE_CASE			) for image in images]

									if do_color_quantize:
																__UpperCamelCase          =  [to_channel_dimension_format(_SCREAMING_SNAKE_CASE     ,    ChannelDimension.LAST			) for image in images]
																# color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
																__UpperCamelCase          =  np.array(_SCREAMING_SNAKE_CASE			)
																__UpperCamelCase          =  color_quantize(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE			).reshape(images.shape[:-1]			)

																# flatten to (batch_size, height*width)
																__UpperCamelCase          =  images.shape[0]
																__UpperCamelCase          =  images.reshape(_SCREAMING_SNAKE_CASE     ,    -1			)

																# We need to convert back to a list of images to keep consistent behaviour across processors.
																__UpperCamelCase          =  list(_SCREAMING_SNAKE_CASE			)
									else:
																__UpperCamelCase          =  [to_channel_dimension_format(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE			) for image in images]

									__UpperCamelCase          =  {"input_ids": images}
									return BatchFeature(data=_SCREAMING_SNAKE_CASE     ,    tensor_type=_SCREAMING_SNAKE_CASE			)







'''simple docstring'''


import functools


def 		_lowercase (SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							):




     '''simple docstring'''





     __A	:       int      =							len(SCREAMING_SNAKE_CASE							)
     __A	:       int      =							len(SCREAMING_SNAKE_CASE							)

     @functools.cache
     def min_distance(SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							) -> 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
          __A	:       Optional[int]      =							int(worda[indexa] != worda[indexa]							)  # current letters not identical
          return min(
              1 + min_distance(indexa + 1       ,					SCREAMING_SNAKE_CASE							)       ,					1 + min_distance(SCREAMING_SNAKE_CASE       ,					indexa + 1							)       ,					diff + min_distance(indexa + 1       ,					indexa + 1							)       ,					)

     return min_distance(0       ,					0							)


if __name__ == "__main__":
      import doctest

      doctest.testmod()

