


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


UpperCAmelCase					:  Any			=						logging.get_logger(__name__)






def      __lowerCamelCase		(				lowerCamelCase__						:		Union[str, Any]   ,				lowerCamelCase__						:		Tuple	):





			'''simple docstring'''




			lowerCamelCase  						=   b.T
			lowerCamelCase  						=   np.sum(np.square(UpperCAmelCase_	)   ,				axis=1	)
			lowerCamelCase  						=   np.sum(np.square(UpperCAmelCase_	)   ,				axis=0	)
			lowerCamelCase  						=   np.matmul(UpperCAmelCase_   ,				UpperCAmelCase_	)
			lowerCamelCase  						=   aa[:, None] - 2 * ab + ba[None, :]
			return d






def      __lowerCamelCase		(				lowerCamelCase__						:		List[str]   ,				lowerCamelCase__						:		int	):





			'''simple docstring'''




			lowerCamelCase  						=   x.reshape(-1   ,				3	)
			lowerCamelCase  						=   squared_euclidean_distance(UpperCAmelCase_   ,				UpperCAmelCase_	)
			return np.argmin(UpperCAmelCase_   ,				axis=1	)







class     __lowercase    (				_snake_case   ):



		"""simple docstring"""

		UpperCamelCase			: Union[str, Any]           =						["pixel_values"]







		def __init__(      self						,	A = None						,	A = True						,	A = None						,	A = PILImageResampling.BILINEAR						,	A = True						,	A = True						,	**A						,	)   ->      Dict:







					'''simple docstring'''







					super().__init__(**_lowerCamelCase     )
					lowerCamelCase  						=   size if size is not None else {'''height''': 2_56, '''width''': 2_56}
					lowerCamelCase  						=   get_size_dict(_lowerCamelCase     )
					lowerCamelCase  						=   np.array(_lowerCamelCase     ) if clusters is not None else None
					lowerCamelCase  						=   do_resize
					lowerCamelCase  						=   size
					lowerCamelCase  						=   resample
					lowerCamelCase  						=   do_normalize
					lowerCamelCase  						=   do_color_quantize







		def  __A					(      self						,	A						,	A						,	A = PILImageResampling.BILINEAR						,	A = None						,	**A						,	)   ->      Optional[int]:







					'''simple docstring'''







					lowerCamelCase  						=   get_size_dict(_lowerCamelCase     )
					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(
					    _lowerCamelCase						,	size=(size["""height"""], size["""width"""])						,	resample=_lowerCamelCase						,	data_format=_lowerCamelCase						,	**_lowerCamelCase     )







		def  __A					(      self						,	A						,	A = None						,	)   ->      str:







					'''simple docstring'''







					lowerCamelCase  						=   rescale(image=_lowerCamelCase						,	scale=1 / 127.5						,	data_format=_lowerCamelCase     )
					lowerCamelCase  						=   image - 1
					return image







		def  __A					(      self						,	A						,	A = None						,	A = None						,	A = None						,	A = None						,	A = None						,	A = None						,	A = None						,	A = ChannelDimension.FIRST						,	**A						,	)   ->      int:







					'''simple docstring'''







					lowerCamelCase  						=   do_resize if do_resize is not None else self.do_resize
					lowerCamelCase  						=   size if size is not None else self.size
					lowerCamelCase  						=   get_size_dict(_lowerCamelCase     )
					lowerCamelCase  						=   resample if resample is not None else self.resample
					lowerCamelCase  						=   do_normalize if do_normalize is not None else self.do_normalize
					lowerCamelCase  						=   do_color_quantize if do_color_quantize is not None else self.do_color_quantize
					lowerCamelCase  						=   clusters if clusters is not None else self.clusters
					lowerCamelCase  						=   np.array(_lowerCamelCase     )

					lowerCamelCase  						=   make_list_of_images(_lowerCamelCase     )

					if not valid_images(_lowerCamelCase     ):
								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.
					lowerCamelCase  						=   [to_numpy_array(_lowerCamelCase     ) for image in images]

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

					if do_normalize:
								lowerCamelCase  						=   [self.normalize(image=_lowerCamelCase     ) for image in images]

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

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

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

					lowerCamelCase  						=   {'''input_ids''': images}
					return BatchFeature(data=_lowerCamelCase						,	tensor_type=_lowerCamelCase     )





def 				__lowerCamelCase						( UpperCAmelCase_						:		int ,  UpperCAmelCase_						:		int						):




      """simple docstring"""





      while b:
            a							,				a						:Optional[Any] 							=       b, a % b
      return a
def 				__lowerCamelCase						( UpperCAmelCase_						:		int ,  UpperCAmelCase_						:		int						):




      """simple docstring"""





      return a if b == 0 else euclidean_gcd_recursive(UpperCAmelCase_ ,  a % b						)
def 				__lowerCamelCase						( ):




      """simple docstring"""





      print(F'''euclidean_gcd(3, 5) = {euclidean_gcd(3 ,  5						)}'''						)
      print(F'''euclidean_gcd(5, 3) = {euclidean_gcd(5 ,  3						)}'''						)
      print(F'''euclidean_gcd(1, 3) = {euclidean_gcd(1 ,  3						)}'''						)
      print(F'''euclidean_gcd(3, 6) = {euclidean_gcd(3 ,  6						)}'''						)
      print(F'''euclidean_gcd(6, 3) = {euclidean_gcd(6 ,  3						)}'''						)

      print(F'''euclidean_gcd_recursive(3, 5) = {euclidean_gcd_recursive(3 ,  5						)}'''						)
      print(F'''euclidean_gcd_recursive(5, 3) = {euclidean_gcd_recursive(5 ,  3						)}'''						)
      print(F'''euclidean_gcd_recursive(1, 3) = {euclidean_gcd_recursive(1 ,  3						)}'''						)
      print(F'''euclidean_gcd_recursive(3, 6) = {euclidean_gcd_recursive(3 ,  6						)}'''						)
      print(F'''euclidean_gcd_recursive(6, 3) = {euclidean_gcd_recursive(6 ,  3						)}'''						)


if __name__ == "__main__":
     main()





