


'''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 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 							__magic_name__(						lowerCamelCase,				lowerCamelCase):
						__lowerCAmelCase							       =				b.T
						__lowerCAmelCase							       =				np.sum(np.square(lowerCamelCase),				axis=1)
						__lowerCAmelCase							       =				np.sum(np.square(lowerCamelCase),				axis=0)
						__lowerCAmelCase							       =				np.matmul(lowerCamelCase,				lowerCamelCase)
						__lowerCAmelCase							       =				aa[:, None] - 2 * ab + ba[None, :]
						return d

def 							__magic_name__(						lowerCamelCase,				lowerCamelCase):
						__lowerCAmelCase							       =				x.reshape(-1,				3)
						__lowerCAmelCase							       =				squared_euclidean_distance(lowerCamelCase,				lowerCamelCase)
						return np.argmin(lowerCamelCase,				axis=1)




class       a__				( __A      ):







				"""simple docstring"""



				__UpperCamelCase    :		Optional[int]	=	['pixel_values']




				def __init__(self       ,			__lowercase = None       ,			__lowercase = True       ,			__lowercase = None       ,			__lowercase = PILImageResampling.BILINEAR       ,			__lowercase = True       ,			__lowercase = True       ,			**__lowercase       ,			):
										super().__init__(**__lowercase		)
										__lowerCAmelCase							       =				size if size is not None else {'''height''': 2_56, '''width''': 2_56}
										__lowerCAmelCase							       =				get_size_dict(__lowercase		)
										__lowerCAmelCase							       =				np.array(__lowercase		) if clusters is not None else None
										__lowerCAmelCase							       =				do_resize
										__lowerCAmelCase							       =				size
										__lowerCAmelCase							       =				resample
										__lowerCAmelCase							       =				do_normalize
										__lowerCAmelCase							       =				do_color_quantize




				def 					_snake_case  (self       ,			__lowercase       ,			__lowercase       ,			__lowercase = PILImageResampling.BILINEAR       ,			__lowercase = None       ,			**__lowercase       ,			):
										__lowerCAmelCase							       =				get_size_dict(__lowercase		)
										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(
										    __lowercase       ,			size=(size['''height'''], size['''width'''])       ,			resample=__lowercase       ,			data_format=__lowercase       ,			**__lowercase		)




				def 					_snake_case  (self       ,			__lowercase       ,			__lowercase = None       ,			):
										__lowerCAmelCase							       =				rescale(image=__lowercase       ,			scale=1 / 1_2_7.5       ,			data_format=__lowercase		)
										__lowerCAmelCase							       =				image - 1
										return image




				def 					_snake_case  (self       ,			__lowercase       ,			__lowercase = None       ,			__lowercase = None       ,			__lowercase = None       ,			__lowercase = None       ,			__lowercase = None       ,			__lowercase = None       ,			__lowercase = None       ,			__lowercase = ChannelDimension.FIRST       ,			**__lowercase       ,			):
										__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(__lowercase		)
										__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(__lowercase		)

										__lowerCAmelCase							       =				make_list_of_images(__lowercase		)

										if not valid_images(__lowercase		):
																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(__lowercase		) for image in images]

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

										if do_normalize:
																__lowerCAmelCase							       =				[self.normalize(image=__lowercase		) for image in images]

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

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

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

										__lowerCAmelCase							       =				{'''input_ids''': images}
										return BatchFeature(data=__lowercase       ,			tensor_type=__lowercase		)





'''simple docstring'''

def 							__magic_name__(						):
						return [
						    a * b * (1_0_0_0 - a - b)
						    for a in range(1,				9_9_9)
						    for b in range(lowerCamelCase,				9_9_9)
						    if (a * a + b * b == (1_0_0_0 - a - b) ** 2)
						][0]


if __name__ == "__main__":
	print(f"""{solution() = }""")

