






'''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


lowerCAmelCase__          =						logging.get_logger(__name__)







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







def       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_)							->     List[Any]:
		UpperCamelCase__				:					Tuple					=					x.reshape(-1     ,      3)
		UpperCamelCase__				:					Dict					=					squared_euclidean_distance(lowerCamelCase_     ,      lowerCamelCase_)
		return np.argmin(lowerCamelCase_     ,      axis=1)







class       __lowercase					(__lowerCamelCase    ):
	_lowerCamelCase              =  ['''pixel_values''']

	def __init__(      self   :						str  ,					UpperCAmelCase_   :						Optional[Union[List[List[int]], np.ndarray]] = None  ,					UpperCAmelCase_   :						bool = True  ,					UpperCAmelCase_   :						Dict[str, int] = None  ,					UpperCAmelCase_   :						PILImageResampling = PILImageResampling.BILINEAR  ,					UpperCAmelCase_   :						bool = True  ,					UpperCAmelCase_   :						bool = True  ,					**UpperCAmelCase_   :						List[Any]  ,					):
			super().__init__(**UpperCAmelCase_)
			UpperCamelCase__				:					Dict					=					size if size is not None else {'height': 256, 'width': 256}
			UpperCamelCase__				:					int					=					get_size_dict(UpperCAmelCase_)
			UpperCamelCase__				:					Tuple					=					np.array(UpperCAmelCase_) if clusters is not None else None
			UpperCamelCase__				:					Dict					=					do_resize
			UpperCamelCase__				:					Union[str, Any]					=					size
			UpperCamelCase__				:					Optional[Any]					=					resample
			UpperCamelCase__				:					Union[str, Any]					=					do_normalize
			UpperCamelCase__				:					Tuple					=					do_color_quantize

	def      __UpperCamelCase							(      self   :						str  ,					UpperCAmelCase_   :						np.ndarray  ,					UpperCAmelCase_   :						Dict[str, int]  ,					UpperCAmelCase_   :						PILImageResampling = PILImageResampling.BILINEAR  ,					UpperCAmelCase_   :						Optional[Union[str, ChannelDimension]] = None  ,					**UpperCAmelCase_   :						Tuple  ,					):
			UpperCamelCase__				:					Optional[Any]					=					get_size_dict(UpperCAmelCase_)
			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(
			    UpperCAmelCase_  ,					size=(size['height'], size['width'])  ,					resample=UpperCAmelCase_  ,					data_format=UpperCAmelCase_  ,					**UpperCAmelCase_)

	def      __UpperCamelCase							(      self   :						str  ,					UpperCAmelCase_   :						np.ndarray  ,					UpperCAmelCase_   :						Optional[Union[str, ChannelDimension]] = None  ,					):
			UpperCamelCase__				:					Optional[int]					=					rescale(image=UpperCAmelCase_  ,					scale=1 / 1_27.5  ,					data_format=UpperCAmelCase_)
			UpperCamelCase__				:					Optional[int]					=					image - 1
			return image





	def      __UpperCamelCase							(      self   :						Dict  ,					UpperCAmelCase_   :						ImageInput  ,					UpperCAmelCase_   :						bool = None  ,					UpperCAmelCase_   :						Dict[str, int] = None  ,					UpperCAmelCase_   :						PILImageResampling = None  ,					UpperCAmelCase_   :						bool = None  ,					UpperCAmelCase_   :						Optional[bool] = None  ,					UpperCAmelCase_   :						Optional[Union[List[List[int]], np.ndarray]] = None  ,					UpperCAmelCase_   :						Optional[Union[str, TensorType]] = None  ,					UpperCAmelCase_   :						Optional[Union[str, ChannelDimension]] = ChannelDimension.FIRST  ,					**UpperCAmelCase_   :						List[str]  ,					):
			UpperCamelCase__				:					Optional[int]					=					do_resize if do_resize is not None else self.do_resize
			UpperCamelCase__				:					str					=					size if size is not None else self.size
			UpperCamelCase__				:					Dict					=					get_size_dict(UpperCAmelCase_)
			UpperCamelCase__				:					int					=					resample if resample is not None else self.resample
			UpperCamelCase__				:					str					=					do_normalize if do_normalize is not None else self.do_normalize
			UpperCamelCase__				:					str					=					do_color_quantize if do_color_quantize is not None else self.do_color_quantize
			UpperCamelCase__				:					Union[str, Any]					=					clusters if clusters is not None else self.clusters
			UpperCamelCase__				:					str					=					np.array(UpperCAmelCase_)

			UpperCamelCase__				:					List[str]					=					make_list_of_images(UpperCAmelCase_)

			if not valid_images(UpperCAmelCase_):
					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__				:					Dict					=					[to_numpy_array(UpperCAmelCase_) for image in images]

			if do_resize:
					UpperCamelCase__				:					Tuple					=					[self.resize(image=UpperCAmelCase_  ,					size=UpperCAmelCase_  ,					resample=UpperCAmelCase_) for image in images]

			if do_normalize:
					UpperCamelCase__				:					Union[str, Any]					=					[self.normalize(image=UpperCAmelCase_) for image in images]

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

					# flatten to (batch_size, height*width)
					UpperCamelCase__				:					Optional[int]					=					images.shape[0]
					UpperCamelCase__				:					int					=					images.reshape(UpperCAmelCase_  ,					-1)

					# We need to convert back to a list of images to keep consistent behaviour across processors.
					UpperCamelCase__				:					Union[str, Any]					=					list(UpperCAmelCase_)
			else:
					UpperCamelCase__				:					int					=					[to_channel_dimension_format(UpperCAmelCase_  ,					UpperCAmelCase_) for image in images]

			UpperCamelCase__				:					Tuple					=					{'input_ids': images}
			return BatchFeature(data=UpperCAmelCase_  ,					tensor_type=UpperCAmelCase_)












'''simple docstring'''




import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize







def       __UpperCAmelCase						(  lowerCamelCase_)							->     float:
		return np.dot(lowerCamelCase_     ,      lowerCamelCase_)







class       __lowercase					:

	def __init__(      self   :						Tuple  ,					*,
	    UpperCAmelCase_   :						float = np.inf  ,					UpperCAmelCase_   :						str = "linear"  ,					UpperCAmelCase_   :						float = 0.0  ,					):
			UpperCamelCase__				:					Union[str, Any]					=					regularization
			UpperCamelCase__				:					Optional[int]					=					gamma
			if kernel == "linear":
					UpperCamelCase__				:					List[str]					=					self.__linear
			elif kernel == "rbf":
					if self.gamma == 0:
							raise ValueError('rbf kernel requires gamma')
					if not isinstance(self.gamma  ,					(float, int)):
							raise ValueError('gamma must be float or int')
					if not self.gamma > 0:
							raise ValueError('gamma must be > 0')
					UpperCamelCase__				:					Union[str, Any]					=					self.__rbf
					# in the future, there could be a default value like in sklearn
					# sklear: def_gamma = 1/(n_features * X.var()) (wiki)
					# previously it was 1/(n_features)
			else:
					UpperCamelCase__				:					Optional[int]					=					F'Unknown kernel: {kernel}'
					raise ValueError(UpperCAmelCase_)

	def      __UpperCamelCase							(      self   :						Any  ,					UpperCAmelCase_   :						ndarray  ,					UpperCAmelCase_   :						ndarray):
			return np.dot(UpperCAmelCase_  ,					UpperCAmelCase_)

	def      __UpperCamelCase							(      self   :						Union[str, Any]  ,					UpperCAmelCase_   :						ndarray  ,					UpperCAmelCase_   :						ndarray):
			return np.exp(-(self.gamma * norm_squared(vectora - vectora)))

	def      __UpperCamelCase							(      self   :						Any  ,					UpperCAmelCase_   :						list[ndarray]  ,					UpperCAmelCase_   :						ndarray):
			UpperCamelCase__				:					Any					=					observations
			UpperCamelCase__				:					Tuple					=					classes

			# using Wolfe's Dual to calculate w.
			# Primal problem: minimize 1/2*norm_squared(w)
			#   constraint: yn(w . xn + b) >= 1
			#
			# With l a vector
			# Dual problem: maximize sum_n(ln) -
			#       1/2 * sum_n(sum_m(ln*lm*yn*ym*xn . xm))
			#   constraint: self.C >= ln >= 0
			#           and sum_n(ln*yn) = 0
			# Then we get w using w = sum_n(ln*yn*xn)
			# At the end we can get b ~= mean(yn - w . xn)
			#
			# Since we use kernels, we only need l_star to calculate b
			# and to classify observations

			((UpperCamelCase__),	)				:					Optional[Any]					=					np.shape(UpperCAmelCase_)

			def to_minimize(UpperCAmelCase_   :						ndarray) -> float:
					UpperCamelCase__				:					Union[str, Any]					=					0
					((UpperCamelCase__),	)				:					int					=					np.shape(UpperCAmelCase_)
					for i in range(UpperCAmelCase_):
							for j in range(UpperCAmelCase_):
									s += (
									    candidate[i]
									    * candidate[j]
									    * classes[i]
									    * classes[j]
									    * self.kernel(observations[i]  ,					observations[j])
									)
					return 1 / 2 * s - sum(UpperCAmelCase_)

			UpperCamelCase__				:					List[str]					=					LinearConstraint(UpperCAmelCase_  ,					0  ,					0)
			UpperCamelCase__				:					Dict					=					Bounds(0  ,					self.regularization)

			UpperCamelCase__				:					Any					=					minimize(
			    UpperCAmelCase_  ,					np.ones(UpperCAmelCase_)  ,					bounds=UpperCAmelCase_  ,					constraints=[ly_contraint]).x
			UpperCamelCase__				:					str					=					l_star

			# calculating mean offset of separation plane to points
			UpperCamelCase__				:					Any					=					0
			for i in range(UpperCAmelCase_):
					for j in range(UpperCAmelCase_):
							s += classes[i] - classes[i] * self.optimum[i] * self.kernel(
							    observations[i]  ,					observations[j])
			UpperCamelCase__				:					List[str]					=					s / n





	def      __UpperCamelCase							(      self   :						str  ,					UpperCAmelCase_   :						ndarray):
			UpperCamelCase__				:					Optional[int]					=					sum(
			    self.optimum[n]
			    * self.classes[n]
			    * self.kernel(self.observations[n]  ,					UpperCAmelCase_)
			    for n in range(len(self.classes)))
			return 1 if s + self.offset >= 0 else -1


if __name__ == "__main__":
		import doctest

		doctest.testmod()




