






"""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 convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    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__)







class     UpperCamelCase_ (__A  ):
	__magic_name__			   =       ['''pixel_values''']





	def __init__(   self      :				Optional[Any]			,			lowerCAmelCase_      :				bool = True			,			lowerCAmelCase_      :				Dict[str, int] = None			,			lowerCAmelCase_      :				PILImageResampling = PILImageResampling.BICUBIC			,			lowerCAmelCase_      :				bool = True			,			lowerCAmelCase_      :				Union[int, float] = 1 / 255			,			lowerCAmelCase_      :				bool = True			,			lowerCAmelCase_      :				Optional[Union[float, List[float]]] = None			,			lowerCAmelCase_      :				Optional[Union[float, List[float]]] = None			,			lowerCAmelCase_      :				bool = True			,			**lowerCAmelCase_      :				Tuple			,			)		->    None:
				super().__init__(**lowerCAmelCase_		)
				UpperCAmelCase_  :				List[str]      =      size if size is not None else {"height": 384, "width": 384}
				UpperCAmelCase_  :				List[str]      =      get_size_dict(lowerCAmelCase_			,			default_to_square=lowerCAmelCase_		)

				UpperCAmelCase_  :				int      =      do_resize
				UpperCAmelCase_  :				int      =      size
				UpperCAmelCase_  :				Optional[Any]      =      resample
				UpperCAmelCase_  :				Dict      =      do_rescale
				UpperCAmelCase_  :				int      =      rescale_factor
				UpperCAmelCase_  :				Dict      =      do_normalize
				UpperCAmelCase_  :				Any      =      image_mean if image_mean is not None else OPENAI_CLIP_MEAN
				UpperCAmelCase_  :				Tuple      =      image_std if image_std is not None else OPENAI_CLIP_STD
				UpperCAmelCase_  :				Dict      =      do_convert_rgb





	def   _SCREAMING_SNAKE_CASE   (   self      :				Optional[Any]			,			lowerCAmelCase_      :				np.ndarray			,			lowerCAmelCase_      :				Dict[str, int]			,			lowerCAmelCase_      :				PILImageResampling = PILImageResampling.BICUBIC			,			lowerCAmelCase_      :				Optional[Union[str, ChannelDimension]] = None			,			**lowerCAmelCase_      :				int			,			)		->    np.ndarray:
				UpperCAmelCase_  :				Any      =      get_size_dict(lowerCAmelCase_			,			default_to_square=lowerCAmelCase_		)
				if "height" not in size or "width" not in size:
							raise ValueError(f"""The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}"""		)
				UpperCAmelCase_  :				Dict      =      (size["height"], size["width"])
				return resize(lowerCAmelCase_			,			size=lowerCAmelCase_			,			resample=lowerCAmelCase_			,			data_format=lowerCAmelCase_			,			**lowerCAmelCase_		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Any			,			lowerCAmelCase_      :				np.ndarray			,			lowerCAmelCase_      :				Union[int, float]			,			lowerCAmelCase_      :				Optional[Union[str, ChannelDimension]] = None			,			**lowerCAmelCase_      :				Any			,			)		->    Optional[Any]:
				return rescale(lowerCAmelCase_			,			scale=lowerCAmelCase_			,			data_format=lowerCAmelCase_			,			**lowerCAmelCase_		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Dict			,			lowerCAmelCase_      :				np.ndarray			,			lowerCAmelCase_      :				Union[float, List[float]]			,			lowerCAmelCase_      :				Union[float, List[float]]			,			lowerCAmelCase_      :				Optional[Union[str, ChannelDimension]] = None			,			**lowerCAmelCase_      :				Optional[int]			,			)		->    np.ndarray:
				return normalize(lowerCAmelCase_			,			mean=lowerCAmelCase_			,			std=lowerCAmelCase_			,			data_format=lowerCAmelCase_			,			**lowerCAmelCase_		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Dict			,			lowerCAmelCase_      :				ImageInput			,			lowerCAmelCase_      :				Optional[bool] = None			,			lowerCAmelCase_      :				Optional[Dict[str, int]] = None			,			lowerCAmelCase_      :				PILImageResampling = None			,			lowerCAmelCase_      :				Optional[bool] = None			,			lowerCAmelCase_      :				Optional[float] = None			,			lowerCAmelCase_      :				Optional[bool] = None			,			lowerCAmelCase_      :				Optional[Union[float, List[float]]] = None			,			lowerCAmelCase_      :				Optional[Union[float, List[float]]] = None			,			lowerCAmelCase_      :				Optional[Union[str, TensorType]] = None			,			lowerCAmelCase_      :				bool = None			,			lowerCAmelCase_      :				ChannelDimension = ChannelDimension.FIRST			,			**lowerCAmelCase_      :				str			,			)		->    PIL.Image.Image:
				UpperCAmelCase_  :				Tuple      =      do_resize if do_resize is not None else self.do_resize
				UpperCAmelCase_  :				Optional[int]      =      resample if resample is not None else self.resample
				UpperCAmelCase_  :				List[Any]      =      do_rescale if do_rescale is not None else self.do_rescale
				UpperCAmelCase_  :				Dict      =      rescale_factor if rescale_factor is not None else self.rescale_factor
				UpperCAmelCase_  :				Dict      =      do_normalize if do_normalize is not None else self.do_normalize
				UpperCAmelCase_  :				Tuple      =      image_mean if image_mean is not None else self.image_mean
				UpperCAmelCase_  :				Union[str, Any]      =      image_std if image_std is not None else self.image_std
				UpperCAmelCase_  :				str      =      do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

				UpperCAmelCase_  :				List[str]      =      size if size is not None else self.size
				UpperCAmelCase_  :				int      =      get_size_dict(lowerCAmelCase_			,			default_to_square=lowerCAmelCase_		)

				UpperCAmelCase_  :				Dict      =      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_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."		)

				# PIL RGBA images are converted to RGB
				if do_convert_rgb:
							UpperCAmelCase_  :				List[Any]      =      [convert_to_rgb(lowerCAmelCase_		) for image in images]

				# All transformations expect numpy arrays.
				UpperCAmelCase_  :				Any      =      [to_numpy_array(lowerCAmelCase_		) for image in images]

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

				if do_rescale:
							UpperCAmelCase_  :				str      =      [self.rescale(image=lowerCAmelCase_			,			scale=lowerCAmelCase_		) for image in images]

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

				UpperCAmelCase_  :				str      =      [to_channel_dimension_format(lowerCAmelCase_			,			lowerCAmelCase_		) for image in images]

				UpperCAmelCase_  :				List[Any]      =      BatchFeature(data={"pixel_values": images}			,			tensor_type=lowerCAmelCase_		)

				return encoded_outputs











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

def        snake_case   (     A__		):
			return np.dot(A__			,A__		)







class     UpperCamelCase_ :





	def __init__(   self      :				int			,			*,
	    lowerCAmelCase_      :				float = np.inf			,			lowerCAmelCase_      :				str = "linear"			,			lowerCAmelCase_      :				float = 0.0			,			)		->    None:
				UpperCAmelCase_  :				List[str]      =      regularization
				UpperCAmelCase_  :				Tuple      =      gamma
				if kernel == "linear":
							UpperCAmelCase_  :				List[Any]      =      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_  :				Tuple      =      f"""Unknown kernel: {kernel}"""
							raise ValueError(lowerCAmelCase_		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Tuple			,			lowerCAmelCase_      :				ndarray			,			lowerCAmelCase_      :				ndarray		)		->    float:
				return np.dot(lowerCAmelCase_			,			lowerCAmelCase_		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Union[str, Any]			,			lowerCAmelCase_      :				ndarray			,			lowerCAmelCase_      :				ndarray		)		->    float:
				return np.exp(-(self.gamma * norm_squared(vectora - vectora		))		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Dict			,			lowerCAmelCase_      :				list[ndarray]			,			lowerCAmelCase_      :				ndarray		)		->    None:
				UpperCAmelCase_  :				int      =      observations
				UpperCAmelCase_  :				Optional[Any]      =      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[int]      =      np.shape(lowerCAmelCase_		)

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

				UpperCAmelCase_  :				int      =      LinearConstraint(lowerCAmelCase_			,			0			,			0		)
				UpperCAmelCase_  :				Dict      =      Bounds(0			,			self.regularization		)

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

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





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


if __name__ == "__main__":
			import doctest

			doctest.testmod()



