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





def       lowerCAmelCase__			(	_UpperCamelCase				: ndarray    )    -> Union[str, Any]:





						"""simple docstring"""






						return np.dot(__SCREAMING_SNAKE_CASE       ,	__SCREAMING_SNAKE_CASE    )



class      lowerCAmelCase_			:



				"""simple docstring"""

				def __init__( self						,			*,
				    lowerCAmelCase = np.inf						,			lowerCAmelCase = "linear"						,			lowerCAmelCase = 0.0						,			):






										"""simple docstring"""

										snake_case	    =	regularization
										snake_case	    =	gamma
										if kernel == "linear":
																snake_case	    =	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'			)
																snake_case	    =	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:
																snake_case	    =	F"""Unknown kernel: {kernel}"""
																raise ValueError(__UpperCamelCase			)

				def 						snake_case       ( self						,			lowerCAmelCase						,			lowerCAmelCase			):






										"""simple docstring"""

										return np.dot(__UpperCamelCase						,			__UpperCamelCase			)

				def 						snake_case       ( self						,			lowerCAmelCase						,			lowerCAmelCase			):






										"""simple docstring"""

										return np.exp(-(self.gamma * norm_squared(vectora - vectora			))			)

				def 						snake_case       ( self						,			lowerCAmelCase						,			lowerCAmelCase			):






										"""simple docstring"""

										snake_case	    =	observations
										snake_case	    =	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

										(snake_case  )	    =	np.shape(__UpperCamelCase			)

										def to_minimize(lowerCAmelCase			) -> float:
																snake_case	    =	0
																(snake_case  )	    =	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			)

										snake_case	    =	LinearConstraint(__UpperCamelCase						,			0						,			0			)
										snake_case	    =	Bounds(0						,			self.regularization			)

										snake_case	    =	minimize(
										    __UpperCamelCase						,			np.ones(__UpperCamelCase			)						,			bounds=__UpperCamelCase						,			constraints=[ly_contraint]			).x
										snake_case	    =	l_star

										# calculating mean offset of separation plane to points
										snake_case	    =	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]			)
										snake_case	    =	s / n

				def 						snake_case       ( self						,			lowerCAmelCase			):






										"""simple docstring"""

										snake_case	    =	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()



"""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 (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_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


__SCREAMING_SNAKE_CASE    	=logging.get_logger(__name__)







class        UpperCamelCase		(			lowercase_  ):
   lowercase							=  ['pixel_values']
   def __init__(			self     ,__UpperCamelCase = True     ,__UpperCamelCase = None     ,__UpperCamelCase = 0.9     ,__UpperCamelCase = PILImageResampling.BICUBIC     ,__UpperCamelCase = True     ,__UpperCamelCase = None     ,__UpperCamelCase = 1 / 255     ,__UpperCamelCase = True     ,__UpperCamelCase = True     ,__UpperCamelCase = None     ,__UpperCamelCase = None     ,**__UpperCamelCase     ,)	->      None:


        '''simple docstring'''


        super().__init__(**__UpperCamelCase			)
        lowercase_	:				Optional[int]													=	size if size is not None else {'shortest_edge': 224}
        lowercase_	:				Union[str, Any]													=	get_size_dict(__UpperCamelCase     ,default_to_square=__UpperCamelCase			)
        lowercase_	:				Union[str, Any]													=	crop_size if crop_size is not None else {'height': 224, 'width': 224}
        lowercase_	:				Optional[int]													=	get_size_dict(__UpperCamelCase     ,param_name='crop_size'			)

        lowercase_	:				List[str]													=	do_resize
        lowercase_	:				List[Any]													=	size
        lowercase_	:				int													=	crop_pct
        lowercase_	:				Dict													=	resample
        lowercase_	:				List[str]													=	do_center_crop
        lowercase_	:				Union[str, Any]													=	crop_size
        lowercase_	:				List[Any]													=	do_rescale
        lowercase_	:				Tuple													=	rescale_factor
        lowercase_	:				Tuple													=	do_normalize
        lowercase_	:				Union[str, Any]													=	image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
        lowercase_	:				int													=	image_std if image_std is not None else IMAGENET_DEFAULT_STD
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase = None     ,__UpperCamelCase = PILImageResampling.BICUBIC     ,__UpperCamelCase = None     ,**__UpperCamelCase     ,)	->      np.ndarray:


        '''simple docstring'''


        lowercase_	:				Any													=	get_size_dict(__UpperCamelCase     ,default_to_square=__UpperCamelCase			)
        if "shortest_edge" not in size and ("height" not in size or "width" not in size):
             raise ValueError(f'''size must contain \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'''			)
        if crop_pct is not None:
             if "shortest_edge" in size:
                  lowercase_	:				Union[str, Any]													=	int(size['shortest_edge'] / crop_pct			)
             elif "height" in size and "width" in size:
                  if size["height"] == size["width"]:
                       lowercase_	:				Tuple													=	int(size['height'] / crop_pct			)
                  else:
                       lowercase_	:				Dict													=	(int(size['height'] / crop_pct			), int(size['width'] / crop_pct			))
             else:
                  raise ValueError('Invalid size for resize: {}'.format(__UpperCamelCase			)			)

             lowercase_	:				int													=	get_resize_output_image_size(__UpperCamelCase     ,size=__UpperCamelCase     ,default_to_square=__UpperCamelCase			)
        else:
             if "shortest_edge" in size:
                  lowercase_	:				Optional[int]													=	get_resize_output_image_size(__UpperCamelCase     ,size=size['shortest_edge']     ,default_to_square=__UpperCamelCase			)
             elif "height" in size and "width" in size:
                  lowercase_	:				Dict													=	(size['height'], size['width'])
             else:
                  raise ValueError('Invalid size for resize: {}'.format(__UpperCamelCase			)			)

        return resize(__UpperCamelCase     ,size=__UpperCamelCase     ,resample=__UpperCamelCase     ,data_format=__UpperCamelCase     ,**__UpperCamelCase			)
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase = None     ,**__UpperCamelCase     ,)	->      np.ndarray:


        '''simple docstring'''


        lowercase_	:				List[Any]													=	get_size_dict(__UpperCamelCase			)
        if "height" not in size or "width" not in size:
             raise ValueError(f'''size must contain \'height\' and \'width\' as keys. Got {size.keys()}'''			)
        return center_crop(__UpperCamelCase     ,size=(size['height'], size['width'])     ,data_format=__UpperCamelCase     ,**__UpperCamelCase			)
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase = None     ,**__UpperCamelCase     ,)	->      str:


        '''simple docstring'''


        return rescale(__UpperCamelCase     ,scale=__UpperCamelCase     ,data_format=__UpperCamelCase     ,**__UpperCamelCase			)
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase = None     ,**__UpperCamelCase     ,)	->      np.ndarray:


        '''simple docstring'''


        return normalize(__UpperCamelCase     ,mean=__UpperCamelCase     ,std=__UpperCamelCase     ,data_format=__UpperCamelCase     ,**__UpperCamelCase			)







   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase = None     ,__UpperCamelCase = None     ,__UpperCamelCase = None     ,__UpperCamelCase = None     ,__UpperCamelCase = None     ,__UpperCamelCase = None     ,__UpperCamelCase = None     ,__UpperCamelCase = None     ,__UpperCamelCase = None     ,__UpperCamelCase = None     ,__UpperCamelCase = None     ,__UpperCamelCase = None     ,__UpperCamelCase = ChannelDimension.FIRST     ,**__UpperCamelCase     ,)	->      PIL.Image.Image:


        '''simple docstring'''


        lowercase_	:				List[Any]													=	do_resize if do_resize is not None else self.do_resize
        lowercase_	:				Optional[int]													=	crop_pct if crop_pct is not None else self.crop_pct
        lowercase_	:				List[str]													=	resample if resample is not None else self.resample
        lowercase_	:				str													=	do_center_crop if do_center_crop is not None else self.do_center_crop
        lowercase_	:				Tuple													=	do_rescale if do_rescale is not None else self.do_rescale
        lowercase_	:				Union[str, Any]													=	rescale_factor if rescale_factor is not None else self.rescale_factor
        lowercase_	:				str													=	do_normalize if do_normalize is not None else self.do_normalize
        lowercase_	:				str													=	image_mean if image_mean is not None else self.image_mean
        lowercase_	:				Tuple													=	image_std if image_std is not None else self.image_std

        lowercase_	:				Optional[Any]													=	size if size is not None else self.size
        lowercase_	:				Tuple													=	get_size_dict(__UpperCamelCase     ,default_to_square=__UpperCamelCase			)
        lowercase_	:				Union[str, Any]													=	crop_size if crop_size is not None else self.crop_size
        lowercase_	:				List[str]													=	get_size_dict(__UpperCamelCase     ,param_name='crop_size'			)

        lowercase_	:				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_center_crop and crop_pct is None:
             raise ValueError('Crop_pct must be specified if do_center_crop 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.'			)

        # All transformations expect numpy arrays.
        lowercase_	:				Optional[Any]													=	[to_numpy_array(__UpperCamelCase			) for image in images]

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

        if do_center_crop:
             lowercase_	:				str													=	[self.center_crop(image=__UpperCamelCase     ,size=__UpperCamelCase			) for image in images]

        if do_rescale:
             lowercase_	:				Any													=	[self.rescale(image=__UpperCamelCase     ,scale=__UpperCamelCase			) for image in images]

        if do_normalize:
             lowercase_	:				int													=	[self.normalize(image=__UpperCamelCase     ,mean=__UpperCamelCase     ,std=__UpperCamelCase			) for image in images]

        lowercase_	:				Dict													=	[to_channel_dimension_format(__UpperCamelCase     ,__UpperCamelCase			) for image in images]

        lowercase_	:				Any													=	{'pixel_values': images}
        return BatchFeature(data=__UpperCamelCase     ,tensor_type=__UpperCamelCase			)

