





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






def     lowerCamelCase__			(__lowerCamelCase	):
     return np.dot(__lowerCamelCase,       __lowerCamelCase	)





class 			lowerCAmelCase__:






       '''simple docstring'''


       def __init__(      self    ,						*,
           __lowerCamelCase = np.inf    ,						__lowerCamelCase = "linear"    ,						__lowerCamelCase = 0.0    ,						)			->     None:
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							regularization
            _SCREAMING_SNAKE_CASE       :	int						    =							gamma
            if kernel == "linear":
                 _SCREAMING_SNAKE_CASE       :	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"							)
                 _SCREAMING_SNAKE_CASE       :	str						    =							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:
                 _SCREAMING_SNAKE_CASE       :	Dict						    =							F"""Unknown kernel: {kernel}"""
                 raise ValueError(__lowerCamelCase							)




       def     UpperCamelCase_			(      self    ,						__lowerCamelCase    ,						__lowerCamelCase							)			->     float:
            return np.dot(__lowerCamelCase    ,						__lowerCamelCase							)




       def     UpperCamelCase_			(      self    ,						__lowerCamelCase    ,						__lowerCamelCase							)			->     float:
            return np.exp(-(self.gamma * norm_squared(vectora - vectora							))							)




       def     UpperCamelCase_			(      self    ,						__lowerCamelCase    ,						__lowerCamelCase							)			->     None:
            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							observations
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							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

            ((_SCREAMING_SNAKE_CASE)    ,			)       :	List[Any]						    =							np.shape(__lowerCamelCase							)

            def to_minimize(__lowerCamelCase							) -> float:
                 _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							0
                 ((_SCREAMING_SNAKE_CASE)    ,			)       :	Any						    =							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							)

            _SCREAMING_SNAKE_CASE       :	List[str]						    =							LinearConstraint(__lowerCamelCase    ,						0    ,						0							)
            _SCREAMING_SNAKE_CASE       :	Tuple						    =							Bounds(0    ,						self.regularization							)

            _SCREAMING_SNAKE_CASE       :	Tuple						    =							minimize(
                __lowerCamelCase    ,						np.ones(__lowerCamelCase							)    ,						bounds=__lowerCamelCase    ,						constraints=[ly_contraint]							).x
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							l_star

            # calculating mean offset of separation plane to points
            _SCREAMING_SNAKE_CASE       :	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]							)
            _SCREAMING_SNAKE_CASE       :	Dict						    =							s / n







       def     UpperCamelCase_			(      self    ,						__lowerCamelCase							)			->     int:
            _SCREAMING_SNAKE_CASE       :	Tuple						    =							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()






from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, Encoder, VectorQuantizer





@dataclass
class 			lowerCAmelCase__( __lowercase    ):






       '''simple docstring'''


       __snake_case 			=    42





class 			lowerCAmelCase__( __lowercase  ,     __lowercase    ):






       '''simple docstring'''


       @register_to_config
       def __init__(      self    ,						__lowerCamelCase = 3    ,						__lowerCamelCase = 3    ,						__lowerCamelCase = ("DownEncoderBlock2D",)    ,						__lowerCamelCase = ("UpDecoderBlock2D",)    ,						__lowerCamelCase = (6_4,)    ,						__lowerCamelCase = 1    ,						__lowerCamelCase = "silu"    ,						__lowerCamelCase = 3    ,						__lowerCamelCase = 3_2    ,						__lowerCamelCase = 2_5_6    ,						__lowerCamelCase = 3_2    ,						__lowerCamelCase = None    ,						__lowerCamelCase = 0.1_8215    ,						__lowerCamelCase = "group"    ,						)			->     List[str]:
            super().__init__()

            # pass init params to Encoder
            _SCREAMING_SNAKE_CASE       :	Tuple						    =							Encoder(
                in_channels=__lowerCamelCase    ,						out_channels=__lowerCamelCase    ,						down_block_types=__lowerCamelCase    ,						block_out_channels=__lowerCamelCase    ,						layers_per_block=__lowerCamelCase    ,						act_fn=__lowerCamelCase    ,						norm_num_groups=__lowerCamelCase    ,						double_z=__lowerCamelCase    ,						)

            _SCREAMING_SNAKE_CASE       :	Dict						    =							vq_embed_dim if vq_embed_dim is not None else latent_channels

            _SCREAMING_SNAKE_CASE       :	str						    =							nn.Convad(__lowerCamelCase    ,						__lowerCamelCase    ,						1							)
            _SCREAMING_SNAKE_CASE       :	Tuple						    =							VectorQuantizer(__lowerCamelCase    ,						__lowerCamelCase    ,						beta=0.25    ,						remap=__lowerCamelCase    ,						sane_index_shape=__lowerCamelCase							)
            _SCREAMING_SNAKE_CASE       :	int						    =							nn.Convad(__lowerCamelCase    ,						__lowerCamelCase    ,						1							)

            # pass init params to Decoder
            _SCREAMING_SNAKE_CASE       :	Dict						    =							Decoder(
                in_channels=__lowerCamelCase    ,						out_channels=__lowerCamelCase    ,						up_block_types=__lowerCamelCase    ,						block_out_channels=__lowerCamelCase    ,						layers_per_block=__lowerCamelCase    ,						act_fn=__lowerCamelCase    ,						norm_num_groups=__lowerCamelCase    ,						norm_type=__lowerCamelCase    ,						)




       @apply_forward_hook
       def     UpperCamelCase_			(      self    ,						__lowerCamelCase    ,						__lowerCamelCase = True							)			->     VQEncoderOutput:
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							self.encoder(__lowerCamelCase							)
            _SCREAMING_SNAKE_CASE       :	Any						    =							self.quant_conv(__lowerCamelCase							)

            if not return_dict:
                 return (h,)

            return VQEncoderOutput(latents=__lowerCamelCase							)




       @apply_forward_hook
       def     UpperCamelCase_			(      self    ,						__lowerCamelCase    ,						__lowerCamelCase = False    ,						__lowerCamelCase = True							)			->     Union[DecoderOutput, torch.FloatTensor]:
            # also go through quantization layer
            if not force_not_quantize:
                 _SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE       :	Optional[int]						    =							self.quantize(__lowerCamelCase							)
            else:
                 _SCREAMING_SNAKE_CASE       :	Dict						    =							h
            _SCREAMING_SNAKE_CASE       :	str						    =							self.post_quant_conv(__lowerCamelCase							)
            _SCREAMING_SNAKE_CASE       :	Dict						    =							self.decoder(__lowerCamelCase    ,						quant if self.config.norm_type == "spatial" else None							)

            if not return_dict:
                 return (dec,)

            return DecoderOutput(sample=__lowerCamelCase							)







       def     UpperCamelCase_			(      self    ,						__lowerCamelCase    ,						__lowerCamelCase = True							)			->     Union[DecoderOutput, torch.FloatTensor]:
            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							sample
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							self.encode(__lowerCamelCase							).latents
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							self.decode(__lowerCamelCase							).sample

            if not return_dict:
                 return (dec,)

            return DecoderOutput(sample=__lowerCamelCase							)