



import gc
import random
import tempfile
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion_safe import StableDiffusionPipelineSafe as StableDiffusionPipeline
from diffusers.utils import floats_tensor, nightly, torch_device
from diffusers.utils.testing_utils import require_torch_gpu



class 	lowerCamelCase__      (							unittest.TestCase):




		'''simple docstring'''



		def 		lowerCAmelCase__				(self			)					->      Tuple:

						"""simple docstring"""






						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()



		@property
		def 		lowerCAmelCase__				(self			)					->      Dict:

						"""simple docstring"""






						lowerCAmelCase__					:    Dict												=					1
						lowerCAmelCase__					:    Optional[Any]												=					3
						lowerCAmelCase__					:    str												=					(32, 32)

						lowerCAmelCase__					:    Dict												=					floats_tensor((batch_size, num_channels) + sizes		,rng=random.Random(0			)			).to(__lowerCAmelCase			)
						return image



		@property
		def 		lowerCAmelCase__				(self			)					->      int:

						"""simple docstring"""






						torch.manual_seed(0			)
						lowerCAmelCase__					:    Optional[Any]												=					UNetaDConditionModel(
						    block_out_channels=(32, 64)		,layers_per_block=2		,sample_size=32		,in_channels=4		,out_channels=4		,down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')		,up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')		,cross_attention_dim=32		,)
						return model



		@property
		def 		lowerCAmelCase__				(self			)					->      List[Any]:

						"""simple docstring"""






						torch.manual_seed(0			)
						lowerCAmelCase__					:    Dict												=					AutoencoderKL(
						    block_out_channels=[32, 64]		,in_channels=3		,out_channels=3		,down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']		,up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']		,latent_channels=4		,)
						return model



		@property
		def 		lowerCAmelCase__				(self			)					->      Tuple:

						"""simple docstring"""






						torch.manual_seed(0			)
						lowerCAmelCase__					:    int												=					CLIPTextConfig(
						    bos_token_id=0		,eos_token_id=2		,hidden_size=32		,intermediate_size=37		,layer_norm_eps=1e-05		,num_attention_heads=4		,num_hidden_layers=5		,pad_token_id=1		,vocab_size=10_00		,)
						return CLIPTextModel(__lowerCAmelCase			)



		@property
		def 		lowerCAmelCase__				(self			)					->      Optional[Any]:

						"""simple docstring"""






						def extract(*__lowerCamelCase		,**__lowerCamelCase			):



										class 	lowerCamelCase__      :




												'''simple docstring'''



												def __init__(self			)					->      Tuple:

																"""simple docstring"""






																lowerCAmelCase__					:    Optional[int]												=					torch.ones([0]			)



												def 		lowerCAmelCase__				(self		,__lowerCamelCase			)					->      List[str]:

																"""simple docstring"""






																self.pixel_values.to(__lowerCAmelCase			)
																return self


										return Out()

						return extract



		def 		lowerCAmelCase__				(self			)					->      List[Any]:

						"""simple docstring"""






						lowerCAmelCase__					:    List[Any]												=					'''cpu'''  # ensure determinism for the device-dependent torch.Generator
						lowerCAmelCase__					:    List[str]												=					self.dummy_cond_unet
						lowerCAmelCase__					:    List[str]												=					DDIMScheduler(
						    beta_start=0.0_0085		,beta_end=0.012		,beta_schedule='''scaled_linear'''		,clip_sample=__lowerCAmelCase		,set_alpha_to_one=__lowerCAmelCase		,)

						lowerCAmelCase__					:    Optional[int]												=					self.dummy_vae
						lowerCAmelCase__					:    Optional[Any]												=					self.dummy_text_encoder
						lowerCAmelCase__					:    List[Any]												=					CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''			)

						# make sure here that pndm scheduler skips prk
						lowerCAmelCase__					:    str												=					StableDiffusionPipeline(
						    unet=__lowerCAmelCase		,scheduler=__lowerCAmelCase		,vae=__lowerCAmelCase		,text_encoder=__lowerCAmelCase		,tokenizer=__lowerCAmelCase		,safety_checker=__lowerCAmelCase		,feature_extractor=self.dummy_extractor		,)
						lowerCAmelCase__					:    Tuple												=					sd_pipe.to(__lowerCAmelCase			)
						sd_pipe.set_progress_bar_config(disable=__lowerCAmelCase			)

						lowerCAmelCase__					:    List[Any]												=					'''A painting of a squirrel eating a burger'''

						lowerCAmelCase__					:    Tuple												=					torch.Generator(device=__lowerCAmelCase			).manual_seed(0			)
						lowerCAmelCase__					:    Tuple												=					sd_pipe([prompt]		,generator=__lowerCAmelCase		,guidance_scale=6.0		,num_inference_steps=2		,output_type='''np'''			)
						lowerCAmelCase__					:    int												=					output.images

						lowerCAmelCase__					:    Union[str, Any]												=					torch.Generator(device=__lowerCAmelCase			).manual_seed(0			)
						lowerCAmelCase__					:    Tuple												=					sd_pipe(
						    [prompt]		,generator=__lowerCAmelCase		,guidance_scale=6.0		,num_inference_steps=2		,output_type='''np'''		,return_dict=__lowerCAmelCase		,)[0]

						lowerCAmelCase__					:    Union[str, Any]												=					image[0, -3:, -3:, -1]
						lowerCAmelCase__					:    Optional[int]												=					image_from_tuple[0, -3:, -3:, -1]

						assert image.shape == (1, 64, 64, 3)
						lowerCAmelCase__					:    Optional[Any]												=					np.array([0.5756, 0.6118, 0.5005, 0.5041, 0.5471, 0.4726, 0.4976, 0.4865, 0.4864]			)

						assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-2
						assert np.abs(image_from_tuple_slice.flatten() - expected_slice			).max() < 1e-2



		def 		lowerCAmelCase__				(self			)					->      List[str]:

						"""simple docstring"""






						lowerCAmelCase__					:    List[str]												=					'''cpu'''  # ensure determinism for the device-dependent torch.Generator
						lowerCAmelCase__					:    Union[str, Any]												=					self.dummy_cond_unet
						lowerCAmelCase__					:    int												=					PNDMScheduler(skip_prk_steps=__lowerCAmelCase			)
						lowerCAmelCase__					:    List[str]												=					self.dummy_vae
						lowerCAmelCase__					:    Any												=					self.dummy_text_encoder
						lowerCAmelCase__					:    Any												=					CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''			)

						# make sure here that pndm scheduler skips prk
						lowerCAmelCase__					:    Dict												=					StableDiffusionPipeline(
						    unet=__lowerCAmelCase		,scheduler=__lowerCAmelCase		,vae=__lowerCAmelCase		,text_encoder=__lowerCAmelCase		,tokenizer=__lowerCAmelCase		,safety_checker=__lowerCAmelCase		,feature_extractor=self.dummy_extractor		,)
						lowerCAmelCase__					:    Dict												=					sd_pipe.to(__lowerCAmelCase			)
						sd_pipe.set_progress_bar_config(disable=__lowerCAmelCase			)

						lowerCAmelCase__					:    Optional[int]												=					'''A painting of a squirrel eating a burger'''
						lowerCAmelCase__					:    Optional[int]												=					torch.Generator(device=__lowerCAmelCase			).manual_seed(0			)
						lowerCAmelCase__					:    int												=					sd_pipe([prompt]		,generator=__lowerCAmelCase		,guidance_scale=6.0		,num_inference_steps=2		,output_type='''np'''			)

						lowerCAmelCase__					:    Dict												=					output.images

						lowerCAmelCase__					:    Optional[int]												=					torch.Generator(device=__lowerCAmelCase			).manual_seed(0			)
						lowerCAmelCase__					:    List[str]												=					sd_pipe(
						    [prompt]		,generator=__lowerCAmelCase		,guidance_scale=6.0		,num_inference_steps=2		,output_type='''np'''		,return_dict=__lowerCAmelCase		,)[0]

						lowerCAmelCase__					:    List[str]												=					image[0, -3:, -3:, -1]
						lowerCAmelCase__					:    List[str]												=					image_from_tuple[0, -3:, -3:, -1]

						assert image.shape == (1, 64, 64, 3)
						lowerCAmelCase__					:    Dict												=					np.array([0.5125, 0.5716, 0.4828, 0.5060, 0.5650, 0.4768, 0.5185, 0.4895, 0.4993]			)

						assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-2
						assert np.abs(image_from_tuple_slice.flatten() - expected_slice			).max() < 1e-2



		def 		lowerCAmelCase__				(self			)					->      Optional[int]:

						"""simple docstring"""






						lowerCAmelCase__					:    str												=					StableDiffusionPipeline.from_pretrained(
						    '''hf-internal-testing/tiny-stable-diffusion-lms-pipe'''		,safety_checker=__lowerCAmelCase			)
						assert isinstance(__lowerCAmelCase		,__lowerCAmelCase			)
						assert isinstance(pipe.scheduler		,__lowerCAmelCase			)
						assert pipe.safety_checker is None

						lowerCAmelCase__					:    int												=					pipe('''example prompt'''		,num_inference_steps=2			).images[0]
						assert image is not None

						# check that there's no error when saving a pipeline with one of the models being None
						with tempfile.TemporaryDirectory() as tmpdirname:
										pipe.save_pretrained(__lowerCAmelCase			)
										lowerCAmelCase__					:    Tuple												=					StableDiffusionPipeline.from_pretrained(__lowerCAmelCase			)

						# sanity check that the pipeline still works
						assert pipe.safety_checker is None
						lowerCAmelCase__					:    Dict												=					pipe('''example prompt'''		,num_inference_steps=2			).images[0]
						assert image is not None



		@unittest.skipIf(torch_device != '''cuda'''		,'''This test requires a GPU'''			)
		def 		lowerCAmelCase__				(self			)					->      Tuple:

						"""simple docstring"""






						lowerCAmelCase__					:    Tuple												=					self.dummy_cond_unet
						lowerCAmelCase__					:    Union[str, Any]												=					PNDMScheduler(skip_prk_steps=__lowerCAmelCase			)
						lowerCAmelCase__					:    Optional[Any]												=					self.dummy_vae
						lowerCAmelCase__					:    Optional[int]												=					self.dummy_text_encoder
						lowerCAmelCase__					:    List[Any]												=					CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''			)

						# put models in fp16
						lowerCAmelCase__					:    Optional[int]												=					unet.half()
						lowerCAmelCase__					:    Union[str, Any]												=					vae.half()
						lowerCAmelCase__					:    Optional[Any]												=					bert.half()

						# make sure here that pndm scheduler skips prk
						lowerCAmelCase__					:    str												=					StableDiffusionPipeline(
						    unet=__lowerCAmelCase		,scheduler=__lowerCAmelCase		,vae=__lowerCAmelCase		,text_encoder=__lowerCAmelCase		,tokenizer=__lowerCAmelCase		,safety_checker=__lowerCAmelCase		,feature_extractor=self.dummy_extractor		,)
						lowerCAmelCase__					:    Dict												=					sd_pipe.to(__lowerCAmelCase			)
						sd_pipe.set_progress_bar_config(disable=__lowerCAmelCase			)

						lowerCAmelCase__					:    List[str]												=					'''A painting of a squirrel eating a burger'''
						lowerCAmelCase__					:    Tuple												=					sd_pipe([prompt]		,num_inference_steps=2		,output_type='''np'''			).images

						assert image.shape == (1, 64, 64, 3)



@nightly
@require_torch_gpu
class 	lowerCamelCase__      (							unittest.TestCase):




		'''simple docstring'''



		def 		lowerCAmelCase__				(self			)					->      List[str]:

						"""simple docstring"""






						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()



		def 		lowerCAmelCase__				(self			)					->      Optional[Any]:

						"""simple docstring"""






						lowerCAmelCase__					:    List[Any]												=					StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5'''		,safety_checker=__lowerCAmelCase			)
						lowerCAmelCase__					:    str												=					LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config			)
						lowerCAmelCase__					:    int												=					sd_pipe.to(__lowerCAmelCase			)
						sd_pipe.set_progress_bar_config(disable=__lowerCAmelCase			)

						lowerCAmelCase__					:    Union[str, Any]												=					(
						    '''portrait of girl with smokey eyes makeup in abandoned hotel, grange clothes, redshift, wide high angle'''
						    ''' coloured polaroid photograph with flash, kodak film, hyper real, stunning moody cinematography, with'''
						    ''' anamorphic lenses, by maripol, fallen angels by wong kar - wai, style of suspiria and neon demon and'''
						    ''' children from bahnhof zoo, detailed '''
						)
						lowerCAmelCase__					:    Dict												=					40_03_66_03_46
						lowerCAmelCase__					:    Tuple												=					7

						# without safety guidance (sld_guidance_scale = 0)
						lowerCAmelCase__					:    str												=					torch.manual_seed(__lowerCAmelCase			)
						lowerCAmelCase__					:    Optional[int]												=					sd_pipe(
						    [prompt]		,generator=__lowerCAmelCase		,guidance_scale=__lowerCAmelCase		,num_inference_steps=50		,output_type='''np'''		,width=5_12		,height=5_12		,sld_guidance_scale=0		,)

						lowerCAmelCase__					:    Optional[Any]												=					output.images
						lowerCAmelCase__					:    Union[str, Any]												=					image[0, -3:, -3:, -1]
						lowerCAmelCase__					:    Optional[int]												=					[0.2278, 0.2231, 0.2249, 0.2333, 0.2303, 0.1885, 0.2273, 0.2144, 0.2176]

						assert image.shape == (1, 5_12, 5_12, 3)

						assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-2

						# without safety guidance (strong configuration)
						lowerCAmelCase__					:    Optional[Any]												=					torch.manual_seed(__lowerCAmelCase			)
						lowerCAmelCase__					:    List[str]												=					sd_pipe(
						    [prompt]		,generator=__lowerCAmelCase		,guidance_scale=__lowerCAmelCase		,num_inference_steps=50		,output_type='''np'''		,width=5_12		,height=5_12		,sld_guidance_scale=20_00		,sld_warmup_steps=7		,sld_threshold=0.025		,sld_momentum_scale=0.5		,sld_mom_beta=0.7		,)

						lowerCAmelCase__					:    str												=					output.images
						lowerCAmelCase__					:    int												=					image[0, -3:, -3:, -1]
						lowerCAmelCase__					:    List[str]												=					[0.2383, 0.2276, 0.236, 0.2192, 0.2186, 0.2053, 0.1971, 0.1901, 0.1719]

						assert image.shape == (1, 5_12, 5_12, 3)

						assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-2



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

						"""simple docstring"""






						lowerCAmelCase__					:    Tuple												=					StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5'''		,safety_checker=__lowerCAmelCase			)
						lowerCAmelCase__					:    Tuple												=					LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config			)
						lowerCAmelCase__					:    Tuple												=					sd_pipe.to(__lowerCAmelCase			)
						sd_pipe.set_progress_bar_config(disable=__lowerCAmelCase			)

						lowerCAmelCase__					:    int												=					'''padme amidala taking a bath artwork, safe for work, no nudity'''
						lowerCAmelCase__					:    Any												=					27_34_97_17_55
						lowerCAmelCase__					:    Dict												=					7

						lowerCAmelCase__					:    int												=					torch.manual_seed(__lowerCAmelCase			)
						lowerCAmelCase__					:    List[Any]												=					sd_pipe(
						    [prompt]		,generator=__lowerCAmelCase		,guidance_scale=__lowerCAmelCase		,num_inference_steps=50		,output_type='''np'''		,width=5_12		,height=5_12		,sld_guidance_scale=0		,)

						lowerCAmelCase__					:    int												=					output.images
						lowerCAmelCase__					:    Optional[Any]												=					image[0, -3:, -3:, -1]
						lowerCAmelCase__					:    List[Any]												=					[0.3502, 0.3622, 0.3396, 0.3642, 0.3478, 0.3318, 0.35, 0.3348, 0.3297]

						assert image.shape == (1, 5_12, 5_12, 3)

						assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-2

						lowerCAmelCase__					:    Union[str, Any]												=					torch.manual_seed(__lowerCAmelCase			)
						lowerCAmelCase__					:    Union[str, Any]												=					sd_pipe(
						    [prompt]		,generator=__lowerCAmelCase		,guidance_scale=__lowerCAmelCase		,num_inference_steps=50		,output_type='''np'''		,width=5_12		,height=5_12		,sld_guidance_scale=20_00		,sld_warmup_steps=7		,sld_threshold=0.025		,sld_momentum_scale=0.5		,sld_mom_beta=0.7		,)

						lowerCAmelCase__					:    Union[str, Any]												=					output.images
						lowerCAmelCase__					:    str												=					image[0, -3:, -3:, -1]
						lowerCAmelCase__					:    Union[str, Any]												=					[0.5531, 0.5206, 0.4895, 0.5156, 0.5182, 0.4751, 0.4802, 0.4803, 0.4443]

						assert image.shape == (1, 5_12, 5_12, 3)

						assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-2



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

						"""simple docstring"""






						lowerCAmelCase__					:    str												=					StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5'''			)
						lowerCAmelCase__					:    List[Any]												=					sd_pipe.to(__lowerCAmelCase			)
						sd_pipe.set_progress_bar_config(disable=__lowerCAmelCase			)

						lowerCAmelCase__					:    str												=					(
						    '''the four horsewomen of the apocalypse, painting by tom of finland, gaston bussiere, craig mullins, j. c.'''
						    ''' leyendecker'''
						)
						lowerCAmelCase__					:    Any												=					10_44_35_52_34
						lowerCAmelCase__					:    Dict												=					12

						lowerCAmelCase__					:    Dict												=					torch.manual_seed(__lowerCAmelCase			)
						lowerCAmelCase__					:    List[Any]												=					sd_pipe(
						    [prompt]		,generator=__lowerCAmelCase		,guidance_scale=__lowerCAmelCase		,num_inference_steps=50		,output_type='''np'''		,width=5_12		,height=5_12		,sld_guidance_scale=0		,)

						lowerCAmelCase__					:    Optional[Any]												=					output.images
						lowerCAmelCase__					:    List[str]												=					image[0, -3:, -3:, -1]
						lowerCAmelCase__					:    Optional[Any]												=					np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]			)

						assert image.shape == (1, 5_12, 5_12, 3)

						assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-7

						lowerCAmelCase__					:    Optional[Any]												=					torch.manual_seed(__lowerCAmelCase			)
						lowerCAmelCase__					:    Tuple												=					sd_pipe(
						    [prompt]		,generator=__lowerCAmelCase		,guidance_scale=__lowerCAmelCase		,num_inference_steps=50		,output_type='''np'''		,width=5_12		,height=5_12		,sld_guidance_scale=20_00		,sld_warmup_steps=7		,sld_threshold=0.025		,sld_momentum_scale=0.5		,sld_mom_beta=0.7		,)

						lowerCAmelCase__					:    Union[str, Any]												=					output.images
						lowerCAmelCase__					:    Union[str, Any]												=					image[0, -3:, -3:, -1]
						lowerCAmelCase__					:    Any												=					np.array([0.5818, 0.6285, 0.6835, 0.6019, 0.625, 0.6754, 0.6096, 0.6334, 0.6561]			)
						assert image.shape == (1, 5_12, 5_12, 3)

						assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-2










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






def      lowerCAmelCase__		(   lowerCamelCase_		:		ndarray):





				'''simple docstring'''

				return np.dot(lowerCamelCase_	,lowerCamelCase_)



class 	lowerCamelCase__      :




		'''simple docstring'''



		def __init__(self		,*,
		    __lowerCamelCase = np.inf		,__lowerCamelCase = "linear"		,__lowerCamelCase = 0.0		,)					->      None:

						"""simple docstring"""






						lowerCAmelCase__					:    Any												=					regularization
						lowerCAmelCase__					:    str												=					gamma
						if kernel == "linear":
										lowerCAmelCase__					:    Dict												=					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'''			)
										lowerCAmelCase__					:    Optional[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:
										lowerCAmelCase__					:    List[str]												=					f"""Unknown kernel: {kernel}"""
										raise ValueError(__lowerCamelCase			)



		def 		lowerCAmelCase__				(self		,__lowerCamelCase		,__lowerCamelCase			)					->      float:

						"""simple docstring"""






						return np.dot(__lowerCamelCase		,__lowerCamelCase			)



		def 		lowerCAmelCase__				(self		,__lowerCamelCase		,__lowerCamelCase			)					->      float:

						"""simple docstring"""






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



		def 		lowerCAmelCase__				(self		,__lowerCamelCase		,__lowerCamelCase			)					->      None:

						"""simple docstring"""






						lowerCAmelCase__					:    str												=					observations
						lowerCAmelCase__					:    Optional[int]												=					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

						((lowerCAmelCase__)						,		)					:    List[str]												=					np.shape(__lowerCamelCase			)

						def to_minimize(__lowerCamelCase			) -> float:
										lowerCAmelCase__					:    List[str]												=					0
										((lowerCAmelCase__)						,		)					:    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			)

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

						lowerCAmelCase__					:    int												=					minimize(
						    __lowerCamelCase		,np.ones(__lowerCamelCase			)		,bounds=__lowerCamelCase		,constraints=[ly_contraint]			).x
						lowerCAmelCase__					:    List[Any]												=					l_star

						# calculating mean offset of separation plane to points
						lowerCAmelCase__					:    Optional[Any]												=					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]			)
						lowerCAmelCase__					:    Dict												=					s / n



		def 		lowerCAmelCase__				(self		,__lowerCamelCase			)					->      int:

						"""simple docstring"""






						lowerCAmelCase__					:    str												=					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()

