






'''simple docstring'''




import gc
import random
import unittest

import numpy as np
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import (
    DiffusionPipeline,
    UnCLIPImageVariationPipeline,
    UnCLIPScheduler,
    UNetaDConditionModel,
    UNetaDModel,
)
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, load_image, require_torch_gpu, skip_mps

from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()







class       __lowercase					(__lowerCamelCase   ,				unittest.TestCase    ):
	_lowerCamelCase              =  UnCLIPImageVariationPipeline
	_lowerCamelCase              =  IMAGE_VARIATION_PARAMS - {'''height''', '''width''', '''guidance_scale'''}
	_lowerCamelCase              =  IMAGE_VARIATION_BATCH_PARAMS

	_lowerCamelCase              =  [
	    '''generator''',
	    '''return_dict''',
	    '''decoder_num_inference_steps''',
	    '''super_res_num_inference_steps''',
	]
	_lowerCamelCase              =  False

	@property
	def      __UpperCamelCase							(      self   :						Tuple):
			return 32

	@property
	def      __UpperCamelCase							(      self   :						Tuple):
			return 32

	@property
	def      __UpperCamelCase							(      self   :						int):
			return self.time_input_dim

	@property
	def      __UpperCamelCase							(      self   :						Any):
			return self.time_input_dim * 4

	@property
	def      __UpperCamelCase							(      self   :						List[Any]):
			return 100

	@property
	def      __UpperCamelCase							(      self   :						Any):
			UpperCamelCase__				:					Any					=					CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip')
			return tokenizer

	@property
	def      __UpperCamelCase							(      self   :						Union[str, Any]):
			torch.manual_seed(0)
			UpperCamelCase__				:					Union[str, Any]					=					CLIPTextConfig(
			    bos_token_id=0  ,					eos_token_id=2  ,					hidden_size=self.text_embedder_hidden_size  ,					projection_dim=self.text_embedder_hidden_size  ,					intermediate_size=37  ,					layer_norm_eps=1e-05  ,					num_attention_heads=4  ,					num_hidden_layers=5  ,					pad_token_id=1  ,					vocab_size=1_000  ,					)
			return CLIPTextModelWithProjection(UpperCAmelCase_)

	@property
	def      __UpperCamelCase							(      self   :						Any):
			torch.manual_seed(0)
			UpperCamelCase__				:					Tuple					=					CLIPVisionConfig(
			    hidden_size=self.text_embedder_hidden_size  ,					projection_dim=self.text_embedder_hidden_size  ,					num_hidden_layers=5  ,					num_attention_heads=4  ,					image_size=32  ,					intermediate_size=37  ,					patch_size=1  ,					)
			return CLIPVisionModelWithProjection(UpperCAmelCase_)

	@property
	def      __UpperCamelCase							(      self   :						Any):
			torch.manual_seed(0)

			UpperCamelCase__				:					Dict					=					{
			    'clip_embeddings_dim': self.text_embedder_hidden_size,
			    'time_embed_dim': self.time_embed_dim,
			    'cross_attention_dim': self.cross_attention_dim,
			}

			UpperCamelCase__				:					int					=					UnCLIPTextProjModel(**UpperCAmelCase_)
			return model

	@property
	def      __UpperCamelCase							(      self   :						Union[str, Any]):
			torch.manual_seed(0)

			UpperCamelCase__				:					str					=					{
			    'sample_size': 32,
			    # RGB in channels
			    'in_channels': 3,
			    # Out channels is double in channels because predicts mean and variance
			    'out_channels': 6,
			    'down_block_types': ('ResnetDownsampleBlock2D', 'SimpleCrossAttnDownBlock2D'),
			    'up_block_types': ('SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'),
			    'mid_block_type': 'UNetMidBlock2DSimpleCrossAttn',
			    'block_out_channels': (self.block_out_channels_a, self.block_out_channels_a * 2),
			    'layers_per_block': 1,
			    'cross_attention_dim': self.cross_attention_dim,
			    'attention_head_dim': 4,
			    'resnet_time_scale_shift': 'scale_shift',
			    'class_embed_type': 'identity',
			}

			UpperCamelCase__				:					Optional[Any]					=					UNetaDConditionModel(**UpperCAmelCase_)
			return model

	@property
	def      __UpperCamelCase							(      self   :						List[str]):
			return {
			    "sample_size": 64,
			    "layers_per_block": 1,
			    "down_block_types": ("ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D"),
			    "up_block_types": ("ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D"),
			    "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
			    "in_channels": 6,
			    "out_channels": 3,
			}

	@property
	def      __UpperCamelCase							(      self   :						List[Any]):
			torch.manual_seed(0)

			UpperCamelCase__				:					Any					=					UNetaDModel(**self.dummy_super_res_kwargs)
			return model

	@property
	def      __UpperCamelCase							(      self   :						Optional[Any]):
			# seeded differently to get different unet than `self.dummy_super_res_first`
			torch.manual_seed(1)

			UpperCamelCase__				:					Any					=					UNetaDModel(**self.dummy_super_res_kwargs)
			return model

	def      __UpperCamelCase							(      self   :						int):
			UpperCamelCase__				:					Optional[int]					=					self.dummy_decoder
			UpperCamelCase__				:					Dict					=					self.dummy_text_proj
			UpperCamelCase__				:					int					=					self.dummy_text_encoder
			UpperCamelCase__				:					Tuple					=					self.dummy_tokenizer
			UpperCamelCase__				:					Optional[Any]					=					self.dummy_super_res_first
			UpperCamelCase__				:					List[str]					=					self.dummy_super_res_last

			UpperCamelCase__				:					Optional[Any]					=					UnCLIPScheduler(
			    variance_type='learned_range'  ,					prediction_type='epsilon'  ,					num_train_timesteps=1_000  ,					)

			UpperCamelCase__				:					Tuple					=					UnCLIPScheduler(
			    variance_type='fixed_small_log'  ,					prediction_type='epsilon'  ,					num_train_timesteps=1_000  ,					)

			UpperCamelCase__				:					List[Any]					=					CLIPImageProcessor(crop_size=32  ,					size=32)

			UpperCamelCase__				:					Optional[Any]					=					self.dummy_image_encoder

			return {
			    "decoder": decoder,
			    "text_encoder": text_encoder,
			    "tokenizer": tokenizer,
			    "text_proj": text_proj,
			    "feature_extractor": feature_extractor,
			    "image_encoder": image_encoder,
			    "super_res_first": super_res_first,
			    "super_res_last": super_res_last,
			    "decoder_scheduler": decoder_scheduler,
			    "super_res_scheduler": super_res_scheduler,
			}

	def      __UpperCamelCase							(      self   :						Union[str, Any]  ,					UpperCAmelCase_   :						Tuple  ,					UpperCAmelCase_   :						Union[str, Any]=0  ,					UpperCAmelCase_   :						Dict=True):
			UpperCamelCase__				:					Optional[int]					=					floats_tensor((1, 3, 32, 32)  ,					rng=random.Random(UpperCAmelCase_)).to(UpperCAmelCase_)
			if str(UpperCAmelCase_).startswith('mps'):
					UpperCamelCase__				:					Optional[int]					=					torch.manual_seed(UpperCAmelCase_)
			else:
					UpperCamelCase__				:					str					=					torch.Generator(device=UpperCAmelCase_).manual_seed(UpperCAmelCase_)

			if pil_image:
					UpperCamelCase__				:					List[Any]					=					input_image * 0.5 + 0.5
					UpperCamelCase__				:					str					=					input_image.clamp(0  ,					1)
					UpperCamelCase__				:					Optional[Any]					=					input_image.cpu().permute(0  ,					2  ,					3  ,					1).float().numpy()
					UpperCamelCase__				:					int					=					DiffusionPipeline.numpy_to_pil(UpperCAmelCase_)[0]

			return {
			    "image": input_image,
			    "generator": generator,
			    "decoder_num_inference_steps": 2,
			    "super_res_num_inference_steps": 2,
			    "output_type": "np",
			}

	def      __UpperCamelCase							(      self   :						List[str]):
			UpperCamelCase__				:					Any					=					'cpu'

			UpperCamelCase__				:					Optional[Any]					=					self.get_dummy_components()

			UpperCamelCase__				:					Dict					=					self.pipeline_class(**UpperCAmelCase_)
			UpperCamelCase__				:					int					=					pipe.to(UpperCAmelCase_)

			pipe.set_progress_bar_config(disable=UpperCAmelCase_)

			UpperCamelCase__				:					Optional[int]					=					self.get_dummy_inputs(UpperCAmelCase_  ,					pil_image=UpperCAmelCase_)

			UpperCamelCase__				:					Any					=					pipe(**UpperCAmelCase_)
			UpperCamelCase__				:					int					=					output.images

			UpperCamelCase__				:					Dict					=					self.get_dummy_inputs(UpperCAmelCase_  ,					pil_image=UpperCAmelCase_)

			UpperCamelCase__				:					List[str]					=					pipe(
			    **UpperCAmelCase_  ,					return_dict=UpperCAmelCase_  ,					)[0]

			UpperCamelCase__				:					int					=					image[0, -3:, -3:, -1]
			UpperCamelCase__				:					Dict					=					image_from_tuple[0, -3:, -3:, -1]

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

			UpperCamelCase__				:					int					=					np.array(
			    [
			        0.99_97,
			        0.00_02,
			        0.99_97,
			        0.99_97,
			        0.99_69,
			        0.00_23,
			        0.99_97,
			        0.99_69,
			        0.99_70,
			    ])

			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      __UpperCamelCase							(      self   :						Dict):
			UpperCamelCase__				:					Tuple					=					'cpu'

			UpperCamelCase__				:					Dict					=					self.get_dummy_components()

			UpperCamelCase__				:					Any					=					self.pipeline_class(**UpperCAmelCase_)
			UpperCamelCase__				:					Any					=					pipe.to(UpperCAmelCase_)

			pipe.set_progress_bar_config(disable=UpperCAmelCase_)

			UpperCamelCase__				:					List[str]					=					self.get_dummy_inputs(UpperCAmelCase_  ,					pil_image=UpperCAmelCase_)

			UpperCamelCase__				:					Union[str, Any]					=					pipe(**UpperCAmelCase_)
			UpperCamelCase__				:					Optional[Any]					=					output.images

			UpperCamelCase__				:					Union[str, Any]					=					self.get_dummy_inputs(UpperCAmelCase_  ,					pil_image=UpperCAmelCase_)

			UpperCamelCase__				:					Dict					=					pipe(
			    **UpperCAmelCase_  ,					return_dict=UpperCAmelCase_  ,					)[0]

			UpperCamelCase__				:					Union[str, Any]					=					image[0, -3:, -3:, -1]
			UpperCamelCase__				:					str					=					image_from_tuple[0, -3:, -3:, -1]

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

			UpperCamelCase__				:					Dict					=					np.array([0.99_97, 0.00_03, 0.99_97, 0.99_97, 0.99_70, 0.00_24, 0.99_97, 0.99_71, 0.99_71])

			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      __UpperCamelCase							(      self   :						Optional[Any]):
			UpperCamelCase__				:					int					=					'cpu'

			UpperCamelCase__				:					int					=					self.get_dummy_components()

			UpperCamelCase__				:					Tuple					=					self.pipeline_class(**UpperCAmelCase_)
			UpperCamelCase__				:					Dict					=					pipe.to(UpperCAmelCase_)

			pipe.set_progress_bar_config(disable=UpperCAmelCase_)

			UpperCamelCase__				:					Dict					=					self.get_dummy_inputs(UpperCAmelCase_  ,					pil_image=UpperCAmelCase_)
			UpperCamelCase__				:					Dict					=					[
			    pipeline_inputs['image'],
			    pipeline_inputs['image'],
			]

			UpperCamelCase__				:					int					=					pipe(**UpperCAmelCase_)
			UpperCamelCase__				:					Optional[int]					=					output.images

			UpperCamelCase__				:					Tuple					=					self.get_dummy_inputs(UpperCAmelCase_  ,					pil_image=UpperCAmelCase_)
			UpperCamelCase__				:					List[str]					=					[
			    tuple_pipeline_inputs['image'],
			    tuple_pipeline_inputs['image'],
			]

			UpperCamelCase__				:					str					=					pipe(
			    **UpperCAmelCase_  ,					return_dict=UpperCAmelCase_  ,					)[0]

			UpperCamelCase__				:					str					=					image[0, -3:, -3:, -1]
			UpperCamelCase__				:					Optional[int]					=					image_from_tuple[0, -3:, -3:, -1]

			assert image.shape == (2, 64, 64, 3)

			UpperCamelCase__				:					Any					=					np.array(
			    [
			        0.99_97,
			        0.99_89,
			        0.00_08,
			        0.00_21,
			        0.99_60,
			        0.00_18,
			        0.00_14,
			        0.00_02,
			        0.99_33,
			    ])

			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      __UpperCamelCase							(      self   :						Dict):
			UpperCamelCase__				:					List[Any]					=					torch.device('cpu')







			class       __lowercase					:
				_lowerCamelCase              =  1


			UpperCamelCase__				:					List[Any]					=					self.get_dummy_components()

			UpperCamelCase__				:					Dict					=					self.pipeline_class(**UpperCAmelCase_)
			UpperCamelCase__				:					str					=					pipe.to(UpperCAmelCase_)

			pipe.set_progress_bar_config(disable=UpperCAmelCase_)

			UpperCamelCase__				:					Dict					=					torch.Generator(device=UpperCAmelCase_).manual_seed(0)
			UpperCamelCase__				:					Union[str, Any]					=					pipe.decoder.dtype
			UpperCamelCase__				:					Optional[int]					=					1

			UpperCamelCase__				:					Optional[Any]					=					(
			    batch_size,
			    pipe.decoder.config.in_channels,
			    pipe.decoder.config.sample_size,
			    pipe.decoder.config.sample_size,
			)
			UpperCamelCase__				:					Optional[int]					=					pipe.prepare_latents(
			    UpperCAmelCase_  ,					dtype=UpperCAmelCase_  ,					device=UpperCAmelCase_  ,					generator=UpperCAmelCase_  ,					latents=UpperCAmelCase_  ,					scheduler=DummyScheduler())

			UpperCamelCase__				:					Any					=					(
			    batch_size,
			    pipe.super_res_first.config.in_channels // 2,
			    pipe.super_res_first.config.sample_size,
			    pipe.super_res_first.config.sample_size,
			)
			UpperCamelCase__				:					str					=					pipe.prepare_latents(
			    UpperCAmelCase_  ,					dtype=UpperCAmelCase_  ,					device=UpperCAmelCase_  ,					generator=UpperCAmelCase_  ,					latents=UpperCAmelCase_  ,					scheduler=DummyScheduler())

			UpperCamelCase__				:					List[str]					=					self.get_dummy_inputs(UpperCAmelCase_  ,					pil_image=UpperCAmelCase_)

			UpperCamelCase__				:					Union[str, Any]					=					pipe(
			    **UpperCAmelCase_  ,					decoder_latents=UpperCAmelCase_  ,					super_res_latents=UpperCAmelCase_).images

			UpperCamelCase__				:					int					=					self.get_dummy_inputs(UpperCAmelCase_  ,					pil_image=UpperCAmelCase_)
			# Don't pass image, instead pass embedding
			UpperCamelCase__				:					Dict					=					pipeline_inputs.pop('image')
			UpperCamelCase__				:					int					=					pipe.image_encoder(UpperCAmelCase_).image_embeds

			UpperCamelCase__				:					int					=					pipe(
			    **UpperCAmelCase_  ,					decoder_latents=UpperCAmelCase_  ,					super_res_latents=UpperCAmelCase_  ,					image_embeddings=UpperCAmelCase_  ,					).images

			# make sure passing text embeddings manually is identical
			assert np.abs(img_out_a - img_out_a).max() < 1e-4

	@skip_mps
	def      __UpperCamelCase							(      self   :						Dict):
			UpperCamelCase__				:					Optional[int]					=					torch_device == 'cpu'

			# Check is relaxed because there is not a torch 2.0 sliced attention added kv processor
			UpperCamelCase__				:					Optional[Any]					=					1e-2

			self._test_attention_slicing_forward_pass(
			    test_max_difference=UpperCAmelCase_  ,					expected_max_diff=UpperCAmelCase_)

	@skip_mps
	def      __UpperCamelCase							(      self   :						str):
			UpperCamelCase__				:					List[Any]					=					torch_device == 'cpu'
			UpperCamelCase__				:					Dict					=					True
			UpperCamelCase__				:					Optional[int]					=					[
			    'decoder_num_inference_steps',
			    'super_res_num_inference_steps',
			]

			self._test_inference_batch_single_identical(
			    test_max_difference=UpperCAmelCase_  ,					relax_max_difference=UpperCAmelCase_  ,					additional_params_copy_to_batched_inputs=UpperCAmelCase_  ,					)

	def      __UpperCamelCase							(      self   :						Any):
			UpperCamelCase__				:					List[Any]					=					[
			    'decoder_num_inference_steps',
			    'super_res_num_inference_steps',
			]

			if torch_device == "mps":
					# TODO: MPS errors with larger batch sizes
					UpperCamelCase__				:					Tuple					=					[2, 3]
					self._test_inference_batch_consistent(
					    batch_sizes=UpperCAmelCase_  ,					additional_params_copy_to_batched_inputs=UpperCAmelCase_  ,					)
			else:
					self._test_inference_batch_consistent(
					    additional_params_copy_to_batched_inputs=UpperCAmelCase_)

	@skip_mps
	def      __UpperCamelCase							(      self   :						List[Any]):
			return super().test_dict_tuple_outputs_equivalent()

	@skip_mps
	def      __UpperCamelCase							(      self   :						str):
			return super().test_save_load_local()





	@skip_mps
	def      __UpperCamelCase							(      self   :						Optional[Any]):
			return super().test_save_load_optional_components()







@slow
@require_torch_gpu
class       __lowercase					(unittest.TestCase    ):

	def      __UpperCamelCase							(      self   :						Optional[int]):
			# clean up the VRAM after each test
			super().tearDown()
			gc.collect()
			torch.cuda.empty_cache()





	def      __UpperCamelCase							(      self   :						Tuple):
			UpperCamelCase__				:					Optional[int]					=					load_image(
			    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unclip/cat.png')
			UpperCamelCase__				:					Dict					=					load_numpy(
			    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
			    '/unclip/karlo_v1_alpha_cat_variation_fp16.npy')

			UpperCamelCase__				:					Any					=					UnCLIPImageVariationPipeline.from_pretrained(
			    'kakaobrain/karlo-v1-alpha-image-variations'  ,					torch_dtype=torch.floataa)
			UpperCamelCase__				:					Dict					=					pipeline.to(UpperCAmelCase_)
			pipeline.set_progress_bar_config(disable=UpperCAmelCase_)

			UpperCamelCase__				:					List[Any]					=					torch.Generator(device='cpu').manual_seed(0)
			UpperCamelCase__				:					Optional[int]					=					pipeline(
			    UpperCAmelCase_  ,					generator=UpperCAmelCase_  ,					output_type='np'  ,					)

			UpperCamelCase__				:					List[Any]					=					output.images[0]

			assert image.shape == (256, 256, 3)

			assert_mean_pixel_difference(UpperCAmelCase_  ,					UpperCAmelCase_  ,					15)












'''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()




