






"""simple docstring"""
import inspect
import unittest
import warnings
from math import ceil, floor

from transformers import LevitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
			import torch

			from transformers import (
			    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
			    MODEL_MAPPING,
			    LevitForImageClassification,
			    LevitForImageClassificationWithTeacher,
			    LevitModel,
			)
			from transformers.models.levit.modeling_levit import LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
			from PIL import Image

			from transformers import LevitImageProcessor







class     UpperCamelCase_ (__A  ):





	def   _SCREAMING_SNAKE_CASE   (   self      :				List[Any]		)		->    int:
				UpperCAmelCase_  :				int      =      self.config_class(**self.inputs_dict		)
				self.parent.assertTrue(hasattr(lowerCAmelCase_			,			"hidden_sizes"		)		)
				self.parent.assertTrue(hasattr(lowerCAmelCase_			,			"num_attention_heads"		)		)







class     UpperCamelCase_ :





	def __init__(   self      :				str			,			lowerCAmelCase_      :				Tuple			,			lowerCAmelCase_      :				Optional[Any]=13			,			lowerCAmelCase_      :				List[str]=64			,			lowerCAmelCase_      :				int=3			,			lowerCAmelCase_      :				Optional[int]=3			,			lowerCAmelCase_      :				List[Any]=2			,			lowerCAmelCase_      :				Optional[int]=1			,			lowerCAmelCase_      :				List[str]=16			,			lowerCAmelCase_      :				int=[128, 256, 384]			,			lowerCAmelCase_      :				str=[4, 6, 8]			,			lowerCAmelCase_      :				int=[2, 3, 4]			,			lowerCAmelCase_      :				List[str]=[16, 16, 16]			,			lowerCAmelCase_      :				Any=0			,			lowerCAmelCase_      :				Dict=[2, 2, 2]			,			lowerCAmelCase_      :				Any=[2, 2, 2]			,			lowerCAmelCase_      :				Dict=0.0_2			,			lowerCAmelCase_      :				Tuple=True			,			lowerCAmelCase_      :				Tuple=True			,			lowerCAmelCase_      :				Tuple=2			,			)		->    int:
				UpperCAmelCase_  :				int      =      parent
				UpperCAmelCase_  :				List[Any]      =      batch_size
				UpperCAmelCase_  :				List[Any]      =      image_size
				UpperCAmelCase_  :				Union[str, Any]      =      num_channels
				UpperCAmelCase_  :				Tuple      =      kernel_size
				UpperCAmelCase_  :				Any      =      stride
				UpperCAmelCase_  :				Optional[int]      =      padding
				UpperCAmelCase_  :				int      =      hidden_sizes
				UpperCAmelCase_  :				Optional[Any]      =      num_attention_heads
				UpperCAmelCase_  :				Tuple      =      depths
				UpperCAmelCase_  :				List[Any]      =      key_dim
				UpperCAmelCase_  :				Dict      =      drop_path_rate
				UpperCAmelCase_  :				Union[str, Any]      =      patch_size
				UpperCAmelCase_  :				Any      =      attention_ratio
				UpperCAmelCase_  :				List[str]      =      mlp_ratio
				UpperCAmelCase_  :				str      =      initializer_range
				UpperCAmelCase_  :				Union[str, Any]      =      [
				    ["Subsample", key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
				    ["Subsample", key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
				]
				UpperCAmelCase_  :				Dict      =      is_training
				UpperCAmelCase_  :				Optional[int]      =      use_labels
				UpperCAmelCase_  :				Tuple      =      num_labels
				UpperCAmelCase_  :				List[Any]      =      initializer_range





	def   _SCREAMING_SNAKE_CASE   (   self      :				str		)		->    Optional[int]:
				UpperCAmelCase_  :				str      =      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

				UpperCAmelCase_  :				int      =      None
				if self.use_labels:
							UpperCAmelCase_  :				List[Any]      =      ids_tensor([self.batch_size]			,			self.num_labels		)

				UpperCAmelCase_  :				Tuple      =      self.get_config()
				return config, pixel_values, labels





	def   _SCREAMING_SNAKE_CASE   (   self      :				List[str]		)		->    Any:
				return LevitConfig(
				    image_size=self.image_size			,			num_channels=self.num_channels			,			kernel_size=self.kernel_size			,			stride=self.stride			,			padding=self.padding			,			patch_size=self.patch_size			,			hidden_sizes=self.hidden_sizes			,			num_attention_heads=self.num_attention_heads			,			depths=self.depths			,			key_dim=self.key_dim			,			drop_path_rate=self.drop_path_rate			,			mlp_ratio=self.mlp_ratio			,			attention_ratio=self.attention_ratio			,			initializer_range=self.initializer_range			,			down_ops=self.down_ops			,			)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Optional[int]			,			lowerCAmelCase_      :				Any			,			lowerCAmelCase_      :				str			,			lowerCAmelCase_      :				Tuple		)		->    Optional[int]:
				UpperCAmelCase_  :				Tuple      =      LevitModel(config=lowerCAmelCase_		)
				model.to(lowerCAmelCase_		)
				model.eval()
				UpperCAmelCase_  :				Optional[Any]      =      model(lowerCAmelCase_		)
				UpperCAmelCase_  :				List[str]      =      (self.image_size, self.image_size)
				UpperCAmelCase_  :				Tuple      =      image_size[0], image_size[1]
				for _ in range(4		):
							UpperCAmelCase_  :				str      =      floor(((height + 2 * self.padding - self.kernel_size) / self.stride) + 1		)
							UpperCAmelCase_  :				List[str]      =      floor(((width + 2 * self.padding - self.kernel_size) / self.stride) + 1		)
				self.parent.assertEqual(
				    result.last_hidden_state.shape			,			(self.batch_size, ceil(height / 4		) * ceil(width / 4		), self.hidden_sizes[-1])			,			)





	def   _SCREAMING_SNAKE_CASE   (   self      :				str			,			lowerCAmelCase_      :				int			,			lowerCAmelCase_      :				Dict			,			lowerCAmelCase_      :				str		)		->    List[str]:
				UpperCAmelCase_  :				List[Any]      =      self.num_labels
				UpperCAmelCase_  :				List[str]      =      LevitForImageClassification(lowerCAmelCase_		)
				model.to(lowerCAmelCase_		)
				model.eval()
				UpperCAmelCase_  :				Dict      =      model(lowerCAmelCase_			,			labels=lowerCAmelCase_		)
				self.parent.assertEqual(result.logits.shape			,			(self.batch_size, self.num_labels)		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Optional[Any]		)		->    Optional[Any]:
				UpperCAmelCase_  :				Tuple      =      self.prepare_config_and_inputs()
				UpperCAmelCase_  :				Optional[int]      =      config_and_inputs
				UpperCAmelCase_  :				Optional[int]      =      {"pixel_values": pixel_values}
				return config, inputs_dict







@require_torch
class     UpperCamelCase_ (__A       ,		__A       ,		unittest.TestCase  ):
	__magic_name__			   =       (
	    (LevitModel, LevitForImageClassification, LevitForImageClassificationWithTeacher)
	    if is_torch_available()
	    else ()
	)
	__magic_name__			   =       (
	    {
	        '''feature-extraction''': LevitModel,
	        '''image-classification''': (LevitForImageClassification, LevitForImageClassificationWithTeacher),
	    }
	    if is_torch_available()
	    else {}
	)

	__magic_name__			   =       False
	__magic_name__			   =       False
	__magic_name__			   =       False
	__magic_name__			   =       False
	__magic_name__			   =       False





	def   _SCREAMING_SNAKE_CASE   (   self      :				int		)		->    str:
				UpperCAmelCase_  :				List[Any]      =      LevitModelTester(self		)
				UpperCAmelCase_  :				Tuple      =      ConfigTester(self			,			config_class=lowerCAmelCase_			,			has_text_modality=lowerCAmelCase_			,			hidden_size=37		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				int		)		->    Any:
				self.create_and_test_config_common_properties()
				self.config_tester.create_and_test_config_to_json_string()
				self.config_tester.create_and_test_config_to_json_file()
				self.config_tester.create_and_test_config_from_and_save_pretrained()
				self.config_tester.create_and_test_config_with_num_labels()
				self.config_tester.check_config_can_be_init_without_params()
				self.config_tester.check_config_arguments_init()





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





	@unittest.skip(reason="Levit does not use inputs_embeds"		)
	def   _SCREAMING_SNAKE_CASE   (   self      :				int		)		->    Any:
				pass





	@unittest.skip(reason="Levit does not support input and output embeddings"		)
	def   _SCREAMING_SNAKE_CASE   (   self      :				Optional[int]		)		->    Any:
				pass





	@unittest.skip(reason="Levit does not output attentions"		)
	def   _SCREAMING_SNAKE_CASE   (   self      :				Tuple		)		->    int:
				pass





	def   _SCREAMING_SNAKE_CASE   (   self      :				Dict		)		->    Optional[Any]:
				UpperCAmelCase_  :				Optional[Any]      =      self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
							UpperCAmelCase_  :				Union[str, Any]      =      model_class(lowerCAmelCase_		)
							UpperCAmelCase_  :				str      =      inspect.signature(model.forward		)
							# signature.parameters is an OrderedDict => so arg_names order is deterministic
							UpperCAmelCase_  :				Dict      =      [*signature.parameters.keys()]

							UpperCAmelCase_  :				List[str]      =      ["pixel_values"]
							self.assertListEqual(arg_names[:1]			,			lowerCAmelCase_		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				int		)		->    List[Any]:
				def check_hidden_states_output(lowerCAmelCase_      :				int			,			lowerCAmelCase_      :				Dict			,			lowerCAmelCase_      :				Any		):
							UpperCAmelCase_  :				Optional[Any]      =      model_class(lowerCAmelCase_		)
							model.to(lowerCAmelCase_		)
							model.eval()

							with torch.no_grad():
										UpperCAmelCase_  :				Tuple      =      model(**self._prepare_for_class(lowerCAmelCase_			,			lowerCAmelCase_		)		)

							UpperCAmelCase_  :				Any      =      outputs.hidden_states

							UpperCAmelCase_  :				Dict      =      len(self.model_tester.depths		) + 1
							self.assertEqual(len(lowerCAmelCase_		)			,			lowerCAmelCase_		)

							UpperCAmelCase_  :				Union[str, Any]      =      (self.model_tester.image_size, self.model_tester.image_size)
							UpperCAmelCase_  :				Tuple      =      image_size[0], image_size[1]
							for _ in range(4		):
										UpperCAmelCase_  :				Dict      =      floor(
										    (
										        (height + 2 * self.model_tester.padding - self.model_tester.kernel_size)
										        / self.model_tester.stride
										    )
										    + 1		)
										UpperCAmelCase_  :				List[str]      =      floor(
										    (
										        (width + 2 * self.model_tester.padding - self.model_tester.kernel_size)
										        / self.model_tester.stride
										    )
										    + 1		)
							# verify the first hidden states (first block)
							self.assertListEqual(
							    list(hidden_states[0].shape[-2:]		)			,			[
							        height * width,
							        self.model_tester.hidden_sizes[0],
							    ]			,			)

				UpperCAmelCase_  :				Dict      =      self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
							UpperCAmelCase_  :				Any      =      True
							check_hidden_states_output(lowerCAmelCase_			,			lowerCAmelCase_			,			lowerCAmelCase_		)

							# check that output_hidden_states also work using config
							del inputs_dict["output_hidden_states"]
							UpperCAmelCase_  :				int      =      True

							check_hidden_states_output(lowerCAmelCase_			,			lowerCAmelCase_			,			lowerCAmelCase_		)





	@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."		)
	def   _SCREAMING_SNAKE_CASE   (   self      :				Optional[Any]		)		->    List[str]:
				pass





	def   _SCREAMING_SNAKE_CASE   (   self      :				Tuple			,			lowerCAmelCase_      :				Union[str, Any]			,			lowerCAmelCase_      :				Optional[int]			,			lowerCAmelCase_      :				Optional[Any]=False		)		->    Optional[Any]:
				UpperCAmelCase_  :				Tuple      =      super()._prepare_for_class(lowerCAmelCase_			,			lowerCAmelCase_			,			return_labels=lowerCAmelCase_		)

				if return_labels:
							if model_class.__name__ == "LevitForImageClassificationWithTeacher":
										del inputs_dict["labels"]

				return inputs_dict





	def   _SCREAMING_SNAKE_CASE   (   self      :				int		)		->    int:
				UpperCAmelCase_  :				Tuple      =      self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*lowerCAmelCase_		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Tuple		)		->    List[Any]:
				UpperCAmelCase_  :				str      =      self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase_		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Union[str, Any]		)		->    Optional[Any]:
				if not self.model_tester.is_training:
							return

				UpperCAmelCase_  :				Dict      =      self.model_tester.prepare_config_and_inputs_for_common()
				UpperCAmelCase_  :				Optional[int]      =      True

				for model_class in self.all_model_classes:
							# LevitForImageClassificationWithTeacher supports inference-only
							if (
							    model_class in get_values(lowerCAmelCase_		)
							    or model_class.__name__ == "LevitForImageClassificationWithTeacher"
							):
										continue
							UpperCAmelCase_  :				str      =      model_class(lowerCAmelCase_		)
							model.to(lowerCAmelCase_		)
							model.train()
							UpperCAmelCase_  :				int      =      self._prepare_for_class(lowerCAmelCase_			,			lowerCAmelCase_			,			return_labels=lowerCAmelCase_		)
							UpperCAmelCase_  :				List[Any]      =      model(**lowerCAmelCase_		).loss
							loss.backward()





	def   _SCREAMING_SNAKE_CASE   (   self      :				Any		)		->    Optional[Any]:
				UpperCAmelCase_  :				Union[str, Any]      =      self.model_tester.prepare_config_and_inputs_for_common()
				if not self.model_tester.is_training:
							return

				UpperCAmelCase_  :				Union[str, Any]      =      False
				UpperCAmelCase_  :				Any      =      True

				for model_class in self.all_model_classes:
							if model_class in get_values(lowerCAmelCase_		) or not model_class.supports_gradient_checkpointing:
										continue
							# LevitForImageClassificationWithTeacher supports inference-only
							if model_class.__name__ == "LevitForImageClassificationWithTeacher":
										continue
							UpperCAmelCase_  :				List[str]      =      model_class(lowerCAmelCase_		)
							model.gradient_checkpointing_enable()
							model.to(lowerCAmelCase_		)
							model.train()
							UpperCAmelCase_  :				int      =      self._prepare_for_class(lowerCAmelCase_			,			lowerCAmelCase_			,			return_labels=lowerCAmelCase_		)
							UpperCAmelCase_  :				Tuple      =      model(**lowerCAmelCase_		).loss
							loss.backward()





	def   _SCREAMING_SNAKE_CASE   (   self      :				Optional[Any]		)		->    Tuple:
				UpperCAmelCase_  :				Tuple      =      self.model_tester.prepare_config_and_inputs_for_common()

				UpperCAmelCase_  :				Tuple      =      [
				    {"title": "multi_label_classification", "num_labels": 2, "dtype": torch.float},
				    {"title": "single_label_classification", "num_labels": 1, "dtype": torch.long},
				    {"title": "regression", "num_labels": 1, "dtype": torch.float},
				]

				for model_class in self.all_model_classes:
							if (
							    model_class
							    not in [
							        *get_values(lowerCAmelCase_		),
							    ]
							    or model_class.__name__ == "LevitForImageClassificationWithTeacher"
							):
										continue

							for problem_type in problem_types:
										with self.subTest(msg=f"""Testing {model_class} with {problem_type['title']}"""		):
													UpperCAmelCase_  :				Union[str, Any]      =      problem_type["title"]
													UpperCAmelCase_  :				Any      =      problem_type["num_labels"]

													UpperCAmelCase_  :				str      =      model_class(lowerCAmelCase_		)
													model.to(lowerCAmelCase_		)
													model.train()

													UpperCAmelCase_  :				List[str]      =      self._prepare_for_class(lowerCAmelCase_			,			lowerCAmelCase_			,			return_labels=lowerCAmelCase_		)

													if problem_type["num_labels"] > 1:
																UpperCAmelCase_  :				Union[str, Any]      =      inputs["labels"].unsqueeze(1		).repeat(1			,			problem_type["num_labels"]		)

													UpperCAmelCase_  :				List[Any]      =      inputs["labels"].to(problem_type["dtype"]		)

													# This tests that we do not trigger the warning form PyTorch "Using a target size that is different
													# to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
													# they have the same size." which is a symptom something in wrong for the regression problem.
													# See https://github.com/huggingface/transformers/issues/11780
													with warnings.catch_warnings(record=lowerCAmelCase_		) as warning_list:
																UpperCAmelCase_  :				Union[str, Any]      =      model(**lowerCAmelCase_		).loss
													for w in warning_list:
																if "Using a target size that is different to the input size" in str(w.message		):
																			raise ValueError(
																			    f"""Something is going wrong in the regression problem: intercepted {w.message}"""		)

													loss.backward()





	@slow
	def   _SCREAMING_SNAKE_CASE   (   self      :				List[str]		)		->    Dict:
				for model_name in LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							UpperCAmelCase_  :				str      =      LevitModel.from_pretrained(lowerCAmelCase_		)
							self.assertIsNotNone(lowerCAmelCase_		)





def        snake_case   (     ):
			UpperCAmelCase_  :				Any      =      Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"		)
			return image







@require_torch
@require_vision
class     UpperCamelCase_ (unittest.TestCase  ):





	@cached_property
	def   _SCREAMING_SNAKE_CASE   (   self      :				Optional[int]		)		->    Any:
				return LevitImageProcessor.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]		)





	@slow
	def   _SCREAMING_SNAKE_CASE   (   self      :				Optional[Any]		)		->    Union[str, Any]:
				UpperCAmelCase_  :				str      =      LevitForImageClassificationWithTeacher.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]		).to(
				    lowerCAmelCase_		)

				UpperCAmelCase_  :				Union[str, Any]      =      self.default_image_processor
				UpperCAmelCase_  :				Tuple      =      prepare_img()
				UpperCAmelCase_  :				Any      =      image_processor(images=lowerCAmelCase_			,			return_tensors="pt"		).to(lowerCAmelCase_		)

				# forward pass
				with torch.no_grad():
							UpperCAmelCase_  :				Optional[Any]      =      model(**lowerCAmelCase_		)

				# verify the logits
				UpperCAmelCase_  :				Tuple      =      torch.Size((1, 1_000)		)
				self.assertEqual(outputs.logits.shape			,			lowerCAmelCase_		)

				UpperCAmelCase_  :				Dict      =      torch.tensor([1.0_4_4_8, -0.3_7_4_5, -1.8_3_1_7]		).to(lowerCAmelCase_		)

				self.assertTrue(torch.allclose(outputs.logits[0, :3]			,			lowerCAmelCase_			,			atol=1e-4		)		)











"""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()



