






"""simple docstring"""
import inspect
import unittest

from transformers import SegformerConfig, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, 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_MAPPING,
			    SegformerForImageClassification,
			    SegformerForSemanticSegmentation,
			    SegformerModel,
			)
			from transformers.models.segformer.modeling_segformer import SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
			from PIL import Image

			from transformers import SegformerImageProcessor







class     UpperCamelCase_ (UpperCamelCase__  ):





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







class     UpperCamelCase_ :





	def __init__(   self      :				List[str]			,			lowerCAmelCase_      :				Optional[Any]			,			lowerCAmelCase_      :				Tuple=13			,			lowerCAmelCase_      :				List[Any]=64			,			lowerCAmelCase_      :				int=3			,			lowerCAmelCase_      :				Dict=4			,			lowerCAmelCase_      :				str=[2, 2, 2, 2]			,			lowerCAmelCase_      :				Optional[Any]=[8, 4, 2, 1]			,			lowerCAmelCase_      :				str=[16, 32, 64, 128]			,			lowerCAmelCase_      :				Any=[1, 4, 8, 16]			,			lowerCAmelCase_      :				Tuple=[1, 2, 4, 8]			,			lowerCAmelCase_      :				List[Any]=True			,			lowerCAmelCase_      :				List[str]=True			,			lowerCAmelCase_      :				str="gelu"			,			lowerCAmelCase_      :				str=0.1			,			lowerCAmelCase_      :				Dict=0.1			,			lowerCAmelCase_      :				Optional[Any]=0.0_2			,			lowerCAmelCase_      :				Union[str, Any]=3			,			lowerCAmelCase_      :				Any=None			,			)		->    List[Any]:
				UpperCAmelCase_  :				str      =      parent
				UpperCAmelCase_  :				Union[str, Any]      =      batch_size
				UpperCAmelCase_  :				Optional[int]      =      image_size
				UpperCAmelCase_  :				str      =      num_channels
				UpperCAmelCase_  :				List[str]      =      num_encoder_blocks
				UpperCAmelCase_  :				str      =      sr_ratios
				UpperCAmelCase_  :				int      =      depths
				UpperCAmelCase_  :				Dict      =      hidden_sizes
				UpperCAmelCase_  :				Union[str, Any]      =      downsampling_rates
				UpperCAmelCase_  :				int      =      num_attention_heads
				UpperCAmelCase_  :				int      =      is_training
				UpperCAmelCase_  :				Optional[int]      =      use_labels
				UpperCAmelCase_  :				List[str]      =      hidden_act
				UpperCAmelCase_  :				Union[str, Any]      =      hidden_dropout_prob
				UpperCAmelCase_  :				Any      =      attention_probs_dropout_prob
				UpperCAmelCase_  :				Any      =      initializer_range
				UpperCAmelCase_  :				Tuple      =      num_labels
				UpperCAmelCase_  :				int      =      scope





	def   _SCREAMING_SNAKE_CASE   (   self      :				str		)		->    List[Any]:
				UpperCAmelCase_  :				Tuple      =      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

				UpperCAmelCase_  :				List[str]      =      None
				if self.use_labels:
							UpperCAmelCase_  :				Tuple      =      ids_tensor([self.batch_size, self.image_size, self.image_size]			,			self.num_labels		)

				UpperCAmelCase_  :				List[Any]      =      self.get_config()
				return config, pixel_values, labels





	def   _SCREAMING_SNAKE_CASE   (   self      :				Dict		)		->    List[Any]:
				return SegformerConfig(
				    image_size=self.image_size			,			num_channels=self.num_channels			,			num_encoder_blocks=self.num_encoder_blocks			,			depths=self.depths			,			hidden_sizes=self.hidden_sizes			,			num_attention_heads=self.num_attention_heads			,			hidden_act=self.hidden_act			,			hidden_dropout_prob=self.hidden_dropout_prob			,			attention_probs_dropout_prob=self.attention_probs_dropout_prob			,			initializer_range=self.initializer_range			,			)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Optional[int]			,			lowerCAmelCase_      :				Tuple			,			lowerCAmelCase_      :				Optional[int]			,			lowerCAmelCase_      :				str		)		->    Tuple:
				UpperCAmelCase_  :				Tuple      =      SegformerModel(config=__A		)
				model.to(__A		)
				model.eval()
				UpperCAmelCase_  :				List[str]      =      model(__A		)
				UpperCAmelCase_  :				int      =      self.image_size // (self.downsampling_rates[-1] * 2)
				self.parent.assertEqual(
				    result.last_hidden_state.shape			,			(self.batch_size, self.hidden_sizes[-1], expected_height, expected_width)		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Tuple			,			lowerCAmelCase_      :				Optional[Any]			,			lowerCAmelCase_      :				Any			,			lowerCAmelCase_      :				Union[str, Any]		)		->    str:
				UpperCAmelCase_  :				Optional[Any]      =      self.num_labels
				UpperCAmelCase_  :				Optional[Any]      =      SegformerForSemanticSegmentation(__A		)
				model.to(__A		)
				model.eval()
				UpperCAmelCase_  :				int      =      model(__A		)
				self.parent.assertEqual(
				    result.logits.shape			,			(self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4)		)
				UpperCAmelCase_  :				Optional[int]      =      model(__A			,			labels=__A		)
				self.parent.assertEqual(
				    result.logits.shape			,			(self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4)		)
				self.parent.assertGreater(result.loss			,			0.0		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Optional[int]			,			lowerCAmelCase_      :				List[Any]			,			lowerCAmelCase_      :				int			,			lowerCAmelCase_      :				List[str]		)		->    Dict:
				UpperCAmelCase_  :				List[Any]      =      1
				UpperCAmelCase_  :				Tuple      =      SegformerForSemanticSegmentation(config=__A		)
				model.to(__A		)
				model.eval()
				UpperCAmelCase_  :				Optional[Any]      =      torch.randint(0			,			1			,			(self.batch_size, self.image_size, self.image_size)		).to(__A		)
				UpperCAmelCase_  :				List[str]      =      model(__A			,			labels=__A		)
				self.parent.assertGreater(result.loss			,			0.0		)





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







@require_torch
class     UpperCamelCase_ (UpperCamelCase__       ,		UpperCamelCase__       ,		unittest.TestCase  ):
	__magic_name__			   =       (
	    (
	        SegformerModel,
	        SegformerForSemanticSegmentation,
	        SegformerForImageClassification,
	    )
	    if is_torch_available()
	    else ()
	)
	__magic_name__			   =       (
	    {
	        '''feature-extraction''': SegformerModel,
	        '''image-classification''': SegformerForImageClassification,
	        '''image-segmentation''': SegformerForSemanticSegmentation,
	    }
	    if is_torch_available()
	    else {}
	)

	__magic_name__			   =       True
	__magic_name__			   =       False
	__magic_name__			   =       False
	__magic_name__			   =       False





	def   _SCREAMING_SNAKE_CASE   (   self      :				int		)		->    Tuple:
				UpperCAmelCase_  :				Optional[Any]      =      SegformerModelTester(self		)
				UpperCAmelCase_  :				str      =      SegformerConfigTester(self			,			config_class=__A		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Any		)		->    int:
				self.config_tester.run_common_tests()





	def   _SCREAMING_SNAKE_CASE   (   self      :				List[Any]		)		->    Union[str, Any]:
				UpperCAmelCase_  :				List[str]      =      self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*__A		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Optional[Any]		)		->    Dict:
				UpperCAmelCase_  :				Dict      =      self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_binary_image_segmentation(*__A		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Union[str, Any]		)		->    Optional[Any]:
				UpperCAmelCase_  :				int      =      self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_image_segmentation(*__A		)





	@unittest.skip("SegFormer does not use inputs_embeds"		)
	def   _SCREAMING_SNAKE_CASE   (   self      :				List[str]		)		->    Optional[Any]:
				pass





	@unittest.skip("SegFormer does not have get_input_embeddings method and get_output_embeddings methods"		)
	def   _SCREAMING_SNAKE_CASE   (   self      :				Union[str, Any]		)		->    Any:
				pass





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

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

							UpperCAmelCase_  :				Union[str, Any]      =      ["pixel_values"]
							self.assertListEqual(arg_names[:1]			,			__A		)





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

				for model_class in self.all_model_classes:
							UpperCAmelCase_  :				Tuple      =      True
							UpperCAmelCase_  :				Any      =      False
							UpperCAmelCase_  :				Dict      =      True
							UpperCAmelCase_  :				List[str]      =      model_class(__A		)
							model.to(__A		)
							model.eval()
							with torch.no_grad():
										UpperCAmelCase_  :				List[str]      =      model(**self._prepare_for_class(__A			,			__A		)		)
							UpperCAmelCase_  :				List[str]      =      outputs.attentions

							UpperCAmelCase_  :				Tuple      =      sum(self.model_tester.depths		)
							self.assertEqual(len(__A		)			,			__A		)

							# check that output_attentions also work using config
							del inputs_dict["output_attentions"]
							UpperCAmelCase_  :				int      =      True
							UpperCAmelCase_  :				Optional[int]      =      model_class(__A		)
							model.to(__A		)
							model.eval()
							with torch.no_grad():
										UpperCAmelCase_  :				Union[str, Any]      =      model(**self._prepare_for_class(__A			,			__A		)		)
							UpperCAmelCase_  :				Union[str, Any]      =      outputs.attentions

							self.assertEqual(len(__A		)			,			__A		)

							# verify the first attentions (first block, first layer)
							UpperCAmelCase_  :				Tuple      =      (self.model_tester.image_size // 4) ** 2
							UpperCAmelCase_  :				Any      =      (self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
							self.assertListEqual(
							    list(attentions[0].shape[-3:]		)			,			[self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]			,			)

							# verify the last attentions (last block, last layer)
							UpperCAmelCase_  :				Optional[Any]      =      (self.model_tester.image_size // 32) ** 2
							UpperCAmelCase_  :				List[str]      =      (self.model_tester.image_size // (32 * self.model_tester.sr_ratios[-1])) ** 2
							self.assertListEqual(
							    list(attentions[-1].shape[-3:]		)			,			[self.model_tester.num_attention_heads[-1], expected_seq_len, expected_reduced_seq_len]			,			)
							UpperCAmelCase_  :				Union[str, Any]      =      len(__A		)

							# Check attention is always last and order is fine
							UpperCAmelCase_  :				Optional[Any]      =      True
							UpperCAmelCase_  :				Tuple      =      True
							UpperCAmelCase_  :				Union[str, Any]      =      model_class(__A		)
							model.to(__A		)
							model.eval()
							with torch.no_grad():
										UpperCAmelCase_  :				Optional[int]      =      model(**self._prepare_for_class(__A			,			__A		)		)

							self.assertEqual(out_len + 1			,			len(__A		)		)

							UpperCAmelCase_  :				Optional[int]      =      outputs.attentions

							self.assertEqual(len(__A		)			,			__A		)
							# verify the first attentions (first block, first layer)
							UpperCAmelCase_  :				Optional[int]      =      (self.model_tester.image_size // 4) ** 2
							UpperCAmelCase_  :				Dict      =      (self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
							self.assertListEqual(
							    list(self_attentions[0].shape[-3:]		)			,			[self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]			,			)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Dict		)		->    Dict:



				def check_hidden_states_output(lowerCAmelCase_      :				Optional[Any]			,			lowerCAmelCase_      :				str			,			lowerCAmelCase_      :				List[str]		):
							UpperCAmelCase_  :				Optional[Any]      =      model_class(__A		)
							model.to(__A		)
							model.eval()

							with torch.no_grad():
										UpperCAmelCase_  :				Union[str, Any]      =      model(**self._prepare_for_class(__A			,			__A		)		)

							UpperCAmelCase_  :				Union[str, Any]      =      outputs.hidden_states

							UpperCAmelCase_  :				int      =      self.model_tester.num_encoder_blocks
							self.assertEqual(len(__A		)			,			__A		)

							# verify the first hidden states (first block)
							self.assertListEqual(
							    list(hidden_states[0].shape[-3:]		)			,			[
							        self.model_tester.hidden_sizes[0],
							        self.model_tester.image_size // 4,
							        self.model_tester.image_size // 4,
							    ]			,			)

				UpperCAmelCase_  ,			UpperCAmelCase_  :				List[str]      =      self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
							UpperCAmelCase_  :				Optional[Any]      =      True
							check_hidden_states_output(__A			,			__A			,			__A		)

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

							check_hidden_states_output(__A			,			__A			,			__A		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				str		)		->    List[Any]:



				if not self.model_tester.is_training:
							return

				UpperCAmelCase_  ,			UpperCAmelCase_  :				Any      =      self.model_tester.prepare_config_and_inputs_for_common()
				UpperCAmelCase_  :				Tuple      =      True

				for model_class in self.all_model_classes:
							if model_class in get_values(__A		):
										continue

							UpperCAmelCase_  :				Optional[Any]      =      model_class(__A		)
							model.to(__A		)
							model.train()
							UpperCAmelCase_  :				Optional[int]      =      self._prepare_for_class(__A			,			__A			,			return_labels=__A		)
							UpperCAmelCase_  :				List[Any]      =      model(**__A		).loss
							loss.backward()





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





	@slow
	def   _SCREAMING_SNAKE_CASE   (   self      :				List[str]		)		->    List[str]:



				for model_name in SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							UpperCAmelCase_  :				Any      =      SegformerModel.from_pretrained(__A		)
							self.assertIsNotNone(__A		)





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







@require_torch
class     UpperCamelCase_ (unittest.TestCase  ):





	@slow
	def   _SCREAMING_SNAKE_CASE   (   self      :				Optional[int]		)		->    Tuple:
				UpperCAmelCase_  :				Dict      =      SegformerImageProcessor(
				    image_scale=(512, 512)			,			keep_ratio=__A			,			align=__A			,			do_random_crop=__A		)
				UpperCAmelCase_  :				Any      =      SegformerForSemanticSegmentation.from_pretrained("nvidia/segformer-b0-finetuned-ade-512-512"		).to(
				    __A		)

				UpperCAmelCase_  :				Union[str, Any]      =      prepare_img()
				UpperCAmelCase_  :				str      =      image_processor(images=__A			,			return_tensors="pt"		)
				UpperCAmelCase_  :				Optional[Any]      =      encoded_inputs.pixel_values.to(__A		)

				with torch.no_grad():
							UpperCAmelCase_  :				Any      =      model(__A		)

				UpperCAmelCase_  :				Tuple      =      torch.Size((1, model.config.num_labels, 128, 128)		)
				self.assertEqual(outputs.logits.shape			,			__A		)

				UpperCAmelCase_  :				int      =      torch.tensor(
				    [
				        [[-4.6_3_1_0, -5.5_2_3_2, -6.2_3_5_6], [-5.1_9_2_1, -6.1_4_4_4, -6.5_9_9_6], [-5.4_4_2_4, -6.2_7_9_0, -6.7_5_7_4]],
				        [[-12.1_391, -13.3_122, -13.9_554], [-12.8_732, -13.9_352, -14.3_563], [-12.9_438, -13.8_226, -14.2_513]],
				        [[-12.5_134, -13.4_686, -14.4_915], [-12.8_669, -14.4_343, -14.7_758], [-13.2_523, -14.5_819, -15.0_694]],
				    ]		).to(__A		)
				self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3]			,			__A			,			atol=1e-4		)		)





	@slow
	def   _SCREAMING_SNAKE_CASE   (   self      :				Dict		)		->    List[Any]:
				UpperCAmelCase_  :				Union[str, Any]      =      SegformerImageProcessor(
				    image_scale=(512, 512)			,			keep_ratio=__A			,			align=__A			,			do_random_crop=__A		)
				UpperCAmelCase_  :				str      =      SegformerForSemanticSegmentation.from_pretrained(
				    "nvidia/segformer-b1-finetuned-cityscapes-1024-1024"		).to(__A		)

				UpperCAmelCase_  :				Dict      =      prepare_img()
				UpperCAmelCase_  :				Dict      =      image_processor(images=__A			,			return_tensors="pt"		)
				UpperCAmelCase_  :				List[str]      =      encoded_inputs.pixel_values.to(__A		)

				with torch.no_grad():
							UpperCAmelCase_  :				List[Any]      =      model(__A		)

				UpperCAmelCase_  :				List[str]      =      torch.Size((1, model.config.num_labels, 128, 128)		)
				self.assertEqual(outputs.logits.shape			,			__A		)

				UpperCAmelCase_  :				Union[str, Any]      =      torch.tensor(
				    [
				        [[-13.5_748, -13.9_111, -12.6_500], [-14.3_500, -15.3_683, -14.2_328], [-14.7_532, -16.0_424, -15.6_087]],
				        [[-17.1_651, -15.8_725, -12.9_653], [-17.2_580, -17.3_718, -14.8_223], [-16.6_058, -16.8_783, -16.7_452]],
				        [[-3.6_4_5_6, -3.0_2_0_9, -1.4_2_0_3], [-3.0_7_9_7, -3.1_9_5_9, -2.0_0_0_0], [-1.8_7_5_7, -1.9_2_1_7, -1.6_9_9_7]],
				    ]		).to(__A		)
				self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3]			,			__A			,			atol=1e-1		)		)





	@slow
	def   _SCREAMING_SNAKE_CASE   (   self      :				Optional[int]		)		->    List[Any]:
				UpperCAmelCase_  :				List[Any]      =      SegformerImageProcessor(
				    image_scale=(512, 512)			,			keep_ratio=__A			,			align=__A			,			do_random_crop=__A		)
				UpperCAmelCase_  :				Optional[int]      =      SegformerForSemanticSegmentation.from_pretrained("nvidia/segformer-b0-finetuned-ade-512-512"		).to(
				    __A		)

				UpperCAmelCase_  :				Union[str, Any]      =      prepare_img()
				UpperCAmelCase_  :				Any      =      image_processor(images=__A			,			return_tensors="pt"		)
				UpperCAmelCase_  :				Dict      =      encoded_inputs.pixel_values.to(__A		)

				with torch.no_grad():
							UpperCAmelCase_  :				str      =      model(__A		)

				UpperCAmelCase_  :				Optional[Any]      =      outputs.logits.detach().cpu()

				UpperCAmelCase_  :				Union[str, Any]      =      image_processor.post_process_semantic_segmentation(outputs=__A			,			target_sizes=[(500, 300)]		)
				UpperCAmelCase_  :				Tuple      =      torch.Size((500, 300)		)
				self.assertEqual(segmentation[0].shape			,			__A		)

				UpperCAmelCase_  :				Optional[int]      =      image_processor.post_process_semantic_segmentation(outputs=__A		)
				UpperCAmelCase_  :				Optional[Any]      =      torch.Size((128, 128)		)
				self.assertEqual(segmentation[0].shape			,			__A		)











"""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()



