"""simple docstring"""






import collections
import inspect
import unittest

from transformers import SwinvaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

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


if is_torch_available():
						import torch
						from torch import nn

						from transformers import SwinvaForImageClassification, SwinvaForMaskedImageModeling, SwinvaModel
						from transformers.models.swinva.modeling_swinva import SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
						from PIL import Image

						from transformers import AutoImageProcessor

class lowerCamelCase		:






					'''simple docstring'''


					def __init__(						self			:				int   , _snake_case			:				str   , _snake_case			:				List[str]=13   , _snake_case			:				Optional[Any]=32   , _snake_case			:				List[str]=2   , _snake_case			:				List[Any]=3   , _snake_case			:				List[str]=16   , _snake_case			:				Dict=[1, 2, 1]   , _snake_case			:				str=[2, 2, 4]   , _snake_case			:				List[Any]=2   , _snake_case			:				Union[str, Any]=2.0   , _snake_case			:				List[str]=True   , _snake_case			:				Dict=0.0   , _snake_case			:				str=0.0   , _snake_case			:				str=0.1   , _snake_case			:				Optional[int]="gelu"   , _snake_case			:				List[Any]=False   , _snake_case			:				List[str]=True   , _snake_case			:				Any=0.02   , _snake_case			:				Optional[Any]=1e-5   , _snake_case			:				List[Any]=True   , _snake_case			:				List[str]=None   , _snake_case			:				Optional[int]=True   , _snake_case			:				Any=10   , _snake_case			:				List[str]=8   , )							->       Optional[int]:
									SCREAMING_SNAKE_CASE__		  =				parent
									SCREAMING_SNAKE_CASE__		  =				batch_size
									SCREAMING_SNAKE_CASE__		  =				image_size
									SCREAMING_SNAKE_CASE__		  =				patch_size
									SCREAMING_SNAKE_CASE__		  =				num_channels
									SCREAMING_SNAKE_CASE__		  =				embed_dim
									SCREAMING_SNAKE_CASE__		  =				depths
									SCREAMING_SNAKE_CASE__		  =				num_heads
									SCREAMING_SNAKE_CASE__		  =				window_size
									SCREAMING_SNAKE_CASE__		  =				mlp_ratio
									SCREAMING_SNAKE_CASE__		  =				qkv_bias
									SCREAMING_SNAKE_CASE__		  =				hidden_dropout_prob
									SCREAMING_SNAKE_CASE__		  =				attention_probs_dropout_prob
									SCREAMING_SNAKE_CASE__		  =				drop_path_rate
									SCREAMING_SNAKE_CASE__		  =				hidden_act
									SCREAMING_SNAKE_CASE__		  =				use_absolute_embeddings
									SCREAMING_SNAKE_CASE__		  =				patch_norm
									SCREAMING_SNAKE_CASE__		  =				layer_norm_eps
									SCREAMING_SNAKE_CASE__		  =				initializer_range
									SCREAMING_SNAKE_CASE__		  =				is_training
									SCREAMING_SNAKE_CASE__		  =				scope
									SCREAMING_SNAKE_CASE__		  =				use_labels
									SCREAMING_SNAKE_CASE__		  =				type_sequence_label_size
									SCREAMING_SNAKE_CASE__		  =				encoder_stride



					def 			lowerCAmelCase_					(						self			:				Optional[Any]		)							->       int:
									SCREAMING_SNAKE_CASE__		  =				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

									SCREAMING_SNAKE_CASE__		  =				None
									if self.use_labels:
													SCREAMING_SNAKE_CASE__		  =				ids_tensor([self.batch_size]   , self.type_sequence_label_size		)

									SCREAMING_SNAKE_CASE__		  =				self.get_config()

									return config, pixel_values, labels



					def 			lowerCAmelCase_					(						self			:				Optional[int]		)							->       str:
									return SwinvaConfig(
									    image_size=self.image_size   , patch_size=self.patch_size   , num_channels=self.num_channels   , embed_dim=self.embed_dim   , depths=self.depths   , num_heads=self.num_heads   , window_size=self.window_size   , mlp_ratio=self.mlp_ratio   , qkv_bias=self.qkv_bias   , hidden_dropout_prob=self.hidden_dropout_prob   , attention_probs_dropout_prob=self.attention_probs_dropout_prob   , drop_path_rate=self.drop_path_rate   , hidden_act=self.hidden_act   , use_absolute_embeddings=self.use_absolute_embeddings   , path_norm=self.patch_norm   , layer_norm_eps=self.layer_norm_eps   , initializer_range=self.initializer_range   , encoder_stride=self.encoder_stride   , )



					def 			lowerCAmelCase_					(						self			:				Optional[Any]   , _snake_case			:				Dict   , _snake_case			:				List[str]   , _snake_case			:				List[str]		)							->       List[Any]:
									SCREAMING_SNAKE_CASE__		  =				SwinvaModel(config=_snake_case		)
									model.to(_snake_case		)
									model.eval()
									SCREAMING_SNAKE_CASE__		  =				model(_snake_case		)

									SCREAMING_SNAKE_CASE__		  =				((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths		) - 1))
									SCREAMING_SNAKE_CASE__		  =				int(config.embed_dim * 2 ** (len(config.depths		) - 1)		)

									self.parent.assertEqual(result.last_hidden_state.shape   , (self.batch_size, expected_seq_len, expected_dim)		)



					def 			lowerCAmelCase_					(						self			:				Optional[Any]   , _snake_case			:				int   , _snake_case			:				str   , _snake_case			:				int		)							->       Any:
									SCREAMING_SNAKE_CASE__		  =				SwinvaForMaskedImageModeling(config=_snake_case		)
									model.to(_snake_case		)
									model.eval()
									SCREAMING_SNAKE_CASE__		  =				model(_snake_case		)
									self.parent.assertEqual(
									    result.logits.shape   , (self.batch_size, self.num_channels, self.image_size, self.image_size)		)

									# test greyscale images
									SCREAMING_SNAKE_CASE__		  =				1
									SCREAMING_SNAKE_CASE__		  =				SwinvaForMaskedImageModeling(_snake_case		)
									model.to(_snake_case		)
									model.eval()

									SCREAMING_SNAKE_CASE__		  =				floats_tensor([self.batch_size, 1, self.image_size, self.image_size]		)
									SCREAMING_SNAKE_CASE__		  =				model(_snake_case		)
									self.parent.assertEqual(result.logits.shape   , (self.batch_size, 1, self.image_size, self.image_size)		)



					def 			lowerCAmelCase_					(						self			:				Optional[Any]   , _snake_case			:				str   , _snake_case			:				Tuple   , _snake_case			:				Any		)							->       int:
									SCREAMING_SNAKE_CASE__		  =				self.type_sequence_label_size
									SCREAMING_SNAKE_CASE__		  =				SwinvaForImageClassification(_snake_case		)
									model.to(_snake_case		)
									model.eval()
									SCREAMING_SNAKE_CASE__		  =				model(_snake_case   , labels=_snake_case		)
									self.parent.assertEqual(result.logits.shape   , (self.batch_size, self.type_sequence_label_size)		)



					def 			lowerCAmelCase_					(						self			:				int		)							->       List[str]:
									SCREAMING_SNAKE_CASE__		  =				self.prepare_config_and_inputs()
									SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__		  =				config_and_inputs
									SCREAMING_SNAKE_CASE__		  =				{"pixel_values": pixel_values}
									return config, inputs_dict



@require_torch
class lowerCamelCase		(_SCREAMING_SNAKE_CASE    ,    _SCREAMING_SNAKE_CASE    ,    unittest.TestCase      ):






					'''simple docstring'''


					a   	=		(
					    (SwinvaModel, SwinvaForImageClassification, SwinvaForMaskedImageModeling) if is_torch_available() else ()
					)
					a   	=		(
					    {"feature-extraction": SwinvaModel, "image-classification": SwinvaForImageClassification}
					    if is_torch_available()
					    else {}
					)

					a   	=		False
					a   	=		False
					a   	=		False
					a   	=		False



					def 			lowerCAmelCase_					(						self			:				Any		)							->       List[str]:
									SCREAMING_SNAKE_CASE__		  =				SwinvaModelTester(self		)
									SCREAMING_SNAKE_CASE__		  =				ConfigTester(self   , config_class=_snake_case   , embed_dim=37		)



					def 			lowerCAmelCase_					(						self			:				Union[str, Any]		)							->       str:
									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 			lowerCAmelCase_					(						self			:				Any		)							->       str:
									SCREAMING_SNAKE_CASE__		  =				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*_snake_case		)



					@unittest.skip(reason="Got `CUDA error: misaligned address` with PyTorch 2.0.0."		)
					def 			lowerCAmelCase_					(						self			:				int		)							->       Optional[Any]:
									pass



					@unittest.skip(reason="Swinv2 does not use inputs_embeds"		)
					def 			lowerCAmelCase_					(						self			:				List[Any]		)							->       Any:
									pass



					def 			lowerCAmelCase_					(						self			:				int		)							->       List[Any]:
									SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__		  =				self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
													SCREAMING_SNAKE_CASE__		  =				model_class(_snake_case		)
													self.assertIsInstance(model.get_input_embeddings()   , (nn.Module)		)
													SCREAMING_SNAKE_CASE__		  =				model.get_output_embeddings()
													self.assertTrue(x is None or isinstance(_snake_case   , nn.Linear		)		)



					def 			lowerCAmelCase_					(						self			:				Dict		)							->       Optional[int]:
									SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__		  =				self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
													SCREAMING_SNAKE_CASE__		  =				model_class(_snake_case		)
													SCREAMING_SNAKE_CASE__		  =				inspect.signature(model.forward		)
													# signature.parameters is an OrderedDict => so arg_names order is deterministic
													SCREAMING_SNAKE_CASE__		  =				[*signature.parameters.keys()]

													SCREAMING_SNAKE_CASE__		  =				["pixel_values"]
													self.assertListEqual(arg_names[:1]   , _snake_case		)



					def 			lowerCAmelCase_					(						self			:				Optional[int]		)							->       List[str]:
									SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__		  =				self.model_tester.prepare_config_and_inputs_for_common()
									SCREAMING_SNAKE_CASE__		  =				True

									for model_class in self.all_model_classes:
													SCREAMING_SNAKE_CASE__		  =				True
													SCREAMING_SNAKE_CASE__		  =				False
													SCREAMING_SNAKE_CASE__		  =				True
													SCREAMING_SNAKE_CASE__		  =				model_class(_snake_case		)
													model.to(_snake_case		)
													model.eval()
													with torch.no_grad():
																	SCREAMING_SNAKE_CASE__		  =				model(**self._prepare_for_class(_snake_case   , _snake_case		)		)
													SCREAMING_SNAKE_CASE__		  =				outputs.attentions
													SCREAMING_SNAKE_CASE__		  =				len(self.model_tester.depths		)
													self.assertEqual(len(_snake_case		)   , _snake_case		)

													# check that output_attentions also work using config
													del inputs_dict["output_attentions"]
													SCREAMING_SNAKE_CASE__		  =				True
													SCREAMING_SNAKE_CASE__		  =				config.window_size**2
													SCREAMING_SNAKE_CASE__		  =				model_class(_snake_case		)
													model.to(_snake_case		)
													model.eval()
													with torch.no_grad():
																	SCREAMING_SNAKE_CASE__		  =				model(**self._prepare_for_class(_snake_case   , _snake_case		)		)
													SCREAMING_SNAKE_CASE__		  =				outputs.attentions
													self.assertEqual(len(_snake_case		)   , _snake_case		)

													self.assertListEqual(
													    list(attentions[0].shape[-3:]		)   , [self.model_tester.num_heads[0], window_size_squared, window_size_squared]   , )
													SCREAMING_SNAKE_CASE__		  =				len(_snake_case		)

													# Check attention is always last and order is fine
													SCREAMING_SNAKE_CASE__		  =				True
													SCREAMING_SNAKE_CASE__		  =				True
													SCREAMING_SNAKE_CASE__		  =				model_class(_snake_case		)
													model.to(_snake_case		)
													model.eval()
													with torch.no_grad():
																	SCREAMING_SNAKE_CASE__		  =				model(**self._prepare_for_class(_snake_case   , _snake_case		)		)

													if hasattr(self.model_tester   , "num_hidden_states_types"		):
																	SCREAMING_SNAKE_CASE__		  =				self.model_tester.num_hidden_states_types
													else:
																	# also another +1 for reshaped_hidden_states
																	SCREAMING_SNAKE_CASE__		  =				2
													self.assertEqual(out_len + added_hidden_states   , len(_snake_case		)		)

													SCREAMING_SNAKE_CASE__		  =				outputs.attentions

													self.assertEqual(len(_snake_case		)   , _snake_case		)

													self.assertListEqual(
													    list(self_attentions[0].shape[-3:]		)   , [self.model_tester.num_heads[0], window_size_squared, window_size_squared]   , )



					def 			lowerCAmelCase_					(						self			:				Tuple   , _snake_case			:				List[Any]   , _snake_case			:				int   , _snake_case			:				Union[str, Any]   , _snake_case			:				Union[str, Any]		)							->       Union[str, Any]:
									SCREAMING_SNAKE_CASE__		  =				model_class(_snake_case		)
									model.to(_snake_case		)
									model.eval()

									with torch.no_grad():
													SCREAMING_SNAKE_CASE__		  =				model(**self._prepare_for_class(_snake_case   , _snake_case		)		)

									SCREAMING_SNAKE_CASE__		  =				outputs.hidden_states

									SCREAMING_SNAKE_CASE__		  =				getattr(
									    self.model_tester   , "expected_num_hidden_layers"   , len(self.model_tester.depths		) + 1		)
									self.assertEqual(len(_snake_case		)   , _snake_case		)

									# Swinv2 has a different seq_length
									SCREAMING_SNAKE_CASE__		  =				(
									    config.patch_size
									    if isinstance(config.patch_size   , collections.abc.Iterable		)
									    else (config.patch_size, config.patch_size)
									)

									SCREAMING_SNAKE_CASE__		  =				(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

									self.assertListEqual(
									    list(hidden_states[0].shape[-2:]		)   , [num_patches, self.model_tester.embed_dim]   , )

									SCREAMING_SNAKE_CASE__		  =				outputs.reshaped_hidden_states
									self.assertEqual(len(_snake_case		)   , _snake_case		)

									SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__		  =				reshaped_hidden_states[0].shape
									SCREAMING_SNAKE_CASE__		  =				(
									    reshaped_hidden_states[0].view(_snake_case   , _snake_case   , height * width		).permute(0   , 2   , 1		)
									)
									self.assertListEqual(
									    list(reshaped_hidden_states.shape[-2:]		)   , [num_patches, self.model_tester.embed_dim]   , )



					def 			lowerCAmelCase_					(						self			:				int		)							->       Optional[int]:
									SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__		  =				self.model_tester.prepare_config_and_inputs_for_common()

									SCREAMING_SNAKE_CASE__		  =				(
									    self.model_tester.image_size
									    if isinstance(self.model_tester.image_size   , collections.abc.Iterable		)
									    else (self.model_tester.image_size, self.model_tester.image_size)
									)

									for model_class in self.all_model_classes:
													SCREAMING_SNAKE_CASE__		  =				True
													self.check_hidden_states_output(_snake_case   , _snake_case   , _snake_case   , _snake_case		)

													# check that output_hidden_states also work using config
													del inputs_dict["output_hidden_states"]
													SCREAMING_SNAKE_CASE__		  =				True

													self.check_hidden_states_output(_snake_case   , _snake_case   , _snake_case   , _snake_case		)



					def 			lowerCAmelCase_					(						self			:				int		)							->       List[Any]:
									SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__		  =				self.model_tester.prepare_config_and_inputs_for_common()
									SCREAMING_SNAKE_CASE__		  =				3

									SCREAMING_SNAKE_CASE__		  =				(
									    self.model_tester.image_size
									    if isinstance(self.model_tester.image_size   , collections.abc.Iterable		)
									    else (self.model_tester.image_size, self.model_tester.image_size)
									)
									SCREAMING_SNAKE_CASE__		  =				(
									    config.patch_size
									    if isinstance(config.patch_size   , collections.abc.Iterable		)
									    else (config.patch_size, config.patch_size)
									)

									SCREAMING_SNAKE_CASE__		  =				image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
									SCREAMING_SNAKE_CASE__		  =				image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

									for model_class in self.all_model_classes:
													SCREAMING_SNAKE_CASE__		  =				True
													self.check_hidden_states_output(_snake_case   , _snake_case   , _snake_case   , (padded_height, padded_width)		)

													# check that output_hidden_states also work using config
													del inputs_dict["output_hidden_states"]
													SCREAMING_SNAKE_CASE__		  =				True
													self.check_hidden_states_output(_snake_case   , _snake_case   , _snake_case   , (padded_height, padded_width)		)



					def 			lowerCAmelCase_					(						self			:				Optional[Any]		)							->       Dict:
									SCREAMING_SNAKE_CASE__		  =				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_masked_image_modeling(*_snake_case		)



					def 			lowerCAmelCase_					(						self			:				Any		)							->       Optional[Any]:
									SCREAMING_SNAKE_CASE__		  =				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*_snake_case		)



					@slow
					def 			lowerCAmelCase_					(						self			:				List[Any]		)							->       Tuple:
									for model_name in SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													SCREAMING_SNAKE_CASE__		  =				SwinvaModel.from_pretrained(_snake_case		)
													self.assertIsNotNone(_snake_case		)



					def 			lowerCAmelCase_					(						self			:				List[str]		)							->       Any:
									SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__		  =				self.model_tester.prepare_config_and_inputs_for_common()

									SCREAMING_SNAKE_CASE__		  =				_config_zero_init(_snake_case		)
									for model_class in self.all_model_classes:
													SCREAMING_SNAKE_CASE__		  =				model_class(config=_snake_case		)
													for name, param in model.named_parameters():
																	if "embeddings" not in name and "logit_scale" not in name and param.requires_grad:
																					self.assertIn(
																					    ((param.data.mean() * 1e9).round() / 1e9).item()   , [0.0, 1.0]   , msg=F"""Parameter {name} of model {model_class} seems not properly initialized"""   , )



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






					'''simple docstring'''


					@cached_property
					def 			lowerCAmelCase_					(						self			:				List[str]		)							->       int:
									return (
									    AutoImageProcessor.from_pretrained("microsoft/swinv2-tiny-patch4-window8-256"		)
									    if is_vision_available()
									    else None
									)



					@slow
					def 			lowerCAmelCase_					(						self			:				List[Any]		)							->       Optional[Any]:
									SCREAMING_SNAKE_CASE__		  =				SwinvaForImageClassification.from_pretrained("microsoft/swinv2-tiny-patch4-window8-256"		).to(
									    _snake_case		)
									SCREAMING_SNAKE_CASE__		  =				self.default_image_processor

									SCREAMING_SNAKE_CASE__		  =				Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"		)
									SCREAMING_SNAKE_CASE__		  =				image_processor(images=_snake_case   , return_tensors="pt"		).to(_snake_case		)

									# forward pass
									with torch.no_grad():
													SCREAMING_SNAKE_CASE__		  =				model(**_snake_case		)

									# verify the logits
									SCREAMING_SNAKE_CASE__		  =				torch.Size((1, 1000)		)
									self.assertEqual(outputs.logits.shape   , _snake_case		)
									SCREAMING_SNAKE_CASE__		  =				torch.tensor([-0.3947, -0.4306, 0.0026]		).to(_snake_case		)
									self.assertTrue(torch.allclose(outputs.logits[0, :3]   , _snake_case   , atol=1e-4		)		)





"""simple docstring"""






# Logistic Regression from scratch

# In[62]:

# In[63]:

# importing all the required libraries

import numpy as np
from matplotlib import pyplot as plt
from sklearn import datasets




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase       )							->   List[str]:
				return 1 / (1 + np.exp(-z       ))




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase       )							->   Dict:
				return (-y * np.log(__UpperCAmelCase       ) - (1 - y) * np.log(1 - h       )).mean()




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase       )							->   Any:
				SCREAMING_SNAKE_CASE__		  =				np.dot(__UpperCAmelCase  ,					__UpperCAmelCase       )
				return np.sum(y * scores - np.log(1 + np.exp(__UpperCAmelCase       )       )       )




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase=70_000       )							->   Optional[Any]:
				SCREAMING_SNAKE_CASE__		  =				np.zeros(x.shape[1]       )

				for iterations in range(__UpperCAmelCase       ):
								SCREAMING_SNAKE_CASE__		  =				np.dot(__UpperCAmelCase  ,					__UpperCAmelCase       )
								SCREAMING_SNAKE_CASE__		  =				sigmoid_function(__UpperCAmelCase       )
								SCREAMING_SNAKE_CASE__		  =				np.dot(x.T  ,					h - y       ) / y.size
								SCREAMING_SNAKE_CASE__		  =				theta - alpha * gradient  # updating the weights
								SCREAMING_SNAKE_CASE__		  =				np.dot(__UpperCAmelCase  ,					__UpperCAmelCase       )
								SCREAMING_SNAKE_CASE__		  =				sigmoid_function(__UpperCAmelCase       )
								SCREAMING_SNAKE_CASE__		  =				cost_function(__UpperCAmelCase  ,					__UpperCAmelCase       )
								if iterations % 100 == 0:
												print(F"""loss: {j} \t"""       )  # printing the loss after every 100 iterations
				return theta


# In[68]:

if __name__ == "__main__":
						_A   					=    datasets.load_iris()
						_A   					=    iris.data[:, :2]
						_A   					=    (iris.target != 0) * 1

						_A   					=    0.1
						_A   					=    logistic_reg(alpha, x, y, max_iterations=7_0_0_0_0)
						print('theta: ', theta)  # printing the theta i.e our weights vector




						def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase       )							->   Union[str, Any]:
										return sigmoid_function(
										    np.dot(__UpperCAmelCase  ,					__UpperCAmelCase       )       )  # predicting the value of probability from the logistic regression algorithm

						plt.figure(figsize=(1_0, 6))
						plt.scatter(x[y == 0][:, 0], x[y == 0][:, 1], color='b', label='0')
						plt.scatter(x[y == 1][:, 0], x[y == 1][:, 1], color='r', label='1')
						((_A)					,			(_A))   					=    (x[:, 0].min(), x[:, 0].max())
						((_A)					,			(_A))   					=    (x[:, 1].min(), x[:, 1].max())
						((_A)					,			(_A))   					=    np.meshgrid(np.linspace(xa_min, xa_max), np.linspace(xa_min, xa_max))
						_A   					=    np.c_[xxa.ravel(), xxa.ravel()]
						_A   					=    predict_prob(grid).reshape(xxa.shape)
						plt.contour(xxa, xxa, probs, [0.5], linewidths=1, colors='black')

						plt.legend()

						plt.show()




