import collections
import tempfile
import unittest

import numpy as np

from transformers.testing_utils import (
    is_pt_flax_cross_test,
    require_flax,
    require_torch,
    require_vision,
    slow,
    torch_device,
)
from transformers.utils import is_flax_available, is_torch_available, is_vision_available

from ...test_modeling_flax_common import floats_tensor, ids_tensor, random_attention_mask
from ..bert.test_modeling_flax_bert import FlaxBertModelTester
from ..clip.test_modeling_flax_clip import FlaxCLIPVisionModelTester
from ..vit.test_modeling_flax_vit import FlaxViTModelTester


if is_flax_available():
      from transformers import (
          FlaxBertModel,
          FlaxCLIPVisionModel,
          FlaxVisionTextDualEncoderModel,
          FlaxViTModel,
          VisionTextDualEncoderConfig,
          VisionTextDualEncoderProcessor,
      )
      from transformers.modeling_flax_pytorch_utils import (
          convert_pytorch_state_dict_to_flax,
          load_flax_weights_in_pytorch_model,
      )


if is_torch_available():
      import torch

      from transformers import VisionTextDualEncoderModel

if is_vision_available():
      from PIL import Image







def      _a					(   lowercase__	:      Dict			):







     '''simple docstring'''


     if isinstance(lowercase__  ,	collections.abc.Iterable			):
          return x
     return (x, x)

@require_flax
class    snake_case       :




  def 						__lowercase(   self  :						Dict  ,			a_  :						List[Any]  ,			a_  :						List[Any]					)->							int:




       """simple docstring"""
       pass




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




       """simple docstring"""
       pass




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




       """simple docstring"""
       pass




  def 						__lowercase(   self  :						Any  ,			a_  :						np.ndarray  ,			a_  :						np.ndarray  ,			a_  :						float					)->							Optional[Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Any      =		np.abs((a - b)					).max()
       self.assertLessEqual(a_  ,			a_  ,			F'''Difference between torch and flax is {diff} (>= {tol}).'''					)




  def 						__lowercase(   self  :						Optional[Any]  ,			a_  :						Optional[int]  ,			a_  :						Any  ,			a_  :						Optional[Any]  ,			a_  :						Dict  ,			a_  :						List[Any]=None  ,			**a_  :						Optional[int]					)->							Optional[int]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Dict      =		VisionTextDualEncoderConfig.from_vision_text_configs(a_  ,			a_					)

       SCREAMING_SNAKE_CASE__					:					Dict      =		FlaxVisionTextDualEncoderModel(a_					)

       SCREAMING_SNAKE_CASE__					:					Dict      =		model(input_ids=a_  ,			pixel_values=a_  ,			attention_mask=a_					)

       self.assertEqual(output['text_embeds'].shape  ,			(input_ids.shape[0], config.projection_dim)					)
       self.assertEqual(output['image_embeds'].shape  ,			(pixel_values.shape[0], config.projection_dim)					)




  def 						__lowercase(   self  :						str  ,			a_  :						List[Any]  ,			a_  :						Dict  ,			a_  :						str  ,			a_  :						Optional[Any]  ,			a_  :						Optional[int]=None  ,			**a_  :						List[Any]					)->							Optional[Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__      ,				SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		self.get_vision_text_model(a_  ,			a_					)
       SCREAMING_SNAKE_CASE__					:					Tuple      =		{'vision_model': vision_model, 'text_model': text_model}
       SCREAMING_SNAKE_CASE__					:					Any      =		FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**a_					)

       SCREAMING_SNAKE_CASE__					:					Dict      =		model(input_ids=a_  ,			pixel_values=a_  ,			attention_mask=a_					)

       self.assertEqual(output['text_embeds'].shape  ,			(input_ids.shape[0], model.config.projection_dim)					)
       self.assertEqual(output['image_embeds'].shape  ,			(pixel_values.shape[0], model.config.projection_dim)					)




  def 						__lowercase(   self  :						Dict  ,			a_  :						Any  ,			a_  :						Any  ,			a_  :						List[str]  ,			a_  :						str  ,			a_  :						int=None  ,			**a_  :						Any					)->							str:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__      ,				SCREAMING_SNAKE_CASE__					:					Dict      =		self.get_vision_text_model(a_  ,			a_					)
       SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		{'vision_model': vision_model, 'text_model': text_model}
       SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**a_					)

       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		model(input_ids=a_  ,			pixel_values=a_  ,			attention_mask=a_					)
       SCREAMING_SNAKE_CASE__					:					List[str]      =		output[0]

       with tempfile.TemporaryDirectory() as tmpdirname:
            model.save_pretrained(a_					)
            SCREAMING_SNAKE_CASE__					:					Dict      =		FlaxVisionTextDualEncoderModel.from_pretrained(a_					)

            SCREAMING_SNAKE_CASE__					:					Tuple      =		model(input_ids=a_  ,			pixel_values=a_  ,			attention_mask=a_					)
            SCREAMING_SNAKE_CASE__					:					List[Any]      =		after_output[0]
            SCREAMING_SNAKE_CASE__					:					Any      =		np.amax(np.abs(out_a - out_a					)					)
            self.assertLessEqual(a_  ,			1e-3					)




  def 						__lowercase(   self  :						Optional[int]  ,			a_  :						List[Any]  ,			a_  :						Optional[int]  ,			a_  :						List[str]  ,			a_  :						int  ,			a_  :						int=None  ,			**a_  :						Union[str, Any]					)->							Any:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__      ,				SCREAMING_SNAKE_CASE__					:					Any      =		self.get_vision_text_model(a_  ,			a_					)
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		{'vision_model': vision_model, 'text_model': text_model}
       SCREAMING_SNAKE_CASE__					:					List[str]      =		FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**a_					)

       SCREAMING_SNAKE_CASE__					:					List[str]      =		model(
           input_ids=a_  ,			pixel_values=a_  ,			attention_mask=a_  ,			output_attentions=a_					)

       SCREAMING_SNAKE_CASE__					:					int      =		output.vision_model_output.attentions
       self.assertEqual(len(a_					)  ,			vision_config.num_hidden_layers					)

       # in ViT, the seq_len equals the number of patches + 1 (we add 1 for the [CLS] token)
       SCREAMING_SNAKE_CASE__					:					Any      =		to_atuple(vision_model.config.image_size					)
       SCREAMING_SNAKE_CASE__					:					Dict      =		to_atuple(vision_model.config.patch_size					)
       SCREAMING_SNAKE_CASE__					:					str      =		(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
       SCREAMING_SNAKE_CASE__					:					Dict      =		num_patches + 1
       self.assertEqual(vision_attentions[0].shape[-3:]  ,			(vision_config.num_attention_heads, seq_len, seq_len)					)

       SCREAMING_SNAKE_CASE__					:					int      =		output.text_model_output.attentions
       self.assertEqual(len(a_					)  ,			text_config.num_hidden_layers					)

       self.assertEqual(
           text_attentions[0].shape[-3:]  ,			(text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1])  ,			)




  def 						__lowercase(   self  :						str  ,			a_  :						Optional[Any]  ,			a_  :						Optional[int]  ,			a_  :						List[str]					)->							Optional[int]:




       """simple docstring"""
       pt_model.to(a_					)
       pt_model.eval()

       # prepare inputs
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		inputs_dict
       SCREAMING_SNAKE_CASE__					:					Tuple      =		{k: torch.tensor(v.tolist()					) for k, v in flax_inputs.items()}

       with torch.no_grad():
            SCREAMING_SNAKE_CASE__					:					Any      =		pt_model(**a_					).to_tuple()

       SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		fx_model(**a_					).to_tuple()
       self.assertEqual(len(a_					)  ,			len(a_					)  ,			'Output lengths differ between Flax and PyTorch'					)
       for fx_output, pt_output in zip(fx_outputs[:4]  ,			pt_outputs[:4]					):
            self.assert_almost_equals(a_  ,			pt_output.numpy()  ,			4e-2					)

       # PT -> Flax
       with tempfile.TemporaryDirectory() as tmpdirname:
            pt_model.save_pretrained(a_					)
            SCREAMING_SNAKE_CASE__					:					str      =		FlaxVisionTextDualEncoderModel.from_pretrained(a_  ,			from_pt=a_					)

       SCREAMING_SNAKE_CASE__					:					int      =		fx_model_loaded(**a_					).to_tuple()
       self.assertEqual(len(a_					)  ,			len(a_					)  ,			'Output lengths differ between Flax and PyTorch'					)
       for fx_output_loaded, pt_output in zip(fx_outputs_loaded[:4]  ,			pt_outputs[:4]					):
            self.assert_almost_equals(a_  ,			pt_output.numpy()  ,			4e-2					)

       # Flax -> PT
       with tempfile.TemporaryDirectory() as tmpdirname:
            fx_model.save_pretrained(a_					)
            SCREAMING_SNAKE_CASE__					:					List[Any]      =		VisionTextDualEncoderModel.from_pretrained(a_  ,			from_flax=a_					)

       pt_model_loaded.to(a_					)
       pt_model_loaded.eval()

       with torch.no_grad():
            SCREAMING_SNAKE_CASE__					:					str      =		pt_model_loaded(**a_					).to_tuple()

       self.assertEqual(len(a_					)  ,			len(a_					)  ,			'Output lengths differ between Flax and PyTorch'					)
       for fx_output, pt_output_loaded in zip(fx_outputs[:4]  ,			pt_outputs_loaded[:4]					):
            self.assert_almost_equals(a_  ,			pt_output_loaded.numpy()  ,			4e-2					)




  def 						__lowercase(   self  :						int  ,			a_  :						List[str]  ,			a_  :						str  ,			a_  :						Tuple					)->							List[str]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Tuple      =		VisionTextDualEncoderConfig.from_vision_text_configs(a_  ,			a_					)

       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		VisionTextDualEncoderModel(a_					)
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		FlaxVisionTextDualEncoderModel(a_					)

       SCREAMING_SNAKE_CASE__					:					Tuple      =		convert_pytorch_state_dict_to_flax(pt_model.state_dict()  ,			a_					)
       SCREAMING_SNAKE_CASE__					:					str      =		fx_state

       self.check_pt_flax_equivalence(a_  ,			a_  ,			a_					)




  def 						__lowercase(   self  :						int  ,			a_  :						List[Any]  ,			a_  :						int  ,			a_  :						List[Any]					)->							Optional[Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Tuple      =		VisionTextDualEncoderConfig.from_vision_text_configs(a_  ,			a_					)

       SCREAMING_SNAKE_CASE__					:					List[str]      =		VisionTextDualEncoderModel(a_					)
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		FlaxVisionTextDualEncoderModel(a_					)

       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		load_flax_weights_in_pytorch_model(a_  ,			fx_model.params					)

       self.check_pt_flax_equivalence(a_  ,			a_  ,			a_					)




  def 						__lowercase(   self  :						Union[str, Any]					)->							Optional[int]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					List[str]      =		self.prepare_config_and_inputs()
       self.check_model_from_pretrained_configs(**a_					)




  def 						__lowercase(   self  :						Any					)->							Any:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					int      =		self.prepare_config_and_inputs()
       self.check_vision_text_dual_encoder_from_pretrained(**a_					)




  def 						__lowercase(   self  :						str					)->							int:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		self.prepare_config_and_inputs()
       self.check_save_load(**a_					)




  def 						__lowercase(   self  :						int					)->							Tuple:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		self.prepare_config_and_inputs()
       self.check_vision_text_output_attention(**a_					)




  @is_pt_flax_cross_test
  def 						__lowercase(   self  :						int					)->							int:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					int      =		self.prepare_config_and_inputs()
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		config_inputs_dict.pop('vision_config'					)
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		config_inputs_dict.pop('text_config'					)

       SCREAMING_SNAKE_CASE__					:					Tuple      =		config_inputs_dict

       self.check_equivalence_pt_to_flax(a_  ,			a_  ,			a_					)
       self.check_equivalence_flax_to_pt(a_  ,			a_  ,			a_					)




  @slow
  def 						__lowercase(   self  :						str					)->							int:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__      ,				SCREAMING_SNAKE_CASE__					:					int      =		self.get_pretrained_model_and_inputs()

       SCREAMING_SNAKE_CASE__					:					Any      =		model_a(**a_					)
       SCREAMING_SNAKE_CASE__					:					str      =		outputs[0]

       with tempfile.TemporaryDirectory() as tmp_dirname:
            model_a.save_pretrained(a_					)
            SCREAMING_SNAKE_CASE__					:					Any      =		FlaxVisionTextDualEncoderModel.from_pretrained(a_					)

            SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		model_a(**a_					)
            SCREAMING_SNAKE_CASE__					:					List[Any]      =		after_outputs[0]
            SCREAMING_SNAKE_CASE__					:					List[Any]      =		np.amax(np.abs(out_a - out_a					)					)
            self.assertLessEqual(a_  ,			1e-5					)




@require_flax
class    snake_case       (   UpperCamelCase_ ,    unittest.TestCase    ):




  def 						__lowercase(   self  :						Union[str, Any]					)->							Optional[int]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Any      =		FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
           'hf-internal-testing/tiny-random-vit'  ,			'hf-internal-testing/tiny-bert'  ,			vision_from_pt=a_  ,			text_from_pt=a_  ,			)
       SCREAMING_SNAKE_CASE__					:					Tuple      =		13
       SCREAMING_SNAKE_CASE__					:					Dict      =		floats_tensor(
           [
               batch_size,
               model.config.vision_config.num_channels,
               model.config.vision_config.image_size,
               model.config.vision_config.image_size,
           ]					)
       SCREAMING_SNAKE_CASE__					:					Tuple      =		ids_tensor([batch_size, 4]  ,			model.config.text_config.vocab_size					)
       SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		random_attention_mask([batch_size, 4]					)
       SCREAMING_SNAKE_CASE__					:					Tuple      =		{'pixel_values': pixel_values, 'input_ids': input_ids, 'attention_mask': attention_mask}

       return model, inputs




  def 						__lowercase(   self  :						List[str]  ,			a_  :						Any  ,			a_  :						Tuple					)->							str:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		FlaxViTModel(a_					)
       SCREAMING_SNAKE_CASE__					:					int      =		FlaxBertModel(a_					)
       return vision_model, text_model




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




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		FlaxViTModelTester(self					)
       SCREAMING_SNAKE_CASE__					:					Dict      =		FlaxBertModelTester(self					)
       SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		vit_model_tester.prepare_config_and_inputs()
       SCREAMING_SNAKE_CASE__					:					Dict      =		bert_model_tester.prepare_config_and_inputs()

       SCREAMING_SNAKE_CASE__      ,				SCREAMING_SNAKE_CASE__					:					List[Any]      =		vision_config_and_inputs

       SCREAMING_SNAKE_CASE__      ,				SCREAMING_SNAKE_CASE__      ,				SCREAMING_SNAKE_CASE__      ,				SCREAMING_SNAKE_CASE__					:					Dict      =		text_config_and_inputs

       # make sure that cross attention layers are added
       return {
           "text_config": text_config,
           "vision_config": vision_config,
           "pixel_values": pixel_values,
           "attention_mask": attention_mask,
           "input_ids": input_ids,
           "token_type_ids": token_type_ids,
       }




@require_torch
class    snake_case       (   UpperCamelCase_ ,    unittest.TestCase    ):




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




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					str      =		FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
           'hf-internal-testing/tiny-random-clip'  ,			'hf-internal-testing/tiny-bert'  ,			vision_from_pt=a_  ,			text_from_pt=a_  ,			)
       SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		13
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		floats_tensor(
           [
               batch_size,
               model.config.vision_config.num_channels,
               model.config.vision_config.image_size,
               model.config.vision_config.image_size,
           ]					)
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		ids_tensor([batch_size, 4]  ,			model.config.text_config.vocab_size					)
       SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		random_attention_mask([batch_size, 4]					)
       SCREAMING_SNAKE_CASE__					:					List[str]      =		{'pixel_values': pixel_values, 'input_ids': input_ids, 'attention_mask': attention_mask}

       return model, inputs




  def 						__lowercase(   self  :						str  ,			a_  :						Optional[int]  ,			a_  :						str					)->							List[Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					str      =		FlaxCLIPVisionModel(a_					)
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		FlaxBertModel(a_					)
       return vision_model, text_model




  def 						__lowercase(   self  :						str					)->							Optional[int]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		FlaxCLIPVisionModelTester(self					)
       SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		FlaxBertModelTester(self					)
       SCREAMING_SNAKE_CASE__					:					Any      =		clip_model_tester.prepare_config_and_inputs()
       SCREAMING_SNAKE_CASE__					:					int      =		bert_model_tester.prepare_config_and_inputs()

       SCREAMING_SNAKE_CASE__      ,				SCREAMING_SNAKE_CASE__					:					List[Any]      =		vision_config_and_inputs

       SCREAMING_SNAKE_CASE__      ,				SCREAMING_SNAKE_CASE__      ,				SCREAMING_SNAKE_CASE__      ,				SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		text_config_and_inputs

       # make sure that cross attention layers are added
       return {
           "text_config": text_config,
           "vision_config": vision_config,
           "pixel_values": pixel_values,
           "attention_mask": attention_mask,
           "input_ids": input_ids,
           "token_type_ids": token_type_ids,
       }




@require_flax
@require_vision
class    snake_case       (   unittest.TestCase    ):




  @slow
  def 						__lowercase(   self  :						Optional[int]					)->							Union[str, Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		FlaxVisionTextDualEncoderModel.from_pretrained('clip-italian/clip-italian'  ,			logit_scale_init_value=1.0					)
       SCREAMING_SNAKE_CASE__					:					Tuple      =		VisionTextDualEncoderProcessor.from_pretrained('clip-italian/clip-italian'					)

       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'					)
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		processor(
           text=['una foto di un gatto', 'una foto di un cane']  ,			images=a_  ,			padding=a_  ,			return_tensors='np'					)

       SCREAMING_SNAKE_CASE__					:					Tuple      =		model(**a_					)

       # verify the logits
       self.assertEqual(outputs.logits_per_image.shape  ,			(inputs.pixel_values.shape[0], inputs.input_ids.shape[0])					)
       self.assertEqual(
           outputs.logits_per_text.shape  ,			(inputs.input_ids.shape[0], inputs.pixel_values.shape[0])  ,			)

       SCREAMING_SNAKE_CASE__					:					Any      =		np.array([[1.228_4727, 0.310_4122]]					)

       self.assertTrue(np.allclose(outputs.logits_per_image  ,			a_  ,			atol=1e-3					)					)







"""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 				A_       ( snake_case__		)					->  str:
			return 1 / (1 + np.exp(-z		))





def 				A_       ( snake_case__       ,					snake_case__		)					->  str:
			return (-y * np.log(snake_case__		) - (1 - y) * np.log(1 - h		)).mean()





def 				A_       ( snake_case__       ,					snake_case__       ,					snake_case__		)					->  Union[str, Any]:
			_UpperCamelCase					:Tuple					     =	np.dot(snake_case__       ,					snake_case__		)
			return np.sum(y * scores - np.log(1 + np.exp(snake_case__		)		)		)





def 				A_       ( snake_case__       ,					snake_case__       ,					snake_case__       ,					snake_case__=7_00_00		)					->  Optional[int]:
			_UpperCamelCase					:Union[str, Any]					     =	np.zeros(x.shape[1]		)

			for iterations in range(snake_case__		):
						_UpperCamelCase					:Optional[int]					     =	np.dot(snake_case__       ,					snake_case__		)
						_UpperCamelCase					:Tuple					     =	sigmoid_function(snake_case__		)
						_UpperCamelCase					:Optional[Any]					     =	np.dot(x.T       ,					h - y		) / y.size
						_UpperCamelCase					:int					     =	theta - alpha * gradient  # updating the weights
						_UpperCamelCase					:Union[str, Any]					     =	np.dot(snake_case__       ,					snake_case__		)
						_UpperCamelCase					:Dict					     =	sigmoid_function(snake_case__		)
						_UpperCamelCase					:Optional[int]					     =	cost_function(snake_case__       ,					snake_case__		)
						if iterations % 1_00 == 0:
									print(f"loss: {j} \t"		)  # printing the loss after every 100 iterations
			return theta


# In[68]:

if __name__ == "__main__":
					UpperCamelCase__				:Union[str, Any]			   =	datasets.load_iris()
					UpperCamelCase__				:Dict			   =	iris.data[:, :2]
					UpperCamelCase__				:Any			   =	(iris.target != 0) * 1

					UpperCamelCase__				:Tuple			   =	0.1
					UpperCamelCase__				:List[str]			   =	logistic_reg(alpha, x, y, max_iterations=70_000)
					print("""theta: """, theta)  # printing the theta i.e our weights vector





					def 				A_       ( snake_case__		)					->  Optional[Any]:
								return sigmoid_function(
								    np.dot(snake_case__       ,					snake_case__		)		)  # predicting the value of probability from the logistic regression algorithm

					plt.figure(figsize=(10, 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""")
					((UpperCamelCase__)   ,			(UpperCamelCase__))				:Union[str, Any]			   =	(x[:, 0].min(), x[:, 0].max())
					((UpperCamelCase__)   ,			(UpperCamelCase__))				:Tuple			   =	(x[:, 1].min(), x[:, 1].max())
					((UpperCamelCase__)   ,			(UpperCamelCase__))				:Tuple			   =	np.meshgrid(np.linspace(xa_min, xa_max), np.linspace(xa_min, xa_max))
					UpperCamelCase__				:List[Any]			   =	np.c_[xxa.ravel(), xxa.ravel()]
					UpperCamelCase__				:List[Any]			   =	predict_prob(grid).reshape(xxa.shape)
					plt.contour(xxa, xxa, probs, [0.5], linewidths=1, colors="""black""")

					plt.legend()




					plt.show()

