


"""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()





"""simple docstring"""






import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding






class      A(  lowerCamelCase__		):

		"""simple docstring"""





		A    =							["image_processor", "tokenizer"]
		A    =							"ViTImageProcessor"
		A    =							("CLIPTokenizer", "CLIPTokenizerFast")
		def __init__( self					,				SCREAMING_SNAKE_CASE__=None					,				SCREAMING_SNAKE_CASE__=None					,				**SCREAMING_SNAKE_CASE__   )      ->	List[str]:

					"""simple docstring"""



					_UpperCamelCase					:List[str]					     =	None
					if "feature_extractor" in kwargs:
								warnings.warn(
								    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
								    ''' instead.'''					,				SCREAMING_SNAKE_CASE__					,				)
								_UpperCamelCase					:List[str]					     =	kwargs.pop('''feature_extractor'''   )

					_UpperCamelCase					:Optional[Any]					     =	image_processor if image_processor is not None else feature_extractor
					if image_processor is None:
								raise ValueError('''You need to specify an `image_processor`.'''   )
					if tokenizer is None:
								raise ValueError('''You need to specify a `tokenizer`.'''   )

					super().__init__(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )
		def __call__( self					,				SCREAMING_SNAKE_CASE__=None					,				SCREAMING_SNAKE_CASE__=None					,				SCREAMING_SNAKE_CASE__=None					,				SCREAMING_SNAKE_CASE__=None					,				**SCREAMING_SNAKE_CASE__   )      ->	Tuple:

					"""simple docstring"""



					if text is None and visual_prompt is None and images is None:
								raise ValueError('''You have to specify either text, visual prompt or images.'''   )

					if text is not None and visual_prompt is not None:
								raise ValueError('''You have to specify exactly one type of prompt. Either text or visual prompt.'''   )

					if text is not None:
								_UpperCamelCase					:Dict					     =	self.tokenizer(SCREAMING_SNAKE_CASE__					,				return_tensors=SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__   )

					if visual_prompt is not None:
								_UpperCamelCase					:Optional[Any]					     =	self.image_processor(SCREAMING_SNAKE_CASE__					,				return_tensors=SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__   )

					if images is not None:
								_UpperCamelCase					:Union[str, Any]					     =	self.image_processor(SCREAMING_SNAKE_CASE__					,				return_tensors=SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__   )

					if visual_prompt is not None and images is not None:
								_UpperCamelCase					:List[str]					     =	{
								    '''pixel_values''': image_features.pixel_values,
								    '''conditional_pixel_values''': prompt_features.pixel_values,
								}
								return encoding
					elif text is not None and images is not None:
								_UpperCamelCase					:List[Any]					     =	image_features.pixel_values
								return encoding
					elif text is not None:
								return encoding
					elif visual_prompt is not None:
								_UpperCamelCase					:Optional[Any]					     =	{
								    '''conditional_pixel_values''': prompt_features.pixel_values,
								}
								return encoding
					else:
								return BatchEncoding(data=dict(**SCREAMING_SNAKE_CASE__   )					,				tensor_type=SCREAMING_SNAKE_CASE__   )
		def   _UpperCamelCase( self					,				*SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__   )      ->	Any:

					"""simple docstring"""



					return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__   )
		def   _UpperCamelCase( self					,				*SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__   )      ->	Tuple:

					"""simple docstring"""



					return self.tokenizer.decode(*SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__   )
		@property
		def   _UpperCamelCase( self   )      ->	Tuple:

					"""simple docstring"""



					warnings.warn(
					    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''					,				SCREAMING_SNAKE_CASE__					,				)
					return self.image_processor_class





		@property
		def   _UpperCamelCase( self   )      ->	Union[str, Any]:

					"""simple docstring"""



					warnings.warn(
					    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''					,				SCREAMING_SNAKE_CASE__					,				)
					return self.image_processor

