




from typing import Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging


a													=			logging.get_logger(__name__)




class       _A				(		__lowercase     ):
		__a					    =       ["""pixel_values"""]






		def __init__(    self						,		_SCREAMING_SNAKE_CASE = True						,		_SCREAMING_SNAKE_CASE = 1 / 255						,		_SCREAMING_SNAKE_CASE = True						,		_SCREAMING_SNAKE_CASE = 8						,		**_SCREAMING_SNAKE_CASE						,		):
								super().__init__(**_SCREAMING_SNAKE_CASE			)

								_UpperCAmelCase      =			do_rescale
								_UpperCAmelCase      =			rescale_factor
								_UpperCAmelCase      =			do_pad
								_UpperCAmelCase      =			pad_size






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE = None						,		**_SCREAMING_SNAKE_CASE			):
								return rescale(_SCREAMING_SNAKE_CASE						,		scale=_SCREAMING_SNAKE_CASE						,		data_format=_SCREAMING_SNAKE_CASE						,		**_SCREAMING_SNAKE_CASE			)






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE = None			):
								_UpperCAmelCase    ,					_UpperCAmelCase      =			get_image_size(_SCREAMING_SNAKE_CASE			)
								_UpperCAmelCase      =			(old_height // size + 1) * size - old_height
								_UpperCAmelCase      =			(old_width // size + 1) * size - old_width

								return pad(_SCREAMING_SNAKE_CASE						,		((0, pad_height), (0, pad_width))						,		mode="""symmetric"""						,		data_format=_SCREAMING_SNAKE_CASE			)






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE = None						,		_SCREAMING_SNAKE_CASE = None						,		_SCREAMING_SNAKE_CASE = None						,		_SCREAMING_SNAKE_CASE = None						,		_SCREAMING_SNAKE_CASE = None						,		_SCREAMING_SNAKE_CASE = ChannelDimension.FIRST						,		**_SCREAMING_SNAKE_CASE						,		):
								_UpperCAmelCase      =			do_rescale if do_rescale is not None else self.do_rescale
								_UpperCAmelCase      =			rescale_factor if rescale_factor is not None else self.rescale_factor
								_UpperCAmelCase      =			do_pad if do_pad is not None else self.do_pad
								_UpperCAmelCase      =			pad_size if pad_size is not None else self.pad_size

								_UpperCAmelCase      =			make_list_of_images(_SCREAMING_SNAKE_CASE			)

								if not valid_images(_SCREAMING_SNAKE_CASE			):
														raise ValueError(
														    """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
														    """torch.Tensor, tf.Tensor or jax.ndarray."""			)

								if do_rescale and rescale_factor is None:
														raise ValueError("""Rescale factor must be specified if do_rescale is True."""			)

								# All transformations expect numpy arrays.
								_UpperCAmelCase      =			[to_numpy_array(_SCREAMING_SNAKE_CASE			) for image in images]

								if do_rescale:
														_UpperCAmelCase      =			[self.rescale(image=_SCREAMING_SNAKE_CASE						,		scale=_SCREAMING_SNAKE_CASE			) for image in images]

								if do_pad:
														_UpperCAmelCase      =			[self.pad(_SCREAMING_SNAKE_CASE						,		size=_SCREAMING_SNAKE_CASE			) for image in images]

								_UpperCAmelCase      =			[to_channel_dimension_format(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			) for image in images]

								_UpperCAmelCase      =			{"""pixel_values""": images}
								return BatchFeature(data=_SCREAMING_SNAKE_CASE						,		tensor_type=_SCREAMING_SNAKE_CASE			)





# 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      (		snake_case       )						->				List[str]:
						return 1 / (1 + np.exp(-z       ))




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case       )						->				Tuple:
						return (-y * np.log(snake_case       ) - (1 - y) * np.log(1 - h       )).mean()




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case       )						->				List[Any]:
						_UpperCAmelCase      =			np.dot(snake_case			,     snake_case       )
						return np.sum(y * scores - np.log(1 + np.exp(snake_case       )       )       )




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case			,     snake_case=7_0_0_0_0       )						->				Tuple:
						_UpperCAmelCase      =			np.zeros(x.shape[1]       )

						for iterations in range(snake_case       ):
												_UpperCAmelCase      =			np.dot(snake_case			,     snake_case       )
												_UpperCAmelCase      =			sigmoid_function(snake_case       )
												_UpperCAmelCase      =			np.dot(x.T			,     h - y       ) / y.size
												_UpperCAmelCase      =			theta - alpha * gradient  # updating the weights
												_UpperCAmelCase      =			np.dot(snake_case			,     snake_case       )
												_UpperCAmelCase      =			sigmoid_function(snake_case       )
												_UpperCAmelCase      =			cost_function(snake_case			,     snake_case       )
												if iterations % 1_0_0 == 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=70000)
				print("theta: ", theta)  # printing the theta i.e our weights vector




				def        _SCREAMING_SNAKE_CASE      (		snake_case       )						->				List[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")
				((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()