


"""simple docstring"""






from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


_A   					=    logging.get_logger(__name__)


if is_vision_available():
						import PIL

class lowerCamelCase		(_SCREAMING_SNAKE_CASE      ):






					'''simple docstring'''


					a   	=		["pixel_values"]



					def __init__(						self			:				Optional[Any]   , _snake_case			:				bool = True   , _snake_case			:				Dict[str, int] = None   , _snake_case			:				PILImageResampling = PILImageResampling.BICUBIC   , _snake_case			:				bool = True   , _snake_case			:				Dict[str, int] = None   , _snake_case			:				bool = True   , _snake_case			:				Union[int, float] = 1 / 255   , _snake_case			:				bool = True   , _snake_case			:				Optional[Union[float, List[float]]] = None   , _snake_case			:				Optional[Union[float, List[float]]] = None   , _snake_case			:				bool = True   , **_snake_case			:				Any   , )							->       None:
									super().__init__(**_snake_case		)
									SCREAMING_SNAKE_CASE__		  =				size if size is not None else {"shortest_edge": 224}
									SCREAMING_SNAKE_CASE__		  =				get_size_dict(_snake_case   , default_to_square=_snake_case		)
									SCREAMING_SNAKE_CASE__		  =				crop_size if crop_size is not None else {"height": 224, "width": 224}
									SCREAMING_SNAKE_CASE__		  =				get_size_dict(_snake_case   , default_to_square=_snake_case   , param_name="crop_size"		)

									SCREAMING_SNAKE_CASE__		  =				do_resize
									SCREAMING_SNAKE_CASE__		  =				size
									SCREAMING_SNAKE_CASE__		  =				resample
									SCREAMING_SNAKE_CASE__		  =				do_center_crop
									SCREAMING_SNAKE_CASE__		  =				crop_size
									SCREAMING_SNAKE_CASE__		  =				do_rescale
									SCREAMING_SNAKE_CASE__		  =				rescale_factor
									SCREAMING_SNAKE_CASE__		  =				do_normalize
									SCREAMING_SNAKE_CASE__		  =				image_mean if image_mean is not None else OPENAI_CLIP_MEAN
									SCREAMING_SNAKE_CASE__		  =				image_std if image_std is not None else OPENAI_CLIP_STD
									SCREAMING_SNAKE_CASE__		  =				do_convert_rgb



					def 			lowerCAmelCase_					(						self			:				int   , _snake_case			:				np.ndarray   , _snake_case			:				Dict[str, int]   , _snake_case			:				PILImageResampling = PILImageResampling.BICUBIC   , _snake_case			:				Optional[Union[str, ChannelDimension]] = None   , **_snake_case			:				int   , )							->       np.ndarray:
									SCREAMING_SNAKE_CASE__		  =				get_size_dict(_snake_case   , default_to_square=_snake_case		)
									if "shortest_edge" not in size:
													raise ValueError(F"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""		)
									SCREAMING_SNAKE_CASE__		  =				get_resize_output_image_size(_snake_case   , size=size["shortest_edge"]   , default_to_square=_snake_case		)
									return resize(_snake_case   , size=_snake_case   , resample=_snake_case   , data_format=_snake_case   , **_snake_case		)



					def 			lowerCAmelCase_					(						self			:				Union[str, Any]   , _snake_case			:				np.ndarray   , _snake_case			:				Dict[str, int]   , _snake_case			:				Optional[Union[str, ChannelDimension]] = None   , **_snake_case			:				Optional[Any]   , )							->       np.ndarray:
									SCREAMING_SNAKE_CASE__		  =				get_size_dict(_snake_case		)
									if "height" not in size or "width" not in size:
													raise ValueError(F"""The `size` parameter must contain the keys (height, width). Got {size.keys()}"""		)
									return center_crop(_snake_case   , size=(size["height"], size["width"])   , data_format=_snake_case   , **_snake_case		)



					def 			lowerCAmelCase_					(						self			:				Optional[Any]   , _snake_case			:				np.ndarray   , _snake_case			:				Union[int, float]   , _snake_case			:				Optional[Union[str, ChannelDimension]] = None   , **_snake_case			:				List[Any]   , )							->       List[str]:
									return rescale(_snake_case   , scale=_snake_case   , data_format=_snake_case   , **_snake_case		)



					def 			lowerCAmelCase_					(						self			:				Optional[int]   , _snake_case			:				np.ndarray   , _snake_case			:				Union[float, List[float]]   , _snake_case			:				Union[float, List[float]]   , _snake_case			:				Optional[Union[str, ChannelDimension]] = None   , **_snake_case			:				Union[str, Any]   , )							->       np.ndarray:
									return normalize(_snake_case   , mean=_snake_case   , std=_snake_case   , data_format=_snake_case   , **_snake_case		)



					def 			lowerCAmelCase_					(						self			:				List[str]   , _snake_case			:				ImageInput   , _snake_case			:				bool = None   , _snake_case			:				Dict[str, int] = None   , _snake_case			:				PILImageResampling = None   , _snake_case			:				bool = None   , _snake_case			:				int = None   , _snake_case			:				bool = None   , _snake_case			:				float = None   , _snake_case			:				bool = None   , _snake_case			:				Optional[Union[float, List[float]]] = None   , _snake_case			:				Optional[Union[float, List[float]]] = None   , _snake_case			:				bool = None   , _snake_case			:				Optional[Union[str, TensorType]] = None   , _snake_case			:				Optional[ChannelDimension] = ChannelDimension.FIRST   , **_snake_case			:				str   , )							->       PIL.Image.Image:
									SCREAMING_SNAKE_CASE__		  =				do_resize if do_resize is not None else self.do_resize
									SCREAMING_SNAKE_CASE__		  =				size if size is not None else self.size
									SCREAMING_SNAKE_CASE__		  =				get_size_dict(_snake_case   , param_name="size"   , default_to_square=_snake_case		)
									SCREAMING_SNAKE_CASE__		  =				resample if resample is not None else self.resample
									SCREAMING_SNAKE_CASE__		  =				do_center_crop if do_center_crop is not None else self.do_center_crop
									SCREAMING_SNAKE_CASE__		  =				crop_size if crop_size is not None else self.crop_size
									SCREAMING_SNAKE_CASE__		  =				get_size_dict(_snake_case   , param_name="crop_size"   , default_to_square=_snake_case		)
									SCREAMING_SNAKE_CASE__		  =				do_rescale if do_rescale is not None else self.do_rescale
									SCREAMING_SNAKE_CASE__		  =				rescale_factor if rescale_factor is not None else self.rescale_factor
									SCREAMING_SNAKE_CASE__		  =				do_normalize if do_normalize is not None else self.do_normalize
									SCREAMING_SNAKE_CASE__		  =				image_mean if image_mean is not None else self.image_mean
									SCREAMING_SNAKE_CASE__		  =				image_std if image_std is not None else self.image_std
									SCREAMING_SNAKE_CASE__		  =				do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

									SCREAMING_SNAKE_CASE__		  =				make_list_of_images(_snake_case		)

									if not valid_images(_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_resize and size is None:
													raise ValueError("Size must be specified if do_resize is True."		)

									if do_center_crop and crop_size is None:
													raise ValueError("Crop size must be specified if do_center_crop is True."		)

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

									if do_normalize and (image_mean is None or image_std is None):
													raise ValueError("Image mean and std must be specified if do_normalize is True."		)

									# PIL RGBA images are converted to RGB
									if do_convert_rgb:
													SCREAMING_SNAKE_CASE__		  =				[convert_to_rgb(_snake_case		) for image in images]

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

									if do_resize:
													SCREAMING_SNAKE_CASE__		  =				[self.resize(image=_snake_case   , size=_snake_case   , resample=_snake_case		) for image in images]

									if do_center_crop:
													SCREAMING_SNAKE_CASE__		  =				[self.center_crop(image=_snake_case   , size=_snake_case		) for image in images]

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

									if do_normalize:
													SCREAMING_SNAKE_CASE__		  =				[self.normalize(image=_snake_case   , mean=_snake_case   , std=_snake_case		) for image in images]

									SCREAMING_SNAKE_CASE__		  =				[to_channel_dimension_format(_snake_case   , _snake_case		) for image in images]

									SCREAMING_SNAKE_CASE__		  =				{"pixel_values": images}
									return BatchFeature(data=_snake_case   , tensor_type=_snake_case		)





"""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()




