





"""simple docstring"""


import numpy as np
import requests





def 							a__	(     )			->     Optional[Any]:

 '''simple docstring'''






 snake_case__										= requests.get(
     "https://raw.githubusercontent.com/yashLadha/The_Math_of_Intelligence/"
     "master/Week1/ADRvsRating.csv"		)
 snake_case__										= response.text.splitlines()
 snake_case__										= []
 for item in lines:
  snake_case__										= item.split(","		)
  data.append(_UpperCAmelCase		)
 data.pop(0		)  # This is for removing the labels from the list
 snake_case__										= np.matrix(_UpperCAmelCase		)
 return dataset





def 							a__	(     _UpperCAmelCase		,   _UpperCAmelCase		,   _UpperCAmelCase		,   _UpperCAmelCase		,   _UpperCAmelCase		)			->     Dict:

 '''simple docstring'''






 snake_case__										= len_data

 snake_case__										= np.dot(_UpperCAmelCase		,   data_x.transpose()		)
 prod -= data_y.transpose()
 snake_case__										= np.dot(_UpperCAmelCase		,   _UpperCAmelCase		)
 snake_case__										= theta - (alpha / n) * sum_grad
 return theta





def 							a__	(     _UpperCAmelCase		,   _UpperCAmelCase		,   _UpperCAmelCase		,   _UpperCAmelCase		)			->     Tuple:

 '''simple docstring'''






 snake_case__										= np.dot(_UpperCAmelCase		,   data_x.transpose()		)
 prod -= data_y.transpose()
 snake_case__										= np.sum(np.square(_UpperCAmelCase		)		)
 snake_case__										= sum_elem / (2 * len_data)
 return error





def 							a__	(     _UpperCAmelCase		,   _UpperCAmelCase		)			->     Optional[int]:

 '''simple docstring'''






 snake_case__										= 1_0_0_0_0_0
 snake_case__										= 0.0001550

 snake_case__										= data_x.shape[1]
 snake_case__										= data_x.shape[0] - 1

 snake_case__										= np.zeros((1, no_features)		)

 for i in range(0		,   _UpperCAmelCase		):
  snake_case__										= run_steep_gradient_descent(_UpperCAmelCase		,   _UpperCAmelCase		,   _UpperCAmelCase		,   _UpperCAmelCase		,   _UpperCAmelCase		)
  snake_case__										= sum_of_square_error(_UpperCAmelCase		,   _UpperCAmelCase		,   _UpperCAmelCase		,   _UpperCAmelCase		)
  print(f"At Iteration {i + 1} - Error is {error:.5f}"		)

 return theta





def 							a__	(     _UpperCAmelCase		,   _UpperCAmelCase		)			->     Optional[Any]:

 '''simple docstring'''






 snake_case__										= sum(abs(y - predicted_y[i]		) for i, y in enumerate(_UpperCAmelCase		)		)
 return total / len(_UpperCAmelCase		)





def 							a__	(     )			->     int:

 '''simple docstring'''






 snake_case__										= collect_dataset()

 snake_case__										= data.shape[0]
 snake_case__										= np.c_[np.ones(_UpperCAmelCase		), data[:, :-1]].astype(_UpperCAmelCase		)
 snake_case__										= data[:, -1].astype(_UpperCAmelCase		)

 snake_case__										= run_linear_regression(_UpperCAmelCase		,   _UpperCAmelCase		)
 snake_case__										= theta.shape[1]
 print("Resultant Feature vector : "		)
 for i in range(0		,   _UpperCAmelCase		):
  print(f"{theta[0, i]:.5f}"		)


if __name__ == "__main__":
   main()








"""simple docstring"""


from typing import List

import PIL
import torch
from PIL import Image

from ...configuration_utils import ConfigMixin
from ...models.modeling_utils import ModelMixin
from ...utils import PIL_INTERPOLATION



class    lowercase_ (   __lowerCAmelCase ,__lowerCAmelCase							):

       def __init__(	self   ):






        '''simple docstring'''

        super().__init__()

        self.register_buffer("watermark_image"      ,    torch.zeros((62, 62, 4)   )   )
        snake_case__										= None

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE=None   ):






        '''simple docstring'''

        snake_case__										= images[0].height
        snake_case__										= images[0].width

        snake_case__										= sample_size or h

        snake_case__										= min(h / sample_size      ,    w / sample_size   )
        snake_case__							,      snake_case__										= (int(h / coef   ), int(w / coef   )) if coef < 1 else (h, w)

        snake_case__							,      snake_case__										= 1024**2, img_w * img_h
        snake_case__										= (Sa / Sa) ** 0.5
        snake_case__							,      snake_case__							,      snake_case__										= int(K * 62   ), img_w - int(14 * K   ), img_h - int(14 * K   )

        if self.watermark_image_as_pil is None:
         snake_case__										= self.watermark_image.to(torch.uinta   ).cpu().numpy()
         snake_case__										= Image.fromarray(__SCREAMING_SNAKE_CASE      ,    mode="RGBA"   )
         snake_case__										= watermark_image

        snake_case__										= self.watermark_image_as_pil.resize(
            (wm_size, wm_size)      ,    PIL_INTERPOLATION["bicubic"]      ,    reducing_gap=__SCREAMING_SNAKE_CASE   )

        for pil_img in images:
         pil_img.paste(__SCREAMING_SNAKE_CASE      ,    box=(wm_x - wm_size, wm_y - wm_size, wm_x, wm_y)      ,    mask=wm_img.split()[-1]   )

        return images

