


"""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__			(				SCREAMING_SNAKE_CASE    :    List[Any]				):


  '''simple docstring'''







  return 1 / (1 + np.exp(-z				))
def 					a__			(				SCREAMING_SNAKE_CASE    :    Dict				,      SCREAMING_SNAKE_CASE    :    Tuple				):


  '''simple docstring'''







  return (-y * np.log(_snake_case				) - (1 - y) * np.log(1 - h				)).mean()
def 					a__			(				SCREAMING_SNAKE_CASE    :    Optional[Any]				,      SCREAMING_SNAKE_CASE    :    Any				,      SCREAMING_SNAKE_CASE    :    Dict				):


  '''simple docstring'''







  lowerCAmelCase     :     Dict								=							np.dot(_snake_case				,      _snake_case				)
  return np.sum(y * scores - np.log(1 + np.exp(_snake_case				)				)				)
def 					a__			(				SCREAMING_SNAKE_CASE    :    str				,      SCREAMING_SNAKE_CASE    :    Optional[int]				,      SCREAMING_SNAKE_CASE    :    Optional[Any]				,      SCREAMING_SNAKE_CASE    :    List[str]=7_0_0_0_0				):


  '''simple docstring'''







  lowerCAmelCase     :     Optional[Any]								=							np.zeros(x.shape[1]				)

  for iterations in range(_snake_case				):
    lowerCAmelCase     :     List[Any]								=							np.dot(_snake_case				,      _snake_case				)
    lowerCAmelCase     :     List[Any]								=							sigmoid_function(_snake_case				)
    lowerCAmelCase     :     int								=							np.dot(x.T				,      h - y				) / y.size
    lowerCAmelCase     :     Tuple								=							theta - alpha * gradient  # updating the weights
    lowerCAmelCase     :     str								=							np.dot(_snake_case				,      _snake_case				)
    lowerCAmelCase     :     Union[str, Any]								=							sigmoid_function(_snake_case				)
    lowerCAmelCase     :     str								=							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__":
     lowerCAmelCase__      				=   datasets.load_iris()
     lowerCAmelCase__      				=   iris.data[:, :2]
     lowerCAmelCase__      				=   (iris.target != 0) * 1

     lowerCAmelCase__      				=   0.1
     lowerCAmelCase__      				=   logistic_reg(alpha, x, y, max_iterations=70_000)
     print('''theta: ''', theta)  # printing the theta i.e our weights vector
     def 					a__			(				SCREAMING_SNAKE_CASE    :    int				):


       '''simple docstring'''







       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''')
     (lowerCAmelCase__)      				=   (x[:, 0].min(), x[:, 0].max())
     (lowerCAmelCase__)      				=   (x[:, 1].min(), x[:, 1].max())
     (lowerCAmelCase__)      				=   np.meshgrid(np.linspace(xa_min, xa_max), np.linspace(xa_min, xa_max))
     lowerCAmelCase__      				=   np.c_[xxa.ravel(), xxa.ravel()]
     lowerCAmelCase__      				=   predict_prob(grid).reshape(xxa.shape)
     plt.contour(xxa, xxa, probs, [0.5], linewidths=1, colors='''black''')

     plt.legend()





     plt.show()








import warnings
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch

from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


_lowerCAmelCase					:			Tuple  				=			logging.get_logger(__name__)  # pylint: disable=invalid-name






def        UpperCamelCase_(							_snake_case   :  Union[List, PIL.Image.Image, torch.Tensor]	):




			"""simple docstring"""






			warnings.warn(
			    'The preprocess method is deprecated and will be removed in a future version. Please'
			    ' use VaeImageProcessor.preprocess instead'       ,     _snake_case       ,     )
			if isinstance(_snake_case       ,     torch.Tensor	):
						return image
			elif isinstance(_snake_case       ,     PIL.Image.Image	):
						__a        =[image]

			if isinstance(image[0]       ,     PIL.Image.Image	):
						__a      ,    __a        =image[0].size
						__a      ,    __a        =(x - x % 8 for x in (w, h))  # resize to integer multiple of 8

						__a        =[np.array(i.resize((w, h)       ,     resample=PIL_INTERPOLATION['lanczos']	)	)[None, :] for i in image]
						__a        =np.concatenate(_snake_case       ,     axis=0	)
						__a        =np.array(_snake_case	).astype(np.floataa	) / 255.0
						__a        =image.transpose(0       ,     3       ,     1       ,     2	)
						__a        =2.0 * image - 1.0
						__a        =torch.from_numpy(_snake_case	)
			elif isinstance(image[0]       ,     torch.Tensor	):
						__a        =torch.cat(_snake_case       ,     dim=0	)
			return image






def        UpperCamelCase_(							_snake_case   :  Union[List, PIL.Image.Image, torch.Tensor]	):




			"""simple docstring"""






			if isinstance(_snake_case       ,     torch.Tensor	):
						return mask
			elif isinstance(_snake_case       ,     PIL.Image.Image	):
						__a        =[mask]

			if isinstance(mask[0]       ,     PIL.Image.Image	):
						__a      ,    __a        =mask[0].size
						__a      ,    __a        =(x - x % 32 for x in (w, h))  # resize to integer multiple of 32
						__a        =[np.array(m.convert('L'	).resize((w, h)       ,     resample=PIL_INTERPOLATION['nearest']	)	)[None, :] for m in mask]
						__a        =np.concatenate(_snake_case       ,     axis=0	)
						__a        =mask.astype(np.floataa	) / 255.0
						__a        =0
						__a        =1
						__a        =torch.from_numpy(_snake_case	)
			elif isinstance(mask[0]       ,     torch.Tensor	):
						__a        =torch.cat(_snake_case       ,     dim=0	)
			return mask






class 	__magic_name__    (			lowerCAmelCase_      ):
			SCREAMING_SNAKE_CASE							   =						42
			SCREAMING_SNAKE_CASE							   =						42





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




						'''simple docstring'''
						super().__init__()
						self.register_modules(unet=__snake_case					,  scheduler=__snake_case					)





			@torch.no_grad()
			def __call__(		self					,  __snake_case					,  __snake_case					,  __snake_case = 250					,  __snake_case = 0.0					,  __snake_case = 10					,  __snake_case = 10					,  __snake_case = None					,  __snake_case = "pil"					,  __snake_case = True					,  )   -> Union[ImagePipelineOutput, Tuple]:




						'''simple docstring'''
						__a        =image

						__a        =_preprocess_image(__snake_case					)
						__a        =original_image.to(device=self.device					,  dtype=self.unet.dtype					)
						__a        =_preprocess_mask(__snake_case					)
						__a        =mask_image.to(device=self.device					,  dtype=self.unet.dtype					)

						__a        =original_image.shape[0]

						# sample gaussian noise to begin the loop
						if isinstance(__snake_case					,  __snake_case					) and len(__snake_case					) != batch_size:
									raise ValueError(
									    f'You have passed a list of generators of length {len(__snake_case					)}, but requested an effective batch'
									    f' size of {batch_size}. Make sure the batch size matches the length of the generators.'					)

						__a        =original_image.shape
						__a        =randn_tensor(__snake_case					,  generator=__snake_case					,  device=self.device					,  dtype=self.unet.dtype					)

						# set step values
						self.scheduler.set_timesteps(__snake_case					,  __snake_case					,  __snake_case					,  self.device					)
						__a        =eta

						__a        =self.scheduler.timesteps[0] + 1
						__a        =generator[0] if isinstance(__snake_case					,  __snake_case					) else generator
						for i, t in enumerate(self.progress_bar(self.scheduler.timesteps					)					):
									if t < t_last:
												# predict the noise residual
												__a        =self.unet(__snake_case					,  __snake_case					).sample
												# compute previous image: x_t -> x_t-1
												__a        =self.scheduler.step(__snake_case					,  __snake_case					,  __snake_case					,  __snake_case					,  __snake_case					,  __snake_case					).prev_sample

									else:
												# compute the reverse: x_t-1 -> x_t
												__a        =self.scheduler.undo_step(__snake_case					,  __snake_case					,  __snake_case					)
									__a        =t

						__a        =(image / 2 + 0.5).clamp(0					,  1					)
						__a        =image.cpu().permute(0					,  2					,  3					,  1					).numpy()
						if output_type == "pil":
									__a        =self.numpy_to_pil(__snake_case					)

						if not return_dict:
									return (image,)

						return ImagePipelineOutput(images=__snake_case					)






