




from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput




class       _A				(		__lowercase     ):
		__a					    =       42
		__a					    =       42






		def __init__(    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			):
								super().__init__()
								self.register_modules(unet=_SCREAMING_SNAKE_CASE						,		scheduler=_SCREAMING_SNAKE_CASE			)






		@torch.no_grad()
		def __call__(    self						,		_SCREAMING_SNAKE_CASE = 1						,		_SCREAMING_SNAKE_CASE = 2000						,		_SCREAMING_SNAKE_CASE = None						,		_SCREAMING_SNAKE_CASE = "pil"						,		_SCREAMING_SNAKE_CASE = True						,		**_SCREAMING_SNAKE_CASE						,		):
								_UpperCAmelCase      =			self.unet.config.sample_size
								_UpperCAmelCase      =			(batch_size, 3, img_size, img_size)

								_UpperCAmelCase      =			self.unet

								_UpperCAmelCase      =			randn_tensor(_SCREAMING_SNAKE_CASE						,		generator=_SCREAMING_SNAKE_CASE			) * self.scheduler.init_noise_sigma
								_UpperCAmelCase      =			sample.to(self.device			)

								self.scheduler.set_timesteps(_SCREAMING_SNAKE_CASE			)
								self.scheduler.set_sigmas(_SCREAMING_SNAKE_CASE			)

								for i, t in enumerate(self.progress_bar(self.scheduler.timesteps			)			):
														_UpperCAmelCase      =			self.scheduler.sigmas[i] * torch.ones(shape[0]						,		device=self.device			)

														# correction step
														for _ in range(self.scheduler.config.correct_steps			):
																				_UpperCAmelCase      =			self.unet(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			).sample
																				_UpperCAmelCase      =			self.scheduler.step_correct(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		generator=_SCREAMING_SNAKE_CASE			).prev_sample

														# prediction step
														_UpperCAmelCase      =			model(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			).sample
														_UpperCAmelCase      =			self.scheduler.step_pred(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		generator=_SCREAMING_SNAKE_CASE			)

														_UpperCAmelCase    ,					_UpperCAmelCase      =			output.prev_sample, output.prev_sample_mean

								_UpperCAmelCase      =			sample_mean.clamp(0						,		1			)
								_UpperCAmelCase      =			sample.cpu().permute(0						,		2						,		3						,		1			).numpy()
								if output_type == "pil":
														_UpperCAmelCase      =			self.numpy_to_pil(_SCREAMING_SNAKE_CASE			)

								if not return_dict:
														return (sample,)

								return ImagePipelineOutput(images=_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()