


import matplotlib.pyplot as plt
import numpy as np






def 	a__ (							A__,			A__,			A__							):
			SCREAMING_SNAKE_CASE_		:    List[Any] 		=				len(A__							)  # Number of training samples
			SCREAMING_SNAKE_CASE_		:    int 		=				np.eye(A__							)  # Initialize weights as identity matrix
			for j in range(A__							):
						SCREAMING_SNAKE_CASE_		:    str 		=				point - x_train[j]
						SCREAMING_SNAKE_CASE_		:    Union[str, Any] 		=				np.exp(diff @ diff.T / (-2.0 * tau**2)							)

			return weights






def 	a__ (							A__,			A__,			A__,			A__							):
			SCREAMING_SNAKE_CASE_		:    List[str] 		=				weight_matrix(A__,			A__,			A__							)
			SCREAMING_SNAKE_CASE_		:    Optional[Any] 		=				np.linalg.inv(x_train.T @ weight_mat @ x_train							) @ (
			    x_train.T @ weight_mat @ y_train.T
			)

			return weight






def 	a__ (							A__,			A__,			A__							):
			SCREAMING_SNAKE_CASE_		:    int 		=				np.zeros(len(A__							)							)  # Initialize array of predictions
			for i, item in enumerate(A__							):
						SCREAMING_SNAKE_CASE_		:    str 		=				item @ local_weight(A__,			A__,			A__,			A__							)

			return y_pred






def 	a__ (							A__,			A__,			A__							):
			import seaborn as sns

			SCREAMING_SNAKE_CASE_		:    Tuple 		=				sns.load_dataset(A__							)
			SCREAMING_SNAKE_CASE_		:    Union[str, Any] 		=				np.array(data[x_name]							)
			SCREAMING_SNAKE_CASE_		:    Tuple 		=				np.array(data[y_name]							)

			SCREAMING_SNAKE_CASE_		:    Optional[int] 		=				np.ones(len(A__							)							)

			# pairing elements of one and x_data
			SCREAMING_SNAKE_CASE_		:    str 		=				np.column_stack((one, x_data)							)

			return x_train, x_data, y_data






def 	a__ (							A__,			A__,			A__,			A__,			A__,			A__,			):
			SCREAMING_SNAKE_CASE_		:    Union[str, Any] 		=				np.sort(A__,			axis=0							)
			plt.scatter(A__,			A__,			color='blue'							)
			plt.plot(
			    x_train_sorted[:, 1],			preds[x_train[:, 1].argsort(0							)],			color='yellow',			linewidth=5,			)
			plt.title('Local Weighted Regression'							)
			plt.xlabel(A__							)
			plt.ylabel(A__							)
			plt.show()


if __name__ == "__main__":
				import doctest

				doctest.testmod()

				# Demo with a dataset from the seaborn module
				lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__ :			List[Any]         =load_data('tips', 'total_bill', 'tip')
				lowerCAmelCase__ :			Optional[Any]         =local_weight_regression(training_data_x, tip, 5)
				plot_preds(training_data_x, predictions, total_bill, tip, 'total_bill', 'tip')









from typing import List, Optional, Tuple, Union

import torch

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







class   __lowercase						(__SCREAMING_SNAKE_CASE     ):





							"""simple docstring"""







							_UpperCAmelCase						     =	42
							_UpperCAmelCase						     =	42






							def __init__(			self ,	lowerCAmelCase__ ,	lowerCAmelCase__					):





										"""simple docstring"""




										super().__init__()

										SCREAMING_SNAKE_CASE_		:    str 		=				PNDMScheduler.from_config(scheduler.config					)

										self.register_modules(unet=lowerCAmelCase__ ,	scheduler=lowerCAmelCase__					)






							@torch.no_grad()
							def __call__(			self ,	lowerCAmelCase__ = 1 ,	lowerCAmelCase__ = 5_0 ,	lowerCAmelCase__ = None ,	lowerCAmelCase__ = "pil" ,	lowerCAmelCase__ = True ,	**lowerCAmelCase__ ,	):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:    Tuple 		=				randn_tensor(
										    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size) ,	generator=lowerCAmelCase__ ,	device=self.device ,	)

										self.scheduler.set_timesteps(lowerCAmelCase__					)
										for t in self.progress_bar(self.scheduler.timesteps					):
													SCREAMING_SNAKE_CASE_		:    Dict 		=				self.unet(lowerCAmelCase__ ,	lowerCAmelCase__					).sample

													SCREAMING_SNAKE_CASE_		:    Optional[Any] 		=				self.scheduler.step(lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__					).prev_sample

										SCREAMING_SNAKE_CASE_		:    Dict 		=				(image / 2 + 0.5).clamp(0 ,	1					)
										SCREAMING_SNAKE_CASE_		:    Any 		=				image.cpu().permute(0 ,	2 ,	3 ,	1					).numpy()
										if output_type == "pil":
													SCREAMING_SNAKE_CASE_		:    str 		=				self.numpy_to_pil(lowerCAmelCase__					)

										if not return_dict:
													return (image,)

										return ImagePipelineOutput(images=lowerCAmelCase__					)





