


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 					__lowerCamelCase	(_a		):
       _lowercase    							=      42
       _lowercase    							=      42







       def __init__(	self:				Optional[Any],A_:				UNetaDModel,A_:				PNDMScheduler			):






            '''simple docstring'''




            super().__init__()

            __UpperCamelCase				=	PNDMScheduler.from_config(scheduler.config			)

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







       @torch.no_grad()
       def __call__(	self:				Any,A_:				int = 1,A_:				int = 50,A_:				Optional[Union[torch.Generator, List[torch.Generator]]] = None,A_:				Optional[str] = "pil",A_:				bool = True,**A_:				Optional[int],):






            '''simple docstring'''




            __UpperCamelCase				=	randn_tensor(
                (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size),generator=A_,device=self.device,)

            self.scheduler.set_timesteps(A_			)
            for t in self.progress_bar(self.scheduler.timesteps			):
                 __UpperCamelCase				=	self.unet(A_,A_			).sample

                 __UpperCamelCase				=	self.scheduler.step(A_,A_,A_			).prev_sample

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

            if not return_dict:
                 return (image,)

            return ImagePipelineOutput(images=A_			)






import matplotlib.pyplot as plt
import numpy as np






def 						_A						(						_lowercase			,       _lowercase			,       _lowercase				)			->				np.ndarray:


     """simple docstring"""


     __UpperCamelCase				=	len(_lowercase				)  # Number of training samples
     __UpperCamelCase				=	np.eye(_lowercase				)  # Initialize weights as identity matrix
     for j in range(_lowercase				):
          __UpperCamelCase				=	point - x_train[j]
          __UpperCamelCase				=	np.exp(diff @ diff.T / (-2.0 * tau**2)				)

     return weights






def 						_A						(						_lowercase			,       _lowercase			,       _lowercase			,       _lowercase				)			->				np.ndarray:


     """simple docstring"""


     __UpperCamelCase				=	weight_matrix(_lowercase			,       _lowercase			,       _lowercase				)
     __UpperCamelCase				=	np.linalg.inv(x_train.T @ weight_mat @ x_train				) @ (
         x_train.T @ weight_mat @ y_train.T
     )

     return weight






def 						_A						(						_lowercase			,       _lowercase			,       _lowercase				)			->				np.ndarray:


     """simple docstring"""


     __UpperCamelCase				=	np.zeros(len(_lowercase				)				)  # Initialize array of predictions
     for i, item in enumerate(_lowercase				):
          __UpperCamelCase				=	item @ local_weight(_lowercase			,       _lowercase			,       _lowercase			,       _lowercase				)

     return y_pred






def 						_A						(						_lowercase			,       _lowercase			,       _lowercase				)			->				tuple[np.ndarray, np.ndarray, np.ndarray]:


     """simple docstring"""


     import seaborn as sns

     __UpperCamelCase				=	sns.load_dataset(_lowercase				)
     __UpperCamelCase				=	np.array(data[x_name]				)
     __UpperCamelCase				=	np.array(data[y_name]				)

     __UpperCamelCase				=	np.ones(len(_lowercase				)				)

     # pairing elements of one and x_data
     __UpperCamelCase				=	np.column_stack((one, x_data)				)

     return x_train, x_data, y_data






def 						_A						(						_lowercase			,       _lowercase			,       _lowercase			,       _lowercase			,       _lowercase			,       _lowercase			,       )			->				None:


     """simple docstring"""


     __UpperCamelCase				=	np.sort(_lowercase			,       axis=0				)
     plt.scatter(_lowercase			,       _lowercase			,       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(_lowercase				)
     plt.ylabel(_lowercase				)
     plt.show()


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    # Demo with a dataset from the seaborn module
    __snake_case    ,					__snake_case    ,					__snake_case     =		load_data('''tips''', '''total_bill''', '''tip''')
    __snake_case     =		local_weight_regression(training_data_x, tip, 5)
    plot_preds(training_data_x, predictions, total_bill, tip, '''total_bill''', '''tip''')


