import matplotlib.pyplot as plt
import numpy as np






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

       return weights






def  __lowerCamelCase						(		lowerCAmelCase__      ,    lowerCAmelCase__      ,    lowerCAmelCase__      ,    lowerCAmelCase__      ):
       lowerCAmelCase__	      =   weight_matrix(lowerCAmelCase__      ,    lowerCAmelCase__      ,    lowerCAmelCase__      )
       lowerCAmelCase__	      =   np.linalg.inv(x_train.T @ weight_mat @ x_train      ) @ (
           x_train.T @ weight_mat @ y_train.T
       )

       return weight






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

       return y_pred






def  __lowerCamelCase						(		lowerCAmelCase__      ,    lowerCAmelCase__      ,    lowerCAmelCase__      ):
       import seaborn as sns

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

       lowerCAmelCase__	      =   np.ones(len(lowerCAmelCase__      )      )

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

       return x_train, x_data, y_data






def  __lowerCamelCase						(		lowerCAmelCase__      ,    lowerCAmelCase__      ,    lowerCAmelCase__      ,    lowerCAmelCase__      ,    lowerCAmelCase__      ,    lowerCAmelCase__      ,    ):
       lowerCAmelCase__	      =   np.sort(lowerCAmelCase__      ,    axis=0      )
       plt.scatter(lowerCAmelCase__      ,    lowerCAmelCase__      ,    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(lowerCAmelCase__      )
       plt.ylabel(lowerCAmelCase__      )
       plt.show()


if __name__ == "__main__":
    import doctest

    doctest.testmod()

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

from typing import Union

import torch
from PIL import Image
from torchvision import transforms as tfms
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
class 							a_					(		SCREAMING_SNAKE_CASE		):






 '''simple docstring'''






 def __init__(     self   :    List[Any]     ,       lowercase__   :    AutoencoderKL     ,       lowercase__   :    CLIPTextModel     ,       lowercase__   :    CLIPTokenizer     ,       lowercase__   :    UNetaDConditionModel     ,       lowercase__   :    Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler]     ,       ):







        '''simple docstring'''



        super().__init__()

        self.register_modules(vae=lowercase__     ,       text_encoder=lowercase__     ,       tokenizer=lowercase__     ,       unet=lowercase__     ,       scheduler=lowercase__)




 def 						__snake_case		(     self   :    List[Any]     ,       lowercase__   :    str):







        '''simple docstring'''



        with torch.no_grad():
               lowerCAmelCase__	      =   self.vae.encode(tfms.ToTensor()(lowercase__).unsqueeze(0).to(self.device) * 2 - 1)
               lowerCAmelCase__	      =   0.18_215 * latent.latent_dist.sample()
        return latent




 def 						__snake_case		(     self   :    Tuple     ,       lowercase__   :    str):







        '''simple docstring'''



        lowerCAmelCase__	      =   (1 / 0.18_215) * latent
        with torch.no_grad():
               lowerCAmelCase__	      =   self.vae.decode(lowercase__).sample
        lowerCAmelCase__	      =   (img / 2 + 0.5).clamp(0     ,       1)
        lowerCAmelCase__	      =   img.detach().cpu().permute(0     ,       2     ,       3     ,       1).numpy()
        lowerCAmelCase__	      =   (img * 255).round().astype('uint8')
        return Image.fromarray(img[0])




 def 						__snake_case		(     self   :    Union[str, Any]     ,       lowercase__   :    Optional[int]):







        '''simple docstring'''



        lowerCAmelCase__	      =   self.tokenizer(
            lowercase__     ,       padding='max_length'     ,       max_length=self.tokenizer.model_max_length     ,       truncation=lowercase__     ,       return_tensors='pt'     ,       )

        lowerCAmelCase__	      =   self.text_encoder(text_input.input_ids.to(self.device))[0]

        lowerCAmelCase__	      =   self.tokenizer(
            ''     ,       padding='max_length'     ,       max_length=self.tokenizer.model_max_length     ,       truncation=lowercase__     ,       return_tensors='pt'     ,       )

        lowerCAmelCase__	      =   self.text_encoder(uncond_input.input_ids.to(self.device))[0]

        return torch.cat([uncond_embedding, text_embedding])




 def __call__(     self   :    List[Any]     ,       lowercase__   :    Image.Image     ,       lowercase__   :    str     ,       lowercase__   :    float = 0.3     ,       lowercase__   :    float = 0.6     ,       lowercase__   :    float = 0.5     ,       lowercase__   :    int = 42     ,       lowercase__   :    int = 50     ,       lowercase__   :    float = 7.5     ,       ):







        '''simple docstring'''



        lowerCAmelCase__	      =   steps - int(kmin * steps)
        lowerCAmelCase__	      =   steps - int(kmax * steps)

        lowerCAmelCase__	      =   self.prep_text(lowercase__)

        self.scheduler.set_timesteps(lowercase__)

        lowerCAmelCase__      ,   lowerCAmelCase__	      =   img.size
        lowerCAmelCase__	      =   self.encode(lowercase__)

        torch.manual_seed(lowercase__)
        lowerCAmelCase__	      =   torch.randn(
            (1, self.unet.config.in_channels, height // 8, width // 8)     ,       ).to(self.device)

        lowerCAmelCase__	      =   self.scheduler.add_noise(
            lowercase__     ,       lowercase__     ,       timesteps=self.scheduler.timesteps[tmax]     ,       )

        lowerCAmelCase__	      =   torch.cat([latents] * 2)

        lowerCAmelCase__	      =   self.scheduler.scale_model_input(lowercase__     ,       self.scheduler.timesteps[tmax])

        with torch.no_grad():
               lowerCAmelCase__	      =   self.unet(
                   lowercase__     ,       self.scheduler.timesteps[tmax]     ,       encoder_hidden_states=lowercase__     ,       ).sample

        lowerCAmelCase__      ,   lowerCAmelCase__	      =   pred.chunk(2)
        lowerCAmelCase__	      =   pred_uncond + guidance_scale * (pred_text - pred_uncond)

        lowerCAmelCase__	      =   self.scheduler.step(lowercase__     ,       self.scheduler.timesteps[tmax]     ,       lowercase__).prev_sample

        for i, t in enumerate(tqdm(self.scheduler.timesteps)):
               if i > tmax:
                      if i < tmin:  # layout generation phase
                             lowerCAmelCase__	      =   self.scheduler.add_noise(
                                 lowercase__     ,       lowercase__     ,       timesteps=lowercase__     ,       )

                             lowerCAmelCase__	      =   (mix_factor * latents) + (
                                 1 - mix_factor
                             ) * orig_latents  # interpolating between layout noise and conditionally generated noise to preserve layout sematics
                             lowerCAmelCase__	      =   torch.cat([input] * 2)

                      else:  # content generation phase
                             lowerCAmelCase__	      =   torch.cat([latents] * 2)

                      lowerCAmelCase__	      =   self.scheduler.scale_model_input(lowercase__     ,       lowercase__)

                      with torch.no_grad():
                             lowerCAmelCase__	      =   self.unet(
                                 lowercase__     ,       lowercase__     ,       encoder_hidden_states=lowercase__     ,       ).sample

                      lowerCAmelCase__      ,   lowerCAmelCase__	      =   pred.chunk(2)
                      lowerCAmelCase__	      =   pred_uncond + guidance_scale * (pred_text - pred_uncond)

                      lowerCAmelCase__	      =   self.scheduler.step(lowercase__     ,       lowercase__     ,       lowercase__).prev_sample

        return self.decode(lowercase__)
