





from typing import List, Optional, Tuple, Union

import torch

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







class    __snake_case	(							__lowerCamelCase      ):





       '''simple docstring'''







       lowerCAmelCase__						=					42
       lowerCAmelCase__						=					42




       def __init__(  self			:   Dict							,    A			:   UNetaDModel							,    A			:   KarrasVeScheduler		):
         super().__init__()
         self.register_modules(unet=A							,    scheduler=A		)






       @torch.no_grad()
       def __call__(  self			:   Union[str, Any]							,    A			:   int = 1							,    A			:   int = 50							,    A			:   Optional[Union[torch.Generator, List[torch.Generator]]] = None							,    A			:   Optional[str] = "pil"							,    A			:   bool = True							,    **A			:   Tuple							,    ):
         __snake_case:						List[Any]			  =						self.unet.config.sample_size
         __snake_case:						Dict			  =						(batch_size, 3, img_size, img_size)

         __snake_case:						List[Any]			  =						self.unet

         # sample x_0 ~ N(0, sigma_0^2 * I)
         __snake_case:						List[str]			  =						randn_tensor(A							,    generator=A							,    device=self.device		) * self.scheduler.init_noise_sigma

         self.scheduler.set_timesteps(A		)

         for t in self.progress_bar(self.scheduler.timesteps		):
           # here sigma_t == t_i from the paper
           __snake_case:						Dict			  =						self.scheduler.schedule[t]
           __snake_case:						str			  =						self.scheduler.schedule[t - 1] if t > 0 else 0

           # 1. Select temporarily increased noise level sigma_hat
           # 2. Add new noise to move from sample_i to sample_hat
           __snake_case  ,       __snake_case:						Any			  =						self.scheduler.add_noise_to_input(A							,    A							,    generator=A		)

           # 3. Predict the noise residual given the noise magnitude `sigma_hat`
           # The model inputs and output are adjusted by following eq. (213) in [1].
           __snake_case:						int			  =						(sigma_hat / 2) * model((sample_hat + 1) / 2							,    sigma_hat / 2		).sample

           # 4. Evaluate dx/dt at sigma_hat
           # 5. Take Euler step from sigma to sigma_prev
           __snake_case:						Any			  =						self.scheduler.step(A							,    A							,    A							,    A		)

           if sigma_prev != 0:
             # 6. Apply 2nd order correction
             # The model inputs and output are adjusted by following eq. (213) in [1].
             __snake_case:						List[str]			  =						(sigma_prev / 2) * model((step_output.prev_sample + 1) / 2							,    sigma_prev / 2		).sample
             __snake_case:						str			  =						self.scheduler.step_correct(
                 A							,    A							,    A							,    A							,    step_output.prev_sample							,    step_output["""derivative"""]							,    )
           __snake_case:						Any			  =						step_output.prev_sample

         __snake_case:						Union[str, Any]			  =						(sample / 2 + 0.5).clamp(0							,    1		)
         __snake_case:						str			  =						sample.cpu().permute(0							,    2							,    3							,    1		).numpy()
         if output_type == "pil":
           __snake_case:						List[Any]			  =						self.numpy_to_pil(A		)

         if not return_dict:
           return (image,)

         return ImagePipelineOutput(images=A		)













from __future__ import annotations

import numpy as np







def       A__							(   SCREAMING_SNAKE_CASE__)       ->   List[str]:
  return np.maximum(0 , SCREAMING_SNAKE_CASE__)


if __name__ == "__main__":
      print(np.array(relu([-1, 0, 5])))  # --> [0, 0, 5]






