'''simple docstring'''
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 lowerCamelCase							(	lowercase__					):




  '''simple docstring'''



  lowerCAmelCase__										=	42
  lowerCAmelCase__										=	42






  def __init__(      self			:       Dict  ,							UpperCAmelCase__			:       UNetaDModel  ,							UpperCAmelCase__			:       KarrasVeScheduler  )				->Optional[Any]:
        super().__init__()
        self.register_modules(unet=__lowercase  ,							scheduler=__lowercase  )






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

        UpperCAmelCase_            =  self.unet

        # sample x_0 ~ N(0, sigma_0^2 * I)
        UpperCAmelCase_            =  randn_tensor(__lowercase  ,							generator=__lowercase  ,							device=self.device  ) * self.scheduler.init_noise_sigma

        self.scheduler.set_timesteps(__lowercase  )

        for t in self.progress_bar(self.scheduler.timesteps  ):
              # here sigma_t == t_i from the paper
              UpperCAmelCase_            =  self.scheduler.schedule[t]
              UpperCAmelCase_            =  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
              UpperCAmelCase_      ,		UpperCAmelCase_            =  self.scheduler.add_noise_to_input(__lowercase  ,							__lowercase  ,							generator=__lowercase  )

              # 3. Predict the noise residual given the noise magnitude `sigma_hat`
              # The model inputs and output are adjusted by following eq. (213) in [1].
              UpperCAmelCase_            =  (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
              UpperCAmelCase_            =  self.scheduler.step(__lowercase  ,							__lowercase  ,							__lowercase  ,							__lowercase  )

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

        UpperCAmelCase_            =  (sample / 2 + 0.5).clamp(0  ,							1  )
        UpperCAmelCase_            =  sample.cpu().permute(0  ,							2  ,							3  ,							1  ).numpy()
        if output_type == "pil":
              UpperCAmelCase_            =  self.numpy_to_pil(__lowercase  )

        if not return_dict:
              return (image,)

        return ImagePipelineOutput(images=__lowercase  )






'''simple docstring'''
lowercase__  :      Union[str, Any]        =					"\n# Transformers 설치 방법\n! pip install transformers datasets\n# 마지막 릴리스 대신 소스에서 설치하려면, 위 명령을 주석으로 바꾸고 아래 명령을 해제하세요.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"

lowercase__  :      str        =					[{"type": "code", "content": INSTALL_CONTENT}]
lowercase__  :      Any        =					{
    "{processor_class}": "FakeProcessorClass",
    "{model_class}": "FakeModelClass",
    "{object_class}": "FakeObjectClass",
}





