

'''simple docstring'''
from typing import List, Optional, Tuple, Union

import torch

from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







class 							A__    (       A__  ):





  def __init__(		self      :	str							,					_a      :	List[Any]							,					_a      :	Dict )	->    Union[str, Any]:





       '''simple docstring'''






       super().__init__()

       # make sure scheduler can always be converted to DDIM
       _SCREAMING_SNAKE_CASE							    =DDIMScheduler.from_config(scheduler.config )

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





  @torch.no_grad()
  def __call__(		self      :	List[Any]							,					_a      :	int = 1							,					_a      :	Optional[Union[torch.Generator, List[torch.Generator]]] = None							,					_a      :	float = 0.0							,					_a      :	int = 50							,					_a      :	Optional[bool] = None							,					_a      :	Optional[str] = "pil"							,					_a      :	bool = True							,					)	->    Union[ImagePipelineOutput, Tuple]:





       '''simple docstring'''






       if isinstance(self.unet.config.sample_size							,					_a ):
            _SCREAMING_SNAKE_CASE							    =(
                batch_size,
                self.unet.config.in_channels,
                self.unet.config.sample_size,
                self.unet.config.sample_size,
            )
       else:
            _SCREAMING_SNAKE_CASE							    =(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

       if isinstance(_a							,					_a ) and len(_a ) != batch_size:
            raise ValueError(
                f"You have passed a list of generators of length {len(_a )}, but requested an effective batch"
                f" size of {batch_size}. Make sure the batch size matches the length of the generators." )

       _SCREAMING_SNAKE_CASE							    =randn_tensor(_a							,					generator=_a							,					device=self.device							,					dtype=self.unet.dtype )

       # set step values
       self.scheduler.set_timesteps(_a )

       for t in self.progress_bar(self.scheduler.timesteps ):
            # 1. predict noise model_output
            _SCREAMING_SNAKE_CASE							    =self.unet(_a							,					_a ).sample

            # 2. predict previous mean of image x_t-1 and add variance depending on eta
            # eta corresponds to η in paper and should be between [0, 1]
            # do x_t -> x_t-1
            _SCREAMING_SNAKE_CASE							    =self.scheduler.step(
                _a							,					_a							,					_a							,					eta=_a							,					use_clipped_model_output=_a							,					generator=_a ).prev_sample

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

       if not return_dict:
            return (image,)

       return ImagePipelineOutput(images=_a )



'''simple docstring'''
lowerCamelCase :      Any					 =     "\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"

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