'''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      ):


  """simple docstring"""

  def __init__(		self      :       Dict   ,				lowercase_      :       Union[str, Any]   ,				lowercase_      :       Optional[int]      ):







    '''simple docstring'''


    super().__init__()

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

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





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







    '''simple docstring'''


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

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

    lowercase_     =       randn_tensor(lowercase_   ,				generator=lowercase_   ,				device=self.device   ,				dtype=self.unet.dtype      )

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

    for t in self.progress_bar(self.scheduler.timesteps      ):
      # 1. predict noise model_output
      lowercase_     =       self.unet(lowercase_   ,				lowercase_      ).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
      lowercase_     =       self.scheduler.step(
          lowercase_   ,				lowercase_   ,				lowercase_   ,				eta=lowercase_   ,				use_clipped_model_output=lowercase_   ,				generator=lowercase_      ).prev_sample

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

    if not return_dict:
      return (image,)

    return ImagePipelineOutput(images=lowercase_      )


'''simple docstring'''


import warnings

from ...utils import logging
from .image_processing_poolformer import PoolFormerImageProcessor


__snake_case						  =		logging.get_logger(__name__)





class       _a    (		__a      ):


  """simple docstring"""

  def __init__(		self      :       int   ,				*lowercase_      :       Tuple   ,				**lowercase_      :       List[Any]      ):







    '''simple docstring'''


    warnings.warn(
        """The class PoolFormerFeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
        """ Please use PoolFormerImageProcessor instead."""   ,				lowercase_   ,				)
    super().__init__(*lowercase_   ,				**lowercase_      )

