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

import torch

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




class _lowerCAmelCase (  snake_case				):







      def __init__(     self     ,						lowercase     ,						lowercase    ):
        super().__init__()
        self.register_modules(unet=lowercase     ,						scheduler=lowercase    )







      @torch.no_grad()
      def __call__(     self     ,						lowercase = 1     ,						lowercase = None     ,						lowercase = 1000     ,						lowercase = "pil"     ,						lowercase = True     ,						):
        # Sample gaussian noise to begin loop
        if isinstance(self.unet.config.sample_size     ,						lowercase    ):
          lowercase							:       Any											=      (
              batch_size,
              self.unet.config.in_channels,
              self.unet.config.sample_size,
              self.unet.config.sample_size,
          )
        else:
          lowercase							:       List[Any]											=      (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

        if self.device.type == "mps":
          # randn does not work reproducibly on mps
          lowercase							:       Any											=      randn_tensor(lowercase     ,						generator=lowercase    )
          lowercase							:       List[Any]											=      image.to(self.device    )
        else:
          lowercase							:       List[str]											=      randn_tensor(lowercase     ,						generator=lowercase     ,						device=self.device    )

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

        for t in self.progress_bar(self.scheduler.timesteps    ):
          # 1. predict noise model_output
          lowercase							:       Optional[Any]											=      self.unet(lowercase     ,						lowercase    ).sample

          # 2. compute previous image: x_t -> x_t-1
          lowercase							:       Dict											=      self.scheduler.step(lowercase     ,						lowercase     ,						lowercase     ,						generator=lowercase    ).prev_sample

        lowercase							:       Any											=      (image / 2 + 0.5).clamp(0     ,						1    )
        lowercase							:       Union[str, Any]											=      image.cpu().permute(0     ,						2     ,						3     ,						1    ).numpy()
        if output_type == "pil":
          lowercase							:       List[Any]											=      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_layoutlmva import LayoutLMvaImageProcessor


_A		:			Optional[int]	      =logging.get_logger(__name__)




class _lowerCAmelCase (  snake_case				):







      def __init__(     self     ,						*lowercase     ,						**lowercase    ):
        warnings.warn(
            '''The class LayoutLMv3FeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
            ''' Please use LayoutLMv3ImageProcessor instead.'''     ,						lowercase     ,						)
        super().__init__(*lowercase     ,						**lowercase    )