





import warnings
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch

from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


UpperCamelCase__											= logging.get_logger(__name__)  # pylint: disable=invalid-name





def     _UpperCamelCase     (a__   :Union[List, PIL.Image.Image, torch.Tensor]       ):






       """simple docstring"""





       warnings.warn(
           """The preprocess method is deprecated and will be removed in a future version. Please"""
           """ use VaeImageProcessor.preprocess instead"""   ,			lowerCamelCase_   ,			)
       if isinstance(lowerCamelCase_   ,			torch.Tensor       ):
              return image
       elif isinstance(lowerCamelCase_   ,			PIL.Image.Image       ):
              UpperCamelCase__				     =			[image]

       if isinstance(image[0]   ,			PIL.Image.Image       ):
              UpperCamelCase__     ,     UpperCamelCase__				     =			image[0].size
              UpperCamelCase__     ,     UpperCamelCase__				     =			(x - x % 8 for x in (w, h))  # resize to integer multiple of 8

              UpperCamelCase__				     =			[np.array(i.resize((w, h)   ,			resample=PIL_INTERPOLATION["""lanczos"""]       )       )[None, :] for i in image]
              UpperCamelCase__				     =			np.concatenate(lowerCamelCase_   ,			axis=0       )
              UpperCamelCase__				     =			np.array(lowerCamelCase_       ).astype(np.floataa       ) / 255.0
              UpperCamelCase__				     =			image.transpose(0   ,			3   ,			1   ,			2       )
              UpperCamelCase__				     =			2.0 * image - 1.0
              UpperCamelCase__				     =			torch.from_numpy(lowerCamelCase_       )
       elif isinstance(image[0]   ,			torch.Tensor       ):
              UpperCamelCase__				     =			torch.cat(lowerCamelCase_   ,			dim=0       )
       return image





def     _UpperCamelCase     (a__   :Union[List, PIL.Image.Image, torch.Tensor]       ):






       """simple docstring"""





       if isinstance(lowerCamelCase_   ,			torch.Tensor       ):
              return mask
       elif isinstance(lowerCamelCase_   ,			PIL.Image.Image       ):
              UpperCamelCase__				     =			[mask]

       if isinstance(mask[0]   ,			PIL.Image.Image       ):
              UpperCamelCase__     ,     UpperCamelCase__				     =			mask[0].size
              UpperCamelCase__     ,     UpperCamelCase__				     =			(x - x % 32 for x in (w, h))  # resize to integer multiple of 32
              UpperCamelCase__				     =			[np.array(m.convert("""L"""       ).resize((w, h)   ,			resample=PIL_INTERPOLATION["""nearest"""]       )       )[None, :] for m in mask]
              UpperCamelCase__				     =			np.concatenate(lowerCamelCase_   ,			axis=0       )
              UpperCamelCase__				     =			mask.astype(np.floataa       ) / 255.0
              UpperCamelCase__				     =			0
              UpperCamelCase__				     =			1
              UpperCamelCase__				     =			torch.from_numpy(lowerCamelCase_       )
       elif isinstance(mask[0]   ,			torch.Tensor       ):
              UpperCamelCase__				     =			torch.cat(lowerCamelCase_   ,			dim=0       )
       return mask

class        __SCREAMING_SNAKE_CASE					(     lowerCAmelCase__       ):
       snake_case     :      UNetaDModel
       snake_case     :      RePaintScheduler






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







       @torch.no_grad()
       def __call__(						self  , __lowerCAmelCase  , __lowerCAmelCase  , __lowerCAmelCase = 250  , __lowerCAmelCase = 0.0  , __lowerCAmelCase = 10  , __lowerCAmelCase = 10  , __lowerCAmelCase = None  , __lowerCAmelCase = "pil"  , __lowerCAmelCase = True  , ):
              UpperCamelCase__				     =			image

              UpperCamelCase__				     =			_preprocess_image(_lowerCamelCase  )
              UpperCamelCase__				     =			original_image.to(device=self.device  , dtype=self.unet.dtype  )
              UpperCamelCase__				     =			_preprocess_mask(_lowerCamelCase  )
              UpperCamelCase__				     =			mask_image.to(device=self.device  , dtype=self.unet.dtype  )

              UpperCamelCase__				     =			original_image.shape[0]

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

              UpperCamelCase__				     =			original_image.shape
              UpperCamelCase__				     =			randn_tensor(_lowerCamelCase  , generator=_lowerCamelCase  , device=self.device  , dtype=self.unet.dtype  )

              # set step values
              self.scheduler.set_timesteps(_lowerCamelCase  , _lowerCamelCase  , _lowerCamelCase  , self.device  )
              UpperCamelCase__				     =			eta

              UpperCamelCase__				     =			self.scheduler.timesteps[0] + 1
              UpperCamelCase__				     =			generator[0] if isinstance(_lowerCamelCase  , _lowerCamelCase  ) else generator
              for i, t in enumerate(self.progress_bar(self.scheduler.timesteps  )  ):
                     if t < t_last:
                            # predict the noise residual
                            UpperCamelCase__				     =			self.unet(_lowerCamelCase  , _lowerCamelCase  ).sample
                            # compute previous image: x_t -> x_t-1
                            UpperCamelCase__				     =			self.scheduler.step(_lowerCamelCase  , _lowerCamelCase  , _lowerCamelCase  , _lowerCamelCase  , _lowerCamelCase  , _lowerCamelCase  ).prev_sample

                     else:
                            # compute the reverse: x_t-1 -> x_t
                            UpperCamelCase__				     =			self.scheduler.undo_step(_lowerCamelCase  , _lowerCamelCase  , _lowerCamelCase  )
                     UpperCamelCase__				     =			t

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

              if not return_dict:
                     return (image,)

              return ImagePipelineOutput(images=_lowerCamelCase  )











from argparse import ArgumentParser

from .env import EnvironmentCommand





def     _UpperCamelCase     ():






							"""simple docstring"""





							UpperCamelCase__				     =			ArgumentParser("""Diffusers CLI tool"""   ,			usage="""diffusers-cli <command> [<args>]"""       )
							UpperCamelCase__				     =			parser.add_subparsers(help="""diffusers-cli command helpers"""       )

							# Register commands
							EnvironmentCommand.register_subcommand(a__       )

							# Let's go
							UpperCamelCase__				     =			parser.parse_args()

							if not hasattr(a__   ,			"""func"""       ):
														parser.print_help()
														exit(1       )

							# Run
							UpperCamelCase__				     =			args.func(a__       )
							service.run()


if __name__ == "__main__":
					main()




