


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 : Union[str, Any] 			=				logging.get_logger(__name__)  # pylint: disable=invalid-name


def      a	(							SCREAMING_SNAKE_CASE_      :		Dict       ):






       """simple docstring"""





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

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

              UpperCamelCase    :	Tuple   	=		[np.array(i.resize((w, h)							,			resample=PIL_INTERPOLATION['''lanczos''']       )       )[None, :] for i in image]
              UpperCamelCase    :	Any   	=		np.concatenate(_SCREAMING_SNAKE_CASE							,			axis=0       )
              UpperCamelCase    :	Optional[int]   	=		np.array(_SCREAMING_SNAKE_CASE       ).astype(np.floataa       ) / 255.0
              UpperCamelCase    :	Union[str, Any]   	=		image.transpose(0							,			3							,			1							,			2       )
              UpperCamelCase    :	Optional[int]   	=		2.0 * image - 1.0
              UpperCamelCase    :	List[Any]   	=		torch.from_numpy(_SCREAMING_SNAKE_CASE       )
       elif isinstance(image[0]							,			torch.Tensor       ):
              UpperCamelCase    :	int   	=		torch.cat(_SCREAMING_SNAKE_CASE							,			dim=0       )
       return image


def      a	(							SCREAMING_SNAKE_CASE_      :		int       ):






       """simple docstring"""





       if isinstance(_SCREAMING_SNAKE_CASE							,			torch.Tensor       ):
              return mask
       elif isinstance(_SCREAMING_SNAKE_CASE							,			PIL.Image.Image       ):
              UpperCamelCase    :	List[Any]   	=		[mask]

       if isinstance(mask[0]							,			PIL.Image.Image       ):
              UpperCamelCase	, UpperCamelCase    :	Union[str, Any]   	=		mask[0].size
              UpperCamelCase	, UpperCamelCase    :	List[Any]   	=		(x - x % 3_2 for x in (w, h))  # resize to integer multiple of 32
              UpperCamelCase    :	Union[str, Any]   	=		[np.array(m.convert('''L'''       ).resize((w, h)							,			resample=PIL_INTERPOLATION['''nearest''']       )       )[None, :] for m in mask]
              UpperCamelCase    :	List[str]   	=		np.concatenate(_SCREAMING_SNAKE_CASE							,			axis=0       )
              UpperCamelCase    :	Optional[Any]   	=		mask.astype(np.floataa       ) / 255.0
              UpperCamelCase    :	List[Any]   	=		0
              UpperCamelCase    :	Tuple   	=		1
              UpperCamelCase    :	int   	=		torch.from_numpy(_SCREAMING_SNAKE_CASE       )
       elif isinstance(mask[0]							,			torch.Tensor       ):
              UpperCamelCase    :	List[Any]   	=		torch.cat(_SCREAMING_SNAKE_CASE							,			dim=0       )
       return mask



class    UpperCAmelCase_	(							snake_case__):
       '''simple docstring'''
       __UpperCamelCase      :     UNetaDModel
       __UpperCamelCase      :     RePaintScheduler






       def __init__(     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              super().__init__()
              self.register_modules(unet=UpperCAmelCase_ ,					scheduler=UpperCAmelCase_						)






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




              """simple docstring"""




              UpperCamelCase    :	Optional[int]   	=		image

              UpperCamelCase    :	int   	=		_preprocess_image(UpperCAmelCase_						)
              UpperCamelCase    :	str   	=		original_image.to(device=self.device ,					dtype=self.unet.dtype						)
              UpperCamelCase    :	Tuple   	=		_preprocess_mask(UpperCAmelCase_						)
              UpperCamelCase    :	str   	=		mask_image.to(device=self.device ,					dtype=self.unet.dtype						)

              UpperCamelCase    :	Union[str, Any]   	=		original_image.shape[0]

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

              UpperCamelCase    :	List[Any]   	=		original_image.shape
              UpperCamelCase    :	Optional[int]   	=		randn_tensor(UpperCAmelCase_ ,					generator=UpperCAmelCase_ ,					device=self.device ,					dtype=self.unet.dtype						)

              # set step values
              self.scheduler.set_timesteps(UpperCAmelCase_ ,					UpperCAmelCase_ ,					UpperCAmelCase_ ,					self.device						)
              UpperCamelCase    :	Any   	=		eta

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

                     else:
                            # compute the reverse: x_t-1 -> x_t
                            UpperCamelCase    :	Optional[Any]   	=		self.scheduler.undo_step(UpperCAmelCase_ ,					UpperCAmelCase_ ,					UpperCAmelCase_						)
                     UpperCamelCase    :	Optional[int]   	=		t

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

              if not return_dict:
                     return (image,)

              return ImagePipelineOutput(images=UpperCAmelCase_						)







def      a	(							SCREAMING_SNAKE_CASE_      :		int       ):






       """simple docstring"""





       if number > 0:
              raise ValueError('''input must be a negative integer'''       )
       UpperCamelCase    :	List[str]   	=		len(bin(SCREAMING_SNAKE_CASE_       )[3:]       )
       UpperCamelCase    :	List[str]   	=		bin(abs(SCREAMING_SNAKE_CASE_       ) - (1 << binary_number_length)       )[3:]
       UpperCamelCase    :	Dict   	=		(
           (
               '''1'''
               + '''0''' * (binary_number_length - len(SCREAMING_SNAKE_CASE_       ))
               + twos_complement_number
           )
           if number < 0
           else '''0'''
       )
       return "0b" + twos_complement_number


if __name__ == "__main__":
  import doctest

  doctest.testmod()

