




"""simple docstring"""





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


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

def        _UpperCamelCase     (      UpperCamelCase				)      ->      Any:


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

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

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

def        _UpperCamelCase     (      UpperCamelCase				)      ->      List[Any]:


			"""simple docstring"""
			if isinstance(UpperCamelCase					,							torch.Tensor				):
						return mask
			elif isinstance(UpperCamelCase					,							PIL.Image.Image				):
						__UpperCAmelCase						:			str												=       [mask]

			if isinstance(mask[0]					,							PIL.Image.Image				):
						__UpperCAmelCase						,  __UpperCAmelCase						:			Dict												=       mask[0].size
						__UpperCAmelCase						,  __UpperCAmelCase						:			Tuple												=       (x - x % 32 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						:			Optional[Any]												=       np.concatenate(UpperCamelCase					,							axis=0				)
						__UpperCAmelCase						:			Dict												=       mask.astype(np.floataa				) / 255.0
						__UpperCAmelCase						:			int												=       0
						__UpperCAmelCase						:			Dict												=       1
						__UpperCAmelCase						:			List[str]												=       torch.from_numpy(UpperCamelCase				)
			elif isinstance(mask[0]					,							torch.Tensor				):
						__UpperCAmelCase						:			List[str]												=       torch.cat(UpperCamelCase					,							dim=0				)
			return mask





class   a__					(						__magic_name__ ):
			lowercase_		=						42
			lowercase_		=						42


			def __init__(   self   :     List[Any]   ,	UpperCamelCase_   :     Dict   ,	UpperCamelCase_   :     List[str]):


						"""simple docstring"""




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


			@torch.no_grad()
			def __call__(   self   :     int   ,	UpperCamelCase_   :     Union[torch.Tensor, PIL.Image.Image]   ,	UpperCamelCase_   :     Union[torch.Tensor, PIL.Image.Image]   ,	UpperCamelCase_   :     int = 250   ,	UpperCamelCase_   :     float = 0.0   ,	UpperCamelCase_   :     int = 10   ,	UpperCamelCase_   :     int = 10   ,	UpperCamelCase_   :     Optional[Union[torch.Generator, List[torch.Generator]]] = None   ,	UpperCamelCase_   :     Optional[str] = "pil"   ,	UpperCamelCase_   :     bool = True   ,	):


						"""simple docstring"""




						__UpperCAmelCase						:			Dict												=       image

						__UpperCAmelCase						:			Any												=       _preprocess_image(UpperCamelCase_)
						__UpperCAmelCase						:			str												=       original_image.to(device=self.device   ,	dtype=self.unet.dtype)
						__UpperCAmelCase						:			List[str]												=       _preprocess_mask(UpperCamelCase_)
						__UpperCAmelCase						:			List[Any]												=       mask_image.to(device=self.device   ,	dtype=self.unet.dtype)

						__UpperCAmelCase						:			Dict												=       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						:			Tuple												=       original_image.shape
						__UpperCAmelCase						:			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						:			List[Any]												=       self.scheduler.timesteps[0] + 1
						__UpperCAmelCase						:			Optional[Any]												=       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						:			Optional[Any]												=       self.unet(UpperCamelCase_   ,	UpperCamelCase_).sample
												# compute previous image: x_t -> x_t-1
												__UpperCAmelCase						:			int												=       self.scheduler.step(UpperCamelCase_   ,	UpperCamelCase_   ,	UpperCamelCase_   ,	UpperCamelCase_   ,	UpperCamelCase_   ,	UpperCamelCase_).prev_sample

									else:
												# compute the reverse: x_t-1 -> x_t
												__UpperCAmelCase						:			Union[str, Any]												=       self.scheduler.undo_step(UpperCamelCase_   ,	UpperCamelCase_   ,	UpperCamelCase_)
									__UpperCAmelCase						:			Tuple												=       t

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

						if not return_dict:
									return (image,)

						return ImagePipelineOutput(images=UpperCamelCase_)









def 						A_							(	_lowerCAmelCase	)		->					bool:
 UpperCamelCase      :   List[Any]			      =  0
 for ch in input_str:
  UpperCamelCase      :   Optional[Any]			      =  ord(_lowerCAmelCase	)
  UpperCamelCase      :   Optional[Any]			      =  pow(2				,  _lowerCAmelCase	)

  # If we already turned on bit for current character's unicode
  if bitmap >> ch_unicode & 1 == 1:
   return False
  bitmap |= ch_bit_index_on
 return True


if __name__ == "__main__":
    import doctest

    doctest.testmod()
