


'''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


lowerCamelCase							:   Optional[int]  						=	logging.get_logger(__name__)  # pylint: disable=invalid-name

def _SCREAMING_SNAKE_CASE      (A       )       ->       List[str]:






       """simple docstring"""







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

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

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

def _SCREAMING_SNAKE_CASE      (A       )       ->       int:






       """simple docstring"""







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

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

class   __lowerCAmelCase						(lowercase__     ):





      '''simple docstring'''






      lowerCAmelCase__					:     Dict  					=							42
      lowerCAmelCase__					:     str  					=							42



      def __init__(self    :    Dict   ,						UpperCamelCase    :    int   ,						UpperCamelCase    :    List[Any] ):

             '''simple docstring'''







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






      @torch.no_grad()
      def __call__(self    :    Dict   ,						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'''







             lowercase__      			= image

             lowercase__      			= _preprocess_image(__lowerCamelCase )
             lowercase__      			= original_image.to(device=self.device   ,						dtype=self.unet.dtype )
             lowercase__      			= _preprocess_mask(__lowerCamelCase )
             lowercase__      			= mask_image.to(device=self.device   ,						dtype=self.unet.dtype )

             lowercase__      			= 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." )

             lowercase__      			= original_image.shape
             lowercase__      			= randn_tensor(__lowerCamelCase   ,						generator=__lowerCamelCase   ,						device=self.device   ,						dtype=self.unet.dtype )

             # set step values
             self.scheduler.set_timesteps(__lowerCamelCase   ,						__lowerCamelCase   ,						__lowerCamelCase   ,						self.device )
             lowercase__      			= eta

             lowercase__      			= self.scheduler.timesteps[0] + 1
             lowercase__      			= 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
                           lowercase__      			= self.unet(__lowerCamelCase   ,						__lowerCamelCase ).sample
                           # compute previous image: x_t -> x_t-1
                           lowercase__      			= self.scheduler.step(__lowerCamelCase   ,						__lowerCamelCase   ,						__lowerCamelCase   ,						__lowerCamelCase   ,						__lowerCamelCase   ,						__lowerCamelCase ).prev_sample

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

             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(__lowerCamelCase )

             if not return_dict:
                    return (image,)

             return ImagePipelineOutput(images=__lowerCamelCase )








class 			_lowercase :


       """simple docstring"""




       def __init__(   self							:			List[Any]					,      __lowerCamelCase							:			int       ):


             '''simple docstring'''



             lowerCamelCase__		:				Optional[Any]    =  size
             lowerCamelCase__		:				List[str]    =  [0] * size
             lowerCamelCase__		:				str    =  [0] * size





       @staticmethod
       def 						lowerCAmelCase     (   __lowerCamelCase							:			int       ):


             '''simple docstring'''



             return index | (index + 1)





       @staticmethod
       def 						lowerCAmelCase     (   __lowerCamelCase							:			int       ):


             '''simple docstring'''



             return (index & (index + 1)) - 1





       def 						lowerCAmelCase     (   self							:			int					,      __lowerCamelCase							:			int					,      __lowerCamelCase							:			int       ):


             '''simple docstring'''



             lowerCamelCase__		:				Union[str, Any]    =  value
             while index < self.size:
                   lowerCamelCase__		:				Tuple    =  self.get_prev(__lowerCamelCase       ) + 1
                   if current_left_border == index:
                         lowerCamelCase__		:				Optional[Any]    =  value
                   else:
                         lowerCamelCase__		:				str    =  max(__lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase       )
                   lowerCamelCase__		:				Dict    =  self.get_next(__lowerCamelCase       )





       def 						lowerCAmelCase     (   self							:			List[Any]					,      __lowerCamelCase							:			int					,      __lowerCamelCase							:			int       ):


             '''simple docstring'''



             right -= 1  # Because of right is exclusive
             lowerCamelCase__		:				str    =  0
             while left <= right:
                   lowerCamelCase__		:				Optional[Any]    =  self.get_prev(__lowerCamelCase       )
                   if left <= current_left:
                         lowerCamelCase__		:				Optional[Any]    =  max(__lowerCamelCase					,      self.tree[right]       )
                         lowerCamelCase__		:				Any    =  current_left
                   else:
                         lowerCamelCase__		:				Optional[Any]    =  max(__lowerCamelCase					,      self.arr[right]       )
                         right -= 1
             return result


if __name__ == "__main__":
    import doctest

    doctest.testmod()


