


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


snake_case    :   Union[str, Any]	    =					logging.get_logger(__name__)  # pylint: disable=invalid-name
def     __lowerCamelCase						(			UpperCAmelCase_						:		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'''			,  UpperCAmelCase_			,  )
      if isinstance(UpperCAmelCase_			,  torch.Tensor						):
            return image
      elif isinstance(UpperCAmelCase_			,  PIL.Image.Image						):
            a						:str										=	[image]

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

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




      """simple docstring"""





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

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







class 		_snake_case  (				_snake_case							):
 SCREAMING_SNAKE_CASE__				=	42
 SCREAMING_SNAKE_CASE__				=	42




 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 ,  ):
       a						:Any										=	image

       a						:Optional[int]										=	_preprocess_image(_lowerCamelCase   )
       a						:Optional[int]										=	original_image.to(device=self.device ,  dtype=self.unet.dtype   )
       a						:List[str]										=	_preprocess_mask(_lowerCamelCase   )
       a						:Tuple										=	mask_image.to(device=self.device ,  dtype=self.unet.dtype   )

       a						:str										=	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.'''   )

       a						:Dict										=	original_image.shape
       a						:Dict										=	randn_tensor(_lowerCamelCase ,  generator=_lowerCamelCase ,  device=self.device ,  dtype=self.unet.dtype   )

       # set step values
       self.scheduler.set_timesteps(_lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase ,  self.device   )
       a						:List[Any]										=	eta

       a						:Tuple										=	self.scheduler.timesteps[0] + 1
       a						:int										=	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
                   a						:Tuple										=	self.unet(_lowerCamelCase ,  _lowerCamelCase   ).sample
                   # compute previous image: x_t -> x_t-1
                   a						:List[Any]										=	self.scheduler.step(_lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase   ).prev_sample

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

       a						:Union[str, Any]										=	(image / 2 + 0.5).clamp(0 ,  1   )
       a						:int										=	image.cpu().permute(0 ,  2 ,  3 ,  1   ).numpy()
       if output_type == "pil":
             a						:Optional[int]										=	self.numpy_to_pil(_lowerCamelCase   )

       if not return_dict:
             return (image,)

       return ImagePipelineOutput(images=_lowerCamelCase   )









def     __lowerCamelCase						(			UpperCAmelCase_						:		int = 100						):




      """simple docstring"""





      a						:List[Any]										=	0
      a						:List[Any]										=	0
      for i in range(1			,  n + 1						):
            sum_of_squares += i**2
            sum_of_ints += i
      return sum_of_ints**2 - sum_of_squares


if __name__ == "__main__":
     print(F"""{solution() = }""")





