


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


def        a__     (					UpperCamelCase_						:      Union[str, Any]						):
   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						):
      UpperCAmelCase__	:int      =      [image]

   if isinstance(image[0],   PIL.Image.Image						):
      UpperCAmelCase__	:Any      =      image[0].size
      UpperCAmelCase__	:List[Any]      =      (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__	:List[str]      =      np.concatenate(_A,   axis=0						)
      UpperCAmelCase__	:List[Any]      =      np.array(_A						).astype(np.floataa						) / 255.0
      UpperCAmelCase__	:List[str]      =      image.transpose(0,   3,   1,   2						)
      UpperCAmelCase__	:Optional[Any]      =      2.0 * image - 1.0
      UpperCAmelCase__	:Optional[int]      =      torch.from_numpy(_A						)
   elif isinstance(image[0],   torch.Tensor						):
      UpperCAmelCase__	:List[str]      =      torch.cat(_A,   dim=0						)
   return image


def        a__     (					UpperCamelCase_						:      Optional[int]						):
   if isinstance(_A,   torch.Tensor						):
      return mask
   elif isinstance(_A,   PIL.Image.Image						):
      UpperCAmelCase__	:int      =      [mask]

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




class 				UpperCAmelCase			(			__snake_case						):
 UpperCAmelCase				=					42
 UpperCAmelCase				=					42


 def __init__(      self							:	List[Any]			,       __lowerCamelCase							:	int			,       __lowerCamelCase							:	str ):
    super().__init__()
    self.register_modules(unet=__lowerCamelCase			,       scheduler=__lowerCamelCase )





 @torch.no_grad()
 def __call__(      self							:	List[str]			,       __lowerCamelCase							:	Union[torch.Tensor, PIL.Image.Image]			,       __lowerCamelCase							:	Union[torch.Tensor, PIL.Image.Image]			,       __lowerCamelCase							:	int = 2_5_0			,       __lowerCamelCase							:	float = 0.0			,       __lowerCamelCase							:	int = 1_0			,       __lowerCamelCase							:	int = 1_0			,       __lowerCamelCase							:	Optional[Union[torch.Generator, List[torch.Generator]]] = None			,       __lowerCamelCase							:	Optional[str] = "pil"			,       __lowerCamelCase							:	bool = True			,       ):
    UpperCAmelCase__	:str      =      image

    UpperCAmelCase__	:Union[str, Any]      =      _preprocess_image(__lowerCamelCase )
    UpperCAmelCase__	:Optional[int]      =      original_image.to(device=self.device			,       dtype=self.unet.dtype )
    UpperCAmelCase__	:str      =      _preprocess_mask(__lowerCamelCase )
    UpperCAmelCase__	:Optional[int]      =      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(__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__	:Any      =      original_image.shape
    UpperCAmelCase__	:str      =      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__	:Dict      =      eta

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

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

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

    if not return_dict:
       return (image,)

    return ImagePipelineOutput(images=__lowerCamelCase )











"""simple docstring"""







def      UpperCamelCase       ( _A  ,				_A )     ->       None:
				lowercase    :      List[Any]       = len(_A )
				print("""The following activities are selected:""" )

				# The first activity is always selected
				lowercase    :      Optional[int]       = 0
				print(_A  ,				end=""",""" )

				# Consider rest of the activities
				for j in range(_A ):
								# If this activity has start time greater than
								# or equal to the finish time of previously
								# selected activity, then select it
								if start[j] >= finish[i]:
												print(_A  ,				end=""",""" )
												lowercase    :      str       = j


if __name__ == "__main__":
				import doctest

				doctest.testmod()

				_lowerCAmelCase            =	[1, 3, 0, 5, 8, 5]
				_lowerCAmelCase            =	[2, 4, 6, 7, 9, 9]
				print_max_activities(start, finish)
