

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




def  _lowercase					(    __lowerCAmelCase							)       ->				Any:
      warnings.warn(
          """The preprocess method is deprecated and will be removed in a future version. Please"""
          """ use VaeImageProcessor.preprocess instead"""  ,					__lowerCAmelCase  ,					)
      if isinstance(__lowerCAmelCase  ,					torch.Tensor							):
            return image
      elif isinstance(__lowerCAmelCase  ,					PIL.Image.Image							):
            SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	[image]

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

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




def  _lowercase					(    __lowerCAmelCase							)       ->				Optional[int]:
      if isinstance(__lowerCAmelCase  ,					torch.Tensor							):
            return mask
      elif isinstance(__lowerCAmelCase  ,					PIL.Image.Image							):
            SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	[mask]

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



class      __a							(UpperCamelCase_):






     '''simple docstring'''
     _SCREAMING_SNAKE_CASE						:UNetaDModel
     _SCREAMING_SNAKE_CASE						:RePaintScheduler

     def __init__(						self	,    _a	,    _a  )  ->      Any:


           """simple docstring"""


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





     @torch.no_grad()
     def __call__(						self	,    _a	,    _a	,    _a = 250	,    _a = 0.0	,    _a = 10	,    _a = 10	,    _a = None	,    _a = "pil"	,    _a = True	,    )  ->      Union[ImagePipelineOutput, Tuple]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      str      		=	image

           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	_preprocess_image(_a  )
           SCREAMING_SNAKE_CASE__   :      int      		=	original_image.to(device=self.device	,    dtype=self.unet.dtype  )
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	_preprocess_mask(_a  )
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	mask_image.to(device=self.device	,    dtype=self.unet.dtype  )

           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	original_image.shape[0]

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

           SCREAMING_SNAKE_CASE__   :      int      		=	original_image.shape
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	randn_tensor(_a	,    generator=_a	,    device=self.device	,    dtype=self.unet.dtype  )

           # set step values
           self.scheduler.set_timesteps(_a	,    _a	,    _a	,    self.device  )
           SCREAMING_SNAKE_CASE__   :      Dict      		=	eta

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

                 else:
                       # compute the reverse: x_t-1 -> x_t
                       SCREAMING_SNAKE_CASE__   :      List[Any]      		=	self.scheduler.undo_step(_a	,    _a	,    _a  )
                 SCREAMING_SNAKE_CASE__   :      int      		=	t

           SCREAMING_SNAKE_CASE__   :      int      		=	(image / 2 + 0.5).clamp(0	,    1  )
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	image.cpu().permute(0	,    2	,    3	,    1  ).numpy()
           if output_type == "pil":
                 SCREAMING_SNAKE_CASE__   :      Tuple      		=	self.numpy_to_pil(_a  )

           if not return_dict:
                 return (image,)

           return ImagePipelineOutput(images=_a  )







"""simple docstring"""


class      __a							:






     '''simple docstring'''

     def __init__(						self	,    _a	,    _a	,    _a  )  ->      str:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Any      		=	name
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	value
           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	weight

     def __repr__(						self  )  ->      List[Any]:


           """simple docstring"""


           return f'''{self.__class__.__name__}({self.name}, {self.value}, {self.weight})'''

     def 					_a			(						self  )  ->      Dict:


           """simple docstring"""


           return self.value

     def 					_a			(						self  )  ->      int:


           """simple docstring"""


           return self.name

     def 					_a			(						self  )  ->      Optional[Any]:


           """simple docstring"""


           return self.weight





     def 					_a			(						self  )  ->      Dict:


           """simple docstring"""


           return self.value / self.weight





def  _lowercase					(    __lowerCAmelCase  ,					__lowerCAmelCase  ,					__lowerCAmelCase							)       ->				Dict:
      SCREAMING_SNAKE_CASE__   :      Any      		=	[]
      for i in range(len(__lowerCAmelCase							)							):
            menu.append(Things(name[i]  ,					value[i]  ,					weight[i]							)							)
      return menu




def  _lowercase					(    __lowerCAmelCase  ,					__lowerCAmelCase  ,					__lowerCAmelCase							)       ->				Union[str, Any]:
      SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	sorted(__lowerCAmelCase  ,					key=__lowerCAmelCase  ,					reverse=__lowerCAmelCase							)
      SCREAMING_SNAKE_CASE__   :      str      		=	[]
      SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	0.0, 0.0
      for i in range(len(__lowerCAmelCase							)							):
            if (total_cost + items_copy[i].get_weight()) <= max_cost:
                  result.append(items_copy[i]							)
                  total_cost += items_copy[i].get_weight()
                  total_value += items_copy[i].get_value()
      return (result, total_value)




def  _lowercase					(    )       ->				List[str]:
      pass


if __name__ == "__main__":
  import doctest

  doctest.testmod()




