




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

def     _SCREAMING_SNAKE_CASE    (							lowercase			: 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'			,							lowercase			,							)
      if isinstance(lowercase			,							torch.Tensor  ):
            return image
      elif isinstance(lowercase			,							PIL.Image.Image  ):
            lowerCamelCase_	  =					[image]

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

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

def     _SCREAMING_SNAKE_CASE    (							lowercase			: Union[List, PIL.Image.Image, torch.Tensor]  ):


      '''simple docstring'''



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

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







class 						A(		UpperCamelCase     ):



 '''simple docstring'''


 UpperCamelCase		     =		42
 UpperCamelCase		     =		42

 def __init__(       self      :						Tuple		,							A_      :						Any		,							A_      :						List[Any]						)     ->			Any:

       """simple docstring"""






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





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

       """simple docstring"""






       lowerCamelCase_	  =					image

       lowerCamelCase_	  =					_preprocess_image(A_						)
       lowerCamelCase_	  =					original_image.to(device=self.device		,							dtype=self.unet.dtype						)
       lowerCamelCase_	  =					_preprocess_mask(A_						)
       lowerCamelCase_	  =					mask_image.to(device=self.device		,							dtype=self.unet.dtype						)

       lowerCamelCase_	  =					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."""						)

       lowerCamelCase_	  =					original_image.shape
       lowerCamelCase_	  =					randn_tensor(A_		,							generator=A_		,							device=self.device		,							dtype=self.unet.dtype						)

       # set step values
       self.scheduler.set_timesteps(A_		,							A_		,							A_		,							self.device						)
       lowerCamelCase_	  =					eta

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

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

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

       if not return_dict:
             return (image,)

       return ImagePipelineOutput(images=A_						)









from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Features, Value
from .base import TaskTemplate







@dataclass(frozen=UpperCamelCase     )
class 						A(		UpperCamelCase     ):



 '''simple docstring'''


 UpperCamelCase		     =		field(default='''language-modeling'''							,   metadata={'''include_in_asdict_even_if_is_default''': True}     )

 UpperCamelCase		     =		Features({'''text''': Value('''string'''     )}     )
 UpperCamelCase		     =		Features({}     )
 UpperCamelCase		     =		"text"

 @property
 def        a__				(       self      :						List[Any]						)     ->			Dict[str, str]:

       """simple docstring"""






       return {self.text_column: "text"}



