





from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
     import PIL


_snake_case  :   str						   =  logging.get_logger(__name__)



class     _UpperCAmelCase							(   _UpperCamelCase   ):

  """simple docstring"""


  a_      =			["""pixel_values"""]






  def __init__(    self			:		List[str] ,  lowerCAmelCase_			:		bool = True ,  lowerCAmelCase_			:		Dict[str, int] = None ,  lowerCAmelCase_			:		PILImageResampling = PIL.Image.BICUBIC ,  lowerCAmelCase_			:		bool = True ,  lowerCAmelCase_			:		Dict[str, int] = None ,  lowerCAmelCase_			:		Union[int, float] = 1 / 2_5_5 ,  lowerCAmelCase_			:		bool = True ,  lowerCAmelCase_			:		bool = True ,  lowerCAmelCase_			:		Optional[Union[float, List[float]]] = None ,  lowerCAmelCase_			:		Optional[Union[float, List[float]]] = None ,  **lowerCAmelCase_			:		Optional[int] ,  )							->						None:
     super().__init__(**lowerCAmelCase_					)
     __lowerCAmelCase							     =     size if size is not None else {'height': 2_5_6, 'width': 2_5_6}
     __lowerCAmelCase							     =     get_size_dict(lowerCAmelCase_					)
     __lowerCAmelCase							     =     crop_size if crop_size is not None else {'height': 2_2_4, 'width': 2_2_4}
     __lowerCAmelCase							     =     get_size_dict(lowerCAmelCase_ ,  param_name='crop_size'					)

     __lowerCAmelCase							     =     do_resize
     __lowerCAmelCase							     =     size
     __lowerCAmelCase							     =     resample
     __lowerCAmelCase							     =     do_center_crop
     __lowerCAmelCase							     =     crop_size
     __lowerCAmelCase							     =     do_rescale
     __lowerCAmelCase							     =     rescale_factor
     __lowerCAmelCase							     =     do_normalize
     __lowerCAmelCase							     =     image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
     __lowerCAmelCase							     =     image_std if image_std is not None else IMAGENET_STANDARD_STD






  def     lowercase		(    self			:		Union[str, Any] ,  lowerCAmelCase_			:		np.ndarray ,  lowerCAmelCase_			:		Dict[str, int] ,  lowerCAmelCase_			:		PILImageResampling = PIL.Image.BICUBIC ,  lowerCAmelCase_			:		Optional[Union[str, ChannelDimension]] = None ,  **lowerCAmelCase_			:		Optional[int] ,  )							->						np.ndarray:
     __lowerCAmelCase							     =     get_size_dict(lowerCAmelCase_					)
     if "height" not in size or "width" not in size:
        raise ValueError(f"""The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"""					)
     return resize(
         lowerCAmelCase_ ,  size=(size['height'], size['width']) ,  resample=lowerCAmelCase_ ,  data_format=lowerCAmelCase_ ,  **lowerCAmelCase_					)






  def     lowercase		(    self			:		Any ,  lowerCAmelCase_			:		np.ndarray ,  lowerCAmelCase_			:		Dict[str, int] ,  lowerCAmelCase_			:		Optional[Union[str, ChannelDimension]] = None ,  **lowerCAmelCase_			:		Any ,  )							->						np.ndarray:
     __lowerCAmelCase							     =     get_size_dict(lowerCAmelCase_					)
     if "height" not in size or "width" not in size:
        raise ValueError(f"""The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"""					)
     return center_crop(lowerCAmelCase_ ,  size=(size['height'], size['width']) ,  data_format=lowerCAmelCase_ ,  **lowerCAmelCase_					)






  def     lowercase		(    self			:		Tuple ,  lowerCAmelCase_			:		np.ndarray ,  lowerCAmelCase_			:		Union[int, float] ,  lowerCAmelCase_			:		Optional[Union[str, ChannelDimension]] = None ,  **lowerCAmelCase_			:		List[str] ,  )							->						Any:
     return rescale(lowerCAmelCase_ ,  scale=lowerCAmelCase_ ,  data_format=lowerCAmelCase_ ,  **lowerCAmelCase_					)






  def     lowercase		(    self			:		str ,  lowerCAmelCase_			:		np.ndarray ,  lowerCAmelCase_			:		Union[float, List[float]] ,  lowerCAmelCase_			:		Union[float, List[float]] ,  lowerCAmelCase_			:		Optional[Union[str, ChannelDimension]] = None ,  **lowerCAmelCase_			:		List[str] ,  )							->						np.ndarray:
     return normalize(lowerCAmelCase_ ,  mean=lowerCAmelCase_ ,  std=lowerCAmelCase_ ,  data_format=lowerCAmelCase_ ,  **lowerCAmelCase_					)






  def     lowercase		(    self			:		int ,  lowerCAmelCase_			:		ImageInput ,  lowerCAmelCase_			:		bool = None ,  lowerCAmelCase_			:		Dict[str, int] = None ,  lowerCAmelCase_			:		str=None ,  lowerCAmelCase_			:		bool = None ,  lowerCAmelCase_			:		Dict[str, int] = None ,  lowerCAmelCase_			:		bool = None ,  lowerCAmelCase_			:		float = None ,  lowerCAmelCase_			:		bool = None ,  lowerCAmelCase_			:		Optional[Union[float, List[float]]] = None ,  lowerCAmelCase_			:		Optional[Union[float, List[float]]] = None ,  lowerCAmelCase_			:		Optional[Union[str, TensorType]] = None ,  lowerCAmelCase_			:		ChannelDimension = ChannelDimension.FIRST ,  **lowerCAmelCase_			:		Optional[Any] ,  )							->						PIL.Image.Image:
     __lowerCAmelCase							     =     do_resize if do_resize is not None else self.do_resize
     __lowerCAmelCase							     =     resample if resample is not None else self.resample
     __lowerCAmelCase							     =     do_center_crop if do_center_crop is not None else self.do_center_crop
     __lowerCAmelCase							     =     do_rescale if do_rescale is not None else self.do_rescale
     __lowerCAmelCase							     =     rescale_factor if rescale_factor is not None else self.rescale_factor
     __lowerCAmelCase							     =     do_normalize if do_normalize is not None else self.do_normalize
     __lowerCAmelCase							     =     image_mean if image_mean is not None else self.image_mean
     __lowerCAmelCase							     =     image_std if image_std is not None else self.image_std

     __lowerCAmelCase							     =     size if size is not None else self.size
     __lowerCAmelCase							     =     get_size_dict(lowerCAmelCase_					)
     __lowerCAmelCase							     =     crop_size if crop_size is not None else self.crop_size
     __lowerCAmelCase							     =     get_size_dict(lowerCAmelCase_ ,  param_name='crop_size'					)

     __lowerCAmelCase							     =     make_list_of_images(lowerCAmelCase_					)

     if not valid_images(lowerCAmelCase_					):
        raise ValueError(
            'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
            'torch.Tensor, tf.Tensor or jax.ndarray.'					)

     if do_resize and size is None or resample is None:
        raise ValueError('Size and resample must be specified if do_resize is True.'					)

     if do_center_crop and crop_size is None:
        raise ValueError('Crop size must be specified if do_center_crop is True.'					)

     if do_rescale and rescale_factor is None:
        raise ValueError('Rescale factor must be specified if do_rescale is True.'					)

     if do_normalize and (image_mean is None or image_std is None):
        raise ValueError('Image mean and std must be specified if do_normalize is True.'					)

     # All transformations expect numpy arrays.
     __lowerCAmelCase							     =     [to_numpy_array(lowerCAmelCase_					) for image in images]

     if do_resize:
        __lowerCAmelCase							     =     [self.resize(image=lowerCAmelCase_ ,  size=lowerCAmelCase_ ,  resample=lowerCAmelCase_					) for image in images]

     if do_center_crop:
        __lowerCAmelCase							     =     [self.center_crop(image=lowerCAmelCase_ ,  size=lowerCAmelCase_					) for image in images]

     if do_rescale:
        __lowerCAmelCase							     =     [self.rescale(image=lowerCAmelCase_ ,  scale=lowerCAmelCase_					) for image in images]

     if do_normalize:
        __lowerCAmelCase							     =     [self.normalize(image=lowerCAmelCase_ ,  mean=lowerCAmelCase_ ,  std=lowerCAmelCase_					) for image in images]

     __lowerCAmelCase							     =     [to_channel_dimension_format(lowerCAmelCase_ ,  lowerCAmelCase_					) for image in images]

     __lowerCAmelCase							     =     {'pixel_values': images}
     return BatchFeature(data=lowerCAmelCase_ ,  tensor_type=lowerCAmelCase_					)








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   a_   (			lowerCAmelCase_				:      Union[List, PIL.Image.Image, torch.Tensor]     ):
   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     ):
      __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(lowerCAmelCase_,     axis=0     )
      __lowerCAmelCase							     =     np.array(lowerCAmelCase_     ).astype(np.floataa     ) / 255.0
      __lowerCAmelCase							     =     image.transpose(0,     3,     1,     2     )
      __lowerCAmelCase							     =     2.0 * image - 1.0
      __lowerCAmelCase							     =     torch.from_numpy(lowerCAmelCase_     )
   elif isinstance(image[0],     torch.Tensor     ):
      __lowerCAmelCase							     =     torch.cat(lowerCAmelCase_,     dim=0     )
   return image

def   a_   (			lowerCAmelCase_				:      Union[List, PIL.Image.Image, torch.Tensor]     ):
   if isinstance(lowerCAmelCase_,     torch.Tensor     ):
      return mask
   elif isinstance(lowerCAmelCase_,     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(lowerCAmelCase_,     axis=0     )
      __lowerCAmelCase							     =     mask.astype(np.floataa     ) / 255.0
      __lowerCAmelCase							     =     0
      __lowerCAmelCase							     =     1
      __lowerCAmelCase							     =     torch.from_numpy(lowerCAmelCase_     )
   elif isinstance(mask[0],     torch.Tensor     ):
      __lowerCAmelCase							     =     torch.cat(lowerCAmelCase_,     dim=0     )
   return mask




class     _UpperCAmelCase   (   _UpperCamelCase   ):

  """simple docstring"""


  a_      =			42
  a_      =			42






  def __init__(    self			:		Optional[Any] ,  lowerCAmelCase_			:		List[str] ,  lowerCAmelCase_			:		List[Any]					)							->						Optional[int]:
     super().__init__()
     self.register_modules(unet=lowerCAmelCase_ ,  scheduler=lowerCAmelCase_					)






  @torch.no_grad()
  def __call__(    self			:		Dict ,  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 ,  )							->						Union[ImagePipelineOutput, Tuple]:
     __lowerCAmelCase							     =     image

     __lowerCAmelCase							     =     _preprocess_image(lowerCAmelCase_					)
     __lowerCAmelCase							     =     original_image.to(device=self.device ,  dtype=self.unet.dtype					)
     __lowerCAmelCase							     =     _preprocess_mask(lowerCAmelCase_					)
     __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(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."""					)

     __lowerCAmelCase							     =     original_image.shape
     __lowerCAmelCase							     =     randn_tensor(lowerCAmelCase_ ,  generator=lowerCAmelCase_ ,  device=self.device ,  dtype=self.unet.dtype					)

     # set step values
     self.scheduler.set_timesteps(lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_ ,  self.device					)
     __lowerCAmelCase							     =     eta

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

        else:
           # compute the reverse: x_t-1 -> x_t
           __lowerCAmelCase							     =     self.scheduler.undo_step(lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_					)
        __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(lowerCAmelCase_					)

     if not return_dict:
        return (image,)

     return ImagePipelineOutput(images=lowerCAmelCase_					)

