





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



def 				lowerCAmelCase_ (							A_):
    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__:	str					=     [image]

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

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



def 				lowerCAmelCase_ (							A_):
    if isinstance(A_    ,torch.Tensor):
        return mask
    elif isinstance(A_    ,PIL.Image.Image):
        UpperCamelCase__:	str					=     [mask]

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




class        _a    (    UpperCamelCase__):

      """simple docstring"""


      UpperCamelCase__  		=       42
      UpperCamelCase__  		=       42






      def __init__(		self:			Any	,    __lowerCamelCase:			Optional[Any]	,    __lowerCamelCase:			Any					):
          '''simple docstring'''


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






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


          UpperCamelCase__:	Optional[int]					=     image

          UpperCamelCase__:	Tuple					=     _preprocess_image(__lowerCamelCase					)
          UpperCamelCase__:	Tuple					=     original_image.to(device=self.device	,    dtype=self.unet.dtype					)
          UpperCamelCase__:	Dict					=     _preprocess_mask(__lowerCamelCase					)
          UpperCamelCase__:	List[Any]					=     mask_image.to(device=self.device	,    dtype=self.unet.dtype					)

          UpperCamelCase__:	Tuple					=     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__:	str					=     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__:	List[Any]					=     eta

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

          if not return_dict:
              return (image,)

          return ImagePipelineOutput(images=__lowerCamelCase					)








from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core


# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before tokenizers

A__:    Union[str, Any]  	=	[
    '''python''',
    '''tqdm''',
    '''regex''',
    '''requests''',
    '''packaging''',
    '''filelock''',
    '''numpy''',
    '''tokenizers''',
    '''huggingface-hub''',
    '''safetensors''',
    '''accelerate''',
    '''pyyaml''',
]

for pkg in pkgs_to_check_at_runtime:
   if pkg in deps:
      if pkg == "tokenizers":
         # must be loaded here, or else tqdm check may fail
         from .utils import is_tokenizers_available

         if not is_tokenizers_available():
            continue  # not required, check version only if installed
      elif pkg == "accelerate":
         # must be loaded here, or else tqdm check may fail
         from .utils import is_accelerate_available

         # Maybe switch to is_torch_available in the future here so that Accelerate is hard dep of
         # Transformers with PyTorch
         if not is_accelerate_available():
            continue  # not required, check version only if installed

      require_version_core(deps[pkg])
   else:
      raise ValueError(f"can't find {pkg} in {deps.keys()}, check dependency_versions_table.py")



def 				lowerCAmelCase_ (							A_    ,A_=None):
    require_version(deps[pkg]    ,A_)

