


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





def   _a		(lowercase__			:   Union[List, PIL.Image.Image, torch.Tensor]						)			->			Optional[int]:






     """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						):
          __snake_case           =						[image]

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

          __snake_case           =						[np.array(i.resize((w, h)							,   resample=PIL_INTERPOLATION['lanczos']						)						)[None, :] for i in image]
          __snake_case           =						np.concatenate(lowercase__							,   axis=0						)
          __snake_case           =						np.array(lowercase__						).astype(np.floataa						) / 2_55.0
          __snake_case           =						image.transpose(0							,   3							,   1							,   2						)
          __snake_case           =						2.0 * image - 1.0
          __snake_case           =						torch.from_numpy(lowercase__						)
     elif isinstance(image[0]							,   torch.Tensor						):
          __snake_case           =						torch.cat(lowercase__							,   dim=0						)
     return image





def   _a		(lowercase__			:   Union[List, PIL.Image.Image, torch.Tensor]						)			->			Dict:






     """simple docstring"""
     if isinstance(lowercase__							,   torch.Tensor						):
          return mask
     elif isinstance(lowercase__							,   PIL.Image.Image						):
          __snake_case           =						[mask]

     if isinstance(mask[0]							,   PIL.Image.Image						):
          __snake_case      ,       __snake_case           =						mask[0].size
          __snake_case      ,       __snake_case           =						(x - x % 3_2 for x in (w, h))  # resize to integer multiple of 32
          __snake_case           =						[np.array(m.convert('L'						).resize((w, h)							,   resample=PIL_INTERPOLATION['nearest']						)						)[None, :] for m in mask]
          __snake_case           =						np.concatenate(lowercase__							,   axis=0						)
          __snake_case           =						mask.astype(np.floataa						) / 2_55.0
          __snake_case           =						0
          __snake_case           =						1
          __snake_case           =						torch.from_numpy(lowercase__						)
     elif isinstance(mask[0]							,   torch.Tensor						):
          __snake_case           =						torch.cat(lowercase__							,   dim=0						)
     return mask




class 			_lowercase (	__lowercase							):
   _SCREAMING_SNAKE_CASE  :  UNetaDModel
   _SCREAMING_SNAKE_CASE  :  RePaintScheduler
   def __init__(						self       :      Optional[int]       ,   SCREAMING_SNAKE_CASE_       :      Tuple       ,   SCREAMING_SNAKE_CASE_       :      Tuple      ) ->							Optional[Any]:
        super().__init__()
        self.register_modules(unet=SCREAMING_SNAKE_CASE_       ,   scheduler=SCREAMING_SNAKE_CASE_      )





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

        __snake_case           =						_preprocess_image(SCREAMING_SNAKE_CASE_      )
        __snake_case           =						original_image.to(device=self.device       ,   dtype=self.unet.dtype      )
        __snake_case           =						_preprocess_mask(SCREAMING_SNAKE_CASE_      )
        __snake_case           =						mask_image.to(device=self.device       ,   dtype=self.unet.dtype      )

        __snake_case           =						original_image.shape[0]

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

        __snake_case           =						original_image.shape
        __snake_case           =						randn_tensor(SCREAMING_SNAKE_CASE_       ,   generator=SCREAMING_SNAKE_CASE_       ,   device=self.device       ,   dtype=self.unet.dtype      )

        # set step values
        self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE_       ,   SCREAMING_SNAKE_CASE_       ,   SCREAMING_SNAKE_CASE_       ,   self.device      )
        __snake_case           =						eta

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

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

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

        if not return_dict:
             return (image,)

        return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE_      )












from datetime import datetime as dt
import os

from github import Github


__magic_name__   :  Dict        =							[
    '''good first issue''',
    '''good second issue''',
    '''good difficult issue''',
    '''feature request''',
    '''new model''',
    '''wip''',
]
def 		lowercase__  (				) ->			Dict:
      """simple docstring"""

      UpperCamelCase					       =  Github(os.environ['GITHUB_TOKEN'])
      UpperCamelCase					       =  g.get_repo('huggingface/transformers')
      UpperCamelCase					       =  repo.get_issues(state='open')

      for issue in open_issues:
            UpperCamelCase					       =  sorted([comment for comment in issue.get_comments()]       ,							key=lambda _UpperCamelCase: i.created_at       ,							reverse=_UpperCamelCase)
            UpperCamelCase					       =  comments[0] if len(_UpperCamelCase) > 0 else None
            if (
                last_comment is not None
                and last_comment.user.login == "github-actions[bot]"
                and (dt.utcnow() - issue.updated_at).days > 7
                and (dt.utcnow() - issue.created_at).days >= 30
                and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels())
            ):
                  # print(f"Would close issue {issue.number} since it has been 7 days of inactivity since bot mention.")
                  issue.edit(state='closed')
            elif (
                (dt.utcnow() - issue.updated_at).days > 23
                and (dt.utcnow() - issue.created_at).days >= 30
                and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels())
            ):
                  # print(f"Would add stale comment to {issue.number}")
                  issue.create_comment(
                      'This issue has been automatically marked as stale because it has not had '
                      'recent activity. If you think this still needs to be addressed '
                      'please comment on this thread.\n\nPlease note that issues that do not follow the '
                      '[contributing guidelines](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md) '
                      'are likely to be ignored.')


if __name__ == "__main__":
       main()



