



'''simple docstring'''





import os
from datetime import datetime as dt

from github import Github


A					:       int									=     [
    """good first issue""",
    """good second issue""",
    """good difficult issue""",
    """enhancement""",
    """new pipeline/model""",
    """new scheduler""",
    """wip""",
]
def 						_a       (    ):
   snake_case       :				Optional[int]		    =Github(os.environ['''GITHUB_TOKEN''']				)
   snake_case       :				str		    =g.get_repo('''huggingface/diffusers'''				)
   snake_case       :				Optional[int]		    =repo.get_issues(state='''open'''				)

   for issue in open_issues:
      snake_case       :				Optional[Any]		    =sorted(issue.get_comments()	,		key=lambda lowerCamelCase_				: i.created_at	,		reverse=snake_case__				)
      snake_case       :				Dict		    =comments[0] if len(snake_case__				) > 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()				)
      ):
         # Closes the issue after 7 days of inactivity since the Stalebot notification.
         issue.edit(state='''closed'''				)
      elif (
          "stale" in issue.get_labels()
          and last_comment is not None
          and last_comment.user.login != "github-actions[bot]"
      ):
         # Opens the issue if someone other than Stalebot commented.
         issue.edit(state='''open'''				)
         issue.remove_from_labels('''stale'''				)
      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()				)
      ):
         # Post a Stalebot notification after 23 days of inactivity.
         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/diffusers/blob/main/CONTRIBUTING.md) '''
             '''are likely to be ignored.'''				)
         issue.add_to_labels('''stale'''				)


if __name__ == "__main__":
      main()









"""simple docstring"""






from typing import List, Optional, Tuple, Union

import torch

from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput






class      A(  lowerCamelCase__		):

		"""simple docstring"""





		def __init__( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )      ->	Optional[Any]:

					"""simple docstring"""



					super().__init__()

					# make sure scheduler can always be converted to DDIM
					_UpperCamelCase					:int					     =	DDIMScheduler.from_config(scheduler.config   )

					self.register_modules(unet=SCREAMING_SNAKE_CASE__					,				scheduler=SCREAMING_SNAKE_CASE__   )





		@torch.no_grad()
		def __call__( self					,				SCREAMING_SNAKE_CASE__ = 1					,				SCREAMING_SNAKE_CASE__ = None					,				SCREAMING_SNAKE_CASE__ = 0.0					,				SCREAMING_SNAKE_CASE__ = 50					,				SCREAMING_SNAKE_CASE__ = None					,				SCREAMING_SNAKE_CASE__ = "pil"					,				SCREAMING_SNAKE_CASE__ = True					,				)      ->	Union[ImagePipelineOutput, Tuple]:

					"""simple docstring"""



					if isinstance(self.unet.config.sample_size					,				SCREAMING_SNAKE_CASE__   ):
								_UpperCamelCase					:Optional[int]					     =	(
								    batch_size,
								    self.unet.config.in_channels,
								    self.unet.config.sample_size,
								    self.unet.config.sample_size,
								)
					else:
								_UpperCamelCase					:Tuple					     =	(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

					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."   )

					_UpperCamelCase					:Optional[int]					     =	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__   )

					for t in self.progress_bar(self.scheduler.timesteps   ):
								# 1. predict noise model_output
								_UpperCamelCase					:List[str]					     =	self.unet(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   ).sample

								# 2. predict previous mean of image x_t-1 and add variance depending on eta
								# eta corresponds to η in paper and should be between [0, 1]
								# do x_t -> x_t-1
								_UpperCamelCase					:Optional[Any]					     =	self.scheduler.step(
								    SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				eta=SCREAMING_SNAKE_CASE__					,				use_clipped_model_output=SCREAMING_SNAKE_CASE__					,				generator=SCREAMING_SNAKE_CASE__   ).prev_sample

					_UpperCamelCase					:str					     =	(image / 2 + 0.5).clamp(0					,				1   )
					_UpperCamelCase					:int					     =	image.cpu().permute(0					,				2					,				3					,				1   ).numpy()
					if output_type == "pil":
								_UpperCamelCase					:Union[str, Any]					     =	self.numpy_to_pil(SCREAMING_SNAKE_CASE__   )

					if not return_dict:
								return (image,)

					return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE__   )

