'''simple docstring'''

import unittest

import torch

from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow


UpperCamelCase__  :						str					=      False


class 					_lowercase						(   unittest.TestCase ):
      '''simple docstring'''


      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_=32				)      ->      Union[str, Any]:



         '''simple docstring'''



         set_seed(0				)
         UpperCAmelCase__				:					Union[str, Any] 					=	UNetaDModel(sample_size=lowerCamelCase_       ,in_channels=3       ,out_channels=3				)
         UpperCAmelCase__				:					Any 					=	torch.optim.SGD(model.parameters()       ,lr=0.0001				)
         return model, optimizer




      @slow
      def 		lowerCAmelCase__       (					self				)      ->      List[str]:



         '''simple docstring'''



         UpperCAmelCase__				:					Optional[Any] 					=	'''cpu'''  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
         UpperCAmelCase__				:					Dict 					=	DDPMScheduler(
             num_train_timesteps=1000       ,beta_start=0.0001       ,beta_end=0.02       ,beta_schedule='''linear'''       ,clip_sample=lowerCamelCase_       ,)
         UpperCAmelCase__				:					Union[str, Any] 					=	DDIMScheduler(
             num_train_timesteps=1000       ,beta_start=0.0001       ,beta_end=0.02       ,beta_schedule='''linear'''       ,clip_sample=lowerCamelCase_       ,)

         assert ddpm_scheduler.config.num_train_timesteps == ddim_scheduler.config.num_train_timesteps

         # shared batches for DDPM and DDIM
         set_seed(0				)
         UpperCAmelCase__				:					Optional[Any] 					=	[torch.randn((4, 3, 32, 32)				).clip(-1       ,1				).to(lowerCamelCase_				) for _ in range(4				)]
         UpperCAmelCase__				:					int 					=	[torch.randn((4, 3, 32, 32)				).to(lowerCamelCase_				) for _ in range(4				)]
         UpperCAmelCase__				:					Union[str, Any] 					=	[torch.randint(0       ,1000       ,(4,)				).long().to(lowerCamelCase_				) for _ in range(4				)]

         # train with a DDPM scheduler
         UpperCAmelCase__					,   UpperCAmelCase__				:					List[str] 					=	self.get_model_optimizer(resolution=32				)
         model.train().to(lowerCamelCase_				)
         for i in range(4				):
            optimizer.zero_grad()
            UpperCAmelCase__				:					Optional[int] 					=	ddpm_scheduler.add_noise(clean_images[i]       ,noise[i]       ,timesteps[i]				)
            UpperCAmelCase__				:					Union[str, Any] 					=	model(lowerCamelCase_       ,timesteps[i]				).sample
            UpperCAmelCase__				:					Optional[int] 					=	torch.nn.functional.mse_loss(lowerCamelCase_       ,noise[i]				)
            loss.backward()
            optimizer.step()
         del model, optimizer

         # recreate the model and optimizer, and retry with DDIM
         UpperCAmelCase__					,   UpperCAmelCase__				:					Union[str, Any] 					=	self.get_model_optimizer(resolution=32				)
         model.train().to(lowerCamelCase_				)
         for i in range(4				):
            optimizer.zero_grad()
            UpperCAmelCase__				:					List[Any] 					=	ddim_scheduler.add_noise(clean_images[i]       ,noise[i]       ,timesteps[i]				)
            UpperCAmelCase__				:					List[Any] 					=	model(lowerCamelCase_       ,timesteps[i]				).sample
            UpperCAmelCase__				:					Any 					=	torch.nn.functional.mse_loss(lowerCamelCase_       ,noise[i]				)
            loss.backward()
            optimizer.step()
         del model, optimizer

         self.assertTrue(torch.allclose(lowerCamelCase_       ,lowerCamelCase_       ,atol=1e-5				)				)
         self.assertTrue(torch.allclose(lowerCamelCase_       ,lowerCamelCase_       ,atol=1e-5				)				)





'''simple docstring'''

import os
from datetime import datetime as dt

from github import Github


UpperCamelCase__  :						str					=      [
    'good first issue',
    'good second issue',
    'good difficult issue',
    'enhancement',
    'new pipeline/model',
    'new scheduler',
    'wip',
]







def __UpperCamelCase( ):
   '''simple docstring'''
   UpperCAmelCase__				:					str 					=	Github(os.environ['''GITHUB_TOKEN''']     )
   UpperCAmelCase__				:					Union[str, Any] 					=	g.get_repo('''huggingface/diffusers'''     )
   UpperCAmelCase__				:					Union[str, Any] 					=	repo.get_issues(state='''open'''     )

   for issue in open_issues:
      UpperCAmelCase__				:					int 					=	sorted(issue.get_comments()	,      key=lambda _A     : i.created_at	,      reverse=_A     )
      UpperCAmelCase__				:					int 					=	comments[0] if len(_A     ) > 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()




