'''simple docstring'''

import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest


class 					_lowercase						(   lowerCAmelCase ):
      '''simple docstring'''


      UpperCAmelCase_   :   List[str]   				= (CMStochasticIterativeScheduler,)
      UpperCAmelCase_   :   Any   				= 10




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



         '''simple docstring'''



         UpperCAmelCase__				:					Any 					=	{
             '''num_train_timesteps''': 201,
             '''sigma_min''': 0.002,
             '''sigma_max''': 80.0,
         }

         config.update(**lowerCamelCase_				)
         return config




      def 		lowerCAmelCase__       (					self				)      ->      Tuple:



         '''simple docstring'''



         UpperCAmelCase__				:					str 					=	10

         UpperCAmelCase__				:					Union[str, Any] 					=	self.get_scheduler_config()
         UpperCAmelCase__				:					Dict 					=	self.scheduler_classes[0](**lowerCamelCase_				)

         scheduler.set_timesteps(lowerCamelCase_				)

         UpperCAmelCase__				:					Optional[Any] 					=	scheduler.timesteps[0]
         UpperCAmelCase__				:					Dict 					=	scheduler.timesteps[1]

         UpperCAmelCase__				:					Dict 					=	self.dummy_sample
         UpperCAmelCase__				:					List[Any] 					=	0.1 * sample

         UpperCAmelCase__				:					Any 					=	scheduler.step(lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_				).prev_sample
         UpperCAmelCase__				:					Union[str, Any] 					=	scheduler.step(lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_				).prev_sample

         self.assertEqual(output_a.shape       ,sample.shape				)
         self.assertEqual(output_a.shape       ,output_a.shape				)




      def 		lowerCAmelCase__       (					self				)      ->      int:



         '''simple docstring'''



         for timesteps in [10, 50, 100, 1000]:
            self.check_over_configs(num_train_timesteps=lowerCamelCase_				)




      def 		lowerCAmelCase__       (					self				)      ->      Tuple:



         '''simple docstring'''



         for clip_denoised in [True, False]:
            self.check_over_configs(clip_denoised=lowerCamelCase_				)




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



         '''simple docstring'''



         UpperCAmelCase__				:					Optional[int] 					=	self.scheduler_classes[0]
         UpperCAmelCase__				:					int 					=	self.get_scheduler_config()
         UpperCAmelCase__				:					Optional[Any] 					=	scheduler_class(**lowerCamelCase_				)

         UpperCAmelCase__				:					List[Any] 					=	1
         scheduler.set_timesteps(lowerCamelCase_				)
         UpperCAmelCase__				:					str 					=	scheduler.timesteps

         UpperCAmelCase__				:					Optional[Any] 					=	torch.manual_seed(0				)

         UpperCAmelCase__				:					Any 					=	self.dummy_model()
         UpperCAmelCase__				:					int 					=	self.dummy_sample_deter * scheduler.init_noise_sigma

         for i, t in enumerate(lowerCamelCase_				):
            # 1. scale model input
            UpperCAmelCase__				:					int 					=	scheduler.scale_model_input(lowerCamelCase_       ,lowerCamelCase_				)

            # 2. predict noise residual
            UpperCAmelCase__				:					Any 					=	model(lowerCamelCase_       ,lowerCamelCase_				)

            # 3. predict previous sample x_t-1
            UpperCAmelCase__				:					List[Any] 					=	scheduler.step(lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_       ,generator=lowerCamelCase_				).prev_sample

            UpperCAmelCase__				:					int 					=	pred_prev_sample

         UpperCAmelCase__				:					Any 					=	torch.sum(torch.abs(lowerCamelCase_				)				)
         UpperCAmelCase__				:					Union[str, Any] 					=	torch.mean(torch.abs(lowerCamelCase_				)				)

         assert abs(result_sum.item() - 192.7614				) < 1e-2
         assert abs(result_mean.item() - 0.2510				) < 1e-3




      def 		lowerCAmelCase__       (					self				)      ->      Any:



         '''simple docstring'''



         UpperCAmelCase__				:					str 					=	self.scheduler_classes[0]
         UpperCAmelCase__				:					List[str] 					=	self.get_scheduler_config()
         UpperCAmelCase__				:					int 					=	scheduler_class(**lowerCamelCase_				)

         UpperCAmelCase__				:					Tuple 					=	[106, 0]
         scheduler.set_timesteps(timesteps=lowerCamelCase_				)
         UpperCAmelCase__				:					List[str] 					=	scheduler.timesteps

         UpperCAmelCase__				:					str 					=	torch.manual_seed(0				)

         UpperCAmelCase__				:					Dict 					=	self.dummy_model()
         UpperCAmelCase__				:					Optional[int] 					=	self.dummy_sample_deter * scheduler.init_noise_sigma

         for t in timesteps:
            # 1. scale model input
            UpperCAmelCase__				:					Any 					=	scheduler.scale_model_input(lowerCamelCase_       ,lowerCamelCase_				)

            # 2. predict noise residual
            UpperCAmelCase__				:					Optional[Any] 					=	model(lowerCamelCase_       ,lowerCamelCase_				)

            # 3. predict previous sample x_t-1
            UpperCAmelCase__				:					Optional[Any] 					=	scheduler.step(lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_       ,generator=lowerCamelCase_				).prev_sample

            UpperCAmelCase__				:					List[Any] 					=	pred_prev_sample

         UpperCAmelCase__				:					str 					=	torch.sum(torch.abs(lowerCamelCase_				)				)
         UpperCAmelCase__				:					List[Any] 					=	torch.mean(torch.abs(lowerCamelCase_				)				)

         assert abs(result_sum.item() - 347.6357				) < 1e-2
         assert abs(result_mean.item() - 0.4527				) < 1e-3




      def 		lowerCAmelCase__       (					self				)      ->      Any:



         '''simple docstring'''



         UpperCAmelCase__				:					int 					=	self.scheduler_classes[0]
         UpperCAmelCase__				:					Tuple 					=	self.get_scheduler_config()
         UpperCAmelCase__				:					List[Any] 					=	scheduler_class(**lowerCamelCase_				)

         UpperCAmelCase__				:					Optional[Any] 					=	[39, 30, 12, 15, 0]

         with self.assertRaises(lowerCamelCase_       ,msg='''`timesteps` must be in descending order.'''				):
            scheduler.set_timesteps(timesteps=lowerCamelCase_				)




      def 		lowerCAmelCase__       (					self				)      ->      List[Any]:



         '''simple docstring'''



         UpperCAmelCase__				:					Any 					=	self.scheduler_classes[0]
         UpperCAmelCase__				:					Optional[int] 					=	self.get_scheduler_config()
         UpperCAmelCase__				:					Any 					=	scheduler_class(**lowerCamelCase_				)

         UpperCAmelCase__				:					Tuple 					=	[39, 30, 12, 1, 0]
         UpperCAmelCase__				:					int 					=	len(lowerCamelCase_				)

         with self.assertRaises(lowerCamelCase_       ,msg='''Can only pass one of `num_inference_steps` or `timesteps`.'''				):
            scheduler.set_timesteps(num_inference_steps=lowerCamelCase_       ,timesteps=lowerCamelCase_				)




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



         '''simple docstring'''



         UpperCAmelCase__				:					List[Any] 					=	self.scheduler_classes[0]
         UpperCAmelCase__				:					Any 					=	self.get_scheduler_config()
         UpperCAmelCase__				:					str 					=	scheduler_class(**lowerCamelCase_				)

         UpperCAmelCase__				:					Tuple 					=	[scheduler.config.num_train_timesteps]

         with self.assertRaises(
             lowerCamelCase_       ,msg='''`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'''       ,):
            scheduler.set_timesteps(timesteps=lowerCamelCase_				)





'''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()




