




import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest






class  _lowerCAmelCase     (  _lowercase	):
      A__		     =		(DDIMParallelScheduler,)
      A__		     =		(('eta', 0.0), ('num_inference_steps', 50))

      def 	__magic_name__(   self      ,				**__UpperCAmelCase		):
        lowerCAmelCase__				:  str		       =     {
            '''num_train_timesteps''': 1000,
            '''beta_start''': 0.0001,
            '''beta_end''': 0.02,
            '''beta_schedule''': '''linear''',
            '''clip_sample''': True,
        }

        config.update(**__a		)
        return config

      def 	__magic_name__(   self      ,				**__UpperCAmelCase		):
        lowerCAmelCase__				:  Union[str, Any]		       =     self.scheduler_classes[0]
        lowerCAmelCase__				:  str		       =     self.get_scheduler_config(**__a		)
        lowerCAmelCase__				:  List[str]		       =     scheduler_class(**__a		)

        lowerCAmelCase__       ,			lowerCAmelCase__				:  Tuple		       =     10, 0.0

        lowerCAmelCase__				:  int		       =     self.dummy_model()
        lowerCAmelCase__				:  List[Any]		       =     self.dummy_sample_deter

        scheduler.set_timesteps(__a		)

        for t in scheduler.timesteps:
          lowerCAmelCase__				:  List[str]		       =     model(__a      ,				__a		)
          lowerCAmelCase__				:  Union[str, Any]		       =     scheduler.step(__a      ,				__a      ,				__a      ,				__a		).prev_sample

        return sample

      def 	__magic_name__(   self		):
        for timesteps in [100, 500, 1000]:
          self.check_over_configs(num_train_timesteps=__a		)

      def 	__magic_name__(   self		):
        for steps_offset in [0, 1]:
          self.check_over_configs(steps_offset=__a		)

        lowerCAmelCase__				:  Union[str, Any]		       =     self.scheduler_classes[0]
        lowerCAmelCase__				:  Optional[int]		       =     self.get_scheduler_config(steps_offset=1		)
        lowerCAmelCase__				:  str		       =     scheduler_class(**__a		)
        scheduler.set_timesteps(5		)
        assert torch.equal(scheduler.timesteps      ,				torch.LongTensor([801, 601, 401, 201, 1]		)		)

      def 	__magic_name__(   self		):
        for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]      ,				[0.002, 0.02, 0.2, 2]		):
          self.check_over_configs(beta_start=__a      ,				beta_end=__a		)

      def 	__magic_name__(   self		):
        for schedule in ["linear", "squaredcos_cap_v2"]:
          self.check_over_configs(beta_schedule=__a		)

      def 	__magic_name__(   self		):
        for prediction_type in ["epsilon", "v_prediction"]:
          self.check_over_configs(prediction_type=__a		)

      def 	__magic_name__(   self		):
        for clip_sample in [True, False]:
          self.check_over_configs(clip_sample=__a		)

      def 	__magic_name__(   self		):
        for timestep_spacing in ["trailing", "leading"]:
          self.check_over_configs(timestep_spacing=__a		)

      def 	__magic_name__(   self		):
        for rescale_betas_zero_snr in [True, False]:
          self.check_over_configs(rescale_betas_zero_snr=__a		)

      def 	__magic_name__(   self		):
        self.check_over_configs(thresholding=__a		)
        for threshold in [0.5, 1.0, 2.0]:
          for prediction_type in ["epsilon", "v_prediction"]:
            self.check_over_configs(
                thresholding=__a      ,				prediction_type=__a      ,				sample_max_value=__a      ,				)

      def 	__magic_name__(   self		):
        for t in [1, 10, 49]:
          self.check_over_forward(time_step=__a		)

      def 	__magic_name__(   self		):
        for t, num_inference_steps in zip([1, 10, 50]      ,				[10, 50, 500]		):
          self.check_over_forward(time_step=__a      ,				num_inference_steps=__a		)

      def 	__magic_name__(   self		):
        for t, eta in zip([1, 10, 49]      ,				[0.0, 0.5, 1.0]		):
          self.check_over_forward(time_step=__a      ,				eta=__a		)

      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  Dict		       =     self.scheduler_classes[0]
        lowerCAmelCase__				:  Optional[int]		       =     self.get_scheduler_config()
        lowerCAmelCase__				:  Union[str, Any]		       =     scheduler_class(**__a		)

        assert torch.sum(torch.abs(scheduler._get_variance(0      ,				0		) - 0.0		)		) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(420      ,				400		) - 0.14771		)		) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(980      ,				960		) - 0.32460		)		) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(0      ,				0		) - 0.0		)		) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(487      ,				486		) - 0.00979		)		) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(999      ,				998		) - 0.02		)		) < 1e-5

      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  int		       =     self.scheduler_classes[0]
        lowerCAmelCase__				:  Union[str, Any]		       =     self.get_scheduler_config()
        lowerCAmelCase__				:  str		       =     scheduler_class(**__a		)

        lowerCAmelCase__       ,			lowerCAmelCase__				:  int		       =     10, 0.0
        scheduler.set_timesteps(__a		)

        lowerCAmelCase__				:  List[Any]		       =     self.dummy_model()
        lowerCAmelCase__				:  str		       =     self.dummy_sample_deter
        lowerCAmelCase__				:  int		       =     self.dummy_sample_deter + 0.1
        lowerCAmelCase__				:  int		       =     self.dummy_sample_deter - 0.1

        lowerCAmelCase__				:  Optional[int]		       =     samplea.shape[0]
        lowerCAmelCase__				:  Optional[Any]		       =     torch.stack([samplea, samplea, samplea]      ,				dim=0		)
        lowerCAmelCase__				:  Any		       =     torch.arange(__a		)[0:3, None].repeat(1      ,				__a		)

        lowerCAmelCase__				:  Optional[Any]		       =     model(samples.flatten(0      ,				1		)      ,				timesteps.flatten(0      ,				1		)		)
        lowerCAmelCase__				:  Any		       =     scheduler.batch_step_no_noise(__a      ,				timesteps.flatten(0      ,				1		)      ,				samples.flatten(0      ,				1		)      ,				__a		)

        lowerCAmelCase__				:  str		       =     torch.sum(torch.abs(__a		)		)
        lowerCAmelCase__				:  Tuple		       =     torch.mean(torch.abs(__a		)		)

        assert abs(result_sum.item() - 1147.7904		) < 1e-2
        assert abs(result_mean.item() - 0.4982		) < 1e-3

      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  Optional[Any]		       =     self.full_loop()

        lowerCAmelCase__				:  Any		       =     torch.sum(torch.abs(__a		)		)
        lowerCAmelCase__				:  Dict		       =     torch.mean(torch.abs(__a		)		)

        assert abs(result_sum.item() - 172.0067		) < 1e-2
        assert abs(result_mean.item() - 0.223967		) < 1e-3

      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  Optional[int]		       =     self.full_loop(prediction_type='''v_prediction'''		)

        lowerCAmelCase__				:  Tuple		       =     torch.sum(torch.abs(__a		)		)
        lowerCAmelCase__				:  List[Any]		       =     torch.mean(torch.abs(__a		)		)

        assert abs(result_sum.item() - 52.5302		) < 1e-2
        assert abs(result_mean.item() - 0.0684		) < 1e-3

      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  Union[str, Any]		       =     self.full_loop(set_alpha_to_one=__a      ,				beta_start=0.01		)
        lowerCAmelCase__				:  Any		       =     torch.sum(torch.abs(__a		)		)
        lowerCAmelCase__				:  int		       =     torch.mean(torch.abs(__a		)		)

        assert abs(result_sum.item() - 149.8295		) < 1e-2
        assert abs(result_mean.item() - 0.1951		) < 1e-3







      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  Dict		       =     self.full_loop(set_alpha_to_one=__a      ,				beta_start=0.01		)
        lowerCAmelCase__				:  Optional[int]		       =     torch.sum(torch.abs(__a		)		)
        lowerCAmelCase__				:  Union[str, Any]		       =     torch.mean(torch.abs(__a		)		)

        assert abs(result_sum.item() - 149.0784		) < 1e-2
        assert abs(result_mean.item() - 0.1941		) < 1e-3







import os
from datetime import datetime as dt

from github import Github


lowerCAmelCase_					    = [
    """good first issue""",
    """good second issue""",
    """good difficult issue""",
    """enhancement""",
    """new pipeline/model""",
    """new scheduler""",
    """wip""",
]



def      __lowerCAmelCase			(					)     ->	List[Any]:
  lowerCAmelCase__				:  str		       =     Github(os.environ['''GITHUB_TOKEN''']      )
  lowerCAmelCase__				:  str		       =     g.get_repo('''huggingface/diffusers'''      )
  lowerCAmelCase__				:  List[str]		       =     repo.get_issues(state='''open'''      )

  for issue in open_issues:
    lowerCAmelCase__				:  Optional[Any]		       =     sorted(issue.get_comments()	,      key=lambda UpperCamelCase      : i.created_at	,      reverse=UpperCamelCase      )
    lowerCAmelCase__				:  int		       =     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()      )
    ):
      # 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()

