"""simple docstring"""


import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest




class  UpperCamelCase					(				lowerCAmelCase__   ):
       SCREAMING_SNAKE_CASE_			=      (UniPCMultistepScheduler,)
       SCREAMING_SNAKE_CASE_			=      (("num_inference_steps", 2_5),)







       def 		a_						( self,							**lowerCAmelCase__)      ->      List[Any]:
            snake_case_					       =					{
                'num_train_timesteps': 1000,
                'beta_start': 0.0001,
                'beta_end': 0.02,
                'beta_schedule': 'linear',
                'solver_order': 2,
                'solver_type': 'bh2',
            }

            config.update(**lowerCAmelCase__)
            return config







       def 		a_						( self,							lowerCAmelCase__=0,							**lowerCAmelCase__)      ->      Any:
            snake_case_					       =					dict(self.forward_default_kwargs)
            snake_case_					       =					kwargs.pop('num_inference_steps',							lowerCAmelCase__)
            snake_case_					       =					self.dummy_sample
            snake_case_					       =					0.1 * sample
            snake_case_					       =					[residual + 0.2, residual + 0.15, residual + 0.10]

            for scheduler_class in self.scheduler_classes:
                 snake_case_					       =					self.get_scheduler_config(**lowerCAmelCase__)
                 snake_case_					       =					scheduler_class(**lowerCAmelCase__)
                 scheduler.set_timesteps(lowerCAmelCase__)
                 # copy over dummy past residuals
                 snake_case_					       =					dummy_past_residuals[: scheduler.config.solver_order]

                 with tempfile.TemporaryDirectory() as tmpdirname:
                      scheduler.save_config(lowerCAmelCase__)
                      snake_case_					       =					scheduler_class.from_pretrained(lowerCAmelCase__)
                      new_scheduler.set_timesteps(lowerCAmelCase__)
                      # copy over dummy past residuals
                      snake_case_					       =					dummy_past_residuals[: new_scheduler.config.solver_order]

                 snake_case_       ,	snake_case_					       =					sample, sample
                 for t in range(lowerCAmelCase__,							time_step + scheduler.config.solver_order + 1):
                      snake_case_					       =					scheduler.step(lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__,							**lowerCAmelCase__).prev_sample
                      snake_case_					       =					new_scheduler.step(lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__,							**lowerCAmelCase__).prev_sample

                      assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical"







       def 		a_						( self,							lowerCAmelCase__=0,							**lowerCAmelCase__)      ->      Dict:
            snake_case_					       =					dict(self.forward_default_kwargs)
            snake_case_					       =					kwargs.pop('num_inference_steps',							lowerCAmelCase__)
            snake_case_					       =					self.dummy_sample
            snake_case_					       =					0.1 * sample
            snake_case_					       =					[residual + 0.2, residual + 0.15, residual + 0.10]

            for scheduler_class in self.scheduler_classes:
                 snake_case_					       =					self.get_scheduler_config()
                 snake_case_					       =					scheduler_class(**lowerCAmelCase__)
                 scheduler.set_timesteps(lowerCAmelCase__)

                 # copy over dummy past residuals (must be after setting timesteps)
                 snake_case_					       =					dummy_past_residuals[: scheduler.config.solver_order]

                 with tempfile.TemporaryDirectory() as tmpdirname:
                      scheduler.save_config(lowerCAmelCase__)
                      snake_case_					       =					scheduler_class.from_pretrained(lowerCAmelCase__)
                      # copy over dummy past residuals
                      new_scheduler.set_timesteps(lowerCAmelCase__)

                      # copy over dummy past residual (must be after setting timesteps)
                      snake_case_					       =					dummy_past_residuals[: new_scheduler.config.solver_order]

                 snake_case_					       =					scheduler.step(lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__,							**lowerCAmelCase__).prev_sample
                 snake_case_					       =					new_scheduler.step(lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__,							**lowerCAmelCase__).prev_sample

                 assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical"







       def 		a_						( self,							lowerCAmelCase__=None,							**lowerCAmelCase__)      ->      List[Any]:
            if scheduler is None:
                 snake_case_					       =					self.scheduler_classes[0]
                 snake_case_					       =					self.get_scheduler_config(**lowerCAmelCase__)
                 snake_case_					       =					scheduler_class(**lowerCAmelCase__)

            snake_case_					       =					self.scheduler_classes[0]
            snake_case_					       =					self.get_scheduler_config(**lowerCAmelCase__)
            snake_case_					       =					scheduler_class(**lowerCAmelCase__)

            snake_case_					       =					10
            snake_case_					       =					self.dummy_model()
            snake_case_					       =					self.dummy_sample_deter
            scheduler.set_timesteps(lowerCAmelCase__)

            for i, t in enumerate(scheduler.timesteps):
                 snake_case_					       =					model(lowerCAmelCase__,							lowerCAmelCase__)
                 snake_case_					       =					scheduler.step(lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__).prev_sample

            return sample







       def 		a_						( self)      ->      List[str]:
            snake_case_					       =					dict(self.forward_default_kwargs)

            snake_case_					       =					kwargs.pop('num_inference_steps',							lowerCAmelCase__)

            for scheduler_class in self.scheduler_classes:
                 snake_case_					       =					self.get_scheduler_config()
                 snake_case_					       =					scheduler_class(**lowerCAmelCase__)

                 snake_case_					       =					self.dummy_sample
                 snake_case_					       =					0.1 * sample

                 if num_inference_steps is not None and hasattr(lowerCAmelCase__,							'set_timesteps'):
                      scheduler.set_timesteps(lowerCAmelCase__)
                 elif num_inference_steps is not None and not hasattr(lowerCAmelCase__,							'set_timesteps'):
                      snake_case_					       =					num_inference_steps

                 # copy over dummy past residuals (must be done after set_timesteps)
                 snake_case_					       =					[residual + 0.2, residual + 0.15, residual + 0.10]
                 snake_case_					       =					dummy_past_residuals[: scheduler.config.solver_order]

                 snake_case_					       =					scheduler.timesteps[5]
                 snake_case_					       =					scheduler.timesteps[6]

                 snake_case_					       =					scheduler.step(lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__,							**lowerCAmelCase__).prev_sample
                 snake_case_					       =					scheduler.step(lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__,							**lowerCAmelCase__).prev_sample

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







       def 		a_						( self)      ->      Tuple:
            # make sure that iterating over schedulers with same config names gives same results
            # for defaults
            snake_case_					       =					UniPCMultistepScheduler(**self.get_scheduler_config())
            snake_case_					       =					self.full_loop(scheduler=lowerCAmelCase__)
            snake_case_					       =					torch.mean(torch.abs(lowerCAmelCase__))

            assert abs(result_mean.item() - 0.2464) < 1e-3

            snake_case_					       =					DPMSolverSinglestepScheduler.from_config(scheduler.config)
            snake_case_					       =					DEISMultistepScheduler.from_config(scheduler.config)
            snake_case_					       =					DPMSolverMultistepScheduler.from_config(scheduler.config)
            snake_case_					       =					UniPCMultistepScheduler.from_config(scheduler.config)

            snake_case_					       =					self.full_loop(scheduler=lowerCAmelCase__)
            snake_case_					       =					torch.mean(torch.abs(lowerCAmelCase__))

            assert abs(result_mean.item() - 0.2464) < 1e-3







       def 		a_						( self)      ->      str:
            for timesteps in [25, 50, 100, 999, 1000]:
                 self.check_over_configs(num_train_timesteps=lowerCAmelCase__)







       def 		a_						( self)      ->      List[Any]:
            self.check_over_configs(thresholding=lowerCAmelCase__)
            for order in [1, 2, 3]:
                 for solver_type in ["bh1", "bh2"]:
                      for threshold in [0.5, 1.0, 2.0]:
                           for prediction_type in ["epsilon", "sample"]:
                                self.check_over_configs(
                                    thresholding=lowerCAmelCase__,							prediction_type=lowerCAmelCase__,							sample_max_value=lowerCAmelCase__,							solver_order=lowerCAmelCase__,							solver_type=lowerCAmelCase__,							)







       def 		a_						( self)      ->      List[str]:
            for prediction_type in ["epsilon", "v_prediction"]:
                 self.check_over_configs(prediction_type=lowerCAmelCase__)







       def 		a_						( self)      ->      Tuple:
            for solver_type in ["bh1", "bh2"]:
                 for order in [1, 2, 3]:
                      for prediction_type in ["epsilon", "sample"]:
                           self.check_over_configs(
                               solver_order=lowerCAmelCase__,							solver_type=lowerCAmelCase__,							prediction_type=lowerCAmelCase__,							)
                           snake_case_					       =					self.full_loop(
                               solver_order=lowerCAmelCase__,							solver_type=lowerCAmelCase__,							prediction_type=lowerCAmelCase__,							)
                           assert not torch.isnan(lowerCAmelCase__).any(), "Samples have nan numbers"







       def 		a_						( self)      ->      Any:
            self.check_over_configs(lower_order_final=lowerCAmelCase__)
            self.check_over_configs(lower_order_final=lowerCAmelCase__)







       def 		a_						( self)      ->      int:
            for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
                 self.check_over_forward(num_inference_steps=lowerCAmelCase__,							time_step=0)







       def 		a_						( self)      ->      Tuple:
            snake_case_					       =					self.full_loop()
            snake_case_					       =					torch.mean(torch.abs(lowerCAmelCase__))

            assert abs(result_mean.item() - 0.2464) < 1e-3







       def 		a_						( self)      ->      str:
            snake_case_					       =					self.full_loop(prediction_type='v_prediction')
            snake_case_					       =					torch.mean(torch.abs(lowerCAmelCase__))

            assert abs(result_mean.item() - 0.1014) < 1e-3







       def 		a_						( self)      ->      Dict:
            snake_case_					       =					self.scheduler_classes[0]
            snake_case_					       =					self.get_scheduler_config(thresholding=lowerCAmelCase__,							dynamic_thresholding_ratio=0)
            snake_case_					       =					scheduler_class(**lowerCAmelCase__)

            snake_case_					       =					10
            snake_case_					       =					self.dummy_model()
            snake_case_					       =					self.dummy_sample_deter.half()
            scheduler.set_timesteps(lowerCAmelCase__)

            for i, t in enumerate(scheduler.timesteps):
                 snake_case_					       =					model(lowerCAmelCase__,							lowerCAmelCase__)
                 snake_case_					       =					scheduler.step(lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__).prev_sample

            assert sample.dtype == torch.floataa







       def 		a_						( self,							**lowerCAmelCase__)      ->      str:
            for scheduler_class in self.scheduler_classes:
                 snake_case_					       =					self.get_scheduler_config(**lowerCAmelCase__)
                 snake_case_					       =					scheduler_class(**lowerCAmelCase__)

                 scheduler.set_timesteps(scheduler.config.num_train_timesteps)
                 assert len(scheduler.timesteps.unique()) == scheduler.num_inference_steps








'''simple docstring'''




import os
from datetime import datetime as dt

from github import Github


__SCREAMING_SNAKE_CASE     :str       =				[
    '''good first issue''',
    '''feature request''',
    '''wip''',
]
def 	UpperCAmelCase_							(					)		->       Optional[Any]:


     '''simple docstring'''



     _UpperCAmelCase     							=   Github(os.environ["GITHUB_TOKEN"]       )
     _UpperCAmelCase     							=   g.get_repo("huggingface/accelerate"       )
     _UpperCAmelCase     							=   repo.get_issues(state="open"       )

     for issue in open_issues:
          _UpperCAmelCase     							=   sorted([comment for comment in issue.get_comments()]			,     key=lambda __lowercase       : i.created_at			,     reverse=__lowercase       )
          _UpperCAmelCase     							=   comments[0] if len(__lowercase       ) > 0 else None
          _UpperCAmelCase     							=   dt.utcnow()
          _UpperCAmelCase     							=   (current_time - issue.updated_at).days
          _UpperCAmelCase     							=   (current_time - issue.created_at).days
          if (
              last_comment is not None
              and last_comment.user.login == "github-actions[bot]"
              and days_since_updated > 7
              and days_since_creation >= 30
              and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()       )
          ):
               # Close issue since it has been 7 days of inactivity since bot mention.
               issue.edit(state="closed"       )
          elif (
              days_since_updated > 23
              and days_since_creation >= 30
              and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()       )
          ):
               # Add stale comment
               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/accelerate/blob/main/CONTRIBUTING.md) "
                   "are likely to be ignored."       )


if __name__ == "__main__":
  main()




