
"""simple docstring"""




import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest


class       __magic_name__							(							SCREAMING_SNAKE_CASE__			):
       UpperCamelCase_ =		(PNDMScheduler,)
       UpperCamelCase_ =		(('''num_inference_steps''', 50),)
       def 				lowercase_				(  self			,					**A_							)  ->						Any:





         """simple docstring"""




         _lowercase:      int   			=  {
             '''num_train_timesteps''': 1000,
             '''beta_start''': 0.00_01,
             '''beta_end''': 0.02,
             '''beta_schedule''': '''linear''',
         }

         config.update(**A_							)
         return config
       def 				lowercase_				(  self			,					A_=0			,					**A_							)  ->						Union[str, Any]:





         """simple docstring"""




         _lowercase:      int   			=  dict(self.forward_default_kwargs							)
         _lowercase:      Tuple   			=  kwargs.pop('''num_inference_steps'''			,					A_							)
         _lowercase:      List[Any]   			=  self.dummy_sample
         _lowercase:      Dict   			=  0.1 * sample
         _lowercase:      Optional[Any]   			=  [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

         for scheduler_class in self.scheduler_classes:
           _lowercase:      Optional[int]   			=  self.get_scheduler_config(**A_							)
           _lowercase:      str   			=  scheduler_class(**A_							)
           scheduler.set_timesteps(A_							)
           # copy over dummy past residuals
           _lowercase:      Tuple   			=  dummy_past_residuals[:]

           with tempfile.TemporaryDirectory() as tmpdirname:
             scheduler.save_config(A_							)
             _lowercase:      Dict   			=  scheduler_class.from_pretrained(A_							)
             new_scheduler.set_timesteps(A_							)
             # copy over dummy past residuals
             _lowercase:      Any   			=  dummy_past_residuals[:]

           _lowercase:      Union[str, Any]   			=  scheduler.step_prk(A_			,					A_			,					A_			,					**A_							).prev_sample
           _lowercase:      int   			=  new_scheduler.step_prk(A_			,					A_			,					A_			,					**A_							).prev_sample

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

           _lowercase:      Dict   			=  scheduler.step_plms(A_			,					A_			,					A_			,					**A_							).prev_sample
           _lowercase:      List[str]   			=  new_scheduler.step_plms(A_			,					A_			,					A_			,					**A_							).prev_sample

           assert torch.sum(torch.abs(output - new_output							)							) < 1E-5, "Scheduler outputs are not identical"
       def 				lowercase_				(  self							)  ->						Tuple:





         """simple docstring"""




         pass
       def 				lowercase_				(  self			,					A_=0			,					**A_							)  ->						Tuple:





         """simple docstring"""




         _lowercase:      List[str]   			=  dict(self.forward_default_kwargs							)
         _lowercase:      str   			=  kwargs.pop('''num_inference_steps'''			,					A_							)
         _lowercase:      str   			=  self.dummy_sample
         _lowercase:      str   			=  0.1 * sample
         _lowercase:      Optional[Any]   			=  [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

         for scheduler_class in self.scheduler_classes:
           _lowercase:      Optional[Any]   			=  self.get_scheduler_config()
           _lowercase:      List[str]   			=  scheduler_class(**A_							)
           scheduler.set_timesteps(A_							)

           # copy over dummy past residuals (must be after setting timesteps)
           _lowercase:      str   			=  dummy_past_residuals[:]

           with tempfile.TemporaryDirectory() as tmpdirname:
             scheduler.save_config(A_							)
             _lowercase:      List[str]   			=  scheduler_class.from_pretrained(A_							)
             # copy over dummy past residuals
             new_scheduler.set_timesteps(A_							)

             # copy over dummy past residual (must be after setting timesteps)
             _lowercase:      Tuple   			=  dummy_past_residuals[:]

           _lowercase:      Optional[Any]   			=  scheduler.step_prk(A_			,					A_			,					A_			,					**A_							).prev_sample
           _lowercase:      Tuple   			=  new_scheduler.step_prk(A_			,					A_			,					A_			,					**A_							).prev_sample

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

           _lowercase:      Optional[int]   			=  scheduler.step_plms(A_			,					A_			,					A_			,					**A_							).prev_sample
           _lowercase:      Optional[Any]   			=  new_scheduler.step_plms(A_			,					A_			,					A_			,					**A_							).prev_sample

           assert torch.sum(torch.abs(output - new_output							)							) < 1E-5, "Scheduler outputs are not identical"
       def 				lowercase_				(  self			,					**A_							)  ->						List[str]:





         """simple docstring"""




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

         _lowercase:      Any   			=  10
         _lowercase:      Tuple   			=  self.dummy_model()
         _lowercase:      List[Any]   			=  self.dummy_sample_deter
         scheduler.set_timesteps(A_							)

         for i, t in enumerate(scheduler.prk_timesteps							):
           _lowercase:      Optional[int]   			=  model(A_			,					A_							)
           _lowercase:      List[Any]   			=  scheduler.step_prk(A_			,					A_			,					A_							).prev_sample

         for i, t in enumerate(scheduler.plms_timesteps							):
           _lowercase:      Union[str, Any]   			=  model(A_			,					A_							)
           _lowercase:      Optional[int]   			=  scheduler.step_plms(A_			,					A_			,					A_							).prev_sample

         return sample
       def 				lowercase_				(  self							)  ->						List[Any]:





         """simple docstring"""




         _lowercase:      Tuple   			=  dict(self.forward_default_kwargs							)

         _lowercase:      Dict   			=  kwargs.pop('''num_inference_steps'''			,					A_							)

         for scheduler_class in self.scheduler_classes:
           _lowercase:      Tuple   			=  self.get_scheduler_config()
           _lowercase:      Tuple   			=  scheduler_class(**A_							)

           _lowercase:      Dict   			=  self.dummy_sample
           _lowercase:      Any   			=  0.1 * sample

           if num_inference_steps is not None and hasattr(A_			,					'''set_timesteps'''							):
             scheduler.set_timesteps(A_							)
           elif num_inference_steps is not None and not hasattr(A_			,					'''set_timesteps'''							):
             _lowercase:      str   			=  num_inference_steps

           # copy over dummy past residuals (must be done after set_timesteps)
           _lowercase:      Optional[int]   			=  [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
           _lowercase:      List[Any]   			=  dummy_past_residuals[:]

           _lowercase:      Union[str, Any]   			=  scheduler.step_prk(A_			,					0			,					A_			,					**A_							).prev_sample
           _lowercase:      Dict   			=  scheduler.step_prk(A_			,					1			,					A_			,					**A_							).prev_sample

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

           _lowercase:      int   			=  scheduler.step_plms(A_			,					0			,					A_			,					**A_							).prev_sample
           _lowercase:      int   			=  scheduler.step_plms(A_			,					1			,					A_			,					**A_							).prev_sample

           self.assertEqual(output_a.shape			,					sample.shape							)
           self.assertEqual(output_a.shape			,					output_a.shape							)
       def 				lowercase_				(  self							)  ->						Union[str, Any]:





         """simple docstring"""




         for timesteps in [100, 1000]:
           self.check_over_configs(num_train_timesteps=A_							)
       def 				lowercase_				(  self							)  ->						Optional[Any]:





         """simple docstring"""




         for steps_offset in [0, 1]:
           self.check_over_configs(steps_offset=A_							)

         _lowercase:      int   			=  self.scheduler_classes[0]
         _lowercase:      Optional[int]   			=  self.get_scheduler_config(steps_offset=1							)
         _lowercase:      Optional[int]   			=  scheduler_class(**A_							)
         scheduler.set_timesteps(10							)
         assert torch.equal(
             scheduler.timesteps			,					torch.LongTensor(
                 [901, 851, 851, 801, 801, 751, 751, 701, 701, 651, 651, 601, 601, 501, 401, 301, 201, 101, 1]							)			,					)
       def 				lowercase_				(  self							)  ->						List[str]:





         """simple docstring"""




         for beta_start, beta_end in zip([0.00_01, 0.0_01]			,					[0.0_02, 0.02]							):
           self.check_over_configs(beta_start=A_			,					beta_end=A_							)
       def 				lowercase_				(  self							)  ->						List[str]:





         """simple docstring"""




         for schedule in ["linear", "squaredcos_cap_v2"]:
           self.check_over_configs(beta_schedule=A_							)
       def 				lowercase_				(  self							)  ->						List[str]:





         """simple docstring"""




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





         """simple docstring"""




         for t in [1, 5, 10]:
           self.check_over_forward(time_step=A_							)
       def 				lowercase_				(  self							)  ->						Tuple:





         """simple docstring"""




         for t, num_inference_steps in zip([1, 5, 10]			,					[10, 50, 100]							):
           self.check_over_forward(num_inference_steps=A_							)
       def 				lowercase_				(  self							)  ->						Optional[int]:





         """simple docstring"""




         _lowercase:      Dict   			=  27

         for scheduler_class in self.scheduler_classes:
           _lowercase:      Any   			=  self.dummy_sample
           _lowercase:      Any   			=  0.1 * sample

           _lowercase:      Dict   			=  self.get_scheduler_config()
           _lowercase:      Any   			=  scheduler_class(**A_							)

           scheduler.set_timesteps(A_							)

           # before power of 3 fix, would error on first step, so we only need to do two
           for i, t in enumerate(scheduler.prk_timesteps[:2]							):
             _lowercase:      List[Any]   			=  scheduler.step_prk(A_			,					A_			,					A_							).prev_sample
       def 				lowercase_				(  self							)  ->						List[str]:





         """simple docstring"""




         with self.assertRaises(A_							):
           _lowercase:      Dict   			=  self.scheduler_classes[0]
           _lowercase:      int   			=  self.get_scheduler_config()
           _lowercase:      Union[str, Any]   			=  scheduler_class(**A_							)

           scheduler.step_plms(self.dummy_sample			,					1			,					self.dummy_sample							).prev_sample
       def 				lowercase_				(  self							)  ->						List[Any]:





         """simple docstring"""




         _lowercase:      int   			=  self.full_loop()
         _lowercase:      str   			=  torch.sum(torch.abs(A_							)							)
         _lowercase:      Any   			=  torch.mean(torch.abs(A_							)							)

         assert abs(result_sum.item() - 1_98.13_18							) < 1E-2
         assert abs(result_mean.item() - 0.25_80							) < 1E-3
       def 				lowercase_				(  self							)  ->						List[str]:





         """simple docstring"""




         _lowercase:      List[Any]   			=  self.full_loop(prediction_type='''v_prediction'''							)
         _lowercase:      str   			=  torch.sum(torch.abs(A_							)							)
         _lowercase:      Tuple   			=  torch.mean(torch.abs(A_							)							)

         assert abs(result_sum.item() - 67.39_86							) < 1E-2
         assert abs(result_mean.item() - 0.08_78							) < 1E-3
       def 				lowercase_				(  self							)  ->						Optional[int]:





         """simple docstring"""




         _lowercase:      List[Any]   			=  self.full_loop(set_alpha_to_one=A_			,					beta_start=0.01							)
         _lowercase:      Optional[int]   			=  torch.sum(torch.abs(A_							)							)
         _lowercase:      Optional[Any]   			=  torch.mean(torch.abs(A_							)							)

         assert abs(result_sum.item() - 2_30.03_99							) < 1E-2
         assert abs(result_mean.item() - 0.29_95							) < 1E-3



       def 				lowercase_				(  self							)  ->						Tuple:





         """simple docstring"""




         _lowercase:      Dict   			=  self.full_loop(set_alpha_to_one=A_			,					beta_start=0.01							)
         _lowercase:      int   			=  torch.sum(torch.abs(A_							)							)
         _lowercase:      Optional[int]   			=  torch.mean(torch.abs(A_							)							)

         assert abs(result_sum.item() - 1_86.94_82							) < 1E-2
         assert abs(result_mean.item() - 0.24_34							) < 1E-3


"""simple docstring"""




import os
from datetime import datetime as dt

from github import Github


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




def 			_lowerCAmelCase		(		):






  """simple docstring"""



  _lowercase:      Optional[int]   			=  Github(os.environ['''GITHUB_TOKEN''']    )
  _lowercase:      Optional[int]   			=  g.get_repo('''huggingface/diffusers'''    )
  _lowercase:      Union[str, Any]   			=  repo.get_issues(state='''open'''    )

  for issue in open_issues:
    _lowercase:      List[Any]   			=  sorted(issue.get_comments()			,					key=lambda _UpperCamelCase    : i.created_at			,					reverse=_UpperCamelCase    )
    _lowercase:      Optional[Any]   			=  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()
