


import os
from datetime import datetime as dt

from github import Github


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


def 		lowercase_  (     ):


			'''simple docstring'''







			__lowercase 					=   Github(os.environ['''GITHUB_TOKEN''']  )
			__lowercase 					=   g.get_repo('''huggingface/diffusers'''  )
			__lowercase 					=   repo.get_issues(state='''open'''  )

			for issue in open_issues:
						__lowercase 					=   sorted(issue.get_comments()       ,			key=lambda _UpperCamelCase  : i.created_at       ,			reverse=_UpperCamelCase  )
						__lowercase 					=   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()






import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest
class 	lowerCamelCase_       (				lowerCAmelCase__				):



					'''simple docstring'''







					__UpperCAmelCase            =				(DEISMultistepScheduler,)
					__UpperCAmelCase            =				(("num_inference_steps", 25),)






					def A      (				self ,       **snake_case_						)   ->			int:







								'''simple docstring'''

								__lowercase 					=   {
								    '''num_train_timesteps''': 1_0_0_0,
								    '''beta_start''': 0.0_0_0_1,
								    '''beta_end''': 0.0_2,
								    '''beta_schedule''': '''linear''',
								    '''solver_order''': 2,
								}

								config.update(**snake_case_						)
								return config






					def A      (				self ,       snake_case_=0 ,       **snake_case_						)   ->			List[Any]:







								'''simple docstring'''

								__lowercase 					=   dict(self.forward_default_kwargs						)
								__lowercase 					=   kwargs.pop('''num_inference_steps''' ,       snake_case_						)
								__lowercase 					=   self.dummy_sample
								__lowercase 					=   0.1 * sample
								__lowercase 					=   [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

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

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

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

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






					def A      (				self						)   ->			Dict:







								'''simple docstring'''

								pass






					def A      (				self ,       snake_case_=0 ,       **snake_case_						)   ->			List[str]:







								'''simple docstring'''

								__lowercase 					=   dict(self.forward_default_kwargs						)
								__lowercase 					=   kwargs.pop('''num_inference_steps''' ,       snake_case_						)
								__lowercase 					=   self.dummy_sample
								__lowercase 					=   0.1 * sample
								__lowercase 					=   [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

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

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

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

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

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

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






					def A      (				self ,       snake_case_=None ,       **snake_case_						)   ->			Optional[int]:







								'''simple docstring'''

								if scheduler is None:
											__lowercase 					=   self.scheduler_classes[0]
											__lowercase 					=   self.get_scheduler_config(**snake_case_						)
											__lowercase 					=   scheduler_class(**snake_case_						)

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

								__lowercase 					=   1_0
								__lowercase 					=   self.dummy_model()
								__lowercase 					=   self.dummy_sample_deter
								scheduler.set_timesteps(snake_case_						)

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

								return sample






					def A      (				self						)   ->			str:







								'''simple docstring'''

								__lowercase 					=   dict(self.forward_default_kwargs						)

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

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

											__lowercase 					=   self.dummy_sample
											__lowercase 					=   0.1 * sample

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

											# copy over dummy past residuals (must be done after set_timesteps)
											__lowercase 					=   [residual + 0.2, residual + 0.1_5, residual + 0.1_0]
											__lowercase 					=   dummy_past_residuals[: scheduler.config.solver_order]

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

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

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






					def A      (				self						)   ->			str:







								'''simple docstring'''

								__lowercase 					=   DEISMultistepScheduler(**self.get_scheduler_config()						)
								__lowercase 					=   self.full_loop(scheduler=snake_case_						)
								__lowercase 					=   torch.mean(torch.abs(snake_case_						)						)

								assert abs(result_mean.item() - 0.2_3_9_1_6						) < 1e-3

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

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

								assert abs(result_mean.item() - 0.2_3_9_1_6						) < 1e-3






					def A      (				self						)   ->			Optional[Any]:







								'''simple docstring'''

								for timesteps in [2_5, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
											self.check_over_configs(num_train_timesteps=snake_case_						)






					def A      (				self						)   ->			Optional[int]:







								'''simple docstring'''

								self.check_over_configs(thresholding=snake_case_						)
								for order in [1, 2, 3]:
											for solver_type in ["logrho"]:
														for threshold in [0.5, 1.0, 2.0]:
																	for prediction_type in ["epsilon", "sample"]:
																				self.check_over_configs(
																				    thresholding=snake_case_ ,       prediction_type=snake_case_ ,       sample_max_value=snake_case_ ,       algorithm_type='''deis''' ,       solver_order=snake_case_ ,       solver_type=snake_case_ ,       )






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







								'''simple docstring'''

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






					def A      (				self						)   ->			Optional[Any]:







								'''simple docstring'''

								for algorithm_type in ["deis"]:
											for solver_type in ["logrho"]:
														for order in [1, 2, 3]:
																	for prediction_type in ["epsilon", "sample"]:
																				self.check_over_configs(
																				    solver_order=snake_case_ ,       solver_type=snake_case_ ,       prediction_type=snake_case_ ,       algorithm_type=snake_case_ ,       )
																				__lowercase 					=   self.full_loop(
																				    solver_order=snake_case_ ,       solver_type=snake_case_ ,       prediction_type=snake_case_ ,       algorithm_type=snake_case_ ,       )
																				assert not torch.isnan(snake_case_						).any(), "Samples have nan numbers"






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







								'''simple docstring'''

								self.check_over_configs(lower_order_final=snake_case_						)
								self.check_over_configs(lower_order_final=snake_case_						)






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







								'''simple docstring'''

								for num_inference_steps in [1, 2, 3, 5, 1_0, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
											self.check_over_forward(num_inference_steps=snake_case_ ,       time_step=0						)






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







								'''simple docstring'''

								__lowercase 					=   self.full_loop()
								__lowercase 					=   torch.mean(torch.abs(snake_case_						)						)

								assert abs(result_mean.item() - 0.2_3_9_1_6						) < 1e-3






					def A      (				self						)   ->			Dict:







								'''simple docstring'''

								__lowercase 					=   self.full_loop(prediction_type='''v_prediction'''						)
								__lowercase 					=   torch.mean(torch.abs(snake_case_						)						)

								assert abs(result_mean.item() - 0.0_9_1						) < 1e-3






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







								'''simple docstring'''

								__lowercase 					=   self.scheduler_classes[0]
								__lowercase 					=   self.get_scheduler_config(thresholding=snake_case_ ,       dynamic_thresholding_ratio=0						)
								__lowercase 					=   scheduler_class(**snake_case_						)

								__lowercase 					=   1_0
								__lowercase 					=   self.dummy_model()
								__lowercase 					=   self.dummy_sample_deter.half()
								scheduler.set_timesteps(snake_case_						)

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

								assert sample.dtype == torch.floataa


