


import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest






class     UpperCamelCase(  _a   ):
					snake_case_		:				Dict										=	(UnCLIPScheduler,)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[Any]     ,    **SCREAMING_SNAKE_CASE	:		List[str]						)			->	Optional[int]:




											'''simple docstring'''

											__snake_case				=		{
											    "num_train_timesteps": 1_0_0_0,
											    "variance_type": "fixed_small_log",
											    "clip_sample": True,
											    "clip_sample_range": 1.0,
											    "prediction_type": "epsilon",
											}

											config.update(**SCREAMING_SNAKE_CASE						)
											return config


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




											'''simple docstring'''

											for timesteps in [1, 5, 1_0_0, 1_0_0_0]:
																	self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		List[Any]						)			->	str:




											'''simple docstring'''

											for variance in ["fixed_small_log", "learned_range"]:
																	self.check_over_configs(variance_type=SCREAMING_SNAKE_CASE						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Dict						)			->	int:




											'''simple docstring'''

											for clip_sample in [True, False]:
																	self.check_over_configs(clip_sample=SCREAMING_SNAKE_CASE						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		List[str]						)			->	Optional[int]:




											'''simple docstring'''

											for clip_sample_range in [1, 5, 1_0, 2_0]:
																	self.check_over_configs(clip_sample_range=SCREAMING_SNAKE_CASE						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		List[str]						)			->	Dict:




											'''simple docstring'''

											for prediction_type in ["epsilon", "sample"]:
																	self.check_over_configs(prediction_type=SCREAMING_SNAKE_CASE						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Union[str, Any]						)			->	Optional[int]:




											'''simple docstring'''

											for time_step in [0, 5_0_0, 9_9_9]:
																	for prev_timestep in [None, 5, 1_0_0, 2_5_0, 5_0_0, 7_5_0]:
																							if prev_timestep is not None and prev_timestep >= time_step:
																													continue

																							self.check_over_forward(time_step=SCREAMING_SNAKE_CASE     ,    prev_timestep=SCREAMING_SNAKE_CASE						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Any						)			->	Tuple:




											'''simple docstring'''

											__snake_case				=		self.scheduler_classes[0]
											__snake_case				=		self.get_scheduler_config(variance_type="fixed_small_log"						)
											__snake_case				=		scheduler_class(**SCREAMING_SNAKE_CASE						)

											assert torch.sum(torch.abs(scheduler._get_variance(0						) - 1.0_0_0_0e-1_0						)						) < 1e-5
											assert torch.sum(torch.abs(scheduler._get_variance(4_8_7						) - 0.0549625						)						) < 1e-5
											assert torch.sum(torch.abs(scheduler._get_variance(9_9_9						) - 0.9994987						)						) < 1e-5


					def 							SCREAMING_SNAKE_CASE_  (						self	:		str						)			->	List[Any]:




											'''simple docstring'''

											__snake_case				=		self.scheduler_classes[0]
											__snake_case				=		self.get_scheduler_config(variance_type="learned_range"						)
											__snake_case				=		scheduler_class(**SCREAMING_SNAKE_CASE						)

											__snake_case				=		0.5

											assert scheduler._get_variance(1     ,    predicted_variance=SCREAMING_SNAKE_CASE						) - -10.1712790 < 1e-5
											assert scheduler._get_variance(4_8_7     ,    predicted_variance=SCREAMING_SNAKE_CASE						) - -5.7998052 < 1e-5
											assert scheduler._get_variance(9_9_9     ,    predicted_variance=SCREAMING_SNAKE_CASE						) - -0.0010011 < 1e-5


					def 							SCREAMING_SNAKE_CASE_  (						self	:		int						)			->	Optional[Any]:




											'''simple docstring'''

											__snake_case				=		self.scheduler_classes[0]
											__snake_case				=		self.get_scheduler_config()
											__snake_case				=		scheduler_class(**SCREAMING_SNAKE_CASE						)

											__snake_case				=		scheduler.timesteps

											__snake_case				=		self.dummy_model()
											__snake_case				=		self.dummy_sample_deter
											__snake_case				=		torch.manual_seed(0						)

											for i, t in enumerate(SCREAMING_SNAKE_CASE						):
																	# 1. predict noise residual
																	__snake_case				=		model(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE						)

																	# 2. predict previous mean of sample x_t-1
																	__snake_case				=		scheduler.step(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    generator=SCREAMING_SNAKE_CASE						).prev_sample

																	__snake_case				=		pred_prev_sample

											__snake_case				=		torch.sum(torch.abs(SCREAMING_SNAKE_CASE						)						)
											__snake_case				=		torch.mean(torch.abs(SCREAMING_SNAKE_CASE						)						)

											assert abs(result_sum.item() - 252.2682495						) < 1e-2
											assert abs(result_mean.item() - 0.3284743						) < 1e-3


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




											'''simple docstring'''

											__snake_case				=		self.scheduler_classes[0]
											__snake_case				=		self.get_scheduler_config()
											__snake_case				=		scheduler_class(**SCREAMING_SNAKE_CASE						)

											scheduler.set_timesteps(2_5						)

											__snake_case				=		scheduler.timesteps

											__snake_case				=		self.dummy_model()
											__snake_case				=		self.dummy_sample_deter
											__snake_case				=		torch.manual_seed(0						)

											for i, t in enumerate(SCREAMING_SNAKE_CASE						):
																	# 1. predict noise residual
																	__snake_case				=		model(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE						)

																	if i + 1 == timesteps.shape[0]:
																							__snake_case				=		None
																	else:
																							__snake_case				=		timesteps[i + 1]

																	# 2. predict previous mean of sample x_t-1
																	__snake_case				=		scheduler.step(
																	    SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    prev_timestep=SCREAMING_SNAKE_CASE     ,    generator=SCREAMING_SNAKE_CASE						).prev_sample

																	__snake_case				=		pred_prev_sample

											__snake_case				=		torch.sum(torch.abs(SCREAMING_SNAKE_CASE						)						)
											__snake_case				=		torch.mean(torch.abs(SCREAMING_SNAKE_CASE						)						)

											assert abs(result_sum.item() - 258.2044983						) < 1e-2
											assert abs(result_mean.item() - 0.3362038						) < 1e-3


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




											'''simple docstring'''

											pass






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




											'''simple docstring'''

											pass









import os
from datetime import datetime as dt

from github import Github


A							:					Union[str, Any]     						=     [
    'good first issue',
    'good second issue',
    'good difficult issue',
    'enhancement',
    'new pipeline/model',
    'new scheduler',
    'wip',
]





def 	_lowerCAmelCase       (    )						->	str:


						'''simple docstring'''






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

						for issue in open_issues:
												__snake_case				=		sorted(issue.get_comments()      ,	key=lambda _lowerCAmelCase : i.created_at      ,	reverse=_lowerCAmelCase )
												__snake_case				=		comments[0] if len(_lowerCAmelCase ) > 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()





