


import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest

class 						__a (							UpperCAmelCase     ):
		_a      :    List[Any]           =      (DEISMultistepScheduler,)
		_a      :    Union[str, Any]           =      (('num_inference_steps', 25),)
		def       UpperCAmelCase__							(     self     ,       **_SCREAMING_SNAKE_CASE		)					->			List[str]:




									"""simple docstring"""





									_UpperCAmelCase											=     {
									    'num_train_timesteps': 1000,
									    'beta_start': 0.0001,
									    'beta_end': 0.02,
									    'beta_schedule': 'linear',
									    'solver_order': 2,
									}

									config.update(**_SCREAMING_SNAKE_CASE		)
									return config
		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE=0     ,       **_SCREAMING_SNAKE_CASE		)					->			List[str]:




									"""simple docstring"""





									_UpperCAmelCase											=     dict(self.forward_default_kwargs		)
									_UpperCAmelCase											=     kwargs.pop('num_inference_steps'     ,       _SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase											=     self.dummy_sample
									_UpperCAmelCase											=     0.1 * sample
									_UpperCAmelCase											=     [residual + 0.2, residual + 0.15, residual + 0.10]

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

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

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

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




									"""simple docstring"""





									pass
		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE=0     ,       **_SCREAMING_SNAKE_CASE		)					->			Any:




									"""simple docstring"""





									_UpperCAmelCase											=     dict(self.forward_default_kwargs		)
									_UpperCAmelCase											=     kwargs.pop('num_inference_steps'     ,       _SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase											=     self.dummy_sample
									_UpperCAmelCase											=     0.1 * sample
									_UpperCAmelCase											=     [residual + 0.2, residual + 0.15, residual + 0.10]

									for scheduler_class in self.scheduler_classes:
																_UpperCAmelCase											=     self.get_scheduler_config()
																_UpperCAmelCase											=     scheduler_class(**_SCREAMING_SNAKE_CASE		)
																scheduler.set_timesteps(_SCREAMING_SNAKE_CASE		)

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

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

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

																_UpperCAmelCase											=     scheduler.step(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       **_SCREAMING_SNAKE_CASE		).prev_sample
																_UpperCAmelCase											=     new_scheduler.step(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       **_SCREAMING_SNAKE_CASE		).prev_sample

																assert torch.sum(torch.abs(output - new_output		)		) < 1e-5, "Scheduler outputs are not identical"
		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE=None     ,       **_SCREAMING_SNAKE_CASE		)					->			Any:




									"""simple docstring"""





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

									_UpperCAmelCase											=     self.scheduler_classes[0]
									_UpperCAmelCase											=     self.get_scheduler_config(**_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase											=     scheduler_class(**_SCREAMING_SNAKE_CASE		)

									_UpperCAmelCase											=     10
									_UpperCAmelCase											=     self.dummy_model()
									_UpperCAmelCase											=     self.dummy_sample_deter
									scheduler.set_timesteps(_SCREAMING_SNAKE_CASE		)

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

									return sample
		def       UpperCAmelCase__							(     self		)					->			Dict:




									"""simple docstring"""





									_UpperCAmelCase											=     dict(self.forward_default_kwargs		)

									_UpperCAmelCase											=     kwargs.pop('num_inference_steps'     ,       _SCREAMING_SNAKE_CASE		)

									for scheduler_class in self.scheduler_classes:
																_UpperCAmelCase											=     self.get_scheduler_config()
																_UpperCAmelCase											=     scheduler_class(**_SCREAMING_SNAKE_CASE		)

																_UpperCAmelCase											=     self.dummy_sample
																_UpperCAmelCase											=     0.1 * sample

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

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

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

																_UpperCAmelCase											=     scheduler.step(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       **_SCREAMING_SNAKE_CASE		).prev_sample
																_UpperCAmelCase											=     scheduler.step(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       **_SCREAMING_SNAKE_CASE		).prev_sample

																self.assertEqual(output_a.shape     ,       sample.shape		)
																self.assertEqual(output_a.shape     ,       output_a.shape		)
		def       UpperCAmelCase__							(     self		)					->			Any:




									"""simple docstring"""





									_UpperCAmelCase											=     DEISMultistepScheduler(**self.get_scheduler_config()		)
									_UpperCAmelCase											=     self.full_loop(scheduler=_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase											=     torch.mean(torch.abs(_SCREAMING_SNAKE_CASE		)		)

									assert abs(result_mean.item() - 0.23916		) < 1e-3

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

									_UpperCAmelCase											=     self.full_loop(scheduler=_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase											=     torch.mean(torch.abs(_SCREAMING_SNAKE_CASE		)		)

									assert abs(result_mean.item() - 0.23916		) < 1e-3
		def       UpperCAmelCase__							(     self		)					->			int:




									"""simple docstring"""





									for timesteps in [25, 50, 100, 999, 1000]:
																self.check_over_configs(num_train_timesteps=_SCREAMING_SNAKE_CASE		)
		def       UpperCAmelCase__							(     self		)					->			int:




									"""simple docstring"""





									self.check_over_configs(thresholding=_SCREAMING_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=_SCREAMING_SNAKE_CASE     ,       prediction_type=_SCREAMING_SNAKE_CASE     ,       sample_max_value=_SCREAMING_SNAKE_CASE     ,       algorithm_type='deis'     ,       solver_order=_SCREAMING_SNAKE_CASE     ,       solver_type=_SCREAMING_SNAKE_CASE     ,       )
		def       UpperCAmelCase__							(     self		)					->			str:




									"""simple docstring"""





									for prediction_type in ["epsilon", "v_prediction"]:
																self.check_over_configs(prediction_type=_SCREAMING_SNAKE_CASE		)
		def       UpperCAmelCase__							(     self		)					->			Optional[int]:




									"""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=_SCREAMING_SNAKE_CASE     ,       solver_type=_SCREAMING_SNAKE_CASE     ,       prediction_type=_SCREAMING_SNAKE_CASE     ,       algorithm_type=_SCREAMING_SNAKE_CASE     ,       )
																																					_UpperCAmelCase											=     self.full_loop(
																																					    solver_order=_SCREAMING_SNAKE_CASE     ,       solver_type=_SCREAMING_SNAKE_CASE     ,       prediction_type=_SCREAMING_SNAKE_CASE     ,       algorithm_type=_SCREAMING_SNAKE_CASE     ,       )
																																					assert not torch.isnan(_SCREAMING_SNAKE_CASE		).any(), "Samples have nan numbers"
		def       UpperCAmelCase__							(     self		)					->			Optional[Any]:




									"""simple docstring"""





									self.check_over_configs(lower_order_final=_SCREAMING_SNAKE_CASE		)
									self.check_over_configs(lower_order_final=_SCREAMING_SNAKE_CASE		)
		def       UpperCAmelCase__							(     self		)					->			int:




									"""simple docstring"""





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




									"""simple docstring"""





									_UpperCAmelCase											=     self.full_loop()
									_UpperCAmelCase											=     torch.mean(torch.abs(_SCREAMING_SNAKE_CASE		)		)

									assert abs(result_mean.item() - 0.23916		) < 1e-3
		def       UpperCAmelCase__							(     self		)					->			Any:




									"""simple docstring"""





									_UpperCAmelCase											=     self.full_loop(prediction_type='v_prediction'		)
									_UpperCAmelCase											=     torch.mean(torch.abs(_SCREAMING_SNAKE_CASE		)		)

									assert abs(result_mean.item() - 0.091		) < 1e-3






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




									"""simple docstring"""





									_UpperCAmelCase											=     self.scheduler_classes[0]
									_UpperCAmelCase											=     self.get_scheduler_config(thresholding=_SCREAMING_SNAKE_CASE     ,       dynamic_thresholding_ratio=0		)
									_UpperCAmelCase											=     scheduler_class(**_SCREAMING_SNAKE_CASE		)

									_UpperCAmelCase											=     10
									_UpperCAmelCase											=     self.dummy_model()
									_UpperCAmelCase											=     self.dummy_sample_deter.half()
									scheduler.set_timesteps(_SCREAMING_SNAKE_CASE		)

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

									assert sample.dtype == torch.floataa




import argparse
import json
import subprocess

def 			lowerCAmelCase__					(     a__:						List[str]     ,    a__:						Optional[int]			)    ->						Union[str, Any]:


							'''simple docstring'''





							_UpperCAmelCase											=     []

							_UpperCAmelCase											=     (
							    F'''curl -H "Accept: application/vnd.github+json" -H "Authorization: Bearer {token}"'''
							    ' https://api.github.com/repos/huggingface/transformers/actions/runners'
							)
							_UpperCAmelCase											=     subprocess.run(a__     ,    shell=a__     ,    stdout=subprocess.PIPE			)
							_UpperCAmelCase											=     output.stdout.decode('utf-8'			)
							_UpperCAmelCase											=     json.loads(a__			)

							_UpperCAmelCase											=     status['runners']
							for runner in runners:
														if runner["name"] in target_runners:
																					if runner["status"] == "offline":
																												offline_runners.append(a__			)

    # save the result so we can report them on Slack
							with open('offline_runners.txt'     ,    'w'			) as fp:
														fp.write(json.dumps(a__			)			)

							if len(a__			) > 0:
														_UpperCAmelCase											=     '\n'.join([x['name'] for x in offline_runners]			)
														raise ValueError(F'''The following runners are offline:\n{failed}'''			)


if __name__ == "__main__":

		def 			lowerCAmelCase__					(     a__:						int			)    ->						List[Any]:


									'''simple docstring'''





									return values.split(','			)

		lowerCAmelCase__    :Optional[Any]       =				argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--target_runners''',
		    default=None,
		    type=list_str,
		    required=True,
		    help='''Comma-separated list of runners to check status.''',
		)

		parser.add_argument(
		    '''--token''', default=None, type=str, required=True, help='''A token that has actions:read permission.'''
		)
		lowerCAmelCase__    :Tuple       =				parser.parse_args()







		get_runner_status(args.target_runners, args.token)
