



import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest



class        lowerCAmelCase__					(  _lowerCamelCase       ):
				A_   :     Optional[Any]								=     (DEISMultistepScheduler,)
				A_   :     int								=     (('num_inference_steps', 2_5),)


				def __UpperCamelCase						(							self      :  Union[str, Any]						, **__UpperCamelCase      :  List[str]       )		->     Dict:
											A      						=    {
											    'num_train_timesteps': 1_000,
											    'beta_start': 0.0_0_0_1,
											    'beta_end': 0.0_2,
											    'beta_schedule': 'linear',
											    'solver_order': 2,
											}

											config.update(**__UpperCamelCase       )
											return config


				def __UpperCamelCase						(							self      :  Union[str, Any]						, __UpperCamelCase      :  str=0						, **__UpperCamelCase      :  Optional[int]       )		->     List[Any]:
											A      						=    dict(self.forward_default_kwargs       )
											A      						=    kwargs.pop('num_inference_steps'						, __UpperCamelCase       )
											A      						=    self.dummy_sample
											A      						=    0.1 * sample
											A      						=    [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

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

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

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

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


				def __UpperCamelCase						(							self      :  str       )		->     Dict:
											pass


				def __UpperCamelCase						(							self      :  List[Any]						, __UpperCamelCase      :  int=0						, **__UpperCamelCase      :  Dict       )		->     List[str]:
											A      						=    dict(self.forward_default_kwargs       )
											A      						=    kwargs.pop('num_inference_steps'						, __UpperCamelCase       )
											A      						=    self.dummy_sample
											A      						=    0.1 * sample
											A      						=    [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

											for scheduler_class in self.scheduler_classes:
																		A      						=    self.get_scheduler_config()
																		A      						=    scheduler_class(**__UpperCamelCase       )
																		scheduler.set_timesteps(__UpperCamelCase       )

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

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

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

																		A      						=    scheduler.step(__UpperCamelCase						, __UpperCamelCase						, __UpperCamelCase						, **__UpperCamelCase       ).prev_sample
																		A      						=    new_scheduler.step(__UpperCamelCase						, __UpperCamelCase						, __UpperCamelCase						, **__UpperCamelCase       ).prev_sample

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


				def __UpperCamelCase						(							self      :  Tuple						, __UpperCamelCase      :  Tuple=None						, **__UpperCamelCase      :  Dict       )		->     Tuple:
											if scheduler is None:
																		A      						=    self.scheduler_classes[0]
																		A      						=    self.get_scheduler_config(**__UpperCamelCase       )
																		A      						=    scheduler_class(**__UpperCamelCase       )

											A      						=    self.scheduler_classes[0]
											A      						=    self.get_scheduler_config(**__UpperCamelCase       )
											A      						=    scheduler_class(**__UpperCamelCase       )

											A      						=    10
											A      						=    self.dummy_model()
											A      						=    self.dummy_sample_deter
											scheduler.set_timesteps(__UpperCamelCase       )

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

											return sample


				def __UpperCamelCase						(							self      :  Union[str, Any]       )		->     List[Any]:
											A      						=    dict(self.forward_default_kwargs       )

											A      						=    kwargs.pop('num_inference_steps'						, __UpperCamelCase       )

											for scheduler_class in self.scheduler_classes:
																		A      						=    self.get_scheduler_config()
																		A      						=    scheduler_class(**__UpperCamelCase       )

																		A      						=    self.dummy_sample
																		A      						=    0.1 * sample

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

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

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

																		A      						=    scheduler.step(__UpperCamelCase						, __UpperCamelCase						, __UpperCamelCase						, **__UpperCamelCase       ).prev_sample
																		A      						=    scheduler.step(__UpperCamelCase						, __UpperCamelCase						, __UpperCamelCase						, **__UpperCamelCase       ).prev_sample

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


				def __UpperCamelCase						(							self      :  Any       )		->     int:
											# make sure that iterating over schedulers with same config names gives same results
											# for defaults
											A      						=    DEISMultistepScheduler(**self.get_scheduler_config()       )
											A      						=    self.full_loop(scheduler=__UpperCamelCase       )
											A      						=    torch.mean(torch.abs(__UpperCamelCase       )       )

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

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

											A      						=    self.full_loop(scheduler=__UpperCamelCase       )
											A      						=    torch.mean(torch.abs(__UpperCamelCase       )       )

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


				def __UpperCamelCase						(							self      :  List[Any]       )		->     Dict:
											for timesteps in [25, 50, 100, 999, 1_000]:
																		self.check_over_configs(num_train_timesteps=__UpperCamelCase       )


				def __UpperCamelCase						(							self      :  str       )		->     List[Any]:
											self.check_over_configs(thresholding=__UpperCamelCase       )
											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=__UpperCamelCase						, prediction_type=__UpperCamelCase						, sample_max_value=__UpperCamelCase						, algorithm_type='deis'						, solver_order=__UpperCamelCase						, solver_type=__UpperCamelCase						, )


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


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


				def __UpperCamelCase						(							self      :  Optional[int]       )		->     Optional[Any]:
											self.check_over_configs(lower_order_final=__UpperCamelCase       )
											self.check_over_configs(lower_order_final=__UpperCamelCase       )


				def __UpperCamelCase						(							self      :  int       )		->     Tuple:
											for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1_000]:
																		self.check_over_forward(num_inference_steps=__UpperCamelCase						, time_step=0       )


				def __UpperCamelCase						(							self      :  Union[str, Any]       )		->     str:
											A      						=    self.full_loop()
											A      						=    torch.mean(torch.abs(__UpperCamelCase       )       )

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


				def __UpperCamelCase						(							self      :  int       )		->     Optional[Any]:
											A      						=    self.full_loop(prediction_type='v_prediction'       )
											A      						=    torch.mean(torch.abs(__UpperCamelCase       )       )

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


				def __UpperCamelCase						(							self      :  List[Any]       )		->     List[str]:
											A      						=    self.scheduler_classes[0]
											A      						=    self.get_scheduler_config(thresholding=__UpperCamelCase						, dynamic_thresholding_ratio=0       )
											A      						=    scheduler_class(**__UpperCamelCase       )

											A      						=    10
											A      						=    self.dummy_model()
											A      						=    self.dummy_sample_deter.half()
											scheduler.set_timesteps(__UpperCamelCase       )

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

											assert sample.dtype == torch.floataa





'''simple docstring'''



def       _UpperCamelCase			(_lowerCamelCase				:  int						)-> int:




     '''simple docstring'''





     if divisor % 5 == 0 or divisor % 2 == 0:
          return 0
     __snake_case            =	1
     __snake_case            =	1
     while repunit:
          __snake_case            =	(10 * repunit + 1) % divisor
          repunit_index += 1
     return repunit_index







def       _UpperCamelCase			(_lowerCamelCase				:  int = 1_00_00_00						)-> int:




     '''simple docstring'''





     __snake_case            =	limit - 1
     if divisor % 2 == 0:
          divisor += 1
     while least_divisible_repunit(_lowerCamelCase						) <= limit:
          divisor += 2
     return divisor


if __name__ == "__main__":
     print(F"""{solution() = }""")






