


'''simple docstring'''
import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest
class      lowercase			(							A__      ):

		"""simple docstring"""

		_a									=       (IPNDMScheduler,)
		_a									=       (('num_inference_steps', 50),)




		def 					lowerCAmelCase__       (	self					,     **UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__     :Any							      =				{'''num_train_timesteps''': 1000}
							config.update(**UpperCamelCase_							)
							return config




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_=0					,     **UpperCamelCase_							):

							'''simple docstring'''


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

							for scheduler_class in self.scheduler_classes:
												UpperCamelCase__     :int							      =				self.get_scheduler_config(**UpperCamelCase_							)
												UpperCamelCase__     :List[Any]							      =				scheduler_class(**UpperCamelCase_							)
												scheduler.set_timesteps(UpperCamelCase_							)
												# copy over dummy past residuals
												UpperCamelCase__     :List[Any]							      =				dummy_past_residuals[:]

												if time_step is None:
																	UpperCamelCase__     :Tuple							      =				scheduler.timesteps[len(scheduler.timesteps							) // 2]

												with tempfile.TemporaryDirectory() as tmpdirname:
																	scheduler.save_config(UpperCamelCase_							)
																	UpperCamelCase__     :str							      =				scheduler_class.from_pretrained(UpperCamelCase_							)
																	new_scheduler.set_timesteps(UpperCamelCase_							)
																	# copy over dummy past residuals
																	UpperCamelCase__     :Tuple							      =				dummy_past_residuals[:]

												UpperCamelCase__     :List[str]							      =				scheduler.step(UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     **UpperCamelCase_							).prev_sample
												UpperCamelCase__     :Union[str, Any]							      =				new_scheduler.step(UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     **UpperCamelCase_							).prev_sample

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

												UpperCamelCase__     :List[Any]							      =				scheduler.step(UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     **UpperCamelCase_							).prev_sample
												UpperCamelCase__     :Dict							      =				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 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							pass




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_=0					,     **UpperCamelCase_							):

							'''simple docstring'''


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

							for scheduler_class in self.scheduler_classes:
												UpperCamelCase__     :Optional[Any]							      =				self.get_scheduler_config()
												UpperCamelCase__     :Any							      =				scheduler_class(**UpperCamelCase_							)
												scheduler.set_timesteps(UpperCamelCase_							)

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

												if time_step is None:
																	UpperCamelCase__     :str							      =				scheduler.timesteps[len(scheduler.timesteps							) // 2]

												with tempfile.TemporaryDirectory() as tmpdirname:
																	scheduler.save_config(UpperCamelCase_							)
																	UpperCamelCase__     :Optional[Any]							      =				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)
																	UpperCamelCase__     :Optional[Any]							      =				dummy_past_residuals[:]

												UpperCamelCase__     :List[str]							      =				scheduler.step(UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     **UpperCamelCase_							).prev_sample
												UpperCamelCase__     :Union[str, Any]							      =				new_scheduler.step(UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     **UpperCamelCase_							).prev_sample

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

												UpperCamelCase__     :List[Any]							      =				scheduler.step(UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     **UpperCamelCase_							).prev_sample
												UpperCamelCase__     :Tuple							      =				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 					lowerCAmelCase__       (	self					,     **UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__     :List[str]							      =				self.scheduler_classes[0]
							UpperCamelCase__     :Dict							      =				self.get_scheduler_config(**UpperCamelCase_							)
							UpperCamelCase__     :Union[str, Any]							      =				scheduler_class(**UpperCamelCase_							)

							UpperCamelCase__     :Dict							      =				10
							UpperCamelCase__     :int							      =				self.dummy_model()
							UpperCamelCase__     :str							      =				self.dummy_sample_deter
							scheduler.set_timesteps(UpperCamelCase_							)

							for i, t in enumerate(scheduler.timesteps							):
												UpperCamelCase__     :List[Any]							      =				model(UpperCamelCase_					,     UpperCamelCase_							)
												UpperCamelCase__     :Any							      =				scheduler.step(UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_							).prev_sample

							for i, t in enumerate(scheduler.timesteps							):
												UpperCamelCase__     :List[Any]							      =				model(UpperCamelCase_					,     UpperCamelCase_							)
												UpperCamelCase__     :List[str]							      =				scheduler.step(UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_							).prev_sample

							return sample




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :Dict							      =				dict(self.forward_default_kwargs							)

							UpperCamelCase__     :int							      =				kwargs.pop('''num_inference_steps'''					,     UpperCamelCase_							)

							for scheduler_class in self.scheduler_classes:
												UpperCamelCase__     :int							      =				self.get_scheduler_config()
												UpperCamelCase__     :Dict							      =				scheduler_class(**UpperCamelCase_							)

												UpperCamelCase__     :List[Any]							      =				self.dummy_sample
												UpperCamelCase__     :Tuple							      =				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'''							):
																	UpperCamelCase__     :Dict							      =				num_inference_steps

												# copy over dummy past residuals (must be done after set_timesteps)
												UpperCamelCase__     :Tuple							      =				[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
												UpperCamelCase__     :Union[str, Any]							      =				dummy_past_residuals[:]

												UpperCamelCase__     :str							      =				scheduler.timesteps[5]
												UpperCamelCase__     :List[str]							      =				scheduler.timesteps[6]

												UpperCamelCase__     :List[Any]							      =				scheduler.step(UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     **UpperCamelCase_							).prev_sample
												UpperCamelCase__     :int							      =				scheduler.step(UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     **UpperCamelCase_							).prev_sample

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

												UpperCamelCase__     :Optional[int]							      =				scheduler.step(UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     **UpperCamelCase_							).prev_sample
												UpperCamelCase__     :List[str]							      =				scheduler.step(UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     **UpperCamelCase_							).prev_sample

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




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							for timesteps in [100, 1000]:
												self.check_over_configs(num_train_timesteps=UpperCamelCase_					,     time_step=UpperCamelCase_							)




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							for t, num_inference_steps in zip([1, 5, 10]					,     [10, 50, 100]							):
												self.check_over_forward(num_inference_steps=UpperCamelCase_					,     time_step=UpperCamelCase_							)




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :int							      =				self.full_loop()
							UpperCamelCase__     :Optional[Any]							      =				torch.mean(torch.abs(UpperCamelCase_							)							)

							assert abs(result_mean.item() - 2540529							) < 10



'''simple docstring'''
from .integrations import (
    is_optuna_available,
    is_ray_available,
    is_sigopt_available,
    is_wandb_available,
    run_hp_search_optuna,
    run_hp_search_ray,
    run_hp_search_sigopt,
    run_hp_search_wandb,
)
from .trainer_utils import (
    HPSearchBackend,
    default_hp_space_optuna,
    default_hp_space_ray,
    default_hp_space_sigopt,
    default_hp_space_wandb,
)
from .utils import logging


__snake_case							   =  logging.get_logger(__name__)
class      lowercase			:

		"""simple docstring"""

		_a									=       42
		_a									=       None




		@staticmethod
		def 					lowerCAmelCase__       (	):

							'''simple docstring'''


							raise NotImplementedError




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     **UpperCamelCase_							):

							'''simple docstring'''


							raise NotImplementedError




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_							):

							'''simple docstring'''


							raise NotImplementedError




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							if not self.is_available():
												raise RuntimeError(
												    F'''You picked the {self.name} backend, but it is not installed. Run {self.pip_install()}.'''							)




		@classmethod
		def 					lowerCAmelCase__       (	cls							):

							'''simple docstring'''


							return F'''`pip install {cls.pip_package or cls.name}`'''



class      lowercase			(							A__      ):

		"""simple docstring"""

		_a									=       'optuna'




		@staticmethod
		def 					lowerCAmelCase__       (	):

							'''simple docstring'''


							return is_optuna_available()




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     **UpperCamelCase_							):

							'''simple docstring'''


							return run_hp_search_optuna(UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     **UpperCamelCase_							)




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_							):

							'''simple docstring'''


							return default_hp_space_optuna(UpperCamelCase_							)



class      lowercase			(							A__      ):

		"""simple docstring"""

		_a									=       'ray'
		_a									=       '\'ray[tune]\''




		@staticmethod
		def 					lowerCAmelCase__       (	):

							'''simple docstring'''


							return is_ray_available()




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     **UpperCamelCase_							):

							'''simple docstring'''


							return run_hp_search_ray(UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     **UpperCamelCase_							)




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_							):

							'''simple docstring'''


							return default_hp_space_ray(UpperCamelCase_							)



class      lowercase			(							A__      ):

		"""simple docstring"""

		_a									=       'sigopt'




		@staticmethod
		def 					lowerCAmelCase__       (	):

							'''simple docstring'''


							return is_sigopt_available()




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     **UpperCamelCase_							):

							'''simple docstring'''


							return run_hp_search_sigopt(UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     **UpperCamelCase_							)




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_							):

							'''simple docstring'''


							return default_hp_space_sigopt(UpperCamelCase_							)



class      lowercase			(							A__      ):

		"""simple docstring"""

		_a									=       'wandb'




		@staticmethod
		def 					lowerCAmelCase__       (	):

							'''simple docstring'''


							return is_wandb_available()




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     **UpperCamelCase_							):

							'''simple docstring'''


							return run_hp_search_wandb(UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     **UpperCamelCase_							)




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_							):

							'''simple docstring'''


							return default_hp_space_wandb(UpperCamelCase_							)



__snake_case							   =  {
    HPSearchBackend(backend.name): backend for backend in [OptunaBackend, RayTuneBackend, SigOptBackend, WandbBackend]
}


def     a   (    )						->			str:



					'''simple docstring'''






					UpperCamelCase__     :List[Any]							      =				[backend for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() if backend.is_available()]
					if len(__a     ) > 0:
										UpperCamelCase__     :Tuple							      =				available_backends[0].name
										if len(__a     ) > 1:
															logger.info(
															    f'''{len(__a     )} hyperparameter search backends available. Using {name} as the default.'''     )
										return name
					raise RuntimeError(
					    '''No hyperparameter search backend available.\n'''
					    + '''\n'''.join(
					        f''' - To install {backend.name} run {backend.pip_install()}'''
					        for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values()     )     )