
'''simple docstring'''







import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest






class 			lowerCAmelCase__       (  lowerCamelCase_					):
					lowerCAmelCase_					       =					(UniPCMultistepScheduler,)
					lowerCAmelCase_					       =					(('''num_inference_steps''', 2_5),)






					def      _snake_case   (       self    ,							**__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_     :					str													=	{
											    '''num_train_timesteps''': 10_00,
											    '''beta_start''': 0.0_001,
											    '''beta_end''': 0.02,
											    '''beta_schedule''': '''linear''',
											    '''solver_order''': 2,
											    '''solver_type''': '''bh2''',
											}

											config.update(**__SCREAMING_SNAKE_CASE		)
											return config






					def      _snake_case   (       self    ,							__SCREAMING_SNAKE_CASE=0    ,							**__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_     :					int													=	dict(self.forward_default_kwargs		)
											lowercase_     :					Optional[int]													=	kwargs.pop('''num_inference_steps'''    ,							__SCREAMING_SNAKE_CASE		)
											lowercase_     :					Dict													=	self.dummy_sample
											lowercase_     :					List[Any]													=	0.1 * sample
											lowercase_     :					Tuple													=	[residual + 0.2, residual + 0.15, residual + 0.10]

											for scheduler_class in self.scheduler_classes:
																	lowercase_     :					Union[str, Any]													=	self.get_scheduler_config(**__SCREAMING_SNAKE_CASE		)
																	lowercase_     :					str													=	scheduler_class(**__SCREAMING_SNAKE_CASE		)
																	scheduler.set_timesteps(__SCREAMING_SNAKE_CASE		)
																	# copy over dummy past residuals
																	lowercase_     :					Optional[Any]													=	dummy_past_residuals[: scheduler.config.solver_order]

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

																	lowercase_						,			lowercase_     :					Optional[Any]													=	sample, sample
																	for t in range(__SCREAMING_SNAKE_CASE    ,							time_step + scheduler.config.solver_order + 1		):
																							lowercase_     :					Optional[int]													=	scheduler.step(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							**__SCREAMING_SNAKE_CASE		).prev_sample
																							lowercase_     :					str													=	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      _snake_case   (       self    ,							__SCREAMING_SNAKE_CASE=0    ,							**__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_     :					Tuple													=	dict(self.forward_default_kwargs		)
											lowercase_     :					Union[str, Any]													=	kwargs.pop('''num_inference_steps'''    ,							__SCREAMING_SNAKE_CASE		)
											lowercase_     :					Tuple													=	self.dummy_sample
											lowercase_     :					Union[str, Any]													=	0.1 * sample
											lowercase_     :					str													=	[residual + 0.2, residual + 0.15, residual + 0.10]

											for scheduler_class in self.scheduler_classes:
																	lowercase_     :					Tuple													=	self.get_scheduler_config()
																	lowercase_     :					Any													=	scheduler_class(**__SCREAMING_SNAKE_CASE		)
																	scheduler.set_timesteps(__SCREAMING_SNAKE_CASE		)

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

																	with tempfile.TemporaryDirectory() as tmpdirname:
																							scheduler.save_config(__SCREAMING_SNAKE_CASE		)
																							lowercase_     :					int													=	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)
																							lowercase_     :					Union[str, Any]													=	dummy_past_residuals[: new_scheduler.config.solver_order]

																	lowercase_     :					Optional[Any]													=	scheduler.step(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							**__SCREAMING_SNAKE_CASE		).prev_sample
																	lowercase_     :					Union[str, Any]													=	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      _snake_case   (       self    ,							__SCREAMING_SNAKE_CASE=None    ,							**__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											if scheduler is None:
																	lowercase_     :					Optional[Any]													=	self.scheduler_classes[0]
																	lowercase_     :					List[Any]													=	self.get_scheduler_config(**__SCREAMING_SNAKE_CASE		)
																	lowercase_     :					Optional[int]													=	scheduler_class(**__SCREAMING_SNAKE_CASE		)

											lowercase_     :					Tuple													=	self.scheduler_classes[0]
											lowercase_     :					Optional[int]													=	self.get_scheduler_config(**__SCREAMING_SNAKE_CASE		)
											lowercase_     :					Optional[int]													=	scheduler_class(**__SCREAMING_SNAKE_CASE		)

											lowercase_     :					Dict													=	10
											lowercase_     :					Optional[Any]													=	self.dummy_model()
											lowercase_     :					str													=	self.dummy_sample_deter
											scheduler.set_timesteps(__SCREAMING_SNAKE_CASE		)

											for i, t in enumerate(scheduler.timesteps		):
																	lowercase_     :					Optional[Any]													=	model(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		)
																	lowercase_     :					Union[str, Any]													=	scheduler.step(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		).prev_sample

											return sample






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					List[Any]													=	dict(self.forward_default_kwargs		)

											lowercase_     :					Optional[int]													=	kwargs.pop('''num_inference_steps'''    ,							__SCREAMING_SNAKE_CASE		)

											for scheduler_class in self.scheduler_classes:
																	lowercase_     :					Dict													=	self.get_scheduler_config()
																	lowercase_     :					Any													=	scheduler_class(**__SCREAMING_SNAKE_CASE		)

																	lowercase_     :					Optional[Any]													=	self.dummy_sample
																	lowercase_     :					Dict													=	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'''		):
																							lowercase_     :					Dict													=	num_inference_steps

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

																	lowercase_     :					Tuple													=	scheduler.timesteps[5]
																	lowercase_     :					Any													=	scheduler.timesteps[6]

																	lowercase_     :					Dict													=	scheduler.step(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							**__SCREAMING_SNAKE_CASE		).prev_sample
																	lowercase_     :					Tuple													=	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      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					Dict													=	UniPCMultistepScheduler(**self.get_scheduler_config()		)
											lowercase_     :					Union[str, Any]													=	self.full_loop(scheduler=__SCREAMING_SNAKE_CASE		)
											lowercase_     :					Any													=	torch.mean(torch.abs(__SCREAMING_SNAKE_CASE		)		)

											assert abs(result_mean.item() - 0.2_464		) < 1E-3

											lowercase_     :					int													=	DPMSolverSinglestepScheduler.from_config(scheduler.config		)
											lowercase_     :					Tuple													=	DEISMultistepScheduler.from_config(scheduler.config		)
											lowercase_     :					int													=	DPMSolverMultistepScheduler.from_config(scheduler.config		)
											lowercase_     :					List[Any]													=	UniPCMultistepScheduler.from_config(scheduler.config		)

											lowercase_     :					List[str]													=	self.full_loop(scheduler=__SCREAMING_SNAKE_CASE		)
											lowercase_     :					List[str]													=	torch.mean(torch.abs(__SCREAMING_SNAKE_CASE		)		)

											assert abs(result_mean.item() - 0.2_464		) < 1E-3






					def      _snake_case   (       self		):


											"""simple docstring"""





											for timesteps in [25, 50, 1_00, 9_99, 10_00]:
																	self.check_over_configs(num_train_timesteps=__SCREAMING_SNAKE_CASE		)






					def      _snake_case   (       self		):


											"""simple docstring"""





											self.check_over_configs(thresholding=__SCREAMING_SNAKE_CASE		)
											for order in [1, 2, 3]:
																	for solver_type in ["bh1", "bh2"]:
																							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    ,							solver_order=__SCREAMING_SNAKE_CASE    ,							solver_type=__SCREAMING_SNAKE_CASE    ,							)






					def      _snake_case   (       self		):


											"""simple docstring"""





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






					def      _snake_case   (       self		):


											"""simple docstring"""





											for solver_type in ["bh1", "bh2"]:
																	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    ,							)
																													lowercase_     :					Union[str, Any]													=	self.full_loop(
																													    solver_order=__SCREAMING_SNAKE_CASE    ,							solver_type=__SCREAMING_SNAKE_CASE    ,							prediction_type=__SCREAMING_SNAKE_CASE    ,							)
																													assert not torch.isnan(__SCREAMING_SNAKE_CASE		).any(), "Samples have nan numbers"






					def      _snake_case   (       self		):


											"""simple docstring"""





											self.check_over_configs(lower_order_final=__SCREAMING_SNAKE_CASE		)
											self.check_over_configs(lower_order_final=__SCREAMING_SNAKE_CASE		)






					def      _snake_case   (       self		):


											"""simple docstring"""





											for num_inference_steps in [1, 2, 3, 5, 10, 50, 1_00, 9_99, 10_00]:
																	self.check_over_forward(num_inference_steps=__SCREAMING_SNAKE_CASE    ,							time_step=0		)






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					Tuple													=	self.full_loop()
											lowercase_     :					Union[str, Any]													=	torch.mean(torch.abs(__SCREAMING_SNAKE_CASE		)		)

											assert abs(result_mean.item() - 0.2_464		) < 1E-3






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					Dict													=	self.full_loop(prediction_type='''v_prediction'''		)
											lowercase_     :					Optional[int]													=	torch.mean(torch.abs(__SCREAMING_SNAKE_CASE		)		)

											assert abs(result_mean.item() - 0.1_014		) < 1E-3






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					List[str]													=	self.scheduler_classes[0]
											lowercase_     :					Tuple													=	self.get_scheduler_config(thresholding=__SCREAMING_SNAKE_CASE    ,							dynamic_thresholding_ratio=0		)
											lowercase_     :					int													=	scheduler_class(**__SCREAMING_SNAKE_CASE		)

											lowercase_     :					Any													=	10
											lowercase_     :					Union[str, Any]													=	self.dummy_model()
											lowercase_     :					Tuple													=	self.dummy_sample_deter.half()
											scheduler.set_timesteps(__SCREAMING_SNAKE_CASE		)

											for i, t in enumerate(scheduler.timesteps		):
																	lowercase_     :					str													=	model(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		)
																	lowercase_     :					str													=	scheduler.step(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		).prev_sample

											assert sample.dtype == torch.floataa






					def      _snake_case   (       self    ,							**__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											for scheduler_class in self.scheduler_classes:
																	lowercase_     :					List[str]													=	self.get_scheduler_config(**__SCREAMING_SNAKE_CASE		)
																	lowercase_     :					Optional[int]													=	scheduler_class(**__SCREAMING_SNAKE_CASE		)

																	scheduler.set_timesteps(scheduler.config.num_train_timesteps		)
																	assert len(scheduler.timesteps.unique()		) == scheduler.num_inference_steps





'''simple docstring'''







import logging
import os

from .state import PartialState






class 			lowerCAmelCase__       (  logging.LoggerAdapter					):






					@staticmethod
					def      _snake_case   (       __SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_     :					Optional[Any]													=	PartialState()
											return not main_process_only or (main_process_only and state.is_main_process)






					def      _snake_case   (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							*__SCREAMING_SNAKE_CASE    ,							**__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											if PartialState._shared_state == {}:
																	raise RuntimeError(
																	    '''You must initialize the accelerate state by calling either `PartialState()` or `Accelerator()` before using the logging utility.'''		)
											lowercase_     :					Tuple													=	kwargs.pop('''main_process_only'''    ,							__SCREAMING_SNAKE_CASE		)
											lowercase_     :					Optional[int]													=	kwargs.pop('''in_order'''    ,							__SCREAMING_SNAKE_CASE		)

											if self.isEnabledFor(__SCREAMING_SNAKE_CASE		):
																	if self._should_log(__SCREAMING_SNAKE_CASE		):
																							lowercase_						,			lowercase_     :					Optional[Any]													=	self.process(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		)
																							self.logger.log(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							*__SCREAMING_SNAKE_CASE    ,							**__SCREAMING_SNAKE_CASE		)

																	elif in_order:
																							lowercase_     :					Optional[Any]													=	PartialState()
																							for i in range(state.num_processes		):
																													if i == state.process_index:
																																			lowercase_						,			lowercase_     :					Optional[int]													=	self.process(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		)
																																			self.logger.log(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							*__SCREAMING_SNAKE_CASE    ,							**__SCREAMING_SNAKE_CASE		)
																													state.wait_for_everyone()







def  snake_case_		(    __SCREAMING_SNAKE_CASE :			str				,				__SCREAMING_SNAKE_CASE :			str = None							):



						"""simple docstring"""





						if log_level is None:
												lowercase_     :					Any													=	os.environ.get('''ACCELERATE_LOG_LEVEL'''				,				__SCREAMING_SNAKE_CASE							)
						lowercase_     :					List[str]													=	logging.getLogger(__SCREAMING_SNAKE_CASE							)
						if log_level is not None:
												logger.setLevel(log_level.upper()							)
												logger.root.setLevel(log_level.upper()							)
						return MultiProcessAdapter(__SCREAMING_SNAKE_CASE				,				{}							)



