
'''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_     :					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				,				{}							)



