






'''simple docstring'''

import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest






class 							__magic_name__     (	UpperCAmelCase_				):







	"""simple docstring"""



	SCREAMING_SNAKE_CASE_					:      int        =  (DPMSolverSinglestepScheduler,)
	SCREAMING_SNAKE_CASE_					:      Any        =  (('num_inference_steps', 2_5),)
	def        lowerCamelCase__		(							self  ,      **_lowercase       )						->  Tuple:
							lowercase_      :   Union[str, Any]        =			{
							    'num_train_timesteps': 1000,
							    'beta_start': 0.00_01,
							    'beta_end': 0.02,
							    'beta_schedule': 'linear',
							    'solver_order': 2,
							    'prediction_type': 'epsilon',
							    'thresholding': False,
							    'sample_max_value': 1.0,
							    'algorithm_type': 'dpmsolver++',
							    'solver_type': 'midpoint',
							    'lambda_min_clipped': -float('inf'       ),
							    'variance_type': None,
							}

							config.update(**_lowercase       )
							return config
	def        lowerCamelCase__		(							self  ,      _lowercase=0  ,      **_lowercase       )						->  Dict:
							lowercase_      :   Union[str, Any]        =			dict(self.forward_default_kwargs       )
							lowercase_      :   List[Any]        =			kwargs.pop('num_inference_steps'  ,      _lowercase       )
							lowercase_      :   List[str]        =			self.dummy_sample
							lowercase_      :   Dict        =			0.1 * sample
							lowercase_      :   List[Any]        =			[residual + 0.2, residual + 0.15, residual + 0.10]

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

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

													lowercase_      :   Any        =			sample, sample
													for t in range(_lowercase  ,      time_step + scheduler.config.solver_order + 1       ):
																			lowercase_      :   Optional[int]        =			scheduler.step(_lowercase  ,      _lowercase  ,      _lowercase  ,      **_lowercase       ).prev_sample
																			lowercase_      :   List[Any]        =			new_scheduler.step(_lowercase  ,      _lowercase  ,      _lowercase  ,      **_lowercase       ).prev_sample

																			assert torch.sum(torch.abs(output - new_output       )       ) < 1E-5, "Scheduler outputs are not identical"
	def        lowerCamelCase__		(							self       )						->  List[str]:
							pass
	def        lowerCamelCase__		(							self  ,      _lowercase=0  ,      **_lowercase       )						->  List[str]:
							lowercase_      :   int        =			dict(self.forward_default_kwargs       )
							lowercase_      :   int        =			kwargs.pop('num_inference_steps'  ,      _lowercase       )
							lowercase_      :   Any        =			self.dummy_sample
							lowercase_      :   Optional[Any]        =			0.1 * sample
							lowercase_      :   Dict        =			[residual + 0.2, residual + 0.15, residual + 0.10]

							for scheduler_class in self.scheduler_classes:
													lowercase_      :   List[str]        =			self.get_scheduler_config()
													lowercase_      :   Any        =			scheduler_class(**_lowercase       )
													scheduler.set_timesteps(_lowercase       )

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

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

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

													lowercase_      :   Dict        =			scheduler.step(_lowercase  ,      _lowercase  ,      _lowercase  ,      **_lowercase       ).prev_sample
													lowercase_      :   Union[str, Any]        =			new_scheduler.step(_lowercase  ,      _lowercase  ,      _lowercase  ,      **_lowercase       ).prev_sample

													assert torch.sum(torch.abs(output - new_output       )       ) < 1E-5, "Scheduler outputs are not identical"
	def        lowerCamelCase__		(							self  ,      _lowercase=None  ,      **_lowercase       )						->  Optional[Any]:
							if scheduler is None:
													lowercase_      :   Union[str, Any]        =			self.scheduler_classes[0]
													lowercase_      :   Optional[Any]        =			self.get_scheduler_config(**_lowercase       )
													lowercase_      :   str        =			scheduler_class(**_lowercase       )

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

							lowercase_      :   Tuple        =			10
							lowercase_      :   List[Any]        =			self.dummy_model()
							lowercase_      :   Any        =			self.dummy_sample_deter
							scheduler.set_timesteps(_lowercase       )

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

							return sample
	def        lowerCamelCase__		(							self       )						->  List[str]:
							lowercase_      :   Optional[Any]        =			DPMSolverSinglestepScheduler(**self.get_scheduler_config()       )
							lowercase_      :   Tuple        =			50
							lowercase_      :   int        =			self.dummy_model()
							lowercase_      :   List[str]        =			self.dummy_sample_deter
							scheduler.set_timesteps(_lowercase       )

							# make sure that the first t is uneven
							for i, t in enumerate(scheduler.timesteps[3:]       ):
													lowercase_      :   int        =			model(_lowercase  ,      _lowercase       )
													lowercase_      :   Any        =			scheduler.step(_lowercase  ,      _lowercase  ,      _lowercase       ).prev_sample

							lowercase_      :   Any        =			torch.mean(torch.abs(_lowercase       )       )

							assert abs(result_mean.item() - 0.25_74       ) < 1E-3
	def        lowerCamelCase__		(							self       )						->  Dict:
							for timesteps in [25, 50, 100, 999, 1000]:
													self.check_over_configs(num_train_timesteps=_lowercase       )
	def        lowerCamelCase__		(							self       )						->  List[str]:
							# make sure that iterating over schedulers with same config names gives same results
							# for defaults
							lowercase_      :   str        =			DPMSolverSinglestepScheduler(**self.get_scheduler_config()       )
							lowercase_      :   List[str]        =			self.full_loop(scheduler=_lowercase       )
							lowercase_      :   Tuple        =			torch.mean(torch.abs(_lowercase       )       )

							assert abs(result_mean.item() - 0.27_91       ) < 1E-3

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

							lowercase_      :   Any        =			self.full_loop(scheduler=_lowercase       )
							lowercase_      :   Tuple        =			torch.mean(torch.abs(_lowercase       )       )

							assert abs(result_mean.item() - 0.27_91       ) < 1E-3
	def        lowerCamelCase__		(							self       )						->  Dict:
							self.check_over_configs(thresholding=_lowercase       )
							for order in [1, 2, 3]:
													for solver_type in ["midpoint", "heun"]:
																			for threshold in [0.5, 1.0, 2.0]:
																									for prediction_type in ["epsilon", "sample"]:
																															self.check_over_configs(
																															    thresholding=_lowercase  ,      prediction_type=_lowercase  ,      sample_max_value=_lowercase  ,      algorithm_type='dpmsolver++'  ,      solver_order=_lowercase  ,      solver_type=_lowercase  ,      )
	def        lowerCamelCase__		(							self       )						->  Optional[int]:
							for prediction_type in ["epsilon", "v_prediction"]:
													self.check_over_configs(prediction_type=_lowercase       )
	def        lowerCamelCase__		(							self       )						->  Dict:
							for algorithm_type in ["dpmsolver", "dpmsolver++"]:
													for solver_type in ["midpoint", "heun"]:
																			for order in [1, 2, 3]:
																									for prediction_type in ["epsilon", "sample"]:
																															self.check_over_configs(
																															    solver_order=_lowercase  ,      solver_type=_lowercase  ,      prediction_type=_lowercase  ,      algorithm_type=_lowercase  ,      )
																															lowercase_      :   Union[str, Any]        =			self.full_loop(
																															    solver_order=_lowercase  ,      solver_type=_lowercase  ,      prediction_type=_lowercase  ,      algorithm_type=_lowercase  ,      )
																															assert not torch.isnan(_lowercase       ).any(), "Samples have nan numbers"
	def        lowerCamelCase__		(							self       )						->  int:
							self.check_over_configs(lower_order_final=_lowercase       )
							self.check_over_configs(lower_order_final=_lowercase       )
	def        lowerCamelCase__		(							self       )						->  Union[str, Any]:
							self.check_over_configs(lambda_min_clipped=-float('inf'       )       )
							self.check_over_configs(lambda_min_clipped=-5.1       )
	def        lowerCamelCase__		(							self       )						->  Tuple:
							self.check_over_configs(variance_type=_lowercase       )
							self.check_over_configs(variance_type='learned_range'       )
	def        lowerCamelCase__		(							self       )						->  Dict:
							for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
													self.check_over_forward(num_inference_steps=_lowercase  ,      time_step=0       )
	def        lowerCamelCase__		(							self       )						->  int:
							lowercase_      :   Tuple        =			self.full_loop()
							lowercase_      :   int        =			torch.mean(torch.abs(_lowercase       )       )

							assert abs(result_mean.item() - 0.27_91       ) < 1E-3
	def        lowerCamelCase__		(							self       )						->  Any:
							lowercase_      :   Optional[Any]        =			self.full_loop(use_karras_sigmas=_lowercase       )
							lowercase_      :   int        =			torch.mean(torch.abs(_lowercase       )       )

							assert abs(result_mean.item() - 0.22_48       ) < 1E-3
	def        lowerCamelCase__		(							self       )						->  Tuple:
							lowercase_      :   Union[str, Any]        =			self.full_loop(prediction_type='v_prediction'       )
							lowercase_      :   str        =			torch.mean(torch.abs(_lowercase       )       )

							assert abs(result_mean.item() - 0.14_53       ) < 1E-3
	def        lowerCamelCase__		(							self       )						->  Optional[Any]:
							lowercase_      :   Tuple        =			self.full_loop(prediction_type='v_prediction'  ,      use_karras_sigmas=_lowercase       )
							lowercase_      :   List[str]        =			torch.mean(torch.abs(_lowercase       )       )

							assert abs(result_mean.item() - 0.06_49       ) < 1E-3







	def        lowerCamelCase__		(							self       )						->  Dict:
							lowercase_      :   Optional[int]        =			self.scheduler_classes[0]
							lowercase_      :   List[str]        =			self.get_scheduler_config(thresholding=_lowercase  ,      dynamic_thresholding_ratio=0       )
							lowercase_      :   Optional[int]        =			scheduler_class(**_lowercase       )

							lowercase_      :   str        =			10
							lowercase_      :   List[str]        =			self.dummy_model()
							lowercase_      :   int        =			self.dummy_sample_deter.half()
							scheduler.set_timesteps(_lowercase       )

							for i, t in enumerate(scheduler.timesteps       ):
													lowercase_      :   Optional[int]        =			model(_lowercase  ,      _lowercase       )
													lowercase_      :   int        =			scheduler.step(_lowercase  ,      _lowercase  ,      _lowercase       ).prev_sample

							assert sample.dtype == torch.floataa








'''simple docstring'''

from ..utils import DummyObject, requires_backends






class 							__magic_name__     (	metaclass=UpperCAmelCase_				):







	"""simple docstring"""



	SCREAMING_SNAKE_CASE_					:      str        =  ['transformers', 'torch', 'note_seq']
	def __init__(							self  ,      *_lowercase  ,      **_lowercase       )						->  Dict:
							requires_backends(self  ,      ['transformers', 'torch', 'note_seq']       )
	@classmethod
	def        lowerCamelCase__		(							cls  ,      *_lowercase  ,      **_lowercase       )						->  List[str]:
							requires_backends(cls  ,      ['transformers', 'torch', 'note_seq']       )







	@classmethod
	def        lowerCamelCase__		(							cls  ,      *_lowercase  ,      **_lowercase       )						->  Dict:
							requires_backends(cls  ,      ['transformers', 'torch', 'note_seq']       )
