



import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest




class   UpperCamelCase_       (     __snake_case    ):
							lowerCamelCase_           =					(DPMSolverSinglestepScheduler,)
							lowerCamelCase_           =					(('num_inference_steps', 25),)


							def      _snake_case  (							self    :int							,				**__A    :int		)   ->   Optional[int]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      {
									    "num_train_timesteps": 1000,
									    "beta_start": 0.0_0_0_1,
									    "beta_end": 0.0_2,
									    "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(**A_		)
									return config


							def      _snake_case  (							self    :List[str]							,				__A    :Dict=0							,				**__A    :str		)   ->   Optional[int]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      dict(self.forward_default_kwargs		)
									SCREAMING_SNAKE_CASE__			=      kwargs.pop("""num_inference_steps"""							,				A_		)
									SCREAMING_SNAKE_CASE__			=      self.dummy_sample
									SCREAMING_SNAKE_CASE__			=      0.1 * sample
									SCREAMING_SNAKE_CASE__			=      [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

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

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

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

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


							def      _snake_case  (							self    :Dict		)   ->   Dict:



									"""simple docstring"""



									pass


							def      _snake_case  (							self    :int							,				__A    :Any=0							,				**__A    :List[Any]		)   ->   str:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      dict(self.forward_default_kwargs		)
									SCREAMING_SNAKE_CASE__			=      kwargs.pop("""num_inference_steps"""							,				A_		)
									SCREAMING_SNAKE_CASE__			=      self.dummy_sample
									SCREAMING_SNAKE_CASE__			=      0.1 * sample
									SCREAMING_SNAKE_CASE__			=      [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

									for scheduler_class in self.scheduler_classes:
											SCREAMING_SNAKE_CASE__			=      self.get_scheduler_config()
											SCREAMING_SNAKE_CASE__			=      scheduler_class(**A_		)
											scheduler.set_timesteps(A_		)

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

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

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

											SCREAMING_SNAKE_CASE__			=      scheduler.step(A_							,				A_							,				A_							,				**A_		).prev_sample
											SCREAMING_SNAKE_CASE__			=      new_scheduler.step(A_							,				A_							,				A_							,				**A_		).prev_sample

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


							def      _snake_case  (							self    :Any							,				__A    :Tuple=None							,				**__A    :List[Any]		)   ->   Optional[int]:



									"""simple docstring"""



									if scheduler is None:
											SCREAMING_SNAKE_CASE__			=      self.scheduler_classes[0]
											SCREAMING_SNAKE_CASE__			=      self.get_scheduler_config(**A_		)
											SCREAMING_SNAKE_CASE__			=      scheduler_class(**A_		)

									SCREAMING_SNAKE_CASE__			=      self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE__			=      self.get_scheduler_config(**A_		)
									SCREAMING_SNAKE_CASE__			=      scheduler_class(**A_		)

									SCREAMING_SNAKE_CASE__			=      10
									SCREAMING_SNAKE_CASE__			=      self.dummy_model()
									SCREAMING_SNAKE_CASE__			=      self.dummy_sample_deter
									scheduler.set_timesteps(A_		)

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

									return sample


							def      _snake_case  (							self    :str		)   ->   List[str]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      DPMSolverSinglestepScheduler(**self.get_scheduler_config()		)
									SCREAMING_SNAKE_CASE__			=      50
									SCREAMING_SNAKE_CASE__			=      self.dummy_model()
									SCREAMING_SNAKE_CASE__			=      self.dummy_sample_deter
									scheduler.set_timesteps(A_		)

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

									SCREAMING_SNAKE_CASE__			=      torch.mean(torch.abs(A_		)		)

									assert abs(result_mean.item() - 0.2_5_7_4		) < 1E-3


							def      _snake_case  (							self    :List[Any]		)   ->   Dict:



									"""simple docstring"""



									for timesteps in [25, 50, 100, 999, 1000]:
											self.check_over_configs(num_train_timesteps=A_		)


							def      _snake_case  (							self    :List[str]		)   ->   Optional[Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      DPMSolverSinglestepScheduler(**self.get_scheduler_config()		)
									SCREAMING_SNAKE_CASE__			=      self.full_loop(scheduler=A_		)
									SCREAMING_SNAKE_CASE__			=      torch.mean(torch.abs(A_		)		)

									assert abs(result_mean.item() - 0.2_7_9_1		) < 1E-3

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

									SCREAMING_SNAKE_CASE__			=      self.full_loop(scheduler=A_		)
									SCREAMING_SNAKE_CASE__			=      torch.mean(torch.abs(A_		)		)

									assert abs(result_mean.item() - 0.2_7_9_1		) < 1E-3


							def      _snake_case  (							self    :Optional[Any]		)   ->   int:



									"""simple docstring"""



									self.check_over_configs(thresholding=A_		)
									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=A_							,				prediction_type=A_							,				sample_max_value=A_							,				algorithm_type="""dpmsolver++"""							,				solver_order=A_							,				solver_type=A_							,				)


							def      _snake_case  (							self    :Dict		)   ->   Dict:



									"""simple docstring"""



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


							def      _snake_case  (							self    :Optional[Any]		)   ->   Any:



									"""simple docstring"""



									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=A_							,				solver_type=A_							,				prediction_type=A_							,				algorithm_type=A_							,				)
																	SCREAMING_SNAKE_CASE__			=      self.full_loop(
																	    solver_order=A_							,				solver_type=A_							,				prediction_type=A_							,				algorithm_type=A_							,				)
																	assert not torch.isnan(A_		).any(), "Samples have nan numbers"


							def      _snake_case  (							self    :List[Any]		)   ->   Optional[Any]:



									"""simple docstring"""



									self.check_over_configs(lower_order_final=A_		)
									self.check_over_configs(lower_order_final=A_		)


							def      _snake_case  (							self    :Tuple		)   ->   List[Any]:



									"""simple docstring"""



									self.check_over_configs(lambda_min_clipped=-float("""inf"""		)		)
									self.check_over_configs(lambda_min_clipped=-5.1		)


							def      _snake_case  (							self    :Optional[Any]		)   ->   List[str]:



									"""simple docstring"""



									self.check_over_configs(variance_type=A_		)
									self.check_over_configs(variance_type="""learned_range"""		)


							def      _snake_case  (							self    :Tuple		)   ->   int:



									"""simple docstring"""



									for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
											self.check_over_forward(num_inference_steps=A_							,				time_step=0		)


							def      _snake_case  (							self    :str		)   ->   Any:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      self.full_loop()
									SCREAMING_SNAKE_CASE__			=      torch.mean(torch.abs(A_		)		)

									assert abs(result_mean.item() - 0.2_7_9_1		) < 1E-3


							def      _snake_case  (							self    :Union[str, Any]		)   ->   Dict:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      self.full_loop(use_karras_sigmas=A_		)
									SCREAMING_SNAKE_CASE__			=      torch.mean(torch.abs(A_		)		)

									assert abs(result_mean.item() - 0.2_2_4_8		) < 1E-3


							def      _snake_case  (							self    :Union[str, Any]		)   ->   List[Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      self.full_loop(prediction_type="""v_prediction"""		)
									SCREAMING_SNAKE_CASE__			=      torch.mean(torch.abs(A_		)		)

									assert abs(result_mean.item() - 0.1_4_5_3		) < 1E-3


							def      _snake_case  (							self    :Optional[int]		)   ->   Optional[Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      self.full_loop(prediction_type="""v_prediction"""							,				use_karras_sigmas=A_		)
									SCREAMING_SNAKE_CASE__			=      torch.mean(torch.abs(A_		)		)

									assert abs(result_mean.item() - 0.0_6_4_9		) < 1E-3


							def      _snake_case  (							self    :Tuple		)   ->   Optional[Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE__			=      self.get_scheduler_config(thresholding=A_							,				dynamic_thresholding_ratio=0		)
									SCREAMING_SNAKE_CASE__			=      scheduler_class(**A_		)

									SCREAMING_SNAKE_CASE__			=      10
									SCREAMING_SNAKE_CASE__			=      self.dummy_model()
									SCREAMING_SNAKE_CASE__			=      self.dummy_sample_deter.half()
									scheduler.set_timesteps(A_		)

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

									assert sample.dtype == torch.floataa






import inspect
import unittest




class   UpperCamelCase_       (     unittest.TestCase    ):


							def      _snake_case  (							self    :str		)   ->   Union[str, Any]:



									"""simple docstring"""



									try:
											import diffusers  # noqa: F401
									except ImportError:
											assert False


							def      _snake_case  (							self    :Any		)   ->   Any:



									"""simple docstring"""



									import diffusers
									from diffusers.dependency_versions_table import deps

									SCREAMING_SNAKE_CASE__			=      inspect.getmembers(__A							,				inspect.isclass		)

									for cls_name, cls_module in all_classes:
											if "dummy_" in cls_module.__module__:
													for backend in cls_module._backends:
															if backend == "k_diffusion":
																	SCREAMING_SNAKE_CASE__			=      """k-diffusion"""
															elif backend == "invisible_watermark":
																	SCREAMING_SNAKE_CASE__			=      """invisible-watermark"""
															assert backend in deps, f'''{backend} is not in the deps table!'''