"""simple docstring"""






import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest
class        snake_case (				__UpperCAmelCase       ):




					"""simple docstring"""

					snake_case__       					=				(DPMSolverSinglestepScheduler,)
					snake_case__       					=				(("num_inference_steps", 25),)







					def 	__lowerCAmelCase		(       self     :			Optional[Any]					,**lowerCamelCase__     :			List[str]    ):
									UpperCAmelCase__    =							{
									    'num_train_timesteps': 1_000,
									    '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(**lowerCamelCase__    )
									return config







					def 	__lowerCAmelCase		(       self     :			Tuple					,lowerCamelCase__     :			Optional[int]=0					,**lowerCamelCase__     :			Optional[int]    ):
									UpperCAmelCase__    =							dict(self.forward_default_kwargs    )
									UpperCAmelCase__    =							kwargs.pop('num_inference_steps'					,lowerCamelCase__    )
									UpperCAmelCase__    =							self.dummy_sample
									UpperCAmelCase__    =							0.1 * sample
									UpperCAmelCase__    =							[residual + 0.2, residual + 0.1_5, residual + 0.1_0]

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

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

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

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







					def 	__lowerCAmelCase		(       self     :			Optional[int]    ):
									pass







					def 	__lowerCAmelCase		(       self     :			List[str]					,lowerCamelCase__     :			List[str]=0					,**lowerCamelCase__     :			Optional[int]    ):
									UpperCAmelCase__    =							dict(self.forward_default_kwargs    )
									UpperCAmelCase__    =							kwargs.pop('num_inference_steps'					,lowerCamelCase__    )
									UpperCAmelCase__    =							self.dummy_sample
									UpperCAmelCase__    =							0.1 * sample
									UpperCAmelCase__    =							[residual + 0.2, residual + 0.1_5, residual + 0.1_0]

									for scheduler_class in self.scheduler_classes:
													UpperCAmelCase__    =							self.get_scheduler_config()
													UpperCAmelCase__    =							scheduler_class(**lowerCamelCase__    )
													scheduler.set_timesteps(lowerCamelCase__    )

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

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

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

													UpperCAmelCase__    =							scheduler.step(lowerCamelCase__					,lowerCamelCase__					,lowerCamelCase__					,**lowerCamelCase__    ).prev_sample
													UpperCAmelCase__    =							new_scheduler.step(lowerCamelCase__					,lowerCamelCase__					,lowerCamelCase__					,**lowerCamelCase__    ).prev_sample

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







					def 	__lowerCAmelCase		(       self     :			Optional[Any]					,lowerCamelCase__     :			str=None					,**lowerCamelCase__     :			Dict    ):
									if scheduler is None:
													UpperCAmelCase__    =							self.scheduler_classes[0]
													UpperCAmelCase__    =							self.get_scheduler_config(**lowerCamelCase__    )
													UpperCAmelCase__    =							scheduler_class(**lowerCamelCase__    )

									UpperCAmelCase__    =							self.scheduler_classes[0]
									UpperCAmelCase__    =							self.get_scheduler_config(**lowerCamelCase__    )
									UpperCAmelCase__    =							scheduler_class(**lowerCamelCase__    )

									UpperCAmelCase__    =							10
									UpperCAmelCase__    =							self.dummy_model()
									UpperCAmelCase__    =							self.dummy_sample_deter
									scheduler.set_timesteps(lowerCamelCase__    )

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

									return sample







					def 	__lowerCAmelCase		(       self     :			Optional[Any]    ):
									UpperCAmelCase__    =							DPMSolverSinglestepScheduler(**self.get_scheduler_config()    )
									UpperCAmelCase__    =							50
									UpperCAmelCase__    =							self.dummy_model()
									UpperCAmelCase__    =							self.dummy_sample_deter
									scheduler.set_timesteps(lowerCamelCase__    )

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

									UpperCAmelCase__    =							torch.mean(torch.abs(lowerCamelCase__    )    )

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







					def 	__lowerCAmelCase		(       self     :			Optional[int]    ):
									for timesteps in [25, 50, 100, 999, 1_000]:
													self.check_over_configs(num_train_timesteps=lowerCamelCase__    )







					def 	__lowerCAmelCase		(       self     :			List[Any]    ):
									# make sure that iterating over schedulers with same config names gives same results
									# for defaults
									UpperCAmelCase__    =							DPMSolverSinglestepScheduler(**self.get_scheduler_config()    )
									UpperCAmelCase__    =							self.full_loop(scheduler=lowerCamelCase__    )
									UpperCAmelCase__    =							torch.mean(torch.abs(lowerCamelCase__    )    )

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

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

									UpperCAmelCase__    =							self.full_loop(scheduler=lowerCamelCase__    )
									UpperCAmelCase__    =							torch.mean(torch.abs(lowerCamelCase__    )    )

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







					def 	__lowerCAmelCase		(       self     :			int    ):
									self.check_over_configs(thresholding=lowerCamelCase__    )
									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=lowerCamelCase__					,prediction_type=lowerCamelCase__					,sample_max_value=lowerCamelCase__					,algorithm_type='dpmsolver++'					,solver_order=lowerCamelCase__					,solver_type=lowerCamelCase__					,)







					def 	__lowerCAmelCase		(       self     :			Any    ):
									for prediction_type in ["epsilon", "v_prediction"]:
													self.check_over_configs(prediction_type=lowerCamelCase__    )







					def 	__lowerCAmelCase		(       self     :			Union[str, Any]    ):
									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=lowerCamelCase__					,solver_type=lowerCamelCase__					,prediction_type=lowerCamelCase__					,algorithm_type=lowerCamelCase__					,)
																									UpperCAmelCase__    =							self.full_loop(
																									    solver_order=lowerCamelCase__					,solver_type=lowerCamelCase__					,prediction_type=lowerCamelCase__					,algorithm_type=lowerCamelCase__					,)
																									assert not torch.isnan(lowerCamelCase__    ).any(), "Samples have nan numbers"







					def 	__lowerCAmelCase		(       self     :			List[Any]    ):
									self.check_over_configs(lower_order_final=lowerCamelCase__    )
									self.check_over_configs(lower_order_final=lowerCamelCase__    )







					def 	__lowerCAmelCase		(       self     :			List[Any]    ):
									self.check_over_configs(lambda_min_clipped=-float('inf'    )    )
									self.check_over_configs(lambda_min_clipped=-5.1    )







					def 	__lowerCAmelCase		(       self     :			Dict    ):
									self.check_over_configs(variance_type=lowerCamelCase__    )
									self.check_over_configs(variance_type='learned_range'    )







					def 	__lowerCAmelCase		(       self     :			List[str]    ):
									for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1_000]:
													self.check_over_forward(num_inference_steps=lowerCamelCase__					,time_step=0    )







					def 	__lowerCAmelCase		(       self     :			Union[str, Any]    ):
									UpperCAmelCase__    =							self.full_loop()
									UpperCAmelCase__    =							torch.mean(torch.abs(lowerCamelCase__    )    )

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







					def 	__lowerCAmelCase		(       self     :			Dict    ):
									UpperCAmelCase__    =							self.full_loop(use_karras_sigmas=lowerCamelCase__    )
									UpperCAmelCase__    =							torch.mean(torch.abs(lowerCamelCase__    )    )

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







					def 	__lowerCAmelCase		(       self     :			Any    ):
									UpperCAmelCase__    =							self.full_loop(prediction_type='v_prediction'    )
									UpperCAmelCase__    =							torch.mean(torch.abs(lowerCamelCase__    )    )

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







					def 	__lowerCAmelCase		(       self     :			Union[str, Any]    ):
									UpperCAmelCase__    =							self.full_loop(prediction_type='v_prediction'					,use_karras_sigmas=lowerCamelCase__    )
									UpperCAmelCase__    =							torch.mean(torch.abs(lowerCamelCase__    )    )

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







					def 	__lowerCAmelCase		(       self     :			List[str]    ):
									UpperCAmelCase__    =							self.scheduler_classes[0]
									UpperCAmelCase__    =							self.get_scheduler_config(thresholding=lowerCamelCase__					,dynamic_thresholding_ratio=0    )
									UpperCAmelCase__    =							scheduler_class(**lowerCamelCase__    )

									UpperCAmelCase__    =							10
									UpperCAmelCase__    =							self.dummy_model()
									UpperCAmelCase__    =							self.dummy_sample_deter.half()
									scheduler.set_timesteps(lowerCamelCase__    )

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

									assert sample.dtype == torch.floataa




"""simple docstring"""






import os
import sys
from contextlib import contextmanager


# Windows only
if os.name == "nt":
						import ctypes
						import msvcrt  # noqa
						class        snake_case (				ctypes.Structure       ):




											"""simple docstring"""

											snake_case__       					=				[("size", ctypes.c_int), ("visible", ctypes.c_byte)]



def 						a_		(      ):
				if os.name == "nt":
								UpperCAmelCase__    =							CursorInfo()
								UpperCAmelCase__    =							ctypes.windll.kernelaa.GetStdHandle(-1_1 )
								ctypes.windll.kernelaa.GetConsoleCursorInfo(lowerCamelCase      ,    ctypes.byref(lowerCamelCase ) )
								UpperCAmelCase__    =							False
								ctypes.windll.kernelaa.SetConsoleCursorInfo(lowerCamelCase      ,    ctypes.byref(lowerCamelCase ) )
				elif os.name == "posix":
								sys.stdout.write('\033[?25l' )
								sys.stdout.flush()



def 						a_		(      ):
				if os.name == "nt":
								UpperCAmelCase__    =							CursorInfo()
								UpperCAmelCase__    =							ctypes.windll.kernelaa.GetStdHandle(-1_1 )
								ctypes.windll.kernelaa.GetConsoleCursorInfo(lowerCamelCase      ,    ctypes.byref(lowerCamelCase ) )
								UpperCAmelCase__    =							True
								ctypes.windll.kernelaa.SetConsoleCursorInfo(lowerCamelCase      ,    ctypes.byref(lowerCamelCase ) )
				elif os.name == "posix":
								sys.stdout.write('\033[?25h' )
								sys.stdout.flush()







@contextmanager
def 						a_		(      ):
				try:
								hide_cursor()
								yield
				finally:
								show_cursor()



