



import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest

class   _A   ( __lowerCAmelCase	):


						'''simple docstring'''





						_snake_case			:     str      							=      (EulerDiscreteScheduler,)
						_snake_case			:     Union[str, Any]      							=      10



						def     _snake_case	(	self	:							Tuple							,      **lowerCamelCase	:							Optional[Any]	):






							'''simple docstring'''



							__lowercase		       =			{
							    """num_train_timesteps""": 1_100,
							    """beta_start""": 0.0001,
							    """beta_end""": 0.02,
							    """beta_schedule""": """linear""",
							}

							config.update(**_UpperCamelCase	)
							return config



						def     _snake_case	(	self	:							List[str]	):






							'''simple docstring'''



							for timesteps in [10, 50, 100, 1_000]:
								self.check_over_configs(num_train_timesteps=_UpperCamelCase	)



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






							'''simple docstring'''



							for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001]							,      [0.0002, 0.002, 0.02]	):
								self.check_over_configs(beta_start=_UpperCamelCase							,      beta_end=_UpperCamelCase	)



						def     _snake_case	(	self	:							Any	):






							'''simple docstring'''



							for schedule in ["linear", "scaled_linear"]:
								self.check_over_configs(beta_schedule=_UpperCamelCase	)



						def     _snake_case	(	self	:							Optional[int]	):






							'''simple docstring'''



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



						def     _snake_case	(	self	:							List[Any]	):






							'''simple docstring'''



							__lowercase		       =			self.scheduler_classes[0]
							__lowercase		       =			self.get_scheduler_config()
							__lowercase		       =			scheduler_class(**_UpperCamelCase	)

							scheduler.set_timesteps(self.num_inference_steps	)

							__lowercase		       =			torch.manual_seed(0	)

							__lowercase		       =			self.dummy_model()
							__lowercase		       =			self.dummy_sample_deter * scheduler.init_noise_sigma
							__lowercase		       =			sample.to(_UpperCamelCase	)

							for i, t in enumerate(scheduler.timesteps	):
								__lowercase		       =			scheduler.scale_model_input(_UpperCamelCase							,      _UpperCamelCase	)

								__lowercase		       =			model(_UpperCamelCase							,      _UpperCamelCase	)

								__lowercase		       =			scheduler.step(_UpperCamelCase							,      _UpperCamelCase							,      _UpperCamelCase							,      generator=_UpperCamelCase	)
								__lowercase		       =			output.prev_sample

							__lowercase		       =			torch.sum(torch.abs(_UpperCamelCase	)	)
							__lowercase		       =			torch.mean(torch.abs(_UpperCamelCase	)	)

							assert abs(result_sum.item() - 10.0_807	) < 1e-2
							assert abs(result_mean.item() - 0.0131	) < 1e-3



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






							'''simple docstring'''



							__lowercase		       =			self.scheduler_classes[0]
							__lowercase		       =			self.get_scheduler_config(prediction_type="v_prediction"	)
							__lowercase		       =			scheduler_class(**_UpperCamelCase	)

							scheduler.set_timesteps(self.num_inference_steps	)

							__lowercase		       =			torch.manual_seed(0	)

							__lowercase		       =			self.dummy_model()
							__lowercase		       =			self.dummy_sample_deter * scheduler.init_noise_sigma
							__lowercase		       =			sample.to(_UpperCamelCase	)

							for i, t in enumerate(scheduler.timesteps	):
								__lowercase		       =			scheduler.scale_model_input(_UpperCamelCase							,      _UpperCamelCase	)

								__lowercase		       =			model(_UpperCamelCase							,      _UpperCamelCase	)

								__lowercase		       =			scheduler.step(_UpperCamelCase							,      _UpperCamelCase							,      _UpperCamelCase							,      generator=_UpperCamelCase	)
								__lowercase		       =			output.prev_sample

							__lowercase		       =			torch.sum(torch.abs(_UpperCamelCase	)	)
							__lowercase		       =			torch.mean(torch.abs(_UpperCamelCase	)	)

							assert abs(result_sum.item() - 0.0002	) < 1e-2
							assert abs(result_mean.item() - 2.2676e-06	) < 1e-3



						def     _snake_case	(	self	:							List[Any]	):






							'''simple docstring'''



							__lowercase		       =			self.scheduler_classes[0]
							__lowercase		       =			self.get_scheduler_config()
							__lowercase		       =			scheduler_class(**_UpperCamelCase	)

							scheduler.set_timesteps(self.num_inference_steps							,      device=_UpperCamelCase	)

							__lowercase		       =			torch.manual_seed(0	)

							__lowercase		       =			self.dummy_model()
							__lowercase		       =			self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
							__lowercase		       =			sample.to(_UpperCamelCase	)

							for t in scheduler.timesteps:
								__lowercase		       =			scheduler.scale_model_input(_UpperCamelCase							,      _UpperCamelCase	)

								__lowercase		       =			model(_UpperCamelCase							,      _UpperCamelCase	)

								__lowercase		       =			scheduler.step(_UpperCamelCase							,      _UpperCamelCase							,      _UpperCamelCase							,      generator=_UpperCamelCase	)
								__lowercase		       =			output.prev_sample

							__lowercase		       =			torch.sum(torch.abs(_UpperCamelCase	)	)
							__lowercase		       =			torch.mean(torch.abs(_UpperCamelCase	)	)

							assert abs(result_sum.item() - 10.0_807	) < 1e-2
							assert abs(result_mean.item() - 0.0131	) < 1e-3



						def     _snake_case	(	self	:							int	):






							'''simple docstring'''



							__lowercase		       =			self.scheduler_classes[0]
							__lowercase		       =			self.get_scheduler_config()
							__lowercase		       =			scheduler_class(**_UpperCamelCase							,      use_karras_sigmas=_UpperCamelCase	)

							scheduler.set_timesteps(self.num_inference_steps							,      device=_UpperCamelCase	)

							__lowercase		       =			torch.manual_seed(0	)

							__lowercase		       =			self.dummy_model()
							__lowercase		       =			self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
							__lowercase		       =			sample.to(_UpperCamelCase	)

							for t in scheduler.timesteps:
								__lowercase		       =			scheduler.scale_model_input(_UpperCamelCase							,      _UpperCamelCase	)

								__lowercase		       =			model(_UpperCamelCase							,      _UpperCamelCase	)

								__lowercase		       =			scheduler.step(_UpperCamelCase							,      _UpperCamelCase							,      _UpperCamelCase							,      generator=_UpperCamelCase	)
								__lowercase		       =			output.prev_sample

							__lowercase		       =			torch.sum(torch.abs(_UpperCamelCase	)	)
							__lowercase		       =			torch.mean(torch.abs(_UpperCamelCase	)	)

							assert abs(result_sum.item() - 124.52_299_499_511_719	) < 1e-2
							assert abs(result_mean.item() - 0.1_6213_9326_3339_9963	) < 1e-3








import os
import sys
from contextlib import contextmanager


# Windows only
if os.name == "nt":
	import ctypes
	import msvcrt  # noqa

	class   _A   ( ctypes.Structure	):


							'''simple docstring'''





							_snake_case			:     Optional[Any]      							=      [("""size""", ctypes.c_int), ("""visible""", ctypes.c_byte)]



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



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



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



