


import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest



class 				A       (			UpperCAmelCase__				):
					'''simple docstring'''






					A__     =					(EulerDiscreteScheduler,)
					A__     =					10

					def     lowerCamelCase__	(self       :		List[str] ,   **_UpperCAmelCase       :		Optional[Any]						)		->  Tuple:




							"""simple docstring"""


							lowercase__	     =						{
							    """num_train_timesteps""": 1100,
							    """beta_start""": 0.0_001,
							    """beta_end""": 0.02,
							    """beta_schedule""": """linear""",
							}

							config.update(**_UpperCAmelCase						)
							return config

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




							"""simple docstring"""


							for timesteps in [10, 50, 100, 1000]:
									self.check_over_configs(num_train_timesteps=_UpperCAmelCase						)

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




							"""simple docstring"""


							for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001] ,   [0.0_002, 0.002, 0.02]						):
									self.check_over_configs(beta_start=_UpperCAmelCase ,   beta_end=_UpperCAmelCase						)

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




							"""simple docstring"""


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

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




							"""simple docstring"""


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

					def     lowerCamelCase__	(self       :		List[str]						)		->  int:




							"""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() - 0.0_131						) < 1E-3

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




							"""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() - 0.0_002						) < 1E-2
							assert abs(result_mean.item() - 2.2_6_7_6E-0_6						) < 1E-3

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




							"""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() - 0.0_131						) < 1E-3


					def     lowerCamelCase__	(self       :		int						)		->  List[Any]:




							"""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() - 0.16_213_932_633_399_963						) < 1E-3



import re





def      lowerCamelCase_					(							lowerCamelCase__       ):
				return [char.split() for char in re.split(r"[^ a-z A-Z 0-9 \s]"      ,					str_       )]





def      lowerCamelCase_					(							lowerCamelCase__       ):
				lowerCamelCase_         =     split_input(str_       )
				return "".join(
				    ["".join([char.capitalize() for char in sub_str]       ) for sub_str in string_split]       )





def      lowerCamelCase_					(							lowerCamelCase__      ,					lowerCamelCase__      ,					lowerCamelCase__       ):
				try:
								lowerCamelCase_         =     split_input(lowerCamelCase__       )
								if upper:
												lowerCamelCase_         =     "".join(
												    [
												        separator.join([char.upper() for char in sub_str]       )
												        for sub_str in string_split
												    ]       )
								else:
												lowerCamelCase_         =     "".join(
												    [
												        separator.join([char.lower() for char in sub_str]       )
												        for sub_str in string_split
												    ]       )
								return res_str
				except IndexError:
								return "not valid string"





def      lowerCamelCase_					(							lowerCamelCase__       ):
				return to_simple_case(lowerCamelCase__       )





def      lowerCamelCase_					(							lowerCamelCase__       ):
				try:
								lowerCamelCase_         =     to_simple_case(lowerCamelCase__       )
								return res_str[0].lower() + res_str[1:]
				except IndexError:
								return "not valid string"





def      lowerCamelCase_					(							lowerCamelCase__      ,					lowerCamelCase__       ):
				return to_complex_case(lowerCamelCase__      ,					lowerCamelCase__      ,					"_"       )





def      lowerCamelCase_					(							lowerCamelCase__      ,					lowerCamelCase__       ):
				return to_complex_case(lowerCamelCase__      ,					lowerCamelCase__      ,					"-"       )


if __name__ == "__main__":
			__import__('''doctest''').testmod()


