



import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest






class 	_a			(      snake_case_  ):
			_UpperCamelCase:   Union[str, Any]	     =		(LMSDiscreteScheduler,)
			_UpperCamelCase:   Optional[Any]	     =		10






			def 							_snake_case   (    self    ,  **lowercase_ )	->					str:
									lowerCAmelCase	:   Dict					     =					{
									    """num_train_timesteps""": 1100,
									    """beta_start""": 0.0_0_0_1,
									    """beta_end""": 0.0_2,
									    """beta_schedule""": """linear""",
									}

									config.update(**lowercase_ )
									return config






			def 							_snake_case   (    self )	->					Optional[Any]:
									for timesteps in [10, 50, 100, 1000]:
															self.check_over_configs(num_train_timesteps=lowercase_ )






			def 							_snake_case   (    self )	->					str:
									for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]    ,  [0.0_0_0_2, 0.0_0_2, 0.0_2] ):
															self.check_over_configs(beta_start=lowercase_    ,  beta_end=lowercase_ )






			def 							_snake_case   (    self )	->					Tuple:
									for schedule in ["linear", "scaled_linear"]:
															self.check_over_configs(beta_schedule=lowercase_ )






			def 							_snake_case   (    self )	->					str:
									for prediction_type in ["epsilon", "v_prediction"]:
															self.check_over_configs(prediction_type=lowercase_ )






			def 							_snake_case   (    self )	->					List[str]:
									for t in [0, 500, 800]:
															self.check_over_forward(time_step=lowercase_ )






			def 							_snake_case   (    self )	->					Optional[int]:
									lowerCAmelCase	:   Union[str, Any]					     =					self.scheduler_classes[0]
									lowerCAmelCase	:   List[Any]					     =					self.get_scheduler_config()
									lowerCAmelCase	:   List[str]					     =					scheduler_class(**lowercase_ )

									scheduler.set_timesteps(self.num_inference_steps )

									lowerCAmelCase	:   Optional[int]					     =					self.dummy_model()
									lowerCAmelCase	:   List[str]					     =					self.dummy_sample_deter * scheduler.init_noise_sigma

									for i, t in enumerate(scheduler.timesteps ):
															lowerCAmelCase	:   List[str]					     =					scheduler.scale_model_input(lowercase_    ,  lowercase_ )

															lowerCAmelCase	:   List[str]					     =					model(lowercase_    ,  lowercase_ )

															lowerCAmelCase	:   List[str]					     =					scheduler.step(lowercase_    ,  lowercase_    ,  lowercase_ )
															lowerCAmelCase	:   str					     =					output.prev_sample

									lowerCAmelCase	:   Optional[Any]					     =					torch.sum(torch.abs(lowercase_ ) )
									lowerCAmelCase	:   Optional[Any]					     =					torch.mean(torch.abs(lowercase_ ) )

									assert abs(result_sum.item() - 1_0_0_6.3_8_8 ) < 1e-2
									assert abs(result_mean.item() - 1.3_1 ) < 1e-3






			def 							_snake_case   (    self )	->					int:
									lowerCAmelCase	:   str					     =					self.scheduler_classes[0]
									lowerCAmelCase	:   Dict					     =					self.get_scheduler_config(prediction_type="""v_prediction""" )
									lowerCAmelCase	:   Optional[int]					     =					scheduler_class(**lowercase_ )

									scheduler.set_timesteps(self.num_inference_steps )

									lowerCAmelCase	:   List[Any]					     =					self.dummy_model()
									lowerCAmelCase	:   int					     =					self.dummy_sample_deter * scheduler.init_noise_sigma

									for i, t in enumerate(scheduler.timesteps ):
															lowerCAmelCase	:   Any					     =					scheduler.scale_model_input(lowercase_    ,  lowercase_ )

															lowerCAmelCase	:   Any					     =					model(lowercase_    ,  lowercase_ )

															lowerCAmelCase	:   List[str]					     =					scheduler.step(lowercase_    ,  lowercase_    ,  lowercase_ )
															lowerCAmelCase	:   Union[str, Any]					     =					output.prev_sample

									lowerCAmelCase	:   Union[str, Any]					     =					torch.sum(torch.abs(lowercase_ ) )
									lowerCAmelCase	:   Optional[int]					     =					torch.mean(torch.abs(lowercase_ ) )

									assert abs(result_sum.item() - 0.0_0_1_7 ) < 1e-2
									assert abs(result_mean.item() - 2.2676e-06 ) < 1e-3






			def 							_snake_case   (    self )	->					int:
									lowerCAmelCase	:   int					     =					self.scheduler_classes[0]
									lowerCAmelCase	:   List[str]					     =					self.get_scheduler_config()
									lowerCAmelCase	:   int					     =					scheduler_class(**lowercase_ )

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

									lowerCAmelCase	:   Any					     =					self.dummy_model()
									lowerCAmelCase	:   Dict					     =					self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
									lowerCAmelCase	:   Dict					     =					sample.to(lowercase_ )

									for i, t in enumerate(scheduler.timesteps ):
															lowerCAmelCase	:   Optional[Any]					     =					scheduler.scale_model_input(lowercase_    ,  lowercase_ )

															lowerCAmelCase	:   Any					     =					model(lowercase_    ,  lowercase_ )

															lowerCAmelCase	:   Optional[Any]					     =					scheduler.step(lowercase_    ,  lowercase_    ,  lowercase_ )
															lowerCAmelCase	:   int					     =					output.prev_sample

									lowerCAmelCase	:   List[str]					     =					torch.sum(torch.abs(lowercase_ ) )
									lowerCAmelCase	:   List[str]					     =					torch.mean(torch.abs(lowercase_ ) )

									assert abs(result_sum.item() - 1_0_0_6.3_8_8 ) < 1e-2
									assert abs(result_mean.item() - 1.3_1 ) < 1e-3






			def 							_snake_case   (    self )	->					Dict:
									lowerCAmelCase	:   Any					     =					self.scheduler_classes[0]
									lowerCAmelCase	:   Optional[Any]					     =					self.get_scheduler_config()
									lowerCAmelCase	:   List[Any]					     =					scheduler_class(**lowercase_    ,  use_karras_sigmas=lowercase_ )

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

									lowerCAmelCase	:   Optional[int]					     =					self.dummy_model()
									lowerCAmelCase	:   Dict					     =					self.dummy_sample_deter.to(lowercase_ ) * scheduler.init_noise_sigma
									lowerCAmelCase	:   List[Any]					     =					sample.to(lowercase_ )

									for t in scheduler.timesteps:
															lowerCAmelCase	:   str					     =					scheduler.scale_model_input(lowercase_    ,  lowercase_ )

															lowerCAmelCase	:   Optional[Any]					     =					model(lowercase_    ,  lowercase_ )

															lowerCAmelCase	:   Any					     =					scheduler.step(lowercase_    ,  lowercase_    ,  lowercase_ )
															lowerCAmelCase	:   List[str]					     =					output.prev_sample

									lowerCAmelCase	:   Tuple					     =					torch.sum(torch.abs(lowercase_ ) )
									lowerCAmelCase	:   Tuple					     =					torch.mean(torch.abs(lowercase_ ) )

									assert abs(result_sum.item() - 3_8_1_2.9_9_2_7 ) < 2e-2
									assert abs(result_mean.item() - 4.9_6_4_8 ) < 1e-3






def  _UpperCAmelCase  (				SCREAMING_SNAKE_CASE__  ):


						'''simple docstring'''




						lowerCAmelCase	:   List[str]					     =					str(SCREAMING_SNAKE_CASE__  ).strip()
						if not bin_string:
												raise ValueError("""Empty string was passed to the function"""  )
						lowerCAmelCase	:   Optional[int]					     =					bin_string[0] == """-"""
						if is_negative:
												lowerCAmelCase	:   Tuple					     =					bin_string[1:]
						if not all(char in """01""" for char in bin_string  ):
												raise ValueError("""Non-binary value was passed to the function"""  )
						lowerCAmelCase	:   Union[str, Any]					     =					0
						for char in bin_string:
												lowerCAmelCase	:   int					     =					2 * decimal_number + int(SCREAMING_SNAKE_CASE__  )
						return -decimal_number if is_negative else decimal_number


if __name__ == "__main__":
		from doctest import testmod

		testmod()

