






import torch

from diffusers import HeunDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest




class 						UpperCAmelCase_  (		SCREAMING_SNAKE_CASE__    ):


						'''simple docstring'''




						A   :      List[str]						 =    (HeunDiscreteScheduler,)
						A   :      int						 =    10






						def 					_lowerCAmelCase				(		self						, **_SCREAMING_SNAKE_CASE )       -> List[str]:
								snake_case_ :							Any         =				{
								    "num_train_timesteps": 1100,
								    "beta_start": 0.0001,
								    "beta_end": 0.02,
								    "beta_schedule": "linear",
								}

								config.update(**lowercase__ )
								return config






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







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






						def 					_lowerCAmelCase				(		self )       -> Any:







								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=lowercase__						, beta_end=lowercase__ )






						def 					_lowerCAmelCase				(		self )       -> Optional[int]:







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






						def 					_lowerCAmelCase				(		self )       -> str:







								for clip_sample_range in [1.0, 2.0, 3.0]:
										self.check_over_configs(clip_sample_range=lowercase__						, clip_sample=lowercase__ )






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







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






						def 					_lowerCAmelCase				(		self )       -> Tuple:
								snake_case_ :							Tuple         =				self.scheduler_classes[0]
								snake_case_ :							str         =				self.get_scheduler_config()
								snake_case_ :							Optional[Any]         =				scheduler_class(**lowercase__ )

								scheduler.set_timesteps(self.num_inference_steps )

								snake_case_ :							Tuple         =				self.dummy_model()
								snake_case_ :							int         =				self.dummy_sample_deter * scheduler.init_noise_sigma
								snake_case_ :							Optional[int]         =				sample.to(lowercase__ )

								for i, t in enumerate(scheduler.timesteps ):
										snake_case_ :							Any         =				scheduler.scale_model_input(lowercase__						, lowercase__ )

										snake_case_ :							List[str]         =				model(lowercase__						, lowercase__ )

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

								snake_case_ :							List[str]         =				torch.sum(torch.abs(lowercase__ ) )
								snake_case_ :							List[str]         =				torch.mean(torch.abs(lowercase__ ) )

								if torch_device in ["cpu", "mps"]:
										assert abs(result_sum.item() - 0.1233 ) < 1e-2
										assert abs(result_mean.item() - 0.0002 ) < 1e-3
								else:
										# CUDA
										assert abs(result_sum.item() - 0.1233 ) < 1e-2
										assert abs(result_mean.item() - 0.0002 ) < 1e-3






						def 					_lowerCAmelCase				(		self )       -> Union[str, Any]:
								snake_case_ :							int         =				self.scheduler_classes[0]
								snake_case_ :							Any         =				self.get_scheduler_config(prediction_type="v_prediction" )
								snake_case_ :							List[str]         =				scheduler_class(**lowercase__ )

								scheduler.set_timesteps(self.num_inference_steps )

								snake_case_ :							Optional[Any]         =				self.dummy_model()
								snake_case_ :							Optional[Any]         =				self.dummy_sample_deter * scheduler.init_noise_sigma
								snake_case_ :							Union[str, Any]         =				sample.to(lowercase__ )

								for i, t in enumerate(scheduler.timesteps ):
										snake_case_ :							Union[str, Any]         =				scheduler.scale_model_input(lowercase__						, lowercase__ )

										snake_case_ :							Dict         =				model(lowercase__						, lowercase__ )

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

								snake_case_ :							Optional[Any]         =				torch.sum(torch.abs(lowercase__ ) )
								snake_case_ :							Tuple         =				torch.mean(torch.abs(lowercase__ ) )

								if torch_device in ["cpu", "mps"]:
										assert abs(result_sum.item() - 4.6934e-07 ) < 1e-2
										assert abs(result_mean.item() - 6.1112e-10 ) < 1e-3
								else:
										# CUDA
										assert abs(result_sum.item() - 4.693428650170972e-07 ) < 1e-2
										assert abs(result_mean.item() - 0.0002 ) < 1e-3






						def 					_lowerCAmelCase				(		self )       -> int:
								snake_case_ :							Dict         =				self.scheduler_classes[0]
								snake_case_ :							str         =				self.get_scheduler_config()
								snake_case_ :							Optional[Any]         =				scheduler_class(**lowercase__ )

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

								snake_case_ :							Dict         =				self.dummy_model()
								snake_case_ :							List[str]         =				self.dummy_sample_deter.to(lowercase__ ) * scheduler.init_noise_sigma

								for t in scheduler.timesteps:
										snake_case_ :							str         =				scheduler.scale_model_input(lowercase__						, lowercase__ )

										snake_case_ :							Union[str, Any]         =				model(lowercase__						, lowercase__ )

										snake_case_ :							Dict         =				scheduler.step(lowercase__						, lowercase__						, lowercase__ )
										snake_case_ :							Tuple         =				output.prev_sample

								snake_case_ :							List[Any]         =				torch.sum(torch.abs(lowercase__ ) )
								snake_case_ :							Optional[Any]         =				torch.mean(torch.abs(lowercase__ ) )

								if str(lowercase__ ).startswith("cpu" ):
										# The following sum varies between 148 and 156 on mps. Why?
										assert abs(result_sum.item() - 0.1233 ) < 1e-2
										assert abs(result_mean.item() - 0.0002 ) < 1e-3
								elif str(lowercase__ ).startswith("mps" ):
										# Larger tolerance on mps
										assert abs(result_mean.item() - 0.0002 ) < 1e-2
								else:
										# CUDA
										assert abs(result_sum.item() - 0.1233 ) < 1e-2
										assert abs(result_mean.item() - 0.0002 ) < 1e-3






						def 					_lowerCAmelCase				(		self )       -> str:
								snake_case_ :							Tuple         =				self.scheduler_classes[0]
								snake_case_ :							Dict         =				self.get_scheduler_config()
								snake_case_ :							Tuple         =				scheduler_class(**lowercase__						, use_karras_sigmas=lowercase__ )

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

								snake_case_ :							str         =				self.dummy_model()
								snake_case_ :							str         =				self.dummy_sample_deter.to(lowercase__ ) * scheduler.init_noise_sigma
								snake_case_ :							Tuple         =				sample.to(lowercase__ )

								for t in scheduler.timesteps:
										snake_case_ :							List[str]         =				scheduler.scale_model_input(lowercase__						, lowercase__ )

										snake_case_ :							int         =				model(lowercase__						, lowercase__ )

										snake_case_ :							Dict         =				scheduler.step(lowercase__						, lowercase__						, lowercase__ )
										snake_case_ :							int         =				output.prev_sample

								snake_case_ :							Tuple         =				torch.sum(torch.abs(lowercase__ ) )
								snake_case_ :							List[Any]         =				torch.mean(torch.abs(lowercase__ ) )

								assert abs(result_sum.item() - 0.0_0015 ) < 1e-2
								assert abs(result_mean.item() - 1.9869554535034695e-07 ) < 1e-2


"""simple docstring"""




def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_			):
					if not isinstance(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			):
										SCREAMING_SNAKE_CASE				=	(
										    'operation can not be conducted on a object of type '
										    f'''{type(SCREAMING_SNAKE_CASE_			).__name__}'''
										)
										raise TypeError(SCREAMING_SNAKE_CASE_			)
					SCREAMING_SNAKE_CASE				=	''
					for _ in range(0,     3_2			):
										bit_string += str(number % 2			)
										SCREAMING_SNAKE_CASE				=	number >> 1
					return bit_string

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_			):
					if number < 0:
										raise ValueError('the value of input must be positive'			)
					elif isinstance(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			):
										raise TypeError('Input value must be a \'int\' type'			)
					elif isinstance(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			):
										raise TypeError('\'<\' not supported between instances of \'str\' and \'int\''			)
					SCREAMING_SNAKE_CASE				=	0
					# iterator over [1 to 32],since we are dealing with 32 bit integer
					for _ in range(1,     3_3			):
										# left shift the bits by unity
										SCREAMING_SNAKE_CASE				=	result << 1
										# get the end bit
										SCREAMING_SNAKE_CASE				=	number % 2
										# right shift the bits by unity
										SCREAMING_SNAKE_CASE				=	number >> 1
										# add that bit to our ans
										SCREAMING_SNAKE_CASE				=	result | end_bit
					return get_reverse_bit_string(SCREAMING_SNAKE_CASE_			)


if __name__ == "__main__":
	import doctest

	doctest.testmod()
