

"""simple docstring"""





import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest







class 							SCREAMING_SNAKE_CASE   (						a_			):






					"""simple docstring"""



					lowercase__												=			(KDPMaDiscreteScheduler,)
					lowercase__												=			10







					def 			__lowerCAmelCase					(			self     :							Optional[int]  ,**lowercase_     :							Dict						):
						lowerCAmelCase__      :		Union[str, Any]    							=      {
						    '''num_train_timesteps''': 1_1_0_0,
						    '''beta_start''': 0.0001,
						    '''beta_end''': 0.02,
						    '''beta_schedule''': '''linear''',
						}

						config.update(**lowercase_						)
						return config







					def 			__lowerCAmelCase					(			self     :							Optional[int]						):
						for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
							self.check_over_configs(num_train_timesteps=lowercase_						)







					def 			__lowerCAmelCase					(			self     :							Dict						):
						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[Any]						):
						for schedule in ["linear", "scaled_linear"]:
							self.check_over_configs(beta_schedule=lowercase_						)







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







					def 			__lowerCAmelCase					(			self     :							Optional[Any]						):
						lowerCAmelCase__      :		Tuple    							=      self.scheduler_classes[0]
						lowerCAmelCase__      :		Dict    							=      self.get_scheduler_config(prediction_type='''v_prediction'''						)
						lowerCAmelCase__      :		List[Any]    							=      scheduler_class(**lowercase_						)

						scheduler.set_timesteps(self.num_inference_steps						)

						lowerCAmelCase__      :		Any    							=      self.dummy_model()
						lowerCAmelCase__      :		Optional[Any]    							=      self.dummy_sample_deter * scheduler.init_noise_sigma
						lowerCAmelCase__      :		Optional[Any]    							=      sample.to(lowercase_						)

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

							lowerCAmelCase__      :		List[Any]    							=      model(lowercase_  ,lowercase_						)

							lowerCAmelCase__      :		int    							=      scheduler.step(lowercase_  ,lowercase_  ,lowercase_						)
							lowerCAmelCase__      :		List[Any]    							=      output.prev_sample

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

						if torch_device in ["cpu", "mps"]:
							assert abs(result_sum.item() - 4.6_934E-07						) < 1E-2
							assert abs(result_mean.item() - 6.1_112E-10						) < 1E-3
						else:
							# CUDA
							assert abs(result_sum.item() - 4.693_428_650_170_972E-07						) < 1E-2
							assert abs(result_mean.item() - 0.0002						) < 1E-3







					def 			__lowerCAmelCase					(			self     :							int						):
						if torch_device == "mps":
							return
						lowerCAmelCase__      :		List[Any]    							=      self.scheduler_classes[0]
						lowerCAmelCase__      :		Optional[int]    							=      self.get_scheduler_config()
						lowerCAmelCase__      :		Optional[Any]    							=      scheduler_class(**lowercase_						)

						scheduler.set_timesteps(self.num_inference_steps						)

						lowerCAmelCase__      :		Optional[Any]    							=      self.dummy_model()
						lowerCAmelCase__      :		List[Any]    							=      self.dummy_sample_deter * scheduler.init_noise_sigma
						lowerCAmelCase__      :		List[str]    							=      sample.to(lowercase_						)

						for i, t in enumerate(scheduler.timesteps						):
							lowerCAmelCase__      :		str    							=      scheduler.scale_model_input(lowercase_  ,lowercase_						)

							lowerCAmelCase__      :		Tuple    							=      model(lowercase_  ,lowercase_						)

							lowerCAmelCase__      :		Any    							=      scheduler.step(lowercase_  ,lowercase_  ,lowercase_						)
							lowerCAmelCase__      :		int    							=      output.prev_sample

						lowerCAmelCase__      :		List[Any]    							=      torch.sum(torch.abs(lowercase_						)						)
						lowerCAmelCase__      :		Union[str, Any]    							=      torch.mean(torch.abs(lowercase_						)						)

						if torch_device in ["cpu", "mps"]:
							assert abs(result_sum.item() - 20.4125						) < 1E-2
							assert abs(result_mean.item() - 0.0266						) < 1E-3
						else:
							# CUDA
							assert abs(result_sum.item() - 20.4125						) < 1E-2
							assert abs(result_mean.item() - 0.0266						) < 1E-3







					def 			__lowerCAmelCase					(			self     :							List[Any]						):
						if torch_device == "mps":
							return
						lowerCAmelCase__      :		Union[str, Any]    							=      self.scheduler_classes[0]
						lowerCAmelCase__      :		List[Any]    							=      self.get_scheduler_config()
						lowerCAmelCase__      :		Dict    							=      scheduler_class(**lowercase_						)

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

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

						for t in scheduler.timesteps:
							lowerCAmelCase__      :		int    							=      scheduler.scale_model_input(lowercase_  ,lowercase_						)

							lowerCAmelCase__      :		Any    							=      model(lowercase_  ,lowercase_						)

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

						lowerCAmelCase__      :		Optional[int]    							=      torch.sum(torch.abs(lowercase_						)						)
						lowerCAmelCase__      :		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() - 20.4125						) < 1E-2
							assert abs(result_mean.item() - 0.0266						) < 1E-3
						else:
							# CUDA
							assert abs(result_sum.item() - 20.4125						) < 1E-2
							assert abs(result_mean.item() - 0.0266						) < 1E-3







"""simple docstring"""





def     __SCREAMING_SNAKE_CASE    ( A_		,	A_					):
	if number < 0 or shift_amount < 0:
		raise ValueError('''both inputs must be positive integers'''					)

	lowerCAmelCase__      :		int    							=      str(bin(A_					)					)
	binary_number += "0" * shift_amount
	return binary_number

def     __SCREAMING_SNAKE_CASE    ( A_		,	A_					):
	if number < 0 or shift_amount < 0:
		raise ValueError('''both inputs must be positive integers'''					)

	lowerCAmelCase__      :		Any    							=      str(bin(A_					)					)[2:]
	if shift_amount >= len(A_					):
		return "0b0"
	lowerCAmelCase__      :		Tuple    							=      binary_number[: len(A_					) - shift_amount]
	return "0b" + shifted_binary_number

def     __SCREAMING_SNAKE_CASE    ( A_		,	A_					):
	if number >= 0:  # Get binary representation of positive number
		lowerCAmelCase__      :		Union[str, Any]    							=      '''0''' + str(bin(A_					)					).strip('''-'''					)[2:]
	else:  # Get binary (2's complement) representation of negative number
		lowerCAmelCase__      :		Optional[Any]    							=      len(bin(A_					)[3:]					)  # Find 2's complement of number
		lowerCAmelCase__      :		Optional[int]    							=      bin(abs(A_					) - (1 << binary_number_length)					)[3:]
		lowerCAmelCase__      :		int    							=      (
		    '''1''' + '''0''' * (binary_number_length - len(A_					)) + binary_number
		)

	if shift_amount >= len(A_					):
		return "0b" + binary_number[0] * len(A_					)
	return (
	    "0b"
	    + binary_number[0] * shift_amount
	    + binary_number[: len(A_					) - shift_amount]
	)


if __name__ == "__main__":
							import doctest

							doctest.testmod()




