
import torch

from diffusers import KDPMaAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest


class 							lowercase   (						_SCREAMING_SNAKE_CASE  ):
		__lowercase			:			Optional[int]      	=							(KDPMaAncestralDiscreteScheduler,)
		__lowercase			:			int      	=							10



		def __UpperCamelCase					(					self							, **A_			)    ->   int:
				"""simple docstring"""
				UpperCamelCase            =	{
				    'num_train_timesteps': 1_100,
				    'beta_start': 0.0001,
				    'beta_end': 0.02,
				    'beta_schedule': 'linear',
				}

				config.update(**A_			)
				return config



		def __UpperCamelCase					(					self			)    ->   Any:
				"""simple docstring"""
				for timesteps in [10, 50, 100, 1_000]:
						self.check_over_configs(num_train_timesteps=A_			)



		def __UpperCamelCase					(					self			)    ->   Optional[int]:
				"""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=A_							, beta_end=A_			)



		def __UpperCamelCase					(					self			)    ->   List[Any]:
				"""simple docstring"""
				for schedule in ["linear", "scaled_linear"]:
						self.check_over_configs(beta_schedule=A_			)



		def __UpperCamelCase					(					self			)    ->   Any:
				"""simple docstring"""
				if torch_device == "mps":
						return
				UpperCamelCase            =	self.scheduler_classes[0]
				UpperCamelCase            =	self.get_scheduler_config()
				UpperCamelCase            =	scheduler_class(**A_			)

				scheduler.set_timesteps(self.num_inference_steps			)

				UpperCamelCase            =	torch.manual_seed(0			)

				UpperCamelCase            =	self.dummy_model()
				UpperCamelCase            =	self.dummy_sample_deter * scheduler.init_noise_sigma
				UpperCamelCase            =	sample.to(A_			)

				for i, t in enumerate(scheduler.timesteps			):
						UpperCamelCase            =	scheduler.scale_model_input(A_							, A_			)

						UpperCamelCase            =	model(A_							, A_			)

						UpperCamelCase            =	scheduler.step(A_							, A_							, A_							, generator=A_			)
						UpperCamelCase            =	output.prev_sample

				UpperCamelCase            =	torch.sum(torch.abs(A_			)			)
				UpperCamelCase            =	torch.mean(torch.abs(A_			)			)

				assert abs(result_sum.item() - 1_3849.3877			) < 1e-2
				assert abs(result_mean.item() - 18.0331			) < 5e-3



		def __UpperCamelCase					(					self			)    ->   Optional[int]:
				"""simple docstring"""
				for prediction_type in ["epsilon", "v_prediction"]:
						self.check_over_configs(prediction_type=A_			)



		def __UpperCamelCase					(					self			)    ->   Any:
				"""simple docstring"""
				if torch_device == "mps":
						return
				UpperCamelCase            =	self.scheduler_classes[0]
				UpperCamelCase            =	self.get_scheduler_config(prediction_type='v_prediction'			)
				UpperCamelCase            =	scheduler_class(**A_			)

				scheduler.set_timesteps(self.num_inference_steps			)

				UpperCamelCase            =	self.dummy_model()
				UpperCamelCase            =	self.dummy_sample_deter * scheduler.init_noise_sigma
				UpperCamelCase            =	sample.to(A_			)

				UpperCamelCase            =	torch.manual_seed(0			)

				for i, t in enumerate(scheduler.timesteps			):
						UpperCamelCase            =	scheduler.scale_model_input(A_							, A_			)

						UpperCamelCase            =	model(A_							, A_			)

						UpperCamelCase            =	scheduler.step(A_							, A_							, A_							, generator=A_			)
						UpperCamelCase            =	output.prev_sample

				UpperCamelCase            =	torch.sum(torch.abs(A_			)			)
				UpperCamelCase            =	torch.mean(torch.abs(A_			)			)

				assert abs(result_sum.item() - 328.9970			) < 1e-2
				assert abs(result_mean.item() - 0.4284			) < 1e-3



		def __UpperCamelCase					(					self			)    ->   str:
				"""simple docstring"""
				if torch_device == "mps":
						return
				UpperCamelCase            =	self.scheduler_classes[0]
				UpperCamelCase            =	self.get_scheduler_config()
				UpperCamelCase            =	scheduler_class(**A_			)

				scheduler.set_timesteps(self.num_inference_steps							, device=A_			)
				UpperCamelCase            =	torch.manual_seed(0			)

				UpperCamelCase            =	self.dummy_model()
				UpperCamelCase            =	self.dummy_sample_deter.to(A_			) * scheduler.init_noise_sigma

				for t in scheduler.timesteps:
						UpperCamelCase            =	scheduler.scale_model_input(A_							, A_			)

						UpperCamelCase            =	model(A_							, A_			)

						UpperCamelCase            =	scheduler.step(A_							, A_							, A_							, generator=A_			)
						UpperCamelCase            =	output.prev_sample

				UpperCamelCase            =	torch.sum(torch.abs(A_			)			)
				UpperCamelCase            =	torch.mean(torch.abs(A_			)			)

				assert abs(result_sum.item() - 1_3849.3818			) < 1e-1
				assert abs(result_mean.item() - 18.0331			) < 1e-3


def  A			(   lowercase					)  ->    str:
		'''simple docstring'''

		if not isinstance(lowercase       ,			lowercase					):
				UpperCamelCase            =	(
				    'operation can not be conducted on a object of type '
				    f'''{type(lowercase					).__name__}'''
				)
				raise TypeError(lowercase					)
		UpperCamelCase            =	''
		for _ in range(0       ,			32					):
				bit_string += str(number % 2					)
				UpperCamelCase            =	number >> 1
		return bit_string


def  A			(   lowercase					)  ->    str:
		'''simple docstring'''

		if number < 0:
				raise ValueError('the value of input must be positive'					)
		elif isinstance(lowercase       ,			lowercase					):
				raise TypeError('Input value must be a \'int\' type'					)
		elif isinstance(lowercase       ,			lowercase					):
				raise TypeError('\'<\' not supported between instances of \'str\' and \'int\''					)
		UpperCamelCase            =	0
		# iterator over [1 to 32],since we are dealing with 32 bit integer
		for _ in range(1       ,			33					):
				# left shift the bits by unity
				UpperCamelCase            =	result << 1
				# get the end bit
				UpperCamelCase            =	number % 2
				# right shift the bits by unity
				UpperCamelCase            =	number >> 1
				# add that bit to our ans
				UpperCamelCase            =	result | end_bit
		return get_reverse_bit_string(lowercase					)


if __name__ == "__main__":
					import doctest

					doctest.testmod()
