

import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest






class     _UpperCAmelCase			(		lowerCAmelCase_):
			__a :       Tuple									=							(LMSDiscreteScheduler,)
			__a :       Tuple									=							1_0
			def 							__snake_case (		self      ,		**_A     )    ->    Union[str, Any]:






				'''simple docstring'''







				_UpperCAmelCase    :							Optional[int]									=					{
				    'num_train_timesteps': 11_00,
				    'beta_start': 0.0001,
				    'beta_end': 0.02,
				    'beta_schedule': 'linear',
				}

				config.update(**__a     )
				return config
			def 							__snake_case (		self     )    ->    int:






				'''simple docstring'''







				for timesteps in [10, 50, 1_00, 10_00]:
					self.check_over_configs(num_train_timesteps=__a     )
			def 							__snake_case (		self     )    ->    List[str]:






				'''simple docstring'''







				for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]      ,		[0.0002, 0.002, 0.02]     ):
					self.check_over_configs(beta_start=__a      ,		beta_end=__a     )
			def 							__snake_case (		self     )    ->    int:






				'''simple docstring'''







				for schedule in ["linear", "scaled_linear"]:
					self.check_over_configs(beta_schedule=__a     )
			def 							__snake_case (		self     )    ->    Any:






				'''simple docstring'''







				for prediction_type in ["epsilon", "v_prediction"]:
					self.check_over_configs(prediction_type=__a     )
			def 							__snake_case (		self     )    ->    Any:






				'''simple docstring'''







				for t in [0, 5_00, 8_00]:
					self.check_over_forward(time_step=__a     )
			def 							__snake_case (		self     )    ->    Union[str, Any]:






				'''simple docstring'''







				_UpperCAmelCase    :							Any									=					self.scheduler_classes[0]
				_UpperCAmelCase    :							Optional[Any]									=					self.get_scheduler_config()
				_UpperCAmelCase    :							Dict									=					scheduler_class(**__a     )

				scheduler.set_timesteps(self.num_inference_steps     )

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

				for i, t in enumerate(scheduler.timesteps     ):
					_UpperCAmelCase    :							Optional[int]									=					scheduler.scale_model_input(__a      ,		__a     )

					_UpperCAmelCase    :							Tuple									=					model(__a      ,		__a     )

					_UpperCAmelCase    :							Union[str, Any]									=					scheduler.step(__a      ,		__a      ,		__a     )
					_UpperCAmelCase    :							Any									=					output.prev_sample

				_UpperCAmelCase    :							List[str]									=					torch.sum(torch.abs(__a     )     )
				_UpperCAmelCase    :							List[Any]									=					torch.mean(torch.abs(__a     )     )

				assert abs(result_sum.item() - 1006.388     ) < 1e-2
				assert abs(result_mean.item() - 1.31     ) < 1e-3
			def 							__snake_case (		self     )    ->    Optional[int]:






				'''simple docstring'''







				_UpperCAmelCase    :							List[Any]									=					self.scheduler_classes[0]
				_UpperCAmelCase    :							str									=					self.get_scheduler_config(prediction_type="""v_prediction"""     )
				_UpperCAmelCase    :							Any									=					scheduler_class(**__a     )

				scheduler.set_timesteps(self.num_inference_steps     )

				_UpperCAmelCase    :							Dict									=					self.dummy_model()
				_UpperCAmelCase    :							Optional[Any]									=					self.dummy_sample_deter * scheduler.init_noise_sigma

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

					_UpperCAmelCase    :							Union[str, Any]									=					model(__a      ,		__a     )

					_UpperCAmelCase    :							Dict									=					scheduler.step(__a      ,		__a      ,		__a     )
					_UpperCAmelCase    :							Any									=					output.prev_sample

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

				assert abs(result_sum.item() - 0.0017     ) < 1e-2
				assert abs(result_mean.item() - 2.26_76e-06     ) < 1e-3
			def 							__snake_case (		self     )    ->    Optional[int]:






				'''simple docstring'''







				_UpperCAmelCase    :							List[Any]									=					self.scheduler_classes[0]
				_UpperCAmelCase    :							Dict									=					self.get_scheduler_config()
				_UpperCAmelCase    :							Tuple									=					scheduler_class(**__a     )

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

				_UpperCAmelCase    :							List[Any]									=					self.dummy_model()
				_UpperCAmelCase    :							List[Any]									=					self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
				_UpperCAmelCase    :							int									=					sample.to(__a     )

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

					_UpperCAmelCase    :							Union[str, Any]									=					model(__a      ,		__a     )

					_UpperCAmelCase    :							Dict									=					scheduler.step(__a      ,		__a      ,		__a     )
					_UpperCAmelCase    :							Union[str, Any]									=					output.prev_sample

				_UpperCAmelCase    :							Optional[Any]									=					torch.sum(torch.abs(__a     )     )
				_UpperCAmelCase    :							Optional[int]									=					torch.mean(torch.abs(__a     )     )

				assert abs(result_sum.item() - 1006.388     ) < 1e-2
				assert abs(result_mean.item() - 1.31     ) < 1e-3
			def 							__snake_case (		self     )    ->    Tuple:






				'''simple docstring'''







				_UpperCAmelCase    :							Dict									=					self.scheduler_classes[0]
				_UpperCAmelCase    :							str									=					self.get_scheduler_config()
				_UpperCAmelCase    :							int									=					scheduler_class(**__a      ,		use_karras_sigmas=__a     )

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

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

				for t in scheduler.timesteps:
					_UpperCAmelCase    :							Optional[Any]									=					scheduler.scale_model_input(__a      ,		__a     )

					_UpperCAmelCase    :							Dict									=					model(__a      ,		__a     )

					_UpperCAmelCase    :							List[Any]									=					scheduler.step(__a      ,		__a      ,		__a     )
					_UpperCAmelCase    :							int									=					output.prev_sample

				_UpperCAmelCase    :							Dict									=					torch.sum(torch.abs(__a     )     )
				_UpperCAmelCase    :							Optional[Any]									=					torch.mean(torch.abs(__a     )     )

				assert abs(result_sum.item() - 3812.9927     ) < 2e-2
				assert abs(result_mean.item() - 4.9648     ) < 1e-3








"""simple docstring"""






def       UpperCamelCase					(			_lowerCAmelCase     :						int )				->			str:
	_UpperCAmelCase    :							Any									=					1
	_UpperCAmelCase    :							List[str]									=					0
	_UpperCAmelCase    :							str									=					1
	_UpperCAmelCase    :							Union[str, Any]									=					1
	_UpperCAmelCase    :							int									=					3
	_UpperCAmelCase    :							int									=					3
	_UpperCAmelCase    :							List[str]									=					limit
	_UpperCAmelCase    :							Optional[Any]									=					0

	_UpperCAmelCase    :							Any									=					""""""
	while counter != decimal + 1:
		if 4 * q + r - t < n * t:
			result += str(_lowerCAmelCase )
			if counter == 0:
				result += "."

			if decimal == counter:
				break

			counter += 1
			_UpperCAmelCase    :							str									=					10 * (r - n * t)
			_UpperCAmelCase    :							List[Any]									=					((10 * (3 * q + r)) // t) - 10 * n
			q *= 10
			_UpperCAmelCase    :							int									=					nr
		else:
			_UpperCAmelCase    :							Any									=					(2 * q + r) * l
			_UpperCAmelCase    :							str									=					(q * (7 * k) + 2 + (r * l)) // (t * l)
			q *= k
			t *= l
			l += 2
			k += 1
			_UpperCAmelCase    :							List[Any]									=					nn
			_UpperCAmelCase    :							Optional[int]									=					nr
	return result
def       UpperCamelCase					(			)				->			None:
	print(f'''{calculate_pi(50 ) = }''' )
	import doctest

	doctest.testmod()


if __name__ == "__main__":
						main()





