




import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest


class 							__UpperCamelCase			( __UpperCAmelCase  ):





				'''simple docstring'''



				__a							: Union[str, Any]										=(EulerDiscreteScheduler,)
				__a							: List[str]										=1_0



				def       __snake_case		( self       , **UpperCAmelCase_						):
											lowerCAmelCase   =		{
											    '''num_train_timesteps''': 11_00,
											    '''beta_start''': 0.0001,
											    '''beta_end''': 0.02,
											    '''beta_schedule''': '''linear''',
											}

											config.update(**UpperCAmelCase_						)
											return config



				def       __snake_case		( self						):
											for timesteps in [10, 50, 1_00, 10_00]:
																		self.check_over_configs(num_train_timesteps=UpperCAmelCase_						)



				def       __snake_case		( self						):
											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=UpperCAmelCase_       , beta_end=UpperCAmelCase_						)



				def       __snake_case		( self						):
											for schedule in ["linear", "scaled_linear"]:
																		self.check_over_configs(beta_schedule=UpperCAmelCase_						)



				def       __snake_case		( self						):
											for prediction_type in ["epsilon", "v_prediction"]:
																		self.check_over_configs(prediction_type=UpperCAmelCase_						)



				def       __snake_case		( self						):
											lowerCAmelCase   =		self.scheduler_classes[0]
											lowerCAmelCase   =		self.get_scheduler_config()
											lowerCAmelCase   =		scheduler_class(**UpperCAmelCase_						)

											scheduler.set_timesteps(self.num_inference_steps						)

											lowerCAmelCase   =		torch.manual_seed(0						)

											lowerCAmelCase   =		self.dummy_model()
											lowerCAmelCase   =		self.dummy_sample_deter * scheduler.init_noise_sigma
											lowerCAmelCase   =		sample.to(UpperCAmelCase_						)

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

																		lowerCAmelCase   =		model(UpperCAmelCase_       , UpperCAmelCase_						)

																		lowerCAmelCase   =		scheduler.step(UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_       , generator=UpperCAmelCase_						)
																		lowerCAmelCase   =		output.prev_sample

											lowerCAmelCase   =		torch.sum(torch.abs(UpperCAmelCase_						)						)
											lowerCAmelCase   =		torch.mean(torch.abs(UpperCAmelCase_						)						)

											assert abs(result_sum.item() - 10.0807						) < 1E-2
											assert abs(result_mean.item() - 0.0131						) < 1E-3



				def       __snake_case		( self						):
											lowerCAmelCase   =		self.scheduler_classes[0]
											lowerCAmelCase   =		self.get_scheduler_config(prediction_type='''v_prediction'''						)
											lowerCAmelCase   =		scheduler_class(**UpperCAmelCase_						)

											scheduler.set_timesteps(self.num_inference_steps						)

											lowerCAmelCase   =		torch.manual_seed(0						)

											lowerCAmelCase   =		self.dummy_model()
											lowerCAmelCase   =		self.dummy_sample_deter * scheduler.init_noise_sigma
											lowerCAmelCase   =		sample.to(UpperCAmelCase_						)

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

																		lowerCAmelCase   =		model(UpperCAmelCase_       , UpperCAmelCase_						)

																		lowerCAmelCase   =		scheduler.step(UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_       , generator=UpperCAmelCase_						)
																		lowerCAmelCase   =		output.prev_sample

											lowerCAmelCase   =		torch.sum(torch.abs(UpperCAmelCase_						)						)
											lowerCAmelCase   =		torch.mean(torch.abs(UpperCAmelCase_						)						)

											assert abs(result_sum.item() - 0.0002						) < 1E-2
											assert abs(result_mean.item() - 2.2_6_7_6E-0_6						) < 1E-3



				def       __snake_case		( self						):
											lowerCAmelCase   =		self.scheduler_classes[0]
											lowerCAmelCase   =		self.get_scheduler_config()
											lowerCAmelCase   =		scheduler_class(**UpperCAmelCase_						)

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

											lowerCAmelCase   =		torch.manual_seed(0						)

											lowerCAmelCase   =		self.dummy_model()
											lowerCAmelCase   =		self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
											lowerCAmelCase   =		sample.to(UpperCAmelCase_						)

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

																		lowerCAmelCase   =		model(UpperCAmelCase_       , UpperCAmelCase_						)

																		lowerCAmelCase   =		scheduler.step(UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_       , generator=UpperCAmelCase_						)
																		lowerCAmelCase   =		output.prev_sample

											lowerCAmelCase   =		torch.sum(torch.abs(UpperCAmelCase_						)						)
											lowerCAmelCase   =		torch.mean(torch.abs(UpperCAmelCase_						)						)

											assert abs(result_sum.item() - 10.0807						) < 1E-2
											assert abs(result_mean.item() - 0.0131						) < 1E-3






				def       __snake_case		( self						):
											lowerCAmelCase   =		self.scheduler_classes[0]
											lowerCAmelCase   =		self.get_scheduler_config()
											lowerCAmelCase   =		scheduler_class(**UpperCAmelCase_       , use_karras_sigmas=UpperCAmelCase_						)

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

											lowerCAmelCase   =		torch.manual_seed(0						)

											lowerCAmelCase   =		self.dummy_model()
											lowerCAmelCase   =		self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
											lowerCAmelCase   =		sample.to(UpperCAmelCase_						)

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

																		lowerCAmelCase   =		model(UpperCAmelCase_       , UpperCAmelCase_						)

																		lowerCAmelCase   =		scheduler.step(UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_       , generator=UpperCAmelCase_						)
																		lowerCAmelCase   =		output.prev_sample

											lowerCAmelCase   =		torch.sum(torch.abs(UpperCAmelCase_						)						)
											lowerCAmelCase   =		torch.mean(torch.abs(UpperCAmelCase_						)						)

											assert abs(result_sum.item() - 124.52299499511719						) < 1E-2
											assert abs(result_mean.item() - 0.16213932633399963						) < 1E-3









def       UpperCAmelCase       (     _snake_case						, _snake_case						, _snake_case							):

							def count_of_possible_combinations(_snake_case							) -> int:
														if target < 0:
																					return 0
														if target == 0:
																					return 1
														return sum(count_of_possible_combinations(target - item							) for item in array							)

							return count_of_possible_combinations(_snake_case							)



def       UpperCAmelCase       (     _snake_case						, _snake_case						, _snake_case							):

							def count_of_possible_combinations_with_dp_array(
							    _snake_case						, _snake_case							) -> int:
														if target < 0:
																					return 0
														if target == 0:
																					return 1
														if dp_array[target] != -1:
																					return dp_array[target]
														lowerCAmelCase   =		sum(
														    count_of_possible_combinations_with_dp_array(target - item						, _snake_case							)
														    for item in array							)
														lowerCAmelCase   =		answer
														return answer

							lowerCAmelCase   =		[-1] * (target + 1)
							return count_of_possible_combinations_with_dp_array(_snake_case						, _snake_case							)



def       UpperCAmelCase       (     _snake_case						, _snake_case						, _snake_case							):
							lowerCAmelCase   =		[0] * (target + 1)
							lowerCAmelCase   =		1

							for i in range(1						, target + 1							):
														for j in range(_snake_case							):
																					if i - array[j] >= 0:
																												dp_array[i] += dp_array[i - array[j]]

							return dp_array[target]


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		UpperCAmelCase_						=3
		UpperCAmelCase_						=5
		UpperCAmelCase_						=[1, 2, 5]
		print(combination_sum_iv(n, array, target))



