





import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest





class 				a			(      snake_case_			):
				SCREAMING_SNAKE_CASE__     :   List[str]		      =     (DDIMParallelScheduler,)
				SCREAMING_SNAKE_CASE__     :   Optional[int]		      =     (('''eta''', 0.0), ('''num_inference_steps''', 50))






				def 			snake_case_    (  self			, **_lowerCAmelCase					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Dict           =   {
										    'num_train_timesteps': 1000,
										    'beta_start': 0.0001,
										    'beta_end': 0.02,
										    'beta_schedule': 'linear',
										    'clip_sample': True,
										}

										config.update(**__lowercase					)
										return config






				def 			snake_case_    (  self			, **_lowerCAmelCase					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Optional[int]           =   self.scheduler_classes[0]
										__SCREAMING_SNAKE_CASE:				Any           =   self.get_scheduler_config(**__lowercase					)
										__SCREAMING_SNAKE_CASE:				Tuple           =   scheduler_class(**__lowercase					)

										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   10, 0.0

										__SCREAMING_SNAKE_CASE:				Dict           =   self.dummy_model()
										__SCREAMING_SNAKE_CASE:				Tuple           =   self.dummy_sample_deter

										scheduler.set_timesteps(__lowercase					)

										for t in scheduler.timesteps:
																__SCREAMING_SNAKE_CASE:				Optional[Any]           =   model(__lowercase			, __lowercase					)
																__SCREAMING_SNAKE_CASE:				Tuple           =   scheduler.step(__lowercase			, __lowercase			, __lowercase			, __lowercase					).prev_sample

										return sample






				def 			snake_case_    (  self					):

										"""simple docstring"""




										for timesteps in [100, 500, 1000]:
																self.check_over_configs(num_train_timesteps=__lowercase					)






				def 			snake_case_    (  self					):

										"""simple docstring"""




										for steps_offset in [0, 1]:
																self.check_over_configs(steps_offset=__lowercase					)

										__SCREAMING_SNAKE_CASE:				int           =   self.scheduler_classes[0]
										__SCREAMING_SNAKE_CASE:				Tuple           =   self.get_scheduler_config(steps_offset=1					)
										__SCREAMING_SNAKE_CASE:				Any           =   scheduler_class(**__lowercase					)
										scheduler.set_timesteps(5					)
										assert torch.equal(scheduler.timesteps			, torch.LongTensor([801, 601, 401, 201, 1]					)					)






				def 			snake_case_    (  self					):

										"""simple docstring"""




										for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]			, [0.002, 0.02, 0.2, 2]					):
																self.check_over_configs(beta_start=__lowercase			, beta_end=__lowercase					)






				def 			snake_case_    (  self					):

										"""simple docstring"""




										for schedule in ["linear", "squaredcos_cap_v2"]:
																self.check_over_configs(beta_schedule=__lowercase					)






				def 			snake_case_    (  self					):

										"""simple docstring"""




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






				def 			snake_case_    (  self					):

										"""simple docstring"""




										for clip_sample in [True, False]:
																self.check_over_configs(clip_sample=__lowercase					)






				def 			snake_case_    (  self					):

										"""simple docstring"""




										for timestep_spacing in ["trailing", "leading"]:
																self.check_over_configs(timestep_spacing=__lowercase					)






				def 			snake_case_    (  self					):

										"""simple docstring"""




										for rescale_betas_zero_snr in [True, False]:
																self.check_over_configs(rescale_betas_zero_snr=__lowercase					)






				def 			snake_case_    (  self					):

										"""simple docstring"""




										self.check_over_configs(thresholding=__lowercase					)
										for threshold in [0.5, 1.0, 2.0]:
																for prediction_type in ["epsilon", "v_prediction"]:
																						self.check_over_configs(
																						    thresholding=__lowercase			, prediction_type=__lowercase			, sample_max_value=__lowercase			, )






				def 			snake_case_    (  self					):

										"""simple docstring"""




										for t in [1, 10, 49]:
																self.check_over_forward(time_step=__lowercase					)






				def 			snake_case_    (  self					):

										"""simple docstring"""




										for t, num_inference_steps in zip([1, 10, 50]			, [10, 50, 500]					):
																self.check_over_forward(time_step=__lowercase			, num_inference_steps=__lowercase					)






				def 			snake_case_    (  self					):

										"""simple docstring"""




										for t, eta in zip([1, 10, 49]			, [0.0, 0.5, 1.0]					):
																self.check_over_forward(time_step=__lowercase			, eta=__lowercase					)






				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				List[str]           =   self.scheduler_classes[0]
										__SCREAMING_SNAKE_CASE:				str           =   self.get_scheduler_config()
										__SCREAMING_SNAKE_CASE:				Optional[int]           =   scheduler_class(**__lowercase					)

										assert torch.sum(torch.abs(scheduler._get_variance(0			, 0					) - 0.0					)					) < 1e-5
										assert torch.sum(torch.abs(scheduler._get_variance(420			, 400					) - 0.14771					)					) < 1e-5
										assert torch.sum(torch.abs(scheduler._get_variance(980			, 960					) - 0.32460					)					) < 1e-5
										assert torch.sum(torch.abs(scheduler._get_variance(0			, 0					) - 0.0					)					) < 1e-5
										assert torch.sum(torch.abs(scheduler._get_variance(487			, 486					) - 0.00979					)					) < 1e-5
										assert torch.sum(torch.abs(scheduler._get_variance(999			, 998					) - 0.02					)					) < 1e-5






				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Tuple           =   self.scheduler_classes[0]
										__SCREAMING_SNAKE_CASE:				Tuple           =   self.get_scheduler_config()
										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   scheduler_class(**__lowercase					)

										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   10, 0.0
										scheduler.set_timesteps(__lowercase					)

										__SCREAMING_SNAKE_CASE:				Optional[int]           =   self.dummy_model()
										__SCREAMING_SNAKE_CASE:				Dict           =   self.dummy_sample_deter
										__SCREAMING_SNAKE_CASE:				int           =   self.dummy_sample_deter + 0.1
										__SCREAMING_SNAKE_CASE:				Tuple           =   self.dummy_sample_deter - 0.1

										__SCREAMING_SNAKE_CASE:				Any           =   samplea.shape[0]
										__SCREAMING_SNAKE_CASE:				str           =   torch.stack([samplea, samplea, samplea]			, dim=0					)
										__SCREAMING_SNAKE_CASE:				Optional[int]           =   torch.arange(__lowercase					)[0:3, None].repeat(1			, __lowercase					)

										__SCREAMING_SNAKE_CASE:				str           =   model(samples.flatten(0			, 1					)			, timesteps.flatten(0			, 1					)					)
										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   scheduler.batch_step_no_noise(__lowercase			, timesteps.flatten(0			, 1					)			, samples.flatten(0			, 1					)			, __lowercase					)

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

										assert abs(result_sum.item() - 1147.7904					) < 1e-2
										assert abs(result_mean.item() - 0.4982					) < 1e-3






				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   self.full_loop()

										__SCREAMING_SNAKE_CASE:				Tuple           =   torch.sum(torch.abs(__lowercase					)					)
										__SCREAMING_SNAKE_CASE:				Optional[int]           =   torch.mean(torch.abs(__lowercase					)					)

										assert abs(result_sum.item() - 172.0067					) < 1e-2
										assert abs(result_mean.item() - 0.223967					) < 1e-3






				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   self.full_loop(prediction_type='''v_prediction'''					)

										__SCREAMING_SNAKE_CASE:				Any           =   torch.sum(torch.abs(__lowercase					)					)
										__SCREAMING_SNAKE_CASE:				Optional[int]           =   torch.mean(torch.abs(__lowercase					)					)

										assert abs(result_sum.item() - 52.5302					) < 1e-2
										assert abs(result_mean.item() - 0.0684					) < 1e-3






				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				List[str]           =   self.full_loop(set_alpha_to_one=__lowercase			, beta_start=0.01					)
										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   torch.sum(torch.abs(__lowercase					)					)
										__SCREAMING_SNAKE_CASE:				List[Any]           =   torch.mean(torch.abs(__lowercase					)					)

										assert abs(result_sum.item() - 149.8295					) < 1e-2
										assert abs(result_mean.item() - 0.1951					) < 1e-3






				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   self.full_loop(set_alpha_to_one=__lowercase			, beta_start=0.01					)
										__SCREAMING_SNAKE_CASE:				Dict           =   torch.sum(torch.abs(__lowercase					)					)
										__SCREAMING_SNAKE_CASE:				Dict           =   torch.mean(torch.abs(__lowercase					)					)

										assert abs(result_sum.item() - 149.0784					) < 1e-2
										assert abs(result_mean.item() - 0.1941					) < 1e-3







'''simple docstring'''
from math import ceil


def       _lowercase							(					UpperCamelCase__							:       int = 1001      ):
 __A	:		int	  =      1

 for i in range(1,					int(ceil(n / 2.0      )      )      ):
  __A	:		Any	  =      2 * i + 1
  __A	:		List[str]	  =      2 * i
  __A	:		str	  =      total + 4 * odd**2 - 6 * even

 return total


if __name__ == "__main__":
 import sys

 if len(sys.argv) == 1:
  print(solution())
 else:
  try:
   UpperCAmelCase_			:  int										=   int(sys.argv[1])
   print(solution(n))
  except ValueError:
   print('Invalid entry - please enter a number')






