






'''simple docstring'''





import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest


class    _lowerCAmelCase			(		_a      ):



				"""simple docstring"""



				_lowercase				:   int      =  (CMStochasticIterativeScheduler,)
				_lowercase				:   List[str]      =  10



				def 					__magic_name__							(      self    :    Optional[int]			,		**UpperCamelCase__    :    Tuple):






											'''simple docstring'''







											snake_case__	  =       {
											    """num_train_timesteps""": 2_0_1,
											    """sigma_min""": 0.0_02,
											    """sigma_max""": 80.0,
											}

											config.update(**_A)
											return config



				def 					__magic_name__							(      self    :    Dict):






											'''simple docstring'''







											snake_case__	  =       1_0

											snake_case__	  =       self.get_scheduler_config()
											snake_case__	  =       self.scheduler_classes[0](**_A)

											scheduler.set_timesteps(_A)

											snake_case__	  =       scheduler.timesteps[0]
											snake_case__	  =       scheduler.timesteps[1]

											snake_case__	  =       self.dummy_sample
											snake_case__	  =       0.1 * sample

											snake_case__	  =       scheduler.step(_A			,		_A			,		_A).prev_sample
											snake_case__	  =       scheduler.step(_A			,		_A			,		_A).prev_sample

											self.assertEqual(output_a.shape			,		sample.shape)
											self.assertEqual(output_a.shape			,		output_a.shape)



				def 					__magic_name__							(      self    :    Union[str, Any]):






											'''simple docstring'''







											for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
																		self.check_over_configs(num_train_timesteps=_A)



				def 					__magic_name__							(      self    :    List[str]):






											'''simple docstring'''







											for clip_denoised in [True, False]:
																		self.check_over_configs(clip_denoised=_A)



				def 					__magic_name__							(      self    :    Optional[int]):






											'''simple docstring'''







											snake_case__	  =       self.scheduler_classes[0]
											snake_case__	  =       self.get_scheduler_config()
											snake_case__	  =       scheduler_class(**_A)

											snake_case__	  =       1
											scheduler.set_timesteps(_A)
											snake_case__	  =       scheduler.timesteps

											snake_case__	  =       torch.manual_seed(0)

											snake_case__	  =       self.dummy_model()
											snake_case__	  =       self.dummy_sample_deter * scheduler.init_noise_sigma

											for i, t in enumerate(_A):
																		# 1. scale model input
																		snake_case__	  =       scheduler.scale_model_input(_A			,		_A)

																		# 2. predict noise residual
																		snake_case__	  =       model(_A			,		_A)

																		# 3. predict previous sample x_t-1
																		snake_case__	  =       scheduler.step(_A			,		_A			,		_A			,		generator=_A).prev_sample

																		snake_case__	  =       pred_prev_sample

											snake_case__	  =       torch.sum(torch.abs(_A))
											snake_case__	  =       torch.mean(torch.abs(_A))

											assert abs(result_sum.item() - 1_92.76_14) < 1E-2
											assert abs(result_mean.item() - 0.25_10) < 1E-3



				def 					__magic_name__							(      self    :    Union[str, Any]):






											'''simple docstring'''







											snake_case__	  =       self.scheduler_classes[0]
											snake_case__	  =       self.get_scheduler_config()
											snake_case__	  =       scheduler_class(**_A)

											snake_case__	  =       [1_0_6, 0]
											scheduler.set_timesteps(timesteps=_A)
											snake_case__	  =       scheduler.timesteps

											snake_case__	  =       torch.manual_seed(0)

											snake_case__	  =       self.dummy_model()
											snake_case__	  =       self.dummy_sample_deter * scheduler.init_noise_sigma

											for t in timesteps:
																		# 1. scale model input
																		snake_case__	  =       scheduler.scale_model_input(_A			,		_A)

																		# 2. predict noise residual
																		snake_case__	  =       model(_A			,		_A)

																		# 3. predict previous sample x_t-1
																		snake_case__	  =       scheduler.step(_A			,		_A			,		_A			,		generator=_A).prev_sample

																		snake_case__	  =       pred_prev_sample

											snake_case__	  =       torch.sum(torch.abs(_A))
											snake_case__	  =       torch.mean(torch.abs(_A))

											assert abs(result_sum.item() - 3_47.63_57) < 1E-2
											assert abs(result_mean.item() - 0.45_27) < 1E-3



				def 					__magic_name__							(      self    :    int):






											'''simple docstring'''







											snake_case__	  =       self.scheduler_classes[0]
											snake_case__	  =       self.get_scheduler_config()
											snake_case__	  =       scheduler_class(**_A)

											snake_case__	  =       [3_9, 3_0, 1_2, 1_5, 0]

											with self.assertRaises(_A			,		msg="""`timesteps` must be in descending order."""):
																		scheduler.set_timesteps(timesteps=_A)



				def 					__magic_name__							(      self    :    str):






											'''simple docstring'''







											snake_case__	  =       self.scheduler_classes[0]
											snake_case__	  =       self.get_scheduler_config()
											snake_case__	  =       scheduler_class(**_A)

											snake_case__	  =       [3_9, 3_0, 1_2, 1, 0]
											snake_case__	  =       len(_A)

											with self.assertRaises(_A			,		msg="""Can only pass one of `num_inference_steps` or `timesteps`."""):
																		scheduler.set_timesteps(num_inference_steps=_A			,		timesteps=_A)



				def 					__magic_name__							(      self    :    Optional[int]):






											'''simple docstring'''







											snake_case__	  =       self.scheduler_classes[0]
											snake_case__	  =       self.get_scheduler_config()
											snake_case__	  =       scheduler_class(**_A)

											snake_case__	  =       [scheduler.config.num_train_timesteps]

											with self.assertRaises(
											    _A			,		msg="""`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}"""			,		):
																		scheduler.set_timesteps(timesteps=_A)








def _UpperCAmelCase   (	a						:	int = 400_0000 ):
							snake_case__	  =       [0, 1]
							snake_case__	  =       0
							while fib[i] <= n:
														fib.append(fib[i] + fib[i + 1] )
														if fib[i + 2] > n:
																					break
														i += 1
							snake_case__	  =       0
							for j in range(len(a ) - 1 ):
														if fib[j] % 2 == 0:
																					total += fib[j]

							return total


if __name__ == "__main__":
	print(F'''{solution() = }''')
