





'''simple docstring'''


from __future__ import annotations




def    __a (     UpperCAmelCase       ,			UpperCAmelCase       ,			UpperCAmelCase       ,			)      ->list[float | int]:



	"""simple docstring"""







	if not all((nth_term, start_term_a, common_ratio_r)    ):
		return []
	A							  =      []
	A							  =      1
	A							  =      common_ratio_r
	for _ in range(int(UpperCAmelCase    )    ):
		if not series:
			series.append(UpperCAmelCase    )
		else:
			power += 1
			series.append(float(start_term_a * multiple    )    )
			A							  =      pow(float(UpperCAmelCase    )       ,			UpperCAmelCase    )
	return series


if __name__ == "__main__":
				import doctest

				doctest.testmod()

				_lowerCamelCase				:	Dict				    =       float(input('Enter the last number (n term) of the Geometric Series'))
				_lowerCamelCase				:	int				    =       float(input('Enter the starting term (a) of the Geometric Series'))
				_lowerCamelCase				:	Any				    =       float(
				    input('Enter the common ratio between two terms (r) of the Geometric Series')
				)
				print('Formula of Geometric Series => a + ar + ar^2 ... +ar^n')
				print(geometric_series(nth_term, start_term_a, common_ratio_r))








'''simple docstring'''


import torch

from diffusers import KDPMaAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest



class 							__UpperCAmelCase  (       A__						):







		'''simple docstring'''







		__lowerCAmelCase 				=    (KDPMaAncestralDiscreteScheduler,)
		__lowerCAmelCase 				=    10







		def        A     (self						:      Any      ,      **_lowerCAmelCase						:      List[Any]      ):
			A							  =      {
			    """num_train_timesteps""": 1100,
			    """beta_start""": 0.0_001,
			    """beta_end""": 0.02,
			    """beta_schedule""": """linear""",
			}

			config.update(**_lowerCAmelCase      )
			return config







		def        A     (self						:      Dict      ):
			for timesteps in [10, 50, 100, 1000]:
				self.check_over_configs(num_train_timesteps=_lowerCAmelCase      )







		def        A     (self						:      Optional[int]      ):
			for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001]      ,      [0.0_002, 0.002, 0.02]      ):
				self.check_over_configs(beta_start=_lowerCAmelCase      ,      beta_end=_lowerCAmelCase      )







		def        A     (self						:      Tuple      ):
			for schedule in ["linear", "scaled_linear"]:
				self.check_over_configs(beta_schedule=_lowerCAmelCase      )







		def        A     (self						:      Dict      ):
			if torch_device == "mps":
				return
			A							  =      self.scheduler_classes[0]
			A							  =      self.get_scheduler_config()
			A							  =      scheduler_class(**_lowerCAmelCase      )

			scheduler.set_timesteps(self.num_inference_steps      )

			A							  =      torch.manual_seed(0      )

			A							  =      self.dummy_model()
			A							  =      self.dummy_sample_deter * scheduler.init_noise_sigma
			A							  =      sample.to(_lowerCAmelCase      )

			for i, t in enumerate(scheduler.timesteps      ):
				A							  =      scheduler.scale_model_input(_lowerCAmelCase      ,      _lowerCAmelCase      )

				A							  =      model(_lowerCAmelCase      ,      _lowerCAmelCase      )

				A							  =      scheduler.step(_lowerCAmelCase      ,      _lowerCAmelCase      ,      _lowerCAmelCase      ,      generator=_lowerCAmelCase      )
				A							  =      output.prev_sample

			A							  =      torch.sum(torch.abs(_lowerCAmelCase      )      )
			A							  =      torch.mean(torch.abs(_lowerCAmelCase      )      )

			assert abs(result_sum.item() - 13_849.3_877      ) < 1e-2
			assert abs(result_mean.item() - 18.0_331      ) < 5e-3







		def        A     (self						:      Union[str, Any]      ):
			for prediction_type in ["epsilon", "v_prediction"]:
				self.check_over_configs(prediction_type=_lowerCAmelCase      )







		def        A     (self						:      Dict      ):
			if torch_device == "mps":
				return
			A							  =      self.scheduler_classes[0]
			A							  =      self.get_scheduler_config(prediction_type="""v_prediction"""      )
			A							  =      scheduler_class(**_lowerCAmelCase      )

			scheduler.set_timesteps(self.num_inference_steps      )

			A							  =      self.dummy_model()
			A							  =      self.dummy_sample_deter * scheduler.init_noise_sigma
			A							  =      sample.to(_lowerCAmelCase      )

			A							  =      torch.manual_seed(0      )

			for i, t in enumerate(scheduler.timesteps      ):
				A							  =      scheduler.scale_model_input(_lowerCAmelCase      ,      _lowerCAmelCase      )

				A							  =      model(_lowerCAmelCase      ,      _lowerCAmelCase      )

				A							  =      scheduler.step(_lowerCAmelCase      ,      _lowerCAmelCase      ,      _lowerCAmelCase      ,      generator=_lowerCAmelCase      )
				A							  =      output.prev_sample

			A							  =      torch.sum(torch.abs(_lowerCAmelCase      )      )
			A							  =      torch.mean(torch.abs(_lowerCAmelCase      )      )

			assert abs(result_sum.item() - 328.9_970      ) < 1e-2
			assert abs(result_mean.item() - 0.4_284      ) < 1e-3







		def        A     (self						:      Optional[int]      ):
			if torch_device == "mps":
				return
			A							  =      self.scheduler_classes[0]
			A							  =      self.get_scheduler_config()
			A							  =      scheduler_class(**_lowerCAmelCase      )

			scheduler.set_timesteps(self.num_inference_steps      ,      device=_lowerCAmelCase      )
			A							  =      torch.manual_seed(0      )

			A							  =      self.dummy_model()
			A							  =      self.dummy_sample_deter.to(_lowerCAmelCase      ) * scheduler.init_noise_sigma

			for t in scheduler.timesteps:
				A							  =      scheduler.scale_model_input(_lowerCAmelCase      ,      _lowerCAmelCase      )

				A							  =      model(_lowerCAmelCase      ,      _lowerCAmelCase      )

				A							  =      scheduler.step(_lowerCAmelCase      ,      _lowerCAmelCase      ,      _lowerCAmelCase      ,      generator=_lowerCAmelCase      )
				A							  =      output.prev_sample

			A							  =      torch.sum(torch.abs(_lowerCAmelCase      )      )
			A							  =      torch.mean(torch.abs(_lowerCAmelCase      )      )

			assert abs(result_sum.item() - 13_849.3_818      ) < 1e-1
			assert abs(result_mean.item() - 18.0_331      ) < 1e-3

