






import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest


class      lowercase__     (			_UpperCAmelCase	):
	a_         =(EulerDiscreteScheduler,)
	a_         =10

	def 		UpperCAmelCase  (							self		,    **__UpperCAmelCase       )->					Any:







							'''simple docstring'''





							lowerCAmelCase__       =						{
							    "num_train_timesteps": 1100,
							    "beta_start": 0.0_001,
							    "beta_end": 0.02,
							    "beta_schedule": "linear",
							}

							config.update(**__UpperCAmelCase       )
							return config

	def 		UpperCAmelCase  (							self       )->					Optional[int]:







							'''simple docstring'''





							for timesteps in [10, 50, 100, 1000]:
													self.check_over_configs(num_train_timesteps=__UpperCAmelCase       )

	def 		UpperCAmelCase  (							self       )->					Tuple:







							'''simple docstring'''





							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=__UpperCAmelCase		,    beta_end=__UpperCAmelCase       )

	def 		UpperCAmelCase  (							self       )->					Optional[Any]:







							'''simple docstring'''





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

	def 		UpperCAmelCase  (							self       )->					str:







							'''simple docstring'''





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

	def 		UpperCAmelCase  (							self       )->					List[Any]:







							'''simple docstring'''





							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() - 10.0_807       ) < 1E-2
							assert abs(result_mean.item() - 0.0_131       ) < 1E-3

	def 		UpperCAmelCase  (							self       )->					List[Any]:







							'''simple docstring'''





							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() - 0.0_002       ) < 1E-2
							assert abs(result_mean.item() - 2.2_6_7_6E-0_6       ) < 1E-3

	def 		UpperCAmelCase  (							self       )->					Optional[int]:







							'''simple docstring'''





							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() - 10.0_807       ) < 1E-2
							assert abs(result_mean.item() - 0.0_131       ) < 1E-3


	def 		UpperCAmelCase  (							self       )->					Tuple:







							'''simple docstring'''





							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() - 124.52_299_499_511_719       ) < 1E-2
							assert abs(result_mean.item() - 0.16_213_932_633_399_963       ) < 1E-3














from __future__ import annotations

from collections.abc import Generator


def        _a  (     ) ->   Generator[int, None, None]:
						"""simple docstring"""







						lowerCAmelCase__       =						{}
						lowerCAmelCase__       =						2
						while True:
												lowerCAmelCase__       =						factor_map.pop(UpperCamelCase_    ,   UpperCamelCase_   )
												if factor:
																		lowerCAmelCase__       =						factor + prime
																		while x in factor_map:
																								x += factor
																		lowerCAmelCase__       =						factor
												else:
																		lowerCAmelCase__       =						prime
																		yield prime
												prime += 1


def        _a  (     UpperCamelCase_					:				float = 1e1_0   ) ->   int:
						"""simple docstring"""







						lowerCAmelCase__       =						sieve()

						lowerCAmelCase__       =						1
						while True:
												lowerCAmelCase__       =						next(UpperCamelCase_   )
												if (2 * prime * n) > limit:
																		return n
												# Ignore the next prime as the reminder will be 2.
												next(UpperCamelCase_   )
												n += 2


if __name__ == "__main__":
	print(solution())






