






"""simple docstring"""
import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest


class _lowerCAmelCase						(    __UpperCAmelCase       ):
						SCREAMING_SNAKE_CASE_:					Tuple 					=				(UniPCMultistepScheduler,)
						SCREAMING_SNAKE_CASE_:					Optional[Any] 					=				(('num_inference_steps', 25),)







						def 			A      (    self   ,				**lowerCAmelCase_							)		->							str:
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    {
										    'num_train_timesteps': 1_0_0_0,
										    'beta_start': 0.0_001,
										    'beta_end': 0.02,
										    'beta_schedule': 'linear',
										    'solver_order': 2,
										    'solver_type': 'bh2',
										}

										config.update(**lowerCAmelCase_							)
										return config







						def 			A      (    self   ,				lowerCAmelCase_=0   ,				**lowerCAmelCase_							)		->							int:
										_SCREAMING_SNAKE_CASE   :		str       		=    dict(self.forward_default_kwargs							)
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    kwargs.pop('num_inference_steps'   ,				lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		Any       		=    self.dummy_sample
										_SCREAMING_SNAKE_CASE   :		str       		=    0.1 * sample
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    [residual + 0.2, residual + 0.15, residual + 0.10]

										for scheduler_class in self.scheduler_classes:
														_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    self.get_scheduler_config(**lowerCAmelCase_							)
														_SCREAMING_SNAKE_CASE   :		List[Any]       		=    scheduler_class(**lowerCAmelCase_							)
														scheduler.set_timesteps(lowerCAmelCase_							)
														# copy over dummy past residuals
														_SCREAMING_SNAKE_CASE   :		List[str]       		=    dummy_past_residuals[: scheduler.config.solver_order]

														with tempfile.TemporaryDirectory() as tmpdirname:
																		scheduler.save_config(lowerCAmelCase_							)
																		_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    scheduler_class.from_pretrained(lowerCAmelCase_							)
																		new_scheduler.set_timesteps(lowerCAmelCase_							)
																		# copy over dummy past residuals
																		_SCREAMING_SNAKE_CASE   :		List[Any]       		=    dummy_past_residuals[: new_scheduler.config.solver_order]

														_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE   :		Any       		=    sample, sample
														for t in range(lowerCAmelCase_   ,				time_step + scheduler.config.solver_order + 1							):
																		_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    scheduler.step(lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				**lowerCAmelCase_							).prev_sample
																		_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    new_scheduler.step(lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				**lowerCAmelCase_							).prev_sample

																		assert torch.sum(torch.abs(output - new_output							)							) < 1e-5, "Scheduler outputs are not identical"







						def 			A      (    self   ,				lowerCAmelCase_=0   ,				**lowerCAmelCase_							)		->							int:
										_SCREAMING_SNAKE_CASE   :		Dict       		=    dict(self.forward_default_kwargs							)
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    kwargs.pop('num_inference_steps'   ,				lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    self.dummy_sample
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    0.1 * sample
										_SCREAMING_SNAKE_CASE   :		int       		=    [residual + 0.2, residual + 0.15, residual + 0.10]

										for scheduler_class in self.scheduler_classes:
														_SCREAMING_SNAKE_CASE   :		Any       		=    self.get_scheduler_config()
														_SCREAMING_SNAKE_CASE   :		Dict       		=    scheduler_class(**lowerCAmelCase_							)
														scheduler.set_timesteps(lowerCAmelCase_							)

														# copy over dummy past residuals (must be after setting timesteps)
														_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    dummy_past_residuals[: scheduler.config.solver_order]

														with tempfile.TemporaryDirectory() as tmpdirname:
																		scheduler.save_config(lowerCAmelCase_							)
																		_SCREAMING_SNAKE_CASE   :		List[str]       		=    scheduler_class.from_pretrained(lowerCAmelCase_							)
																		# copy over dummy past residuals
																		new_scheduler.set_timesteps(lowerCAmelCase_							)

																		# copy over dummy past residual (must be after setting timesteps)
																		_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    dummy_past_residuals[: new_scheduler.config.solver_order]

														_SCREAMING_SNAKE_CASE   :		Any       		=    scheduler.step(lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				**lowerCAmelCase_							).prev_sample
														_SCREAMING_SNAKE_CASE   :		Any       		=    new_scheduler.step(lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				**lowerCAmelCase_							).prev_sample

														assert torch.sum(torch.abs(output - new_output							)							) < 1e-5, "Scheduler outputs are not identical"







						def 			A      (    self   ,				lowerCAmelCase_=None   ,				**lowerCAmelCase_							)		->							List[Any]:
										if scheduler is None:
														_SCREAMING_SNAKE_CASE   :		Tuple       		=    self.scheduler_classes[0]
														_SCREAMING_SNAKE_CASE   :		str       		=    self.get_scheduler_config(**lowerCAmelCase_							)
														_SCREAMING_SNAKE_CASE   :		Dict       		=    scheduler_class(**lowerCAmelCase_							)

										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    self.scheduler_classes[0]
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    self.get_scheduler_config(**lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		str       		=    scheduler_class(**lowerCAmelCase_							)

										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    1_0
										_SCREAMING_SNAKE_CASE   :		str       		=    self.dummy_model()
										_SCREAMING_SNAKE_CASE   :		str       		=    self.dummy_sample_deter
										scheduler.set_timesteps(lowerCAmelCase_							)

										for i, t in enumerate(scheduler.timesteps							):
														_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    model(lowerCAmelCase_   ,				lowerCAmelCase_							)
														_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    scheduler.step(lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_							).prev_sample

										return sample







						def 			A      (    self							)		->							Dict:
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    dict(self.forward_default_kwargs							)

										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    kwargs.pop('num_inference_steps'   ,				lowerCAmelCase_							)

										for scheduler_class in self.scheduler_classes:
														_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    self.get_scheduler_config()
														_SCREAMING_SNAKE_CASE   :		int       		=    scheduler_class(**lowerCAmelCase_							)

														_SCREAMING_SNAKE_CASE   :		Dict       		=    self.dummy_sample
														_SCREAMING_SNAKE_CASE   :		Tuple       		=    0.1 * sample

														if num_inference_steps is not None and hasattr(lowerCAmelCase_   ,				'set_timesteps'							):
																		scheduler.set_timesteps(lowerCAmelCase_							)
														elif num_inference_steps is not None and not hasattr(lowerCAmelCase_   ,				'set_timesteps'							):
																		_SCREAMING_SNAKE_CASE   :		str       		=    num_inference_steps

														# copy over dummy past residuals (must be done after set_timesteps)
														_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    [residual + 0.2, residual + 0.15, residual + 0.10]
														_SCREAMING_SNAKE_CASE   :		Tuple       		=    dummy_past_residuals[: scheduler.config.solver_order]

														_SCREAMING_SNAKE_CASE   :		int       		=    scheduler.timesteps[5]
														_SCREAMING_SNAKE_CASE   :		Any       		=    scheduler.timesteps[6]

														_SCREAMING_SNAKE_CASE   :		Dict       		=    scheduler.step(lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				**lowerCAmelCase_							).prev_sample
														_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    scheduler.step(lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				**lowerCAmelCase_							).prev_sample

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







						def 			A      (    self							)		->							Dict:
										# make sure that iterating over schedulers with same config names gives same results
										# for defaults
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    UniPCMultistepScheduler(**self.get_scheduler_config()							)
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    self.full_loop(scheduler=lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    torch.mean(torch.abs(lowerCAmelCase_							)							)

										assert abs(result_mean.item() - 0.2_464							) < 1e-3

										_SCREAMING_SNAKE_CASE   :		List[str]       		=    DPMSolverSinglestepScheduler.from_config(scheduler.config							)
										_SCREAMING_SNAKE_CASE   :		int       		=    DEISMultistepScheduler.from_config(scheduler.config							)
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    DPMSolverMultistepScheduler.from_config(scheduler.config							)
										_SCREAMING_SNAKE_CASE   :		str       		=    UniPCMultistepScheduler.from_config(scheduler.config							)

										_SCREAMING_SNAKE_CASE   :		List[str]       		=    self.full_loop(scheduler=lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		Any       		=    torch.mean(torch.abs(lowerCAmelCase_							)							)

										assert abs(result_mean.item() - 0.2_464							) < 1e-3







						def 			A      (    self							)		->							str:
										for timesteps in [2_5, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
														self.check_over_configs(num_train_timesteps=lowerCAmelCase_							)







						def 			A      (    self							)		->							Optional[Any]:
										self.check_over_configs(thresholding=lowerCAmelCase_							)
										for order in [1, 2, 3]:
														for solver_type in ["bh1", "bh2"]:
																		for threshold in [0.5, 1.0, 2.0]:
																						for prediction_type in ["epsilon", "sample"]:
																										self.check_over_configs(
																										    thresholding=lowerCAmelCase_   ,				prediction_type=lowerCAmelCase_   ,				sample_max_value=lowerCAmelCase_   ,				solver_order=lowerCAmelCase_   ,				solver_type=lowerCAmelCase_   ,				)







						def 			A      (    self							)		->							int:
										for prediction_type in ["epsilon", "v_prediction"]:
														self.check_over_configs(prediction_type=lowerCAmelCase_							)







						def 			A      (    self							)		->							Optional[int]:
										for solver_type in ["bh1", "bh2"]:
														for order in [1, 2, 3]:
																		for prediction_type in ["epsilon", "sample"]:
																						self.check_over_configs(
																						    solver_order=lowerCAmelCase_   ,				solver_type=lowerCAmelCase_   ,				prediction_type=lowerCAmelCase_   ,				)
																						_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    self.full_loop(
																						    solver_order=lowerCAmelCase_   ,				solver_type=lowerCAmelCase_   ,				prediction_type=lowerCAmelCase_   ,				)
																						assert not torch.isnan(lowerCAmelCase_							).any(), "Samples have nan numbers"







						def 			A      (    self							)		->							Tuple:
										self.check_over_configs(lower_order_final=lowerCAmelCase_							)
										self.check_over_configs(lower_order_final=lowerCAmelCase_							)







						def 			A      (    self							)		->							int:
										for num_inference_steps in [1, 2, 3, 5, 1_0, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
														self.check_over_forward(num_inference_steps=lowerCAmelCase_   ,				time_step=0							)







						def 			A      (    self							)		->							str:
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    self.full_loop()
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    torch.mean(torch.abs(lowerCAmelCase_							)							)

										assert abs(result_mean.item() - 0.2_464							) < 1e-3







						def 			A      (    self							)		->							List[str]:
										_SCREAMING_SNAKE_CASE   :		Dict       		=    self.full_loop(prediction_type='v_prediction'							)
										_SCREAMING_SNAKE_CASE   :		Any       		=    torch.mean(torch.abs(lowerCAmelCase_							)							)

										assert abs(result_mean.item() - 0.1_014							) < 1e-3







						def 			A      (    self							)		->							Union[str, Any]:
										_SCREAMING_SNAKE_CASE   :		Any       		=    self.scheduler_classes[0]
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    self.get_scheduler_config(thresholding=lowerCAmelCase_   ,				dynamic_thresholding_ratio=0							)
										_SCREAMING_SNAKE_CASE   :		Any       		=    scheduler_class(**lowerCAmelCase_							)

										_SCREAMING_SNAKE_CASE   :		Dict       		=    1_0
										_SCREAMING_SNAKE_CASE   :		int       		=    self.dummy_model()
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    self.dummy_sample_deter.half()
										scheduler.set_timesteps(lowerCAmelCase_							)

										for i, t in enumerate(scheduler.timesteps							):
														_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    model(lowerCAmelCase_   ,				lowerCAmelCase_							)
														_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    scheduler.step(lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_							).prev_sample

										assert sample.dtype == torch.floataa







						def 			A      (    self   ,				**lowerCAmelCase_							)		->							Tuple:
										for scheduler_class in self.scheduler_classes:
														_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    self.get_scheduler_config(**lowerCAmelCase_							)
														_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    scheduler_class(**lowerCAmelCase_							)

														scheduler.set_timesteps(scheduler.config.num_train_timesteps							)
														assert len(scheduler.timesteps.unique()							) == scheduler.num_inference_steps









"""simple docstring"""
from __future__ import annotations

from collections.abc import Generator





def 				lowercase__		(					):
				_SCREAMING_SNAKE_CASE   :		dict[int, int]       		=    {}
				_SCREAMING_SNAKE_CASE   :		List[Any]       		=    2
				while True:
								_SCREAMING_SNAKE_CASE   :		List[Any]       		=    factor_map.pop(lowerCamelCase,						lowerCamelCase       )
								if factor:
												_SCREAMING_SNAKE_CASE   :		str       		=    factor + prime
												while x in factor_map:
																x += factor
												_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    factor
								else:
												_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    prime
												yield prime
								prime += 1





def 				lowercase__		(					lowerCamelCase = 1E10       ):
				_SCREAMING_SNAKE_CASE   :		Dict       		=    sieve()

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


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

