






import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest


class     __SCREAMING_SNAKE_CASE					(   __lowercase):
						_SCREAMING_SNAKE_CASE	:       Dict   =					(DDIMParallelScheduler,)
						_SCREAMING_SNAKE_CASE	:       str   =					(('''eta''', 0.0), ('''num_inference_steps''', 50))






						def       UpperCamelCase__      (	self     ,						**_UpperCamelCase   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						{
												    'num_train_timesteps': 10_00,
												    'beta_start': 0.00_01,
												    'beta_end': 0.02,
												    'beta_schedule': 'linear',
												    'clip_sample': True,
												}

												config.update(**_UpperCamelCase   )
												return config






						def       UpperCamelCase__      (	self     ,						**_UpperCamelCase   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						self.scheduler_classes[0]
												lowerCAmelCase__    						=						self.get_scheduler_config(**_UpperCamelCase   )
												lowerCAmelCase__    						=						scheduler_class(**_UpperCamelCase   )

												lowerCAmelCase__						,       lowerCAmelCase__    						=						10, 0.0

												lowerCAmelCase__    						=						self.dummy_model()
												lowerCAmelCase__    						=						self.dummy_sample_deter

												scheduler.set_timesteps(_UpperCamelCase   )

												for t in scheduler.timesteps:
																		lowerCAmelCase__    						=						model(_UpperCamelCase     ,						_UpperCamelCase   )
																		lowerCAmelCase__    						=						scheduler.step(_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase   ).prev_sample

												return sample






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												for timesteps in [1_00, 5_00, 10_00]:
																		self.check_over_configs(num_train_timesteps=_UpperCamelCase   )






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												for steps_offset in [0, 1]:
																		self.check_over_configs(steps_offset=_UpperCamelCase   )

												lowerCAmelCase__    						=						self.scheduler_classes[0]
												lowerCAmelCase__    						=						self.get_scheduler_config(steps_offset=1   )
												lowerCAmelCase__    						=						scheduler_class(**_UpperCamelCase   )
												scheduler.set_timesteps(5   )
												assert torch.equal(scheduler.timesteps     ,						torch.LongTensor([8_01, 6_01, 4_01, 2_01, 1]   )   )






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]     ,						[0.0_02, 0.02, 0.2, 2]   ):
																		self.check_over_configs(beta_start=_UpperCamelCase     ,						beta_end=_UpperCamelCase   )






						def       UpperCamelCase__      (	self   ):






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






						def       UpperCamelCase__      (	self   ):






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






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												for clip_sample in [True, False]:
																		self.check_over_configs(clip_sample=_UpperCamelCase   )






						def       UpperCamelCase__      (	self   ):






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






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												for rescale_betas_zero_snr in [True, False]:
																		self.check_over_configs(rescale_betas_zero_snr=_UpperCamelCase   )






						def       UpperCamelCase__      (	self   ):






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






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												for t in [1, 10, 49]:
																		self.check_over_forward(time_step=_UpperCamelCase   )






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												for t, num_inference_steps in zip([1, 10, 50]     ,						[10, 50, 5_00]   ):
																		self.check_over_forward(time_step=_UpperCamelCase     ,						num_inference_steps=_UpperCamelCase   )






						def       UpperCamelCase__      (	self   ):






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






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						self.scheduler_classes[0]
												lowerCAmelCase__    						=						self.get_scheduler_config()
												lowerCAmelCase__    						=						scheduler_class(**_UpperCamelCase   )

												assert torch.sum(torch.abs(scheduler._get_variance(0     ,						0   ) - 0.0   )   ) < 1E-5
												assert torch.sum(torch.abs(scheduler._get_variance(4_20     ,						4_00   ) - 0.1_47_71   )   ) < 1E-5
												assert torch.sum(torch.abs(scheduler._get_variance(9_80     ,						9_60   ) - 0.3_24_60   )   ) < 1E-5
												assert torch.sum(torch.abs(scheduler._get_variance(0     ,						0   ) - 0.0   )   ) < 1E-5
												assert torch.sum(torch.abs(scheduler._get_variance(4_87     ,						4_86   ) - 0.0_09_79   )   ) < 1E-5
												assert torch.sum(torch.abs(scheduler._get_variance(9_99     ,						9_98   ) - 0.02   )   ) < 1E-5






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						self.scheduler_classes[0]
												lowerCAmelCase__    						=						self.get_scheduler_config()
												lowerCAmelCase__    						=						scheduler_class(**_UpperCamelCase   )

												lowerCAmelCase__						,       lowerCAmelCase__    						=						10, 0.0
												scheduler.set_timesteps(_UpperCamelCase   )

												lowerCAmelCase__    						=						self.dummy_model()
												lowerCAmelCase__    						=						self.dummy_sample_deter
												lowerCAmelCase__    						=						self.dummy_sample_deter + 0.1
												lowerCAmelCase__    						=						self.dummy_sample_deter - 0.1

												lowerCAmelCase__    						=						samplea.shape[0]
												lowerCAmelCase__    						=						torch.stack([samplea, samplea, samplea]     ,						dim=0   )
												lowerCAmelCase__    						=						torch.arange(_UpperCamelCase   )[0:3, None].repeat(1     ,						_UpperCamelCase   )

												lowerCAmelCase__    						=						model(samples.flatten(0     ,						1   )     ,						timesteps.flatten(0     ,						1   )   )
												lowerCAmelCase__    						=						scheduler.batch_step_no_noise(_UpperCamelCase     ,						timesteps.flatten(0     ,						1   )     ,						samples.flatten(0     ,						1   )     ,						_UpperCamelCase   )

												lowerCAmelCase__    						=						torch.sum(torch.abs(_UpperCamelCase   )   )
												lowerCAmelCase__    						=						torch.mean(torch.abs(_UpperCamelCase   )   )

												assert abs(result_sum.item() - 11_47.79_04   ) < 1E-2
												assert abs(result_mean.item() - 0.49_82   ) < 1E-3






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						self.full_loop()

												lowerCAmelCase__    						=						torch.sum(torch.abs(_UpperCamelCase   )   )
												lowerCAmelCase__    						=						torch.mean(torch.abs(_UpperCamelCase   )   )

												assert abs(result_sum.item() - 1_72.00_67   ) < 1E-2
												assert abs(result_mean.item() - 0.22_39_67   ) < 1E-3






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						self.full_loop(prediction_type='v_prediction'   )

												lowerCAmelCase__    						=						torch.sum(torch.abs(_UpperCamelCase   )   )
												lowerCAmelCase__    						=						torch.mean(torch.abs(_UpperCamelCase   )   )

												assert abs(result_sum.item() - 52.53_02   ) < 1E-2
												assert abs(result_mean.item() - 0.06_84   ) < 1E-3






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						self.full_loop(set_alpha_to_one=_UpperCamelCase     ,						beta_start=0.01   )
												lowerCAmelCase__    						=						torch.sum(torch.abs(_UpperCamelCase   )   )
												lowerCAmelCase__    						=						torch.mean(torch.abs(_UpperCamelCase   )   )

												assert abs(result_sum.item() - 1_49.82_95   ) < 1E-2
												assert abs(result_mean.item() - 0.19_51   ) < 1E-3







						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						self.full_loop(set_alpha_to_one=_UpperCamelCase     ,						beta_start=0.01   )
												lowerCAmelCase__    						=						torch.sum(torch.abs(_UpperCamelCase   )   )
												lowerCAmelCase__    						=						torch.mean(torch.abs(_UpperCamelCase   )   )

												assert abs(result_sum.item() - 1_49.07_84   ) < 1E-2
												assert abs(result_mean.item() - 0.19_41   ) < 1E-3













from __future__ import annotations

from functools import lru_cache
from math import ceil

__snake_case    :    Union[str, Any]         =     1_00

__snake_case    :    Tuple         =     set(range(3, NUM_PRIMES, 2))
primes.add(2)
__snake_case    :    int

for prime in range(3, ceil(NUM_PRIMES**0.5), 2):
					if prime not in primes:
										continue
					primes.difference_update(set(range(prime * prime, NUM_PRIMES, prime)))







@lru_cache(maxsize=100		)
def 	_UpperCamelCase   (    UpperCamelCase_       :					int		)  ->   set[int]:

						"""simple docstring"""



						if number_to_partition < 0:
												return set()
						elif number_to_partition == 0:
												return {1}

						lowerCAmelCase__    						=						set()
						lowerCAmelCase__    						=						42
						lowerCAmelCase__    						=						42

						for prime in primes:
												if prime > number_to_partition:
																		continue
												for sub in partition(number_to_partition - prime		):
																		ret.add(sub * prime		)

						return ret







def 	_UpperCamelCase   (    UpperCamelCase_       :					int = 5000		)  ->   int | None:

						"""simple docstring"""



						for number_to_partition in range(1    ,       UpperCamelCase_		):
												if len(partition(UpperCamelCase_		)		) > number_unique_partitions:
																		return number_to_partition
						return None


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





