






import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest
class    _UpperCAmelCase      ( A__			):

				"""simple docstring"""




				lowercase__       							= (DPMSolverSinglestepScheduler,)
				lowercase__       							= (("""num_inference_steps""", 25),)




				def        lowercase__     (  self		:					List[Any],						**lowerCamelCase		:					str						):

								'''simple docstring'''
								lowercase__     				=    {
								    '''num_train_timesteps''': 1_000,
								    '''beta_start''': 0.0001,
								    '''beta_end''': 0.02,
								    '''beta_schedule''': '''linear''',
								    '''solver_order''': 2,
								    '''prediction_type''': '''epsilon''',
								    '''thresholding''': False,
								    '''sample_max_value''': 1.0,
								    '''algorithm_type''': '''dpmsolver++''',
								    '''solver_type''': '''midpoint''',
								    '''lambda_min_clipped''': -float('''inf'''						),
								    '''variance_type''': None,
								}

								config.update(**lowerCamelCase						)
								return config




				def        lowercase__     (  self		:					str,						lowerCamelCase		:					str=0,						**lowerCamelCase		:					int						):

								'''simple docstring'''
								lowercase__     				=    dict(self.forward_default_kwargs						)
								lowercase__     				=    kwargs.pop('''num_inference_steps''',						lowerCamelCase						)
								lowercase__     				=    self.dummy_sample
								lowercase__     				=    0.1 * sample
								lowercase__     				=    [residual + 0.2, residual + 0.15, residual + 0.10]

								for scheduler_class in self.scheduler_classes:
												lowercase__     				=    self.get_scheduler_config(**lowerCamelCase						)
												lowercase__     				=    scheduler_class(**lowerCamelCase						)
												scheduler.set_timesteps(lowerCamelCase						)
												# copy over dummy past residuals
												lowercase__     				=    dummy_past_residuals[: scheduler.config.solver_order]

												with tempfile.TemporaryDirectory() as tmpdirname:
																scheduler.save_config(lowerCamelCase						)
																lowercase__     				=    scheduler_class.from_pretrained(lowerCamelCase						)
																new_scheduler.set_timesteps(lowerCamelCase						)
																# copy over dummy past residuals
																lowercase__     				=    dummy_past_residuals[: new_scheduler.config.solver_order]

												lowercase__				,		lowercase__     				=    sample, sample
												for t in range(lowerCamelCase,						time_step + scheduler.config.solver_order + 1						):
																lowercase__     				=    scheduler.step(lowerCamelCase,						lowerCamelCase,						lowerCamelCase,						**lowerCamelCase						).prev_sample
																lowercase__     				=    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        lowercase__     (  self		:					Union[str, Any]						):

								'''simple docstring'''
								pass




				def        lowercase__     (  self		:					Optional[Any],						lowerCamelCase		:					Optional[int]=0,						**lowerCamelCase		:					Dict						):

								'''simple docstring'''
								lowercase__     				=    dict(self.forward_default_kwargs						)
								lowercase__     				=    kwargs.pop('''num_inference_steps''',						lowerCamelCase						)
								lowercase__     				=    self.dummy_sample
								lowercase__     				=    0.1 * sample
								lowercase__     				=    [residual + 0.2, residual + 0.15, residual + 0.10]

								for scheduler_class in self.scheduler_classes:
												lowercase__     				=    self.get_scheduler_config()
												lowercase__     				=    scheduler_class(**lowerCamelCase						)
												scheduler.set_timesteps(lowerCamelCase						)

												# copy over dummy past residuals (must be after setting timesteps)
												lowercase__     				=    dummy_past_residuals[: scheduler.config.solver_order]

												with tempfile.TemporaryDirectory() as tmpdirname:
																scheduler.save_config(lowerCamelCase						)
																lowercase__     				=    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)
																lowercase__     				=    dummy_past_residuals[: new_scheduler.config.solver_order]

												lowercase__     				=    scheduler.step(lowerCamelCase,						lowerCamelCase,						lowerCamelCase,						**lowerCamelCase						).prev_sample
												lowercase__     				=    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        lowercase__     (  self		:					Optional[Any],						lowerCamelCase		:					List[str]=None,						**lowerCamelCase		:					Optional[Any]						):

								'''simple docstring'''
								if scheduler is None:
												lowercase__     				=    self.scheduler_classes[0]
												lowercase__     				=    self.get_scheduler_config(**lowerCamelCase						)
												lowercase__     				=    scheduler_class(**lowerCamelCase						)

								lowercase__     				=    self.scheduler_classes[0]
								lowercase__     				=    self.get_scheduler_config(**lowerCamelCase						)
								lowercase__     				=    scheduler_class(**lowerCamelCase						)

								lowercase__     				=    10
								lowercase__     				=    self.dummy_model()
								lowercase__     				=    self.dummy_sample_deter
								scheduler.set_timesteps(lowerCamelCase						)

								for i, t in enumerate(scheduler.timesteps						):
												lowercase__     				=    model(lowerCamelCase,						lowerCamelCase						)
												lowercase__     				=    scheduler.step(lowerCamelCase,						lowerCamelCase,						lowerCamelCase						).prev_sample

								return sample




				def        lowercase__     (  self		:					str						):

								'''simple docstring'''
								lowercase__     				=    DPMSolverSinglestepScheduler(**self.get_scheduler_config()						)
								lowercase__     				=    50
								lowercase__     				=    self.dummy_model()
								lowercase__     				=    self.dummy_sample_deter
								scheduler.set_timesteps(lowerCamelCase						)

								# make sure that the first t is uneven
								for i, t in enumerate(scheduler.timesteps[3:]						):
												lowercase__     				=    model(lowerCamelCase,						lowerCamelCase						)
												lowercase__     				=    scheduler.step(lowerCamelCase,						lowerCamelCase,						lowerCamelCase						).prev_sample

								lowercase__     				=    torch.mean(torch.abs(lowerCamelCase						)						)

								assert abs(result_mean.item() - 0.2574						) < 1E-3




				def        lowercase__     (  self		:					Tuple						):

								'''simple docstring'''
								for timesteps in [25, 50, 100, 999, 1_000]:
												self.check_over_configs(num_train_timesteps=lowerCamelCase						)




				def        lowercase__     (  self		:					str						):

								'''simple docstring'''
								# make sure that iterating over schedulers with same config names gives same results
								# for defaults
								lowercase__     				=    DPMSolverSinglestepScheduler(**self.get_scheduler_config()						)
								lowercase__     				=    self.full_loop(scheduler=lowerCamelCase						)
								lowercase__     				=    torch.mean(torch.abs(lowerCamelCase						)						)

								assert abs(result_mean.item() - 0.2791						) < 1E-3

								lowercase__     				=    DEISMultistepScheduler.from_config(scheduler.config						)
								lowercase__     				=    DPMSolverMultistepScheduler.from_config(scheduler.config						)
								lowercase__     				=    UniPCMultistepScheduler.from_config(scheduler.config						)
								lowercase__     				=    DPMSolverSinglestepScheduler.from_config(scheduler.config						)

								lowercase__     				=    self.full_loop(scheduler=lowerCamelCase						)
								lowercase__     				=    torch.mean(torch.abs(lowerCamelCase						)						)

								assert abs(result_mean.item() - 0.2791						) < 1E-3




				def        lowercase__     (  self		:					Optional[Any]						):

								'''simple docstring'''
								self.check_over_configs(thresholding=lowerCamelCase						)
								for order in [1, 2, 3]:
												for solver_type in ["midpoint", "heun"]:
																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,						algorithm_type='''dpmsolver++''',						solver_order=lowerCamelCase,						solver_type=lowerCamelCase,						)




				def        lowercase__     (  self		:					Dict						):

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




				def        lowercase__     (  self		:					Tuple						):

								'''simple docstring'''
								for algorithm_type in ["dpmsolver", "dpmsolver++"]:
												for solver_type in ["midpoint", "heun"]:
																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,						algorithm_type=lowerCamelCase,						)
																								lowercase__     				=    self.full_loop(
																								    solver_order=lowerCamelCase,						solver_type=lowerCamelCase,						prediction_type=lowerCamelCase,						algorithm_type=lowerCamelCase,						)
																								assert not torch.isnan(lowerCamelCase						).any(), "Samples have nan numbers"




				def        lowercase__     (  self		:					Optional[Any]						):

								'''simple docstring'''
								self.check_over_configs(lower_order_final=lowerCamelCase						)
								self.check_over_configs(lower_order_final=lowerCamelCase						)




				def        lowercase__     (  self		:					List[str]						):

								'''simple docstring'''
								self.check_over_configs(lambda_min_clipped=-float('''inf'''						)						)
								self.check_over_configs(lambda_min_clipped=-5.1						)




				def        lowercase__     (  self		:					Any						):

								'''simple docstring'''
								self.check_over_configs(variance_type=lowerCamelCase						)
								self.check_over_configs(variance_type='''learned_range'''						)




				def        lowercase__     (  self		:					Dict						):

								'''simple docstring'''
								for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1_000]:
												self.check_over_forward(num_inference_steps=lowerCamelCase,						time_step=0						)




				def        lowercase__     (  self		:					Optional[int]						):

								'''simple docstring'''
								lowercase__     				=    self.full_loop()
								lowercase__     				=    torch.mean(torch.abs(lowerCamelCase						)						)

								assert abs(result_mean.item() - 0.2791						) < 1E-3




				def        lowercase__     (  self		:					List[Any]						):

								'''simple docstring'''
								lowercase__     				=    self.full_loop(use_karras_sigmas=lowerCamelCase						)
								lowercase__     				=    torch.mean(torch.abs(lowerCamelCase						)						)

								assert abs(result_mean.item() - 0.2248						) < 1E-3




				def        lowercase__     (  self		:					Any						):

								'''simple docstring'''
								lowercase__     				=    self.full_loop(prediction_type='''v_prediction'''						)
								lowercase__     				=    torch.mean(torch.abs(lowerCamelCase						)						)

								assert abs(result_mean.item() - 0.1453						) < 1E-3




				def        lowercase__     (  self		:					Optional[int]						):

								'''simple docstring'''
								lowercase__     				=    self.full_loop(prediction_type='''v_prediction''',						use_karras_sigmas=lowerCamelCase						)
								lowercase__     				=    torch.mean(torch.abs(lowerCamelCase						)						)

								assert abs(result_mean.item() - 0.0649						) < 1E-3




				def        lowercase__     (  self		:					Optional[int]						):

								'''simple docstring'''
								lowercase__     				=    self.scheduler_classes[0]
								lowercase__     				=    self.get_scheduler_config(thresholding=lowerCamelCase,						dynamic_thresholding_ratio=0						)
								lowercase__     				=    scheduler_class(**lowerCamelCase						)

								lowercase__     				=    10
								lowercase__     				=    self.dummy_model()
								lowercase__     				=    self.dummy_sample_deter.half()
								scheduler.set_timesteps(lowerCamelCase						)

								for i, t in enumerate(scheduler.timesteps						):
												lowercase__     				=    model(lowerCamelCase,						lowerCamelCase						)
												lowercase__     				=    scheduler.step(lowerCamelCase,						lowerCamelCase,						lowerCamelCase						).prev_sample

								assert sample.dtype == torch.floataa












from collections import defaultdict
from math import gcd






def 						a    (		lowerCamelCase_ = 150_0000       ):






				'''simple docstring'''
				lowercase__     				=    defaultdict(lowerCamelCase_       )
				lowercase__     				=    2
				while 2 * euclid_m * (euclid_m + 1) <= limit:
								for euclid_n in range((euclid_m % 2) + 1	,     lowerCamelCase_	,     2       ):
												if gcd(lowerCamelCase_	,     lowerCamelCase_       ) > 1:
																continue
												lowercase__     				=    2 * euclid_m * (euclid_m + euclid_n)
												for perimeter in range(lowerCamelCase_	,     limit + 1	,     lowerCamelCase_       ):
																frequencies[perimeter] += 1
								euclid_m += 1
				return sum(1 for frequency in frequencies.values() if frequency == 1       )


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




