





import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest






class  lowercase    (      UpperCAmelCase__       ):






					"""simple docstring"""



					snake_case_						  =			(DDPMScheduler,)





					def       _UpperCamelCase       (    self						:     Dict ,					**a_						:     int							):

						"""simple docstring"""



						lowerCamelCase__										=			{
						    """num_train_timesteps""": 10_00,
						    """beta_start""": 0.0_0_0_1,
						    """beta_end""": 0.0_2,
						    """beta_schedule""": """linear""",
						    """variance_type""": """fixed_small""",
						    """clip_sample""": True,
						}

						config.update(**__lowerCAmelCase							)
						return config





					def       _UpperCamelCase       (    self						:     str							):

						"""simple docstring"""



						for timesteps in [1, 5, 1_00, 10_00]:
							self.check_over_configs(num_train_timesteps=__lowerCAmelCase							)





					def       _UpperCamelCase       (    self						:     str							):

						"""simple docstring"""



						for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1] ,					[0.0_0_2, 0.0_2, 0.2, 2]							):
							self.check_over_configs(beta_start=__lowerCAmelCase ,					beta_end=__lowerCAmelCase							)





					def       _UpperCamelCase       (    self						:     Optional[int]							):

						"""simple docstring"""



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





					def       _UpperCamelCase       (    self						:     str							):

						"""simple docstring"""



						for variance in ["fixed_small", "fixed_large", "other"]:
							self.check_over_configs(variance_type=__lowerCAmelCase							)





					def       _UpperCamelCase       (    self						:     Any							):

						"""simple docstring"""



						for clip_sample in [True, False]:
							self.check_over_configs(clip_sample=__lowerCAmelCase							)





					def       _UpperCamelCase       (    self						:     Tuple							):

						"""simple docstring"""



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





					def       _UpperCamelCase       (    self						:     Optional[Any]							):

						"""simple docstring"""



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





					def       _UpperCamelCase       (    self						:     Union[str, Any]							):

						"""simple docstring"""



						for t in [0, 5_00, 9_99]:
							self.check_over_forward(time_step=__lowerCAmelCase							)





					def       _UpperCamelCase       (    self						:     List[str]							):

						"""simple docstring"""



						lowerCamelCase__										=			self.scheduler_classes[0]
						lowerCamelCase__										=			self.get_scheduler_config()
						lowerCamelCase__										=			scheduler_class(**__lowerCAmelCase							)

						assert torch.sum(torch.abs(scheduler._get_variance(0							) - 0.0							)							) < 1e-5
						assert torch.sum(torch.abs(scheduler._get_variance(4_87							) - 0.0_0_9_7_9							)							) < 1e-5
						assert torch.sum(torch.abs(scheduler._get_variance(9_99							) - 0.0_2							)							) < 1e-5





					def       _UpperCamelCase       (    self						:     str							):

						"""simple docstring"""



						lowerCamelCase__										=			self.scheduler_classes[0]
						lowerCamelCase__										=			self.get_scheduler_config()
						lowerCamelCase__										=			scheduler_class(**__lowerCAmelCase							)

						lowerCamelCase__										=			len(__lowerCAmelCase							)

						lowerCamelCase__										=			self.dummy_model()
						lowerCamelCase__										=			self.dummy_sample_deter
						lowerCamelCase__										=			torch.manual_seed(0							)

						for t in reversed(range(__lowerCAmelCase							)							):
							# 1. predict noise residual
							lowerCamelCase__										=			model(__lowerCAmelCase ,					__lowerCAmelCase							)

							# 2. predict previous mean of sample x_t-1
							lowerCamelCase__										=			scheduler.step(__lowerCAmelCase ,					__lowerCAmelCase ,					__lowerCAmelCase ,					generator=__lowerCAmelCase							).prev_sample

							# if t > 0:
							#     noise = self.dummy_sample_deter
							#     variance = scheduler.get_variance(t) ** (0.5) * noise
							#
							# sample = pred_prev_sample + variance
							lowerCamelCase__										=			pred_prev_sample

						lowerCamelCase__										=			torch.sum(torch.abs(__lowerCAmelCase							)							)
						lowerCamelCase__										=			torch.mean(torch.abs(__lowerCAmelCase							)							)

						assert abs(result_sum.item() - 2_5_8.9_6_0_6							) < 1e-2
						assert abs(result_mean.item() - 0.3_3_7_2							) < 1e-3





					def       _UpperCamelCase       (    self						:     int							):

						"""simple docstring"""



						lowerCamelCase__										=			self.scheduler_classes[0]
						lowerCamelCase__										=			self.get_scheduler_config(prediction_type="""v_prediction"""							)
						lowerCamelCase__										=			scheduler_class(**__lowerCAmelCase							)

						lowerCamelCase__										=			len(__lowerCAmelCase							)

						lowerCamelCase__										=			self.dummy_model()
						lowerCamelCase__										=			self.dummy_sample_deter
						lowerCamelCase__										=			torch.manual_seed(0							)

						for t in reversed(range(__lowerCAmelCase							)							):
							# 1. predict noise residual
							lowerCamelCase__										=			model(__lowerCAmelCase ,					__lowerCAmelCase							)

							# 2. predict previous mean of sample x_t-1
							lowerCamelCase__										=			scheduler.step(__lowerCAmelCase ,					__lowerCAmelCase ,					__lowerCAmelCase ,					generator=__lowerCAmelCase							).prev_sample

							# if t > 0:
							#     noise = self.dummy_sample_deter
							#     variance = scheduler.get_variance(t) ** (0.5) * noise
							#
							# sample = pred_prev_sample + variance
							lowerCamelCase__										=			pred_prev_sample

						lowerCamelCase__										=			torch.sum(torch.abs(__lowerCAmelCase							)							)
						lowerCamelCase__										=			torch.mean(torch.abs(__lowerCAmelCase							)							)

						assert abs(result_sum.item() - 2_0_2.0_2_9_6							) < 1e-2
						assert abs(result_mean.item() - 0.2_6_3_1							) < 1e-3





					def       _UpperCamelCase       (    self						:     Tuple							):

						"""simple docstring"""



						lowerCamelCase__										=			self.scheduler_classes[0]
						lowerCamelCase__										=			self.get_scheduler_config()
						lowerCamelCase__										=			scheduler_class(**__lowerCAmelCase							)

						lowerCamelCase__										=			[1_00, 87, 50, 1, 0]

						scheduler.set_timesteps(timesteps=__lowerCAmelCase							)

						lowerCamelCase__										=			scheduler.timesteps

						for i, timestep in enumerate(__lowerCAmelCase							):
							if i == len(__lowerCAmelCase							) - 1:
								lowerCamelCase__										=			-1
							else:
								lowerCamelCase__										=			timesteps[i + 1]

							lowerCamelCase__										=			scheduler.previous_timestep(__lowerCAmelCase							)
							lowerCamelCase__										=			prev_t.item()

							self.assertEqual(__lowerCAmelCase ,					__lowerCAmelCase							)





					def       _UpperCamelCase       (    self						:     Any							):

						"""simple docstring"""



						lowerCamelCase__										=			self.scheduler_classes[0]
						lowerCamelCase__										=			self.get_scheduler_config()
						lowerCamelCase__										=			scheduler_class(**__lowerCAmelCase							)

						lowerCamelCase__										=			[1_00, 87, 50, 51, 0]

						with self.assertRaises(__lowerCAmelCase ,					msg="""`custom_timesteps` must be in descending order."""							):
							scheduler.set_timesteps(timesteps=__lowerCAmelCase							)





					def       _UpperCamelCase       (    self						:     List[Any]							):

						"""simple docstring"""



						lowerCamelCase__										=			self.scheduler_classes[0]
						lowerCamelCase__										=			self.get_scheduler_config()
						lowerCamelCase__										=			scheduler_class(**__lowerCAmelCase							)

						lowerCamelCase__										=			[1_00, 87, 50, 1, 0]
						lowerCamelCase__										=			len(__lowerCAmelCase							)

						with self.assertRaises(__lowerCAmelCase ,					msg="""Can only pass one of `num_inference_steps` or `custom_timesteps`."""							):
							scheduler.set_timesteps(num_inference_steps=__lowerCAmelCase ,					timesteps=__lowerCAmelCase							)





					def       _UpperCamelCase       (    self						:     Any							):

						"""simple docstring"""



						lowerCamelCase__										=			self.scheduler_classes[0]
						lowerCamelCase__										=			self.get_scheduler_config()
						lowerCamelCase__										=			scheduler_class(**__lowerCAmelCase							)

						lowerCamelCase__										=			[scheduler.config.num_train_timesteps]

						with self.assertRaises(
						    __lowerCAmelCase ,					msg="""`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}""" ,					):
							scheduler.set_timesteps(timesteps=__lowerCAmelCase							)






'''simple docstring'''


from math import isqrt, loga
def 						lowerCamelCase__   (				a							):
						__snake_case						 =   [True] * max_number
						for i in range(2    ,	isqrt(max_number - 1							) + 1							):
												if is_prime[i]:
																		for j in range(i**2    ,	a    ,	a							):
																								__snake_case						 =   False

						return [i for i in range(2    ,	a							) if is_prime[i]]
def 						lowerCamelCase__   (				a = 800800    ,	a = 800800							):
						__snake_case						 =   degree * loga(a							)
						__snake_case						 =   int(a							)
						__snake_case						 =   calculate_prime_numbers(a							)

						__snake_case						 =   0
						__snake_case						 =   0
						__snake_case						 =   len(a							) - 1
						while left < right:
												while (
												    prime_numbers[right] * loga(prime_numbers[left]							)
												    + prime_numbers[left] * loga(prime_numbers[right]							)
												    > upper_bound
												):
																		right -= 1
												hybrid_integers_count += right - left
												left += 1

						return hybrid_integers_count


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

