




import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest







class 						lowercase							(   UpperCamelCase__		):
     _a							 =						(UnCLIPScheduler,)

     def 					a__	(  self		,     **_a				)						->							Any:
           _A							:     Dict			=						{
               """num_train_timesteps""": 1000,
               """variance_type""": """fixed_small_log""",
               """clip_sample""": True,
               """clip_sample_range""": 1.0,
               """prediction_type""": """epsilon""",
           }

           config.update(**_a				)
           return config

     def 					a__	(  self				)						->							Union[str, Any]:
           for timesteps in [1, 5, 100, 1000]:
                 self.check_over_configs(num_train_timesteps=_a				)

     def 					a__	(  self				)						->							str:
           for variance in ["fixed_small_log", "learned_range"]:
                 self.check_over_configs(variance_type=_a				)

     def 					a__	(  self				)						->							List[str]:
           for clip_sample in [True, False]:
                 self.check_over_configs(clip_sample=_a				)

     def 					a__	(  self				)						->							Optional[Any]:
           for clip_sample_range in [1, 5, 10, 20]:
                 self.check_over_configs(clip_sample_range=_a				)

     def 					a__	(  self				)						->							str:
           for prediction_type in ["epsilon", "sample"]:
                 self.check_over_configs(prediction_type=_a				)

     def 					a__	(  self				)						->							Tuple:
           for time_step in [0, 500, 999]:
                 for prev_timestep in [None, 5, 100, 250, 500, 750]:
                       if prev_timestep is not None and prev_timestep >= time_step:
                             continue

                       self.check_over_forward(time_step=_a		,     prev_timestep=_a				)

     def 					a__	(  self				)						->							str:
           _A							:     str			=						self.scheduler_classes[0]
           _A							:     str			=						self.get_scheduler_config(variance_type="""fixed_small_log"""				)
           _A							:     Optional[Any]			=						scheduler_class(**_a				)

           assert torch.sum(torch.abs(scheduler._get_variance(0				) - 1.00_00e-10				)				) < 1e-5
           assert torch.sum(torch.abs(scheduler._get_variance(487				) - 0.0549625				)				) < 1e-5
           assert torch.sum(torch.abs(scheduler._get_variance(999				) - 0.9994987				)				) < 1e-5

     def 					a__	(  self				)						->							int:
           _A							:     Dict			=						self.scheduler_classes[0]
           _A							:     List[str]			=						self.get_scheduler_config(variance_type="""learned_range"""				)
           _A							:     Optional[Any]			=						scheduler_class(**_a				)

           _A							:     str			=						0.5

           assert scheduler._get_variance(1		,     predicted_variance=_a				) - -10.1712790 < 1e-5
           assert scheduler._get_variance(487		,     predicted_variance=_a				) - -5.7998052 < 1e-5
           assert scheduler._get_variance(999		,     predicted_variance=_a				) - -0.0010011 < 1e-5

     def 					a__	(  self				)						->							Any:
           _A							:     Any			=						self.scheduler_classes[0]
           _A							:     Tuple			=						self.get_scheduler_config()
           _A							:     Optional[Any]			=						scheduler_class(**_a				)

           _A							:     Optional[int]			=						scheduler.timesteps

           _A							:     Union[str, Any]			=						self.dummy_model()
           _A							:     Any			=						self.dummy_sample_deter
           _A							:     List[str]			=						torch.manual_seed(0				)

           for i, t in enumerate(_a				):
                 # 1. predict noise residual
                 _A							:     Dict			=						model(_a		,     _a				)

                 # 2. predict previous mean of sample x_t-1
                 _A							:     List[Any]			=						scheduler.step(_a		,     _a		,     _a		,     generator=_a				).prev_sample

                 _A							:     Any			=						pred_prev_sample

           _A							:     int			=						torch.sum(torch.abs(_a				)				)
           _A							:     Optional[int]			=						torch.mean(torch.abs(_a				)				)

           assert abs(result_sum.item() - 252.2682495				) < 1e-2
           assert abs(result_mean.item() - 0.3284743				) < 1e-3

     def 					a__	(  self				)						->							Any:
           _A							:     Dict			=						self.scheduler_classes[0]
           _A							:     Optional[int]			=						self.get_scheduler_config()
           _A							:     int			=						scheduler_class(**_a				)

           scheduler.set_timesteps(25				)

           _A							:     List[str]			=						scheduler.timesteps

           _A							:     str			=						self.dummy_model()
           _A							:     Union[str, Any]			=						self.dummy_sample_deter
           _A							:     str			=						torch.manual_seed(0				)

           for i, t in enumerate(_a				):
                 # 1. predict noise residual
                 _A							:     List[Any]			=						model(_a		,     _a				)

                 if i + 1 == timesteps.shape[0]:
                       _A							:     Dict			=						None
                 else:
                       _A							:     Optional[Any]			=						timesteps[i + 1]

                 # 2. predict previous mean of sample x_t-1
                 _A							:     Any			=						scheduler.step(
                     _a		,     _a		,     _a		,     prev_timestep=_a		,     generator=_a				).prev_sample

                 _A							:     int			=						pred_prev_sample

           _A							:     Optional[int]			=						torch.sum(torch.abs(_a				)				)
           _A							:     int			=						torch.mean(torch.abs(_a				)				)

           assert abs(result_sum.item() - 258.2044983				) < 1e-2
           assert abs(result_mean.item() - 0.3362038				) < 1e-3

     def 					a__	(  self				)						->							Dict:
           pass



     def 					a__	(  self				)						->							Optional[Any]:
           pass



'''simple docstring'''







from collections import defaultdict
from math import gcd







def 				SCREAMING_SNAKE_CASE__	(   __A = 1_500_000	)       ->	int:
	_snake_case   						=		defaultdict(__A	)
	_snake_case   						=		2
	while 2 * euclid_m * (euclid_m + 1) <= limit:
		for euclid_n in range((euclid_m % 2) + 1			,				__A			,				2	):
			if gcd(__A			,				__A	) > 1:
				continue
			_snake_case   						=		2 * euclid_m * (euclid_m + euclid_n)
			for perimeter in range(__A			,				limit + 1			,				__A	):
				frequencies[perimeter] += 1
		euclid_m += 1
	return sum(1 for frequency in frequencies.values() if frequency == 1	)


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

