






import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest






class  UpperCamelCase    (			A_   ):
 UpperCamelCase      : Optional[Any]    =      (DDIMScheduler,)
 UpperCamelCase      : Optional[Any]    =      (('''eta''', 0.0), ('''num_inference_steps''', 50))


 def _lowercase			(  self	:		List[Any]							,		**UpperCAmelCase__	:		str							)      -> Optional[Any]:
   _a						: Any    =			{
       '''num_train_timesteps''': 1000,
       '''beta_start''': 0.0_0_0_1,
       '''beta_end''': 0.0_2,
       '''beta_schedule''': '''linear''',
       '''clip_sample''': True,
   }

   config.update(**UpperCAmelCase__							)
   return config


 def _lowercase			(  self	:		str							,		**UpperCAmelCase__	:		int							)      -> int:
   _a						: str    =			self.scheduler_classes[0]
   _a						: Dict    =			self.get_scheduler_config(**UpperCAmelCase__							)
   _a						: List[str]    =			scheduler_class(**UpperCAmelCase__							)

   _a						: int    =			10, 0.0

   _a						: List[str]    =			self.dummy_model()
   _a						: int    =			self.dummy_sample_deter

   scheduler.set_timesteps(UpperCAmelCase__							)

   for t in scheduler.timesteps:
     _a						: Union[str, Any]    =			model(UpperCAmelCase__							,		UpperCAmelCase__							)
     _a						: List[Any]    =			scheduler.step(UpperCAmelCase__							,		UpperCAmelCase__							,		UpperCAmelCase__							,		UpperCAmelCase__							).prev_sample

   return sample


 def _lowercase			(  self	:		Dict							)      -> Optional[int]:



   for timesteps in [100, 500, 1000]:
     self.check_over_configs(num_train_timesteps=UpperCAmelCase__							)


 def _lowercase			(  self	:		List[Any]							)      -> List[str]:



   for steps_offset in [0, 1]:
     self.check_over_configs(steps_offset=UpperCAmelCase__							)

   _a						: List[Any]    =			self.scheduler_classes[0]
   _a						: Tuple    =			self.get_scheduler_config(steps_offset=1							)
   _a						: Union[str, Any]    =			scheduler_class(**UpperCAmelCase__							)
   scheduler.set_timesteps(5							)
   assert torch.equal(scheduler.timesteps							,		torch.LongTensor([801, 601, 401, 201, 1]							)							)


 def _lowercase			(  self	:		Optional[Any]							)      -> Union[str, Any]:



   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=UpperCAmelCase__							,		beta_end=UpperCAmelCase__							)


 def _lowercase			(  self	:		Any							)      -> Union[str, Any]:



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


 def _lowercase			(  self	:		Optional[int]							)      -> Optional[Any]:



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


 def _lowercase			(  self	:		Optional[Any]							)      -> Optional[int]:



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


 def _lowercase			(  self	:		Any							)      -> int:



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


 def _lowercase			(  self	:		List[str]							)      -> Union[str, Any]:



   for rescale_betas_zero_snr in [True, False]:
     self.check_over_configs(rescale_betas_zero_snr=UpperCAmelCase__							)


 def _lowercase			(  self	:		Optional[Any]							)      -> List[str]:
   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 _lowercase			(  self	:		str							)      -> List[Any]:



   for t in [1, 10, 49]:
     self.check_over_forward(time_step=UpperCAmelCase__							)


 def _lowercase			(  self	:		Optional[int]							)      -> Any:



   for t, num_inference_steps in zip([1, 10, 50]							,		[10, 50, 500]							):
     self.check_over_forward(time_step=UpperCAmelCase__							,		num_inference_steps=UpperCAmelCase__							)


 def _lowercase			(  self	:		Optional[Any]							)      -> Union[str, Any]:



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


 def _lowercase			(  self	:		List[Any]							)      -> Tuple:
   _a						: int    =			self.scheduler_classes[0]
   _a						: str    =			self.get_scheduler_config()
   _a						: int    =			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(420							,		400							) - 0.1_4_7_7_1							)							) < 1E-5
   assert torch.sum(torch.abs(scheduler._get_variance(980							,		960							) - 0.3_2_4_6_0							)							) < 1E-5
   assert torch.sum(torch.abs(scheduler._get_variance(0							,		0							) - 0.0							)							) < 1E-5
   assert torch.sum(torch.abs(scheduler._get_variance(487							,		486							) - 0.0_0_9_7_9							)							) < 1E-5
   assert torch.sum(torch.abs(scheduler._get_variance(999							,		998							) - 0.0_2							)							) < 1E-5


 def _lowercase			(  self	:		List[Any]							)      -> List[Any]:
   _a						: Any    =			self.full_loop()

   _a						: Optional[Any]    =			torch.sum(torch.abs(UpperCAmelCase__							)							)
   _a						: Union[str, Any]    =			torch.mean(torch.abs(UpperCAmelCase__							)							)

   assert abs(result_sum.item() - 1_7_2.0_0_6_7							) < 1E-2
   assert abs(result_mean.item() - 0.2_2_3_9_6_7							) < 1E-3


 def _lowercase			(  self	:		Union[str, Any]							)      -> Dict:
   _a						: List[Any]    =			self.full_loop(prediction_type="""v_prediction"""							)

   _a						: int    =			torch.sum(torch.abs(UpperCAmelCase__							)							)
   _a						: Union[str, Any]    =			torch.mean(torch.abs(UpperCAmelCase__							)							)

   assert abs(result_sum.item() - 5_2.5_3_0_2							) < 1E-2
   assert abs(result_mean.item() - 0.0_6_8_4							) < 1E-3


 def _lowercase			(  self	:		Any							)      -> str:
   _a						: Tuple    =			self.full_loop(set_alpha_to_one=UpperCAmelCase__							,		beta_start=0.0_1							)
   _a						: List[Any]    =			torch.sum(torch.abs(UpperCAmelCase__							)							)
   _a						: Any    =			torch.mean(torch.abs(UpperCAmelCase__							)							)

   assert abs(result_sum.item() - 1_4_9.8_2_9_5							) < 1E-2
   assert abs(result_mean.item() - 0.1_9_5_1							) < 1E-3







 def _lowercase			(  self	:		Any							)      -> Optional[int]:
   _a						: int    =			self.full_loop(set_alpha_to_one=UpperCAmelCase__							,		beta_start=0.0_1							)
   _a						: Optional[int]    =			torch.sum(torch.abs(UpperCAmelCase__							)							)
   _a						: Dict    =			torch.mean(torch.abs(UpperCAmelCase__							)							)

   assert abs(result_sum.item() - 1_4_9.0_7_8_4							) < 1E-2
   assert abs(result_mean.item() - 0.1_9_4_1							) < 1E-3









"""simple docstring"""




from __future__ import annotations

import math
from itertools import permutations

def     lowerCAmelCase__  (  UpperCamelCase__       ):



  '''simple docstring'''

  if 1 < number < 4:
    # 2 and 3 are primes
    return True
  elif number < 2 or number % 2 == 0 or number % 3 == 0:
    # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
    return False

  # All primes number are in format of 6k +/- 1
  for i in range(5  ,     int(math.sqrt(UpperCamelCase__       ) + 1       )  ,     6       ):
    if number % i == 0 or number % (i + 2) == 0:
      return False
  return True

def     lowerCAmelCase__  (  UpperCamelCase__ = 7       ):



  '''simple docstring'''
  _a						: Tuple    =			"""""".join(str(UpperCamelCase__       ) for i in range(1  ,     n + 1       )       )
  _a						: str    =			[int("""""".join(UpperCamelCase__       )       ) for i in permutations(UpperCamelCase__  ,     UpperCamelCase__       )]
  _a						: Union[str, Any]    =			[num for num in perm_list if is_prime(UpperCamelCase__       )]
  return max(UpperCamelCase__       ) if pandigitals else 0


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

