
import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest






class     UpperCAmelCase						(		__lowercase					):
   A__   :						List[Any]			=	(UnCLIPScheduler,)



   def 							_SCREAMING_SNAKE_CASE  (self						:   Dict       ,		**snake_case__						:   Optional[Any]					)						->      Tuple:


          '''simple docstring'''


          snake_case			:  str     							= {
              "num_train_timesteps": 10_00,
              "variance_type": "fixed_small_log",
              "clip_sample": True,
              "clip_sample_range": 1.0,
              "prediction_type": "epsilon",
          }

          config.update(**SCREAMING_SNAKE_CASE_					)
          return config



   def 							_SCREAMING_SNAKE_CASE  (self						:   str					)						->      Tuple:


          '''simple docstring'''


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



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


          '''simple docstring'''


          for variance in ["fixed_small_log", "learned_range"]:
                 self.check_over_configs(variance_type=SCREAMING_SNAKE_CASE_					)



   def 							_SCREAMING_SNAKE_CASE  (self						:   Dict					)						->      Dict:


          '''simple docstring'''


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



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


          '''simple docstring'''


          for clip_sample_range in [1, 5, 10, 20]:
                 self.check_over_configs(clip_sample_range=SCREAMING_SNAKE_CASE_					)



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


          '''simple docstring'''


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



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


          '''simple docstring'''


          for time_step in [0, 5_00, 9_99]:
                 for prev_timestep in [None, 5, 1_00, 2_50, 5_00, 7_50]:
                        if prev_timestep is not None and prev_timestep >= time_step:
                               continue

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



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


          '''simple docstring'''


          snake_case			:  Tuple     							= self.scheduler_classes[0]
          snake_case			:  Tuple     							= self.get_scheduler_config(variance_type="fixed_small_log"					)
          snake_case			:  int     							= scheduler_class(**SCREAMING_SNAKE_CASE_					)

          assert torch.sum(torch.abs(scheduler._get_variance(0					) - 1.00_00e-10					)					) < 1e-5
          assert torch.sum(torch.abs(scheduler._get_variance(4_87					) - 0.0549625					)					) < 1e-5
          assert torch.sum(torch.abs(scheduler._get_variance(9_99					) - 0.9994987					)					) < 1e-5



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


          '''simple docstring'''


          snake_case			:  str     							= self.scheduler_classes[0]
          snake_case			:  Any     							= self.get_scheduler_config(variance_type="learned_range"					)
          snake_case			:  List[str]     							= scheduler_class(**SCREAMING_SNAKE_CASE_					)

          snake_case			:  Union[str, Any]     							= 0.5

          assert scheduler._get_variance(1       ,		predicted_variance=SCREAMING_SNAKE_CASE_					) - -10.1712790 < 1e-5
          assert scheduler._get_variance(4_87       ,		predicted_variance=SCREAMING_SNAKE_CASE_					) - -5.7998052 < 1e-5
          assert scheduler._get_variance(9_99       ,		predicted_variance=SCREAMING_SNAKE_CASE_					) - -0.0010011 < 1e-5



   def 							_SCREAMING_SNAKE_CASE  (self						:   List[str]					)						->      List[str]:


          '''simple docstring'''


          snake_case			:  List[Any]     							= self.scheduler_classes[0]
          snake_case			:  Optional[int]     							= self.get_scheduler_config()
          snake_case			:  List[Any]     							= scheduler_class(**SCREAMING_SNAKE_CASE_					)

          snake_case			:  List[Any]     							= scheduler.timesteps

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

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

                 # 2. predict previous mean of sample x_t-1
                 snake_case			:  List[str]     							= scheduler.step(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		generator=SCREAMING_SNAKE_CASE_					).prev_sample

                 snake_case			:  Optional[Any]     							= pred_prev_sample

          snake_case			:  List[Any]     							= torch.sum(torch.abs(SCREAMING_SNAKE_CASE_					)					)
          snake_case			:  Any     							= torch.mean(torch.abs(SCREAMING_SNAKE_CASE_					)					)

          assert abs(result_sum.item() - 2_52.2_68_24_95					) < 1e-2
          assert abs(result_mean.item() - 0.3284743					) < 1e-3



   def 							_SCREAMING_SNAKE_CASE  (self						:   str					)						->      Optional[int]:


          '''simple docstring'''


          snake_case			:  int     							= self.scheduler_classes[0]
          snake_case			:  Any     							= self.get_scheduler_config()
          snake_case			:  Dict     							= scheduler_class(**SCREAMING_SNAKE_CASE_					)

          scheduler.set_timesteps(25					)

          snake_case			:  Dict     							= scheduler.timesteps

          snake_case			:  Optional[int]     							= self.dummy_model()
          snake_case			:  Any     							= self.dummy_sample_deter
          snake_case			:  Optional[int]     							= torch.manual_seed(0					)

          for i, t in enumerate(SCREAMING_SNAKE_CASE_					):
                 # 1. predict noise residual
                 snake_case			:  Optional[int]     							= model(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_					)

                 if i + 1 == timesteps.shape[0]:
                        snake_case			:  str     							= None
                 else:
                        snake_case			:  Union[str, Any]     							= timesteps[i + 1]

                 # 2. predict previous mean of sample x_t-1
                 snake_case			:  Dict     							= scheduler.step(
                     SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		prev_timestep=SCREAMING_SNAKE_CASE_       ,		generator=SCREAMING_SNAKE_CASE_					).prev_sample

                 snake_case			:  Optional[int]     							= pred_prev_sample

          snake_case			:  Any     							= torch.sum(torch.abs(SCREAMING_SNAKE_CASE_					)					)
          snake_case			:  Optional[int]     							= torch.mean(torch.abs(SCREAMING_SNAKE_CASE_					)					)

          assert abs(result_sum.item() - 2_58.2_04_49_83					) < 1e-2
          assert abs(result_mean.item() - 0.3362038					) < 1e-3



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


          '''simple docstring'''


          pass



   def 							_SCREAMING_SNAKE_CASE  (self						:   int					)						->      Any:


          '''simple docstring'''


          pass







"""simple docstring"""




from __future__ import annotations

import math



def 							_lowerCamelCase			(			__a     ):

		if num <= 0:
				SCREAMING_SNAKE_CASE_    			=			F'{num}: Invalid input, please enter a positive integer.'
				raise ValueError(__a     )

		SCREAMING_SNAKE_CASE_    			=			[True] * (num + 1)
		SCREAMING_SNAKE_CASE_    			=			[]
		SCREAMING_SNAKE_CASE_    			=			2
		SCREAMING_SNAKE_CASE_    			=			int(math.sqrt(__a     )     )

		while start <= end:
				# If start is a prime
				if sieve[start] is True:
						prime.append(__a     )

						# Set multiples of start be False
						for i in range(start * start,				num + 1,				__a     ):
								if sieve[i] is True:
										SCREAMING_SNAKE_CASE_    			=			False

				start += 1

		for j in range(end + 1,				num + 1     ):
				if sieve[j] is True:
						prime.append(__a     )

		return prime


if __name__ == "__main__":
						print(prime_sieve(int(input('Enter a positive integer: ').strip())))