


'''simple docstring'''



import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest



class 			a       (	lowercase_ ):


     """simple docstring"""






     SCREAMING_SNAKE_CASE :				List[Any]							     =     (DEISMultistepScheduler,)
     SCREAMING_SNAKE_CASE :				str							     =     (('''num_inference_steps''', 2_5),)

     def 				lowerCamelCase__				(       self  :	Any							,				**snake_case  :	str    )		->							str:
          __UpperCAmelCase			:				List[str]													=    {
              '''num_train_timesteps''': 1000,
              '''beta_start''': 0.0_001,
              '''beta_end''': 0.02,
              '''beta_schedule''': '''linear''',
              '''solver_order''': 2,
          }

          config.update(**UpperCamelCase__    )
          return config

     def 				lowerCamelCase__				(       self  :	Optional[Any]							,				snake_case  :	Tuple=0							,				**snake_case  :	Optional[Any]    )		->							Dict:
          __UpperCAmelCase			:				List[Any]													=    dict(self.forward_default_kwargs    )
          __UpperCAmelCase			:				Optional[Any]													=    kwargs.pop('''num_inference_steps'''							,				UpperCamelCase__    )
          __UpperCAmelCase			:				List[str]													=    self.dummy_sample
          __UpperCAmelCase			:				Dict													=    0.1 * sample
          __UpperCAmelCase			:				Tuple													=    [residual + 0.2, residual + 0.15, residual + 0.10]

          for scheduler_class in self.scheduler_classes:
               __UpperCAmelCase			:				Optional[Any]													=    self.get_scheduler_config(**UpperCamelCase__    )
               __UpperCAmelCase			:				Dict													=    scheduler_class(**UpperCamelCase__    )
               scheduler.set_timesteps(UpperCamelCase__    )
               # copy over dummy past residuals
               __UpperCAmelCase			:				List[Any]													=    dummy_past_residuals[: scheduler.config.solver_order]

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

               __UpperCAmelCase							,     __UpperCAmelCase			:				Optional[int]													=    sample, sample
               for t in range(UpperCamelCase__							,				time_step + scheduler.config.solver_order + 1    ):
                    __UpperCAmelCase			:				Optional[Any]													=    scheduler.step(UpperCamelCase__							,				UpperCamelCase__							,				UpperCamelCase__							,				**UpperCamelCase__    ).prev_sample
                    __UpperCAmelCase			:				List[Any]													=    new_scheduler.step(UpperCamelCase__							,				UpperCamelCase__							,				UpperCamelCase__							,				**UpperCamelCase__    ).prev_sample

                    assert torch.sum(torch.abs(output - new_output    )    ) < 1E-5, "Scheduler outputs are not identical"

     def 				lowerCamelCase__				(       self  :	Union[str, Any]    )		->							Tuple:
          pass

     def 				lowerCamelCase__				(       self  :	Dict							,				snake_case  :	int=0							,				**snake_case  :	Optional[int]    )		->							int:
          __UpperCAmelCase			:				Tuple													=    dict(self.forward_default_kwargs    )
          __UpperCAmelCase			:				Optional[Any]													=    kwargs.pop('''num_inference_steps'''							,				UpperCamelCase__    )
          __UpperCAmelCase			:				Any													=    self.dummy_sample
          __UpperCAmelCase			:				List[Any]													=    0.1 * sample
          __UpperCAmelCase			:				Dict													=    [residual + 0.2, residual + 0.15, residual + 0.10]

          for scheduler_class in self.scheduler_classes:
               __UpperCAmelCase			:				int													=    self.get_scheduler_config()
               __UpperCAmelCase			:				int													=    scheduler_class(**UpperCamelCase__    )
               scheduler.set_timesteps(UpperCamelCase__    )

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

               with tempfile.TemporaryDirectory() as tmpdirname:
                    scheduler.save_config(UpperCamelCase__    )
                    __UpperCAmelCase			:				Optional[Any]													=    scheduler_class.from_pretrained(UpperCamelCase__    )
                    # copy over dummy past residuals
                    new_scheduler.set_timesteps(UpperCamelCase__    )

                    # copy over dummy past residual (must be after setting timesteps)
                    __UpperCAmelCase			:				Optional[Any]													=    dummy_past_residuals[: new_scheduler.config.solver_order]

               __UpperCAmelCase			:				str													=    scheduler.step(UpperCamelCase__							,				UpperCamelCase__							,				UpperCamelCase__							,				**UpperCamelCase__    ).prev_sample
               __UpperCAmelCase			:				Any													=    new_scheduler.step(UpperCamelCase__							,				UpperCamelCase__							,				UpperCamelCase__							,				**UpperCamelCase__    ).prev_sample

               assert torch.sum(torch.abs(output - new_output    )    ) < 1E-5, "Scheduler outputs are not identical"

     def 				lowerCamelCase__				(       self  :	int							,				snake_case  :	List[str]=None							,				**snake_case  :	List[Any]    )		->							List[str]:







          if scheduler is None:
               __UpperCAmelCase			:				str													=    self.scheduler_classes[0]
               __UpperCAmelCase			:				Dict													=    self.get_scheduler_config(**UpperCamelCase__    )
               __UpperCAmelCase			:				Union[str, Any]													=    scheduler_class(**UpperCamelCase__    )

          __UpperCAmelCase			:				Any													=    self.scheduler_classes[0]
          __UpperCAmelCase			:				List[str]													=    self.get_scheduler_config(**UpperCamelCase__    )
          __UpperCAmelCase			:				str													=    scheduler_class(**UpperCamelCase__    )

          __UpperCAmelCase			:				Union[str, Any]													=    10
          __UpperCAmelCase			:				Any													=    self.dummy_model()
          __UpperCAmelCase			:				List[Any]													=    self.dummy_sample_deter
          scheduler.set_timesteps(UpperCamelCase__    )

          for i, t in enumerate(scheduler.timesteps    ):
               __UpperCAmelCase			:				Optional[int]													=    model(UpperCamelCase__							,				UpperCamelCase__    )
               __UpperCAmelCase			:				Any													=    scheduler.step(UpperCamelCase__							,				UpperCamelCase__							,				UpperCamelCase__    ).prev_sample

          return sample

     def 				lowerCamelCase__				(       self  :	str    )		->							Any:
          __UpperCAmelCase			:				List[Any]													=    dict(self.forward_default_kwargs    )

          __UpperCAmelCase			:				Any													=    kwargs.pop('''num_inference_steps'''							,				UpperCamelCase__    )

          for scheduler_class in self.scheduler_classes:
               __UpperCAmelCase			:				Dict													=    self.get_scheduler_config()
               __UpperCAmelCase			:				str													=    scheduler_class(**UpperCamelCase__    )

               __UpperCAmelCase			:				Dict													=    self.dummy_sample
               __UpperCAmelCase			:				Optional[int]													=    0.1 * sample

               if num_inference_steps is not None and hasattr(UpperCamelCase__							,				'''set_timesteps'''    ):
                    scheduler.set_timesteps(UpperCamelCase__    )
               elif num_inference_steps is not None and not hasattr(UpperCamelCase__							,				'''set_timesteps'''    ):
                    __UpperCAmelCase			:				str													=    num_inference_steps

               # copy over dummy past residuals (must be done after set_timesteps)
               __UpperCAmelCase			:				List[str]													=    [residual + 0.2, residual + 0.15, residual + 0.10]
               __UpperCAmelCase			:				Union[str, Any]													=    dummy_past_residuals[: scheduler.config.solver_order]

               __UpperCAmelCase			:				Dict													=    scheduler.timesteps[5]
               __UpperCAmelCase			:				Union[str, Any]													=    scheduler.timesteps[6]

               __UpperCAmelCase			:				List[str]													=    scheduler.step(UpperCamelCase__							,				UpperCamelCase__							,				UpperCamelCase__							,				**UpperCamelCase__    ).prev_sample
               __UpperCAmelCase			:				Dict													=    scheduler.step(UpperCamelCase__							,				UpperCamelCase__							,				UpperCamelCase__							,				**UpperCamelCase__    ).prev_sample

               self.assertEqual(output_a.shape							,				sample.shape    )
               self.assertEqual(output_a.shape							,				output_a.shape    )

     def 				lowerCamelCase__				(       self  :	Optional[int]    )		->							str:
          __UpperCAmelCase			:				List[Any]													=    DEISMultistepScheduler(**self.get_scheduler_config()    )
          __UpperCAmelCase			:				Union[str, Any]													=    self.full_loop(scheduler=UpperCamelCase__    )
          __UpperCAmelCase			:				Union[str, Any]													=    torch.mean(torch.abs(UpperCamelCase__    )    )

          assert abs(result_mean.item() - 0.23_916    ) < 1E-3

          __UpperCAmelCase			:				Dict													=    DPMSolverSinglestepScheduler.from_config(scheduler.config    )
          __UpperCAmelCase			:				Optional[int]													=    DPMSolverMultistepScheduler.from_config(scheduler.config    )
          __UpperCAmelCase			:				Union[str, Any]													=    UniPCMultistepScheduler.from_config(scheduler.config    )
          __UpperCAmelCase			:				Optional[Any]													=    DEISMultistepScheduler.from_config(scheduler.config    )

          __UpperCAmelCase			:				Dict													=    self.full_loop(scheduler=UpperCamelCase__    )
          __UpperCAmelCase			:				Optional[int]													=    torch.mean(torch.abs(UpperCamelCase__    )    )

          assert abs(result_mean.item() - 0.23_916    ) < 1E-3

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







          for timesteps in [25, 50, 100, 999, 1000]:
               self.check_over_configs(num_train_timesteps=UpperCamelCase__    )

     def 				lowerCamelCase__				(       self  :	str    )		->							Optional[int]:
          self.check_over_configs(thresholding=UpperCamelCase__    )
          for order in [1, 2, 3]:
               for solver_type in ["logrho"]:
                    for threshold in [0.5, 1.0, 2.0]:
                         for prediction_type in ["epsilon", "sample"]:
                              self.check_over_configs(
                                  thresholding=UpperCamelCase__							,				prediction_type=UpperCamelCase__							,				sample_max_value=UpperCamelCase__							,				algorithm_type='''deis'''							,				solver_order=UpperCamelCase__							,				solver_type=UpperCamelCase__							,				)

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







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

     def 				lowerCamelCase__				(       self  :	Dict    )		->							Any:







          for algorithm_type in ["deis"]:
               for solver_type in ["logrho"]:
                    for order in [1, 2, 3]:
                         for prediction_type in ["epsilon", "sample"]:
                              self.check_over_configs(
                                  solver_order=UpperCamelCase__							,				solver_type=UpperCamelCase__							,				prediction_type=UpperCamelCase__							,				algorithm_type=UpperCamelCase__							,				)
                              __UpperCAmelCase			:				Any													=    self.full_loop(
                                  solver_order=UpperCamelCase__							,				solver_type=UpperCamelCase__							,				prediction_type=UpperCamelCase__							,				algorithm_type=UpperCamelCase__							,				)
                              assert not torch.isnan(UpperCamelCase__    ).any(), "Samples have nan numbers"

     def 				lowerCamelCase__				(       self  :	Dict    )		->							Any:
          self.check_over_configs(lower_order_final=UpperCamelCase__    )
          self.check_over_configs(lower_order_final=UpperCamelCase__    )

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







          for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
               self.check_over_forward(num_inference_steps=UpperCamelCase__							,				time_step=0    )

     def 				lowerCamelCase__				(       self  :	Optional[Any]    )		->							int:
          __UpperCAmelCase			:				Dict													=    self.full_loop()
          __UpperCAmelCase			:				int													=    torch.mean(torch.abs(UpperCamelCase__    )    )

          assert abs(result_mean.item() - 0.23_916    ) < 1E-3

     def 				lowerCamelCase__				(       self  :	Union[str, Any]    )		->							Tuple:
          __UpperCAmelCase			:				int													=    self.full_loop(prediction_type='''v_prediction'''    )
          __UpperCAmelCase			:				Optional[int]													=    torch.mean(torch.abs(UpperCamelCase__    )    )

          assert abs(result_mean.item() - 0.091    ) < 1E-3






     def 				lowerCamelCase__				(       self  :	Any    )		->							List[Any]:
          __UpperCAmelCase			:				Union[str, Any]													=    self.scheduler_classes[0]
          __UpperCAmelCase			:				Tuple													=    self.get_scheduler_config(thresholding=UpperCamelCase__							,				dynamic_thresholding_ratio=0    )
          __UpperCAmelCase			:				Optional[Any]													=    scheduler_class(**UpperCamelCase__    )

          __UpperCAmelCase			:				Dict													=    10
          __UpperCAmelCase			:				Any													=    self.dummy_model()
          __UpperCAmelCase			:				List[str]													=    self.dummy_sample_deter.half()
          scheduler.set_timesteps(UpperCamelCase__    )

          for i, t in enumerate(scheduler.timesteps    ):
               __UpperCAmelCase			:				int													=    model(UpperCamelCase__							,				UpperCamelCase__    )
               __UpperCAmelCase			:				str													=    scheduler.step(UpperCamelCase__							,				UpperCamelCase__							,				UpperCamelCase__    ).prev_sample

          assert sample.dtype == torch.floataa



'''simple docstring'''



from __future__ import annotations




def 			_a			(		_lowercase      :			int						):





     '''simple docstring'''






     __UpperCAmelCase			:				str													=    [True] * limit
     __UpperCAmelCase			:				Tuple													=    False
     __UpperCAmelCase			:				List[str]													=    False
     __UpperCAmelCase			:				List[Any]													=    True

     for i in range(3    ,      int(limit**0.5 + 1						)    ,      2						):
          __UpperCAmelCase			:				Dict													=    i * 2
          while index < limit:
               __UpperCAmelCase			:				Optional[int]													=    False
               __UpperCAmelCase			:				Union[str, Any]													=    index + i

     __UpperCAmelCase			:				Optional[Any]													=    [2]

     for i in range(3    ,      _lowercase    ,      2						):
          if is_prime[i]:
               primes.append(_lowercase						)

     return primes




def 			_a			(		_lowercase      :			int = 1000000						):





     '''simple docstring'''






     __UpperCAmelCase			:				List[str]													=    prime_sieve(_lowercase						)
     __UpperCAmelCase			:				Optional[Any]													=    0
     __UpperCAmelCase			:				int													=    0

     for i in range(len(_lowercase						)						):
          for j in range(i + length    ,      len(_lowercase						)						):
               __UpperCAmelCase			:				Optional[Any]													=    sum(primes[i:j]						)
               if sol >= ceiling:
                    break

               if sol in primes:
                    __UpperCAmelCase			:				Any													=    j - i
                    __UpperCAmelCase			:				List[Any]													=    sol

     return largest


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