



import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest




class  snake_case__						(	lowerCAmelCase_					):






     """simple docstring"""


     _SCREAMING_SNAKE_CASE            =     (DPMSolverMultistepScheduler,)
     _SCREAMING_SNAKE_CASE            =     (("""num_inference_steps""", 25),)






     def        lowercase_       (	self  :  Union[str, Any],			**_snake_case  :  str )       ->Tuple:
            snake_case__							:     int            =     {
                'num_train_timesteps': 1_0_0_0,
                'beta_start': 0.0_0_0_1,
                'beta_end': 0.0_2,
                'beta_schedule': 'linear',
                'solver_order': 2,
                'prediction_type': 'epsilon',
                'thresholding': False,
                'sample_max_value': 1.0,
                'algorithm_type': 'dpmsolver++',
                'solver_type': 'midpoint',
                'lower_order_final': False,
                'lambda_min_clipped': -float('inf' ),
                'variance_type': None,
            }

            config.update(**_snake_case )
            return config






     def        lowercase_       (	self  :  Tuple,			_snake_case  :  Optional[int]=0,			**_snake_case  :  Optional[int] )       ->Dict:
            snake_case__							:     Tuple            =     dict(self.forward_default_kwargs )
            snake_case__							:     Union[str, Any]            =     kwargs.pop('num_inference_steps',			_snake_case )
            snake_case__							:     Optional[Any]            =     self.dummy_sample
            snake_case__							:     Optional[int]            =     0.1 * sample
            snake_case__							:     Optional[Any]            =     [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

            for scheduler_class in self.scheduler_classes:
                   snake_case__							:     int            =     self.get_scheduler_config(**_snake_case )
                   snake_case__							:     Any            =     scheduler_class(**_snake_case )
                   scheduler.set_timesteps(_snake_case )
                   # copy over dummy past residuals
                   snake_case__							:     Optional[Any]            =     dummy_past_residuals[: scheduler.config.solver_order]

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

                   snake_case__     ,    snake_case__							:     Optional[int]            =     sample, sample
                   for t in range(_snake_case,			time_step + scheduler.config.solver_order + 1 ):
                          snake_case__							:     str            =     scheduler.step(_snake_case,			_snake_case,			_snake_case,			**_snake_case ).prev_sample
                          snake_case__							:     Dict            =     new_scheduler.step(_snake_case,			_snake_case,			_snake_case,			**_snake_case ).prev_sample

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






     def        lowercase_       (	self  :  str )       ->Any:
            pass






     def        lowercase_       (	self  :  Optional[Any],			_snake_case  :  Optional[Any]=0,			**_snake_case  :  Any )       ->Any:
            snake_case__							:     Any            =     dict(self.forward_default_kwargs )
            snake_case__							:     List[str]            =     kwargs.pop('num_inference_steps',			_snake_case )
            snake_case__							:     str            =     self.dummy_sample
            snake_case__							:     Dict            =     0.1 * sample
            snake_case__							:     Dict            =     [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

            for scheduler_class in self.scheduler_classes:
                   snake_case__							:     int            =     self.get_scheduler_config()
                   snake_case__							:     List[str]            =     scheduler_class(**_snake_case )
                   scheduler.set_timesteps(_snake_case )

                   # copy over dummy past residuals (must be after setting timesteps)
                   snake_case__							:     List[str]            =     dummy_past_residuals[: scheduler.config.solver_order]

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

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

                   snake_case__							:     int            =     scheduler.step(_snake_case,			_snake_case,			_snake_case,			**_snake_case ).prev_sample
                   snake_case__							:     str            =     new_scheduler.step(_snake_case,			_snake_case,			_snake_case,			**_snake_case ).prev_sample

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






     def        lowercase_       (	self  :  Dict,			_snake_case  :  int=None,			**_snake_case  :  str )       ->Union[str, Any]:
            if scheduler is None:
                   snake_case__							:     Union[str, Any]            =     self.scheduler_classes[0]
                   snake_case__							:     Optional[int]            =     self.get_scheduler_config(**_snake_case )
                   snake_case__							:     str            =     scheduler_class(**_snake_case )

            snake_case__							:     Optional[int]            =     1_0
            snake_case__							:     Optional[int]            =     self.dummy_model()
            snake_case__							:     Dict            =     self.dummy_sample_deter
            scheduler.set_timesteps(_snake_case )

            for i, t in enumerate(scheduler.timesteps ):
                   snake_case__							:     Optional[Any]            =     model(_snake_case,			_snake_case )
                   snake_case__							:     Tuple            =     scheduler.step(_snake_case,			_snake_case,			_snake_case ).prev_sample

            return sample






     def        lowercase_       (	self  :  Dict )       ->int:
            snake_case__							:     str            =     dict(self.forward_default_kwargs )

            snake_case__							:     Any            =     kwargs.pop('num_inference_steps',			_snake_case )

            for scheduler_class in self.scheduler_classes:
                   snake_case__							:     Any            =     self.get_scheduler_config()
                   snake_case__							:     Union[str, Any]            =     scheduler_class(**_snake_case )

                   snake_case__							:     Optional[int]            =     self.dummy_sample
                   snake_case__							:     str            =     0.1 * sample

                   if num_inference_steps is not None and hasattr(_snake_case,			'set_timesteps' ):
                          scheduler.set_timesteps(_snake_case )
                   elif num_inference_steps is not None and not hasattr(_snake_case,			'set_timesteps' ):
                          snake_case__							:     Tuple            =     num_inference_steps

                   # copy over dummy past residuals (must be done after set_timesteps)
                   snake_case__							:     Tuple            =     [residual + 0.2, residual + 0.1_5, residual + 0.1_0]
                   snake_case__							:     Any            =     dummy_past_residuals[: scheduler.config.solver_order]

                   snake_case__							:     Dict            =     scheduler.timesteps[5]
                   snake_case__							:     Dict            =     scheduler.timesteps[6]

                   snake_case__							:     str            =     scheduler.step(_snake_case,			_snake_case,			_snake_case,			**_snake_case ).prev_sample
                   snake_case__							:     Tuple            =     scheduler.step(_snake_case,			_snake_case,			_snake_case,			**_snake_case ).prev_sample

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






     def        lowercase_       (	self  :  Tuple )       ->Optional[Any]:
            for timesteps in [2_5, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
                   self.check_over_configs(num_train_timesteps=_snake_case )






     def        lowercase_       (	self  :  Dict )       ->Optional[Any]:
            self.check_over_configs(thresholding=_snake_case )
            for order in [1, 2, 3]:
                   for solver_type in ["midpoint", "heun"]:
                          for threshold in [0.5, 1.0, 2.0]:
                                 for prediction_type in ["epsilon", "sample"]:
                                        self.check_over_configs(
                                            thresholding=_snake_case,			prediction_type=_snake_case,			sample_max_value=_snake_case,			algorithm_type='dpmsolver++',			solver_order=_snake_case,			solver_type=_snake_case,			)






     def        lowercase_       (	self  :  str )       ->Optional[int]:
            for prediction_type in ["epsilon", "v_prediction"]:
                   self.check_over_configs(prediction_type=_snake_case )






     def        lowercase_       (	self  :  Dict )       ->Union[str, Any]:
            for algorithm_type in ["dpmsolver", "dpmsolver++", "sde-dpmsolver", "sde-dpmsolver++"]:
                   for solver_type in ["midpoint", "heun"]:
                          for order in [1, 2, 3]:
                                 for prediction_type in ["epsilon", "sample"]:
                                        if algorithm_type in ["sde-dpmsolver", "sde-dpmsolver++"]:
                                               if order == 3:
                                                      continue
                                        else:
                                               self.check_over_configs(
                                                   solver_order=_snake_case,			solver_type=_snake_case,			prediction_type=_snake_case,			algorithm_type=_snake_case,			)
                                        snake_case__							:     Dict            =     self.full_loop(
                                            solver_order=_snake_case,			solver_type=_snake_case,			prediction_type=_snake_case,			algorithm_type=_snake_case,			)
                                        assert not torch.isnan(_snake_case ).any(), "Samples have nan numbers"






     def        lowercase_       (	self  :  int )       ->Dict:
            self.check_over_configs(lower_order_final=_snake_case )
            self.check_over_configs(lower_order_final=_snake_case )






     def        lowercase_       (	self  :  List[str] )       ->Dict:
            self.check_over_configs(lambda_min_clipped=-float('inf' ) )
            self.check_over_configs(lambda_min_clipped=-5.1 )






     def        lowercase_       (	self  :  Dict )       ->str:
            self.check_over_configs(variance_type=_snake_case )
            self.check_over_configs(variance_type='learned_range' )






     def        lowercase_       (	self  :  Optional[Any] )       ->Any:
            for num_inference_steps in [1, 2, 3, 5, 1_0, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
                   self.check_over_forward(num_inference_steps=_snake_case,			time_step=0 )






     def        lowercase_       (	self  :  Tuple )       ->List[Any]:
            snake_case__							:     List[str]            =     self.full_loop()
            snake_case__							:     str            =     torch.mean(torch.abs(_snake_case ) )

            assert abs(result_mean.item() - 0.3_3_0_1 ) < 1e-3






     def        lowercase_       (	self  :  Dict )       ->Dict:
            snake_case__							:     Any            =     self.full_loop(thresholding=_snake_case,			dynamic_thresholding_ratio=0.8_7,			sample_max_value=0.5 )
            snake_case__							:     List[str]            =     torch.mean(torch.abs(_snake_case ) )

            assert abs(result_mean.item() - 1.1_3_6_4 ) < 1e-3






     def        lowercase_       (	self  :  List[str] )       ->Optional[int]:
            snake_case__							:     Tuple            =     self.full_loop(prediction_type='v_prediction' )
            snake_case__							:     Optional[int]            =     torch.mean(torch.abs(_snake_case ) )

            assert abs(result_mean.item() - 0.2_2_5_1 ) < 1e-3






     def        lowercase_       (	self  :  Union[str, Any] )       ->Optional[Any]:
            snake_case__							:     Optional[int]            =     self.full_loop(prediction_type='v_prediction',			use_karras_sigmas=_snake_case )
            snake_case__							:     Optional[int]            =     torch.mean(torch.abs(_snake_case ) )

            assert abs(result_mean.item() - 0.2_0_9_6 ) < 1e-3






     def        lowercase_       (	self  :  List[str] )       ->List[str]:
            # make sure that iterating over schedulers with same config names gives same results
            # for defaults
            snake_case__							:     int            =     DPMSolverMultistepScheduler(**self.get_scheduler_config() )
            snake_case__							:     str            =     self.full_loop(scheduler=_snake_case )
            snake_case__							:     Union[str, Any]            =     torch.mean(torch.abs(_snake_case ) )

            assert abs(result_mean.item() - 0.3_3_0_1 ) < 1e-3

            snake_case__							:     Dict            =     DPMSolverSinglestepScheduler.from_config(scheduler.config )
            snake_case__							:     Optional[int]            =     UniPCMultistepScheduler.from_config(scheduler.config )
            snake_case__							:     Any            =     DEISMultistepScheduler.from_config(scheduler.config )
            snake_case__							:     List[str]            =     DPMSolverMultistepScheduler.from_config(scheduler.config )

            snake_case__							:     Any            =     self.full_loop(scheduler=_snake_case )
            snake_case__							:     Dict            =     torch.mean(torch.abs(_snake_case ) )

            assert abs(result_mean.item() - 0.3_3_0_1 ) < 1e-3






     def        lowercase_       (	self  :  List[Any] )       ->Optional[int]:
            snake_case__							:     Optional[int]            =     self.scheduler_classes[0]
            snake_case__							:     Optional[int]            =     self.get_scheduler_config(thresholding=_snake_case,			dynamic_thresholding_ratio=0 )
            snake_case__							:     Union[str, Any]            =     scheduler_class(**_snake_case )

            snake_case__							:     int            =     1_0
            snake_case__							:     Optional[Any]            =     self.dummy_model()
            snake_case__							:     int            =     self.dummy_sample_deter.half()
            scheduler.set_timesteps(_snake_case )

            for i, t in enumerate(scheduler.timesteps ):
                   snake_case__							:     List[Any]            =     model(_snake_case,			_snake_case )
                   snake_case__							:     List[Any]            =     scheduler.step(_snake_case,			_snake_case,			_snake_case ).prev_sample

            assert sample.dtype == torch.floataa






     def        lowercase_       (	self  :  Dict,			**_snake_case  :  Dict )       ->List[str]:
            for scheduler_class in self.scheduler_classes:
                   snake_case__							:     Any            =     self.get_scheduler_config(**_snake_case )
                   snake_case__							:     int            =     scheduler_class(**_snake_case )

                   scheduler.set_timesteps(scheduler.config.num_train_timesteps )
                   assert len(scheduler.timesteps.unique() ) == scheduler.num_inference_steps











import math
def 	lowercase_							(A			:			int				):

       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(A				) + 1				)		, 6				):
              if number % i == 0 or number % (i + 2) == 0:
                     return False
       return True
def 	lowercase_							(A			:			int = 1_0_0_0		, A			:			int = 1_0_0_0				):
       snake_case__							:     str            =     [0, 0, 0]  # length, a, b
       for a in range((a_limit * -1) + 1		, A				):
              for b in range(2		, A				):
                     if is_prime(A				):
                            snake_case__							:     Optional[Any]            =     0
                            snake_case__							:     Optional[int]            =     0
                            while is_prime((n**2) + (a * n) + b				):
                                   count += 1
                                   n += 1
                            if count > longest[0]:
                                   snake_case__							:     Union[str, Any]            =     [count, a, b]
       snake_case__							:     List[str]            =     longest[1] * longest[2]
       return ans


if __name__ == "__main__":
 print(solution(1_000, 1_000))






