


import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest


class     lowercase__  (					__SCREAMING_SNAKE_CASE		):
  A__=  (DPMSolverSinglestepScheduler,)
  A__=  (('num_inference_steps', 25),)






  def 			_UpperCAmelCase  (					self      :					Dict		,						**_lowercase      :					Optional[int] ):




         """simple docstring"""







         UpperCAmelCase__										=     {
             "num_train_timesteps": 10_00,
             "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",
             "lambda_min_clipped": -float("inf" ),
             "variance_type": None,
         }

         config.update(**_lowercase )
         return config






  def 			_UpperCAmelCase  (					self      :					str		,						_lowercase      :					Optional[Any]=0		,						**_lowercase      :					Optional[Any] ):




         """simple docstring"""







         UpperCAmelCase__										=     dict(self.forward_default_kwargs )
         UpperCAmelCase__										=     kwargs.pop("num_inference_steps"		,						_lowercase )
         UpperCAmelCase__										=     self.dummy_sample
         UpperCAmelCase__										=     0.1 * sample
         UpperCAmelCase__										=     [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

         for scheduler_class in self.scheduler_classes:
                UpperCAmelCase__										=     self.get_scheduler_config(**_lowercase )
                UpperCAmelCase__										=     scheduler_class(**_lowercase )
                scheduler.set_timesteps(_lowercase )
                # copy over dummy past residuals
                UpperCAmelCase__										=     dummy_past_residuals[: scheduler.config.solver_order]

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

                UpperCAmelCase__						,			UpperCAmelCase__										=     sample, sample
                for t in range(_lowercase		,						time_step + scheduler.config.solver_order + 1 ):
                       UpperCAmelCase__										=     scheduler.step(_lowercase		,						_lowercase		,						_lowercase		,						**_lowercase ).prev_sample
                       UpperCAmelCase__										=     new_scheduler.step(_lowercase		,						_lowercase		,						_lowercase		,						**_lowercase ).prev_sample

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






  def 			_UpperCAmelCase  (					self      :					List[Any] ):




         """simple docstring"""







         pass






  def 			_UpperCAmelCase  (					self      :					Optional[int]		,						_lowercase      :					Any=0		,						**_lowercase      :					Optional[int] ):




         """simple docstring"""







         UpperCAmelCase__										=     dict(self.forward_default_kwargs )
         UpperCAmelCase__										=     kwargs.pop("num_inference_steps"		,						_lowercase )
         UpperCAmelCase__										=     self.dummy_sample
         UpperCAmelCase__										=     0.1 * sample
         UpperCAmelCase__										=     [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

         for scheduler_class in self.scheduler_classes:
                UpperCAmelCase__										=     self.get_scheduler_config()
                UpperCAmelCase__										=     scheduler_class(**_lowercase )
                scheduler.set_timesteps(_lowercase )

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

                with tempfile.TemporaryDirectory() as tmpdirname:
                       scheduler.save_config(_lowercase )
                       UpperCAmelCase__										=     scheduler_class.from_pretrained(_lowercase )
                       # copy over dummy past residuals
                       new_scheduler.set_timesteps(_lowercase )

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

                UpperCAmelCase__										=     scheduler.step(_lowercase		,						_lowercase		,						_lowercase		,						**_lowercase ).prev_sample
                UpperCAmelCase__										=     new_scheduler.step(_lowercase		,						_lowercase		,						_lowercase		,						**_lowercase ).prev_sample

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






  def 			_UpperCAmelCase  (					self      :					str		,						_lowercase      :					int=None		,						**_lowercase      :					Union[str, Any] ):




         """simple docstring"""







         if scheduler is None:
                UpperCAmelCase__										=     self.scheduler_classes[0]
                UpperCAmelCase__										=     self.get_scheduler_config(**_lowercase )
                UpperCAmelCase__										=     scheduler_class(**_lowercase )

         UpperCAmelCase__										=     self.scheduler_classes[0]
         UpperCAmelCase__										=     self.get_scheduler_config(**_lowercase )
         UpperCAmelCase__										=     scheduler_class(**_lowercase )

         UpperCAmelCase__										=     10
         UpperCAmelCase__										=     self.dummy_model()
         UpperCAmelCase__										=     self.dummy_sample_deter
         scheduler.set_timesteps(_lowercase )

         for i, t in enumerate(scheduler.timesteps ):
                UpperCAmelCase__										=     model(_lowercase		,						_lowercase )
                UpperCAmelCase__										=     scheduler.step(_lowercase		,						_lowercase		,						_lowercase ).prev_sample

         return sample






  def 			_UpperCAmelCase  (					self      :					Optional[Any] ):




         """simple docstring"""







         UpperCAmelCase__										=     DPMSolverSinglestepScheduler(**self.get_scheduler_config() )
         UpperCAmelCase__										=     50
         UpperCAmelCase__										=     self.dummy_model()
         UpperCAmelCase__										=     self.dummy_sample_deter
         scheduler.set_timesteps(_lowercase )

         # make sure that the first t is uneven
         for i, t in enumerate(scheduler.timesteps[3:] ):
                UpperCAmelCase__										=     model(_lowercase		,						_lowercase )
                UpperCAmelCase__										=     scheduler.step(_lowercase		,						_lowercase		,						_lowercase ).prev_sample

         UpperCAmelCase__										=     torch.mean(torch.abs(_lowercase ) )

         assert abs(result_mean.item() - 0.2_5_7_4 ) < 1E-3






  def 			_UpperCAmelCase  (					self      :					Dict ):




         """simple docstring"""







         for timesteps in [25, 50, 1_00, 9_99, 10_00]:
                self.check_over_configs(num_train_timesteps=_lowercase )






  def 			_UpperCAmelCase  (					self      :					Optional[Any] ):




         """simple docstring"""







         UpperCAmelCase__										=     DPMSolverSinglestepScheduler(**self.get_scheduler_config() )
         UpperCAmelCase__										=     self.full_loop(scheduler=_lowercase )
         UpperCAmelCase__										=     torch.mean(torch.abs(_lowercase ) )

         assert abs(result_mean.item() - 0.2_7_9_1 ) < 1E-3

         UpperCAmelCase__										=     DEISMultistepScheduler.from_config(scheduler.config )
         UpperCAmelCase__										=     DPMSolverMultistepScheduler.from_config(scheduler.config )
         UpperCAmelCase__										=     UniPCMultistepScheduler.from_config(scheduler.config )
         UpperCAmelCase__										=     DPMSolverSinglestepScheduler.from_config(scheduler.config )

         UpperCAmelCase__										=     self.full_loop(scheduler=_lowercase )
         UpperCAmelCase__										=     torch.mean(torch.abs(_lowercase ) )

         assert abs(result_mean.item() - 0.2_7_9_1 ) < 1E-3






  def 			_UpperCAmelCase  (					self      :					Dict ):




         """simple docstring"""







         self.check_over_configs(thresholding=_lowercase )
         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=_lowercase		,						prediction_type=_lowercase		,						sample_max_value=_lowercase		,						algorithm_type="dpmsolver++"		,						solver_order=_lowercase		,						solver_type=_lowercase		,						)






  def 			_UpperCAmelCase  (					self      :					Union[str, Any] ):




         """simple docstring"""







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






  def 			_UpperCAmelCase  (					self      :					Optional[int] ):




         """simple docstring"""







         for algorithm_type in ["dpmsolver", "dpmsolver++"]:
                for solver_type in ["midpoint", "heun"]:
                       for order in [1, 2, 3]:
                              for prediction_type in ["epsilon", "sample"]:
                                     self.check_over_configs(
                                         solver_order=_lowercase		,						solver_type=_lowercase		,						prediction_type=_lowercase		,						algorithm_type=_lowercase		,						)
                                     UpperCAmelCase__										=     self.full_loop(
                                         solver_order=_lowercase		,						solver_type=_lowercase		,						prediction_type=_lowercase		,						algorithm_type=_lowercase		,						)
                                     assert not torch.isnan(_lowercase ).any(), "Samples have nan numbers"






  def 			_UpperCAmelCase  (					self      :					Tuple ):




         """simple docstring"""







         self.check_over_configs(lower_order_final=_lowercase )
         self.check_over_configs(lower_order_final=_lowercase )






  def 			_UpperCAmelCase  (					self      :					List[Any] ):




         """simple docstring"""







         self.check_over_configs(lambda_min_clipped=-float("inf" ) )
         self.check_over_configs(lambda_min_clipped=-5.1 )






  def 			_UpperCAmelCase  (					self      :					str ):




         """simple docstring"""







         self.check_over_configs(variance_type=_lowercase )
         self.check_over_configs(variance_type="learned_range" )






  def 			_UpperCAmelCase  (					self      :					List[Any] ):




         """simple docstring"""







         for num_inference_steps in [1, 2, 3, 5, 10, 50, 1_00, 9_99, 10_00]:
                self.check_over_forward(num_inference_steps=_lowercase		,						time_step=0 )






  def 			_UpperCAmelCase  (					self      :					Tuple ):




         """simple docstring"""







         UpperCAmelCase__										=     self.full_loop()
         UpperCAmelCase__										=     torch.mean(torch.abs(_lowercase ) )

         assert abs(result_mean.item() - 0.2_7_9_1 ) < 1E-3






  def 			_UpperCAmelCase  (					self      :					Any ):




         """simple docstring"""







         UpperCAmelCase__										=     self.full_loop(use_karras_sigmas=_lowercase )
         UpperCAmelCase__										=     torch.mean(torch.abs(_lowercase ) )

         assert abs(result_mean.item() - 0.2_2_4_8 ) < 1E-3






  def 			_UpperCAmelCase  (					self      :					Dict ):




         """simple docstring"""







         UpperCAmelCase__										=     self.full_loop(prediction_type="v_prediction" )
         UpperCAmelCase__										=     torch.mean(torch.abs(_lowercase ) )

         assert abs(result_mean.item() - 0.1_4_5_3 ) < 1E-3






  def 			_UpperCAmelCase  (					self      :					Optional[Any] ):




         """simple docstring"""







         UpperCAmelCase__										=     self.full_loop(prediction_type="v_prediction"		,						use_karras_sigmas=_lowercase )
         UpperCAmelCase__										=     torch.mean(torch.abs(_lowercase ) )

         assert abs(result_mean.item() - 0.0_6_4_9 ) < 1E-3






  def 			_UpperCAmelCase  (					self      :					Optional[Any] ):




         """simple docstring"""







         UpperCAmelCase__										=     self.scheduler_classes[0]
         UpperCAmelCase__										=     self.get_scheduler_config(thresholding=_lowercase		,						dynamic_thresholding_ratio=0 )
         UpperCAmelCase__										=     scheduler_class(**_lowercase )

         UpperCAmelCase__										=     10
         UpperCAmelCase__										=     self.dummy_model()
         UpperCAmelCase__										=     self.dummy_sample_deter.half()
         scheduler.set_timesteps(_lowercase )

         for i, t in enumerate(scheduler.timesteps ):
                UpperCAmelCase__										=     model(_lowercase		,						_lowercase )
                UpperCAmelCase__										=     scheduler.step(_lowercase		,						_lowercase		,						_lowercase ).prev_sample

         assert sample.dtype == torch.floataa









from __future__ import annotations





def __UpperCAmelCase       (		__A			)						->					list[int]:



       '''simple docstring'''





       UpperCAmelCase__										=     [True] * limit
       UpperCAmelCase__										=     False
       UpperCAmelCase__										=     False
       UpperCAmelCase__										=     True

       for i in range(3 ,						int(limit**0.5 + 1			) ,						2			):
              UpperCAmelCase__										=     i * 2
              while index < limit:
                     UpperCAmelCase__										=     False
                     UpperCAmelCase__										=     index + i

       UpperCAmelCase__										=     [2]

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

       return primes





def __UpperCAmelCase       (		__A = 1_0_0_0_0_0_0			)						->					int:



       '''simple docstring'''





       UpperCAmelCase__										=     prime_sieve(__A			)
       UpperCAmelCase__										=     0
       UpperCAmelCase__										=     0

       for i in range(len(__A			)			):
              for j in range(i + length ,						len(__A			)			):
                     UpperCAmelCase__										=     sum(primes[i:j]			)
                     if sol >= ceiling:
                            break

                     if sol in primes:
                            UpperCAmelCase__										=     j - i
                            UpperCAmelCase__										=     sol

       return largest


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





