

import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest



class 		a_	(							lowerCamelCase_					):



      """simple docstring"""







      __UpperCAmelCase      		=				(DPMSolverMultistepScheduler,)
      __UpperCAmelCase      		=				(('num_inference_steps', 25),)
      def 	_lowerCAmelCase					(				self					:							Any      ,**snake_case					:							int    ):
           SCREAMING_SNAKE_CASE										={
               'num_train_timesteps': 1000,
               'beta_start': 0.0_001,
               'beta_end': 0.02,
               '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 	_lowerCAmelCase					(				self					:							Any      ,snake_case					:							str=0      ,**snake_case					:							int    ):
           SCREAMING_SNAKE_CASE										=dict(self.forward_default_kwargs    )
           SCREAMING_SNAKE_CASE										=kwargs.pop('num_inference_steps'      ,snake_case    )
           SCREAMING_SNAKE_CASE										=self.dummy_sample
           SCREAMING_SNAKE_CASE										=0.1 * sample
           SCREAMING_SNAKE_CASE										=[residual + 0.2, residual + 0.15, residual + 0.10]

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

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

                SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE										=sample, sample
                for t in range(snake_case      ,time_step + scheduler.config.solver_order + 1    ):
                     SCREAMING_SNAKE_CASE										=scheduler.step(snake_case      ,snake_case      ,snake_case      ,**snake_case    ).prev_sample
                     SCREAMING_SNAKE_CASE										=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 	_lowerCAmelCase					(				self					:							str    ):
           pass
      def 	_lowerCAmelCase					(				self					:							str      ,snake_case					:							Optional[int]=0      ,**snake_case					:							Tuple    ):
           SCREAMING_SNAKE_CASE										=dict(self.forward_default_kwargs    )
           SCREAMING_SNAKE_CASE										=kwargs.pop('num_inference_steps'      ,snake_case    )
           SCREAMING_SNAKE_CASE										=self.dummy_sample
           SCREAMING_SNAKE_CASE										=0.1 * sample
           SCREAMING_SNAKE_CASE										=[residual + 0.2, residual + 0.15, residual + 0.10]

           for scheduler_class in self.scheduler_classes:
                SCREAMING_SNAKE_CASE										=self.get_scheduler_config()
                SCREAMING_SNAKE_CASE										=scheduler_class(**snake_case    )
                scheduler.set_timesteps(snake_case    )

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

                with tempfile.TemporaryDirectory() as tmpdirname:
                     scheduler.save_config(snake_case    )
                     SCREAMING_SNAKE_CASE										=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)
                     SCREAMING_SNAKE_CASE										=dummy_past_residuals[: new_scheduler.config.solver_order]

                SCREAMING_SNAKE_CASE										=scheduler.step(snake_case      ,snake_case      ,snake_case      ,**snake_case    ).prev_sample
                SCREAMING_SNAKE_CASE										=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 	_lowerCAmelCase					(				self					:							List[str]      ,snake_case					:							str=None      ,**snake_case					:							Optional[Any]    ):
           if scheduler is None:
                SCREAMING_SNAKE_CASE										=self.scheduler_classes[0]
                SCREAMING_SNAKE_CASE										=self.get_scheduler_config(**snake_case    )
                SCREAMING_SNAKE_CASE										=scheduler_class(**snake_case    )

           SCREAMING_SNAKE_CASE										=10
           SCREAMING_SNAKE_CASE										=self.dummy_model()
           SCREAMING_SNAKE_CASE										=self.dummy_sample_deter
           scheduler.set_timesteps(snake_case    )

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

           return sample
      def 	_lowerCAmelCase					(				self					:							Optional[int]    ):
           SCREAMING_SNAKE_CASE										=dict(self.forward_default_kwargs    )

           SCREAMING_SNAKE_CASE										=kwargs.pop('num_inference_steps'      ,snake_case    )

           for scheduler_class in self.scheduler_classes:
                SCREAMING_SNAKE_CASE										=self.get_scheduler_config()
                SCREAMING_SNAKE_CASE										=scheduler_class(**snake_case    )

                SCREAMING_SNAKE_CASE										=self.dummy_sample
                SCREAMING_SNAKE_CASE										=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'    ):
                     SCREAMING_SNAKE_CASE										=num_inference_steps

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

                SCREAMING_SNAKE_CASE										=scheduler.timesteps[5]
                SCREAMING_SNAKE_CASE										=scheduler.timesteps[6]

                SCREAMING_SNAKE_CASE										=scheduler.step(snake_case      ,snake_case      ,snake_case      ,**snake_case    ).prev_sample
                SCREAMING_SNAKE_CASE										=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 	_lowerCAmelCase					(				self					:							List[str]    ):
           for timesteps in [25, 50, 100, 999, 1000]:
                self.check_over_configs(num_train_timesteps=snake_case    )
      def 	_lowerCAmelCase					(				self					:							Tuple    ):
           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 	_lowerCAmelCase					(				self					:							Any    ):
           for prediction_type in ["epsilon", "v_prediction"]:
                self.check_over_configs(prediction_type=snake_case    )
      def 	_lowerCAmelCase					(				self					:							Tuple    ):
           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      ,)
                               SCREAMING_SNAKE_CASE										=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 	_lowerCAmelCase					(				self					:							Any    ):
           self.check_over_configs(lower_order_final=snake_case    )
           self.check_over_configs(lower_order_final=snake_case    )
      def 	_lowerCAmelCase					(				self					:							List[str]    ):
           self.check_over_configs(lambda_min_clipped=-float('inf'    )    )
           self.check_over_configs(lambda_min_clipped=-5.1    )
      def 	_lowerCAmelCase					(				self					:							int    ):
           self.check_over_configs(variance_type=snake_case    )
           self.check_over_configs(variance_type='learned_range'    )
      def 	_lowerCAmelCase					(				self					:							Tuple    ):
           for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
                self.check_over_forward(num_inference_steps=snake_case      ,time_step=0    )
      def 	_lowerCAmelCase					(				self					:							Optional[int]    ):
           SCREAMING_SNAKE_CASE										=self.full_loop()
           SCREAMING_SNAKE_CASE										=torch.mean(torch.abs(snake_case    )    )

           assert abs(result_mean.item() - 0.3_301    ) < 1e-3
      def 	_lowerCAmelCase					(				self					:							Optional[int]    ):
           SCREAMING_SNAKE_CASE										=self.full_loop(thresholding=snake_case      ,dynamic_thresholding_ratio=0.87      ,sample_max_value=0.5    )
           SCREAMING_SNAKE_CASE										=torch.mean(torch.abs(snake_case    )    )

           assert abs(result_mean.item() - 1.1_364    ) < 1e-3
      def 	_lowerCAmelCase					(				self					:							List[str]    ):
           SCREAMING_SNAKE_CASE										=self.full_loop(prediction_type='v_prediction'    )
           SCREAMING_SNAKE_CASE										=torch.mean(torch.abs(snake_case    )    )

           assert abs(result_mean.item() - 0.2_251    ) < 1e-3
      def 	_lowerCAmelCase					(				self					:							str    ):
           SCREAMING_SNAKE_CASE										=self.full_loop(prediction_type='v_prediction'      ,use_karras_sigmas=snake_case    )
           SCREAMING_SNAKE_CASE										=torch.mean(torch.abs(snake_case    )    )

           assert abs(result_mean.item() - 0.2_096    ) < 1e-3
      def 	_lowerCAmelCase					(				self					:							List[Any]    ):
           # make sure that iterating over schedulers with same config names gives same results
           # for defaults
           SCREAMING_SNAKE_CASE										=DPMSolverMultistepScheduler(**self.get_scheduler_config()    )
           SCREAMING_SNAKE_CASE										=self.full_loop(scheduler=snake_case    )
           SCREAMING_SNAKE_CASE										=torch.mean(torch.abs(snake_case    )    )

           assert abs(result_mean.item() - 0.3_301    ) < 1e-3

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

           SCREAMING_SNAKE_CASE										=self.full_loop(scheduler=snake_case    )
           SCREAMING_SNAKE_CASE										=torch.mean(torch.abs(snake_case    )    )

           assert abs(result_mean.item() - 0.3_301    ) < 1e-3
      def 	_lowerCAmelCase					(				self					:							str    ):
           SCREAMING_SNAKE_CASE										=self.scheduler_classes[0]
           SCREAMING_SNAKE_CASE										=self.get_scheduler_config(thresholding=snake_case      ,dynamic_thresholding_ratio=0    )
           SCREAMING_SNAKE_CASE										=scheduler_class(**snake_case    )

           SCREAMING_SNAKE_CASE										=10
           SCREAMING_SNAKE_CASE										=self.dummy_model()
           SCREAMING_SNAKE_CASE										=self.dummy_sample_deter.half()
           scheduler.set_timesteps(snake_case    )

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

           assert sample.dtype == torch.floataa


      def 	_lowerCAmelCase					(				self					:							Optional[Any]      ,**snake_case					:							Union[str, Any]    ):
           for scheduler_class in self.scheduler_classes:
                SCREAMING_SNAKE_CASE										=self.get_scheduler_config(**snake_case    )
                SCREAMING_SNAKE_CASE										=scheduler_class(**snake_case    )

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




import decimal







def       snake_case__    (       ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE										=0
     SCREAMING_SNAKE_CASE										=decimal.Context(prec=105      )
     for i in range(2,   100      ):
          SCREAMING_SNAKE_CASE										=decimal.Decimal(lowerCAmelCase_      )
          SCREAMING_SNAKE_CASE										=number.sqrt(lowerCAmelCase_      )
          if len(str(lowerCAmelCase_      )      ) > 1:
               answer += int(str(lowerCAmelCase_      )[0]      )
               SCREAMING_SNAKE_CASE										=str(lowerCAmelCase_      )[2:101]
               answer += sum(int(lowerCAmelCase_      ) for x in sqrt_number_str      )
     return answer


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 print(f'{solution() = }')

