


"""simple docstring"""






import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest


class     __lowerCamelCase  (					a__				):

     '''simple docstring'''
     A_       :	Optional[int]    =		(DEISMultistepScheduler,)
     A_       :	Dict    =		(('num_inference_steps', 25),)


     def 							_UpperCAmelCase      (      self    ,				**__UpperCAmelCase							)       ->							Optional[Any]:
          _a    	=				{
              '''num_train_timesteps''': 1000,
              '''beta_start''': 0.0001,
              '''beta_end''': 0.02,
              '''beta_schedule''': '''linear''',
              '''solver_order''': 2,
          }

          config.update(**__UpperCAmelCase							)
          return config


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase=0    ,				**__UpperCAmelCase							)       ->							Any:
          _a    	=				dict(self.forward_default_kwargs							)
          _a    	=				kwargs.pop('''num_inference_steps'''    ,				__UpperCAmelCase							)
          _a    	=				self.dummy_sample
          _a    	=				0.1 * sample
          _a    	=				[residual + 0.2, residual + 0.15, residual + 0.10]

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

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

               _a	,      _a    	=				sample, sample
               for t in range(__UpperCAmelCase    ,				time_step + scheduler.config.solver_order + 1							):
                    _a    	=				scheduler.step(__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				**__UpperCAmelCase							).prev_sample
                    _a    	=				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 							_UpperCAmelCase      (      self							)       ->							str:
          pass


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase=0    ,				**__UpperCAmelCase							)       ->							List[str]:
          _a    	=				dict(self.forward_default_kwargs							)
          _a    	=				kwargs.pop('''num_inference_steps'''    ,				__UpperCAmelCase							)
          _a    	=				self.dummy_sample
          _a    	=				0.1 * sample
          _a    	=				[residual + 0.2, residual + 0.15, residual + 0.10]

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

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

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

               _a    	=				scheduler.step(__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				**__UpperCAmelCase							).prev_sample
               _a    	=				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 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase=None    ,				**__UpperCAmelCase							)       ->							Optional[Any]:
          if scheduler is None:
               _a    	=				self.scheduler_classes[0]
               _a    	=				self.get_scheduler_config(**__UpperCAmelCase							)
               _a    	=				scheduler_class(**__UpperCAmelCase							)

          _a    	=				self.scheduler_classes[0]
          _a    	=				self.get_scheduler_config(**__UpperCAmelCase							)
          _a    	=				scheduler_class(**__UpperCAmelCase							)

          _a    	=				10
          _a    	=				self.dummy_model()
          _a    	=				self.dummy_sample_deter
          scheduler.set_timesteps(__UpperCAmelCase							)

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

          return sample


     def 							_UpperCAmelCase      (      self							)       ->							Optional[int]:
          _a    	=				dict(self.forward_default_kwargs							)

          _a    	=				kwargs.pop('''num_inference_steps'''    ,				__UpperCAmelCase							)

          for scheduler_class in self.scheduler_classes:
               _a    	=				self.get_scheduler_config()
               _a    	=				scheduler_class(**__UpperCAmelCase							)

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

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

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

               _a    	=				scheduler.step(__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				**__UpperCAmelCase							).prev_sample
               _a    	=				scheduler.step(__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				**__UpperCAmelCase							).prev_sample

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


     def 							_UpperCAmelCase      (      self							)       ->							Union[str, Any]:
          # make sure that iterating over schedulers with same config names gives same results
          # for defaults
          _a    	=				DEISMultistepScheduler(**self.get_scheduler_config()							)
          _a    	=				self.full_loop(scheduler=__UpperCAmelCase							)
          _a    	=				torch.mean(torch.abs(__UpperCAmelCase							)							)

          assert abs(result_mean.item() - 0.23916							) < 1e-3

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

          _a    	=				self.full_loop(scheduler=__UpperCAmelCase							)
          _a    	=				torch.mean(torch.abs(__UpperCAmelCase							)							)

          assert abs(result_mean.item() - 0.23916							) < 1e-3


     def 							_UpperCAmelCase      (      self							)       ->							Any:
          for timesteps in [25, 50, 100, 999, 1000]:
               self.check_over_configs(num_train_timesteps=__UpperCAmelCase							)


     def 							_UpperCAmelCase      (      self							)       ->							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 							_UpperCAmelCase      (      self							)       ->							int:
          for prediction_type in ["epsilon", "v_prediction"]:
               self.check_over_configs(prediction_type=__UpperCAmelCase							)


     def 							_UpperCAmelCase      (      self							)       ->							Dict:
          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    ,				)
                              _a    	=				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 							_UpperCAmelCase      (      self							)       ->							Dict:
          self.check_over_configs(lower_order_final=__UpperCAmelCase							)
          self.check_over_configs(lower_order_final=__UpperCAmelCase							)


     def 							_UpperCAmelCase      (      self							)       ->							str:
          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 							_UpperCAmelCase      (      self							)       ->							Optional[Any]:
          _a    	=				self.full_loop()
          _a    	=				torch.mean(torch.abs(__UpperCAmelCase							)							)

          assert abs(result_mean.item() - 0.23916							) < 1e-3


     def 							_UpperCAmelCase      (      self							)       ->							List[Any]:
          _a    	=				self.full_loop(prediction_type='''v_prediction'''							)
          _a    	=				torch.mean(torch.abs(__UpperCAmelCase							)							)

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





     def 							_UpperCAmelCase      (      self							)       ->							Tuple:
          _a    	=				self.scheduler_classes[0]
          _a    	=				self.get_scheduler_config(thresholding=__UpperCAmelCase    ,				dynamic_thresholding_ratio=0							)
          _a    	=				scheduler_class(**__UpperCAmelCase							)

          _a    	=				10
          _a    	=				self.dummy_model()
          _a    	=				self.dummy_sample_deter.half()
          scheduler.set_timesteps(__UpperCAmelCase							)

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

          assert sample.dtype == torch.floataa



"""simple docstring"""






def       A_   (   _lowerCAmelCase     :					int = 10**12       ):






     """simple docstring"""

     _a    	=				1
     _a    	=				0

     _a    	=				1
     _a    	=				1

     while numerator <= 2 * min_total - 1:
          prev_numerator += 2 * numerator
          numerator += 2 * prev_numerator

          prev_denominator += 2 * denominator
          denominator += 2 * prev_denominator

     return (denominator + 1) // 2


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