




import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest




class  __lowerCAmelCase							(				lowerCAmelCase):
       _a			       =    (DPMSolverMultistepScheduler,)
       _a			       =    (('''num_inference_steps''', 25),)






       def        SCREAMING_SNAKE_CASE     ( self:       List[Any]							,       **_lowerCAmelCase:       Optional[int]					):
         lowercase     :Optional[Any]   						=    {
             "num_train_timesteps": 10_00,
             "beta_start": 0.00_01,
             "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(**_lowerCAmelCase					)
         return config






       def        SCREAMING_SNAKE_CASE     ( self:       str							,       _lowerCAmelCase:       str=0							,       **_lowerCAmelCase:       Union[str, Any]					):
         lowercase     :Optional[int]   						=    dict(self.forward_default_kwargs					)
         lowercase     :Union[str, Any]   						=    kwargs.pop("num_inference_steps"							,       _lowerCAmelCase					)
         lowercase     :str   						=    self.dummy_sample
         lowercase     :List[str]   						=    0.1 * sample
         lowercase     :Any   						=    [residual + 0.2, residual + 0.15, residual + 0.10]

         for scheduler_class in self.scheduler_classes:
           lowercase     :int   						=    self.get_scheduler_config(**_lowerCAmelCase					)
           lowercase     :str   						=    scheduler_class(**_lowerCAmelCase					)
           scheduler.set_timesteps(_lowerCAmelCase					)
           # copy over dummy past residuals
           lowercase     :Dict   						=    dummy_past_residuals[: scheduler.config.solver_order]

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

           lowercase						,     lowercase     :Dict   						=    sample, sample
           for t in range(_lowerCAmelCase							,       time_step + scheduler.config.solver_order + 1					):
             lowercase     :str   						=    scheduler.step(_lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase							,       **_lowerCAmelCase					).prev_sample
             lowercase     :Dict   						=    new_scheduler.step(_lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase							,       **_lowerCAmelCase					).prev_sample

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






       def        SCREAMING_SNAKE_CASE     ( self:       Dict					):
         pass






       def        SCREAMING_SNAKE_CASE     ( self:       Dict							,       _lowerCAmelCase:       Tuple=0							,       **_lowerCAmelCase:       Tuple					):
         lowercase     :Optional[int]   						=    dict(self.forward_default_kwargs					)
         lowercase     :Optional[Any]   						=    kwargs.pop("num_inference_steps"							,       _lowerCAmelCase					)
         lowercase     :List[Any]   						=    self.dummy_sample
         lowercase     :List[Any]   						=    0.1 * sample
         lowercase     :Any   						=    [residual + 0.2, residual + 0.15, residual + 0.10]

         for scheduler_class in self.scheduler_classes:
           lowercase     :int   						=    self.get_scheduler_config()
           lowercase     :Optional[int]   						=    scheduler_class(**_lowerCAmelCase					)
           scheduler.set_timesteps(_lowerCAmelCase					)

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

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

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

           lowercase     :Any   						=    scheduler.step(_lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase							,       **_lowerCAmelCase					).prev_sample
           lowercase     :Optional[Any]   						=    new_scheduler.step(_lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase							,       **_lowerCAmelCase					).prev_sample

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






       def        SCREAMING_SNAKE_CASE     ( self:       Dict							,       _lowerCAmelCase:       Union[str, Any]=None							,       **_lowerCAmelCase:       int					):
         if scheduler is None:
           lowercase     :Tuple   						=    self.scheduler_classes[0]
           lowercase     :str   						=    self.get_scheduler_config(**_lowerCAmelCase					)
           lowercase     :Any   						=    scheduler_class(**_lowerCAmelCase					)

         lowercase     :List[str]   						=    10
         lowercase     :Tuple   						=    self.dummy_model()
         lowercase     :Optional[Any]   						=    self.dummy_sample_deter
         scheduler.set_timesteps(_lowerCAmelCase					)

         for i, t in enumerate(scheduler.timesteps					):
           lowercase     :Optional[Any]   						=    model(_lowerCAmelCase							,       _lowerCAmelCase					)
           lowercase     :str   						=    scheduler.step(_lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase					).prev_sample

         return sample






       def        SCREAMING_SNAKE_CASE     ( self:       Any					):
         lowercase     :List[Any]   						=    dict(self.forward_default_kwargs					)

         lowercase     :Union[str, Any]   						=    kwargs.pop("num_inference_steps"							,       _lowerCAmelCase					)

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

           lowercase     :str   						=    self.dummy_sample
           lowercase     :Tuple   						=    0.1 * sample

           if num_inference_steps is not None and hasattr(_lowerCAmelCase							,       "set_timesteps"					):
             scheduler.set_timesteps(_lowerCAmelCase					)
           elif num_inference_steps is not None and not hasattr(_lowerCAmelCase							,       "set_timesteps"					):
             lowercase     :Tuple   						=    num_inference_steps

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

           lowercase     :List[str]   						=    scheduler.timesteps[5]
           lowercase     :List[str]   						=    scheduler.timesteps[6]

           lowercase     :int   						=    scheduler.step(_lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase							,       **_lowerCAmelCase					).prev_sample
           lowercase     :List[str]   						=    scheduler.step(_lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase							,       **_lowerCAmelCase					).prev_sample

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






       def        SCREAMING_SNAKE_CASE     ( self:       Union[str, Any]					):
         for timesteps in [25, 50, 1_00, 9_99, 10_00]:
           self.check_over_configs(num_train_timesteps=_lowerCAmelCase					)






       def        SCREAMING_SNAKE_CASE     ( self:       Union[str, Any]					):
         self.check_over_configs(thresholding=_lowerCAmelCase					)
         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=_lowerCAmelCase							,       prediction_type=_lowerCAmelCase							,       sample_max_value=_lowerCAmelCase							,       algorithm_type="dpmsolver++"							,       solver_order=_lowerCAmelCase							,       solver_type=_lowerCAmelCase							,       )






       def        SCREAMING_SNAKE_CASE     ( self:       Any					):
         for prediction_type in ["epsilon", "v_prediction"]:
           self.check_over_configs(prediction_type=_lowerCAmelCase					)






       def        SCREAMING_SNAKE_CASE     ( self:       str					):
         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=_lowerCAmelCase							,       solver_type=_lowerCAmelCase							,       prediction_type=_lowerCAmelCase							,       algorithm_type=_lowerCAmelCase							,       )
                 lowercase     :List[Any]   						=    self.full_loop(
                     solver_order=_lowerCAmelCase							,       solver_type=_lowerCAmelCase							,       prediction_type=_lowerCAmelCase							,       algorithm_type=_lowerCAmelCase							,       )
                 assert not torch.isnan(_lowerCAmelCase					).any(), "Samples have nan numbers"






       def        SCREAMING_SNAKE_CASE     ( self:       List[str]					):
         self.check_over_configs(lower_order_final=_lowerCAmelCase					)
         self.check_over_configs(lower_order_final=_lowerCAmelCase					)






       def        SCREAMING_SNAKE_CASE     ( self:       Dict					):
         self.check_over_configs(lambda_min_clipped=-float("inf"					)					)
         self.check_over_configs(lambda_min_clipped=-5.1					)






       def        SCREAMING_SNAKE_CASE     ( self:       str					):
         self.check_over_configs(variance_type=_lowerCAmelCase					)
         self.check_over_configs(variance_type="learned_range"					)






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






       def        SCREAMING_SNAKE_CASE     ( self:       Union[str, Any]					):
         lowercase     :Optional[Any]   						=    self.full_loop()
         lowercase     :List[str]   						=    torch.mean(torch.abs(_lowerCAmelCase					)					)

         assert abs(result_mean.item() - 0.33_01					) < 1e-3






       def        SCREAMING_SNAKE_CASE     ( self:       List[str]					):
         lowercase     :Optional[Any]   						=    self.full_loop(thresholding=_lowerCAmelCase							,       dynamic_thresholding_ratio=0.87							,       sample_max_value=0.5					)
         lowercase     :Optional[int]   						=    torch.mean(torch.abs(_lowerCAmelCase					)					)

         assert abs(result_mean.item() - 1.13_64					) < 1e-3






       def        SCREAMING_SNAKE_CASE     ( self:       Optional[Any]					):
         lowercase     :List[Any]   						=    self.full_loop(prediction_type="v_prediction"					)
         lowercase     :str   						=    torch.mean(torch.abs(_lowerCAmelCase					)					)

         assert abs(result_mean.item() - 0.22_51					) < 1e-3






       def        SCREAMING_SNAKE_CASE     ( self:       List[str]					):
         lowercase     :Dict   						=    self.full_loop(prediction_type="v_prediction"							,       use_karras_sigmas=_lowerCAmelCase					)
         lowercase     :str   						=    torch.mean(torch.abs(_lowerCAmelCase					)					)

         assert abs(result_mean.item() - 0.20_96					) < 1e-3






       def        SCREAMING_SNAKE_CASE     ( self:       Any					):
         # make sure that iterating over schedulers with same config names gives same results
         # for defaults
         lowercase     :int   						=    DPMSolverMultistepScheduler(**self.get_scheduler_config()					)
         lowercase     :List[Any]   						=    self.full_loop(scheduler=_lowerCAmelCase					)
         lowercase     :List[Any]   						=    torch.mean(torch.abs(_lowerCAmelCase					)					)

         assert abs(result_mean.item() - 0.33_01					) < 1e-3

         lowercase     :Any   						=    DPMSolverSinglestepScheduler.from_config(scheduler.config					)
         lowercase     :str   						=    UniPCMultistepScheduler.from_config(scheduler.config					)
         lowercase     :Union[str, Any]   						=    DEISMultistepScheduler.from_config(scheduler.config					)
         lowercase     :Any   						=    DPMSolverMultistepScheduler.from_config(scheduler.config					)

         lowercase     :Optional[Any]   						=    self.full_loop(scheduler=_lowerCAmelCase					)
         lowercase     :List[Any]   						=    torch.mean(torch.abs(_lowerCAmelCase					)					)

         assert abs(result_mean.item() - 0.33_01					) < 1e-3






       def        SCREAMING_SNAKE_CASE     ( self:       Union[str, Any]					):
         lowercase     :Union[str, Any]   						=    self.scheduler_classes[0]
         lowercase     :str   						=    self.get_scheduler_config(thresholding=_lowerCAmelCase							,       dynamic_thresholding_ratio=0					)
         lowercase     :str   						=    scheduler_class(**_lowerCAmelCase					)

         lowercase     :Dict   						=    10
         lowercase     :List[str]   						=    self.dummy_model()
         lowercase     :str   						=    self.dummy_sample_deter.half()
         scheduler.set_timesteps(_lowerCAmelCase					)

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

         assert sample.dtype == torch.floataa






       def        SCREAMING_SNAKE_CASE     ( self:       int							,       **_lowerCAmelCase:       List[Any]					):
         for scheduler_class in self.scheduler_classes:
           lowercase     :Any   						=    self.get_scheduler_config(**_lowerCAmelCase					)
           lowercase     :Tuple   						=    scheduler_class(**_lowerCAmelCase					)

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









import decimal



def    UpperCAmelCase__	(  ):
  lowercase     :int   						=    0
  lowercase     :str   						=    decimal.Context(prec=105 )
  for i in range(2,      100 ):
    lowercase     :int   						=    decimal.Decimal(lowerCamelCase )
    lowercase     :str   						=    number.sqrt(lowerCamelCase )
    if len(str(lowerCamelCase ) ) > 1:
      answer += int(str(lowerCamelCase )[0] )
      lowercase     :int   						=    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() = }''')



