

"""simple docstring"""





import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest




class    _lowerCAmelCase    (      lowercase  ):







 """simple docstring"""







 __UpperCAmelCase      :   int  						=      (DPMSolverMultistepScheduler,)
 __UpperCAmelCase      :   List[str]  						=      (("num_inference_steps", 2_5),)


 def    _lowercase					(     self				:     Any,   **UpperCAmelCase__				:     Optional[Any]      ):
    __lowercase								=		{
        "num_train_timesteps": 1_0_0_0,
        "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(**UpperCAmelCase__      )
    return config


 def    _lowercase					(     self				:     List[Any],   UpperCAmelCase__				:     List[str]=0,   **UpperCAmelCase__				:     Any      ):
    __lowercase								=		dict(self.forward_default_kwargs      )
    __lowercase								=		kwargs.pop("num_inference_steps",   UpperCAmelCase__      )
    __lowercase								=		self.dummy_sample
    __lowercase								=		0.1 * sample
    __lowercase								=		[residual + 0.2, residual + 0.15, residual + 0.10]

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

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

       __lowercase						,__lowercase								=		sample, sample
       for t in range(UpperCAmelCase__,   time_step + scheduler.config.solver_order + 1      ):
          __lowercase								=		scheduler.step(UpperCAmelCase__,   UpperCAmelCase__,   UpperCAmelCase__,   **UpperCAmelCase__      ).prev_sample
          __lowercase								=		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    _lowercase					(     self				:     Optional[Any]      ):
    pass


 def    _lowercase					(     self				:     Optional[Any],   UpperCAmelCase__				:     List[Any]=0,   **UpperCAmelCase__				:     int      ):
    __lowercase								=		dict(self.forward_default_kwargs      )
    __lowercase								=		kwargs.pop("num_inference_steps",   UpperCAmelCase__      )
    __lowercase								=		self.dummy_sample
    __lowercase								=		0.1 * sample
    __lowercase								=		[residual + 0.2, residual + 0.15, residual + 0.10]

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

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

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

       __lowercase								=		scheduler.step(UpperCAmelCase__,   UpperCAmelCase__,   UpperCAmelCase__,   **UpperCAmelCase__      ).prev_sample
       __lowercase								=		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    _lowercase					(     self				:     Tuple,   UpperCAmelCase__				:     List[Any]=None,   **UpperCAmelCase__				:     Dict      ):
    if scheduler is None:
       __lowercase								=		self.scheduler_classes[0]
       __lowercase								=		self.get_scheduler_config(**UpperCAmelCase__      )
       __lowercase								=		scheduler_class(**UpperCAmelCase__      )

    __lowercase								=		1_0
    __lowercase								=		self.dummy_model()
    __lowercase								=		self.dummy_sample_deter
    scheduler.set_timesteps(UpperCAmelCase__      )

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

    return sample


 def    _lowercase					(     self				:     Optional[Any]      ):
    __lowercase								=		dict(self.forward_default_kwargs      )

    __lowercase								=		kwargs.pop("num_inference_steps",   UpperCAmelCase__      )

    for scheduler_class in self.scheduler_classes:
       __lowercase								=		self.get_scheduler_config()
       __lowercase								=		scheduler_class(**UpperCAmelCase__      )

       __lowercase								=		self.dummy_sample
       __lowercase								=		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"      ):
          __lowercase								=		num_inference_steps

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

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

       __lowercase								=		scheduler.step(UpperCAmelCase__,   UpperCAmelCase__,   UpperCAmelCase__,   **UpperCAmelCase__      ).prev_sample
       __lowercase								=		scheduler.step(UpperCAmelCase__,   UpperCAmelCase__,   UpperCAmelCase__,   **UpperCAmelCase__      ).prev_sample

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


 def    _lowercase					(     self				:     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=UpperCAmelCase__      )


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


 def    _lowercase					(     self				:     Union[str, Any]      ):
    for prediction_type in ["epsilon", "v_prediction"]:
       self.check_over_configs(prediction_type=UpperCAmelCase__      )


 def    _lowercase					(     self				:     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=UpperCAmelCase__,   solver_type=UpperCAmelCase__,   prediction_type=UpperCAmelCase__,   algorithm_type=UpperCAmelCase__,   )
                __lowercase								=		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    _lowercase					(     self				:     Any      ):
    self.check_over_configs(lower_order_final=UpperCAmelCase__      )
    self.check_over_configs(lower_order_final=UpperCAmelCase__      )


 def    _lowercase					(     self				:     List[str]      ):
    self.check_over_configs(lambda_min_clipped=-float("inf"      )      )
    self.check_over_configs(lambda_min_clipped=-5.1      )


 def    _lowercase					(     self				:     Optional[int]      ):
    self.check_over_configs(variance_type=UpperCAmelCase__      )
    self.check_over_configs(variance_type="learned_range"      )


 def    _lowercase					(     self				:     List[str]      ):
    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=UpperCAmelCase__,   time_step=0      )


 def    _lowercase					(     self				:     str      ):
    __lowercase								=		self.full_loop()
    __lowercase								=		torch.mean(torch.abs(UpperCAmelCase__      )      )

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


 def    _lowercase					(     self				:     Optional[int]      ):
    __lowercase								=		self.full_loop(thresholding=UpperCAmelCase__,   dynamic_thresholding_ratio=0.87,   sample_max_value=0.5      )
    __lowercase								=		torch.mean(torch.abs(UpperCAmelCase__      )      )

    assert abs(result_mean.item() - 1.1_364      ) < 1E-3


 def    _lowercase					(     self				:     int      ):
    __lowercase								=		self.full_loop(prediction_type="v_prediction"      )
    __lowercase								=		torch.mean(torch.abs(UpperCAmelCase__      )      )

    assert abs(result_mean.item() - 0.2_251      ) < 1E-3


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

    assert abs(result_mean.item() - 0.2_096      ) < 1E-3


 def    _lowercase					(     self				:     Union[str, Any]      ):
    # make sure that iterating over schedulers with same config names gives same results
    # for defaults
    __lowercase								=		DPMSolverMultistepScheduler(**self.get_scheduler_config()      )
    __lowercase								=		self.full_loop(scheduler=UpperCAmelCase__      )
    __lowercase								=		torch.mean(torch.abs(UpperCAmelCase__      )      )

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

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

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

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


 def    _lowercase					(     self				:     Optional[int]      ):
    __lowercase								=		self.scheduler_classes[0]
    __lowercase								=		self.get_scheduler_config(thresholding=UpperCAmelCase__,   dynamic_thresholding_ratio=0      )
    __lowercase								=		scheduler_class(**UpperCAmelCase__      )

    __lowercase								=		1_0
    __lowercase								=		self.dummy_model()
    __lowercase								=		self.dummy_sample_deter.half()
    scheduler.set_timesteps(UpperCAmelCase__      )

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

    assert sample.dtype == torch.floataa



 def    _lowercase					(     self				:     int,   **UpperCAmelCase__				:     Any      ):
    for scheduler_class in self.scheduler_classes:
       __lowercase								=		self.get_scheduler_config(**UpperCAmelCase__      )
       __lowercase								=		scheduler_class(**UpperCAmelCase__      )

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







"""simple docstring"""





import math
from decimal import Decimal, getcontext




def     _A    (    UpperCamelCase_	:			int = 4000000)      ->		int:
   '''simple docstring'''


   try:
      __lowercase								=		int(UpperCamelCase_)
   except (TypeError, ValueError):
      raise TypeError("Parameter n must be int or castable to int.")
   if n <= 0:
      raise ValueError("Parameter n must be greater than or equal to one.")
   __lowercase								=		100
   __lowercase								=		(Decimal(5) ** Decimal(0.5) + 1) / Decimal(2)

   __lowercase								=		(math.floor(math.log(n * (phi + 2),     UpperCamelCase_) - 1) // 3) * 3 + 2
   __lowercase								=		Decimal(round(phi ** Decimal(index + 1))) / (phi + 2)
   __lowercase								=		num // 2
   return int(UpperCamelCase_)


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




