





"""simple docstring"""




import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest



class     _UpperCAmelCase   (   _a   ):

  """simple docstring"""


  a_      =			(UniPCMultistepScheduler,)
  a_      =			(("""num_inference_steps""", 25),)






  def     lowercase		(    self			:		List[Any] ,  **lowerCAmelCase_			:		str					)							->						Optional[Any]:
     __lowerCAmelCase							     =     {
         """num_train_timesteps""": 1_0_0_0,
         """beta_start""": 0.00_01,
         """beta_end""": 0.02,
         """beta_schedule""": """linear""",
         """solver_order""": 2,
         """solver_type""": """bh2""",
     }

     config.update(**snake_case_					)
     return config






  def     lowercase		(    self			:		Dict ,  lowerCAmelCase_			:		Union[str, Any]=0 ,  **lowerCAmelCase_			:		int					)							->						Optional[int]:
     __lowerCAmelCase							     =     dict(self.forward_default_kwargs					)
     __lowerCAmelCase							     =     kwargs.pop('num_inference_steps' ,  snake_case_					)
     __lowerCAmelCase							     =     self.dummy_sample
     __lowerCAmelCase							     =     0.1 * sample
     __lowerCAmelCase							     =     [residual + 0.2, residual + 0.15, residual + 0.10]

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

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

        __lowerCAmelCase							     =     sample, sample
        for t in range(snake_case_ ,  time_step + scheduler.config.solver_order + 1					):
           __lowerCAmelCase							     =     scheduler.step(snake_case_ ,  snake_case_ ,  snake_case_ ,  **snake_case_					).prev_sample
           __lowerCAmelCase							     =     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     lowercase		(    self			:		List[Any] ,  lowerCAmelCase_			:		List[str]=0 ,  **lowerCAmelCase_			:		Tuple					)							->						Dict:
     __lowerCAmelCase							     =     dict(self.forward_default_kwargs					)
     __lowerCAmelCase							     =     kwargs.pop('num_inference_steps' ,  snake_case_					)
     __lowerCAmelCase							     =     self.dummy_sample
     __lowerCAmelCase							     =     0.1 * sample
     __lowerCAmelCase							     =     [residual + 0.2, residual + 0.15, residual + 0.10]

     for scheduler_class in self.scheduler_classes:
        __lowerCAmelCase							     =     self.get_scheduler_config()
        __lowerCAmelCase							     =     scheduler_class(**snake_case_					)
        scheduler.set_timesteps(snake_case_					)

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

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

        __lowerCAmelCase							     =     scheduler.step(snake_case_ ,  snake_case_ ,  snake_case_ ,  **snake_case_					).prev_sample
        __lowerCAmelCase							     =     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     lowercase		(    self			:		Dict ,  lowerCAmelCase_			:		List[Any]=None ,  **lowerCAmelCase_			:		Tuple					)							->						int:
     if scheduler is None:
        __lowerCAmelCase							     =     self.scheduler_classes[0]
        __lowerCAmelCase							     =     self.get_scheduler_config(**snake_case_					)
        __lowerCAmelCase							     =     scheduler_class(**snake_case_					)

     __lowerCAmelCase							     =     self.scheduler_classes[0]
     __lowerCAmelCase							     =     self.get_scheduler_config(**snake_case_					)
     __lowerCAmelCase							     =     scheduler_class(**snake_case_					)

     __lowerCAmelCase							     =     1_0
     __lowerCAmelCase							     =     self.dummy_model()
     __lowerCAmelCase							     =     self.dummy_sample_deter
     scheduler.set_timesteps(snake_case_					)

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

     return sample






  def     lowercase		(    self			:		Optional[Any]					)							->						List[str]:
     __lowerCAmelCase							     =     dict(self.forward_default_kwargs					)

     __lowerCAmelCase							     =     kwargs.pop('num_inference_steps' ,  snake_case_					)

     for scheduler_class in self.scheduler_classes:
        __lowerCAmelCase							     =     self.get_scheduler_config()
        __lowerCAmelCase							     =     scheduler_class(**snake_case_					)

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

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

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

        __lowerCAmelCase							     =     scheduler.step(snake_case_ ,  snake_case_ ,  snake_case_ ,  **snake_case_					).prev_sample
        __lowerCAmelCase							     =     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     lowercase		(    self			:		Tuple					)							->						Union[str, Any]:
     # make sure that iterating over schedulers with same config names gives same results
     # for defaults
     __lowerCAmelCase							     =     UniPCMultistepScheduler(**self.get_scheduler_config()					)
     __lowerCAmelCase							     =     self.full_loop(scheduler=snake_case_					)
     __lowerCAmelCase							     =     torch.mean(torch.abs(snake_case_					)					)

     assert abs(result_mean.item() - 0.24_64					) < 1e-3

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

     __lowerCAmelCase							     =     self.full_loop(scheduler=snake_case_					)
     __lowerCAmelCase							     =     torch.mean(torch.abs(snake_case_					)					)

     assert abs(result_mean.item() - 0.24_64					) < 1e-3






  def     lowercase		(    self			:		str					)							->						Union[str, 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=snake_case_					)






  def     lowercase		(    self			:		Any					)							->						str:
     self.check_over_configs(thresholding=snake_case_					)
     for order in [1, 2, 3]:
        for solver_type in ["bh1", "bh2"]:
           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_ ,  solver_order=snake_case_ ,  solver_type=snake_case_ ,  )






  def     lowercase		(    self			:		List[str]					)							->						List[Any]:
     for prediction_type in ["epsilon", "v_prediction"]:
        self.check_over_configs(prediction_type=snake_case_					)






  def     lowercase		(    self			:		List[Any]					)							->						int:
     for solver_type in ["bh1", "bh2"]:
        for order in [1, 2, 3]:
           for prediction_type in ["epsilon", "sample"]:
              self.check_over_configs(
                  solver_order=snake_case_ ,  solver_type=snake_case_ ,  prediction_type=snake_case_ ,  )
              __lowerCAmelCase							     =     self.full_loop(
                  solver_order=snake_case_ ,  solver_type=snake_case_ ,  prediction_type=snake_case_ ,  )
              assert not torch.isnan(snake_case_					).any(), "Samples have nan numbers"






  def     lowercase		(    self			:		Dict					)							->						Any:
     self.check_over_configs(lower_order_final=snake_case_					)
     self.check_over_configs(lower_order_final=snake_case_					)






  def     lowercase		(    self			:		List[Any]					)							->						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=snake_case_ ,  time_step=0					)






  def     lowercase		(    self			:		Any					)							->						Optional[Any]:
     __lowerCAmelCase							     =     self.full_loop()
     __lowerCAmelCase							     =     torch.mean(torch.abs(snake_case_					)					)

     assert abs(result_mean.item() - 0.24_64					) < 1e-3






  def     lowercase		(    self			:		str					)							->						str:
     __lowerCAmelCase							     =     self.full_loop(prediction_type='v_prediction'					)
     __lowerCAmelCase							     =     torch.mean(torch.abs(snake_case_					)					)

     assert abs(result_mean.item() - 0.10_14					) < 1e-3






  def     lowercase		(    self			:		Optional[Any]					)							->						int:
     __lowerCAmelCase							     =     self.scheduler_classes[0]
     __lowerCAmelCase							     =     self.get_scheduler_config(thresholding=snake_case_ ,  dynamic_thresholding_ratio=0					)
     __lowerCAmelCase							     =     scheduler_class(**snake_case_					)

     __lowerCAmelCase							     =     1_0
     __lowerCAmelCase							     =     self.dummy_model()
     __lowerCAmelCase							     =     self.dummy_sample_deter.half()
     scheduler.set_timesteps(snake_case_					)

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

     assert sample.dtype == torch.floataa






  def     lowercase		(    self			:		Optional[int] ,  **lowerCAmelCase_			:		List[Any]					)							->						Union[str, Any]:
     for scheduler_class in self.scheduler_classes:
        __lowerCAmelCase							     =     self.get_scheduler_config(**snake_case_					)
        __lowerCAmelCase							     =     scheduler_class(**snake_case_					)

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








from math import factorial



class     _UpperCAmelCase   :

  """simple docstring"""


  def __init__(    self			:		Dict ,  lowerCAmelCase_			:		Optional[int] ,  lowerCAmelCase_			:		str					)							->						Union[str, Any]:
     __lowerCAmelCase							     =     real
     if isinstance(lowerCAmelCase_ ,  lowerCAmelCase_					):
        __lowerCAmelCase							     =     [1] * rank
     else:
        __lowerCAmelCase							     =     rank






  def __repr__(    self			:		Optional[Any]					)							->						Tuple:
     return (
         f"""{self.real}+"""
         f"""{"+".join(str(lowerCAmelCase_					)+"E"+str(n+1					)for n,dual in enumerate(self.duals					)					)}"""
     )






  def     lowercase		(    self			:		str					)							->						Dict:
     __lowerCAmelCase							     =     self.duals.copy()
     while cur[-1] == 0:
        cur.pop(-1					)
     return Dual(self.real ,  lowerCAmelCase_					)






  def __add__(    self			:		Union[str, Any] ,  lowerCAmelCase_			:		Tuple					)							->						Optional[Any]:
     if not isinstance(lowerCAmelCase_ ,  lowerCAmelCase_					):
        return Dual(self.real + other ,  self.duals					)
     __lowerCAmelCase							     =     self.duals.copy()
     __lowerCAmelCase							     =     other.duals.copy()
     if len(lowerCAmelCase_					) > len(lowerCAmelCase_					):
        o_dual.extend([1] * (len(lowerCAmelCase_					) - len(lowerCAmelCase_					))					)
     elif len(lowerCAmelCase_					) < len(lowerCAmelCase_					):
        s_dual.extend([1] * (len(lowerCAmelCase_					) - len(lowerCAmelCase_					))					)
     __lowerCAmelCase							     =     []
     for i in range(len(lowerCAmelCase_					)					):
        new_duals.append(s_dual[i] + o_dual[i]					)
     return Dual(self.real + other.real ,  lowerCAmelCase_					)

  a_      =			__add__






  def __sub__(    self			:		int ,  lowerCAmelCase_			:		Dict					)							->						Optional[Any]:
     return self + other * -1






  def __mul__(    self			:		int ,  lowerCAmelCase_			:		Optional[int]					)							->						Dict:
     if not isinstance(lowerCAmelCase_ ,  lowerCAmelCase_					):
        __lowerCAmelCase							     =     []
        for i in self.duals:
           new_duals.append(i * other					)
        return Dual(self.real * other ,  lowerCAmelCase_					)
     __lowerCAmelCase							     =     [0] * (len(self.duals					) + len(other.duals					) + 1)
     for i, item in enumerate(self.duals					):
        for j, jtem in enumerate(other.duals					):
           new_duals[i + j + 1] += item * jtem
     for k in range(len(self.duals					)					):
        new_duals[k] += self.duals[k] * other.real
     for index in range(len(other.duals					)					):
        new_duals[index] += other.duals[index] * self.real
     return Dual(self.real * other.real ,  lowerCAmelCase_					)

  a_      =			__mul__






  def __truediv__(    self			:		Union[str, Any] ,  lowerCAmelCase_			:		str					)							->						Dict:
     if not isinstance(lowerCAmelCase_ ,  lowerCAmelCase_					):
        __lowerCAmelCase							     =     []
        for i in self.duals:
           new_duals.append(i / other					)
        return Dual(self.real / other ,  lowerCAmelCase_					)
     raise ValueError






  def __floordiv__(    self			:		str ,  lowerCAmelCase_			:		List[Any]					)							->						Union[str, Any]:
     if not isinstance(lowerCAmelCase_ ,  lowerCAmelCase_					):
        __lowerCAmelCase							     =     []
        for i in self.duals:
           new_duals.append(i // other					)
        return Dual(self.real // other ,  lowerCAmelCase_					)
     raise ValueError






  def __pow__(    self			:		Tuple ,  lowerCAmelCase_			:		Dict					)							->						List[str]:
     if n < 0 or isinstance(lowerCAmelCase_ ,  lowerCAmelCase_					):
        raise ValueError('power must be a positive integer'					)
     if n == 0:
        return 1
     if n == 1:
        return self
     __lowerCAmelCase							     =     self
     for _ in range(n - 1					):
        x *= self
     return x





def   a_   (			lowerCAmelCase_				:      List[str],     lowerCAmelCase_				:      Optional[Any],     lowerCAmelCase_				:      Optional[Any]     ):
   if not callable(lowerCAmelCase_     ):
      raise ValueError('differentiate() requires a function as input for func'     )
   if not isinstance(lowerCAmelCase_,     (float, int)     ):
      raise ValueError('differentiate() requires a float as input for position'     )
   if not isinstance(lowerCAmelCase_,     lowerCAmelCase_     ):
      raise ValueError('differentiate() requires an int as input for order'     )
   __lowerCAmelCase							     =     Dual(lowerCAmelCase_,     1     )
   __lowerCAmelCase							     =     func(lowerCAmelCase_     )
   if order == 0:
      return result.real
   return result.duals[order - 1] * factorial(lowerCAmelCase_     )


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     def   a_   (			lowerCAmelCase_				:      int     ):
        return y**2 * y**4




     print(differentiate(f, 9, 2))

