





"""simple docstring"""




import tempfile
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVeScheduler

class 			_UpperCAmelCase  (       unittest.TestCase		):



      '''simple docstring'''
      lowercase_				:			Any        =       (ScoreSdeVeScheduler,)
      lowercase_				:			str        =       ()

      @property
      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        A_  :	Optional[Any]    						=  4
        A_  :	Union[str, Any]    						=  3
        A_  :	Tuple    						=  8
        A_  :	Tuple    						=  8

        A_  :	List[Any]    						=  torch.rand((batch_size, num_channels, height, width)  )

        return sample

      @property
      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        A_  :	Dict    						=  4
        A_  :	List[Any]    						=  3
        A_  :	Optional[Any]    						=  8
        A_  :	Union[str, Any]    						=  8

        A_  :	List[str]    						=  batch_size * num_channels * height * width
        A_  :	Dict    						=  torch.arange(snake_case_  )
        A_  :	Any    						=  sample.reshape(snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_  )
        A_  :	Optional[int]    						=  sample / num_elems
        A_  :	str    						=  sample.permute(3 ,				0 ,				1 ,				2  )

        return sample

      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        def model(snake_case_ ,				snake_case_ ,				*snake_case_  ):
          return sample * t / (t + 1)

        return model

      def  lowerCamelCase_      (					self ,				**snake_case_  ):

        """simple docstring"""

        A_  :	Optional[Any]    						=  {
            'num_train_timesteps': 2_0_0_0,
            'snr': 0.15,
            'sigma_min': 0.01,
            'sigma_max': 1_3_4_8,
            'sampling_eps': 1E-5,
        }

        config.update(**snake_case_  )
        return config

      def  lowerCamelCase_      (					self ,				snake_case_=0 ,				**snake_case_  ):

        """simple docstring"""

        A_  :	List[str]    						=  dict(self.forward_default_kwargs  )

        for scheduler_class in self.scheduler_classes:
          A_  :	Any    						=  self.dummy_sample
          A_  :	List[Any]    						=  0.1 * sample

          A_  :	Optional[int]    						=  self.get_scheduler_config(**snake_case_  )
          A_  :	str    						=  scheduler_class(**snake_case_  )

          with tempfile.TemporaryDirectory() as tmpdirname:
            scheduler.save_config(snake_case_  )
            A_  :	int    						=  scheduler_class.from_pretrained(snake_case_  )

          A_  :	int    						=  scheduler.step_pred(
              snake_case_ ,				snake_case_ ,				snake_case_ ,				generator=torch.manual_seed(0  ) ,				**snake_case_  ).prev_sample
          A_  :	Union[str, Any]    						=  new_scheduler.step_pred(
              snake_case_ ,				snake_case_ ,				snake_case_ ,				generator=torch.manual_seed(0  ) ,				**snake_case_  ).prev_sample

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

          A_  :	str    						=  scheduler.step_correct(snake_case_ ,				snake_case_ ,				generator=torch.manual_seed(0  ) ,				**snake_case_  ).prev_sample
          A_  :	Optional[Any]    						=  new_scheduler.step_correct(
              snake_case_ ,				snake_case_ ,				generator=torch.manual_seed(0  ) ,				**snake_case_  ).prev_sample

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

      def  lowerCamelCase_      (					self ,				snake_case_=0 ,				**snake_case_  ):

        """simple docstring"""

        A_  :	str    						=  dict(self.forward_default_kwargs  )
        kwargs.update(snake_case_  )

        for scheduler_class in self.scheduler_classes:
          A_  :	str    						=  self.dummy_sample
          A_  :	List[str]    						=  0.1 * sample

          A_  :	Dict    						=  self.get_scheduler_config()
          A_  :	int    						=  scheduler_class(**snake_case_  )

          with tempfile.TemporaryDirectory() as tmpdirname:
            scheduler.save_config(snake_case_  )
            A_  :	Optional[Any]    						=  scheduler_class.from_pretrained(snake_case_  )

          A_  :	Union[str, Any]    						=  scheduler.step_pred(
              snake_case_ ,				snake_case_ ,				snake_case_ ,				generator=torch.manual_seed(0  ) ,				**snake_case_  ).prev_sample
          A_  :	Tuple    						=  new_scheduler.step_pred(
              snake_case_ ,				snake_case_ ,				snake_case_ ,				generator=torch.manual_seed(0  ) ,				**snake_case_  ).prev_sample

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

          A_  :	Dict    						=  scheduler.step_correct(snake_case_ ,				snake_case_ ,				generator=torch.manual_seed(0  ) ,				**snake_case_  ).prev_sample
          A_  :	List[Any]    						=  new_scheduler.step_correct(
              snake_case_ ,				snake_case_ ,				generator=torch.manual_seed(0  ) ,				**snake_case_  ).prev_sample

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

      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        for timesteps in [1_0, 1_0_0, 1_0_0_0]:
          self.check_over_configs(num_train_timesteps=snake_case_  )

      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        for sigma_min, sigma_max in zip([0.00_01, 0.0_01, 0.01] ,				[1, 1_0_0, 1_0_0_0]  ):
          self.check_over_configs(sigma_min=snake_case_ ,				sigma_max=snake_case_  )

      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        for t in [0.1, 0.5, 0.75]:
          self.check_over_forward(time_step=snake_case_  )

      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        A_  :	int    						=  dict(self.forward_default_kwargs  )

        A_  :	int    						=  self.scheduler_classes[0]
        A_  :	str    						=  self.get_scheduler_config()
        A_  :	str    						=  scheduler_class(**snake_case_  )

        A_  :	Dict    						=  3

        A_  :	Any    						=  self.dummy_model()
        A_  :	Union[str, Any]    						=  self.dummy_sample_deter

        scheduler.set_sigmas(snake_case_  )
        scheduler.set_timesteps(snake_case_  )
        A_  :	Any    						=  torch.manual_seed(0  )

        for i, t in enumerate(scheduler.timesteps  ):
          A_  :	Optional[int]    						=  scheduler.sigmas[i]

          for _ in range(scheduler.config.correct_steps  ):
            with torch.no_grad():
              A_  :	str    						=  model(snake_case_ ,				snake_case_  )
            A_  :	List[str]    						=  scheduler.step_correct(snake_case_ ,				snake_case_ ,				generator=snake_case_ ,				**snake_case_  ).prev_sample

          with torch.no_grad():
            A_  :	Optional[Any]    						=  model(snake_case_ ,				snake_case_  )

          A_  :	Optional[int]    						=  scheduler.step_pred(snake_case_ ,				snake_case_ ,				snake_case_ ,				generator=snake_case_ ,				**snake_case_  )
          A_  :	List[str]    						=  output.prev_sample, output.prev_sample_mean

        A_  :	Optional[int]    						=  torch.sum(torch.abs(snake_case_  )  )
        A_  :	Any    						=  torch.mean(torch.abs(snake_case_  )  )

        assert np.isclose(result_sum.item() ,				1_4_3_7_2_7_5_8_5_2_8.0  )
        assert np.isclose(result_mean.item() ,				1_8_7_1_4_5_3_0.0  )

      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        A_  :	List[Any]    						=  dict(self.forward_default_kwargs  )

        A_  :	Any    						=  kwargs.pop('num_inference_steps' ,				snake_case_  )

        for scheduler_class in self.scheduler_classes:
          A_  :	Tuple    						=  self.get_scheduler_config()
          A_  :	Any    						=  scheduler_class(**snake_case_  )

          A_  :	str    						=  self.dummy_sample
          A_  :	Any    						=  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'  ):
            A_  :	Any    						=  num_inference_steps

          A_  :	Dict    						=  scheduler.step_pred(snake_case_ ,				0 ,				snake_case_ ,				generator=torch.manual_seed(0  ) ,				**snake_case_  ).prev_sample
          A_  :	int    						=  scheduler.step_pred(snake_case_ ,				1 ,				snake_case_ ,				generator=torch.manual_seed(0  ) ,				**snake_case_  ).prev_sample

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






"""simple docstring"""




import math as m





def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase=0.001 ):






  """simple docstring"""





  return (f(a + h ) - f(a - h )) / (2 * h)





def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase=0			, _UpperCAmelCase=100			, _UpperCAmelCase=0.0_001			, _UpperCAmelCase=1E-6			, _UpperCAmelCase=False ):






  """simple docstring"""





  A_  :	Optional[int]    						=  xa  # set the initial guess
  A_  :	Any    						=  [a]
  A_  :	str    						=  abs(f(_UpperCAmelCase ) )
  A_  :	str    						=  lambda _UpperCAmelCase : calc_derivative(_UpperCAmelCase			, _UpperCAmelCase			, h=_UpperCAmelCase )  # noqa: E731  Derivative of f(x)
  for _ in range(_UpperCAmelCase ):
    if fa(_UpperCAmelCase ) == 0:
      raise ValueError('No converging solution found' )
    A_  :	Union[str, Any]    						=  a - f(_UpperCAmelCase ) / fa(_UpperCAmelCase )  # Calculate the next estimate
    if logsteps:
      steps.append(_UpperCAmelCase )
    if error < maxerror:
      break
  else:
    raise ValueError('Iteration limit reached, no converging solution found' )
  if logsteps:
    # If logstep is true, then log intermediate steps
    return a, error, steps
  return a, error


if __name__ == "__main__":
   from matplotlib import pyplot as plt

   lowerCamelCase_					:						str      = lambda x: m.tanh(x) ** 2 - m.exp(3 * x)  # noqa: E731
   lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_					:						int      = newton_raphson(
       f, xa=10, maxiter=10_00, step=1E-6, logsteps=True
   )
   plt.plot([abs(f(x)) for x in steps])
   plt.xlabel('step')
   plt.ylabel('error')
   plt.show()
   print(F"solution = {{{solution:f}}}, error = {{{error:f}}}")