






"""simple docstring"""


import tempfile
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVeScheduler






class 		snake_case    (    unittest.TestCase			):
    # TODO adapt with class SchedulerCommonTest (scheduler needs Numpy Integration)
    __magic_name__       	=	(ScoreSdeVeScheduler,)
    __magic_name__       	=	()





    @property
    def 							lowerCamelCase__ (				self      :   List[Any]     ):


      '''simple docstring'''




      a	:     Optional[int]							=  4
      a	:     Optional[Any]							=  3
      a	:     str							=  8
      a	:     int							=  8

      a	:     Optional[Any]							=  torch.rand((batch_size, num_channels, height, width)     )

      return sample





    @property
    def 							lowerCamelCase__ (				self      :   Tuple     ):


      '''simple docstring'''




      a	:     Any							=  4
      a	:     List[str]							=  3
      a	:     Dict							=  8
      a	:     str							=  8

      a	:     Tuple							=  batch_size * num_channels * height * width
      a	:     List[Any]							=  torch.arange(A     )
      a	:     Tuple							=  sample.reshape(A							,						A							,						A							,						A     )
      a	:     Union[str, Any]							=  sample / num_elems
      a	:     Optional[Any]							=  sample.permute(3							,						0							,						1							,						2     )

      return sample





    def 							lowerCamelCase__ (				self      :   Any     ):


      '''simple docstring'''




      def model(A      :   Dict							,						A      :   List[str]							,						*A      :   List[Any]     ):
        return sample * t / (t + 1)

      return model





    def 							lowerCamelCase__ (				self      :   Tuple							,						**A      :   List[Any]     ):


      '''simple docstring'''




      a	:     List[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(**A     )
      return config





    def 							lowerCamelCase__ (				self      :   Any							,						A      :   List[Any]=0							,						**A      :   int     ):


      '''simple docstring'''




      a	:     Union[str, Any]							=  dict(self.forward_default_kwargs     )

      for scheduler_class in self.scheduler_classes:
        a	:     Optional[int]							=  self.dummy_sample
        a	:     str							=  0.1 * sample

        a	:     int							=  self.get_scheduler_config(**A     )
        a	:     Union[str, Any]							=  scheduler_class(**A     )

        with tempfile.TemporaryDirectory() as tmpdirname:
          scheduler.save_config(A     )
          a	:     Dict							=  scheduler_class.from_pretrained(A     )

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

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

        a	:     Union[str, Any]							=  scheduler.step_correct(A							,						A							,						generator=torch.manual_seed(0     )							,						**A     ).prev_sample
        a	:     Optional[int]							=  new_scheduler.step_correct(
            A							,						A							,						generator=torch.manual_seed(0     )							,						**A     ).prev_sample

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





    def 							lowerCamelCase__ (				self      :   List[str]							,						A      :   int=0							,						**A      :   Any     ):


      '''simple docstring'''




      a	:     Union[str, Any]							=  dict(self.forward_default_kwargs     )
      kwargs.update(A     )

      for scheduler_class in self.scheduler_classes:
        a	:     Optional[int]							=  self.dummy_sample
        a	:     Any							=  0.1 * sample

        a	:     Optional[Any]							=  self.get_scheduler_config()
        a	:     Dict							=  scheduler_class(**A     )

        with tempfile.TemporaryDirectory() as tmpdirname:
          scheduler.save_config(A     )
          a	:     int							=  scheduler_class.from_pretrained(A     )

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

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

        a	:     Optional[Any]							=  scheduler.step_correct(A							,						A							,						generator=torch.manual_seed(0     )							,						**A     ).prev_sample
        a	:     Optional[int]							=  new_scheduler.step_correct(
            A							,						A							,						generator=torch.manual_seed(0     )							,						**A     ).prev_sample

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





    def 							lowerCamelCase__ (				self      :   List[Any]     ):


      '''simple docstring'''




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





    def 							lowerCamelCase__ (				self      :   Dict     ):


      '''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=A							,						sigma_max=A     )





    def 							lowerCamelCase__ (				self      :   List[Any]     ):


      '''simple docstring'''




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





    def 							lowerCamelCase__ (				self      :   Tuple     ):


      '''simple docstring'''




      a	:     Union[str, Any]							=  dict(self.forward_default_kwargs     )

      a	:     int							=  self.scheduler_classes[0]
      a	:     Any							=  self.get_scheduler_config()
      a	:     int							=  scheduler_class(**A     )

      a	:     int							=  3

      a	:     Any							=  self.dummy_model()
      a	:     Tuple							=  self.dummy_sample_deter

      scheduler.set_sigmas(A     )
      scheduler.set_timesteps(A     )
      a	:     Union[str, 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	:     List[Any]							=  model(A							,						A     )
          a	:     Union[str, Any]							=  scheduler.step_correct(A							,						A							,						generator=A							,						**A     ).prev_sample

        with torch.no_grad():
          a	:     Tuple							=  model(A							,						A     )

        a	:     Optional[Any]							=  scheduler.step_pred(A							,						A							,						A							,						generator=A							,						**A     )
        a,	a	:     Any							=  output.prev_sample, output.prev_sample_mean

      a	:     Union[str, Any]							=  torch.sum(torch.abs(A     )     )
      a	:     Any							=  torch.mean(torch.abs(A     )     )

      assert np.isclose(result_sum.item()							,						1_43_72_75_85_28.0     )
      assert np.isclose(result_mean.item()							,						18_71_45_30.0     )





    def 							lowerCamelCase__ (				self      :   List[str]     ):


      '''simple docstring'''




      a	:     Optional[int]							=  dict(self.forward_default_kwargs     )

      a	:     str							=  kwargs.pop('num_inference_steps'							,						A     )

      for scheduler_class in self.scheduler_classes:
        a	:     List[str]							=  self.get_scheduler_config()
        a	:     Optional[int]							=  scheduler_class(**A     )

        a	:     Optional[int]							=  self.dummy_sample
        a	:     List[Any]							=  0.1 * sample

        if num_inference_steps is not None and hasattr(A							,						'set_timesteps'     ):
          scheduler.set_timesteps(A     )
        elif num_inference_steps is not None and not hasattr(A							,						'set_timesteps'     ):
          a	:     Any							=  num_inference_steps

        a	:     Optional[Any]							=  scheduler.step_pred(A							,						0							,						A							,						generator=torch.manual_seed(0     )							,						**A     ).prev_sample
        a	:     Any							=  scheduler.step_pred(A							,						1							,						A							,						generator=torch.manual_seed(0     )							,						**A     ).prev_sample

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












"""simple docstring"""


from __future__ import annotations




def      snake_case    (A_ :list[int | float]      ,     A_ :int      ,     A_ :int ):







  '''simple docstring'''

  if len(A_ ) == 0:
    raise ValueError('find_min() arg is an empty sequence' )
  if (
      left >= len(A_ )
      or left < -len(A_ )
      or right >= len(A_ )
      or right < -len(A_ )
  ):
    raise IndexError('list index out of range' )
  if left == right:
    return nums[left]
  a	:     Optional[Any]							=  (left + right) >> 1  # the middle
  a	:     List[str]							=  find_min(A_      ,     A_      ,     A_ )  # find min in range[left, mid]
  a	:     Union[str, Any]							=  find_min(A_      ,     mid + 1      ,     A_ )  # find min in range[mid + 1, right]

  return left_min if left_min <= right_min else right_min


if __name__ == "__main__":
   import doctest

   doctest.testmod(verbose=True)




