

'''simple docstring'''



import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest



class 				_snake_case					(   a_			):
  SCREAMING_SNAKE_CASE			:       Optional[int]				=     (UniPCMultistepScheduler,)
  SCREAMING_SNAKE_CASE			:       List[Any]				=     (('''num_inference_steps''', 25),)

  def   _SCREAMING_SNAKE_CASE				(				self      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      lowerCAmelCase 						=							{
          'num_train_timesteps': 10_00,
          'beta_start': 0.0_001,
          'beta_end': 0.02,
          'beta_schedule': 'linear',
          'solver_order': 2,
          'solver_type': 'bh2',
      }

      config.update(**_SCREAMING_SNAKE_CASE	)
      return config

  def   _SCREAMING_SNAKE_CASE				(				self      , _SCREAMING_SNAKE_CASE=0      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      lowerCAmelCase 						=							dict(self.forward_default_kwargs	)
      lowerCAmelCase 						=							kwargs.pop('num_inference_steps'      , _SCREAMING_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(**_SCREAMING_SNAKE_CASE	)
          lowerCAmelCase 						=							scheduler_class(**_SCREAMING_SNAKE_CASE	)
          scheduler.set_timesteps(_SCREAMING_SNAKE_CASE	)
          # copy over dummy past residuals
          lowerCAmelCase 						=							dummy_past_residuals[: scheduler.config.solver_order]

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

          lowerCAmelCase						,   lowerCAmelCase 						=							sample, sample
          for t in range(_SCREAMING_SNAKE_CASE      , time_step + scheduler.config.solver_order + 1	):
              lowerCAmelCase 						=							scheduler.step(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	).prev_sample
              lowerCAmelCase 						=							new_scheduler.step(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	).prev_sample

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

  def   _SCREAMING_SNAKE_CASE				(				self      , _SCREAMING_SNAKE_CASE=0      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      lowerCAmelCase 						=							dict(self.forward_default_kwargs	)
      lowerCAmelCase 						=							kwargs.pop('num_inference_steps'      , _SCREAMING_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(**_SCREAMING_SNAKE_CASE	)
          scheduler.set_timesteps(_SCREAMING_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(_SCREAMING_SNAKE_CASE	)
              lowerCAmelCase 						=							scheduler_class.from_pretrained(_SCREAMING_SNAKE_CASE	)
              # copy over dummy past residuals
              new_scheduler.set_timesteps(_SCREAMING_SNAKE_CASE	)

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

          lowerCAmelCase 						=							scheduler.step(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	).prev_sample
          lowerCAmelCase 						=							new_scheduler.step(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	).prev_sample

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

  def   _SCREAMING_SNAKE_CASE				(				self      , _SCREAMING_SNAKE_CASE=None      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      if scheduler is None:
          lowerCAmelCase 						=							self.scheduler_classes[0]
          lowerCAmelCase 						=							self.get_scheduler_config(**_SCREAMING_SNAKE_CASE	)
          lowerCAmelCase 						=							scheduler_class(**_SCREAMING_SNAKE_CASE	)

      lowerCAmelCase 						=							self.scheduler_classes[0]
      lowerCAmelCase 						=							self.get_scheduler_config(**_SCREAMING_SNAKE_CASE	)
      lowerCAmelCase 						=							scheduler_class(**_SCREAMING_SNAKE_CASE	)

      lowerCAmelCase 						=							10
      lowerCAmelCase 						=							self.dummy_model()
      lowerCAmelCase 						=							self.dummy_sample_deter
      scheduler.set_timesteps(_SCREAMING_SNAKE_CASE	)

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

      return sample

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							dict(self.forward_default_kwargs	)

      lowerCAmelCase 						=							kwargs.pop('num_inference_steps'      , _SCREAMING_SNAKE_CASE	)

      for scheduler_class in self.scheduler_classes:
          lowerCAmelCase 						=							self.get_scheduler_config()
          lowerCAmelCase 						=							scheduler_class(**_SCREAMING_SNAKE_CASE	)

          lowerCAmelCase 						=							self.dummy_sample
          lowerCAmelCase 						=							0.1 * sample

          if num_inference_steps is not None and hasattr(_SCREAMING_SNAKE_CASE      , 'set_timesteps'	):
              scheduler.set_timesteps(_SCREAMING_SNAKE_CASE	)
          elif num_inference_steps is not None and not hasattr(_SCREAMING_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(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	).prev_sample
          lowerCAmelCase 						=							scheduler.step(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	).prev_sample

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

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							UniPCMultistepScheduler(**self.get_scheduler_config()	)
      lowerCAmelCase 						=							self.full_loop(scheduler=_SCREAMING_SNAKE_CASE	)
      lowerCAmelCase 						=							torch.mean(torch.abs(_SCREAMING_SNAKE_CASE	)	)

      assert abs(result_mean.item() - 0.2_464	) < 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=_SCREAMING_SNAKE_CASE	)
      lowerCAmelCase 						=							torch.mean(torch.abs(_SCREAMING_SNAKE_CASE	)	)

      assert abs(result_mean.item() - 0.2_464	) < 1e-3

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      for timesteps in [25, 50, 1_00, 9_99, 10_00]:
          self.check_over_configs(num_train_timesteps=_SCREAMING_SNAKE_CASE	)

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      self.check_over_configs(thresholding=_SCREAMING_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=_SCREAMING_SNAKE_CASE      , prediction_type=_SCREAMING_SNAKE_CASE      , sample_max_value=_SCREAMING_SNAKE_CASE      , solver_order=_SCREAMING_SNAKE_CASE      , solver_type=_SCREAMING_SNAKE_CASE      , )

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      for prediction_type in ["epsilon", "v_prediction"]:
          self.check_over_configs(prediction_type=_SCREAMING_SNAKE_CASE	)

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      for solver_type in ["bh1", "bh2"]:
          for order in [1, 2, 3]:
              for prediction_type in ["epsilon", "sample"]:
                  self.check_over_configs(
                      solver_order=_SCREAMING_SNAKE_CASE      , solver_type=_SCREAMING_SNAKE_CASE      , prediction_type=_SCREAMING_SNAKE_CASE      , )
                  lowerCAmelCase 						=							self.full_loop(
                      solver_order=_SCREAMING_SNAKE_CASE      , solver_type=_SCREAMING_SNAKE_CASE      , prediction_type=_SCREAMING_SNAKE_CASE      , )
                  assert not torch.isnan(_SCREAMING_SNAKE_CASE	).any(), "Samples have nan numbers"

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      self.check_over_configs(lower_order_final=_SCREAMING_SNAKE_CASE	)
      self.check_over_configs(lower_order_final=_SCREAMING_SNAKE_CASE	)

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

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

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							self.full_loop()
      lowerCAmelCase 						=							torch.mean(torch.abs(_SCREAMING_SNAKE_CASE	)	)

      assert abs(result_mean.item() - 0.2_464	) < 1e-3

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							self.full_loop(prediction_type='v_prediction'	)
      lowerCAmelCase 						=							torch.mean(torch.abs(_SCREAMING_SNAKE_CASE	)	)

      assert abs(result_mean.item() - 0.1_014	) < 1e-3

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							self.scheduler_classes[0]
      lowerCAmelCase 						=							self.get_scheduler_config(thresholding=_SCREAMING_SNAKE_CASE      , dynamic_thresholding_ratio=0	)
      lowerCAmelCase 						=							scheduler_class(**_SCREAMING_SNAKE_CASE	)

      lowerCAmelCase 						=							10
      lowerCAmelCase 						=							self.dummy_model()
      lowerCAmelCase 						=							self.dummy_sample_deter.half()
      scheduler.set_timesteps(_SCREAMING_SNAKE_CASE	)

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

      assert sample.dtype == torch.floataa


  def   _SCREAMING_SNAKE_CASE				(				self      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      for scheduler_class in self.scheduler_classes:
          lowerCAmelCase 						=							self.get_scheduler_config(**_SCREAMING_SNAKE_CASE	)
          lowerCAmelCase 						=							scheduler_class(**_SCREAMING_SNAKE_CASE	)

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








'''simple docstring'''



def       snake_case   (	snake_case      : list					,		snake_case      : list					,		snake_case      : int					,		snake_case      : int					,		snake_case      : int			) ->  int:


    """simple docstring"""


    if index == number_of_items:
        return 0
    lowerCAmelCase 						=							0
    lowerCAmelCase 						=							0
    lowerCAmelCase 						=							knapsack(snake_case					,		snake_case					,		snake_case					,		snake_case					,		index + 1			)
    if weights[index] <= max_weight:
        lowerCAmelCase 						=							values[index] + knapsack(
            snake_case					,		snake_case					,		snake_case					,		max_weight - weights[index]					,		index + 1			)
    return max(snake_case					,		snake_case			)


if __name__ == "__main__":
     import doctest

     doctest.testmod()





