






import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest


class 						_a       (  UpperCAmelCase__    ):






  """simple docstring"""





  A_						=   (DEISMultistepScheduler,)
  A_						=   (("""num_inference_steps""", 25),)




  def    _UpperCAmelCase     (     self		,		**_UpperCAmelCase )			-> Optional[Any]:
     UpperCamelCase_									=     {
         'num_train_timesteps': 1000,
         'beta_start': 0.0_0_0_1,
         'beta_end': 0.0_2,
         'beta_schedule': 'linear',
         'solver_order': 2,
     }

     config.update(**_UpperCAmelCase )
     return config




  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase=0		,		**_UpperCAmelCase )			-> str:
     UpperCamelCase_									=     dict(self.forward_default_kwargs )
     UpperCamelCase_									=     kwargs.pop('num_inference_steps'		,		_UpperCAmelCase )
     UpperCamelCase_									=     self.dummy_sample
     UpperCamelCase_									=     0.1 * sample
     UpperCamelCase_									=     [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

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

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

        UpperCamelCase_		,      UpperCamelCase_									=     sample, sample
        for t in range(_UpperCAmelCase		,		time_step + scheduler.config.solver_order + 1 ):
           UpperCamelCase_									=     scheduler.step(_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		**_UpperCAmelCase ).prev_sample
           UpperCamelCase_									=     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    _UpperCAmelCase     (     self )			-> List[Any]:
     pass




  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase=0		,		**_UpperCAmelCase )			-> int:
     UpperCamelCase_									=     dict(self.forward_default_kwargs )
     UpperCamelCase_									=     kwargs.pop('num_inference_steps'		,		_UpperCAmelCase )
     UpperCamelCase_									=     self.dummy_sample
     UpperCamelCase_									=     0.1 * sample
     UpperCamelCase_									=     [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

     for scheduler_class in self.scheduler_classes:
        UpperCamelCase_									=     self.get_scheduler_config()
        UpperCamelCase_									=     scheduler_class(**_UpperCAmelCase )
        scheduler.set_timesteps(_UpperCAmelCase )

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

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

        UpperCamelCase_									=     scheduler.step(_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		**_UpperCAmelCase ).prev_sample
        UpperCamelCase_									=     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    _UpperCAmelCase     (     self		,		_UpperCAmelCase=None		,		**_UpperCAmelCase )			-> Optional[Any]:
     if scheduler is None:
        UpperCamelCase_									=     self.scheduler_classes[0]
        UpperCamelCase_									=     self.get_scheduler_config(**_UpperCAmelCase )
        UpperCamelCase_									=     scheduler_class(**_UpperCAmelCase )

     UpperCamelCase_									=     self.scheduler_classes[0]
     UpperCamelCase_									=     self.get_scheduler_config(**_UpperCAmelCase )
     UpperCamelCase_									=     scheduler_class(**_UpperCAmelCase )

     UpperCamelCase_									=     10
     UpperCamelCase_									=     self.dummy_model()
     UpperCamelCase_									=     self.dummy_sample_deter
     scheduler.set_timesteps(_UpperCAmelCase )

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

     return sample




  def    _UpperCAmelCase     (     self )			-> Any:
     UpperCamelCase_									=     dict(self.forward_default_kwargs )

     UpperCamelCase_									=     kwargs.pop('num_inference_steps'		,		_UpperCAmelCase )

     for scheduler_class in self.scheduler_classes:
        UpperCamelCase_									=     self.get_scheduler_config()
        UpperCamelCase_									=     scheduler_class(**_UpperCAmelCase )

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

        # copy over dummy past residuals (must be done after set_timesteps)
        UpperCamelCase_									=     [residual + 0.2, residual + 0.1_5, residual + 0.1_0]
        UpperCamelCase_									=     dummy_past_residuals[: scheduler.config.solver_order]

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

        UpperCamelCase_									=     scheduler.step(_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		**_UpperCAmelCase ).prev_sample
        UpperCamelCase_									=     scheduler.step(_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		**_UpperCAmelCase ).prev_sample

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




  def    _UpperCAmelCase     (     self )			-> str:
     # make sure that iterating over schedulers with same config names gives same results
     # for defaults
     UpperCamelCase_									=     DEISMultistepScheduler(**self.get_scheduler_config() )
     UpperCamelCase_									=     self.full_loop(scheduler=_UpperCAmelCase )
     UpperCamelCase_									=     torch.mean(torch.abs(_UpperCAmelCase ) )

     assert abs(result_mean.item() - 0.2_3_9_1_6 ) < 1e-3

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

     UpperCamelCase_									=     self.full_loop(scheduler=_UpperCAmelCase )
     UpperCamelCase_									=     torch.mean(torch.abs(_UpperCAmelCase ) )

     assert abs(result_mean.item() - 0.2_3_9_1_6 ) < 1e-3




  def    _UpperCAmelCase     (     self )			-> List[str]:
     for timesteps in [25, 50, 100, 999, 1000]:
        self.check_over_configs(num_train_timesteps=_UpperCAmelCase )




  def    _UpperCAmelCase     (     self )			-> Dict:
     self.check_over_configs(thresholding=_UpperCAmelCase )
     for order in [1, 2, 3]:
        for solver_type in ["logrho"]:
           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='deis'		,		solver_order=_UpperCAmelCase		,		solver_type=_UpperCAmelCase		,		)




  def    _UpperCAmelCase     (     self )			-> Optional[int]:
     for prediction_type in ["epsilon", "v_prediction"]:
        self.check_over_configs(prediction_type=_UpperCAmelCase )




  def    _UpperCAmelCase     (     self )			-> List[str]:
     for algorithm_type in ["deis"]:
        for solver_type in ["logrho"]:
           for order in [1, 2, 3]:
              for prediction_type in ["epsilon", "sample"]:
                 self.check_over_configs(
                     solver_order=_UpperCAmelCase		,		solver_type=_UpperCAmelCase		,		prediction_type=_UpperCAmelCase		,		algorithm_type=_UpperCAmelCase		,		)
                 UpperCamelCase_									=     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    _UpperCAmelCase     (     self )			-> List[str]:
     self.check_over_configs(lower_order_final=_UpperCAmelCase )
     self.check_over_configs(lower_order_final=_UpperCAmelCase )




  def    _UpperCAmelCase     (     self )			-> str:
     for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
        self.check_over_forward(num_inference_steps=_UpperCAmelCase		,		time_step=0 )




  def    _UpperCAmelCase     (     self )			-> Any:
     UpperCamelCase_									=     self.full_loop()
     UpperCamelCase_									=     torch.mean(torch.abs(_UpperCAmelCase ) )

     assert abs(result_mean.item() - 0.2_3_9_1_6 ) < 1e-3




  def    _UpperCAmelCase     (     self )			-> List[Any]:
     UpperCamelCase_									=     self.full_loop(prediction_type='v_prediction' )
     UpperCamelCase_									=     torch.mean(torch.abs(_UpperCAmelCase ) )

     assert abs(result_mean.item() - 0.0_9_1 ) < 1e-3




  def    _UpperCAmelCase     (     self )			-> Any:
     UpperCamelCase_									=     self.scheduler_classes[0]
     UpperCamelCase_									=     self.get_scheduler_config(thresholding=_UpperCAmelCase		,		dynamic_thresholding_ratio=0 )
     UpperCamelCase_									=     scheduler_class(**_UpperCAmelCase )

     UpperCamelCase_									=     10
     UpperCamelCase_									=     self.dummy_model()
     UpperCamelCase_									=     self.dummy_sample_deter.half()
     scheduler.set_timesteps(_UpperCAmelCase )

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

     assert sample.dtype == torch.floataa













def     _snake_case    (__lowercase    ,      __lowercase    ,      __lowercase):
   return not any(
       neighbour == 1 and colored_vertices[i] == color
       for i, neighbour in enumerate(__lowercase))






def     _snake_case    (__lowercase    ,      __lowercase    ,      __lowercase    ,      __lowercase):

   # Base Case
   if index == len(__lowercase):
      return True

   # Recursive Step
   for i in range(__lowercase):
      if valid_coloring(graph[index]    ,      __lowercase    ,      __lowercase):
         # Color current vertex
         UpperCamelCase_									=     i
         # Validate coloring
         if util_color(__lowercase    ,      __lowercase    ,      __lowercase    ,      index + 1):
            return True
         # Backtrack
         UpperCamelCase_									=     -1
   return False






def     _snake_case    (__lowercase    ,      __lowercase):
   UpperCamelCase_									=     [-1] * len(__lowercase)

   if util_color(__lowercase    ,      __lowercase    ,      __lowercase    ,      0):
      return colored_vertices

   return []





