

import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest


class     lowerCamelCase_   ( _lowerCAmelCase):
 _lowerCAmelCase       		=  (DPMSolverMultistepScheduler,)
 _lowerCAmelCase       		=  (('''num_inference_steps''', 25),)




 def 						a					( self     ,     **_UpperCAmelCase       ):
   __UpperCAmelCase  :     Dict		=    {
       'num_train_timesteps': 1_000,
       'beta_start': 0.0_0_0_1,
       'beta_end': 0.0_2,
       'beta_schedule': 'linear',
       'solver_order': 2,
       'prediction_type': 'epsilon',
       'thresholding': False,
       'sample_max_value': 1.0,
       'algorithm_type': 'dpmsolver++',
       'solver_type': 'midpoint',
       'lower_order_final': False,
       'lambda_min_clipped': -float('inf'       ),
       'variance_type': None,
   }

   config.update(**_UpperCAmelCase       )
   return config




 def 						a					( self     ,     _UpperCAmelCase=0     ,     **_UpperCAmelCase       ):
   __UpperCAmelCase  :     Optional[Any]		=    dict(self.forward_default_kwargs       )
   __UpperCAmelCase  :     Tuple		=    kwargs.pop('num_inference_steps'     ,     _UpperCAmelCase       )
   __UpperCAmelCase  :     Dict		=    self.dummy_sample
   __UpperCAmelCase  :     List[str]		=    0.1 * sample
   __UpperCAmelCase  :     List[str]		=    [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

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

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

     __UpperCAmelCase, __UpperCAmelCase  :     str		=    sample, sample
     for t in range(_UpperCAmelCase     ,     time_step + scheduler.config.solver_order + 1       ):
       __UpperCAmelCase  :     str		=    scheduler.step(_UpperCAmelCase     ,     _UpperCAmelCase     ,     _UpperCAmelCase     ,     **_UpperCAmelCase       ).prev_sample
       __UpperCAmelCase  :     Tuple		=    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 						a					( self       ):
   pass




 def 						a					( self     ,     _UpperCAmelCase=0     ,     **_UpperCAmelCase       ):
   __UpperCAmelCase  :     Tuple		=    dict(self.forward_default_kwargs       )
   __UpperCAmelCase  :     Union[str, Any]		=    kwargs.pop('num_inference_steps'     ,     _UpperCAmelCase       )
   __UpperCAmelCase  :     Any		=    self.dummy_sample
   __UpperCAmelCase  :     Tuple		=    0.1 * sample
   __UpperCAmelCase  :     List[Any]		=    [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

   for scheduler_class in self.scheduler_classes:
     __UpperCAmelCase  :     Tuple		=    self.get_scheduler_config()
     __UpperCAmelCase  :     Dict		=    scheduler_class(**_UpperCAmelCase       )
     scheduler.set_timesteps(_UpperCAmelCase       )

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

     with tempfile.TemporaryDirectory() as tmpdirname:
       scheduler.save_config(_UpperCAmelCase       )
       __UpperCAmelCase  :     List[Any]		=    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  :     Optional[Any]		=    dummy_past_residuals[: new_scheduler.config.solver_order]

     __UpperCAmelCase  :     str		=    scheduler.step(_UpperCAmelCase     ,     _UpperCAmelCase     ,     _UpperCAmelCase     ,     **_UpperCAmelCase       ).prev_sample
     __UpperCAmelCase  :     List[Any]		=    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 						a					( self     ,     _UpperCAmelCase=None     ,     **_UpperCAmelCase       ):
   if scheduler is None:
     __UpperCAmelCase  :     Optional[Any]		=    self.scheduler_classes[0]
     __UpperCAmelCase  :     Optional[int]		=    self.get_scheduler_config(**_UpperCAmelCase       )
     __UpperCAmelCase  :     Union[str, Any]		=    scheduler_class(**_UpperCAmelCase       )

   __UpperCAmelCase  :     Optional[int]		=    10
   __UpperCAmelCase  :     str		=    self.dummy_model()
   __UpperCAmelCase  :     str		=    self.dummy_sample_deter
   scheduler.set_timesteps(_UpperCAmelCase       )

   for i, t in enumerate(scheduler.timesteps       ):
     __UpperCAmelCase  :     Optional[Any]		=    model(_UpperCAmelCase     ,     _UpperCAmelCase       )
     __UpperCAmelCase  :     Tuple		=    scheduler.step(_UpperCAmelCase     ,     _UpperCAmelCase     ,     _UpperCAmelCase       ).prev_sample

   return sample




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

   __UpperCAmelCase  :     List[str]		=    kwargs.pop('num_inference_steps'     ,     _UpperCAmelCase       )

   for scheduler_class in self.scheduler_classes:
     __UpperCAmelCase  :     str		=    self.get_scheduler_config()
     __UpperCAmelCase  :     Any		=    scheduler_class(**_UpperCAmelCase       )

     __UpperCAmelCase  :     int		=    self.dummy_sample
     __UpperCAmelCase  :     Tuple		=    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  :     Optional[Any]		=    num_inference_steps

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

     __UpperCAmelCase  :     Any		=    scheduler.timesteps[5]
     __UpperCAmelCase  :     str		=    scheduler.timesteps[6]

     __UpperCAmelCase  :     List[str]		=    scheduler.step(_UpperCAmelCase     ,     _UpperCAmelCase     ,     _UpperCAmelCase     ,     **_UpperCAmelCase       ).prev_sample
     __UpperCAmelCase  :     List[str]		=    scheduler.step(_UpperCAmelCase     ,     _UpperCAmelCase     ,     _UpperCAmelCase     ,     **_UpperCAmelCase       ).prev_sample

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




 def 						a					( self       ):
   for timesteps in [25, 50, 100, 999, 1_000]:
     self.check_over_configs(num_train_timesteps=_UpperCAmelCase       )




 def 						a					( self       ):
   self.check_over_configs(thresholding=_UpperCAmelCase       )
   for order in [1, 2, 3]:
     for solver_type in ["midpoint", "heun"]:
       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='dpmsolver++'     ,     solver_order=_UpperCAmelCase     ,     solver_type=_UpperCAmelCase     ,     )




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




 def 						a					( self       ):
   for algorithm_type in ["dpmsolver", "dpmsolver++", "sde-dpmsolver", "sde-dpmsolver++"]:
     for solver_type in ["midpoint", "heun"]:
       for order in [1, 2, 3]:
         for prediction_type in ["epsilon", "sample"]:
           if algorithm_type in ["sde-dpmsolver", "sde-dpmsolver++"]:
             if order == 3:
               continue
           else:
             self.check_over_configs(
                 solver_order=_UpperCAmelCase     ,     solver_type=_UpperCAmelCase     ,     prediction_type=_UpperCAmelCase     ,     algorithm_type=_UpperCAmelCase     ,     )
           __UpperCAmelCase  :     List[Any]		=    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 						a					( self       ):
   self.check_over_configs(lower_order_final=_UpperCAmelCase       )
   self.check_over_configs(lower_order_final=_UpperCAmelCase       )




 def 						a					( self       ):
   self.check_over_configs(lambda_min_clipped=-float('inf'       )       )
   self.check_over_configs(lambda_min_clipped=-5.1       )




 def 						a					( self       ):
   self.check_over_configs(variance_type=_UpperCAmelCase       )
   self.check_over_configs(variance_type='learned_range'       )




 def 						a					( self       ):
   for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1_000]:
     self.check_over_forward(num_inference_steps=_UpperCAmelCase     ,     time_step=0       )




 def 						a					( self       ):
   __UpperCAmelCase  :     Optional[Any]		=    self.full_loop()
   __UpperCAmelCase  :     Optional[Any]		=    torch.mean(torch.abs(_UpperCAmelCase       )       )

   assert abs(result_mean.item() - 0.3_3_0_1       ) < 1E-3




 def 						a					( self       ):
   __UpperCAmelCase  :     str		=    self.full_loop(thresholding=_UpperCAmelCase     ,     dynamic_thresholding_ratio=0.8_7     ,     sample_max_value=0.5       )
   __UpperCAmelCase  :     Optional[Any]		=    torch.mean(torch.abs(_UpperCAmelCase       )       )

   assert abs(result_mean.item() - 1.1_3_6_4       ) < 1E-3




 def 						a					( self       ):
   __UpperCAmelCase  :     List[str]		=    self.full_loop(prediction_type='v_prediction'       )
   __UpperCAmelCase  :     List[Any]		=    torch.mean(torch.abs(_UpperCAmelCase       )       )

   assert abs(result_mean.item() - 0.2_2_5_1       ) < 1E-3




 def 						a					( self       ):
   __UpperCAmelCase  :     Any		=    self.full_loop(prediction_type='v_prediction'     ,     use_karras_sigmas=_UpperCAmelCase       )
   __UpperCAmelCase  :     Optional[int]		=    torch.mean(torch.abs(_UpperCAmelCase       )       )

   assert abs(result_mean.item() - 0.2_0_9_6       ) < 1E-3




 def 						a					( self       ):
   # make sure that iterating over schedulers with same config names gives same results
   # for defaults
   __UpperCAmelCase  :     Union[str, Any]		=    DPMSolverMultistepScheduler(**self.get_scheduler_config()       )
   __UpperCAmelCase  :     Any		=    self.full_loop(scheduler=_UpperCAmelCase       )
   __UpperCAmelCase  :     List[Any]		=    torch.mean(torch.abs(_UpperCAmelCase       )       )

   assert abs(result_mean.item() - 0.3_3_0_1       ) < 1E-3

   __UpperCAmelCase  :     List[Any]		=    DPMSolverSinglestepScheduler.from_config(scheduler.config       )
   __UpperCAmelCase  :     List[str]		=    UniPCMultistepScheduler.from_config(scheduler.config       )
   __UpperCAmelCase  :     Union[str, Any]		=    DEISMultistepScheduler.from_config(scheduler.config       )
   __UpperCAmelCase  :     str		=    DPMSolverMultistepScheduler.from_config(scheduler.config       )

   __UpperCAmelCase  :     str		=    self.full_loop(scheduler=_UpperCAmelCase       )
   __UpperCAmelCase  :     Optional[Any]		=    torch.mean(torch.abs(_UpperCAmelCase       )       )

   assert abs(result_mean.item() - 0.3_3_0_1       ) < 1E-3




 def 						a					( self       ):
   __UpperCAmelCase  :     Any		=    self.scheduler_classes[0]
   __UpperCAmelCase  :     int		=    self.get_scheduler_config(thresholding=_UpperCAmelCase     ,     dynamic_thresholding_ratio=0       )
   __UpperCAmelCase  :     str		=    scheduler_class(**_UpperCAmelCase       )

   __UpperCAmelCase  :     Optional[int]		=    10
   __UpperCAmelCase  :     List[Any]		=    self.dummy_model()
   __UpperCAmelCase  :     int		=    self.dummy_sample_deter.half()
   scheduler.set_timesteps(_UpperCAmelCase       )

   for i, t in enumerate(scheduler.timesteps       ):
     __UpperCAmelCase  :     Union[str, Any]		=    model(_UpperCAmelCase     ,     _UpperCAmelCase       )
     __UpperCAmelCase  :     int		=    scheduler.step(_UpperCAmelCase     ,     _UpperCAmelCase     ,     _UpperCAmelCase       ).prev_sample

   assert sample.dtype == torch.floataa




 def 						a					( self     ,     **_UpperCAmelCase       ):
   for scheduler_class in self.scheduler_classes:
     __UpperCAmelCase  :     int		=    self.get_scheduler_config(**_UpperCAmelCase       )
     __UpperCAmelCase  :     Optional[Any]		=    scheduler_class(**_UpperCAmelCase       )

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








def    _snake_case		(snake_case_				:    list						,					snake_case_				:    list     )       ->		list:
  __UpperCAmelCase  :     int		=    len(snake_case_     )
  __UpperCAmelCase  :     int		=    len(snake_case_     )
  __UpperCAmelCase  :     int		=    (
      first_input_list_length
      if first_input_list_length > second_input_list_length
      else second_input_list_length
  )
  __UpperCAmelCase  :     list		=    []
  for char_count in range(snake_case_     ):
    if char_count < first_input_list_length:
      output_result_list.append(first_input_list[char_count]     )
    if char_count < second_input_list_length:
      output_result_list.append(second_input_list[char_count]     )

  return output_result_list


if __name__ == "__main__":
      print(alternative_list_arrange(['''A''', '''B''', '''C'''], [1, 2, 3, 4, 5]), end=''' ''')





