





"""simple docstring"""
import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest







class       a							(   __SCREAMING_SNAKE_CASE  ):
 snake_case__             =       (DDPMScheduler,)



 def    UpperCamelCase__  (     self				,       **_snake_case     ):







  """simple docstring"""






  lowerCAmelCase    				= {
      'num_train_timesteps': 10_00,
      'beta_start': 0.0_001,
      'beta_end': 0.02,
      'beta_schedule': 'linear',
      'variance_type': 'fixed_small',
      'clip_sample': True,
  }

  config.update(**__lowerCamelCase     )
  return config



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  for timesteps in [1, 5, 1_00, 10_00]:
   self.check_over_configs(num_train_timesteps=__lowerCamelCase     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1]				,       [0.002, 0.02, 0.2, 2]     ):
   self.check_over_configs(beta_start=__lowerCamelCase				,       beta_end=__lowerCamelCase     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  for schedule in ["linear", "squaredcos_cap_v2"]:
   self.check_over_configs(beta_schedule=__lowerCamelCase     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  for variance in ["fixed_small", "fixed_large", "other"]:
   self.check_over_configs(variance_type=__lowerCamelCase     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  for clip_sample in [True, False]:
   self.check_over_configs(clip_sample=__lowerCamelCase     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  self.check_over_configs(thresholding=__lowerCamelCase     )
  for threshold in [0.5, 1.0, 2.0]:
   for prediction_type in ["epsilon", "sample", "v_prediction"]:
    self.check_over_configs(
        thresholding=__lowerCamelCase				,       prediction_type=__lowerCamelCase				,       sample_max_value=__lowerCamelCase				,       )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






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



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  for t in [0, 5_00, 9_99]:
   self.check_over_forward(time_step=__lowerCamelCase     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






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

  assert torch.sum(torch.abs(scheduler._get_variance(0     ) - 0.0     )     ) < 1E-5
  assert torch.sum(torch.abs(scheduler._get_variance(4_87     ) - 0.00_979     )     ) < 1E-5
  assert torch.sum(torch.abs(scheduler._get_variance(9_99     ) - 0.02     )     ) < 1E-5



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






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

  lowerCAmelCase    				= len(__lowerCamelCase     )

  lowerCAmelCase    				= self.dummy_model()
  lowerCAmelCase    				= self.dummy_sample_deter
  lowerCAmelCase    				= torch.manual_seed(0     )

  for t in reversed(range(__lowerCamelCase     )     ):
   # 1. predict noise residual
   lowerCAmelCase    				= model(__lowerCamelCase				,       __lowerCamelCase     )

   # 2. predict previous mean of sample x_t-1
   lowerCAmelCase    				= scheduler.step(__lowerCamelCase				,       __lowerCamelCase				,       __lowerCamelCase				,       generator=__lowerCamelCase     ).prev_sample

   # if t > 0:
   #     noise = self.dummy_sample_deter
   #     variance = scheduler.get_variance(t) ** (0.5) * noise
   #
   # sample = pred_prev_sample + variance
   lowerCAmelCase    				= pred_prev_sample

  lowerCAmelCase    				= torch.sum(torch.abs(__lowerCamelCase     )     )
  lowerCAmelCase    				= torch.mean(torch.abs(__lowerCamelCase     )     )

  assert abs(result_sum.item() - 2_58.96_06     ) < 1E-2
  assert abs(result_mean.item() - 0.3_372     ) < 1E-3



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= self.scheduler_classes[0]
  lowerCAmelCase    				= self.get_scheduler_config(prediction_type='v_prediction'     )
  lowerCAmelCase    				= scheduler_class(**__lowerCamelCase     )

  lowerCAmelCase    				= len(__lowerCamelCase     )

  lowerCAmelCase    				= self.dummy_model()
  lowerCAmelCase    				= self.dummy_sample_deter
  lowerCAmelCase    				= torch.manual_seed(0     )

  for t in reversed(range(__lowerCamelCase     )     ):
   # 1. predict noise residual
   lowerCAmelCase    				= model(__lowerCamelCase				,       __lowerCamelCase     )

   # 2. predict previous mean of sample x_t-1
   lowerCAmelCase    				= scheduler.step(__lowerCamelCase				,       __lowerCamelCase				,       __lowerCamelCase				,       generator=__lowerCamelCase     ).prev_sample

   # if t > 0:
   #     noise = self.dummy_sample_deter
   #     variance = scheduler.get_variance(t) ** (0.5) * noise
   #
   # sample = pred_prev_sample + variance
   lowerCAmelCase    				= pred_prev_sample

  lowerCAmelCase    				= torch.sum(torch.abs(__lowerCamelCase     )     )
  lowerCAmelCase    				= torch.mean(torch.abs(__lowerCamelCase     )     )

  assert abs(result_sum.item() - 2_02.02_96     ) < 1E-2
  assert abs(result_mean.item() - 0.2_631     ) < 1E-3



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






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

  lowerCAmelCase    				= [1_00, 87, 50, 1, 0]

  scheduler.set_timesteps(timesteps=__lowerCamelCase     )

  lowerCAmelCase    				= scheduler.timesteps

  for i, timestep in enumerate(__lowerCamelCase     ):
   if i == len(__lowerCamelCase     ) - 1:
    lowerCAmelCase    				= -1
   else:
    lowerCAmelCase    				= timesteps[i + 1]

   lowerCAmelCase    				= scheduler.previous_timestep(__lowerCamelCase     )
   lowerCAmelCase    				= prev_t.item()

   self.assertEqual(__lowerCamelCase				,       __lowerCamelCase     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






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

  lowerCAmelCase    				= [1_00, 87, 50, 51, 0]

  with self.assertRaises(__lowerCamelCase				,       msg='`custom_timesteps` must be in descending order.'     ):
   scheduler.set_timesteps(timesteps=__lowerCamelCase     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






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

  lowerCAmelCase    				= [1_00, 87, 50, 1, 0]
  lowerCAmelCase    				= len(__lowerCamelCase     )

  with self.assertRaises(__lowerCamelCase				,       msg='Can only pass one of `num_inference_steps` or `custom_timesteps`.'     ):
   scheduler.set_timesteps(num_inference_steps=__lowerCamelCase				,       timesteps=__lowerCamelCase     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






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

  lowerCAmelCase    				= [scheduler.config.num_train_timesteps]

  with self.assertRaises(
      __lowerCamelCase				,       msg='`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'				,       ):
   scheduler.set_timesteps(timesteps=__lowerCamelCase     )










"""simple docstring"""






from ..utils import DummyObject, requires_backends



class     UpperCAmelCase    (	metaclass=__SCREAMING_SNAKE_CASE  ):
   A__		:	Optional[int]           =  ['''torch''', '''scipy''']



   def __init__(       self    :	Any							,       *__lowerCamelCase    :	List[Any]							,       **__lowerCamelCase    :	Any				):



     """simple docstring"""
     requires_backends(self							,       ['''torch''', '''scipy''']				)



   @classmethod
   def    __UpperCAmelCase    (       cls    :	Dict							,       *__lowerCamelCase    :	List[str]							,       **__lowerCamelCase    :	Tuple				):



     """simple docstring"""
     requires_backends(cls							,       ['''torch''', '''scipy''']				)





   @classmethod
   def    __UpperCAmelCase    (       cls    :	int							,       *__lowerCamelCase    :	Union[str, Any]							,       **__lowerCamelCase    :	Tuple				):



     """simple docstring"""
     requires_backends(cls							,       ['''torch''', '''scipy''']				)





