import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest





class 		_lowercase						(					snake_case_       ):
 lowercase				     =   (DDPMScheduler,)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[str]   ,      **snake_case	:							Optional[Any]			)   ->						str:

  """simple docstring"""





  UpperCamelCase_					:						Any =					{
      'num_train_timesteps': 1_0_0_0,
      'beta_start': 0.0001,
      'beta_end': 0.02,
      'beta_schedule': 'linear',
      'variance_type': 'fixed_small',
      'clip_sample': True,
  }

  config.update(**lowerCamelCase_			)
  return config

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Optional[Any]			)   ->						Any:

  """simple docstring"""





  for timesteps in [1, 5, 1_0_0, 1_0_0_0]:
   self.check_over_configs(num_train_timesteps=lowerCamelCase_			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Any			)   ->						Any:

  """simple docstring"""





  for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]   ,      [0.002, 0.02, 0.2, 2]			):
   self.check_over_configs(beta_start=lowerCamelCase_   ,      beta_end=lowerCamelCase_			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[Any]			)   ->						Tuple:

  """simple docstring"""





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

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Tuple			)   ->						Optional[int]:

  """simple docstring"""





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

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Optional[Any]			)   ->						List[Any]:

  """simple docstring"""





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

 def 						SCREAMING_SNAKE_CASE__   ( self	:							int			)   ->						Union[str, Any]:

  """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 						SCREAMING_SNAKE_CASE__   ( self	:							List[Any]			)   ->						Optional[int]:

  """simple docstring"""





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

 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[str]			)   ->						Optional[Any]:

  """simple docstring"""





  for t in [0, 5_0_0, 9_9_9]:
   self.check_over_forward(time_step=lowerCamelCase_			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[str]			)   ->						List[Any]:

  """simple docstring"""





  UpperCamelCase_					:						Optional[Any] =					self.scheduler_classes[0]
  UpperCamelCase_					:						Optional[int] =					self.get_scheduler_config()
  UpperCamelCase_					:						str =					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_8_7			) - 0.00979			)			) < 1e-5
  assert torch.sum(torch.abs(scheduler._get_variance(9_9_9			) - 0.02			)			) < 1e-5

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Union[str, Any]			)   ->						Any:

  """simple docstring"""





  UpperCamelCase_					:						List[Any] =					self.scheduler_classes[0]
  UpperCamelCase_					:						Union[str, Any] =					self.get_scheduler_config()
  UpperCamelCase_					:						List[Any] =					scheduler_class(**lowerCamelCase_			)

  UpperCamelCase_					:						List[Any] =					len(lowerCamelCase_			)

  UpperCamelCase_					:						Union[str, Any] =					self.dummy_model()
  UpperCamelCase_					:						List[Any] =					self.dummy_sample_deter
  UpperCamelCase_					:						Any =					torch.manual_seed(0			)

  for t in reversed(range(lowerCamelCase_			)			):
   # 1. predict noise residual
   UpperCamelCase_					:						Any =					model(lowerCamelCase_   ,      lowerCamelCase_			)

   # 2. predict previous mean of sample x_t-1
   UpperCamelCase_					:						Union[str, Any] =					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
   UpperCamelCase_					:						List[str] =					pred_prev_sample

  UpperCamelCase_					:						int =					torch.sum(torch.abs(lowerCamelCase_			)			)
  UpperCamelCase_					:						Optional[int] =					torch.mean(torch.abs(lowerCamelCase_			)			)

  assert abs(result_sum.item() - 258.9606			) < 1e-2
  assert abs(result_mean.item() - 0.3372			) < 1e-3

 def 						SCREAMING_SNAKE_CASE__   ( self	:							str			)   ->						List[Any]:

  """simple docstring"""





  UpperCamelCase_					:						Union[str, Any] =					self.scheduler_classes[0]
  UpperCamelCase_					:						Union[str, Any] =					self.get_scheduler_config(prediction_type='v_prediction'			)
  UpperCamelCase_					:						List[Any] =					scheduler_class(**lowerCamelCase_			)

  UpperCamelCase_					:						List[str] =					len(lowerCamelCase_			)

  UpperCamelCase_					:						Optional[Any] =					self.dummy_model()
  UpperCamelCase_					:						int =					self.dummy_sample_deter
  UpperCamelCase_					:						Optional[Any] =					torch.manual_seed(0			)

  for t in reversed(range(lowerCamelCase_			)			):
   # 1. predict noise residual
   UpperCamelCase_					:						List[Any] =					model(lowerCamelCase_   ,      lowerCamelCase_			)

   # 2. predict previous mean of sample x_t-1
   UpperCamelCase_					:						Optional[Any] =					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
   UpperCamelCase_					:						List[str] =					pred_prev_sample

  UpperCamelCase_					:						Any =					torch.sum(torch.abs(lowerCamelCase_			)			)
  UpperCamelCase_					:						Tuple =					torch.mean(torch.abs(lowerCamelCase_			)			)

  assert abs(result_sum.item() - 202.0296			) < 1e-2
  assert abs(result_mean.item() - 0.2631			) < 1e-3

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Any			)   ->						Dict:

  """simple docstring"""





  UpperCamelCase_					:						Any =					self.scheduler_classes[0]
  UpperCamelCase_					:						List[str] =					self.get_scheduler_config()
  UpperCamelCase_					:						Optional[int] =					scheduler_class(**lowerCamelCase_			)

  UpperCamelCase_					:						Tuple =					[1_0_0, 8_7, 5_0, 1, 0]

  scheduler.set_timesteps(timesteps=lowerCamelCase_			)

  UpperCamelCase_					:						str =					scheduler.timesteps

  for i, timestep in enumerate(lowerCamelCase_			):
   if i == len(lowerCamelCase_			) - 1:
    UpperCamelCase_					:						Union[str, Any] =					-1
   else:
    UpperCamelCase_					:						Any =					timesteps[i + 1]

   UpperCamelCase_					:						Optional[int] =					scheduler.previous_timestep(lowerCamelCase_			)
   UpperCamelCase_					:						Tuple =					prev_t.item()

   self.assertEqual(lowerCamelCase_   ,      lowerCamelCase_			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Any			)   ->						Optional[int]:

  """simple docstring"""





  UpperCamelCase_					:						Dict =					self.scheduler_classes[0]
  UpperCamelCase_					:						Union[str, Any] =					self.get_scheduler_config()
  UpperCamelCase_					:						Optional[Any] =					scheduler_class(**lowerCamelCase_			)

  UpperCamelCase_					:						Dict =					[1_0_0, 8_7, 5_0, 5_1, 0]

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

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Optional[int]			)   ->						Dict:

  """simple docstring"""





  UpperCamelCase_					:						Optional[Any] =					self.scheduler_classes[0]
  UpperCamelCase_					:						Dict =					self.get_scheduler_config()
  UpperCamelCase_					:						Dict =					scheduler_class(**lowerCamelCase_			)

  UpperCamelCase_					:						Optional[int] =					[1_0_0, 8_7, 5_0, 1, 0]
  UpperCamelCase_					:						List[Any] =					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 						SCREAMING_SNAKE_CASE__   ( self	:							Any			)   ->						Union[str, Any]:

  """simple docstring"""





  UpperCamelCase_					:						int =					self.scheduler_classes[0]
  UpperCamelCase_					:						Tuple =					self.get_scheduler_config()
  UpperCamelCase_					:						Dict =					scheduler_class(**lowerCamelCase_			)

  UpperCamelCase_					:						List[Any] =					[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_			)








def     a(      A       :    list       )		->       list:
					"""simple docstring"""





					if any(not isinstance(A ,     A       ) or x < 0 for x in sequence       ):
										raise TypeError("Sequence must be list of non-negative integers"       )
					for _ in range(len(A       )       ):
										for i, (rod_upper, rod_lower) in enumerate(zip(A ,     sequence[1:]       )       ):
															if rod_upper > rod_lower:
																				sequence[i] -= rod_upper - rod_lower
																				sequence[i + 1] += rod_upper - rod_lower
					return sequence


if __name__ == "__main__":
				assert bead_sort([5, 4, 3, 2, 1]) == [1, 2, 3, 4, 5]
				assert bead_sort([7, 9, 4, 3, 5]) == [3, 4, 5, 7, 9]
