



import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest







class 		snake_case						(	SCREAMING_SNAKE_CASE_  ):



   '''simple docstring'''






   snake_case_					: Union[str, Any]          =		(DDPMScheduler,)

   def      UpperCamelCase_  (					self							:			List[Any]				,		**lowerCAmelCase							:			int)				->				Any:

    """simple docstring"""







    _snake_case    :  Union[str, Any]					=					{
        """num_train_timesteps""": 1000,
        """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							:			List[str])				->				Any:

    """simple docstring"""







    for timesteps in [1, 5, 100, 1000]:
     self.check_over_configs(num_train_timesteps=lowerCAmelCase)

   def      UpperCamelCase_  (					self							:			str)				->				int:

    """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							:			int)				->				Tuple:

    """simple docstring"""







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

   def      UpperCamelCase_  (					self							:			int)				->				int:

    """simple docstring"""







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

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

    """simple docstring"""







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

   def      UpperCamelCase_  (					self							:			Tuple)				->				Tuple:

    """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							:			Optional[Any])				->				List[Any]:

    """simple docstring"""







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

   def      UpperCamelCase_  (					self							:			Dict)				->				Dict:

    """simple docstring"""







    for t in [0, 500, 999]:
     self.check_over_forward(time_step=lowerCAmelCase)

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

    """simple docstring"""







    _snake_case    :  str					=					self.scheduler_classes[0]
    _snake_case    :  List[Any]					=					self.get_scheduler_config()
    _snake_case    :  List[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(487) - 0.00_979)) < 1E-5
    assert torch.sum(torch.abs(scheduler._get_variance(999) - 0.02)) < 1E-5

   def      UpperCamelCase_  (					self							:			Optional[int])				->				int:

    """simple docstring"""







    _snake_case    :  Any					=					self.scheduler_classes[0]
    _snake_case    :  Union[str, Any]					=					self.get_scheduler_config()
    _snake_case    :  int					=					scheduler_class(**lowerCAmelCase)

    _snake_case    :  List[Any]					=					len(lowerCAmelCase)

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

    for t in reversed(range(lowerCAmelCase)):
     # 1. predict noise residual
     _snake_case    :  List[str]					=					model(lowerCAmelCase				,		lowerCAmelCase)

     # 2. predict previous mean of sample x_t-1
     _snake_case    :  Optional[int]					=					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
     _snake_case    :  int					=					pred_prev_sample

    _snake_case    :  Dict					=					torch.sum(torch.abs(lowerCAmelCase))
    _snake_case    :  str					=					torch.mean(torch.abs(lowerCAmelCase))

    assert abs(result_sum.item() - 258.9_606) < 1E-2
    assert abs(result_mean.item() - 0.3_372) < 1E-3

   def      UpperCamelCase_  (					self							:			List[str])				->				str:

    """simple docstring"""







    _snake_case    :  List[Any]					=					self.scheduler_classes[0]
    _snake_case    :  Optional[int]					=					self.get_scheduler_config(prediction_type="""v_prediction""")
    _snake_case    :  str					=					scheduler_class(**lowerCAmelCase)

    _snake_case    :  Optional[Any]					=					len(lowerCAmelCase)

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

    for t in reversed(range(lowerCAmelCase)):
     # 1. predict noise residual
     _snake_case    :  Union[str, Any]					=					model(lowerCAmelCase				,		lowerCAmelCase)

     # 2. predict previous mean of sample x_t-1
     _snake_case    :  List[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
     _snake_case    :  Optional[int]					=					pred_prev_sample

    _snake_case    :  Any					=					torch.sum(torch.abs(lowerCAmelCase))
    _snake_case    :  Tuple					=					torch.mean(torch.abs(lowerCAmelCase))

    assert abs(result_sum.item() - 202.0_296) < 1E-2
    assert abs(result_mean.item() - 0.2_631) < 1E-3

   def      UpperCamelCase_  (					self							:			Optional[int])				->				int:

    """simple docstring"""







    _snake_case    :  Any					=					self.scheduler_classes[0]
    _snake_case    :  Any					=					self.get_scheduler_config()
    _snake_case    :  Union[str, Any]					=					scheduler_class(**lowerCAmelCase)

    _snake_case    :  Dict					=					[100, 87, 50, 1, 0]

    scheduler.set_timesteps(timesteps=lowerCAmelCase)

    _snake_case    :  str					=					scheduler.timesteps

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

     _snake_case    :  Tuple					=					scheduler.previous_timestep(lowerCAmelCase)
     _snake_case    :  List[str]					=					prev_t.item()

     self.assertEqual(lowerCAmelCase				,		lowerCAmelCase)

   def      UpperCamelCase_  (					self							:			Tuple)				->				str:

    """simple docstring"""







    _snake_case    :  Optional[int]					=					self.scheduler_classes[0]
    _snake_case    :  Tuple					=					self.get_scheduler_config()
    _snake_case    :  Any					=					scheduler_class(**lowerCAmelCase)

    _snake_case    :  int					=					[100, 87, 50, 51, 0]

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

   def      UpperCamelCase_  (					self							:			Tuple)				->				Any:

    """simple docstring"""







    _snake_case    :  Union[str, Any]					=					self.scheduler_classes[0]
    _snake_case    :  Any					=					self.get_scheduler_config()
    _snake_case    :  Dict					=					scheduler_class(**lowerCAmelCase)

    _snake_case    :  List[str]					=					[100, 87, 50, 1, 0]
    _snake_case    :  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      UpperCamelCase_  (					self							:			List[Any])				->				Any:

    """simple docstring"""







    _snake_case    :  List[str]					=					self.scheduler_classes[0]
    _snake_case    :  Union[str, Any]					=					self.get_scheduler_config()
    _snake_case    :  Optional[Any]					=					scheduler_class(**lowerCAmelCase)

    _snake_case    :  str					=					[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)





from __future__ import annotations

from math import pi

# Define the Reduced Planck Constant ℏ (H bar), speed of light C, value of
# Pi and the function
a__    =	1.0_54_57_18_17E-34  # unit of ℏ : J * s

a__    =	3E8  # unit of c : m * s^-1



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	float				,       SCREAMING_SNAKE_CASE__       :	float				,       SCREAMING_SNAKE_CASE__       :	float				)  ->     dict[str, float]:

 if (force, area, distance).count(0				) != 1:
  raise ValueError("""One and only one argument must be 0"""				)
 if force < 0:
  raise ValueError("""Magnitude of force can not be negative"""				)
 if distance < 0:
  raise ValueError("""Distance can not be negative"""				)
 if area < 0:
  raise ValueError("""Area can not be negative"""				)
 if force == 0:
  _snake_case    :  Union[str, Any]					=					(REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (
      240 * (distance) ** 4
  )
  return {"force": force}
 elif area == 0:
  _snake_case    :  Optional[Any]					=					(240 * force * (distance) ** 4) / (
      REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2
  )
  return {"area": area}
 elif distance == 0:
  _snake_case    :  str					=					(
      (REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (240 * force)
  ) ** (1 / 4)
  return {"distance": distance}
 raise ValueError("""One and only one argument must be 0"""				)


# Run doctest
if __name__ == "__main__":
  import doctest

  doctest.testmod()
