



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__      :				Optional[int]   	=			1.0_54_57_18_17E-34  # unit of ℏ : J * s

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

def   _lowerCAmelCase   (						A__						,  A__						,  A__	):

      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:
            lowercase__     =  (REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (
                240 * (distance) ** 4
            )
            return {"force": force}
      elif area == 0:
            lowercase__     =  (240 * force * (distance) ** 4) / (
                REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2
            )
            return {"area": area}
      elif distance == 0:
            lowercase__     =  (
                (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()





import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest



class       UpperCAmelCase__(							lowerCamelCase	):

   '''simple docstring'''




   A    :  str       				=    (DDIMParallelScheduler,)
   A    :  Any       				=    (("eta", 0.0), ("num_inference_steps", 50))


   def 			UpperCAmelCase		(						self  :			Union[str, Any]			,				**lowerCAmelCase  :			Optional[int])	->				Dict:




         """simple docstring"""

         lowercase__     =  {
             'num_train_timesteps': 10_00,
             'beta_start': 0.00_01,
             'beta_end': 0.02,
             'beta_schedule': 'linear',
             'clip_sample': True,
         }

         config.update(**lowerCAmelCase)
         return config


   def 			UpperCAmelCase		(						self  :			int			,				**lowerCAmelCase  :			str)	->				Union[str, Any]:




         """simple docstring"""

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

         lowercase__,	lowercase__     =  10, 0.0

         lowercase__     =  self.dummy_model()
         lowercase__     =  self.dummy_sample_deter

         scheduler.set_timesteps(lowerCAmelCase)

         for t in scheduler.timesteps:
               lowercase__     =  model(lowerCAmelCase			,				lowerCAmelCase)
               lowercase__     =  scheduler.step(lowerCAmelCase			,				lowerCAmelCase			,				lowerCAmelCase			,				lowerCAmelCase).prev_sample

         return sample


   def 			UpperCAmelCase		(						self  :			Tuple)	->				int:




         """simple docstring"""

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


   def 			UpperCAmelCase		(						self  :			Tuple)	->				Any:




         """simple docstring"""

         for steps_offset in [0, 1]:
               self.check_over_configs(steps_offset=lowerCAmelCase)

         lowercase__     =  self.scheduler_classes[0]
         lowercase__     =  self.get_scheduler_config(steps_offset=1)
         lowercase__     =  scheduler_class(**lowerCAmelCase)
         scheduler.set_timesteps(5)
         assert torch.equal(scheduler.timesteps			,				torch.LongTensor([8_01, 6_01, 4_01, 2_01, 1]))


   def 			UpperCAmelCase		(						self  :			str)	->				Tuple:




         """simple docstring"""

         for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]			,				[0.0_02, 0.02, 0.2, 2]):
               self.check_over_configs(beta_start=lowerCAmelCase			,				beta_end=lowerCAmelCase)


   def 			UpperCAmelCase		(						self  :			Optional[int])	->				str:




         """simple docstring"""

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


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




         """simple docstring"""

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


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




         """simple docstring"""

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


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




         """simple docstring"""

         for timestep_spacing in ["trailing", "leading"]:
               self.check_over_configs(timestep_spacing=lowerCAmelCase)


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




         """simple docstring"""

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


   def 			UpperCAmelCase		(						self  :			List[str])	->				Optional[int]:




         """simple docstring"""

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


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




         """simple docstring"""

         for t in [1, 10, 49]:
               self.check_over_forward(time_step=lowerCAmelCase)


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




         """simple docstring"""

         for t, num_inference_steps in zip([1, 10, 50]			,				[10, 50, 5_00]):
               self.check_over_forward(time_step=lowerCAmelCase			,				num_inference_steps=lowerCAmelCase)


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




         """simple docstring"""

         for t, eta in zip([1, 10, 49]			,				[0.0, 0.5, 1.0]):
               self.check_over_forward(time_step=lowerCAmelCase			,				eta=lowerCAmelCase)


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




         """simple docstring"""

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

         assert torch.sum(torch.abs(scheduler._get_variance(0			,				0) - 0.0)) < 1E-5
         assert torch.sum(torch.abs(scheduler._get_variance(4_20			,				4_00) - 0.1_47_71)) < 1E-5
         assert torch.sum(torch.abs(scheduler._get_variance(9_80			,				9_60) - 0.3_24_60)) < 1E-5
         assert torch.sum(torch.abs(scheduler._get_variance(0			,				0) - 0.0)) < 1E-5
         assert torch.sum(torch.abs(scheduler._get_variance(4_87			,				4_86) - 0.0_09_79)) < 1E-5
         assert torch.sum(torch.abs(scheduler._get_variance(9_99			,				9_98) - 0.02)) < 1E-5


   def 			UpperCAmelCase		(						self  :			Dict)	->				Tuple:




         """simple docstring"""

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

         lowercase__,	lowercase__     =  10, 0.0
         scheduler.set_timesteps(lowerCAmelCase)

         lowercase__     =  self.dummy_model()
         lowercase__     =  self.dummy_sample_deter
         lowercase__     =  self.dummy_sample_deter + 0.1
         lowercase__     =  self.dummy_sample_deter - 0.1

         lowercase__     =  samplea.shape[0]
         lowercase__     =  torch.stack([samplea, samplea, samplea]			,				dim=0)
         lowercase__     =  torch.arange(lowerCAmelCase)[0:3, None].repeat(1			,				lowerCAmelCase)

         lowercase__     =  model(samples.flatten(0			,				1)			,				timesteps.flatten(0			,				1))
         lowercase__     =  scheduler.batch_step_no_noise(lowerCAmelCase			,				timesteps.flatten(0			,				1)			,				samples.flatten(0			,				1)			,				lowerCAmelCase)

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

         assert abs(result_sum.item() - 11_47.79_04) < 1E-2
         assert abs(result_mean.item() - 0.49_82) < 1E-3


   def 			UpperCAmelCase		(						self  :			Any)	->				int:




         """simple docstring"""

         lowercase__     =  self.full_loop()

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

         assert abs(result_sum.item() - 1_72.00_67) < 1E-2
         assert abs(result_mean.item() - 0.22_39_67) < 1E-3


   def 			UpperCAmelCase		(						self  :			int)	->				List[Any]:




         """simple docstring"""

         lowercase__     =  self.full_loop(prediction_type='v_prediction')

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

         assert abs(result_sum.item() - 52.53_02) < 1E-2
         assert abs(result_mean.item() - 0.06_84) < 1E-3


   def 			UpperCAmelCase		(						self  :			str)	->				Dict:




         """simple docstring"""

         lowercase__     =  self.full_loop(set_alpha_to_one=lowerCAmelCase			,				beta_start=0.01)
         lowercase__     =  torch.sum(torch.abs(lowerCAmelCase))
         lowercase__     =  torch.mean(torch.abs(lowerCAmelCase))

         assert abs(result_sum.item() - 1_49.82_95) < 1E-2
         assert abs(result_mean.item() - 0.19_51) < 1E-3



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




         """simple docstring"""

         lowercase__     =  self.full_loop(set_alpha_to_one=lowerCAmelCase			,				beta_start=0.01)
         lowercase__     =  torch.sum(torch.abs(lowerCAmelCase))
         lowercase__     =  torch.mean(torch.abs(lowerCAmelCase))

         assert abs(result_sum.item() - 1_49.07_84) < 1E-2
         assert abs(result_mean.item() - 0.19_41) < 1E-3
