






import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest



class 							_UpperCamelCase			(a_			):
      snake_case_  =							(DDIMParallelScheduler,)
      snake_case_  =							(("eta", 0.0), ("num_inference_steps", 50))


      def  __UpperCAmelCase		(	self					,						**__UpperCamelCase	)->	Tuple:
           __lowerCAmelCase								=					{
               "num_train_timesteps": 1_0_0_0,
               "beta_start": 0.0_0_0_1,
               "beta_end": 0.0_2,
               "beta_schedule": "linear",
               "clip_sample": True,
           }

           config.update(**UpperCamelCase_	)
           return config


      def  __UpperCAmelCase		(	self					,						**__UpperCamelCase	)->	str:
           __lowerCAmelCase								=					self.scheduler_classes[0]
           __lowerCAmelCase								=					self.get_scheduler_config(**UpperCamelCase_	)
           __lowerCAmelCase								=					scheduler_class(**UpperCamelCase_	)

           __lowerCAmelCase								=					1_0, 0.0

           __lowerCAmelCase								=					self.dummy_model()
           __lowerCAmelCase								=					self.dummy_sample_deter

           scheduler.set_timesteps(UpperCamelCase_	)

           for t in scheduler.timesteps:
                __lowerCAmelCase								=					model(UpperCamelCase_					,						UpperCamelCase_	)
                __lowerCAmelCase								=					scheduler.step(UpperCamelCase_					,						UpperCamelCase_					,						UpperCamelCase_					,						UpperCamelCase_	).prev_sample

           return sample


      def  __UpperCAmelCase		(	self	)->	Tuple:
           for timesteps in [1_0_0, 5_0_0, 1_0_0_0]:
                self.check_over_configs(num_train_timesteps=UpperCamelCase_	)


      def  __UpperCAmelCase		(	self	)->	Optional[int]:
           for steps_offset in [0, 1]:
                self.check_over_configs(steps_offset=UpperCamelCase_	)

           __lowerCAmelCase								=					self.scheduler_classes[0]
           __lowerCAmelCase								=					self.get_scheduler_config(steps_offset=1	)
           __lowerCAmelCase								=					scheduler_class(**UpperCamelCase_	)
           scheduler.set_timesteps(5	)
           assert torch.equal(scheduler.timesteps					,						torch.LongTensor([8_0_1, 6_0_1, 4_0_1, 2_0_1, 1]	)	)


      def  __UpperCAmelCase		(	self	)->	List[Any]:
           for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]					,						[0.0_0_2, 0.0_2, 0.2, 2]	):
                self.check_over_configs(beta_start=UpperCamelCase_					,						beta_end=UpperCamelCase_	)


      def  __UpperCAmelCase		(	self	)->	Any:
           for schedule in ["linear", "squaredcos_cap_v2"]:
                self.check_over_configs(beta_schedule=UpperCamelCase_	)


      def  __UpperCAmelCase		(	self	)->	List[Any]:
           for prediction_type in ["epsilon", "v_prediction"]:
                self.check_over_configs(prediction_type=UpperCamelCase_	)


      def  __UpperCAmelCase		(	self	)->	Optional[Any]:
           for clip_sample in [True, False]:
                self.check_over_configs(clip_sample=UpperCamelCase_	)


      def  __UpperCAmelCase		(	self	)->	int:
           for timestep_spacing in ["trailing", "leading"]:
                self.check_over_configs(timestep_spacing=UpperCamelCase_	)


      def  __UpperCAmelCase		(	self	)->	int:
           for rescale_betas_zero_snr in [True, False]:
                self.check_over_configs(rescale_betas_zero_snr=UpperCamelCase_	)


      def  __UpperCAmelCase		(	self	)->	Any:
           self.check_over_configs(thresholding=UpperCamelCase_	)
           for threshold in [0.5, 1.0, 2.0]:
                for prediction_type in ["epsilon", "v_prediction"]:
                     self.check_over_configs(
                         thresholding=UpperCamelCase_					,						prediction_type=UpperCamelCase_					,						sample_max_value=UpperCamelCase_					,						)


      def  __UpperCAmelCase		(	self	)->	Optional[int]:
           for t in [1, 1_0, 4_9]:
                self.check_over_forward(time_step=UpperCamelCase_	)


      def  __UpperCAmelCase		(	self	)->	Union[str, Any]:
           for t, num_inference_steps in zip([1, 1_0, 5_0]					,						[1_0, 5_0, 5_0_0]	):
                self.check_over_forward(time_step=UpperCamelCase_					,						num_inference_steps=UpperCamelCase_	)


      def  __UpperCAmelCase		(	self	)->	Dict:
           for t, eta in zip([1, 1_0, 4_9]					,						[0.0, 0.5, 1.0]	):
                self.check_over_forward(time_step=UpperCamelCase_					,						eta=UpperCamelCase_	)


      def  __UpperCAmelCase		(	self	)->	Optional[int]:
           __lowerCAmelCase								=					self.scheduler_classes[0]
           __lowerCAmelCase								=					self.get_scheduler_config()
           __lowerCAmelCase								=					scheduler_class(**UpperCamelCase_	)

           assert torch.sum(torch.abs(scheduler._get_variance(0					,						0	) - 0.0	)	) < 1e-5
           assert torch.sum(torch.abs(scheduler._get_variance(4_2_0					,						4_0_0	) - 0.1_4_7_7_1	)	) < 1e-5
           assert torch.sum(torch.abs(scheduler._get_variance(9_8_0					,						9_6_0	) - 0.3_2_4_6_0	)	) < 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_8_7					,						4_8_6	) - 0.0_0_9_7_9	)	) < 1e-5
           assert torch.sum(torch.abs(scheduler._get_variance(9_9_9					,						9_9_8	) - 0.0_2	)	) < 1e-5


      def  __UpperCAmelCase		(	self	)->	Optional[int]:
           __lowerCAmelCase								=					self.scheduler_classes[0]
           __lowerCAmelCase								=					self.get_scheduler_config()
           __lowerCAmelCase								=					scheduler_class(**UpperCamelCase_	)

           __lowerCAmelCase								=					1_0, 0.0
           scheduler.set_timesteps(UpperCamelCase_	)

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

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

           __lowerCAmelCase								=					model(samples.flatten(0					,						1	)					,						timesteps.flatten(0					,						1	)	)
           __lowerCAmelCase								=					scheduler.batch_step_no_noise(UpperCamelCase_					,						timesteps.flatten(0					,						1	)					,						samples.flatten(0					,						1	)					,						UpperCamelCase_	)

           __lowerCAmelCase								=					torch.sum(torch.abs(UpperCamelCase_	)	)
           __lowerCAmelCase								=					torch.mean(torch.abs(UpperCamelCase_	)	)

           assert abs(result_sum.item() - 1_1_4_7.7_9_0_4	) < 1e-2
           assert abs(result_mean.item() - 0.4_9_8_2	) < 1e-3


      def  __UpperCAmelCase		(	self	)->	int:
           __lowerCAmelCase								=					self.full_loop()

           __lowerCAmelCase								=					torch.sum(torch.abs(UpperCamelCase_	)	)
           __lowerCAmelCase								=					torch.mean(torch.abs(UpperCamelCase_	)	)

           assert abs(result_sum.item() - 1_7_2.0_0_6_7	) < 1e-2
           assert abs(result_mean.item() - 0.2_2_3_9_6_7	) < 1e-3


      def  __UpperCAmelCase		(	self	)->	Any:
           __lowerCAmelCase								=					self.full_loop(prediction_type="v_prediction"	)

           __lowerCAmelCase								=					torch.sum(torch.abs(UpperCamelCase_	)	)
           __lowerCAmelCase								=					torch.mean(torch.abs(UpperCamelCase_	)	)

           assert abs(result_sum.item() - 5_2.5_3_0_2	) < 1e-2
           assert abs(result_mean.item() - 0.0_6_8_4	) < 1e-3


      def  __UpperCAmelCase		(	self	)->	Union[str, Any]:
           # We specify different beta, so that the first alpha is 0.99
           __lowerCAmelCase								=					self.full_loop(set_alpha_to_one=UpperCamelCase_					,						beta_start=0.0_1	)
           __lowerCAmelCase								=					torch.sum(torch.abs(UpperCamelCase_	)	)
           __lowerCAmelCase								=					torch.mean(torch.abs(UpperCamelCase_	)	)

           assert abs(result_sum.item() - 1_4_9.8_2_9_5	) < 1e-2
           assert abs(result_mean.item() - 0.1_9_5_1	) < 1e-3


      def  __UpperCAmelCase		(	self	)->	Optional[int]:
           # We specify different beta, so that the first alpha is 0.99
           __lowerCAmelCase								=					self.full_loop(set_alpha_to_one=UpperCamelCase_					,						beta_start=0.0_1	)
           __lowerCAmelCase								=					torch.sum(torch.abs(UpperCamelCase_	)	)
           __lowerCAmelCase								=					torch.mean(torch.abs(UpperCamelCase_	)	)

           assert abs(result_sum.item() - 1_4_9.0_7_8_4	) < 1e-2
           assert abs(result_mean.item() - 0.1_9_4_1	) < 1e-3












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
lowerCamelCase			:					int         =	1.0_5457_1817E-34  # unit of ℏ : J * s

lowerCamelCase			:					Union[str, Any]         =	3E8  # unit of c : m * s^-1





def       __lowerCAmelCase      (						__snake_case					, __snake_case					, __snake_case       ):

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






