

"""simple docstring"""





import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest




class 					_lowerCAmelCase  (	lowercase   ):







 """simple docstring"""







 __UpperCAmelCase	:					Tuple   		=	(DDPMScheduler,)


 def 					_lowercase (			self							:			Optional[int],					**UpperCAmelCase__							:			List[Any]	):
    __lowercase								=						{
        "num_train_timesteps": 1_0_0_0,
        "beta_start": 0.0_001,
        "beta_end": 0.02,
        "beta_schedule": "linear",
        "variance_type": "fixed_small",
        "clip_sample": True,
    }

    config.update(**UpperCAmelCase__	)
    return config


 def 					_lowercase (			self							:			Tuple	):
    for timesteps in [1, 5, 1_0_0, 1_0_0_0]:
       self.check_over_configs(num_train_timesteps=UpperCAmelCase__	)


 def 					_lowercase (			self							:			Any	):
    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=UpperCAmelCase__,					beta_end=UpperCAmelCase__	)


 def 					_lowercase (			self							:			Tuple	):
    for schedule in ["linear", "squaredcos_cap_v2"]:
       self.check_over_configs(beta_schedule=UpperCAmelCase__	)


 def 					_lowercase (			self							:			int	):
    for variance in ["fixed_small", "fixed_large", "other"]:
       self.check_over_configs(variance_type=UpperCAmelCase__	)


 def 					_lowercase (			self							:			str	):
    for clip_sample in [True, False]:
       self.check_over_configs(clip_sample=UpperCAmelCase__	)


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


 def 					_lowercase (			self							:			Tuple	):
    for prediction_type in ["epsilon", "sample", "v_prediction"]:
       self.check_over_configs(prediction_type=UpperCAmelCase__	)


 def 					_lowercase (			self							:			Dict	):
    for t in [0, 5_0_0, 9_9_9]:
       self.check_over_forward(time_step=UpperCAmelCase__	)


 def 					_lowercase (			self							:			str	):
    __lowercase								=						self.scheduler_classes[0]
    __lowercase								=						self.get_scheduler_config()
    __lowercase								=						scheduler_class(**UpperCAmelCase__	)

    assert torch.sum(torch.abs(scheduler._get_variance(0	) - 0.0	)	) < 1E-5
    assert torch.sum(torch.abs(scheduler._get_variance(4_8_7	) - 0.00_979	)	) < 1E-5
    assert torch.sum(torch.abs(scheduler._get_variance(9_9_9	) - 0.02	)	) < 1E-5


 def 					_lowercase (			self							:			Union[str, Any]	):
    __lowercase								=						self.scheduler_classes[0]
    __lowercase								=						self.get_scheduler_config()
    __lowercase								=						scheduler_class(**UpperCAmelCase__	)

    __lowercase								=						len(UpperCAmelCase__	)

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

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

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

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

    __lowercase								=						torch.sum(torch.abs(UpperCAmelCase__	)	)
    __lowercase								=						torch.mean(torch.abs(UpperCAmelCase__	)	)

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


 def 					_lowercase (			self							:			List[str]	):
    __lowercase								=						self.scheduler_classes[0]
    __lowercase								=						self.get_scheduler_config(prediction_type="v_prediction"	)
    __lowercase								=						scheduler_class(**UpperCAmelCase__	)

    __lowercase								=						len(UpperCAmelCase__	)

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

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

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

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

    __lowercase								=						torch.sum(torch.abs(UpperCAmelCase__	)	)
    __lowercase								=						torch.mean(torch.abs(UpperCAmelCase__	)	)

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


 def 					_lowercase (			self							:			Optional[Any]	):
    __lowercase								=						self.scheduler_classes[0]
    __lowercase								=						self.get_scheduler_config()
    __lowercase								=						scheduler_class(**UpperCAmelCase__	)

    __lowercase								=						[1_0_0, 8_7, 5_0, 1, 0]

    scheduler.set_timesteps(timesteps=UpperCAmelCase__	)

    __lowercase								=						scheduler.timesteps

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

       __lowercase								=						scheduler.previous_timestep(UpperCAmelCase__	)
       __lowercase								=						prev_t.item()

       self.assertEqual(UpperCAmelCase__,					UpperCAmelCase__	)


 def 					_lowercase (			self							:			Union[str, Any]	):
    __lowercase								=						self.scheduler_classes[0]
    __lowercase								=						self.get_scheduler_config()
    __lowercase								=						scheduler_class(**UpperCAmelCase__	)

    __lowercase								=						[1_0_0, 8_7, 5_0, 5_1, 0]

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


 def 					_lowercase (			self							:			List[Any]	):
    __lowercase								=						self.scheduler_classes[0]
    __lowercase								=						self.get_scheduler_config()
    __lowercase								=						scheduler_class(**UpperCAmelCase__	)

    __lowercase								=						[1_0_0, 8_7, 5_0, 1, 0]
    __lowercase								=						len(UpperCAmelCase__	)

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



 def 					_lowercase (			self							:			Optional[int]	):
    __lowercase								=						self.scheduler_classes[0]
    __lowercase								=						self.get_scheduler_config()
    __lowercase								=						scheduler_class(**UpperCAmelCase__	)

    __lowercase								=						[scheduler.config.num_train_timesteps]

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







"""simple docstring"""





def   _A  (  UpperCamelCase_     :       list[int])	->						float:
   '''simple docstring'''


   if not nums:  # Makes sure that the list is not empty
      raise ValueError("List is empty")

   __lowercase								=						sum(UpperCamelCase_) / len(UpperCamelCase_)  # Calculate the average
   return sum(abs(x - average) for x in nums) / len(UpperCamelCase_)


if __name__ == "__main__":
   import doctest

   doctest.testmod()




