



'''simple docstring'''


import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest







class 						UpperCAmelCase						(		__a):


 '''simple docstring'''




 __magic_name__       :			List[str]	=   (DDIMScheduler,)
 __magic_name__       :			List[str]	=   (("eta", 0.0), ("num_inference_steps", 50))




 def    lowercase_	( self				,	**lowerCAmelCase_)			->   Any:



      """simple docstring"""







      a_									={
          "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(**lowerCAmelCase_)
      return config




 def    lowercase_	( self				,	**lowerCAmelCase_)			->   Optional[Any]:



      """simple docstring"""







      a_									=self.scheduler_classes[0]
      a_									=self.get_scheduler_config(**lowerCAmelCase_)
      a_									=scheduler_class(**lowerCAmelCase_)

      a_		,    a_									=1_0, 0.0

      a_									=self.dummy_model()
      a_									=self.dummy_sample_deter

      scheduler.set_timesteps(lowerCAmelCase_)

      for t in scheduler.timesteps:
           a_									=model(lowerCAmelCase_				,	lowerCAmelCase_)
           a_									=scheduler.step(lowerCAmelCase_				,	lowerCAmelCase_				,	lowerCAmelCase_				,	lowerCAmelCase_).prev_sample

      return sample




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



      """simple docstring"""







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




 def    lowercase_	( self)			->   Dict:



      """simple docstring"""







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

      a_									=self.scheduler_classes[0]
      a_									=self.get_scheduler_config(steps_offset=1)
      a_									=scheduler_class(**lowerCAmelCase_)
      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    lowercase_	( self)			->   Optional[Any]:



      """simple docstring"""







      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=lowerCAmelCase_				,	beta_end=lowerCAmelCase_)




 def    lowercase_	( self)			->   Any:



      """simple docstring"""







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




 def    lowercase_	( self)			->   int:



      """simple docstring"""







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




 def    lowercase_	( self)			->   int:



      """simple docstring"""







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




 def    lowercase_	( self)			->   Any:



      """simple docstring"""







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




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



      """simple docstring"""







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




 def    lowercase_	( self)			->   Any:



      """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    lowercase_	( self)			->   Optional[int]:



      """simple docstring"""







      for t in [1, 1_0, 4_9]:
           self.check_over_forward(time_step=lowerCAmelCase_)




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



      """simple docstring"""







      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=lowerCAmelCase_				,	num_inference_steps=lowerCAmelCase_)




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



      """simple docstring"""







      for t, eta in zip([1, 1_0, 4_9]				,	[0.0, 0.5, 1.0]):
           self.check_over_forward(time_step=lowerCAmelCase_				,	eta=lowerCAmelCase_)




 def    lowercase_	( self)			->   Dict:



      """simple docstring"""







      a_									=self.scheduler_classes[0]
      a_									=self.get_scheduler_config()
      a_									=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_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    lowercase_	( self)			->   Optional[Any]:



      """simple docstring"""







      a_									=self.full_loop()

      a_									=torch.sum(torch.abs(lowerCAmelCase_))
      a_									=torch.mean(torch.abs(lowerCAmelCase_))

      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    lowercase_	( self)			->   List[str]:



      """simple docstring"""







      a_									=self.full_loop(prediction_type="v_prediction")

      a_									=torch.sum(torch.abs(lowerCAmelCase_))
      a_									=torch.mean(torch.abs(lowerCAmelCase_))

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



      """simple docstring"""







      a_									=self.full_loop(set_alpha_to_one=lowerCAmelCase_				,	beta_start=0.0_1)
      a_									=torch.sum(torch.abs(lowerCAmelCase_))
      a_									=torch.mean(torch.abs(lowerCAmelCase_))

      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    lowercase_	( self)			->   Dict:



      """simple docstring"""







      a_									=self.full_loop(set_alpha_to_one=lowerCAmelCase_				,	beta_start=0.0_1)
      a_									=torch.sum(torch.abs(lowerCAmelCase_))
      a_									=torch.mean(torch.abs(lowerCAmelCase_))

      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







'''simple docstring'''


from dataclasses import dataclass
from typing import Callable, Optional

import datasets







@dataclass
class 						UpperCAmelCase						(		datasets.BuilderConfig):


 '''simple docstring'''




 __magic_name__       :			Optional[Callable]	=   None
 __magic_name__       :			Optional[dict]	=   None
 __magic_name__       :			Optional[datasets.Features]	=   None




 def    lowercase_	( self)			->   List[Any]:



      """simple docstring"""







      assert self.generator is not None, "generator must be specified"

      if self.gen_kwargs is None:
           a_									={}







class 						UpperCAmelCase						(		datasets.GeneratorBasedBuilder):


 '''simple docstring'''




 __magic_name__       :			Tuple	=   GeneratorConfig




 def    lowercase_	( self)			->   Tuple:



      """simple docstring"""







      return datasets.DatasetInfo(features=self.config.features)




 def    lowercase_	( self				,	lowerCAmelCase_)			->   int:



      """simple docstring"""







      return [datasets.SplitGenerator(name=datasets.Split.TRAIN				,	gen_kwargs=self.config.gen_kwargs)]




 def    lowercase_	( self				,	**lowerCAmelCase_)			->   List[Any]:



      """simple docstring"""







      for idx, ex in enumerate(self.config.generator(**lowerCAmelCase_)):
           yield idx, ex


