"""simple docstring"""




import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest
class 						__A							(			SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:      Tuple		 =		(DDIMParallelScheduler,)
     _UpperCamelCase							:      Optional[Any]		 =		(("eta", 0.0), ("num_inference_steps", 50))






     def    __A  (		self    ,					**a__  ):
       _lowerCAmelCase						:	Any        =					{
           """num_train_timesteps""": 1000,
           """beta_start""": 0.0_0_0_1,
           """beta_end""": 0.0_2,
           """beta_schedule""": """linear""",
           """clip_sample""": True,
       }

       config.update(**a__  )
       return config






     def    __A  (		self    ,					**a__  ):
       _lowerCAmelCase						:	Dict        =					self.scheduler_classes[0]
       _lowerCAmelCase						:	Optional[Any]        =					self.get_scheduler_config(**a__  )
       _lowerCAmelCase						:	Dict        =					scheduler_class(**a__  )

       _lowerCAmelCase      ,     _lowerCAmelCase						:	List[Any]        =					10, 0.0

       _lowerCAmelCase						:	Any        =					self.dummy_model()
       _lowerCAmelCase						:	Optional[Any]        =					self.dummy_sample_deter

       scheduler.set_timesteps(a__  )

       for t in scheduler.timesteps:
         _lowerCAmelCase						:	str        =					model(a__    ,					a__  )
         _lowerCAmelCase						:	Tuple        =					scheduler.step(a__    ,					a__    ,					a__    ,					a__  ).prev_sample

       return sample






     def    __A  (		self  ):
       for timesteps in [100, 500, 1000]:
         self.check_over_configs(num_train_timesteps=a__  )






     def    __A  (		self  ):
       for steps_offset in [0, 1]:
         self.check_over_configs(steps_offset=a__  )

       _lowerCAmelCase						:	Union[str, Any]        =					self.scheduler_classes[0]
       _lowerCAmelCase						:	Optional[Any]        =					self.get_scheduler_config(steps_offset=1  )
       _lowerCAmelCase						:	List[Any]        =					scheduler_class(**a__  )
       scheduler.set_timesteps(5  )
       assert torch.equal(scheduler.timesteps    ,					torch.LongTensor([801, 601, 401, 201, 1]  )  )






     def    __A  (		self  ):
       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=a__    ,					beta_end=a__  )






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






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






     def    __A  (		self  ):
       for clip_sample in [True, False]:
         self.check_over_configs(clip_sample=a__  )






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






     def    __A  (		self  ):
       for rescale_betas_zero_snr in [True, False]:
         self.check_over_configs(rescale_betas_zero_snr=a__  )






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






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






     def    __A  (		self  ):
       for t, num_inference_steps in zip([1, 10, 50]    ,					[10, 50, 500]  ):
         self.check_over_forward(time_step=a__    ,					num_inference_steps=a__  )






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






     def    __A  (		self  ):
       _lowerCAmelCase						:	int        =					self.scheduler_classes[0]
       _lowerCAmelCase						:	List[str]        =					self.get_scheduler_config()
       _lowerCAmelCase						:	Optional[int]        =					scheduler_class(**a__  )

       assert torch.sum(torch.abs(scheduler._get_variance(0    ,					0  ) - 0.0  )  ) < 1e-5
       assert torch.sum(torch.abs(scheduler._get_variance(420    ,					400  ) - 0.1_4_7_7_1  )  ) < 1e-5
       assert torch.sum(torch.abs(scheduler._get_variance(980    ,					960  ) - 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(487    ,					486  ) - 0.0_0_9_7_9  )  ) < 1e-5
       assert torch.sum(torch.abs(scheduler._get_variance(999    ,					998  ) - 0.0_2  )  ) < 1e-5






     def    __A  (		self  ):
       _lowerCAmelCase						:	int        =					self.scheduler_classes[0]
       _lowerCAmelCase						:	List[Any]        =					self.get_scheduler_config()
       _lowerCAmelCase						:	Tuple        =					scheduler_class(**a__  )

       _lowerCAmelCase      ,     _lowerCAmelCase						:	Any        =					10, 0.0
       scheduler.set_timesteps(a__  )

       _lowerCAmelCase						:	List[str]        =					self.dummy_model()
       _lowerCAmelCase						:	List[Any]        =					self.dummy_sample_deter
       _lowerCAmelCase						:	Optional[int]        =					self.dummy_sample_deter + 0.1
       _lowerCAmelCase						:	Tuple        =					self.dummy_sample_deter - 0.1

       _lowerCAmelCase						:	Tuple        =					samplea.shape[0]
       _lowerCAmelCase						:	int        =					torch.stack([samplea, samplea, samplea]    ,					dim=0  )
       _lowerCAmelCase						:	List[Any]        =					torch.arange(a__  )[0:3, None].repeat(1    ,					a__  )

       _lowerCAmelCase						:	Any        =					model(samples.flatten(0    ,					1  )    ,					timesteps.flatten(0    ,					1  )  )
       _lowerCAmelCase						:	Union[str, Any]        =					scheduler.batch_step_no_noise(a__    ,					timesteps.flatten(0    ,					1  )    ,					samples.flatten(0    ,					1  )    ,					a__  )

       _lowerCAmelCase						:	Tuple        =					torch.sum(torch.abs(a__  )  )
       _lowerCAmelCase						:	str        =					torch.mean(torch.abs(a__  )  )

       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    __A  (		self  ):
       _lowerCAmelCase						:	List[Any]        =					self.full_loop()

       _lowerCAmelCase						:	Any        =					torch.sum(torch.abs(a__  )  )
       _lowerCAmelCase						:	Optional[int]        =					torch.mean(torch.abs(a__  )  )

       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    __A  (		self  ):
       _lowerCAmelCase						:	Optional[Any]        =					self.full_loop(prediction_type="""v_prediction"""  )

       _lowerCAmelCase						:	int        =					torch.sum(torch.abs(a__  )  )
       _lowerCAmelCase						:	Optional[int]        =					torch.mean(torch.abs(a__  )  )

       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    __A  (		self  ):
       # We specify different beta, so that the first alpha is 0.99
       _lowerCAmelCase						:	Union[str, Any]        =					self.full_loop(set_alpha_to_one=a__    ,					beta_start=0.0_1  )
       _lowerCAmelCase						:	List[Any]        =					torch.sum(torch.abs(a__  )  )
       _lowerCAmelCase						:	Tuple        =					torch.mean(torch.abs(a__  )  )

       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    __A  (		self  ):
       # We specify different beta, so that the first alpha is 0.99
       _lowerCAmelCase						:	List[str]        =					self.full_loop(set_alpha_to_one=a__    ,					beta_start=0.0_1  )
       _lowerCAmelCase						:	Optional[int]        =					torch.sum(torch.abs(a__  )  )
       _lowerCAmelCase						:	int        =					torch.mean(torch.abs(a__  )  )

       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"""




def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	str      ,_lowerCamelCase						:	bool = False     )	->				str:

  if not isinstance(_lowerCamelCase      ,_lowerCamelCase     ):
    _lowerCAmelCase						:	Union[str, Any]        =					f"Expected string as input, found {type(_lowerCamelCase     )}"
    raise ValueError(_lowerCamelCase     )
  if not isinstance(_lowerCamelCase      ,_lowerCamelCase     ):
    _lowerCAmelCase						:	Union[str, Any]        =					f"Expected boolean as use_pascal parameter, found {type(_lowerCamelCase     )}"
    raise ValueError(_lowerCamelCase     )

  _lowerCAmelCase						:	str        =					input_str.split("""_"""     )

  _lowerCAmelCase						:	Optional[int]        =					0 if use_pascal else 1

  _lowerCAmelCase						:	List[Any]        =					words[start_index:]

  _lowerCAmelCase						:	Union[str, Any]        =					[word[0].upper() + word[1:] for word in words_to_capitalize]

  _lowerCAmelCase						:	List[Any]        =					"""""" if use_pascal else words[0]

  return "".join([initial_word, *capitalized_words]     )


if __name__ == "__main__":
     from doctest import testmod

     testmod()




