code: 
  
  
  
  
  
  
  """simple docstring"""
  
  
  
  
  
  
  import torch
  
  from diffusers import DDIMScheduler
  
  from .test_schedulers import SchedulerCommonTest
  class 						__lowerCAmelCase   (			UpperCAmelCase       ):
  
  
  
  
  
  
  
    '''simple docstring'''
  
  
    __lowerCamelCase       :    int     	=			(DDIMScheduler,)
    __lowerCamelCase       :    List[Any]     	=			(("eta", 0.0), ("num_inference_steps", 50))
    def 					UpperCamelCase__   (      self:		Union[str, Any]				,			**UpperCamelCase_:		int					):
       UpperCamelCase_     ={
           "num_train_timesteps": 1000,
           "beta_start": 0.0001,
           "beta_end": 0.02,
           "beta_schedule": "linear",
           "clip_sample": True,
       }
  
       config.update(**UpperCamelCase_					)
       return config
    def 					UpperCamelCase__   (      self:		str				,			**UpperCamelCase_:		List[Any]					):
       UpperCamelCase_     =self.scheduler_classes[0]
       UpperCamelCase_     =self.get_scheduler_config(**UpperCamelCase_					)
       UpperCamelCase_     =scheduler_class(**UpperCamelCase_					)
  
       UpperCamelCase_    ,							UpperCamelCase_     =10, 0.0
  
       UpperCamelCase_     =self.dummy_model()
       UpperCamelCase_     =self.dummy_sample_deter
  
       scheduler.set_timesteps(UpperCamelCase_					)
  
       for t in scheduler.timesteps:
          UpperCamelCase_     =model(UpperCamelCase_				,			UpperCamelCase_					)
          UpperCamelCase_     =scheduler.step(UpperCamelCase_				,			UpperCamelCase_				,			UpperCamelCase_				,			UpperCamelCase_					).prev_sample
  
       return sample
    def 					UpperCamelCase__   (      self:		int					):
       for timesteps in [100, 500, 1000]:
          self.check_over_configs(num_train_timesteps=UpperCamelCase_					)
    def 					UpperCamelCase__   (      self:		Tuple					):
       for steps_offset in [0, 1]:
          self.check_over_configs(steps_offset=UpperCamelCase_					)
  
       UpperCamelCase_     =self.scheduler_classes[0]
       UpperCamelCase_     =self.get_scheduler_config(steps_offset=1					)
       UpperCamelCase_     =scheduler_class(**UpperCamelCase_					)
       scheduler.set_timesteps(5					)
       assert torch.equal(scheduler.timesteps				,			torch.LongTensor([801, 601, 401, 201, 1]					)					)
    def 					UpperCamelCase__   (      self:		Union[str, Any]					):
       for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]				,			[0.002, 0.02, 0.2, 2]					):
          self.check_over_configs(beta_start=UpperCamelCase_				,			beta_end=UpperCamelCase_					)
    def 					UpperCamelCase__   (      self:		Dict					):
       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:		Tuple					):
       for clip_sample in [True, False]:
          self.check_over_configs(clip_sample=UpperCamelCase_					)
    def 					UpperCamelCase__   (      self:		Any					):
       for timestep_spacing in ["trailing", "leading"]:
          self.check_over_configs(timestep_spacing=UpperCamelCase_					)
    def 					UpperCamelCase__   (      self:		Optional[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, 10, 49]:
          self.check_over_forward(time_step=UpperCamelCase_					)
    def 					UpperCamelCase__   (      self:		Tuple					):
       for t, num_inference_steps in zip([1, 10, 50]				,			[10, 50, 500]					):
          self.check_over_forward(time_step=UpperCamelCase_				,			num_inference_steps=UpperCamelCase_					)
    def 					UpperCamelCase__   (      self:		Union[str, Any]					):
       for t, eta in zip([1, 10, 49]				,			[0.0, 0.5, 1.0]					):
          self.check_over_forward(time_step=UpperCamelCase_				,			eta=UpperCamelCase_					)
    def 					UpperCamelCase__   (      self:		Dict					):
       UpperCamelCase_     =self.scheduler_classes[0]
       UpperCamelCase_     =self.get_scheduler_config()
       UpperCamelCase_     =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(420				,			400					) - 0.1_4771					)					) < 1e-5
       assert torch.sum(torch.abs(scheduler._get_variance(980				,			960					) - 0.3_2460					)					) < 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_0979					)					) < 1e-5
       assert torch.sum(torch.abs(scheduler._get_variance(999				,			998					) - 0.02					)					) < 1e-5
    def 					UpperCamelCase__   (      self:		Optional[Any]					):
       UpperCamelCase_     =self.full_loop()
  
       UpperCamelCase_     =torch.sum(torch.abs(UpperCamelCase_					)					)
       UpperCamelCase_     =torch.mean(torch.abs(UpperCamelCase_					)					)
  
       assert abs(result_sum.item() - 172.0067					) < 1e-2
       assert abs(result_mean.item() - 0.22_3967					) < 1e-3
    def 					UpperCamelCase__   (      self:		Optional[int]					):
       UpperCamelCase_     =self.full_loop(prediction_type="v_prediction"					)
  
       UpperCamelCase_     =torch.sum(torch.abs(UpperCamelCase_					)					)
       UpperCamelCase_     =torch.mean(torch.abs(UpperCamelCase_					)					)
  
       assert abs(result_sum.item() - 52.5302					) < 1e-2
       assert abs(result_mean.item() - 0.0684					) < 1e-3
    def 					UpperCamelCase__   (      self:		Tuple					):
       # We specify different beta, so that the first alpha is 0.99
       UpperCamelCase_     =self.full_loop(set_alpha_to_one=UpperCamelCase_				,			beta_start=0.01					)
       UpperCamelCase_     =torch.sum(torch.abs(UpperCamelCase_					)					)
       UpperCamelCase_     =torch.mean(torch.abs(UpperCamelCase_					)					)
  
       assert abs(result_sum.item() - 149.8295					) < 1e-2
       assert abs(result_mean.item() - 0.1951					) < 1e-3
  
  
  
  
  
    def 					UpperCamelCase__   (      self:		Dict					):
       # We specify different beta, so that the first alpha is 0.99
       UpperCamelCase_     =self.full_loop(set_alpha_to_one=UpperCamelCase_				,			beta_start=0.01					)
       UpperCamelCase_     =torch.sum(torch.abs(UpperCamelCase_					)					)
       UpperCamelCase_     =torch.mean(torch.abs(UpperCamelCase_					)					)
  
       assert abs(result_sum.item() - 149.0784					) < 1e-2
       assert abs(result_mean.item() - 0.1941					) < 1e-3
  
style_context: 
  
  
  
  
  
  
  """simple docstring"""
  
  
  
  
  
  
  from .hash_table import HashTable
  from .number_theory.prime_numbers import is_prime, next_prime
  class 						__lowerCAmelCase   (			UpperCAmelCase       ):
  
  
  
  
  
  
  
    '''simple docstring'''
  
  
    def __init__(      self:		List[Any]				,			*UpperCamelCase_:		Dict				,			**UpperCamelCase_:		Optional[int]					):
       super().__init__(*UpperCamelCase_				,			**UpperCamelCase_					)
    def 					UpperCamelCase__   (      self:		Any				,			UpperCamelCase_:		Optional[int]				,			UpperCamelCase_:		Optional[int]					):
       UpperCamelCase_     =(
           next_prime(value % self.size_table					)
           if not is_prime(value % self.size_table					)
           else value % self.size_table
       )  # gt = bigger than
       return next_prime_gt - (data % next_prime_gt)
    def 					UpperCamelCase__   (      self:		str				,			UpperCamelCase_:		List[str]				,			UpperCamelCase_:		str				,			UpperCamelCase_:		Tuple					):
       return (increment * self.__hash_function_a(UpperCamelCase_				,			UpperCamelCase_					)) % self.size_table
  
  
  
  
  
    def 					UpperCamelCase__   (      self:		str				,			UpperCamelCase_:		int				,			UpperCamelCase_:		str=None					):
       UpperCamelCase_     =1
       UpperCamelCase_     =self.hash_function(UpperCamelCase_					)
  
       while self.values[new_key] is not None and self.values[new_key] != key:
          UpperCamelCase_     =(
              self.__hash_double_function(UpperCamelCase_				,			UpperCamelCase_				,			UpperCamelCase_					)
              if self.balanced_factor() >= self.lim_charge
              else None
          )
          if new_key is None:
             break
          else:
             i += 1
  
       return new_key
  