code: 
  
  
  '''simple docstring'''
  
  
  
  
  
  import torch
  
  from diffusers import KDPMaAncestralDiscreteScheduler
  from diffusers.utils import torch_device
  
  from .test_schedulers import SchedulerCommonTest
  class 	a__				(				a_      ):
  
  
  
  
  
  			'''simple docstring'''
  
  			A	:							str							= (KDPMaAncestralDiscreteScheduler,)
  			A	:							Union[str, Any]							= 10
  			def 				lowerCAmelCase    (     self       : List[Any] ,						**lowerCAmelCase_       : List[str]  )			->		List[Any]:
  						__A=  {
  						    'num_train_timesteps': 1_100,
  						    'beta_start': 0.00_01,
  						    'beta_end': 0.02,
  						    'beta_schedule': 'linear',
  						}
  
  						config.update(**lowerCAmelCase_  )
  						return config
  			def 				lowerCAmelCase    (     self       : List[str]  )			->		Tuple:
  						for timesteps in [10, 50, 100, 1_000]:
  									self.check_over_configs(num_train_timesteps=lowerCAmelCase_  )
  			def 				lowerCAmelCase    (     self       : Dict  )			->		str:
  						for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01] ,						[0.00_02, 0.0_02, 0.02]  ):
  									self.check_over_configs(beta_start=lowerCAmelCase_ ,						beta_end=lowerCAmelCase_  )
  			def 				lowerCAmelCase    (     self       : Optional[Any]  )			->		Any:
  						for schedule in ["linear", "scaled_linear"]:
  									self.check_over_configs(beta_schedule=lowerCAmelCase_  )
  			def 				lowerCAmelCase    (     self       : Optional[int]  )			->		Dict:
  						if torch_device == "mps":
  									return
  						__A=  self.scheduler_classes[0]
  						__A=  self.get_scheduler_config()
  						__A=  scheduler_class(**lowerCAmelCase_  )
  
  						scheduler.set_timesteps(self.num_inference_steps  )
  
  						__A=  torch.manual_seed(0  )
  
  						__A=  self.dummy_model()
  						__A=  self.dummy_sample_deter * scheduler.init_noise_sigma
  						__A=  sample.to(lowerCAmelCase_  )
  
  						for i, t in enumerate(scheduler.timesteps  ):
  									__A=  scheduler.scale_model_input(lowerCAmelCase_ ,						lowerCAmelCase_  )
  
  									__A=  model(lowerCAmelCase_ ,						lowerCAmelCase_  )
  
  									__A=  scheduler.step(lowerCAmelCase_ ,						lowerCAmelCase_ ,						lowerCAmelCase_ ,						generator=lowerCAmelCase_  )
  									__A=  output.prev_sample
  
  						__A=  torch.sum(torch.abs(lowerCAmelCase_  )  )
  						__A=  torch.mean(torch.abs(lowerCAmelCase_  )  )
  
  						assert abs(result_sum.item() - 1_38_49.38_77  ) < 1E-2
  						assert abs(result_mean.item() - 18.03_31  ) < 5E-3
  			def 				lowerCAmelCase    (     self       : Union[str, Any]  )			->		Tuple:
  						for prediction_type in ["epsilon", "v_prediction"]:
  									self.check_over_configs(prediction_type=lowerCAmelCase_  )
  			def 				lowerCAmelCase    (     self       : Any  )			->		Union[str, Any]:
  						if torch_device == "mps":
  									return
  						__A=  self.scheduler_classes[0]
  						__A=  self.get_scheduler_config(prediction_type='v_prediction'  )
  						__A=  scheduler_class(**lowerCAmelCase_  )
  
  						scheduler.set_timesteps(self.num_inference_steps  )
  
  						__A=  self.dummy_model()
  						__A=  self.dummy_sample_deter * scheduler.init_noise_sigma
  						__A=  sample.to(lowerCAmelCase_  )
  
  						__A=  torch.manual_seed(0  )
  
  						for i, t in enumerate(scheduler.timesteps  ):
  									__A=  scheduler.scale_model_input(lowerCAmelCase_ ,						lowerCAmelCase_  )
  
  									__A=  model(lowerCAmelCase_ ,						lowerCAmelCase_  )
  
  									__A=  scheduler.step(lowerCAmelCase_ ,						lowerCAmelCase_ ,						lowerCAmelCase_ ,						generator=lowerCAmelCase_  )
  									__A=  output.prev_sample
  
  						__A=  torch.sum(torch.abs(lowerCAmelCase_  )  )
  						__A=  torch.mean(torch.abs(lowerCAmelCase_  )  )
  
  						assert abs(result_sum.item() - 3_28.99_70  ) < 1E-2
  						assert abs(result_mean.item() - 0.42_84  ) < 1E-3
  
  			def 				lowerCAmelCase    (     self       : List[Any]  )			->		Dict:
  						if torch_device == "mps":
  									return
  						__A=  self.scheduler_classes[0]
  						__A=  self.get_scheduler_config()
  						__A=  scheduler_class(**lowerCAmelCase_  )
  
  						scheduler.set_timesteps(self.num_inference_steps ,						device=lowerCAmelCase_  )
  						__A=  torch.manual_seed(0  )
  
  						__A=  self.dummy_model()
  						__A=  self.dummy_sample_deter.to(lowerCAmelCase_  ) * scheduler.init_noise_sigma
  
  						for t in scheduler.timesteps:
  									__A=  scheduler.scale_model_input(lowerCAmelCase_ ,						lowerCAmelCase_  )
  
  									__A=  model(lowerCAmelCase_ ,						lowerCAmelCase_  )
  
  									__A=  scheduler.step(lowerCAmelCase_ ,						lowerCAmelCase_ ,						lowerCAmelCase_ ,						generator=lowerCAmelCase_  )
  									__A=  output.prev_sample
  
  						__A=  torch.sum(torch.abs(lowerCAmelCase_  )  )
  						__A=  torch.mean(torch.abs(lowerCAmelCase_  )  )
  
  						assert abs(result_sum.item() - 1_38_49.38_18  ) < 1E-1
  						assert abs(result_mean.item() - 18.03_31  ) < 1E-3
  
  
  
  
style_context: 
  
  
  '''simple docstring'''
  
  
  
  
  
  from collections import OrderedDict
  from typing import Mapping
  
  from ...configuration_utils import PretrainedConfig
  from ...onnx import OnnxConfig
  from ...utils import logging
  
  
  UpperCAmelCase__      =							logging.get_logger(__name__)
  
  UpperCAmelCase__      =							{
      '''google/mobilebert-uncased''': '''https://huggingface.co/google/mobilebert-uncased/resolve/main/config.json'''
  }
  class 	a__				(				a_      ):
  
  
  
  
  
  			'''simple docstring'''
  
  			A	:							int							= MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
  			A	:							Optional[int]							= '''mobilebert'''
  			def __init__(     self       : Optional[int] ,						lowerCAmelCase_       : str=30_522 ,						lowerCAmelCase_       : List[Any]=512 ,						lowerCAmelCase_       : Optional[int]=24 ,						lowerCAmelCase_       : str=4 ,						lowerCAmelCase_       : List[str]=512 ,						lowerCAmelCase_       : List[str]="relu" ,						lowerCAmelCase_       : Dict=0.0 ,						lowerCAmelCase_       : List[Any]=0.1 ,						lowerCAmelCase_       : Optional[Any]=512 ,						lowerCAmelCase_       : str=2 ,						lowerCAmelCase_       : Dict=0.02 ,						lowerCAmelCase_       : Any=1E-12 ,						lowerCAmelCase_       : List[Any]=0 ,						lowerCAmelCase_       : Union[str, Any]=128 ,						lowerCAmelCase_       : Any=True ,						lowerCAmelCase_       : Optional[Any]=True ,						lowerCAmelCase_       : List[Any]=128 ,						lowerCAmelCase_       : Dict=False ,						lowerCAmelCase_       : Tuple=True ,						lowerCAmelCase_       : Any=4 ,						lowerCAmelCase_       : Optional[Any]="no_norm" ,						lowerCAmelCase_       : Optional[Any]=True ,						lowerCAmelCase_       : Union[str, Any]=None ,						**lowerCAmelCase_       : Union[str, Any] ,						)			->		List[Any]:
  						super().__init__(pad_token_id=lowerCAmelCase_ ,						**lowerCAmelCase_  )
  
  						__A=  vocab_size
  						__A=  hidden_size
  						__A=  num_hidden_layers
  						__A=  num_attention_heads
  						__A=  hidden_act
  						__A=  intermediate_size
  						__A=  hidden_dropout_prob
  						__A=  attention_probs_dropout_prob
  						__A=  max_position_embeddings
  						__A=  type_vocab_size
  						__A=  initializer_range
  						__A=  layer_norm_eps
  						__A=  embedding_size
  						__A=  trigram_input
  						__A=  use_bottleneck
  						__A=  intra_bottleneck_size
  						__A=  use_bottleneck_attention
  						__A=  key_query_shared_bottleneck
  						__A=  num_feedforward_networks
  						__A=  normalization_type
  						__A=  classifier_activation
  
  						if self.use_bottleneck:
  									__A=  intra_bottleneck_size
  						else:
  									__A=  hidden_size
  
  						__A=  classifier_dropout
  
  
  class 	a__				(				a_      ):
  
  
  
  
  
  			'''simple docstring'''
  
  			@property
  			def 				lowerCAmelCase    (     self       : List[str]  )			->		Mapping[str, Mapping[int, str]]:
  						if self.task == "multiple-choice":
  									__A=  {0: 'batch', 1: 'choice', 2: 'sequence'}
  						else:
  									__A=  {0: 'batch', 1: 'sequence'}
  						return OrderedDict(
  						    [
  						        ('input_ids', dynamic_axis),
  						        ('attention_mask', dynamic_axis),
  						        ('token_type_ids', dynamic_axis),
  						    ]  )
  
  
  
  