code: 
  
  
  
  import torch
  import torch.nn.functional as F
  
  from diffusers import VQDiffusionScheduler
  
  from .test_schedulers import SchedulerCommonTest
  
  
  
  
  
  
  class 			UpperCAmelCase_						(       _UpperCamelCase		):
        __SCREAMING_SNAKE_CASE		:     Optional[int]				  =				(VQDiffusionScheduler,)
  
  
  
        def 		snake_case_      (  self  :			List[str]  ,			**A  :			Union[str, Any]		):
             _UpperCAmelCase    :			str              =					{
                 "num_vec_classes": 4_0_9_7,
                 "num_train_timesteps": 1_0_0,
             }
  
             config.update(**A		)
             return config
  
  
  
        def 		snake_case_      (  self  :			Any  ,			A  :			Optional[int]		):
             _UpperCAmelCase    :			Optional[int]              =					4
             _UpperCAmelCase    :			Tuple              =					8
             _UpperCAmelCase    :			Tuple              =					8
  
             _UpperCAmelCase    :			Tuple              =					torch.randint(0  ,			A  ,			(batch_size, height * width)		)
  
             return sample
  
  
  
        @property
        def 		snake_case_      (  self  :			List[Any]		):
             assert False
  
  
  
        def 		snake_case_      (  self  :			int  ,			A  :			int		):
             def model(A  :			List[str]  ,			A  :			List[str]  ,			*A  :			int		):
                  _UpperCAmelCase    :			List[Any]              =					sample.shape
                  _UpperCAmelCase    :			Tuple              =					torch.rand((batch_size, num_vec_classes - 1, num_latent_pixels)		)
                  _UpperCAmelCase    :			Union[str, Any]              =					F.log_softmax(logits.double()  ,			dim=1		).float()
                  return return_value
  
             return model
  
  
  
        def 		snake_case_      (  self  :			Union[str, Any]		):
             for timesteps in [2, 5, 1_0_0, 1_0_0_0]:
                  self.check_over_configs(num_train_timesteps=A		)
  
  
  
        def 		snake_case_      (  self  :			List[Any]		):
             for num_vec_classes in [5, 1_0_0, 1_0_0_0, 4_0_0_0]:
                  self.check_over_configs(num_vec_classes=A		)
  
  
  
        def 		snake_case_      (  self  :			Optional[Any]		):
             for t in [0, 5_0, 9_9]:
                  self.check_over_forward(time_step=A		)
  
  
  
        def 		snake_case_      (  self  :			str		):
             pass
  
style_context: 
  
  
  
  """simple docstring"""
  
  
  
  
  from arguments import TokenizerTrainingArguments
  from datasets import load_dataset
  from tqdm import tqdm
  
  from transformers import AutoTokenizer, HfArgumentParser
  from transformers.models.gpta.tokenization_gpta import bytes_to_unicode
  
  def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		str=10       )						->     List[Any]:
  
  
  
  
  
       '''simple docstring'''
  
       for _ in tqdm(range(0		,						args.n_examples		,						SCREAMING_SNAKE_CASE__       )       ):
            yield [next(SCREAMING_SNAKE_CASE__       )[args.text_column] for _ in range(SCREAMING_SNAKE_CASE__       )]
  
  
  # Configuration
  _lowerCAmelCase       :      Optional[Any]							=							HfArgumentParser(TokenizerTrainingArguments)
  _lowerCAmelCase       :      Dict							=							parser.parse_args()
  
  # Base tokenizer
  _lowerCAmelCase       :      int							=							AutoTokenizer.from_pretrained(args.base_tokenizer)
  _lowerCAmelCase       :      List[str]							=							list(bytes_to_unicode().values())
  
  # Load dataset
  _lowerCAmelCase       :      Dict							=							load_dataset(args.dataset_name, split="train", streaming=True)
  _lowerCAmelCase       :      List[Any]							=							iter(dataset)
  
  
  # Training and saving
  _lowerCAmelCase       :      List[str]							=							tokenizer.train_new_from_iterator(
      batch_iterator(), vocab_size=args.vocab_size, initial_alphabet=base_vocab
  )
  new_tokenizer.save_pretrained(args.tokenizer_name, push_to_hub=args.push_to_hub)
  