code: 
  
  
  
  import torch
  
  from accelerate import Accelerator, DistributedType
  
  
  
  
  
  
  class 			UpperCAmelCase_						:
  
  
  
        def __enter__(  self  :			Optional[int]		):
             if self.enabled:
                  _UpperCAmelCase    :			Any              =					self.model.no_sync()
                  self.model_sync_obj.__enter__()
  
             return self
  
  
  
        def __exit__(  self  :			Optional[Any]  ,			A  :			List[str]  ,			A  :			List[Any]  ,			A  :			Union[str, Any]		):
             if self.enabled:
                  # Average all models on exit
                  self._sync_and_avg_model_params()
                  self.model_sync_obj.__exit__(__SCREAMING_SNAKE_CASE  ,			__SCREAMING_SNAKE_CASE  ,			__SCREAMING_SNAKE_CASE		)
  
  
  
        def __init__(  self  :			List[str]  ,			A  :			str  ,			A  :			int  ,			A  :			Dict  ,			A  :			Any = True		):
             if accelerator.distributed_type not in [
                 DistributedType.NO,
                 DistributedType.MULTI_CPU,
                 DistributedType.MULTI_GPU,
             ]:
                  raise NotImplementedError("LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)"		)
             _UpperCAmelCase    :			Any              =					enabled and accelerator.distributed_type != DistributedType.NO
             _UpperCAmelCase    :			Optional[int]              =					0
             if self.enabled:
                  _UpperCAmelCase    :			int              =					accelerator
                  _UpperCAmelCase    :			int              =					model
                  _UpperCAmelCase    :			str              =					local_sgd_steps
  
  
  
        def 		snake_case_      (  self  :			Optional[int]		):
             self.num_steps += 1
             if not self.enabled:
                  return
  
             if self.num_steps % self.local_sgd_steps == 0:
                  self._sync_and_avg_model_params()
  
  
  
        def 		snake_case_      (  self  :			Tuple		):
             self.accelerator.wait_for_everyone()
             with self.accelerator.autocast():
                  for param in self.model.parameters():
                       _UpperCAmelCase    :			Any              =					self.accelerator.reduce(param.data  ,			reduction="mean"		)
  
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)
  