



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"		)





"""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)
