




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      lowerCAmelCase_     (					lowerCamelCase:    Union[str, Any]=10  )   ->     Optional[int]:


      '''simple docstring'''




      for _ in tqdm(range(0       ,			args.n_examples       ,			lowerCamelCase  )  ):
            yield [next(lowerCamelCase  )[args.text_column] for _ in range(lowerCamelCase  )]


# Configuration
__SCREAMING_SNAKE_CASE      :				Optional[int]				      =HfArgumentParser(TokenizerTrainingArguments)
__SCREAMING_SNAKE_CASE      :				List[str]				      =parser.parse_args()

# Base tokenizer
__SCREAMING_SNAKE_CASE      :				Tuple				      =AutoTokenizer.from_pretrained(args.base_tokenizer)
__SCREAMING_SNAKE_CASE      :				Tuple				      =list(bytes_to_unicode().values())

# Load dataset
__SCREAMING_SNAKE_CASE      :				List[str]				      =load_dataset(args.dataset_name, split="""train""", streaming=True)
__SCREAMING_SNAKE_CASE      :				Any				      =iter(dataset)


# Training and saving
__SCREAMING_SNAKE_CASE      :				Dict				      =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)





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__SCREAMING_SNAKE_CASE      :				int				      ={"""configuration_nat""": ["""NAT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """NatConfig"""]}


try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __SCREAMING_SNAKE_CASE      :				Optional[int]				      =[
          """NAT_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """NatForImageClassification""",
          """NatModel""",
          """NatPreTrainedModel""",
          """NatBackbone""",
      ]

if TYPE_CHECKING:
      from .configuration_nat import NAT_PRETRAINED_CONFIG_ARCHIVE_MAP, NatConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_nat import (
                NAT_PRETRAINED_MODEL_ARCHIVE_LIST,
                NatBackbone,
                NatForImageClassification,
                NatModel,
                NatPreTrainedModel,
            )

else:
      import sys

      __SCREAMING_SNAKE_CASE      :				Any				      =_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)