




'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


a		:	int       =      {
    '''configuration_deta''': ['''DETA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DetaConfig'''],
}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a		:	Dict       =      ['''DetaImageProcessor''']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a		:	Any       =      [
      '''DETA_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''DetaForObjectDetection''',
      '''DetaModel''',
      '''DetaPreTrainedModel''',
  ]


if TYPE_CHECKING:
  from .configuration_deta import DETA_PRETRAINED_CONFIG_ARCHIVE_MAP, DetaConfig

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .image_processing_deta import DetaImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_deta import (
        DETA_PRETRAINED_MODEL_ARCHIVE_LIST,
        DetaForObjectDetection,
        DetaModel,
        DetaPreTrainedModel,
    )

else:
  import sys

  a		:	List[str]       =      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






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  (		A_  :	List[Any]=1_0      )			->			List[str]:

   '''simple docstring'''






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


# Configuration
__UpperCAmelCase      			=  HfArgumentParser(TokenizerTrainingArguments)
__UpperCAmelCase      			=  parser.parse_args()

# Base tokenizer
__UpperCAmelCase      			=  AutoTokenizer.from_pretrained(args.base_tokenizer)
__UpperCAmelCase      			=  list(bytes_to_unicode().values())

# Load dataset
__UpperCAmelCase      			=  load_dataset(args.dataset_name, split="""train""", streaming=True)
__UpperCAmelCase      			=  iter(dataset)


# Training and saving
__UpperCAmelCase      			=  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)

