





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:		Tuple=10 )->						Any:
  for _ in tqdm(range(0			,							args.n_examples			,							lowerCAmelCase ) ):
    yield [next(lowerCAmelCase )[args.text_column] for _ in range(lowerCAmelCase )]


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

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

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


# Training and saving
lowerCAmelCase_        =			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


lowerCamelCase_				:		int       						=       {
    """configuration_fsmt""": ["""FSMT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FSMTConfig"""],
    """tokenization_fsmt""": ["""FSMTTokenizer"""],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCamelCase_				:		Dict       						=       ["""FSMTForConditionalGeneration""", """FSMTModel""", """PretrainedFSMTModel"""]


if TYPE_CHECKING:
	from .configuration_fsmt import FSMT_PRETRAINED_CONFIG_ARCHIVE_MAP, FSMTConfig
	from .tokenization_fsmt import FSMTTokenizer

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_fsmt import FSMTForConditionalGeneration, FSMTModel, PretrainedFSMTModel

else:
	import sys

	lowerCamelCase_				:		Dict       						=       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




