
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 					A       ( UpperCamelCase_   :       Optional[int]=10 )   ->    int:







			'''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
UpperCAmelCase__				:	Union[str, Any]					=						HfArgumentParser(TokenizerTrainingArguments)
UpperCAmelCase__				:	Optional[Any]					=						parser.parse_args()

# Base tokenizer
UpperCAmelCase__				:	Union[str, Any]					=						AutoTokenizer.from_pretrained(args.base_tokenizer)
UpperCAmelCase__				:	Any					=						list(bytes_to_unicode().values())

# Load dataset
UpperCAmelCase__				:	Optional[int]					=						load_dataset(args.dataset_name, split="train", streaming=True)
UpperCAmelCase__				:	Union[str, Any]					=						iter(dataset)


# Training and saving
UpperCAmelCase__				:	Optional[Any]					=						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)







'''simple docstring'''

UpperCAmelCase__				:	List[str]					=						"\n# Transformers installation\n! pip install transformers datasets evaluate\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"

UpperCAmelCase__				:	Any					=						[{"type": "code", "content": INSTALL_CONTENT}]
UpperCAmelCase__				:	Union[str, Any]					=						{
    "{processor_class}": "FakeProcessorClass",
    "{model_class}": "FakeModelClass",
    "{object_class}": "FakeObjectClass",
}





