



"""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    __SCREAMING_SNAKE_CASE		( lowerCamelCase_:       str=1_0   ):
						"""simple docstring"""






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


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

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

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


# Training and saving
A    =	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"""







from ...configuration_utils import PretrainedConfig
from ...utils import logging


A    =	logging.get_logger(__name__)

A    =	{
    'uw-madison/yoso-4096': 'https://huggingface.co/uw-madison/yoso-4096/resolve/main/config.json',
    # See all YOSO models at https://huggingface.co/models?filter=yoso
}

class 			_a     (   SCREAMING_SNAKE_CASE__):
			__magic_name__    				=    """yoso"""



			def __init__( self       :							List[str]      ,  _lowercase       :							List[str]=50265      ,  _lowercase       :							List[str]=768      ,  _lowercase       :							Optional[int]=12      ,  _lowercase       :							str=12      ,  _lowercase       :							List[Any]=3072      ,  _lowercase       :							Dict="gelu"      ,  _lowercase       :							int=0.1      ,  _lowercase       :							Optional[Any]=0.1      ,  _lowercase       :							Dict=4096      ,  _lowercase       :							Optional[Any]=1      ,  _lowercase       :							Any=0.02      ,  _lowercase       :							Union[str, Any]=1E-12      ,  _lowercase       :							int="absolute"      ,  _lowercase       :							Any=True      ,  _lowercase       :							Dict=9      ,  _lowercase       :							int=64      ,  _lowercase       :							Any=None      ,  _lowercase       :							List[str]=True      ,  _lowercase       :							Optional[Any]=True      ,  _lowercase       :							Any=1      ,  _lowercase       :							int=0      ,  _lowercase       :							List[str]=2      ,  **_lowercase       :							List[Any]      ,  )       ->				Union[str, Any]:
									super().__init__(pad_token_id=_lowercase      ,  bos_token_id=_lowercase      ,  eos_token_id=_lowercase      ,  **_lowercase		)

									snake_case      : List[str]							=   vocab_size
									snake_case      : Tuple							=   max_position_embeddings
									snake_case      : Optional[Any]							=   hidden_size
									snake_case      : Tuple							=   num_hidden_layers
									snake_case      : Tuple							=   num_attention_heads
									snake_case      : Any							=   intermediate_size
									snake_case      : Union[str, Any]							=   hidden_act
									snake_case      : int							=   hidden_dropout_prob
									snake_case      : Dict							=   attention_probs_dropout_prob
									snake_case      : Any							=   initializer_range
									snake_case      : Optional[Any]							=   type_vocab_size
									snake_case      : List[str]							=   layer_norm_eps
									snake_case      : Dict							=   position_embedding_type
									snake_case      : Tuple							=   use_expectation
									snake_case      : Dict							=   hash_code_len
									snake_case      : Optional[Any]							=   num_hash
									snake_case      : List[str]							=   conv_window
									snake_case      : Optional[Any]							=   use_fast_hash
									snake_case      : Tuple							=   lsh_backward



