





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    (   _lowercase :  Optional[int]=1_0							)  ->Optional[int]:
		for _ in tqdm(range(0			,    args.n_examples			,    _lowercase							)							):
				yield [next(_lowercase							)[args.text_column] for _ in range(_lowercase							)]


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


__lowerCAmelCase 	=      logging.get_logger(__name__)

__lowerCAmelCase 	=      {
    "MCG-NJU/videomae-base": "https://huggingface.co/MCG-NJU/videomae-base/resolve/main/config.json",
}




class 	_UpperCAmelCase						( _lowercase       ):
					__A        = "videomae"




					def __init__(    self     ,   __lowerCAmelCase=224     ,   __lowerCAmelCase=16     ,   __lowerCAmelCase=3     ,   __lowerCAmelCase=16     ,   __lowerCAmelCase=2     ,   __lowerCAmelCase=768     ,   __lowerCAmelCase=12     ,   __lowerCAmelCase=12     ,   __lowerCAmelCase=3_072     ,   __lowerCAmelCase="gelu"     ,   __lowerCAmelCase=0.0     ,   __lowerCAmelCase=0.0     ,   __lowerCAmelCase=0.0_2     ,   __lowerCAmelCase=1E-1_2     ,   __lowerCAmelCase=True     ,   __lowerCAmelCase=True     ,   __lowerCAmelCase=6     ,   __lowerCAmelCase=384     ,   __lowerCAmelCase=4     ,   __lowerCAmelCase=1_536     ,   __lowerCAmelCase=True     ,   **__lowerCAmelCase     ,   )    ->	Any:
							super().__init__(**__lowerCAmelCase							)

							UpperCamelCase__								=						image_size
							UpperCamelCase__								=						patch_size
							UpperCamelCase__								=						num_channels
							UpperCamelCase__								=						num_frames
							UpperCamelCase__								=						tubelet_size

							UpperCamelCase__								=						hidden_size
							UpperCamelCase__								=						num_hidden_layers
							UpperCamelCase__								=						num_attention_heads
							UpperCamelCase__								=						intermediate_size
							UpperCamelCase__								=						hidden_act
							UpperCamelCase__								=						hidden_dropout_prob
							UpperCamelCase__								=						attention_probs_dropout_prob
							UpperCamelCase__								=						initializer_range
							UpperCamelCase__								=						layer_norm_eps
							UpperCamelCase__								=						qkv_bias
							UpperCamelCase__								=						use_mean_pooling

							UpperCamelCase__								=						decoder_num_attention_heads
							UpperCamelCase__								=						decoder_hidden_size
							UpperCamelCase__								=						decoder_num_hidden_layers
							UpperCamelCase__								=						decoder_intermediate_size
							UpperCamelCase__								=						norm_pix_loss





