



"""simple docstring"""







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


UpperCAmelCase:  Optional[int]			     =				logging.get_logger(__name__)

UpperCAmelCase:  Any			     =				{
    """ZinengTang/tvlt-base""": """https://huggingface.co/ZinengTang/tvlt-base/blob/main/config.json""",
}





class   UpperCamelCase	(     snake_case       ):




	"""simple docstring"""
	SCREAMING_SNAKE_CASE_							:       str						       =  "tvlt"






	def __init__(     self				,UpperCAmelCase_=2_24				,UpperCAmelCase_=20_48				,UpperCAmelCase_=1_28				,UpperCAmelCase_=[16, 16]				,UpperCAmelCase_=[16, 16]				,UpperCAmelCase_=3				,UpperCAmelCase_=1				,UpperCAmelCase_=8				,UpperCAmelCase_=7_68				,UpperCAmelCase_=12				,UpperCAmelCase_=12				,UpperCAmelCase_=30_72				,UpperCAmelCase_="gelu"				,UpperCAmelCase_=0.0				,UpperCAmelCase_=0.0				,UpperCAmelCase_=0.02				,UpperCAmelCase_=1E-6				,UpperCAmelCase_=True				,UpperCAmelCase_=False				,UpperCAmelCase_=16				,UpperCAmelCase_=5_12				,UpperCAmelCase_=8				,UpperCAmelCase_=20_48				,UpperCAmelCase_=0.75				,UpperCAmelCase_=0.15				,UpperCAmelCase_="frame-level"				,UpperCAmelCase_=True				,UpperCAmelCase_=True				,UpperCAmelCase_="classification"				,**UpperCAmelCase_				,):
			super().__init__(**UpperCAmelCase_   )

			if audio_mask_type not in ("frame-level", "patch_level"):
					raise ValueError(
					    """audio_mask_type must be one of two acceptable strategies - {'frame_level', 'patch-level') """
					    f"""got {audio_mask_type}"""   )

			_lowercase				: List[Any]     =   image_size
			_lowercase				: Optional[int]     =   spectrogram_length
			_lowercase				: Dict     =   frequency_length
			_lowercase				: Dict     =   image_patch_size
			_lowercase				: Any     =   audio_patch_size
			_lowercase				: Tuple     =   num_image_channels
			_lowercase				: Union[str, Any]     =   num_audio_channels
			_lowercase				: Optional[Any]     =   num_frames

			_lowercase				: str     =   hidden_size
			_lowercase				: Optional[Any]     =   num_hidden_layers
			_lowercase				: Dict     =   num_attention_heads
			_lowercase				: Any     =   intermediate_size
			_lowercase				: Dict     =   hidden_act
			_lowercase				: Optional[Any]     =   hidden_dropout_prob
			_lowercase				: int     =   attention_probs_dropout_prob
			_lowercase				: List[str]     =   initializer_range
			_lowercase				: int     =   layer_norm_eps
			_lowercase				: List[str]     =   qkv_bias
			_lowercase				: Dict     =   use_mean_pooling

			_lowercase				: int     =   decoder_num_attention_heads
			_lowercase				: Tuple     =   decoder_hidden_size
			_lowercase				: List[str]     =   decoder_num_hidden_layers
			_lowercase				: Any     =   decoder_intermediate_size
			_lowercase				: List[str]     =   pixel_mask_ratio
			_lowercase				: Optional[Any]     =   audio_mask_ratio
			_lowercase				: Optional[int]     =   audio_mask_type

			_lowercase				: Optional[Any]     =   task_matching
			_lowercase				: List[str]     =   task_mae
			_lowercase				: Tuple     =   loss_type








"""simple docstring"""







import argparse
import logging

import datasets
from tokenizers import Tokenizer, decoders, normalizers, pre_tokenizers, processors
from tokenizers.models import Unigram
from tokenizers.trainers import UnigramTrainer

from transformers import AlbertTokenizerFast


UpperCAmelCase:  List[str]			     =				logging.getLogger(__name__)


def  __SCREAMING_SNAKE_CASE (  ):
		_lowercase				: List[Any]     =   argparse.ArgumentParser(description="""Train a unigram tokenizer on the wikitext dataset."""      )
		parser.add_argument(
		    """--dataset_name"""   ,				type=__UpperCAmelCase   ,				default="""wikitext"""   ,				help="""Name of the training. Explore datasets at: hf.co/datasets."""   ,				)
		parser.add_argument(
		    """--dataset_config"""   ,				type=__UpperCAmelCase   ,				default="""wikitext-103-raw-v1"""   ,				help="""Configuration name of the dataset."""      )
		parser.add_argument(
		    """--batch_size"""   ,				type=__UpperCAmelCase   ,				default=1000   ,				help="""Batch size during training."""   ,				)
		parser.add_argument(
		    """--vocab_size"""   ,				type=__UpperCAmelCase   ,				default=10048   ,				help="""Size of the desired vocabulary."""   ,				)
		parser.add_argument(
		    """--limit"""   ,				default=__UpperCAmelCase   ,				type=__UpperCAmelCase   ,				help="""Limit the number of shards (used for debugging)."""   ,				)
		parser.add_argument(
		    """--export_to_hub"""   ,				action="""store_true"""   ,				)

		_lowercase				: Any     =   parser.parse_args()
		return args


def  __SCREAMING_SNAKE_CASE (  __UpperCAmelCase      ):
		_lowercase				: List[Any]     =   datasets.load_dataset(args.dataset_name   ,				args.dataset_config   ,				split="""train"""      )

		if args.limit is not None:
				_lowercase				: Tuple     =   min(len(__UpperCAmelCase      )   ,				args.limit      )
				_lowercase				: Optional[Any]     =   dataset.select(range(__UpperCAmelCase      )      )
				logger.info(F"""Limiting the dataset to {args.limit} entries."""      )

		def batch_iterator():
				for i in range(0   ,				len(__UpperCAmelCase      )   ,				args.batch_size      ):
						yield dataset[i : i + args.batch_size]["text"]

    # Prepare the tokenizer.
		_lowercase				: Optional[int]     =   Tokenizer(Unigram()      )
		_lowercase				: List[str]     =   normalizers.Sequence([normalizers.Replace("""``"""   ,				"""\""""      ), normalizers.Replace("""''"""   ,				"""\""""      )]      )
		_lowercase				: str     =   pre_tokenizers.Metaspace()

		# Prepare the trainer.
		_lowercase				: int     =   UnigramTrainer(
		    unk_token="""<unk>"""   ,				special_tokens=["""[CLS]""", """[SEP]""", """<unk>""", """<pad>""", """[MASK]"""]   ,				vocab_size=args.vocab_size   ,				)

		logger.info("""Training the tokenizer."""      )
		tokenizer.train_from_iterator(batch_iterator()   ,				trainer=__UpperCAmelCase      )
		logger.info("""Tokenizer training complete!"""      )

		_lowercase				: int     =   tokenizer.token_to_id("""[CLS]"""      )
		_lowercase				: Optional[Any]     =   tokenizer.token_to_id("""[SEP]"""      )
		_lowercase				: Optional[int]     =   processors.TemplateProcessing(
		    single="""[CLS]:0 $A:0 [SEP]:0"""   ,				pair="""[CLS]:0 $A:0 [SEP]:0 $B:1 [SEP]:1"""   ,				special_tokens=[
		        ("""[CLS]""", cls_token_id),
		        ("""[SEP]""", sep_token_id),
		    ]   ,				)
		_lowercase				: Union[str, Any]     =   decoders.Metaspace()

		if args.export_to_hub:
				logger.info("""Exporting the trained tokenzier to Hub."""      )
				_lowercase				: List[Any]     =   AlbertTokenizerFast(tokenizer_object=__UpperCAmelCase      )
				new_tokenizer.push_to_hub("""unigram-tokenizer-dataset"""      )


if __name__ == "__main__":
		UpperCAmelCase:  List[Any]			     =				parse_args()
		main(args)



