
'''simple docstring'''







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


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

__SCREAMING_SNAKE_CASE		:  Any        =      {
    """ZinengTang/tvlt-base""": """https://huggingface.co/ZinengTang/tvlt-base/blob/main/config.json""",
}
class  SCREAMING_SNAKE_CASE__       (  __UpperCamelCase   ):
	lowercase__	    =							"tvlt"
	def __init__(				self  ,    __UpperCamelCase=224  ,    __UpperCamelCase=2048  ,    __UpperCamelCase=128  ,    __UpperCamelCase=[16, 16]  ,    __UpperCamelCase=[16, 16]  ,    __UpperCamelCase=3  ,    __UpperCamelCase=1  ,    __UpperCamelCase=8  ,    __UpperCamelCase=768  ,    __UpperCamelCase=12  ,    __UpperCamelCase=12  ,    __UpperCamelCase=3072  ,    __UpperCamelCase="gelu"  ,    __UpperCamelCase=0.0  ,    __UpperCamelCase=0.0  ,    __UpperCamelCase=0.0_2  ,    __UpperCamelCase=1E-6  ,    __UpperCamelCase=True  ,    __UpperCamelCase=False  ,    __UpperCamelCase=16  ,    __UpperCamelCase=512  ,    __UpperCamelCase=8  ,    __UpperCamelCase=2048  ,    __UpperCamelCase=0.7_5  ,    __UpperCamelCase=0.1_5  ,    __UpperCamelCase="frame-level"  ,    __UpperCamelCase=True  ,    __UpperCamelCase=True  ,    __UpperCamelCase="classification"  ,    **__UpperCamelCase  ,    ):



					'''simple docstring'''






					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}"""			)

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

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

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

					__a       :     Optional[Any]								=	task_matching
					__a       :     List[str]								=	task_mae
					__a       :     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)



