



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


A__       =			logging.get_logger(__name__)

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





class    __lowerCAmelCase					(	lowerCamelCase__			):
				__lowerCamelCase					=							'''videomae'''




				def __init__(				self		,							_snake_case=224		,							_snake_case=16		,							_snake_case=3		,							_snake_case=16		,							_snake_case=2		,							_snake_case=768		,							_snake_case=12		,							_snake_case=12		,							_snake_case=3072		,							_snake_case="gelu"		,							_snake_case=0.0		,							_snake_case=0.0		,							_snake_case=0.02		,							_snake_case=1e-12		,							_snake_case=True		,							_snake_case=True		,							_snake_case=6		,							_snake_case=384		,							_snake_case=4		,							_snake_case=1536		,							_snake_case=True		,							**_snake_case		,							):



									"""simple docstring"""





									super().__init__(**_snake_case						)

									_lowerCAmelCase         =       image_size
									_lowerCAmelCase         =       patch_size
									_lowerCAmelCase         =       num_channels
									_lowerCAmelCase         =       num_frames
									_lowerCAmelCase         =       tubelet_size

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

									_lowerCAmelCase         =       decoder_num_attention_heads
									_lowerCAmelCase         =       decoder_hidden_size
									_lowerCAmelCase         =       decoder_num_hidden_layers
									_lowerCAmelCase         =       decoder_intermediate_size
									_lowerCAmelCase         =       norm_pix_loss







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 				UpperCamelCase_	(					)->   Dict:




						'''simple docstring'''
						__magic_name__					=      argparse.ArgumentParser(description='Train a unigram tokenizer on the wikitext dataset.'  )
						parser.add_argument(
						    '--dataset_name'						,      type=__snake_case						,      default='wikitext'						,      help='Name of the training. Explore datasets at: hf.co/datasets.'						,      )
						parser.add_argument(
						    '--dataset_config'						,      type=__snake_case						,      default='wikitext-103-raw-v1'						,      help='Configuration name of the dataset.'  )
						parser.add_argument(
						    '--batch_size'						,      type=__snake_case						,      default=10_00						,      help='Batch size during training.'						,      )
						parser.add_argument(
						    '--vocab_size'						,      type=__snake_case						,      default=1_00_48						,      help='Size of the desired vocabulary.'						,      )
						parser.add_argument(
						    '--limit'						,      default=__snake_case						,      type=__snake_case						,      help='Limit the number of shards (used for debugging).'						,      )
						parser.add_argument(
						    '--export_to_hub'						,      action='store_true'						,      )

						__magic_name__					=      parser.parse_args()
						return args
def 				UpperCamelCase_	(					__snake_case      :    Any  )->   Optional[int]:




						'''simple docstring'''
						__magic_name__					=      datasets.load_dataset(args.dataset_name						,      args.dataset_config						,      split='train'  )

						if args.limit is not None:
												__magic_name__					=      min(len(__snake_case  )						,      args.limit  )
												__magic_name__					=      dataset.select(range(__snake_case  )  )
												logger.info(F'''Limiting the dataset to {args.limit} entries.'''  )

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

    # Prepare the tokenizer.
						__magic_name__					=      Tokenizer(Unigram()  )
						__magic_name__					=      normalizers.Sequence([normalizers.Replace('``'						,      '"'  ), normalizers.Replace('\'\''						,      '"'  )]  )
						__magic_name__					=      pre_tokenizers.Metaspace()

						# Prepare the trainer.
						__magic_name__					=      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=__snake_case  )
						logger.info('Tokenizer training complete!'  )

						__magic_name__					=      tokenizer.token_to_id('[CLS]'  )
						__magic_name__					=      tokenizer.token_to_id('[SEP]'  )
						__magic_name__					=      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),
						    ]						,      )
						__magic_name__					=      decoders.Metaspace()

						if args.export_to_hub:
												logger.info('Exporting the trained tokenzier to Hub.'  )
												__magic_name__					=      AlbertTokenizerFast(tokenizer_object=__snake_case  )
												new_tokenizer.push_to_hub('unigram-tokenizer-dataset'  )


if __name__ == "__main__":
					UpperCamelCase			:					Dict         =							parse_args()
					main(args)

