



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


_lowerCamelCase         =	logging.get_logger(__name__)

_lowerCamelCase         =	{
    """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    _SCREAMING_SNAKE_CASE						(UpperCamelCase			):
     lowerCAmelCase       =      """yoso"""





     def __init__(							self			:				Optional[Any]  ,	UpperCamelCase			:				Any=5_0_2_6_5  ,	UpperCamelCase			:				Dict=7_6_8  ,	UpperCamelCase			:				Any=1_2  ,	UpperCamelCase			:				List[str]=1_2  ,	UpperCamelCase			:				Tuple=3_0_7_2  ,	UpperCamelCase			:				Any="gelu"  ,	UpperCamelCase			:				Optional[int]=0.1  ,	UpperCamelCase			:				Tuple=0.1  ,	UpperCamelCase			:				List[Any]=4_0_9_6  ,	UpperCamelCase			:				List[str]=1  ,	UpperCamelCase			:				str=0.0_2  ,	UpperCamelCase			:				List[Any]=1E-12  ,	UpperCamelCase			:				Optional[int]="absolute"  ,	UpperCamelCase			:				Optional[Any]=True  ,	UpperCamelCase			:				Optional[Any]=9  ,	UpperCamelCase			:				Union[str, Any]=6_4  ,	UpperCamelCase			:				Any=None  ,	UpperCamelCase			:				Dict=True  ,	UpperCamelCase			:				Optional[Any]=True  ,	UpperCamelCase			:				Union[str, Any]=1  ,	UpperCamelCase			:				Optional[int]=0  ,	UpperCamelCase			:				Dict=2  ,	**UpperCamelCase			:				Tuple  ,	)->List[str]:
      super().__init__(pad_token_id=UpperCamelCase  ,	bos_token_id=UpperCamelCase  ,	eos_token_id=UpperCamelCase  ,	**UpperCamelCase					)

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







'''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


_lowercase		:					str													=		logging.getLogger(__name__)




def 				lowerCamelCase      ( )     -> Tuple:
							lowercase_   :     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_   :     List[Any]									=			parser.parse_args()
							return args




def 				lowerCamelCase      ( UpperCAmelCase__			:   Optional[int]					)     -> List[Any]:
							lowercase_   :     Any									=			datasets.load_dataset(args.dataset_name , args.dataset_config , split="""train"""					)

							if args.limit is not None:
														lowercase_   :     Optional[Any]									=			min(len(UpperCAmelCase__					) , args.limit					)
														lowercase_   :     Dict									=			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_   :     Optional[int]									=			normalizers.Sequence([normalizers.Replace("""``""" , """\""""					), normalizers.Replace("""''""" , """\""""					)]					)
							lowercase_   :     List[Any]									=			pre_tokenizers.Metaspace()

							# Prepare the trainer.
							lowercase_   :     str									=			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_   :     Dict									=			tokenizer.token_to_id("""[CLS]"""					)
							lowercase_   :     Any									=			tokenizer.token_to_id("""[SEP]"""					)
							lowercase_   :     Optional[Any]									=			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_   :     int									=			decoders.Metaspace()

							if args.export_to_hub:
														logger.info("""Exporting the trained tokenzier to Hub."""					)
														lowercase_   :     Any									=			AlbertTokenizerFast(tokenizer_object=UpperCAmelCase__					)
														new_tokenizer.push_to_hub("""unigram-tokenizer-dataset"""					)


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





