
'''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					    =		logging.getLogger(__name__)


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

						_lowerCAmelCase  						= parser.parse_args()
						return args


def A      (__lowerCamelCase      :Tuple		):
						_lowerCAmelCase  						= datasets.load_dataset(args.dataset_name ,	args.dataset_config ,	split="""train"""		)

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

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

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

						# Prepare the trainer.
						_lowerCAmelCase  						= 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=__lowerCamelCase		)
						logger.info("""Tokenizer training complete!"""		)

						_lowerCAmelCase  						= tokenizer.token_to_id("""[CLS]"""		)
						_lowerCAmelCase  						= tokenizer.token_to_id("""[SEP]"""		)
						_lowerCAmelCase  						= 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),
						    ] ,	)
						_lowerCAmelCase  						= decoders.Metaspace()

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


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



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


SCREAMING_SNAKE_CASE_						=					{'configuration_hubert': ['HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'HubertConfig']}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE_						=					[
					    'HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'HubertForCTC',
					    'HubertForSequenceClassification',
					    'HubertModel',
					    'HubertPreTrainedModel',
					]


try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE_						=					[
					    'TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'TFHubertForCTC',
					    'TFHubertModel',
					    'TFHubertPreTrainedModel',
					]

if TYPE_CHECKING:
					from .configuration_hubert import HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, HubertConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_hubert import (
										    HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    HubertForCTC,
										    HubertForSequenceClassification,
										    HubertModel,
										    HubertPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_hubert import (
										    TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFHubertForCTC,
										    TFHubertModel,
										    TFHubertPreTrainedModel,
										)


else:
					import sys

					SCREAMING_SNAKE_CASE_						=					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


