





"""simple docstring"""





import os
from string import ascii_letters

lowerCAmelCase__        =      ascii_letters + ''' \t\n'''


def    a__       (       ):




		"""simple docstring"""


		UpperCamelCase =						os.path.split(os.path.realpath(_SCREAMING_SNAKE_CASE     )     )
		UpperCamelCase =						{}
		with open(path[0] + "/dictionary.txt"     ) as dictionary_file:
				for word in dictionary_file.read().split("\n"     ):
						UpperCamelCase =						None
		return english_words


lowerCAmelCase__        =      load_dictionary()


def    a__       (       _SCREAMING_SNAKE_CASE     ):




		"""simple docstring"""


		UpperCamelCase =						message.upper()
		UpperCamelCase =						remove_non_letters(_SCREAMING_SNAKE_CASE     )
		UpperCamelCase =						message.split()
		UpperCamelCase =						len([word for word in possible_words if word in ENGLISH_WORDS]     )
		return float(_SCREAMING_SNAKE_CASE     ) / len(_SCREAMING_SNAKE_CASE     )


def    a__       (       _SCREAMING_SNAKE_CASE     ):




		"""simple docstring"""


		return "".join(symbol for symbol in message if symbol in LETTERS_AND_SPACE     )


def    a__       (       _SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE = 20	,						_SCREAMING_SNAKE_CASE = 85     ):




		"""simple docstring"""


		UpperCamelCase =						get_english_count(_SCREAMING_SNAKE_CASE     ) * 100 >= word_percentage
		UpperCamelCase =						len(remove_non_letters(_SCREAMING_SNAKE_CASE     )     )
		UpperCamelCase =						(float(_SCREAMING_SNAKE_CASE     ) / len(_SCREAMING_SNAKE_CASE     )) * 100
		UpperCamelCase =						message_letters_percentage >= letter_percentage
		return words_match and letters_match


if __name__ == "__main__":
						import doctest

						doctest.testmod()













"""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


lowerCAmelCase__        =      logging.getLogger(__name__)


def    a__       (       ):




		"""simple docstring"""


		UpperCamelCase =						argparse.ArgumentParser(description="Train a unigram tokenizer on the wikitext dataset."     )
		parser.add_argument(
		    "--dataset_name"	,						type=_SCREAMING_SNAKE_CASE	,						default="wikitext"	,						help="Name of the training. Explore datasets at: hf.co/datasets."	,						)
		parser.add_argument(
		    "--dataset_config"	,						type=_SCREAMING_SNAKE_CASE	,						default="wikitext-103-raw-v1"	,						help="Configuration name of the dataset."     )
		parser.add_argument(
		    "--batch_size"	,						type=_SCREAMING_SNAKE_CASE	,						default=1_000	,						help="Batch size during training."	,						)
		parser.add_argument(
		    "--vocab_size"	,						type=_SCREAMING_SNAKE_CASE	,						default=10_048	,						help="Size of the desired vocabulary."	,						)
		parser.add_argument(
		    "--limit"	,						default=_SCREAMING_SNAKE_CASE	,						type=_SCREAMING_SNAKE_CASE	,						help="Limit the number of shards (used for debugging)."	,						)
		parser.add_argument(
		    "--export_to_hub"	,						action="store_true"	,						)

		UpperCamelCase =						parser.parse_args()
		return args


def    a__       (       _SCREAMING_SNAKE_CASE     ):




		"""simple docstring"""


		UpperCamelCase =						datasets.load_dataset(args.dataset_name	,						args.dataset_config	,						split="train"     )

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

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

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

		# Prepare the trainer.
		UpperCamelCase =						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=_SCREAMING_SNAKE_CASE     )
		logger.info("Tokenizer training complete!"     )

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

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


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






