

"""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


_SCREAMING_SNAKE_CASE       :  Union[str, Any]        = logging.getLogger(__name__)



def        _lowerCAmelCase      (						):


					'''simple docstring'''






					UpperCamelCase__		:    List[str]         =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=1_000      ,							help='''Batch size during training.'''      ,							)
					parser.add_argument(
					    '''--vocab_size'''      ,							type=UpperCAmelCase      ,							default=10_048      ,							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'''      ,							)

					UpperCamelCase__		:    Tuple         =parser.parse_args()
					return args



def        _lowerCAmelCase      (						UpperCAmelCase     :							List[Any]				):


					'''simple docstring'''






					UpperCamelCase__		:    int         =datasets.load_dataset(args.dataset_name      ,							args.dataset_config      ,							split='''train'''				)

					if args.limit is not None:
										UpperCamelCase__		:    List[Any]         =min(len(UpperCAmelCase				)      ,							args.limit				)
										UpperCamelCase__		:    int         =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.
					UpperCamelCase__		:    Dict         =Tokenizer(Unigram()				)
					UpperCamelCase__		:    Dict         =normalizers.Sequence([normalizers.Replace('''``'''      ,							'''"'''				), normalizers.Replace('''\'\''''      ,							'''"'''				)]				)
					UpperCamelCase__		:    str         =pre_tokenizers.Metaspace()

					# Prepare the trainer.
					UpperCamelCase__		:    Dict         =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!'''				)

					UpperCamelCase__		:    Any         =tokenizer.token_to_id('''[CLS]'''				)
					UpperCamelCase__		:    int         =tokenizer.token_to_id('''[SEP]'''				)
					UpperCamelCase__		:    Union[str, 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),
					    ]      ,							)
					UpperCamelCase__		:    Union[str, Any]         =decoders.Metaspace()

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


if __name__ == "__main__":
				_SCREAMING_SNAKE_CASE       :  Optional[Any]        = parse_args()
				main(args)





"""simple docstring"""





from __future__ import annotations

from collections.abc import Iterable
from dataclasses import dataclass
from typing import Any

class     __a   :

			"""simple docstring"""

			SCREAMING_SNAKE_CASE_						     =						('_front', '_back', '_len')

			@dataclass
			class     __a   :

						"""simple docstring"""

						SCREAMING_SNAKE_CASE_						     =						None
						SCREAMING_SNAKE_CASE_						     =						None
						SCREAMING_SNAKE_CASE_						     =						None



			class     __a   :

						"""simple docstring"""

						SCREAMING_SNAKE_CASE_						     =						('_cur',)






						def __init__(							self     :					Optional[int]						,  lowercase_     :					Deque._Node | None						):
											UpperCamelCase__		:    List[str]         =cur






						def __iter__(							self     :					List[str]						):
											return self






						def    _lowerCAmelCase			(							self     :					List[Any]						):
											if self._cur is None:
																# finished iterating
																raise StopIteration
											UpperCamelCase__		:    str         =self._cur.val
											UpperCamelCase__		:    Any         =self._cur.next_node

											return val






			def __init__(							self     :					str						,  lowercase_     :					Iterable[Any] | None = None						):
								UpperCamelCase__		:    Any         =None
								UpperCamelCase__		:    Any         =None
								UpperCamelCase__		:    int         =0

								if iterable is not None:
													# append every value to the deque
													for val in iterable:
																		self.append(lowercase_						)






			def    _lowerCAmelCase			(							self     :					str						,  lowercase_     :					Any						):
								UpperCamelCase__		:    Union[str, Any]         =self._Node(lowercase_						,  lowercase_						,  lowercase_						)
								if self.is_empty():
													# front = back
													UpperCamelCase__		:    Any         =node
													UpperCamelCase__		:    List[Any]         =1
								else:
													# connect nodes
													UpperCamelCase__		:    Any         =node
													UpperCamelCase__		:    Optional[Any]         =self._back
													UpperCamelCase__		:    str         =node  # assign new back to the new node

													self._len += 1

													# make sure there were no errors
													assert not self.is_empty(), "Error on appending value."






			def    _lowerCAmelCase			(							self     :					Optional[Any]						,  lowercase_     :					Any						):
								UpperCamelCase__		:    str         =self._Node(lowercase_						,  lowercase_						,  lowercase_						)
								if self.is_empty():
													# front = back
													UpperCamelCase__		:    str         =node
													UpperCamelCase__		:    Any         =1
								else:
													# connect nodes
													UpperCamelCase__		:    int         =self._front
													UpperCamelCase__		:    int         =node
													UpperCamelCase__		:    Any         =node  # assign new front to the new node

													self._len += 1

													# make sure there were no errors
													assert not self.is_empty(), "Error on appending value."






			def    _lowerCAmelCase			(							self     :					List[Any]						,  lowercase_     :					Iterable[Any]						):
								for val in iterable:
													self.append(lowercase_						)






			def    _lowerCAmelCase			(							self     :					Dict						,  lowercase_     :					Iterable[Any]						):
								for val in iterable:
													self.appendleft(lowercase_						)






			def    _lowerCAmelCase			(							self     :					Optional[int]						):
								assert not self.is_empty(), "Deque is empty."

								UpperCamelCase__		:    List[Any]         =self._back
								UpperCamelCase__		:    Any         =self._back.prev_node  # set new back
								# drop the last node - python will deallocate memory automatically
								UpperCamelCase__		:    List[str]         =None

								self._len -= 1

								return topop.val






			def    _lowerCAmelCase			(							self     :					str						):
								assert not self.is_empty(), "Deque is empty."

								UpperCamelCase__		:    List[str]         =self._front
								UpperCamelCase__		:    List[Any]         =self._front.next_node  # set new front and drop the first node
								UpperCamelCase__		:    Dict         =None

								self._len -= 1

								return topop.val






			def    _lowerCAmelCase			(							self     :					Dict						):
								return self._front is None






			def __len__(							self     :					Any						):
								return self._len






			def __eq__(							self     :					Dict						,  lowercase_     :					object						):

								if not isinstance(lowercase_						,  lowercase_						):
													return NotImplemented

								UpperCamelCase__		:    Union[str, Any]         =self._front
								UpperCamelCase__		:    Union[str, Any]         =other._front

								# if the length of the dequeues are not the same, they are not equal
								if len(self						) != len(lowercase_						):
													return False

								while me is not None and oth is not None:
													# compare every value
													if me.val != oth.val:
																		return False
													UpperCamelCase__		:    Union[str, Any]         =me.next_node
													UpperCamelCase__		:    Union[str, Any]         =oth.next_node

								return True






			def __iter__(							self     :					Optional[int]						):
								return Deque._Iterator(self._front						)






			def __repr__(							self     :					Tuple						):
								UpperCamelCase__		:    List[Any]         =[]
								UpperCamelCase__		:    Optional[int]         =self._front
								while aux is not None:
													# append the values in a list to display
													values_list.append(aux.val						)
													UpperCamelCase__		:    Optional[Any]         =aux.next_node

								return f'''[{', '.join(repr(lowercase_						) for val in values_list						)}]'''


if __name__ == "__main__":
				import doctest

				doctest.testmod()


