



'''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


_A					:     Optional[Any]      =logging.getLogger(__name__)







def     __UpperCamelCase					(				)			->      Union[str, Any]:
	_lowercase       :							Optional[int]  	=		argparse.ArgumentParser(description='Train a unigram tokenizer on the wikitext dataset.'       )
	parser.add_argument(
	    '--dataset_name',					type=_lowercase,					default='wikitext',					help='Name of the training. Explore datasets at: hf.co/datasets.',					)
	parser.add_argument(
	    '--dataset_config',					type=_lowercase,					default='wikitext-103-raw-v1',					help='Configuration name of the dataset.'       )
	parser.add_argument(
	    '--batch_size',					type=_lowercase,					default=1000,					help='Batch size during training.',					)
	parser.add_argument(
	    '--vocab_size',					type=_lowercase,					default=1_0048,					help='Size of the desired vocabulary.',					)
	parser.add_argument(
	    '--limit',					default=_lowercase,					type=_lowercase,					help='Limit the number of shards (used for debugging).',					)
	parser.add_argument(
	    '--export_to_hub',					action='store_true',					)

	_lowercase       :							Tuple  	=		parser.parse_args()
	return args







def     __UpperCamelCase					(				_lowercase       )			->      Union[str, Any]:
	_lowercase       :							List[str]  	=		datasets.load_dataset(args.dataset_name,					args.dataset_config,					split='train'       )

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

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

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

	# Prepare the trainer.
	_lowercase       :							Union[str, Any]  	=		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=_lowercase       )
	logger.info('Tokenizer training complete!'       )

	_lowercase       :							List[Any]  	=		tokenizer.token_to_id('[CLS]'       )
	_lowercase       :							Optional[Any]  	=		tokenizer.token_to_id('[SEP]'       )
	_lowercase       :							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       :							Tuple  	=		decoders.Metaspace()

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


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





'''simple docstring'''

import json
import os
from typing import List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_A					:     Optional[Any]      =logging.get_logger(__name__)

_A					:     Optional[Any]      ={
    '''vocab_file''': '''vocab.json''',
    '''merges_file''': '''merges.txt''',
}

_A					:     str      ={
    '''vocab_file''': {
        '''microsoft/biogpt''': '''https://huggingface.co/microsoft/biogpt/resolve/main/vocab.json''',
    },
    '''merges_file''': {'''microsoft/biogpt''': '''https://huggingface.co/microsoft/biogpt/resolve/main/merges.txt'''},
}

_A					:     List[Any]      ={
    '''microsoft/biogpt''': 1_0_2_4,
}







def     __UpperCamelCase					(				_lowercase       )			->      Dict:
	_lowercase       :							List[str]  	=		set()
	_lowercase       :							str  	=		word[0]
	for char in word[1:]:
		pairs.add((prev_char, char)       )
		_lowercase       :							str  	=		char
	return pairs






class 		lowerCamelCase__   (    A  ):




					'''simple docstring'''



					A_         =    VOCAB_FILES_NAMES
					A_         =    PRETRAINED_VOCAB_FILES_MAP
					A_         =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					A_         =    ["""input_ids""", """attention_mask"""]




					def __init__(	self					:						Union[str, Any]  ,	UpperCamelCase_					:						Dict  ,	UpperCamelCase_					:						List[str]  ,	UpperCamelCase_					:						List[Any]="<unk>"  ,	UpperCamelCase_					:						str="<s>"  ,	UpperCamelCase_					:						str="</s>"  ,	UpperCamelCase_					:						int="</s>"  ,	UpperCamelCase_					:						Tuple="<pad>"  ,	**UpperCamelCase_					:						Any  ,	)	->							Tuple:



						'''simple docstring'''





						super().__init__(
						    bos_token=UpperCamelCase_  ,	eos_token=UpperCamelCase_  ,	sep_token=UpperCamelCase_  ,	unk_token=UpperCamelCase_  ,	pad_token=UpperCamelCase_  ,	**UpperCamelCase_  ,	)

						try:
							import sacremoses
						except ImportError:
							raise ImportError(
							    'You need to install sacremoses to use BioGptTokenizer. '
							    'See https://pypi.org/project/sacremoses/ for installation.'       )

						_lowercase       :							Dict  	=		'en'
						_lowercase       :							Any  	=		sacremoses
						# cache of sm.MosesTokenizer instance
						_lowercase       :							Union[str, Any]  	=		{}
						_lowercase       :							List[str]  	=		{}
						with open(UpperCamelCase_  ,	encoding='utf-8'       ) as vocab_handle:
							_lowercase       :							Optional[int]  	=		json.load(UpperCamelCase_       )
						_lowercase       :							str  	=		{v: k for k, v in self.encoder.items()}
						with open(UpperCamelCase_  ,	encoding='utf-8'       ) as merges_handle:
							_lowercase       :							Tuple  	=		merges_handle.read().split('\n'       )[:-1]
						_lowercase       :							List[str]  	=		[tuple(merge.split()[:2]       ) for merge in merges]
						_lowercase       :							str  	=		dict(zip(UpperCamelCase_  ,	range(len(UpperCamelCase_       )       )       )       )
						_lowercase       :							Union[str, Any]  	=		{}




					@property
					def     __UpperCAmelCase  (	self					:						Tuple       )	->							List[str]:



						'''simple docstring'''





						return len(self.encoder       )




					def     __UpperCAmelCase  (	self					:						Tuple       )	->							List[str]:



						'''simple docstring'''





						return dict(self.encoder  ,	**self.added_tokens_encoder       )




					def     __UpperCAmelCase  (	self					:						Optional[Any]  ,	UpperCamelCase_					:						int  ,	UpperCamelCase_					:						int       )	->							Optional[Any]:



						'''simple docstring'''





						if lang not in self.cache_moses_tokenizer:
							_lowercase       :							Union[str, Any]  	=		self.sm.MosesTokenizer(lang=UpperCamelCase_       )
							_lowercase       :							int  	=		moses_tokenizer
						return self.cache_moses_tokenizer[lang].tokenize(
						    UpperCamelCase_  ,	aggressive_dash_splits=UpperCamelCase_  ,	return_str=UpperCamelCase_  ,	escape=UpperCamelCase_       )




					def     __UpperCAmelCase  (	self					:						Any  ,	UpperCamelCase_					:						Dict  ,	UpperCamelCase_					:						str       )	->							List[str]:



						'''simple docstring'''





						if lang not in self.cache_moses_detokenizer:
							_lowercase       :							str  	=		self.sm.MosesDetokenizer(lang=UpperCamelCase_       )
							_lowercase       :							List[Any]  	=		moses_detokenizer
						return self.cache_moses_detokenizer[lang].detokenize(UpperCamelCase_       )




					def     __UpperCAmelCase  (	self					:						Optional[int]  ,	UpperCamelCase_					:						Optional[int]       )	->							Optional[Any]:



						'''simple docstring'''





						_lowercase       :							Dict  	=		tuple(token[:-1]       ) + (token[-1] + '</w>',)
						if token in self.cache:
							return self.cache[token]
						_lowercase       :							Dict  	=		get_pairs(UpperCamelCase_       )

						if not pairs:
							return token + "</w>"

						while True:
							_lowercase       :							str  	=		min(UpperCamelCase_  ,	key=lambda UpperCamelCase_       : self.bpe_ranks.get(UpperCamelCase_  ,	float('inf'       )       )       )
							if bigram not in self.bpe_ranks:
								break
							_lowercase	,     _lowercase       :							int  	=		bigram
							_lowercase       :							Any  	=		[]
							_lowercase       :							str  	=		0
							while i < len(UpperCamelCase_       ):
								try:
									_lowercase       :							Union[str, Any]  	=		word.index(UpperCamelCase_  ,	UpperCamelCase_       )
								except ValueError:
									new_word.extend(word[i:]       )
									break
								else:
									new_word.extend(word[i:j]       )
									_lowercase       :							str  	=		j

								if word[i] == first and i < len(UpperCamelCase_       ) - 1 and word[i + 1] == second:
									new_word.append(first + second       )
									i += 2
								else:
									new_word.append(word[i]       )
									i += 1
							_lowercase       :							Optional[int]  	=		tuple(UpperCamelCase_       )
							_lowercase       :							Tuple  	=		new_word
							if len(UpperCamelCase_       ) == 1:
								break
							else:
								_lowercase       :							List[str]  	=		get_pairs(UpperCamelCase_       )
						_lowercase       :							Optional[int]  	=		' '.join(UpperCamelCase_       )
						if word == "\n  </w>":
							_lowercase       :							Union[str, Any]  	=		'\n</w>'
						_lowercase       :							List[Any]  	=		word
						return word




					def     __UpperCAmelCase  (	self					:						List[Any]  ,	UpperCamelCase_					:						int  ,	UpperCamelCase_					:						List[str]=False       )	->							int:



						'''simple docstring'''





						if bypass_tokenizer:
							_lowercase       :							Optional[Any]  	=		text.split()
						else:
							_lowercase       :							List[str]  	=		self.moses_tokenize(UpperCamelCase_  ,	self.lang       )

						_lowercase       :							int  	=		[]
						for token in text:
							if token:
								split_tokens.extend(list(self.bpe(UpperCamelCase_       ).split(' '       )       )       )

						return split_tokens




					def     __UpperCAmelCase  (	self					:						Union[str, Any]  ,	UpperCamelCase_					:						Optional[int]       )	->							int:



						'''simple docstring'''





						return self.encoder.get(UpperCamelCase_  ,	self.encoder.get(self.unk_token       )       )




					def     __UpperCAmelCase  (	self					:						List[Any]  ,	UpperCamelCase_					:						Union[str, Any]       )	->							Tuple:



						'''simple docstring'''





						return self.decoder.get(UpperCamelCase_  ,	self.unk_token       )




					def     __UpperCAmelCase  (	self					:						Optional[int]  ,	UpperCamelCase_					:						Optional[Any]       )	->							List[Any]:



						'''simple docstring'''





						_lowercase       :							Dict  	=		[t.replace(' '  ,	''       ).replace('</w>'  ,	' '       ) for t in tokens]
						_lowercase       :							List[str]  	=		''.join(UpperCamelCase_       ).split()
						# detokenize
						_lowercase       :							Dict  	=		self.moses_detokenize(UpperCamelCase_  ,	self.lang       )
						return text




					def     __UpperCAmelCase  (	self					:						Optional[int]  ,	UpperCamelCase_					:						List[int]  ,	UpperCamelCase_					:						Optional[List[int]] = None       )	->							List[int]:



						'''simple docstring'''





						if token_ids_a is None:
							return [self.sep_token_id] + token_ids_a
						_lowercase       :							List[Any]  	=		[self.sep_token_id]
						return sep + token_ids_a + sep + token_ids_a




					def     __UpperCAmelCase  (	self					:						Union[str, Any]  ,	UpperCamelCase_					:						List[int]  ,	UpperCamelCase_					:						Optional[List[int]] = None  ,	UpperCamelCase_					:						bool = False       )	->							List[int]:



						'''simple docstring'''





						if already_has_special_tokens:
							return super().get_special_tokens_mask(
							    token_ids_a=UpperCamelCase_  ,	token_ids_a=UpperCamelCase_  ,	already_has_special_tokens=UpperCamelCase_       )
						# no bos used in fairseq
						if token_ids_a is not None:
							return [1] + ([0] * len(UpperCamelCase_       )) + [1] + ([0] * len(UpperCamelCase_       ))
						return [1] + ([0] * len(UpperCamelCase_       ))




					def     __UpperCAmelCase  (	self					:						List[Any]  ,	UpperCamelCase_					:						List[int]  ,	UpperCamelCase_					:						Optional[List[int]] = None       )	->							List[int]:



						'''simple docstring'''





						_lowercase       :							Dict  	=		[self.sep_token_id]

						# no bos used in fairseq
						if token_ids_a is None:
							return len(token_ids_a + sep       ) * [0]
						return len(token_ids_a + sep       ) * [0] + len(token_ids_a + sep       ) * [1]




					def     __UpperCAmelCase  (	self					:						Tuple  ,	UpperCamelCase_					:						str  ,	UpperCamelCase_					:						Optional[str] = None       )	->							Tuple[str]:



						'''simple docstring'''





						if not os.path.isdir(UpperCamelCase_       ):
							logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''       )
							return
						_lowercase       :							Any  	=		os.path.join(
						    UpperCamelCase_  ,	(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']       )
						_lowercase       :							Union[str, Any]  	=		os.path.join(
						    UpperCamelCase_  ,	(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file']       )

						with open(UpperCamelCase_  ,	'w'  ,	encoding='utf-8'       ) as f:
							f.write(json.dumps(self.encoder  ,	indent=2  ,	sort_keys=UpperCamelCase_  ,	ensure_ascii=UpperCamelCase_       ) + '\n'       )

						_lowercase       :							List[str]  	=		0
						with open(UpperCamelCase_  ,	'w'  ,	encoding='utf-8'       ) as writer:
							for bpe_tokens, token_index in sorted(self.bpe_ranks.items()  ,	key=lambda UpperCamelCase_       : kv[1]       ):
								if index != token_index:
									logger.warning(
									    F'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
									    ' Please check that the tokenizer is not corrupted!'       )
									_lowercase       :							Tuple  	=		token_index
								writer.write(' '.join(UpperCamelCase_       ) + '\n'       )
								index += 1

						return vocab_file, merge_file




					def __getstate__(	self					:						Optional[int]       )	->							List[Any]:



						'''simple docstring'''





						_lowercase       :							List[str]  	=		self.__dict__.copy()
						_lowercase       :							Dict  	=		None
						return state




					def __setstate__(	self					:						List[str]  ,	UpperCamelCase_					:						Dict       )	->							List[Any]:



						'''simple docstring'''





						_lowercase       :							List[str]  	=		d

						try:
							import sacremoses
						except ImportError:
							raise ImportError(
							    'You need to install sacremoses to use XLMTokenizer. '
							    'See https://pypi.org/project/sacremoses/ for installation.'       )

						_lowercase       :							str  	=		sacremoses
