



'''simple docstring'''

import argparse
import json
from typing import List

from ltp import LTP

from transformers import BertTokenizer







def 						__UpperCamelCase				(							_lowercase					) ->			Dict:
	# This defines a "chinese character" as anything in the CJK Unicode block:
	#   https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
	#
	# Note that the CJK Unicode block is NOT all Japanese and Korean characters,
	# despite its name. The modern Korean Hangul alphabet is a different block,
	# as is Japanese Hiragana and Katakana. Those alphabets are used to write
	# space-separated words, so they are not treated specially and handled
	# like the all of the other languages.
	if (
	    (cp >= 0x4E_00 and cp <= 0x9F_FF)
	    or (cp >= 0x34_00 and cp <= 0x4D_BF)  #
	    or (cp >= 0x2_00_00 and cp <= 0x2_A6_DF)  #
	    or (cp >= 0x2_A7_00 and cp <= 0x2_B7_3F)  #
	    or (cp >= 0x2_B7_40 and cp <= 0x2_B8_1F)  #
	    or (cp >= 0x2_B8_20 and cp <= 0x2_CE_AF)  #
	    or (cp >= 0xF9_00 and cp <= 0xFA_FF)
	    or (cp >= 0x2_F8_00 and cp <= 0x2_FA_1F)  #
	):  #
		return True

	return False







def 						__UpperCamelCase				(							_lowercase					) ->			Dict:
	# word like '180' or '身高' or '神'
	for char in word:
		_lowercase					:       Union[str, Any]       =		ord(_lowercase					)
		if not _is_chinese_char(_lowercase					):
			return 0
	return 1







def 						__UpperCamelCase				(							_lowercase					) ->			List[str]:
	_lowercase					:       Optional[Any]       =		set()

	for token in tokens:
		_lowercase					:       Any       =		len(_lowercase					) > 1 and is_chinese(_lowercase					)
		if chinese_word:
			word_set.add(_lowercase					)
	_lowercase					:       List[Any]       =		list(_lowercase					)
	return word_list







def 						__UpperCamelCase				(							_lowercase,				_lowercase					) ->			List[str]:
	if not chinese_word_set:
		return bert_tokens
	_lowercase					:       str       =		max([len(_lowercase					) for w in chinese_word_set]					)

	_lowercase					:       int       =		bert_tokens
	_lowercase  ,    _lowercase					:       List[Any]       =		0, len(_lowercase					)
	while start < end:
		_lowercase					:       Dict       =		True
		if is_chinese(bert_word[start]					):
			_lowercase					:       int       =		min(end - start,				_lowercase					)
			for i in range(_lowercase,				1,				-1					):
				_lowercase					:       List[str]       =		''.join(bert_word[start : start + i]					)
				if whole_word in chinese_word_set:
					for j in range(start + 1,				start + i					):
						_lowercase					:       Dict       =		'##' + bert_word[j]
					_lowercase					:       Optional[int]       =		start + i
					_lowercase					:       Optional[Any]       =		False
					break
		if single_word:
			start += 1
	return bert_word







def 						__UpperCamelCase				(							_lowercase,				_lowercase,				_lowercase					) ->			Optional[int]:
	_lowercase					:       Any       =		[]

	for i in range(0,				len(_lowercase					),				100					):
		_lowercase					:       Tuple       =		ltp_tokenizer.seg(lines[i : i + 100]					)[0]
		_lowercase					:       Dict       =		[get_chinese_word(_lowercase					) for r in res]
		ltp_res.extend(_lowercase					)
	assert len(_lowercase					) == len(_lowercase					)

	_lowercase					:       str       =		[]
	for i in range(0,				len(_lowercase					),				100					):
		_lowercase					:       int       =		bert_tokenizer(lines[i : i + 100],				add_special_tokens=_lowercase,				truncation=_lowercase,				max_length=512					)
		bert_res.extend(res['input_ids']					)
	assert len(_lowercase					) == len(_lowercase					)

	_lowercase					:       Optional[Any]       =		[]
	for input_ids, chinese_word in zip(_lowercase,				_lowercase					):
		_lowercase					:       Optional[Any]       =		[]
		for id in input_ids:
			_lowercase					:       str       =		bert_tokenizer._convert_id_to_token(_lowercase					)
			input_tokens.append(_lowercase					)
		_lowercase					:       Any       =		add_sub_symbol(_lowercase,				_lowercase					)
		_lowercase					:       str       =		[]
		# We only save pos of chinese subwords start with ##, which mean is part of a whole word.
		for i, token in enumerate(_lowercase					):
			if token[:2] == "##":
				_lowercase					:       str       =		token[2:]
				# save chinese tokens' pos
				if len(_lowercase					) == 1 and _is_chinese_char(ord(_lowercase					)					):
					ref_id.append(_lowercase					)
		ref_ids.append(_lowercase					)

	assert len(_lowercase					) == len(_lowercase					)

	return ref_ids







def 						__UpperCamelCase				(							_lowercase					) ->			Tuple:
	# For Chinese (Ro)Bert, the best result is from : RoBERTa-wwm-ext (https://github.com/ymcui/Chinese-BERT-wwm)
	# If we want to fine-tune these model, we have to use same tokenizer : LTP (https://github.com/HIT-SCIR/ltp)
	with open(args.file_name,				'r',				encoding='utf-8'					) as f:
		_lowercase					:       int       =		f.readlines()
	_lowercase					:       str       =		[line.strip() for line in data if len(_lowercase					) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
	_lowercase					:       Union[str, Any]       =		LTP(args.ltp					)  # faster in GPU device
	_lowercase					:       Union[str, Any]       =		BertTokenizer.from_pretrained(args.bert					)

	_lowercase					:       Optional[Any]       =		prepare_ref(_lowercase,				_lowercase,				_lowercase					)

	with open(args.save_path,				'w',				encoding='utf-8'					) as f:
		_lowercase					:       Optional[Any]       =		[json.dumps(_lowercase					) + '\n' for ref in ref_ids]
		f.writelines(_lowercase					)


if __name__ == "__main__":
		_A				:    Any     						=argparse.ArgumentParser(description='''prepare_chinese_ref''')
		parser.add_argument(
		    '''--file_name''',
		    type=str,
		    default='''./resources/chinese-demo.txt''',
		    help='''file need process, same as training data in lm''',
		)
		parser.add_argument(
		    '''--ltp''', type=str, default='''./resources/ltp''', help='''resources for LTP tokenizer, usually a path'''
		)
		parser.add_argument('''--bert''', type=str, default='''./resources/robert''', help='''resources for Bert tokenizer''')
		parser.add_argument('''--save_path''', type=str, default='''./resources/ref.txt''', help='''path to save res''')

		_A				:    Optional[int]     						=parser.parse_args()
		main(args)





'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_A				:    Optional[Any]     						={'''configuration_xlnet''': ['''XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XLNetConfig''']}

try:
		if not is_sentencepiece_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_A				:    Tuple     						=['''XLNetTokenizer''']

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_A				:    str     						=['''XLNetTokenizerFast''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_A				:    Any     						=[
		    '''XLNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''XLNetForMultipleChoice''',
		    '''XLNetForQuestionAnswering''',
		    '''XLNetForQuestionAnsweringSimple''',
		    '''XLNetForSequenceClassification''',
		    '''XLNetForTokenClassification''',
		    '''XLNetLMHeadModel''',
		    '''XLNetModel''',
		    '''XLNetPreTrainedModel''',
		    '''load_tf_weights_in_xlnet''',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_A				:    str     						=[
		    '''TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''TFXLNetForMultipleChoice''',
		    '''TFXLNetForQuestionAnsweringSimple''',
		    '''TFXLNetForSequenceClassification''',
		    '''TFXLNetForTokenClassification''',
		    '''TFXLNetLMHeadModel''',
		    '''TFXLNetMainLayer''',
		    '''TFXLNetModel''',
		    '''TFXLNetPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_xlnet import XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLNetConfig

		try:
				if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_xlnet import XLNetTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_xlnet_fast import XLNetTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_xlnet import (
				    XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
				    XLNetForMultipleChoice,
				    XLNetForQuestionAnswering,
				    XLNetForQuestionAnsweringSimple,
				    XLNetForSequenceClassification,
				    XLNetForTokenClassification,
				    XLNetLMHeadModel,
				    XLNetModel,
				    XLNetPreTrainedModel,
				    load_tf_weights_in_xlnet,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_xlnet import (
				    TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFXLNetForMultipleChoice,
				    TFXLNetForQuestionAnsweringSimple,
				    TFXLNetForSequenceClassification,
				    TFXLNetForTokenClassification,
				    TFXLNetLMHeadModel,
				    TFXLNetMainLayer,
				    TFXLNetModel,
				    TFXLNetPreTrainedModel,
				)

else:
		import sys

		_A				:    str     						=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
