'''simple docstring'''


import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer





def       lowerCAmelCase					(		UpperCamelCase__				:   List[str]       ):


	"""simple docstring"""
	# 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 >= 0X4_E00 and cp <= 0X9_FFF)
	    or (cp >= 0X3_400 and cp <= 0X4_DBF)  #
	    or (cp >= 0X20_000 and cp <= 0X2A_6DF)  #
	    or (cp >= 0X2A_700 and cp <= 0X2B_73F)  #
	    or (cp >= 0X2B_740 and cp <= 0X2B_81F)  #
	    or (cp >= 0X2B_820 and cp <= 0X2C_EAF)  #
	    or (cp >= 0XF_900 and cp <= 0XF_AFF)
	    or (cp >= 0X2F_800 and cp <= 0X2F_A1F)  #
	):  #
		return True

	return False





def       lowerCAmelCase					(		UpperCamelCase__				:   str       ):


	"""simple docstring"""
	# word like '180' or '身高' or '神'
	for char in word:
		__UpperCAmelCase							=			ord(UpperCamelCase__       )
		if not _is_chinese_char(UpperCamelCase__       ):
			return 0
	return 1





def       lowerCAmelCase					(		UpperCamelCase__				:   List[str]       ):


	"""simple docstring"""
	__UpperCAmelCase							=			set()

	for token in tokens:
		__UpperCAmelCase							=			len(UpperCamelCase__       ) > 1 and is_chinese(UpperCamelCase__       )
		if chinese_word:
			word_set.add(UpperCamelCase__       )
	__UpperCAmelCase							=			list(UpperCamelCase__       )
	return word_list





def       lowerCAmelCase					(		UpperCamelCase__				:   List[str]    ,					UpperCamelCase__				:   set()       ):


	"""simple docstring"""
	if not chinese_word_set:
		return bert_tokens
	__UpperCAmelCase							=			max([len(UpperCamelCase__       ) for w in chinese_word_set]       )

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





def       lowerCAmelCase					(		UpperCamelCase__				:   List[str]    ,					UpperCamelCase__				:   LTP    ,					UpperCamelCase__				:   BertTokenizer       ):


	"""simple docstring"""
	__UpperCAmelCase							=			[]

	for i in range(0    ,					len(UpperCamelCase__       )    ,					1_0_0       ):
		__UpperCAmelCase							=			ltp_tokenizer.pipeline(lines[i : i + 1_0_0]    ,					tasks=['''cws''']       ).cws
		__UpperCAmelCase							=			[get_chinese_word(UpperCamelCase__       ) for r in res]
		ltp_res.extend(UpperCamelCase__       )
	assert len(UpperCamelCase__       ) == len(UpperCamelCase__       )

	__UpperCAmelCase							=			[]
	for i in range(0    ,					len(UpperCamelCase__       )    ,					1_0_0       ):
		__UpperCAmelCase							=			bert_tokenizer(lines[i : i + 1_0_0]    ,					add_special_tokens=UpperCamelCase__    ,					truncation=UpperCamelCase__    ,					max_length=5_1_2       )
		bert_res.extend(res['''input_ids''']       )
	assert len(UpperCamelCase__       ) == len(UpperCamelCase__       )

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

	assert len(UpperCamelCase__       ) == len(UpperCamelCase__       )

	return ref_ids





def       lowerCAmelCase					(		UpperCamelCase__				:   List[str]       ):


	"""simple docstring"""
	# 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:
		__UpperCAmelCase							=			f.readlines()
	__UpperCAmelCase							=			[line.strip() for line in data if len(UpperCamelCase__       ) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
	__UpperCAmelCase							=			LTP(args.ltp       )  # faster in GPU device
	__UpperCAmelCase							=			BertTokenizer.from_pretrained(args.bert       )

	__UpperCAmelCase							=			prepare_ref(UpperCamelCase__    ,					UpperCamelCase__    ,					UpperCamelCase__       )

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


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

					__lowerCAmelCase	:       Optional[Any]						       =	parser.parse_args()
					main(args)

'''simple docstring'''


from pathlib import Path

import fire





def       lowerCAmelCase					(		UpperCamelCase__				:   str    ,					UpperCamelCase__				:   str    ,					UpperCamelCase__				:   int       ):


	"""simple docstring"""
	__UpperCAmelCase							=			Path(UpperCamelCase__       )
	__UpperCAmelCase							=			Path(UpperCamelCase__       )
	dest_dir.mkdir(exist_ok=UpperCamelCase__       )
	for path in src_dir.iterdir():
		__UpperCAmelCase							=			[x.rstrip() for x in list(path.open().readlines()       )][:n]
		__UpperCAmelCase							=			dest_dir.joinpath(path.name       )
		print(UpperCamelCase__       )
		dest_path.open('''w'''       ).write('''\n'''.join(UpperCamelCase__       )       )


if __name__ == "__main__":
					fire.Fire(minify)
