

import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer

def        lowerCamelCase__       (    _a):
	# 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__       (    _a):
	# word like '180' or '身高' or '神'
	for char in word:
		SCREAMING_SNAKE_CASE      :					str    =				ord(lowerCAmelCase_)
		if not _is_chinese_char(lowerCAmelCase_):
			return 0
	return 1

def        lowerCamelCase__       (    _a):
	SCREAMING_SNAKE_CASE      :					List[Any]    =				set()

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

def        lowerCamelCase__       (    _a							,	_a):
	if not chinese_word_set:
		return bert_tokens
	SCREAMING_SNAKE_CASE      :					List[str]    =				max([len(lowerCAmelCase_) for w in chinese_word_set])

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

def        lowerCamelCase__       (    _a							,	_a							,	_a):
	SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				[]

	for i in range(0							,	len(lowerCAmelCase_)							,	100):
		SCREAMING_SNAKE_CASE      :					str    =				ltp_tokenizer.pipeline(lines[i : i + 100]							,	tasks=["cws"]).cws
		SCREAMING_SNAKE_CASE      :					Any    =				[get_chinese_word(lowerCAmelCase_) for r in res]
		ltp_res.extend(lowerCAmelCase_)
	assert len(lowerCAmelCase_) == len(lowerCAmelCase_)

	SCREAMING_SNAKE_CASE      :					str    =				[]
	for i in range(0							,	len(lowerCAmelCase_)							,	100):
		SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				bert_tokenizer(lines[i : i + 100]							,	add_special_tokens=lowerCAmelCase_							,	truncation=lowerCAmelCase_							,	max_length=512)
		bert_res.extend(res["input_ids"])
	assert len(lowerCAmelCase_) == len(lowerCAmelCase_)

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

	assert len(lowerCAmelCase_) == len(lowerCAmelCase_)

	return ref_ids

def        lowerCamelCase__       (    _a):
	# 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:
		SCREAMING_SNAKE_CASE      :					Tuple    =				f.readlines()
	SCREAMING_SNAKE_CASE      :					Optional[int]    =				[line.strip() for line in data if len(lowerCAmelCase_) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
	SCREAMING_SNAKE_CASE      :					List[Any]    =				LTP(args.ltp)  # faster in GPU device
	SCREAMING_SNAKE_CASE      :					int    =				BertTokenizer.from_pretrained(args.bert)

	SCREAMING_SNAKE_CASE      :					Tuple    =				prepare_ref(lowerCAmelCase_							,	lowerCAmelCase_							,	lowerCAmelCase_)

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


if __name__ == "__main__":
			a_     =			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',
			)

			a_     =			parser.parse_args()
			main(args)



from typing import Union

import fire
import torch
from tqdm import tqdm



def 	lowerCAmelCase       (						lowerCAmelCase_       ,      lowerCAmelCase_ = "cpu"       ,      lowerCAmelCase_ = None       )->       None:
				lowerCAmelCase_       :					str			 = torch.load(lowerCAmelCase_       ,      map_location=lowerCAmelCase_       )
				for k, v in tqdm(state_dict.items()       ):
								if not isinstance(lowerCAmelCase_       ,      torch.Tensor       ):
												raise TypeError('''FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin'''       )
								lowerCAmelCase_       :					int			 = v.half()
				if save_path is None:  # overwrite src_path
								lowerCAmelCase_       :					Tuple			 = src_path
				torch.save(lowerCAmelCase_       ,      lowerCAmelCase_       )


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