



import argparse
import json
from typing import List

from ltp import LTP

from transformers import BertTokenizer






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase )	->						Tuple:
				# 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     SCREAMING_SNAKE_CASE(      __UpperCamelCase )	->						Dict:
				# word like '180' or '身高' or '神'
				for char in word:
								a__	:							List[Any]      						= ord(__UpperCamelCase )
								if not _is_chinese_char(__UpperCamelCase ):
												return 0
				return 1






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase )	->						Optional[Any]:
				a__	:							Optional[int]      						= set()

				for token in tokens:
								a__	:							int      						= len(__UpperCamelCase ) > 1 and is_chinese(__UpperCamelCase )
								if chinese_word:
												word_set.add(__UpperCamelCase )
				a__	:							str      						= list(__UpperCamelCase )
				return word_list






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase	,				__UpperCamelCase )	->						Optional[Any]:
				if not chinese_word_set:
								return bert_tokens
				a__	:							List[Any]      						= max([len(__UpperCamelCase ) for w in chinese_word_set] )

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






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase )	->						Union[str, Any]:
				a__	:							Optional[Any]      						= []

				for i in range(0	,				len(__UpperCamelCase )	,				1_00 ):
								a__	:							List[Any]      						= ltp_tokenizer.seg(lines[i : i + 1_00] )[0]
								a__	:							List[str]      						= [get_chinese_word(__UpperCamelCase ) for r in res]
								ltp_res.extend(__UpperCamelCase )
				assert len(__UpperCamelCase ) == len(__UpperCamelCase )

				a__	:							Dict      						= []
				for i in range(0	,				len(__UpperCamelCase )	,				1_00 ):
								a__	:							List[str]      						= bert_tokenizer(lines[i : i + 1_00]	,				add_special_tokens=__UpperCamelCase	,				truncation=__UpperCamelCase	,				max_length=5_12 )
								bert_res.extend(res["input_ids"] )
				assert len(__UpperCamelCase ) == len(__UpperCamelCase )

				a__	:							Optional[int]      						= []
				for input_ids, chinese_word in zip(__UpperCamelCase	,				__UpperCamelCase ):
								a__	:							str      						= []
								for id in input_ids:
												a__	:							Optional[int]      						= bert_tokenizer._convert_id_to_token(__UpperCamelCase )
												input_tokens.append(__UpperCamelCase )
								a__	:							str      						= add_sub_symbol(__UpperCamelCase	,				__UpperCamelCase )
								a__	:							int      						= []
								# 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] == "##":
																a__	:							Tuple      						= 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     SCREAMING_SNAKE_CASE(      __UpperCamelCase )	->						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:
								a__	:							Optional[int]      						= f.readlines()
				a__	:							Union[str, Any]      						= [line.strip() for line in data if len(__UpperCamelCase ) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
				a__	:							Any      						= LTP(args.ltp )  # faster in GPU device
				a__	:							Union[str, Any]      						= BertTokenizer.from_pretrained(args.bert )

				a__	:							Optional[Any]      						= prepare_ref(__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase )

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


if __name__ == "__main__":
						lowerCamelCase		       =      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""")

						lowerCamelCase		       =      parser.parse_args()
						main(args)







# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
#   python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# since the output of this script is fed into Makefile commands it doesn't print a newline after the results

import re
import subprocess
import sys


lowerCamelCase		       =      subprocess.check_output("""git merge-base main HEAD""".split()).decode("""utf-8""")
lowerCamelCase		       =      subprocess.check_output(F'git diff --name-only {fork_point_sha}'.split()).decode("""utf-8""").split()

lowerCamelCase		       =      """|""".join(sys.argv[1:])
lowerCamelCase		       =      re.compile(RF'^({joined_dirs}).*?\.py$')

lowerCamelCase		       =      [x for x in modified_files if regex.match(x)]
print(""" """.join(relevant_modified_files), end="""""")


