
import argparse
import json
from typing import List

from ltp import LTP

from transformers import BertTokenizer



def        UpperCamelCase    (							lowerCAmelCase__		):
					'''simple docstring'''







					if (
					    (cp >= 0x4e00 and cp <= 0x9fff)
					    or (cp >= 0x3400 and cp <= 0x4dbf)  #
					    or (cp >= 0x20000 and cp <= 0x2a6df)  #
					    or (cp >= 0x2a700 and cp <= 0x2b73f)  #
					    or (cp >= 0x2b740 and cp <= 0x2b81f)  #
					    or (cp >= 0x2b820 and cp <= 0x2ceaf)  #
					    or (cp >= 0xf900 and cp <= 0xfaff)
					    or (cp >= 0x2f800 and cp <= 0x2fa1f)  #
					):  #
										return True

					return False



def        UpperCamelCase    (							lowerCAmelCase__		):
					'''simple docstring'''







					for char in word:
										lowercase							=						ord(__UpperCamelCase		)
										if not _is_chinese_char(__UpperCamelCase		):
															return 0
					return 1



def        UpperCamelCase    (							lowerCAmelCase__		):
					'''simple docstring'''
					lowercase							=						set()

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



def        UpperCamelCase    (							lowerCAmelCase__ ,			lowerCAmelCase__		):
					'''simple docstring'''







					if not chinese_word_set:
										return bert_tokens
					lowercase							=						max([len(__UpperCamelCase		) for w in chinese_word_set]		)

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



def        UpperCamelCase    (							lowerCAmelCase__ ,			lowerCAmelCase__ ,			lowerCAmelCase__		):
					'''simple docstring'''
					lowercase							=						[]

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

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

					lowercase							=						[]
					for input_ids, chinese_word in zip(__UpperCamelCase ,			__UpperCamelCase		):
										lowercase							=						[]
										for id in input_ids:
															lowercase							=						bert_tokenizer._convert_id_to_token(__UpperCamelCase		)
															input_tokens.append(__UpperCamelCase		)
										lowercase							=						add_sub_symbol(__UpperCamelCase ,			__UpperCamelCase		)
										lowercase							=						[]
										# 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] == "##":
																				lowercase							=						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        UpperCamelCase    (							lowerCAmelCase__		):
					'''simple docstring'''







					with open(args.file_name ,			'''r''' ,			encoding='''utf-8'''		) as f:
										lowercase							=						f.readlines()
					lowercase							=						[line.strip() for line in data if len(__UpperCamelCase		) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
					lowercase							=						LTP(args.ltp		)  # faster in GPU device
					lowercase							=						BertTokenizer.from_pretrained(args.bert		)

					lowercase							=						prepare_ref(__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase		)

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


if __name__ == "__main__":
							lowercase__		:List[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")

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












"""simple docstring"""


import warnings

from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor


lowercase_				     =     logging.get_logger(__name__)


class 					snake_case		(							_lowerCAmelCase						):


   '''simple docstring'''

   def __init__(	self :     Optional[int],       *_lowerCamelCase :     Union[str, Any],       **_lowerCamelCase :     Dict  ):

     '''simple docstring'''
     warnings.warn(
         '''The class SegformerFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
         ''' Please use SegformerImageProcessor instead.''',       _lowerCamelCase,       )
     super().__init__(*_lowerCamelCase,       **_lowerCamelCase  )




