
import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer


def 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__		):
      # 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 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__		):
      # word like '180' or '身高' or '神'
      for char in word:
            UpperCamelCase__     : Optional[Any]											=					ord(UpperCamelCase__		)
            if not _is_chinese_char(UpperCamelCase__		):
                  return 0
      return 1


def 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__		):
      UpperCamelCase__     : List[Any]											=					set()

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


def 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__			,		UpperCamelCase__		):
      if not chinese_word_set:
            return bert_tokens
      UpperCamelCase__     : str											=					max([len(UpperCamelCase__		) for w in chinese_word_set]		)

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


def 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__		):
      UpperCamelCase__     : Optional[Any]											=					[]

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

      UpperCamelCase__     : Optional[Any]											=					[]
      for i in range(0			,		len(UpperCamelCase__		)			,		1_0_0		):
            UpperCamelCase__     : List[str]											=					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__     : Any											=					[]
      for input_ids, chinese_word in zip(UpperCamelCase__			,		UpperCamelCase__		):
            UpperCamelCase__     : Tuple											=					[]
            for id in input_ids:
                  UpperCamelCase__     : Dict											=					bert_tokenizer._convert_id_to_token(UpperCamelCase__		)
                  input_tokens.append(UpperCamelCase__		)
            UpperCamelCase__     : str											=					add_sub_symbol(UpperCamelCase__			,		UpperCamelCase__		)
            UpperCamelCase__     : Dict											=					[]
            # 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__     : int											=					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__		):
      # 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__     : str											=					f.readlines()
      UpperCamelCase__     : List[str]											=					[line.strip() for line in data if len(UpperCamelCase__		) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
      UpperCamelCase__     : int											=					LTP(args.ltp		)  # faster in GPU device
      UpperCamelCase__     : Optional[Any]											=					BertTokenizer.from_pretrained(args.bert		)

      UpperCamelCase__     : int											=					prepare_ref(UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__		)

      with open(args.save_path			,		'''w'''			,		encoding='''utf-8'''		) as f:
            UpperCamelCase__     : List[str]											=					[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",
        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  =parser.parse_args()
    main(args)




from __future__ import annotations

from collections.abc import Iterable, Iterator
from dataclasses import dataclass

lowerCamelCase  =(3, 9, -1_1, 0, 7, 5, 1, -1)
lowerCamelCase  =(4, 6, 2, 0, 8, 1_0, 3, -2)




@dataclass
class 					_lowerCamelCase			:





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		 =	42
     SCREAMING_SNAKE_CASE_		 =	42




class 					_lowerCamelCase			:





     """simple docstring"""







     def __init__(				self    ,    __SCREAMING_SNAKE_CASE	)	-> None:




           """simple docstring"""







           UpperCamelCase__     : Node | None											=					None
           for i in sorted(__SCREAMING_SNAKE_CASE    ,    reverse=__SCREAMING_SNAKE_CASE	):
                 UpperCamelCase__     : List[str]											=					Node(__SCREAMING_SNAKE_CASE    ,    self.head	)






     def __iter__(				self	)	-> Iterator[int]:




           """simple docstring"""







           UpperCamelCase__     : int											=					self.head
           while node:
                 yield node.data
                 UpperCamelCase__     : List[Any]											=					node.next_node






     def __len__(				self	)	-> int:




           """simple docstring"""







           return sum(1 for _ in self	)






     def __str__(				self	)	-> str:




           """simple docstring"""







           return " -> ".join([str(__SCREAMING_SNAKE_CASE	) for node in self]	)


def 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__			,		UpperCamelCase__		):
      return SortedLinkedList(list(UpperCamelCase__		) + list(UpperCamelCase__		)		)


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    lowerCamelCase  =SortedLinkedList
    print(merge_lists(SSL(test_data_odd), SSL(test_data_even)))


