
import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer







def 						_snake_case(  SCREAMING_SNAKE_CASE__				) ->    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_e_0_0 and cp <= 0x9_f_f_f)
           or (cp >= 0x3_4_0_0 and cp <= 0x4_d_b_f)  #
           or (cp >= 0x2_0_0_0_0 and cp <= 0x2_a_6_d_f)  #
           or (cp >= 0x2_a_7_0_0 and cp <= 0x2_b_7_3_f)  #
           or (cp >= 0x2_b_7_4_0 and cp <= 0x2_b_8_1_f)  #
           or (cp >= 0x2_b_8_2_0 and cp <= 0x2_c_e_a_f)  #
           or (cp >= 0xf_9_0_0 and cp <= 0xf_a_f_f)
           or (cp >= 0x2_f_8_0_0 and cp <= 0x2_f_a_1_f)  #
       ):  #
              return True

       return False







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







def 						_snake_case(  SCREAMING_SNAKE_CASE__				) ->    List[Any]:
       lowercase							:     Union[str, Any]       	=      set()

       for token in tokens:
              lowercase							:     List[str]       	=      len(SCREAMING_SNAKE_CASE__				) > 1 and is_chinese(SCREAMING_SNAKE_CASE__				)
              if chinese_word:
                     word_set.add(SCREAMING_SNAKE_CASE__				)
       lowercase							:     Union[str, Any]       	=      list(SCREAMING_SNAKE_CASE__				)
       return word_list







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    int:
       if not chinese_word_set:
              return bert_tokens
       lowercase							:     List[str]       	=      max([len(SCREAMING_SNAKE_CASE__				) for w in chinese_word_set]				)

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







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    str:
       lowercase							:     Any       	=      []

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

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

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

       assert len(SCREAMING_SNAKE_CASE__				) == len(SCREAMING_SNAKE_CASE__				)

       return ref_ids







def 						_snake_case(  SCREAMING_SNAKE_CASE__				) ->    int:
       # 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:
              lowercase							:     Dict       	=      f.readlines()
       lowercase							:     Tuple       	=      [line.strip() for line in data if len(SCREAMING_SNAKE_CASE__				) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
       lowercase							:     Tuple       	=      LTP(args.ltp				)  # faster in GPU device
       lowercase							:     Dict       	=      BertTokenizer.from_pretrained(args.bert				)

       lowercase							:     Any       	=      prepare_ref(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)

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


if __name__ == "__main__":
  lowercase  :					Tuple						    =							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""",
  )

  lowercase  :					Tuple						    =							parser.parse_args()
  main(args)


from __future__ import annotations

from collections import namedtuple
from dataclasses import dataclass

@dataclass
class __snake_case		:
    _a		:						int
    _a		:						TreeNode | None=				None
    _a		:						TreeNode | None=				None






lowercase  :					Dict						    =							namedtuple("""CoinsDistribResult""", """moves excess""")







def 						_snake_case(  SCREAMING_SNAKE_CASE__				) ->    int:

       if root is None:
              return 0

       # Validation
       def count_nodes(SCREAMING_SNAKE_CASE__				) -> int:
              if node is None:
                     return 0

              return count_nodes(node.left				) + count_nodes(node.right				) + 1

       def count_coins(SCREAMING_SNAKE_CASE__				) -> int:
              if node is None:
                     return 0

              return count_coins(node.left				) + count_coins(node.right				) + node.data

       if count_nodes(SCREAMING_SNAKE_CASE__				) != count_coins(SCREAMING_SNAKE_CASE__				):
              raise ValueError("""The nodes number should be same as the number of coins"""				)

       # Main calculation
       def get_distrib(SCREAMING_SNAKE_CASE__				) -> CoinsDistribResult:

              if node is None:
                     return CoinsDistribResult(0					,	1				)

              lowercase	,			lowercase							:     int       	=      get_distrib(node.left				)
              lowercase	,			lowercase							:     List[Any]       	=      get_distrib(node.right				)

              lowercase							:     Optional[Any]       	=      1 - left_distrib_excess
              lowercase							:     Union[str, Any]       	=      1 - right_distrib_excess

              lowercase							:     List[Any]       	=      (
                  left_distrib_moves
                  + right_distrib_moves
                  + abs(SCREAMING_SNAKE_CASE__				)
                  + abs(SCREAMING_SNAKE_CASE__				)
              )
              lowercase							:     Any       	=      node.data - coins_to_left - coins_to_right

              return CoinsDistribResult(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)

       return get_distrib(SCREAMING_SNAKE_CASE__				)[0]


if __name__ == "__main__":
  import doctest

  doctest.testmod()
