"""simple docstring"""


import argparse
import json
from typing import List

from ltp import LTP

from transformers import BertTokenizer






def      __UpperCAmelCase       (      lowercase		):







  """simple docstring"""
  # 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      __UpperCAmelCase       (      lowercase		):







  """simple docstring"""
  # word like '180' or '身高' or '神'
  for char in word:
    _UpperCAmelCase    			=   ord(lowercase		)
    if not _is_chinese_char(lowercase		):
      return 0
  return 1






def      __UpperCAmelCase       (      lowercase		):







  """simple docstring"""
  _UpperCAmelCase    			=   set()

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






def      __UpperCAmelCase       (      lowercase ,lowercase		):







  """simple docstring"""
  if not chinese_word_set:
    return bert_tokens
  _UpperCAmelCase    			=   max([len(lowercase		) for w in chinese_word_set]		)

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






def      __UpperCAmelCase       (      lowercase ,lowercase ,lowercase		):







  """simple docstring"""
  _UpperCAmelCase    			=   []

  for i in range(0 ,len(lowercase		) ,1_00		):
    _UpperCAmelCase    			=   ltp_tokenizer.seg(lines[i : i + 1_00]		)[0]
    _UpperCAmelCase    			=   [get_chinese_word(lowercase		) for r in res]
    ltp_res.extend(lowercase		)
  assert len(lowercase		) == len(lowercase		)

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

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

  assert len(lowercase		) == len(lowercase		)

  return ref_ids






def      __UpperCAmelCase       (      lowercase		):







  """simple docstring"""
  # 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    			=   f.readlines()
  _UpperCAmelCase    			=   [line.strip() for line in data if len(lowercase		) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
  _UpperCAmelCase    			=   LTP(args.ltp		)  # faster in GPU device
  _UpperCAmelCase    			=   BertTokenizer.from_pretrained(args.bert		)

  _UpperCAmelCase    			=   prepare_ref(lowercase ,lowercase ,lowercase		)

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


if __name__ == "__main__":
    UpperCAmelCase__			=    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""")

    UpperCAmelCase__			=    parser.parse_args()
    main(args)

"""simple docstring"""


import heapq






def      __UpperCAmelCase       (      lowercase		):







  """simple docstring"""
  _UpperCAmelCase    			=   []

  # for each node and his adjacency list add them and the rank of the node to queue
  # using heapq module the queue will be filled like a Priority Queue
  # heapq works with a min priority queue, so I used -1*len(v) to build it
  for key, value in graph.items():
    # O(log(n))
    heapq.heappush(lowercase ,[-1 * len(lowercase		), (key, value)]		)

  # chosen_vertices = set of chosen vertices
  _UpperCAmelCase    			=   set()

  # while queue isn't empty and there are still edges
  #   (queue[0][0] is the rank of the node with max rank)
  while queue and queue[0][0] != 0:
    # extract vertex with max rank from queue and add it to chosen_vertices
    _UpperCAmelCase    			=   heapq.heappop(lowercase		)[1][0]
    chosen_vertices.add(lowercase		)

    # Remove all arcs adjacent to argmax
    for elem in queue:
      # if v haven't adjacent node, skip
      if elem[0] == 0:
        continue
      # if argmax is reachable from elem
      # remove argmax from elem's adjacent list and update his rank
      if argmax in elem[1][1]:
        _UpperCAmelCase    			=   elem[1][1].index(lowercase		)
        del elem[1][1][index]
        elem[0] += 1
        # re-order the queue
    heapq.heapify(lowercase		)
  return chosen_vertices


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    UpperCAmelCase__			=    {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
    print(F'''Minimum vertex cover:\n{greedy_min_vertex_cover(graph)}''')
