
"""simple docstring"""
import argparse
import json
from typing import List

from ltp import LTP

from transformers import BertTokenizer





def    _lowerCAmelCase    (     lowerCamelCase__							:							List[str]				)  ->    Optional[Any]:




  if (
      (cp >= 0x4E00 and cp <= 0x9FFF)
      or (cp >= 0x3400 and cp <= 0x4DBF)  #
      or (cp >= 0x2_0000 and cp <= 0x2_A6DF)  #
      or (cp >= 0x2_A700 and cp <= 0x2_B73F)  #
      or (cp >= 0x2_B740 and cp <= 0x2_B81F)  #
      or (cp >= 0x2_B820 and cp <= 0x2_CEAF)  #
      or (cp >= 0xF900 and cp <= 0xFAFF)
      or (cp >= 0x2_F800 and cp <= 0x2_FA1F)  #
  ):  #
    return True

  return False





def    _lowerCAmelCase    (     lowerCamelCase__							:							str				)  ->    Tuple:




  for char in word:
    _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = ord(_A				)
    if not _is_chinese_char(_A				):
      return 0
  return 1





def    _lowerCAmelCase    (     lowerCamelCase__							:							List[str]				)  ->    List[str]:
  _SCREAMING_SNAKE_CASE   :				Any							    = set()

  for token in tokens:
    _SCREAMING_SNAKE_CASE   :				Optional[int]							    = len(_A				) > 1 and is_chinese(_A				)
    if chinese_word:
      word_set.add(_A				)
  _SCREAMING_SNAKE_CASE   :				Dict							    = list(_A				)
  return word_list





def    _lowerCAmelCase    (     lowerCamelCase__							:							List[str],						lowerCamelCase__							:							set()				)  ->    Tuple:




  if not chinese_word_set:
    return bert_tokens
  _SCREAMING_SNAKE_CASE   :				List[str]							    = max([len(_A				) for w in chinese_word_set]				)

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





def    _lowerCAmelCase    (     lowerCamelCase__							:							List[str],						lowerCamelCase__							:							LTP,						lowerCamelCase__							:							BertTokenizer				)  ->    int:
  _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = []

  for i in range(0,						len(_A				),						1_0_0				):
    _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = ltp_tokenizer.seg(lines[i : i + 1_0_0]				)[0]
    _SCREAMING_SNAKE_CASE   :				int							    = [get_chinese_word(_A				) for r in res]
    ltp_res.extend(_A				)
  assert len(_A				) == len(_A				)

  _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = []
  for i in range(0,						len(_A				),						1_0_0				):
    _SCREAMING_SNAKE_CASE   :				int							    = bert_tokenizer(lines[i : i + 1_0_0],						add_special_tokens=_A,						truncation=_A,						max_length=5_1_2				)
    bert_res.extend(res["input_ids"]				)
  assert len(_A				) == len(_A				)

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

  assert len(_A				) == len(_A				)

  return ref_ids





def    _lowerCAmelCase    (     lowerCamelCase__							:							Dict				)  ->    Optional[int]:




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

  _SCREAMING_SNAKE_CASE   :				Dict							    = prepare_ref(_A,						_A,						_A				)

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


if __name__ == "__main__":
      lowercase_       :						Optional[int]     					=	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_       :						Any     					=	parser.parse_args()
      main(args)





"""simple docstring"""
import os

import pytest
import yaml

from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict





@pytest.mark.parametrize(
    "files",						[
        ["full:README.md", "dataset_infos.json"],
        ["empty:README.md", "dataset_infos.json"],
        ["dataset_infos.json"],
        ["full:README.md"],
    ],						)
def    _lowerCAmelCase    (     lowerCamelCase__							:							str,						lowerCamelCase__							:							str				)  ->    Optional[int]:
  _SCREAMING_SNAKE_CASE   :				Optional[int]							    = tmp_path_factory.mktemp("dset_infos_dir"				)
  if "full:README.md" in files:
    with open(dataset_infos_dir / "README.md",						"w"				) as f:
      f.write("---\ndataset_info:\n  dataset_size: 42\n---"				)
  if "empty:README.md" in files:
    with open(dataset_infos_dir / "README.md",						"w"				) as f:
      f.write(""				)
    # we want to support dataset_infos.json for backward compatibility
  if "dataset_infos.json" in files:
    with open(dataset_infos_dir / "dataset_infos.json",						"w"				) as f:
      f.write("{\"default\": {\"dataset_size\": 42}}"				)
  _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = DatasetInfosDict.from_directory(lowerCamelCase__				)
  assert dataset_infos
  assert dataset_infos["default"].dataset_size == 4_2





@pytest.mark.parametrize(
    "dataset_info",						[
        DatasetInfo(),
        DatasetInfo(
            description="foo",						features=Features({"a": Value("int32"				)}				),						builder_name="builder",						config_name="config",						version="1.0.0",						splits=[{"name": "train"}],						download_size=4_2,						),
    ],						)
def    _lowerCAmelCase    (     lowerCamelCase__							:							Union[str, Any],						lowerCamelCase__							:							DatasetInfo				)  ->    List[Any]:
  _SCREAMING_SNAKE_CASE   :				Dict							    = str(lowerCamelCase__				)
  dataset_info.write_to_directory(lowerCamelCase__				)
  _SCREAMING_SNAKE_CASE   :				int							    = DatasetInfo.from_directory(lowerCamelCase__				)
  assert dataset_info == reloaded
  assert os.path.exists(os.path.join(lowerCamelCase__,						"dataset_info.json"				)				)





def    _lowerCAmelCase    (     )  ->    Union[str, Any]:
  _SCREAMING_SNAKE_CASE   :				str							    = DatasetInfo(
      description="foo",						citation="bar",						homepage="https://foo.bar",						license="CC0",						features=Features({"a": Value("int32"				)}				),						post_processed={},						supervised_keys=(),						task_templates=[],						builder_name="builder",						config_name="config",						version="1.0.0",						splits=[{"name": "train", "num_examples": 4_2}],						download_checksums={},						download_size=1_3_3_7,						post_processing_size=4_4_2,						dataset_size=1_2_3_4,						size_in_bytes=1_3_3_7 + 4_4_2 + 1_2_3_4,						)
  _SCREAMING_SNAKE_CASE   :				List[str]							    = dataset_info._to_yaml_dict()
  assert sorted(lowerCamelCase__				) == sorted(DatasetInfo._INCLUDED_INFO_IN_YAML				)
  for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
    assert key in dataset_info_yaml_dict
    assert isinstance(dataset_info_yaml_dict[key],						(list, dict, int, str)				)
  _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = yaml.safe_dump(lowerCamelCase__				)
  _SCREAMING_SNAKE_CASE   :				List[Any]							    = yaml.safe_load(lowerCamelCase__				)
  assert dataset_info_yaml_dict == reloaded





def    _lowerCAmelCase    (     )  ->    Tuple:
  _SCREAMING_SNAKE_CASE   :				Dict							    = DatasetInfo()
  _SCREAMING_SNAKE_CASE   :				List[Any]							    = dataset_info._to_yaml_dict()
  assert dataset_info_yaml_dict == {}





@pytest.mark.parametrize(
    "dataset_infos_dict",						[
        DatasetInfosDict(),
        DatasetInfosDict({"default": DatasetInfo()}				),
        DatasetInfosDict({"my_config_name": DatasetInfo()}				),
        DatasetInfosDict(
            {
                "default": DatasetInfo(
                    description="foo",						features=Features({"a": Value("int32"				)}				),						builder_name="builder",						config_name="config",						version="1.0.0",						splits=[{"name": "train"}],						download_size=4_2,						)
            }				),
        DatasetInfosDict(
            {
                "v1": DatasetInfo(dataset_size=4_2				),
                "v2": DatasetInfo(dataset_size=1_3_3_7				),
            }				),
    ],						)
def    _lowerCAmelCase    (     lowerCamelCase__							:							Any,						lowerCamelCase__							:							DatasetInfosDict				)  ->    Optional[int]:
  _SCREAMING_SNAKE_CASE   :				str							    = str(lowerCamelCase__				)
  dataset_infos_dict.write_to_directory(lowerCamelCase__				)
  _SCREAMING_SNAKE_CASE   :				str							    = DatasetInfosDict.from_directory(lowerCamelCase__				)

  # the config_name of the dataset_infos_dict take over the attribute
  for config_name, dataset_info in dataset_infos_dict.items():
    _SCREAMING_SNAKE_CASE   :				List[Any]							    = config_name
    # the yaml representation doesn't include fields like description or citation
    # so we just test that we can recover what we can from the yaml
    _SCREAMING_SNAKE_CASE   :				Dict							    = DatasetInfo._from_yaml_dict(dataset_info._to_yaml_dict()				)
  assert dataset_infos_dict == reloaded

  if dataset_infos_dict:
    assert os.path.exists(os.path.join(lowerCamelCase__,						"README.md"				)				)



