

import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer







def 						_lowercase					(	lowercase__		):
      # 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 						_lowercase					(	lowercase__		):
      # word like '180' or '身高' or '神'
      for char in word:
            __lowerCAmelCase   :		Optional[int]   =   ord(UpperCamelCase_		)
            if not _is_chinese_char(UpperCamelCase_		):
                  return 0
      return 1







def 						_lowercase					(	lowercase__		):
      __lowerCAmelCase   :		Optional[int]   =   set()

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







def 						_lowercase					(	lowercase__  ,      lowercase__		):
      if not chinese_word_set:
            return bert_tokens
      __lowerCAmelCase   :		List[str]   =   max([len(UpperCamelCase_		) for w in chinese_word_set]		)

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







def 						_lowercase					(	lowercase__  ,      lowercase__  ,      lowercase__		):
      __lowerCAmelCase   :		Optional[int]   =   []

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

      __lowerCAmelCase   :		int   =   []
      for i in range(0  ,      len(UpperCamelCase_		)  ,      1_0_0		):
            __lowerCAmelCase   :		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_		)

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

      __lowerCAmelCase   :		Tuple   =   prepare_ref(UpperCamelCase_  ,      UpperCamelCase_  ,      UpperCamelCase_		)

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


if __name__ == "__main__":
 _UpperCamelCase					     =						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",
 )

 _UpperCamelCase					     =						parser.parse_args()
 main(args)







# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position

_lowercase    	=		"2.13.1"

import platform

import pyarrow
from packaging import version


if version.parse(platform.python_version()) < version.parse("3.7"):
   raise ImportWarning(
       "To use `datasets`, Python>=3.7 is required, and the current version of Python doesn't match this condition."
   )

if version.parse(pyarrow.__version__).major < 8:
   raise ImportWarning(
       "To use `datasets`, the module `pyarrow>=8.0.0` is required, and the current version of `pyarrow` doesn't match this condition.\n"
       "If you are running this in a Google Colab, you should probably just restart the runtime to use the right version of `pyarrow`."
   )

del platform
del pyarrow
del version

from .arrow_dataset import Dataset
from .arrow_reader import ReadInstruction
from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder
from .combine import concatenate_datasets, interleave_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .download import *
from .features import *
from .fingerprint import disable_caching, enable_caching, is_caching_enabled, set_caching_enabled
from .info import DatasetInfo, MetricInfo
from .inspect import (
    get_dataset_config_info,
    get_dataset_config_names,
    get_dataset_infos,
    get_dataset_split_names,
    inspect_dataset,
    inspect_metric,
    list_datasets,
    list_metrics,
)
from .iterable_dataset import IterableDataset
from .load import load_dataset, load_dataset_builder, load_from_disk, load_metric
from .metric import Metric
from .splits import (
    NamedSplit,
    NamedSplitAll,
    Split,
    SplitBase,
    SplitDict,
    SplitGenerator,
    SplitInfo,
    SubSplitInfo,
    percent,
)
from .tasks import *
from .utils import *
from .utils import logging


# deprecated modules
from datasets import arrow_dataset as _arrow_dataset  # isort:skip
from datasets import utils as _utils  # isort:skip
from datasets.utils import download_manager as _deprecated_download_manager  # isort:skip

_lowercase    	=		concatenate_datasets
_lowercase    	=		DownloadConfig
_lowercase    	=		DownloadManager
_lowercase    	=		DownloadMode
_lowercase    	=		DownloadConfig
_lowercase    	=		DownloadMode
_lowercase    	=		DownloadManager

del _arrow_dataset, _utils, _deprecated_download_manager
