



import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer



def 		lowerCAmelCase__     (       UpperCamelCase_					:							List[str]						)->	List[Any]:
    # 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 		lowerCAmelCase__     (       UpperCamelCase_					:							str						)->	Optional[int]:
    # word like '180' or '身高' or '神'
    for char in word:
        A__					=       ord(UpperCamelCase_						)
        if not _is_chinese_char(UpperCamelCase_						):
            return 0
    return 1



def 		lowerCAmelCase__     (       UpperCamelCase_					:							List[str]						)->	Union[str, Any]:
    A__					=       set()

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



def 		lowerCAmelCase__     (       UpperCamelCase_					:							List[str]			,							UpperCamelCase_					:							set()						)->	Dict:
    if not chinese_word_set:
        return bert_tokens
    A__					=       max([len(UpperCamelCase_						) for w in chinese_word_set]						)

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



def 		lowerCAmelCase__     (       UpperCamelCase_					:							List[str]			,							UpperCamelCase_					:							LTP			,							UpperCamelCase_					:							BertTokenizer						)->	Optional[int]:
    A__					=       []

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

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

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

    A__					=       prepare_ref(UpperCamelCase_			,							UpperCamelCase_			,							UpperCamelCase_						)

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


if __name__ == "__main__":
   _lowercase    	=		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    	=		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
