





'''simple docstring'''

import argparse
import json
from typing import List

from ltp import LTP

from transformers import BertTokenizer





def 						UpperCamelCase    (							_lowerCamelCase : 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 >= 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 						UpperCamelCase    (							_lowerCamelCase : str	):
    # word like '180' or '身高' or '神'
    for char in word:
        A__    =      ord(_lowerCamelCase	)
        if not _is_chinese_char(_lowerCamelCase	):
            return 0
    return 1





def 						UpperCamelCase    (							_lowerCamelCase : List[str]	):
    A__    =      set()

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





def 						UpperCamelCase    (							_lowerCamelCase : List[str]						,	_lowerCamelCase : set()	):
    if not chinese_word_set:
        return bert_tokens
    A__    =      max([len(_lowerCamelCase	) for w in chinese_word_set]	)

    A__    =      bert_tokens
    A__,     A__    =      0, len(_lowerCamelCase	)
    while start < end:
        A__    =      True
        if is_chinese(bert_word[start]	):
            A__    =      min(end - start						,	_lowerCamelCase	)
            for i in range(_lowerCamelCase						,	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 						UpperCamelCase    (							_lowerCamelCase : List[str]						,	_lowerCamelCase : LTP						,	_lowerCamelCase : BertTokenizer	):
    A__    =      []

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

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

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

    assert len(_lowerCamelCase	) == len(_lowerCamelCase	)

    return ref_ids





def 						UpperCamelCase    (							_lowerCamelCase : 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(_lowerCamelCase	) > 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(_lowerCamelCase						,	_lowerCamelCase						,	_lowerCamelCase	)

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


if __name__ == "__main__":
  __lowerCAmelCase							:     Dict		     =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")

  __lowerCAmelCase							:     List[Any]		     =parser.parse_args()
  main(args)










'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__lowerCAmelCase							:     Any		     ={
    "configuration_swinv2": ["SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP", "Swinv2Config"],
}


try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __lowerCAmelCase							:     Tuple		     =[
      "SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST",
      "Swinv2ForImageClassification",
      "Swinv2ForMaskedImageModeling",
      "Swinv2Model",
      "Swinv2PreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_swinva import SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinvaConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_swinva import (
        SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST,
        SwinvaForImageClassification,
        SwinvaForMaskedImageModeling,
        SwinvaModel,
        SwinvaPreTrainedModel,
    )


else:
  import sys

  __lowerCAmelCase							:     List[Any]		     =_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



