





"""simple docstring"""


import argparse
import json
from typing import List

from ltp import LTP

from transformers import BertTokenizer




def        lowercase__     (       lowercase_      )       ->		Any:




      """simple docstring"""

      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        lowercase__     (       lowercase_      )       ->		Optional[Any]:




      """simple docstring"""

      for char in word:
            _UpperCamelCase   :						List[str]    =							ord(lowercase_      )
            if not _is_chinese_char(lowercase_      ):
                  return 0
      return 1




def        lowercase__     (       lowercase_      )       ->		Dict:




      """simple docstring"""

      _UpperCamelCase   :						List[Any]    =							set()

      for token in tokens:
            _UpperCamelCase   :						Union[str, Any]    =							len(lowercase_      ) > 1 and is_chinese(lowercase_      )
            if chinese_word:
                  word_set.add(lowercase_      )
      _UpperCamelCase   :						List[Any]    =							list(lowercase_      )
      return word_list




def        lowercase__     (       lowercase_		,lowercase_      )       ->		Optional[Any]:




      """simple docstring"""

      if not chinese_word_set:
            return bert_tokens
      _UpperCamelCase   :						Tuple    =							max([len(lowercase_      ) for w in chinese_word_set]      )

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




def        lowercase__     (       lowercase_		,lowercase_		,lowercase_      )       ->		List[str]:




      """simple docstring"""

      _UpperCamelCase   :						Optional[Any]    =							[]

      for i in range(0		,len(lowercase_      )		,100      ):
            _UpperCamelCase   :						Dict    =							ltp_tokenizer.seg(lines[i : i + 100]      )[0]
            _UpperCamelCase   :						int    =							[get_chinese_word(lowercase_      ) for r in res]
            ltp_res.extend(lowercase_      )
      assert len(lowercase_      ) == len(lowercase_      )

      _UpperCamelCase   :						Any    =							[]
      for i in range(0		,len(lowercase_      )		,100      ):
            _UpperCamelCase   :						str    =							bert_tokenizer(lines[i : i + 100]		,add_special_tokens=lowercase_		,truncation=lowercase_		,max_length=512      )
            bert_res.extend(res["input_ids"]      )
      assert len(lowercase_      ) == len(lowercase_      )

      _UpperCamelCase   :						str    =							[]
      for input_ids, chinese_word in zip(lowercase_		,lowercase_      ):
            _UpperCamelCase   :						Any    =							[]
            for id in input_ids:
                  _UpperCamelCase   :						Tuple    =							bert_tokenizer._convert_id_to_token(lowercase_      )
                  input_tokens.append(lowercase_      )
            _UpperCamelCase   :						List[str]    =							add_sub_symbol(lowercase_		,lowercase_      )
            _UpperCamelCase   :						Union[str, Any]    =							[]
            # 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   :						str    =							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        lowercase__     (       lowercase_      )       ->		Optional[Any]:




      """simple docstring"""

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

      _UpperCamelCase   :						Optional[int]    =							prepare_ref(lowercase_		,lowercase_		,lowercase_      )

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


if __name__ == "__main__":
  lowerCamelCase__						= 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__						= parser.parse_args()
  main(args)







"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


lowerCamelCase__						= {
    "configuration_resnet": ["RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "ResNetConfig", "ResNetOnnxConfig"]
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowerCamelCase__						= [
      "RESNET_PRETRAINED_MODEL_ARCHIVE_LIST",
      "ResNetForImageClassification",
      "ResNetModel",
      "ResNetPreTrainedModel",
      "ResNetBackbone",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowerCamelCase__						= [
      "TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST",
      "TFResNetForImageClassification",
      "TFResNetModel",
      "TFResNetPreTrainedModel",
  ]

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowerCamelCase__						= [
      "FlaxResNetForImageClassification",
      "FlaxResNetModel",
      "FlaxResNetPreTrainedModel",
  ]

if TYPE_CHECKING:
  from .configuration_resnet import RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ResNetConfig, ResNetOnnxConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_resnet import (
        RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
        ResNetBackbone,
        ResNetForImageClassification,
        ResNetModel,
        ResNetPreTrainedModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_resnet import (
        TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFResNetForImageClassification,
        TFResNetModel,
        TFResNetPreTrainedModel,
    )

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel, FlaxResNetPreTrainedModel


else:
  import sys

  lowerCamelCase__						= _LazyModule(__name__, globals()["__file__"], _import_structure)
