

'''simple docstring'''







from typing import TYPE_CHECKING

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


__lowerCamelCase		       =    {
    '''configuration_roformer''': ['''ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RoFormerConfig''', '''RoFormerOnnxConfig'''],
    '''tokenization_roformer''': ['''RoFormerTokenizer'''],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __lowerCamelCase		       =    ['''RoFormerTokenizerFast''']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __lowerCamelCase		       =    [
          '''ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''RoFormerForCausalLM''',
          '''RoFormerForMaskedLM''',
          '''RoFormerForMultipleChoice''',
          '''RoFormerForQuestionAnswering''',
          '''RoFormerForSequenceClassification''',
          '''RoFormerForTokenClassification''',
          '''RoFormerLayer''',
          '''RoFormerModel''',
          '''RoFormerPreTrainedModel''',
          '''load_tf_weights_in_roformer''',
      ]


try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __lowerCamelCase		       =    [
          '''TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''TFRoFormerForCausalLM''',
          '''TFRoFormerForMaskedLM''',
          '''TFRoFormerForMultipleChoice''',
          '''TFRoFormerForQuestionAnswering''',
          '''TFRoFormerForSequenceClassification''',
          '''TFRoFormerForTokenClassification''',
          '''TFRoFormerLayer''',
          '''TFRoFormerModel''',
          '''TFRoFormerPreTrainedModel''',
      ]


try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __lowerCamelCase		       =    [
          '''FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''FlaxRoFormerForMaskedLM''',
          '''FlaxRoFormerForMultipleChoice''',
          '''FlaxRoFormerForQuestionAnswering''',
          '''FlaxRoFormerForSequenceClassification''',
          '''FlaxRoFormerForTokenClassification''',
          '''FlaxRoFormerModel''',
          '''FlaxRoFormerPreTrainedModel''',
      ]


if TYPE_CHECKING:
      from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
      from .tokenization_roformer import RoFormerTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_roformer_fast import RoFormerTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_roformer import (
                ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                RoFormerForCausalLM,
                RoFormerForMaskedLM,
                RoFormerForMultipleChoice,
                RoFormerForQuestionAnswering,
                RoFormerForSequenceClassification,
                RoFormerForTokenClassification,
                RoFormerLayer,
                RoFormerModel,
                RoFormerPreTrainedModel,
                load_tf_weights_in_roformer,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_roformer import (
                TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFRoFormerForCausalLM,
                TFRoFormerForMaskedLM,
                TFRoFormerForMultipleChoice,
                TFRoFormerForQuestionAnswering,
                TFRoFormerForSequenceClassification,
                TFRoFormerForTokenClassification,
                TFRoFormerLayer,
                TFRoFormerModel,
                TFRoFormerPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_roformer import (
                FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                FlaxRoFormerForMaskedLM,
                FlaxRoFormerForMultipleChoice,
                FlaxRoFormerForQuestionAnswering,
                FlaxRoFormerForSequenceClassification,
                FlaxRoFormerForTokenClassification,
                FlaxRoFormerModel,
                FlaxRoFormerPreTrainedModel,
            )


else:
      import sys

      __lowerCamelCase		       =    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



'''simple docstring'''







import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer
def       UpperCAmelCase__    (      UpperCAmelCase__				)			->			Optional[int]:
       # 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__    (      UpperCAmelCase__				)			->			List[Any]:
       # 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       UpperCAmelCase__    (      UpperCAmelCase__				)			->			int:
       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       UpperCAmelCase__    (      UpperCAmelCase__,       UpperCAmelCase__				)			->			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       UpperCAmelCase__    (      UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__				)			->			Dict:
       A_						=      []

       for i in range(0,       len(UpperCAmelCase__				),       1_00				):
              A_						=      ltp_tokenizer.pipeline(lines[i : i + 1_00],       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_00				):
              A_						=      bert_tokenizer(lines[i : i + 1_00],       add_special_tokens=UpperCAmelCase__,       truncation=UpperCAmelCase__,       max_length=5_12				)
              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       UpperCAmelCase__    (      UpperCAmelCase__				)			->			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__":
      __lowerCamelCase		       =    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''',
      )

      __lowerCamelCase		       =    parser.parse_args()
      main(args)
