





import argparse
import json
from typing import List

from ltp import LTP

from transformers import BertTokenizer


def     lowerCamelCase_	(      lowerCAmelCase:	Dict				)->		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 >= 0x4_E00 and cp <= 0x9_FFF)
      or (cp >= 0x3_400 and cp <= 0x4_DBF)  #
      or (cp >= 0x20_000 and cp <= 0x2A_6DF)  #
      or (cp >= 0x2A_700 and cp <= 0x2B_73F)  #
      or (cp >= 0x2B_740 and cp <= 0x2B_81F)  #
      or (cp >= 0x2B_820 and cp <= 0x2C_EAF)  #
      or (cp >= 0xF_900 and cp <= 0xF_AFF)
      or (cp >= 0x2F_800 and cp <= 0x2F_A1F)  #
  ):  #
    return True

  return False


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


def     lowerCamelCase_	(      lowerCAmelCase:	List[str]				)->		int:
  _snake_case   :				List[Any]														=	set()

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


def     lowerCamelCase_	(      lowerCAmelCase:	List[str]    ,							lowerCAmelCase:	set()				)->		Optional[int]:
  if not chinese_word_set:
    return bert_tokens
  _snake_case   :				Optional[Any]														=	max([len(lowerCAmelCase				) for w in chinese_word_set]				)

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


def     lowerCamelCase_	(      lowerCAmelCase:	List[str]    ,							lowerCAmelCase:	LTP    ,							lowerCAmelCase:	BertTokenizer				)->		List[str]:
  _snake_case   :				str														=	[]

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

  _snake_case   :				List[Any]														=	[]
  for i in range(0    ,							len(lowerCAmelCase				)    ,							1_00				):
    _snake_case   :				int														=	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				)

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

  _snake_case   :				Optional[Any]														=	prepare_ref(lowerCAmelCase    ,							lowerCAmelCase    ,							lowerCAmelCase				)

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


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)











from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCAmelCase_						   =    logging.get_logger(__name__)

lowerCAmelCase_						   =    {
    """roberta-base""": """https://huggingface.co/roberta-base/resolve/main/config.json""",
    """roberta-large""": """https://huggingface.co/roberta-large/resolve/main/config.json""",
    """roberta-large-mnli""": """https://huggingface.co/roberta-large-mnli/resolve/main/config.json""",
    """distilroberta-base""": """https://huggingface.co/distilroberta-base/resolve/main/config.json""",
    """roberta-base-openai-detector""": """https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json""",
    """roberta-large-openai-detector""": """https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json""",
}



class        _lowerCAmelCase    (			UpperCAmelCase_							):


  '''simple docstring'''
  a_							:       int      					="""roberta"""







  def __init__(      self      :					int      ,				UpperCamelCase      :					Tuple=5_02_65      ,				UpperCamelCase      :					Any=7_68      ,				UpperCamelCase      :					List[Any]=12      ,				UpperCamelCase      :					str=12      ,				UpperCamelCase      :					Dict=30_72      ,				UpperCamelCase      :					Any="gelu"      ,				UpperCamelCase      :					List[Any]=0.1      ,				UpperCamelCase      :					Optional[Any]=0.1      ,				UpperCamelCase      :					Optional[Any]=5_12      ,				UpperCamelCase      :					List[str]=2      ,				UpperCamelCase      :					Optional[Any]=0.02      ,				UpperCamelCase      :					Tuple=1e-1_2      ,				UpperCamelCase      :					str=1      ,				UpperCamelCase      :					int=0      ,				UpperCamelCase      :					Any=2      ,				UpperCamelCase      :					int="absolute"      ,				UpperCamelCase      :					int=True      ,				UpperCamelCase      :					List[Any]=None      ,				**UpperCamelCase      :					Any      ,				):


    '''simple docstring'''





    super().__init__(pad_token_id=UpperCamelCase      ,				bos_token_id=UpperCamelCase      ,				eos_token_id=UpperCamelCase      ,				**UpperCamelCase							)

    _snake_case   :				Any														=	vocab_size
    _snake_case   :				List[str]														=	hidden_size
    _snake_case   :				List[str]														=	num_hidden_layers
    _snake_case   :				Dict														=	num_attention_heads
    _snake_case   :				List[str]														=	hidden_act
    _snake_case   :				Union[str, Any]														=	intermediate_size
    _snake_case   :				Union[str, Any]														=	hidden_dropout_prob
    _snake_case   :				Optional[int]														=	attention_probs_dropout_prob
    _snake_case   :				Dict														=	max_position_embeddings
    _snake_case   :				Optional[int]														=	type_vocab_size
    _snake_case   :				Tuple														=	initializer_range
    _snake_case   :				int														=	layer_norm_eps
    _snake_case   :				Dict														=	position_embedding_type
    _snake_case   :				Union[str, Any]														=	use_cache
    _snake_case   :				str														=	classifier_dropout




class        _lowerCAmelCase    (			UpperCAmelCase_							):


  '''simple docstring'''







  @property
  def UpperCamelCase_ (      self      :					Dict							):


    '''simple docstring'''





    if self.task == "multiple-choice":
      _snake_case   :				Optional[Any]														=	{0: 'batch', 1: 'choice', 2: 'sequence'}
    else:
      _snake_case   :				Dict														=	{0: 'batch', 1: 'sequence'}
    return OrderedDict(
        [
            ('input_ids', dynamic_axis),
            ('attention_mask', dynamic_axis),
        ]							)




