



import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer




def      __UpperCamelCase     (  _lowerCAmelCase		)	->  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      __UpperCamelCase     (  _lowerCAmelCase		)	->  List[str]:



       """simple docstring"""






       for char in word:
              A			:	Union[str, Any]										=       ord(_lowerCAmelCase		)
              if not _is_chinese_char(_lowerCAmelCase		):
                     return 0
       return 1




def      __UpperCamelCase     (  _lowerCAmelCase		)	->  Optional[int]:



       """simple docstring"""






       A			:	int										=       set()

       for token in tokens:
              A			:	Tuple										=       len(_lowerCAmelCase		) > 1 and is_chinese(_lowerCAmelCase		)
              if chinese_word:
                     word_set.add(_lowerCAmelCase		)
       A			:	Optional[Any]										=       list(_lowerCAmelCase		)
       return word_list




def      __UpperCamelCase     (  _lowerCAmelCase				,					_lowerCAmelCase		)	->  str:



       """simple docstring"""






       if not chinese_word_set:
              return bert_tokens
       A			:	str										=       max([len(_lowerCAmelCase		) for w in chinese_word_set]		)

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




def      __UpperCamelCase     (  _lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase		)	->  int:



       """simple docstring"""






       A			:	Any										=       []

       for i in range(0				,					len(_lowerCAmelCase		)				,					100		):
              A			:	str										=       ltp_tokenizer.pipeline(lines[i : i + 100]				,					tasks=["""cws"""]		).cws
              A			:	Optional[Any]										=       [get_chinese_word(_lowerCAmelCase		) for r in res]
              ltp_res.extend(_lowerCAmelCase		)
       assert len(_lowerCAmelCase		) == len(_lowerCAmelCase		)

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

       A			:	Any										=       []
       for input_ids, chinese_word in zip(_lowerCAmelCase				,					_lowerCAmelCase		):
              A			:	Dict										=       []
              for id in input_ids:
                     A			:	Optional[int]										=       bert_tokenizer._convert_id_to_token(_lowerCAmelCase		)
                     input_tokens.append(_lowerCAmelCase		)
              A			:	List[Any]										=       add_sub_symbol(_lowerCAmelCase				,					_lowerCAmelCase		)
              A			:	str										=       []
              # 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			:	Dict										=       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:



       """simple docstring"""






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

       A			:	Optional[Any]										=       prepare_ref(_lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase		)

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


if __name__ == "__main__":
     SCREAMING_SNAKE_CASE_:Optional[Any]					=						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""",
     )

     SCREAMING_SNAKE_CASE_:int					=						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


SCREAMING_SNAKE_CASE_:int					=						logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_:Any					=						{
    """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 SCREAMING_SNAKE_CASE__					(		SCREAMING_SNAKE_CASE__   ):




    '''simple docstring'''






    __lowerCamelCase      :			Any =				"roberta"






    def __init__(	self,		lowerCamelCase__=5_0265,		lowerCamelCase__=768,		lowerCamelCase__=12,		lowerCamelCase__=12,		lowerCamelCase__=3072,		lowerCamelCase__="gelu",		lowerCamelCase__=0.1,		lowerCamelCase__=0.1,		lowerCamelCase__=512,		lowerCamelCase__=2,		lowerCamelCase__=0.02,		lowerCamelCase__=1e-12,		lowerCamelCase__=1,		lowerCamelCase__=0,		lowerCamelCase__=2,		lowerCamelCase__="absolute",		lowerCamelCase__=True,		lowerCamelCase__=None,		**lowerCamelCase__,		):
        super().__init__(pad_token_id=lowerCamelCase__,		bos_token_id=lowerCamelCase__,		eos_token_id=lowerCamelCase__,		**lowerCamelCase__		)

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






class SCREAMING_SNAKE_CASE__					(		SCREAMING_SNAKE_CASE__   ):




    '''simple docstring'''






    @property
    def       _lowerCAmelCase (	self		):
        if self.task == "multiple-choice":
            A			:	Optional[Any]										=       {0: """batch""", 1: """choice""", 2: """sequence"""}
        else:
            A			:	int										=       {0: """batch""", 1: """sequence"""}
        return OrderedDict(
            [
                ("""input_ids""", dynamic_axis),
                ("""attention_mask""", dynamic_axis),
            ]		)


