



'''simple docstring'''


import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer







def 				__lowerCamelCase				(   A__    )       ->	Optional[Any]:






   """simple docstring"""
   # 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 >= 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 				__lowerCamelCase				(   A__    )       ->	Dict:






   """simple docstring"""
   # word like '180' or '身高' or '神'
   for char in word:
      UpperCamelCase					=  ord(__UpperCamelCase    )
      if not _is_chinese_char(__UpperCamelCase    ):
         return 0
   return 1







def 				__lowerCamelCase				(   A__    )       ->	int:






   """simple docstring"""
   UpperCamelCase					=  set()

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







def 				__lowerCamelCase				(   A__    ,						A__    )       ->	List[Any]:






   """simple docstring"""
   if not chinese_word_set:
      return bert_tokens
   UpperCamelCase					=  max([len(__UpperCamelCase    ) for w in chinese_word_set]    )

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







def 				__lowerCamelCase				(   A__    ,						A__    ,						A__    )       ->	Optional[int]:






   """simple docstring"""
   UpperCamelCase					=  []

   for i in range(0    ,						len(__UpperCamelCase    )    ,						100    ):
      UpperCamelCase					=  ltp_tokenizer.pipeline(lines[i : i + 100]    ,						tasks=['cws']    ).cws
      UpperCamelCase					=  [get_chinese_word(__UpperCamelCase    ) for r in res]
      ltp_res.extend(__UpperCamelCase    )
   assert len(__UpperCamelCase    ) == len(__UpperCamelCase    )

   UpperCamelCase					=  []
   for i in range(0    ,						len(__UpperCamelCase    )    ,						100    ):
      UpperCamelCase					=  bert_tokenizer(lines[i : i + 100]    ,						add_special_tokens=__UpperCamelCase    ,						truncation=__UpperCamelCase    ,						max_length=512    )
      bert_res.extend(res['input_ids']    )
   assert len(__UpperCamelCase    ) == len(__UpperCamelCase    )

   UpperCamelCase					=  []
   for input_ids, chinese_word in zip(__UpperCamelCase    ,						__UpperCamelCase    ):
      UpperCamelCase					=  []
      for id in input_ids:
         UpperCamelCase					=  bert_tokenizer._convert_id_to_token(__UpperCamelCase    )
         input_tokens.append(__UpperCamelCase    )
      UpperCamelCase					=  add_sub_symbol(__UpperCamelCase    ,						__UpperCamelCase    )
      UpperCamelCase					=  []
      # 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] == "##":
            UpperCamelCase					=  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 				__lowerCamelCase				(   A__    )       ->	str:






   """simple docstring"""
   # 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:
      UpperCamelCase					=  f.readlines()
   UpperCamelCase					=  [line.strip() for line in data if len(__UpperCamelCase    ) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
   UpperCamelCase					=  LTP(args.ltp    )  # faster in GPU device
   UpperCamelCase					=  BertTokenizer.from_pretrained(args.bert    )

   UpperCamelCase					=  prepare_ref(__UpperCamelCase    ,						__UpperCamelCase    ,						__UpperCamelCase    )

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


if __name__ == "__main__":
   _lowerCamelCase	:       Union[str, 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",
   )

   _lowerCamelCase	:       int      	=	parser.parse_args()
   main(args)


import warnings

from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum

from ...processing_utils import ProcessorMixin


if is_torch_available():
      import torch


class      lowerCamelCase    (SCREAMING_SNAKE_CASE__    ):
  """simple docstring"""







  lowerCamelCase__   =  '''char'''
  lowerCamelCase__   =  '''bpe'''
  lowerCamelCase__   =  '''wp'''



A   :     Tuple       =				(DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)


class      lowerCamelCase    (SCREAMING_SNAKE_CASE__    ):
  """simple docstring"""







  lowerCamelCase__   =  ['''image_processor''', '''char_tokenizer''']
  lowerCamelCase__   =  '''ViTImageProcessor'''
  lowerCamelCase__   =  '''MgpstrTokenizer'''







  def __init__(							self       :				Optional[Any]     ,   __magic_name__       :				Tuple=None     ,   __magic_name__       :				int=None     ,   **__magic_name__       :				Optional[Any]      )     ->		str:
        SCREAMING_SNAKE_CASE_      =     None
        if "feature_extractor" in kwargs:
              warnings.warn(
                  "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
                  " instead."     ,   __magic_name__     ,   )
              SCREAMING_SNAKE_CASE_      =     kwargs.pop("feature_extractor"      )

        SCREAMING_SNAKE_CASE_      =     image_processor if image_processor is not None else feature_extractor
        if image_processor is None:
              raise ValueError("You need to specify an `image_processor`."      )
        if tokenizer is None:
              raise ValueError("You need to specify a `tokenizer`."      )

        SCREAMING_SNAKE_CASE_      =     tokenizer
        SCREAMING_SNAKE_CASE_      =     AutoTokenizer.from_pretrained("gpt2"      )
        SCREAMING_SNAKE_CASE_      =     AutoTokenizer.from_pretrained("bert-base-uncased"      )

        super().__init__(__magic_name__     ,   __magic_name__      )







  def __call__(							self       :				Dict     ,   __magic_name__       :				Union[str, Any]=None     ,   __magic_name__       :				Union[str, Any]=None     ,   __magic_name__       :				Dict=None     ,   **__magic_name__       :				Tuple      )     ->		int:
        if images is None and text is None:
              raise ValueError("You need to specify either an `images` or `text` input to process."      )

        if images is not None:
              SCREAMING_SNAKE_CASE_      =     self.image_processor(__magic_name__     ,   return_tensors=__magic_name__     ,   **__magic_name__      )
        if text is not None:
              SCREAMING_SNAKE_CASE_      =     self.char_tokenizer(__magic_name__     ,   return_tensors=__magic_name__     ,   **__magic_name__      )

        if text is None:
              return inputs
        elif images is None:
              return encodings
        else:
              SCREAMING_SNAKE_CASE_      =     encodings["input_ids"]
              return inputs







  def       __A   (							self       :				Tuple     ,   __magic_name__       :				int      )     ->		Any:
        SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_      =     sequences
        SCREAMING_SNAKE_CASE_      =     char_preds.size(0      )

        SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_      =     self._decode_helper(__magic_name__     ,   "char"      )
        SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_      =     self._decode_helper(__magic_name__     ,   "bpe"      )
        SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_      =     self._decode_helper(__magic_name__     ,   "wp"      )

        SCREAMING_SNAKE_CASE_      =     []
        SCREAMING_SNAKE_CASE_      =     []
        for i in range(__magic_name__      ):
              SCREAMING_SNAKE_CASE_      =     [char_scores[i], bpe_scores[i], wp_scores[i]]
              SCREAMING_SNAKE_CASE_      =     [char_strs[i], bpe_strs[i], wp_strs[i]]
              SCREAMING_SNAKE_CASE_      =     scores.index(max(__magic_name__      )      )
              final_strs.append(strs[max_score_index]      )
              final_scores.append(scores[max_score_index]      )

        SCREAMING_SNAKE_CASE_      =     {}
        SCREAMING_SNAKE_CASE_      =     final_strs
        SCREAMING_SNAKE_CASE_      =     final_scores
        SCREAMING_SNAKE_CASE_      =     char_strs
        SCREAMING_SNAKE_CASE_      =     bpe_strs
        SCREAMING_SNAKE_CASE_      =     wp_strs
        return out







  def       __A   (							self       :				int     ,   __magic_name__       :				List[Any]     ,   __magic_name__       :				str      )     ->		Any:
        if format == DecodeType.CHARACTER:
              SCREAMING_SNAKE_CASE_      =     self.char_decode
              SCREAMING_SNAKE_CASE_      =     1
              SCREAMING_SNAKE_CASE_      =     "[s]"
        elif format == DecodeType.BPE:
              SCREAMING_SNAKE_CASE_      =     self.bpe_decode
              SCREAMING_SNAKE_CASE_      =     2
              SCREAMING_SNAKE_CASE_      =     "#"
        elif format == DecodeType.WORDPIECE:
              SCREAMING_SNAKE_CASE_      =     self.wp_decode
              SCREAMING_SNAKE_CASE_      =     102
              SCREAMING_SNAKE_CASE_      =     "[SEP]"
        else:
              raise ValueError(F'''Format {format} is not supported.'''      )

        SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_      =     [], []
        SCREAMING_SNAKE_CASE_      =     pred_logits.size(0      )
        SCREAMING_SNAKE_CASE_      =     pred_logits.size(1      )
        SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_      =     pred_logits.topk(1     ,   dim=-1     ,   largest=__magic_name__     ,   sorted=__magic_name__      )
        SCREAMING_SNAKE_CASE_      =     preds_index.view(-1     ,   __magic_name__      )[:, 1:]
        SCREAMING_SNAKE_CASE_      =     decoder(__magic_name__      )
        SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_      =     torch.nn.functional.softmax(__magic_name__     ,   dim=2      ).max(dim=2      )
        SCREAMING_SNAKE_CASE_      =     preds_max_prob[:, 1:]

        for index in range(__magic_name__      ):
              SCREAMING_SNAKE_CASE_      =     preds_str[index].find(__magic_name__      )
              SCREAMING_SNAKE_CASE_      =     preds_str[index][:pred_eos]
              SCREAMING_SNAKE_CASE_      =     preds_index[index].cpu().tolist()
              SCREAMING_SNAKE_CASE_      =     pred_index.index(__magic_name__      ) if eos_token in pred_index else -1
              SCREAMING_SNAKE_CASE_      =     preds_max_prob[index][: pred_eos_index + 1]
              SCREAMING_SNAKE_CASE_      =     pred_max_prob.cumprod(dim=0      )[-1] if pred_max_prob.nelement() != 0 else 0.0
              dec_strs.append(__magic_name__      )
              conf_scores.append(__magic_name__      )

        return dec_strs, conf_scores







  def       __A   (							self       :				Any     ,   __magic_name__       :				Dict      )     ->		List[str]:
        SCREAMING_SNAKE_CASE_      =     [seq.replace(" "     ,   ""      ) for seq in self.char_tokenizer.batch_decode(__magic_name__      )]
        return decode_strs







  def       __A   (							self       :				Any     ,   __magic_name__       :				Union[str, Any]      )     ->		Tuple:
        return self.bpe_tokenizer.batch_decode(__magic_name__      )







  def       __A   (							self       :				str     ,   __magic_name__       :				Dict      )     ->		List[Any]:
        SCREAMING_SNAKE_CASE_      =     [seq.replace(" "     ,   ""      ) for seq in self.wp_tokenizer.batch_decode(__magic_name__      )]
        return decode_strs


