'''simple docstring'''
import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer







def  _UpperCamelCase (     _a	:	List[str]			):




    """simple docstring"""



    if (
        (cp >= 0X4_e_0_0 and cp <= 0X9_f_f_f)
        or (cp >= 0X3_4_0_0 and cp <= 0X4_d_b_f)  #
        or (cp >= 0X2_0_0_0_0 and cp <= 0X2_a_6_d_f)  #
        or (cp >= 0X2_a_7_0_0 and cp <= 0X2_b_7_3_f)  #
        or (cp >= 0X2_b_7_4_0 and cp <= 0X2_b_8_1_f)  #
        or (cp >= 0X2_b_8_2_0 and cp <= 0X2_c_e_a_f)  #
        or (cp >= 0Xf_9_0_0 and cp <= 0Xf_a_f_f)
        or (cp >= 0X2_f_8_0_0 and cp <= 0X2_f_a_1_f)  #
    ):  #
        return True

    return False







def  _UpperCamelCase (     _a	:	str			):




    """simple docstring"""



    for char in word:
        __UpperCamelCase	:	Optional[Any]		    =  ord(_a			)
        if not _is_chinese_char(_a			):
            return 0
    return 1







def  _UpperCamelCase (     _a	:	List[str]			):




    """simple docstring"""



    __UpperCamelCase	:	str		    =  set()

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







def  _UpperCamelCase (     _a	:	List[str]				,     _a	:	set()			):




    """simple docstring"""



    if not chinese_word_set:
        return bert_tokens
    __UpperCamelCase	:	List[Any]		    =  max([len(_a			) for w in chinese_word_set]			)

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







def  _UpperCamelCase (     _a	:	List[str]				,     _a	:	LTP				,     _a	:	BertTokenizer			):




    """simple docstring"""



    __UpperCamelCase	:	Any		    =  []

    for i in range(0				,     len(_a			)				,     1_0_0			):
        __UpperCamelCase	:	List[Any]		    =  ltp_tokenizer.pipeline(lines[i : i + 1_0_0]				,     tasks=['cws']			).cws
        __UpperCamelCase	:	List[Any]		    =  [get_chinese_word(_a			) for r in res]
        ltp_res.extend(_a			)
    assert len(_a			) == len(_a			)

    __UpperCamelCase	:	str		    =  []
    for i in range(0				,     len(_a			)				,     1_0_0			):
        __UpperCamelCase	:	Union[str, Any]		    =  bert_tokenizer(lines[i : i + 1_0_0]				,     add_special_tokens=_a				,     truncation=_a				,     max_length=5_1_2			)
        bert_res.extend(res['input_ids']			)
    assert len(_a			) == len(_a			)

    __UpperCamelCase	:	Tuple		    =  []
    for input_ids, chinese_word in zip(_a				,     _a			):
        __UpperCamelCase	:	Tuple		    =  []
        for id in input_ids:
            __UpperCamelCase	:	List[Any]		    =  bert_tokenizer._convert_id_to_token(_a			)
            input_tokens.append(_a			)
        __UpperCamelCase	:	List[Any]		    =  add_sub_symbol(_a				,     _a			)
        __UpperCamelCase	:	int		    =  []
        # We only save pos of chinese subwords start with ##, which mean is part of a whole word.
        for i, token in enumerate(_a			):
            if token[:2] == "##":
                __UpperCamelCase	:	Any		    =  token[2:]
                # save chinese tokens' pos
                if len(_a			) == 1 and _is_chinese_char(ord(_a			)			):
                    ref_id.append(_a			)
        ref_ids.append(_a			)

    assert len(_a			) == len(_a			)

    return ref_ids







def  _UpperCamelCase (     _a	:	int			):




    """simple docstring"""



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

    __UpperCamelCase	:	Tuple		    =  prepare_ref(_a				,     _a				,     _a			)

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


if __name__ == "__main__":
  a=  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''',
  )

  a=  parser.parse_args()
  main(args)




'''simple docstring'''
import json
import os
import unittest

from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class 			__lowercase		(						_lowerCamelCase			,							unittest.TestCase						):







     """simple docstring"""






     SCREAMING_SNAKE_CASE__		   =						OpenAIGPTTokenizer
     SCREAMING_SNAKE_CASE__		   =						OpenAIGPTTokenizerFast
     SCREAMING_SNAKE_CASE__		   =						True
     SCREAMING_SNAKE_CASE__		   =						False







     def lowerCAmelCase     (		self    ):
         super().setUp()

         # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
         __UpperCamelCase	:	List[Any]		    =  [
             'l',
             'o',
             'w',
             'e',
             'r',
             's',
             't',
             'i',
             'd',
             'n',
             'w</w>',
             'r</w>',
             't</w>',
             'lo',
             'low',
             'er</w>',
             'low</w>',
             'lowest</w>',
             'newer</w>',
             'wider</w>',
             '<unk>',
         ]
         __UpperCamelCase	:	List[str]		    =  dict(zip(_lowerCamelCase     ,			range(len(_lowerCamelCase    )    )    )    )
         __UpperCamelCase	:	Union[str, Any]		    =  ['#version: 0.2', 'l o', 'lo w', 'e r</w>', '']

         __UpperCamelCase	:	Optional[int]		    =  os.path.join(self.tmpdirname     ,			VOCAB_FILES_NAMES['vocab_file']    )
         __UpperCamelCase	:	Tuple		    =  os.path.join(self.tmpdirname     ,			VOCAB_FILES_NAMES['merges_file']    )
         with open(self.vocab_file     ,			'w'    ) as fp:
             fp.write(json.dumps(_lowerCamelCase    )    )
         with open(self.merges_file     ,			'w'    ) as fp:
             fp.write('\n'.join(_lowerCamelCase    )    )







     def lowerCAmelCase     (		self     ,			_lowerCamelCase    ):
         return "lower newer", "lower newer"







     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	Dict		    =  OpenAIGPTTokenizer(self.vocab_file     ,			self.merges_file    )

         __UpperCamelCase	:	List[Any]		    =  'lower'
         __UpperCamelCase	:	Union[str, Any]		    =  ['low', 'er</w>']
         __UpperCamelCase	:	Tuple		    =  tokenizer.tokenize(_lowerCamelCase    )
         self.assertListEqual(_lowerCamelCase     ,			_lowerCamelCase    )

         __UpperCamelCase	:	List[str]		    =  tokens + ['<unk>']
         __UpperCamelCase	:	Optional[Any]		    =  [1_4, 1_5, 2_0]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCamelCase    )     ,			_lowerCamelCase    )







     def lowerCAmelCase     (		self     ,			_lowerCamelCase=1_5    ):
         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
             with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""    ):
                 __UpperCamelCase	:	Dict		    =  self.rust_tokenizer_class.from_pretrained(_lowerCamelCase     ,			**_lowerCamelCase    )

                 # Simple input
                 __UpperCamelCase	:	int		    =  'This is a simple input'
                 __UpperCamelCase	:	Any		    =  ['This is a simple input 1', 'This is a simple input 2']
                 __UpperCamelCase	:	Union[str, Any]		    =  ('This is a simple input', 'This is a pair')
                 __UpperCamelCase	:	Any		    =  [
                     ('This is a simple input 1', 'This is a simple input 2'),
                     ('This is a simple pair 1', 'This is a simple pair 2'),
                 ]

                 # Simple input tests
                 self.assertRaises(_lowerCamelCase     ,			tokenizer_r.encode     ,			_lowerCamelCase     ,			max_length=_lowerCamelCase     ,			padding='max_length'    )

                 # Simple input
                 self.assertRaises(_lowerCamelCase     ,			tokenizer_r.encode_plus     ,			_lowerCamelCase     ,			max_length=_lowerCamelCase     ,			padding='max_length'    )

                 # Simple input
                 self.assertRaises(
                     _lowerCamelCase     ,			tokenizer_r.batch_encode_plus     ,			_lowerCamelCase     ,			max_length=_lowerCamelCase     ,			padding='max_length'     ,			)

                 # Pair input
                 self.assertRaises(_lowerCamelCase     ,			tokenizer_r.encode     ,			_lowerCamelCase     ,			max_length=_lowerCamelCase     ,			padding='max_length'    )

                 # Pair input
                 self.assertRaises(_lowerCamelCase     ,			tokenizer_r.encode_plus     ,			_lowerCamelCase     ,			max_length=_lowerCamelCase     ,			padding='max_length'    )

                 # Pair input
                 self.assertRaises(
                     _lowerCamelCase     ,			tokenizer_r.batch_encode_plus     ,			_lowerCamelCase     ,			max_length=_lowerCamelCase     ,			padding='max_length'     ,			)







     def lowerCAmelCase     (		self    ):
         pass






@require_ftfy
@require_spacy
@require_tokenizers
class 			__lowercase		(						_lowerCamelCase						):







     """simple docstring"""






     pass



