





import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer

def      UpperCAmelCase_    (    _UpperCAmelCase							:Any				)	->				Optional[Any]:
     '''simple docstring'''


     if (
         (cp >= 0x4E00 and cp <= 0x9FFF)
         or (cp >= 0x3400 and cp <= 0x4DBF)  #
         or (cp >= 0x20000 and cp <= 0x2A6DF)  #
         or (cp >= 0x2A700 and cp <= 0x2B73F)  #
         or (cp >= 0x2B740 and cp <= 0x2B81F)  #
         or (cp >= 0x2B820 and cp <= 0x2CEAF)  #
         or (cp >= 0xF900 and cp <= 0xFAFF)
         or (cp >= 0x2F800 and cp <= 0x2FA1F)  #
     ):  #
          return True

     return False

def      UpperCAmelCase_    (    _UpperCAmelCase							:str				)	->				Optional[Any]:
     '''simple docstring'''


     for char in word:
          A_          =       ord(_UpperCAmelCase				)
          if not _is_chinese_char(_UpperCAmelCase				):
               return 0
     return 1

def      UpperCAmelCase_    (    _UpperCAmelCase							:List[str]				)	->				Dict:
     '''simple docstring'''


     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							:List[str]  ,						_UpperCAmelCase							:set()				)	->				int:
     '''simple docstring'''


     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							:List[str]  ,						_UpperCAmelCase							:LTP  ,						_UpperCAmelCase							:BertTokenizer				)	->				Optional[Any]:
     '''simple docstring'''


     A_          =       []

     for i in range(0  ,						len(_UpperCAmelCase				)  ,						100				):
          A_          =       ltp_tokenizer.pipeline(lines[i : i + 100]  ,						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				)  ,						100				):
          A_          =       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				)

     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							:Dict				)	->				List[str]:
     '''simple docstring'''


     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__":
      a__      :     Optional[int]         =		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__      :     Tuple         =		parser.parse_args()
      main(args)










import shutil
import tempfile
import unittest

from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio

from .test_feature_extraction_clap import floats_list





@require_torchaudio
@require_sentencepiece
class UpperCAmelCase_				( unittest.TestCase ):


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       '''laion/clap-htsat-unfused'''
       A_          =       tempfile.mkdtemp()


  def 						__UpperCAmelCase     (							self ,**__snake_case ):



       """simple docstring"""





       return RobertaTokenizer.from_pretrained(self.checkpoint ,**__snake_case )


  def 						__UpperCAmelCase     (							self ,**__snake_case ):



       """simple docstring"""





       return ClapFeatureExtractor.from_pretrained(self.checkpoint ,**__snake_case )


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       shutil.rmtree(self.tmpdirname )


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       self.get_tokenizer()
       A_          =       self.get_feature_extractor()

       A_          =       ClapProcessor(tokenizer=__snake_case ,feature_extractor=__snake_case )

       processor.save_pretrained(self.tmpdirname )
       A_          =       ClapProcessor.from_pretrained(self.tmpdirname )

       self.assertEqual(processor.tokenizer.get_vocab() ,tokenizer.get_vocab() )
       self.assertIsInstance(processor.tokenizer ,__snake_case )

       self.assertEqual(processor.feature_extractor.to_json_string() ,feature_extractor.to_json_string() )
       self.assertIsInstance(processor.feature_extractor ,__snake_case )


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       ClapProcessor(tokenizer=self.get_tokenizer() ,feature_extractor=self.get_feature_extractor() )
       processor.save_pretrained(self.tmpdirname )

       A_          =       self.get_tokenizer(bos_token='''(BOS)''' ,eos_token='''(EOS)''' )
       A_          =       self.get_feature_extractor(do_normalize=__snake_case ,padding_value=1.0 )

       A_          =       ClapProcessor.from_pretrained(
           self.tmpdirname ,bos_token='''(BOS)''' ,eos_token='''(EOS)''' ,do_normalize=__snake_case ,padding_value=1.0 )

       self.assertEqual(processor.tokenizer.get_vocab() ,tokenizer_add_kwargs.get_vocab() )
       self.assertIsInstance(processor.tokenizer ,__snake_case )

       self.assertEqual(processor.feature_extractor.to_json_string() ,feature_extractor_add_kwargs.to_json_string() )
       self.assertIsInstance(processor.feature_extractor ,__snake_case )


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       self.get_feature_extractor()
       A_          =       self.get_tokenizer()

       A_          =       ClapProcessor(tokenizer=__snake_case ,feature_extractor=__snake_case )

       A_          =       floats_list((3, 1_0_0_0) )

       A_          =       feature_extractor(__snake_case ,return_tensors='''np''' )
       A_          =       processor(audios=__snake_case ,return_tensors='''np''' )

       for key in input_feat_extract.keys():
            self.assertAlmostEqual(input_feat_extract[key].sum() ,input_processor[key].sum() ,delta=1E-2 )


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       self.get_feature_extractor()
       A_          =       self.get_tokenizer()

       A_          =       ClapProcessor(tokenizer=__snake_case ,feature_extractor=__snake_case )

       A_          =       '''This is a test string'''

       A_          =       processor(text=__snake_case )

       A_          =       tokenizer(__snake_case )

       for key in encoded_tok.keys():
            self.assertListEqual(encoded_tok[key] ,encoded_processor[key] )


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       self.get_feature_extractor()
       A_          =       self.get_tokenizer()

       A_          =       ClapProcessor(tokenizer=__snake_case ,feature_extractor=__snake_case )

       A_          =       [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

       A_          =       processor.batch_decode(__snake_case )
       A_          =       tokenizer.batch_decode(__snake_case )

       self.assertListEqual(__snake_case ,__snake_case )




  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       self.get_feature_extractor()
       A_          =       self.get_tokenizer()

       A_          =       ClapProcessor(tokenizer=__snake_case ,feature_extractor=__snake_case )

       self.assertListEqual(
           processor.model_input_names[2:] ,feature_extractor.model_input_names ,msg='''`processor` and `feature_extractor` model input names do not match''' ,)



