





import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase				)    ->  str:
    # 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 >= 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  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase				)    ->  Any:
    # word like '180' or '身高' or '神'
    for char in word:
        lowerCamelCase__					:   int      				=	ord(_UpperCAmelCase				)
        if not _is_chinese_char(_UpperCAmelCase				):
            return 0
    return 1


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase				)    ->  Optional[Any]:
    lowerCamelCase__					:   Dict      				=	set()

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


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase		,			_UpperCAmelCase				)    ->  List[str]:
    if not chinese_word_set:
        return bert_tokens
    lowerCamelCase__					:   List[Any]      				=	max([len(_UpperCAmelCase				) for w in chinese_word_set]				)

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


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase				)    ->  Any:
    lowerCamelCase__					:   int      				=	[]

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

    lowerCamelCase__					:   Tuple      				=	[]
    for i in range(0		,			len(_UpperCAmelCase				)		,			100				):
        lowerCamelCase__					:   Optional[Any]      				=	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				)

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

    lowerCamelCase__					:   Optional[int]      				=	prepare_ref(_UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase				)

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


if __name__ == "__main__":
    _UpperCAmelCase       : 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""",
    )

    _UpperCAmelCase       : str			     =       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      lowerCAmelCase							(      unittest.TestCase    ):
 def   A_			(   self						:   str			)    ->			Union[str, Any]:
     lowerCamelCase__					:   Optional[int]      				=	'laion/clap-htsat-unfused'
     lowerCamelCase__					:   List[Any]      				=	tempfile.mkdtemp()
 def   A_			(   self						:   Optional[int]    ,  **UpperCAmelCase						:   int			)    ->			Optional[Any]:
     return RobertaTokenizer.from_pretrained(self.checkpoint    ,  **UpperCAmelCase			)
 def   A_			(   self						:   Union[str, Any]    ,  **UpperCAmelCase						:   Any			)    ->			List[str]:
     return ClapFeatureExtractor.from_pretrained(self.checkpoint    ,  **UpperCAmelCase			)
 def   A_			(   self						:   List[str]			)    ->			Dict:
     shutil.rmtree(self.tmpdirname			)
 def   A_			(   self						:   Optional[Any]			)    ->			Optional[Any]:
     lowerCamelCase__					:   Tuple      				=	self.get_tokenizer()
     lowerCamelCase__					:   Dict      				=	self.get_feature_extractor()

     lowerCamelCase__					:   Dict      				=	ClapProcessor(tokenizer=UpperCAmelCase    ,  feature_extractor=UpperCAmelCase			)

     processor.save_pretrained(self.tmpdirname			)
     lowerCamelCase__					:   List[Any]      				=	ClapProcessor.from_pretrained(self.tmpdirname			)

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

     self.assertEqual(processor.feature_extractor.to_json_string()    ,  feature_extractor.to_json_string()			)
     self.assertIsInstance(processor.feature_extractor    ,  UpperCAmelCase			)
 def   A_			(   self						:   str			)    ->			int:
     lowerCamelCase__					:   Optional[Any]      				=	ClapProcessor(tokenizer=self.get_tokenizer()    ,  feature_extractor=self.get_feature_extractor()			)
     processor.save_pretrained(self.tmpdirname			)

     lowerCamelCase__					:   Dict      				=	self.get_tokenizer(bos_token='(BOS)'    ,  eos_token='(EOS)'			)
     lowerCamelCase__					:   Dict      				=	self.get_feature_extractor(do_normalize=UpperCAmelCase    ,  padding_value=1.0			)

     lowerCamelCase__					:   str      				=	ClapProcessor.from_pretrained(
         self.tmpdirname    ,  bos_token='(BOS)'    ,  eos_token='(EOS)'    ,  do_normalize=UpperCAmelCase    ,  padding_value=1.0			)

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

     self.assertEqual(processor.feature_extractor.to_json_string()    ,  feature_extractor_add_kwargs.to_json_string()			)
     self.assertIsInstance(processor.feature_extractor    ,  UpperCAmelCase			)
 def   A_			(   self						:   List[Any]			)    ->			int:
     lowerCamelCase__					:   Any      				=	self.get_feature_extractor()
     lowerCamelCase__					:   str      				=	self.get_tokenizer()

     lowerCamelCase__					:   Optional[Any]      				=	ClapProcessor(tokenizer=UpperCAmelCase    ,  feature_extractor=UpperCAmelCase			)

     lowerCamelCase__					:   Optional[Any]      				=	floats_list((3, 1000)			)

     lowerCamelCase__					:   Tuple      				=	feature_extractor(UpperCAmelCase    ,  return_tensors='np'			)
     lowerCamelCase__					:   Tuple      				=	processor(audios=UpperCAmelCase    ,  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   A_			(   self						:   List[str]			)    ->			Tuple:
     lowerCamelCase__					:   Dict      				=	self.get_feature_extractor()
     lowerCamelCase__					:   List[Any]      				=	self.get_tokenizer()

     lowerCamelCase__					:   Any      				=	ClapProcessor(tokenizer=UpperCAmelCase    ,  feature_extractor=UpperCAmelCase			)

     lowerCamelCase__					:   Union[str, Any]      				=	'This is a test string'

     lowerCamelCase__					:   Union[str, Any]      				=	processor(text=UpperCAmelCase			)

     lowerCamelCase__					:   Any      				=	tokenizer(UpperCAmelCase			)

     for key in encoded_tok.keys():
         self.assertListEqual(encoded_tok[key]    ,  encoded_processor[key]			)
 def   A_			(   self						:   Optional[int]			)    ->			Tuple:
     lowerCamelCase__					:   Tuple      				=	self.get_feature_extractor()
     lowerCamelCase__					:   List[str]      				=	self.get_tokenizer()

     lowerCamelCase__					:   Optional[int]      				=	ClapProcessor(tokenizer=UpperCAmelCase    ,  feature_extractor=UpperCAmelCase			)

     lowerCamelCase__					:   int      				=	[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

     lowerCamelCase__					:   List[Any]      				=	processor.batch_decode(UpperCAmelCase			)
     lowerCamelCase__					:   Tuple      				=	tokenizer.batch_decode(UpperCAmelCase			)

     self.assertListEqual(UpperCAmelCase    ,  UpperCAmelCase			)







 def   A_			(   self						:   Optional[Any]			)    ->			int:
     lowerCamelCase__					:   str      				=	self.get_feature_extractor()
     lowerCamelCase__					:   str      				=	self.get_tokenizer()

     lowerCamelCase__					:   Dict      				=	ClapProcessor(tokenizer=UpperCAmelCase    ,  feature_extractor=UpperCAmelCase			)

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





