



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					,							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)







import inspect
import re
from hashlib import shaaaa
from typing import Dict, List

from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql  # noqa F401
from .text import text




def      __UpperCamelCase     (  _lowerCAmelCase		)	->  str:



    """simple docstring"""






    A			:	int										=       []
    for line in lines:
        A			:	int										=       re.sub(R"""#.*"""				,					""""""				,					_lowerCAmelCase		)  # remove comments
        if line:
            filtered_lines.append(_lowerCAmelCase		)
    A			:	Tuple										=       """\n""".join(_lowerCAmelCase		)

    # Make a hash from all this code
    A			:	Union[str, Any]										=       full_str.encode("""utf-8"""		)
    return shaaaa(_lowerCAmelCase		).hexdigest()


# get importable module names and hash for caching
SCREAMING_SNAKE_CASE_:List[Any]					=						{
    """csv""": (csv.__name__, _hash_python_lines(inspect.getsource(csv).splitlines())),
    """json""": (json.__name__, _hash_python_lines(inspect.getsource(json).splitlines())),
    """pandas""": (pandas.__name__, _hash_python_lines(inspect.getsource(pandas).splitlines())),
    """parquet""": (parquet.__name__, _hash_python_lines(inspect.getsource(parquet).splitlines())),
    """arrow""": (arrow.__name__, _hash_python_lines(inspect.getsource(arrow).splitlines())),
    """text""": (text.__name__, _hash_python_lines(inspect.getsource(text).splitlines())),
    """imagefolder""": (imagefolder.__name__, _hash_python_lines(inspect.getsource(imagefolder).splitlines())),
    """audiofolder""": (audiofolder.__name__, _hash_python_lines(inspect.getsource(audiofolder).splitlines())),
}

# Used to infer the module to use based on the data files extensions
SCREAMING_SNAKE_CASE_:Optional[Any]					=						{
    """.csv""": ("""csv""", {}),
    """.tsv""": ("""csv""", {"""sep""": """\t"""}),
    """.json""": ("""json""", {}),
    """.jsonl""": ("""json""", {}),
    """.parquet""": ("""parquet""", {}),
    """.arrow""": ("""arrow""", {}),
    """.txt""": ("""text""", {}),
}
_EXTENSION_TO_MODULE.update({ext: ("""imagefolder""", {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ("""imagefolder""", {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext: ("""audiofolder""", {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ("""audiofolder""", {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
SCREAMING_SNAKE_CASE_:Optional[int]					=						{"""imagefolder""", """audiofolder"""}

# Used to filter data files based on extensions given a module name
SCREAMING_SNAKE_CASE_:Dict[str, List[str]]					=						{}
for _ext, (_module, _) in _EXTENSION_TO_MODULE.items():
     _MODULE_TO_EXTENSIONS.setdefault(_module, []).append(_ext)

_MODULE_TO_EXTENSIONS["imagefolder"].append(""".zip""")




_MODULE_TO_EXTENSIONS["audiofolder"].append(""".zip""")


