





"""simple docstring"""



import argparse
import os
import re


a_ =			"""src/transformers/models/auto"""


# re pattern that matches mapping introductions:
#    SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
a_ =			re.compile(R"""[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict""")
# re pattern that matches identifiers in mappings
a_ =			re.compile(R"""\s*\(\s*\"(\S[^\"]+)\"""")







def 		__lowercase     (   snake_case_  :  Optional[Any]				,snake_case_  :  bool = False						)   ->List[str]:



    '''simple docstring'''







    with open(snake_case_				,'''r'''				,encoding='''utf-8'''						) as f:
        __A    :						Union[str, Any]						 =     f.read()

    __A    :						Any						 =     content.split('''\n'''						)
    __A    :						Dict						 =     []
    __A    :						Optional[int]						 =     0
    while line_idx < len(snake_case_						):
        if _re_intro_mapping.search(lines[line_idx]						) is not None:
            __A    :						str						 =     len(re.search(r'''^(\s*)\S'''				,lines[line_idx]						).groups()[0]						) + 8
            # Start of a new mapping!
            while not lines[line_idx].startswith(''' ''' * indent + '''('''						):
                new_lines.append(lines[line_idx]						)
                line_idx += 1

            __A    :						int						 =     []
            while lines[line_idx].strip() != "]":
                # Blocks either fit in one line or not
                if lines[line_idx].strip() == "(":
                    __A    :						int						 =     line_idx
                    while not lines[line_idx].startswith(''' ''' * indent + ''')'''						):
                        line_idx += 1
                    blocks.append('''\n'''.join(lines[start_idx : line_idx + 1]						)						)
                else:
                    blocks.append(lines[line_idx]						)
                line_idx += 1

            # Sort blocks by their identifiers
            __A    :						Optional[Any]						 =     sorted(snake_case_				,key=lambda snake_case_						: _re_identifier.search(snake_case_						).groups()[0]						)
            new_lines += blocks
        else:
            new_lines.append(lines[line_idx]						)
            line_idx += 1

    if overwrite:
        with open(snake_case_				,'''w'''				,encoding='''utf-8'''						) as f:
            f.write('''\n'''.join(snake_case_						)						)
    elif "\n".join(snake_case_						) != content:
        return True







def 		__lowercase     (   snake_case_  :  bool = False						)   ->str:



    '''simple docstring'''







    __A    :						Dict						 =     [os.path.join(snake_case_				,snake_case_						) for f in os.listdir(snake_case_						) if f.endswith('''.py'''						)]
    __A    :						str						 =     [sort_auto_mapping(snake_case_				,overwrite=snake_case_						) for fname in fnames]

    if not overwrite and any(snake_case_						):
        __A    :						int						 =     [f for f, d in zip(snake_case_				,snake_case_						) if d]
        raise ValueError(
            F"""The following files have auto mappings that need sorting: {', '.join(snake_case_						)}. Run `make style` to fix"""
            ''' this.'''						)


if __name__ == "__main__":
       a_ =			argparse.ArgumentParser()
       parser.add_argument("""--check_only""", action="""store_true""", help="""Whether to only check or fix style.""")
       a_ =			parser.parse_args()

       sort_all_auto_mappings(not args.check_only)











"""simple docstring"""



import argparse
import json
from typing import List

from ltp import LTP

from transformers import BertTokenizer







def 		__lowercase     (   snake_case_  :  int						)   ->Tuple:



    '''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 		__lowercase     (   snake_case_  :  str						)   ->Dict:



    '''simple docstring'''







    for char in word:
        __A    :						int						 =     ord(snake_case_						)
        if not _is_chinese_char(snake_case_						):
            return 0
    return 1







def 		__lowercase     (   snake_case_  :  List[str]						)   ->List[Any]:



    '''simple docstring'''







    __A    :						str						 =     set()

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







def 		__lowercase     (   snake_case_  :  List[str]				,snake_case_  :  set()						)   ->Any:



    '''simple docstring'''







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

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







def 		__lowercase     (   snake_case_  :  List[str]				,snake_case_  :  LTP				,snake_case_  :  BertTokenizer						)   ->Dict:



    '''simple docstring'''







    __A    :						Optional[Any]						 =     []

    for i in range(0				,len(snake_case_						)				,100						):
        __A    :						int						 =     ltp_tokenizer.seg(lines[i : i + 100]						)[0]
        __A    :						List[Any]						 =     [get_chinese_word(snake_case_						) for r in res]
        ltp_res.extend(snake_case_						)
    assert len(snake_case_						) == len(snake_case_						)

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

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

    assert len(snake_case_						) == len(snake_case_						)

    return ref_ids







def 		__lowercase     (   snake_case_  :  int						)   ->List[Any]:



    '''simple docstring'''







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

    __A    :						Optional[Any]						 =     prepare_ref(snake_case_				,snake_case_				,snake_case_						)

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


if __name__ == "__main__":
       a_ =			argparse.ArgumentParser(description="""prepare_chinese_ref""")
       parser.add_argument(
           """--file_name""",
           type=str,
           default="""./resources/chinese-demo.txt""",
           help="""file need process, same as training data in lm""",
       )
       parser.add_argument(
           """--ltp""", type=str, default="""./resources/ltp""", help="""resources for LTP tokenizer, usually a path"""
       )
       parser.add_argument("""--bert""", type=str, default="""./resources/robert""", help="""resources for Bert tokenizer""")
       parser.add_argument("""--save_path""", type=str, default="""./resources/ref.txt""", help="""path to save res""")

       a_ =			parser.parse_args()
       main(args)




