





import argparse
import os
import re


A__:    List[str]  	=	'''src/transformers/models/auto'''


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



def 				lowerCAmelCase_ (							A_    ,A_ = False):
    with open(A_    ,"r"    ,encoding="utf-8") as f:
        UpperCamelCase__:	Optional[Any]					=     f.read()

    UpperCamelCase__:	Optional[Any]					=     content.split("\n")
    UpperCamelCase__:	Union[str, Any]					=     []
    UpperCamelCase__:	str					=     0
    while line_idx < len(A_):
        if _re_intro_mapping.search(lines[line_idx]) is not None:
            UpperCamelCase__:	Optional[int]					=     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

            UpperCamelCase__:	List[Any]					=     []
            while lines[line_idx].strip() != "]":
                # Blocks either fit in one line or not
                if lines[line_idx].strip() == "(":
                    UpperCamelCase__:	Any					=     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
            UpperCamelCase__:	List[Any]					=     sorted(A_    ,key=lambda A_: _re_identifier.search(A_).groups()[0])
            new_lines += blocks
        else:
            new_lines.append(lines[line_idx])
            line_idx += 1

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



def 				lowerCAmelCase_ (							A_ = False):
    UpperCamelCase__:	str					=     [os.path.join(A_    ,A_) for f in os.listdir(A_) if f.endswith(".py")]
    UpperCamelCase__:	List[str]					=     [sort_auto_mapping(A_    ,overwrite=A_) for fname in fnames]

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


if __name__ == "__main__":
   A__:    List[str]  	=	argparse.ArgumentParser()
   parser.add_argument('''--check_only''', action='''store_true''', help='''Whether to only check or fix style.''')
   A__:    Dict  	=	parser.parse_args()

   sort_all_auto_mappings(not args.check_only)








import json
from typing import List, Optional, Tuple

from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoFormerTokenizer
from .tokenization_utils import JiebaPreTokenizer


A__:    List[Any]  	=	logging.get_logger(__name__)

A__:    str  	=	{'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

A__:    List[Any]  	=	{
    '''vocab_file''': {
        '''junnyu/roformer_chinese_small''': '''https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/vocab.txt''',
        '''junnyu/roformer_chinese_base''': '''https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/vocab.txt''',
        '''junnyu/roformer_chinese_char_small''': (
            '''https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/vocab.txt'''
        ),
        '''junnyu/roformer_chinese_char_base''': (
            '''https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/vocab.txt'''
        ),
        '''junnyu/roformer_small_discriminator''': (
            '''https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/vocab.txt'''
        ),
        '''junnyu/roformer_small_generator''': (
            '''https://huggingface.co/junnyu/roformer_small_generator/resolve/main/vocab.txt'''
        ),
    }
}

A__:    str  	=	{
    '''junnyu/roformer_chinese_small''': 1536,
    '''junnyu/roformer_chinese_base''': 1536,
    '''junnyu/roformer_chinese_char_small''': 512,
    '''junnyu/roformer_chinese_char_base''': 512,
    '''junnyu/roformer_small_discriminator''': 128,
    '''junnyu/roformer_small_generator''': 128,
}


A__:    int  	=	{
    '''junnyu/roformer_chinese_small''': {'''do_lower_case''': True},
    '''junnyu/roformer_chinese_base''': {'''do_lower_case''': True},
    '''junnyu/roformer_chinese_char_small''': {'''do_lower_case''': True},
    '''junnyu/roformer_chinese_char_base''': {'''do_lower_case''': True},
    '''junnyu/roformer_small_discriminator''': {'''do_lower_case''': True},
    '''junnyu/roformer_small_generator''': {'''do_lower_case''': True},
}




class        _a    (    UpperCamelCase__):

      """simple docstring"""


      UpperCamelCase__  		=       VOCAB_FILES_NAMES
      UpperCamelCase__  		=       PRETRAINED_VOCAB_FILES_MAP
      UpperCamelCase__  		=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      UpperCamelCase__  		=       PRETRAINED_INIT_CONFIGURATION
      UpperCamelCase__  		=       RoFormerTokenizer






      def __init__(		self:			int	,    __lowerCamelCase:			Optional[Any]=None	,    __lowerCamelCase:			Any=None	,    __lowerCamelCase:			str=True	,    __lowerCamelCase:			Any="[UNK]"	,    __lowerCamelCase:			int="[SEP]"	,    __lowerCamelCase:			Optional[int]="[PAD]"	,    __lowerCamelCase:			Optional[int]="[CLS]"	,    __lowerCamelCase:			Tuple="[MASK]"	,    __lowerCamelCase:			List[str]=True	,    __lowerCamelCase:			List[Any]=None	,    **__lowerCamelCase:			Dict	,    ):
          '''simple docstring'''


          super().__init__(
              __lowerCamelCase	,    tokenizer_file=__lowerCamelCase	,    do_lower_case=__lowerCamelCase	,    unk_token=__lowerCamelCase	,    sep_token=__lowerCamelCase	,    pad_token=__lowerCamelCase	,    cls_token=__lowerCamelCase	,    mask_token=__lowerCamelCase	,    tokenize_chinese_chars=__lowerCamelCase	,    strip_accents=__lowerCamelCase	,    **__lowerCamelCase	,    )

          UpperCamelCase__:	int					=     json.loads(self.backend_tokenizer.normalizer.__getstate__()					)
          if (
              pre_tok_state.get("lowercase"	,    __lowerCamelCase					) != do_lower_case
              or pre_tok_state.get("strip_accents"	,    __lowerCamelCase					) != strip_accents
          ):
              UpperCamelCase__:	int					=     getattr(__lowerCamelCase	,    pre_tok_state.pop("type"					)					)
              UpperCamelCase__:	Any					=     do_lower_case
              UpperCamelCase__:	Optional[int]					=     strip_accents
              UpperCamelCase__:	Any					=     pre_tok_class(**__lowerCamelCase					)

          UpperCamelCase__:	Tuple					=     do_lower_case






      def __getstate__(		self:			Optional[int]					):
          '''simple docstring'''


          UpperCamelCase__:	List[Any]					=     self.__dict__.copy()
          UpperCamelCase__:	Dict					=     BertPreTokenizer()
          return state






      def __setstate__(		self:			Dict	,    __lowerCamelCase:			Optional[Any]					):
          '''simple docstring'''


          UpperCamelCase__:	str					=     d
          UpperCamelCase__:	List[Any]					=     self.__dict__["_tokenizer"].get_vocab()
          UpperCamelCase__:	str					=     PreTokenizer.custom(JiebaPreTokenizer(__lowerCamelCase					)					)






      def 			UpperCAmelCase_       (		self:			int	,    __lowerCamelCase:			Optional[int]	,    __lowerCamelCase:			Dict=None					):
          '''simple docstring'''


          UpperCamelCase__:	int					=     [self.cls_token_id] + token_ids_a + [self.sep_token_id]

          if token_ids_a:
              output += token_ids_a + [self.sep_token_id]

          return output






      def 			UpperCAmelCase_       (		self:			Optional[int]	,    __lowerCamelCase:			List[int]	,    __lowerCamelCase:			Optional[List[int]] = None					):
          '''simple docstring'''


          UpperCamelCase__:	Tuple					=     [self.sep_token_id]
          UpperCamelCase__:	str					=     [self.cls_token_id]
          if token_ids_a is None:
              return len(cls + token_ids_a + sep					) * [0]
          return len(cls + token_ids_a + sep					) * [0] + len(token_ids_a + sep					) * [1]






      def 			UpperCAmelCase_       (		self:			Tuple	,    __lowerCamelCase:			str	,    __lowerCamelCase:			Optional[str] = None					):
          '''simple docstring'''


          UpperCamelCase__:	Dict					=     self._tokenizer.model.save(__lowerCamelCase	,    name=__lowerCamelCase					)
          return tuple(__lowerCamelCase					)






      def 			UpperCAmelCase_       (		self:			Dict	,    __lowerCamelCase:			Any	,    __lowerCamelCase:			Any=None	,    __lowerCamelCase:			Dict=None	,    __lowerCamelCase:			List[str]=False	,    **__lowerCamelCase:			int	,    ):
          '''simple docstring'''


          UpperCamelCase__:	List[str]					=     BertPreTokenizer()
          return super().save_pretrained(__lowerCamelCase	,    __lowerCamelCase	,    __lowerCamelCase	,    __lowerCamelCase	,    **__lowerCamelCase					)

