



"""simple docstring"""







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


__snake_case						:					Dict	       =      logging.get_logger(__name__)

__snake_case						:					Optional[int]	       =      {'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

__snake_case						:					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'''
        ),
    }
}

__snake_case						:					Any	       =      {
    '''junnyu/roformer_chinese_small''': 1_536,
    '''junnyu/roformer_chinese_base''': 1_536,
    '''junnyu/roformer_chinese_char_small''': 512,
    '''junnyu/roformer_chinese_char_base''': 512,
    '''junnyu/roformer_small_discriminator''': 128,
    '''junnyu/roformer_small_generator''': 128,
}


__snake_case						:					List[str]	       =      {
    '''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    UpperCamelCase	(       UpperCAmelCase_					):





     """simple docstring"""






     _lowerCamelCase       :			List[str]					 =VOCAB_FILES_NAMES
     _lowerCamelCase       :			List[str]					 =PRETRAINED_VOCAB_FILES_MAP
     _lowerCamelCase       :			Tuple					 =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     _lowerCamelCase       :			Tuple					 =PRETRAINED_INIT_CONFIGURATION
     _lowerCamelCase       :			str					 =RoFormerTokenizer





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

          A__      						= 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
          ):
               A__      						= getattr(__lowerCamelCase						,   pre_tok_state.pop('''type'''			)			)
               A__      						= do_lower_case
               A__      						= strip_accents
               A__      						= pre_tok_class(**__lowerCamelCase			)

          A__      						= do_lower_case





     def __getstate__(       self			:				Optional[Any]			):
          A__      						= self.__dict__.copy()
          A__      						= BertPreTokenizer()
          return state





     def __setstate__(       self			:				List[str]						,   _lowerCamelCase			:				List[Any]			):
          A__      						= d
          A__      						= self.__dict__['''_tokenizer'''].get_vocab()
          A__      						= PreTokenizer.custom(JiebaPreTokenizer(__lowerCamelCase			)			)





     def     A__	(       self			:				Any						,   _lowerCamelCase			:				Optional[int]						,   _lowerCamelCase			:				int=None			):
          A__      						= [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     A__	(       self			:				List[str]						,   _lowerCamelCase			:				List[int]						,   _lowerCamelCase			:				Optional[List[int]] = None			):
          A__      						= [self.sep_token_id]
          A__      						= [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     A__	(       self			:				List[Any]						,   _lowerCamelCase			:				str						,   _lowerCamelCase			:				Optional[str] = None			):
          A__      						= self._tokenizer.model.save(__lowerCamelCase						,   name=__lowerCamelCase			)
          return tuple(__lowerCamelCase			)





     def     A__	(       self			:				Optional[Any]						,   _lowerCamelCase			:				Any						,   _lowerCamelCase			:				Tuple=None						,   _lowerCamelCase			:				str=None						,   _lowerCamelCase			:				Any=False						,   **_lowerCamelCase			:				List[str]						,   ):
          A__      						= BertPreTokenizer()
          return super().save_pretrained(__lowerCamelCase						,   __lowerCamelCase						,   __lowerCamelCase						,   __lowerCamelCase						,   **__lowerCamelCase			)










SCREAMING_SNAKE_CASE_   :      dict[str, float]			=   {
    "joule": 1.0,
    "kilojoule": 1000,
    "megajoule": 100_0000,
    "gigajoule": 10_0000_0000,
    "wattsecond": 1.0,
    "watthour": 3600,
    "kilowatthour": 360_0000,
    "newtonmeter": 1.0,
    "calorie_nutr": 4186.8,
    "kilocalorie_nutr": 418_6800.00,
    "electronvolt": 1.6_0_2_1_7_6_6_3_4e-1_9,
    "britishthermalunit_it": 1055.0_5585,
    "footpound": 1.35_5818,
}



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case							)		->    float:
		if to_type not in ENERGY_CONVERSION or from_type not in ENERGY_CONVERSION:
				__lowercase											=     (
				    F"Incorrect 'from_type' or 'to_type' value: {from_type!r}, {to_type!r}\n"
				    F"Valid values are: {', '.join(snake_case							)}"
				)
				raise ValueError(snake_case							)
		return value * ENERGY_CONVERSION[from_type] / ENERGY_CONVERSION[to_type]


if __name__ == "__main__":
		import doctest

		doctest.testmod()




