




'''simple docstring'''


from __future__ import annotations


def 						SCREAMING_SNAKE_CASE       (      a_	:							float				,					a_	:							float				,					a_	:							float			):
  if (voltage, current, resistance).count(0			) != 1:
    raise ValueError('One and only one argument must be 0'			)
  if resistance < 0:
    raise ValueError('Resistance cannot be negative'			)
  if voltage == 0:
    return {"voltage": float(current * resistance			)}
  elif current == 0:
    return {"current": voltage / resistance}
  elif resistance == 0:
    return {"resistance": voltage / current}
  else:
    raise ValueError('Exactly one argument must be 0'			)


if __name__ == "__main__":
  import doctest

  doctest.testmod()









'''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


UpperCAmelCase_ 		=      logging.get_logger(__name__)

UpperCAmelCase_ 		=      {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

UpperCAmelCase_ 		=      {
    "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"
        ),
    }
}

UpperCAmelCase_ 		=      {
    "junnyu/roformer_chinese_small": 15_36,
    "junnyu/roformer_chinese_base": 15_36,
    "junnyu/roformer_chinese_char_small": 5_12,
    "junnyu/roformer_chinese_char_base": 5_12,
    "junnyu/roformer_small_discriminator": 1_28,
    "junnyu/roformer_small_generator": 1_28,
}


UpperCAmelCase_ 		=      {
    "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    __lowercase						(  __magic_name__ ):
   _a							=      VOCAB_FILES_NAMES
   _a							=      PRETRAINED_VOCAB_FILES_MAP
   _a							=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _a							=      PRETRAINED_INIT_CONFIGURATION
   _a							=      RoFormerTokenizer







   def __init__(	self      ,							UpperCamelCase=None      ,							UpperCamelCase=None      ,							UpperCamelCase=True      ,							UpperCamelCase="[UNK]"      ,							UpperCamelCase="[SEP]"      ,							UpperCamelCase="[PAD]"      ,							UpperCamelCase="[CLS]"      ,							UpperCamelCase="[MASK]"      ,							UpperCamelCase=True      ,							UpperCamelCase=None      ,							**UpperCamelCase      ,							)				->    int:
     super().__init__(
         UpperCamelCase      ,							tokenizer_file=UpperCamelCase      ,							do_lower_case=UpperCamelCase      ,							unk_token=UpperCamelCase      ,							sep_token=UpperCamelCase      ,							pad_token=UpperCamelCase      ,							cls_token=UpperCamelCase      ,							mask_token=UpperCamelCase      ,							tokenize_chinese_chars=UpperCamelCase      ,							strip_accents=UpperCamelCase      ,							**UpperCamelCase      ,							)

     __a			=					json.loads(self.backend_tokenizer.normalizer.__getstate__()      )
     if (
         pre_tok_state.get('lowercase'      ,							UpperCamelCase      ) != do_lower_case
         or pre_tok_state.get('strip_accents'      ,							UpperCamelCase      ) != strip_accents
     ):
       __a			=					getattr(UpperCamelCase      ,							pre_tok_state.pop('type'      )      )
       __a			=					do_lower_case
       __a			=					strip_accents
       __a			=					pre_tok_class(**UpperCamelCase      )

     __a			=					do_lower_case







   def __getstate__(	self      )				->    List[Any]:
     __a			=					self.__dict__.copy()
     __a			=					BertPreTokenizer()
     return state







   def __setstate__(	self      ,							UpperCamelCase      )				->    Optional[int]:
     __a			=					d
     __a			=					self.__dict__['_tokenizer'].get_vocab()
     __a			=					PreTokenizer.custom(JiebaPreTokenizer(UpperCamelCase      )      )







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase=None      )				->    int:
     __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 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase = None      )				->    List[int]:
     __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 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase = None      )				->    Tuple[str]:
     __a			=					self._tokenizer.model.save(UpperCamelCase      ,							name=UpperCamelCase      )
     return tuple(UpperCamelCase      )







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase=None      ,							UpperCamelCase=None      ,							UpperCamelCase=False      ,							**UpperCamelCase      ,							)				->    List[Any]:
     __a			=					BertPreTokenizer()
     return super().save_pretrained(UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      ,							**UpperCamelCase      )



