



from __future__ import annotations

# Define the Gravitational Constant G and the function
__lowerCamelCase								=   6.67_43E-11  # unit of G : m^3 * kg^-1 * s^-2




def       UpperCAmelCase__	(							__snake_case  ,			__snake_case  ,			__snake_case  ,			__snake_case							)				->	dict[str, float]:
  _A          =      mass_a * mass_a

  if (force, mass_a, mass_a, distance).count(0							) != 1:
    raise ValueError('''One and only one argument must be 0'''							)
  if force < 0:
    raise ValueError('''Gravitational force can not be negative'''							)
  if distance < 0:
    raise ValueError('''Distance can not be negative'''							)
  if mass_a < 0 or mass_a < 0:
    raise ValueError('''Mass can not be negative'''							)
  if force == 0:
    _A          =      GRAVITATIONAL_CONSTANT * product_of_mass / (distance**2)
    return {"force": force}
  elif mass_a == 0:
    _A          =      (force) * (distance**2) / (GRAVITATIONAL_CONSTANT * mass_a)
    return {"mass_1": mass_a}
  elif mass_a == 0:
    _A          =      (force) * (distance**2) / (GRAVITATIONAL_CONSTANT * mass_a)
    return {"mass_2": mass_a}
  elif distance == 0:
    _A          =      (GRAVITATIONAL_CONSTANT * product_of_mass / (force)) ** 0.5
    return {"distance": distance}
  raise ValueError('''One and only one argument must be 0'''							)


# Run doctest
if __name__ == "__main__":
    import doctest

    doctest.testmod()




import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_deberta import DebertaTokenizer


if TYPE_CHECKING:
    from transformers.pipelines.conversational import Conversation


__lowerCamelCase								=   logging.get_logger(__name__)

__lowerCamelCase								=   {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

__lowerCamelCase								=   {
    """vocab_file""": {
        """microsoft/deberta-base""": """https://huggingface.co/microsoft/deberta-base/resolve/main/vocab.json""",
        """microsoft/deberta-large""": """https://huggingface.co/microsoft/deberta-large/resolve/main/vocab.json""",
        """microsoft/deberta-xlarge""": """https://huggingface.co/microsoft/deberta-xlarge/resolve/main/vocab.json""",
        """microsoft/deberta-base-mnli""": """https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/vocab.json""",
        """microsoft/deberta-large-mnli""": """https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/vocab.json""",
        """microsoft/deberta-xlarge-mnli""": (
            """https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/vocab.json"""
        ),
    },
    """merges_file""": {
        """microsoft/deberta-base""": """https://huggingface.co/microsoft/deberta-base/resolve/main/merges.txt""",
        """microsoft/deberta-large""": """https://huggingface.co/microsoft/deberta-large/resolve/main/merges.txt""",
        """microsoft/deberta-xlarge""": """https://huggingface.co/microsoft/deberta-xlarge/resolve/main/merges.txt""",
        """microsoft/deberta-base-mnli""": """https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/merges.txt""",
        """microsoft/deberta-large-mnli""": """https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/merges.txt""",
        """microsoft/deberta-xlarge-mnli""": (
            """https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/merges.txt"""
        ),
    },
}

__lowerCamelCase								=   {
    """microsoft/deberta-base""": 5_1_2,
    """microsoft/deberta-large""": 5_1_2,
    """microsoft/deberta-xlarge""": 5_1_2,
    """microsoft/deberta-base-mnli""": 5_1_2,
    """microsoft/deberta-large-mnli""": 5_1_2,
    """microsoft/deberta-xlarge-mnli""": 5_1_2,
}

__lowerCamelCase								=   {
    """microsoft/deberta-base""": {"""do_lower_case""": False},
    """microsoft/deberta-large""": {"""do_lower_case""": False},
}



class 						_snake_case (     lowerCamelCase			):





 """simple docstring"""



 lowerCamelCase_       =     VOCAB_FILES_NAMES
 lowerCamelCase_       =     PRETRAINED_VOCAB_FILES_MAP
 lowerCamelCase_       =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 lowerCamelCase_       =     ['''input_ids''', '''attention_mask''', '''token_type_ids''']
 lowerCamelCase_       =     DebertaTokenizer
 def __init__(  self		,      a=None		,      a=None		,      a=None		,      a="replace"		,      a="[CLS]"		,      a="[SEP]"		,      a="[SEP]"		,      a="[CLS]"		,      a="[UNK]"		,      a="[PAD]"		,      a="[MASK]"		,      a=False		,      **a		,      )		->							Any:




   """simple docstring"""







   super().__init__(
       a		,      a		,      tokenizer_file=a		,      errors=a		,      bos_token=a		,      eos_token=a		,      unk_token=a		,      sep_token=a		,      cls_token=a		,      pad_token=a		,      mask_token=a		,      add_prefix_space=a		,      **a		,      )
   _A          =      kwargs.pop('''add_bos_token'''		,      a   )

   _A          =      json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()   )
   if pre_tok_state.get('''add_prefix_space'''		,      a   ) != add_prefix_space:
     _A          =      getattr(a		,      pre_tok_state.pop('''type'''   )   )
     _A          =      add_prefix_space
     _A          =      pre_tok_class(**a   )

   _A          =      add_prefix_space
 @property
 def lowercase_						(  self   )		->							str:




   """simple docstring"""







   if self._mask_token is None:
     if self.verbose:
       logger.error('''Using mask_token, but it is not set yet.'''   )
     return None
   return str(self._mask_token   )
 @mask_token.setter
 def lowercase_						(  self		,      a   )		->							Dict:




   """simple docstring"""







   _A          =      AddedToken(a		,      lstrip=a		,      rstrip=a   ) if isinstance(a		,      a   ) else value
   _A          =      value
 def lowercase_						(  self		,      a		,      a = None   )		->							List[int]:




   """simple docstring"""







   if token_ids_a is None:
     return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
   _A          =      [self.cls_token_id]
   _A          =      [self.sep_token_id]
   return cls + token_ids_a + sep + token_ids_a + sep
 def lowercase_						(  self		,      a		,      a = None   )		->							List[int]:




   """simple docstring"""







   _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 lowercase_						(  self		,      *a		,      **a   )		->							BatchEncoding:




   """simple docstring"""







   _A          =      kwargs.get('''is_split_into_words'''		,      a   )
   assert self.add_prefix_space or not is_split_into_words, (
       f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
       "to use it with pretokenized inputs."
   )

   return super()._batch_encode_plus(*a		,      **a   )
 def lowercase_						(  self		,      *a		,      **a   )		->							BatchEncoding:




   """simple docstring"""







   _A          =      kwargs.get('''is_split_into_words'''		,      a   )

   assert self.add_prefix_space or not is_split_into_words, (
       f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
       "to use it with pretokenized inputs."
   )

   return super()._encode_plus(*a		,      **a   )
 def lowercase_						(  self		,      a		,      a = None   )		->							Tuple[str]:




   """simple docstring"""







   _A          =      self._tokenizer.model.save(a		,      name=a   )
   return tuple(a   )



 def lowercase_						(  self		,      a   )		->							List[int]:




   """simple docstring"""







   _A          =      []
   for is_user, text in conversation.iter_texts():
     input_ids.extend(self.encode(a		,      add_special_tokens=a   ) + [self.eos_token_id]   )

   if len(a   ) > self.model_max_length:
     _A          =      input_ids[-self.model_max_length :]
   return input_ids