



'''simple docstring'''


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


lowercase											=      logging.get_logger(__name__)

lowercase											=      {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

lowercase											=      {
    '''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'''
        ),
    },
}

lowercase											=      {
    '''microsoft/deberta-base''': 512,
    '''microsoft/deberta-large''': 512,
    '''microsoft/deberta-xlarge''': 512,
    '''microsoft/deberta-base-mnli''': 512,
    '''microsoft/deberta-large-mnli''': 512,
    '''microsoft/deberta-xlarge-mnli''': 512,
}

lowercase											=      {
    '''microsoft/deberta-base''': {'''do_lower_case''': False},
    '''microsoft/deberta-large''': {'''do_lower_case''': False},
}







class 						UpperCAmelCase						(		__a):


 '''simple docstring'''




 __magic_name__       :			Optional[int]	=   VOCAB_FILES_NAMES
 __magic_name__       :			List[str]	=   PRETRAINED_VOCAB_FILES_MAP
 __magic_name__       :			Optional[Any]	=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __magic_name__       :			Tuple	=   ["input_ids", "attention_mask", "token_type_ids"]
 __magic_name__       :			int	=   DebertaTokenizer




 def __init__( self				,	lowerCAmelCase_=None				,	lowerCAmelCase_=None				,	lowerCAmelCase_=None				,	lowerCAmelCase_="replace"				,	lowerCAmelCase_="[CLS]"				,	lowerCAmelCase_="[SEP]"				,	lowerCAmelCase_="[SEP]"				,	lowerCAmelCase_="[CLS]"				,	lowerCAmelCase_="[UNK]"				,	lowerCAmelCase_="[PAD]"				,	lowerCAmelCase_="[MASK]"				,	lowerCAmelCase_=False				,	**lowerCAmelCase_				,	)			->   Optional[Any]:



      """simple docstring"""







      super().__init__(
          lowerCAmelCase_				,	lowerCAmelCase_				,	tokenizer_file=lowerCAmelCase_				,	errors=lowerCAmelCase_				,	bos_token=lowerCAmelCase_				,	eos_token=lowerCAmelCase_				,	unk_token=lowerCAmelCase_				,	sep_token=lowerCAmelCase_				,	cls_token=lowerCAmelCase_				,	pad_token=lowerCAmelCase_				,	mask_token=lowerCAmelCase_				,	add_prefix_space=lowerCAmelCase_				,	**lowerCAmelCase_				,	)
      a_									=kwargs.pop("add_bos_token"				,	lowerCAmelCase_)

      a_									=json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
      if pre_tok_state.get("add_prefix_space"				,	lowerCAmelCase_) != add_prefix_space:
           a_									=getattr(lowerCAmelCase_				,	pre_tok_state.pop("type"))
           a_									=add_prefix_space
           a_									=pre_tok_class(**lowerCAmelCase_)

      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				,	lowerCAmelCase_)			->   Optional[int]:



      """simple docstring"""







      a_									=AddedToken(lowerCAmelCase_				,	lstrip=lowerCAmelCase_				,	rstrip=lowerCAmelCase_) if isinstance(lowerCAmelCase_				,	lowerCAmelCase_) else value
      a_									=value




 def    lowercase_	( self				,	lowerCAmelCase_				,	lowerCAmelCase_ = 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				,	lowerCAmelCase_				,	lowerCAmelCase_ = 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				,	*lowerCAmelCase_				,	**lowerCAmelCase_)			->   BatchEncoding:



      """simple docstring"""







      a_									=kwargs.get("is_split_into_words"				,	lowerCAmelCase_)
      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(*lowerCAmelCase_				,	**lowerCAmelCase_)




 def    lowercase_	( self				,	*lowerCAmelCase_				,	**lowerCAmelCase_)			->   BatchEncoding:



      """simple docstring"""







      a_									=kwargs.get("is_split_into_words"				,	lowerCAmelCase_)

      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(*lowerCAmelCase_				,	**lowerCAmelCase_)




 def    lowercase_	( self				,	lowerCAmelCase_				,	lowerCAmelCase_ = None)			->   Tuple[str]:



      """simple docstring"""







      a_									=self._tokenizer.model.save(lowerCAmelCase_				,	name=lowerCAmelCase_)
      return tuple(lowerCAmelCase_)




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



      """simple docstring"""







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

      if len(lowerCAmelCase_) > self.model_max_length:
           a_									=input_ids[-self.model_max_length :]
      return input_ids







'''simple docstring'''


lowercase											=      (1, 153, 370, 371, 1_634, 24_678_051, 115_132_219_018_763_992_565_095_597_973_971_522_401)
lowercase											=      (-153, -1, 0, 1.2, 200, "A", [], {}, None)






def 			UpperCAmelCase_							(		lowercase__							):


     '''simple docstring'''







     if not isinstance(lowercase__							,						lowercase__							) or n < 1:
          return False

     # Initialization of sum and number of digits.
     a_									=0
     a_									=0
     a_									=n
     # Calculation of digits of the number
     while temp > 0:
          number_of_digits += 1
          temp //= 1_0
     # Dividing number into separate digits and find Armstrong number
     a_									=n
     while temp > 0:
          a_									=temp % 1_0
          total += rem**number_of_digits
          temp //= 1_0
     return n == total






def 			UpperCAmelCase_							(		lowercase__							):


     '''simple docstring'''







     if not isinstance(lowercase__							,						lowercase__							) or n < 1:
          return False

     # Init a "histogram" of the digits
     a_									=[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
     a_									=0
     a_									=0
     a_									=n
     while temp > 0:
          a_		,    a_									=divmod(lowercase__							,						1_0							)
          digit_histogram[rem] += 1
          digit_total += 1

     for cnt, i in zip(lowercase__							,						range(len(lowercase__							)							)							):
          total += cnt * i**digit_total

     return n == total






def 			UpperCAmelCase_							(		lowercase__							):


     '''simple docstring'''







     if not isinstance(lowercase__							,						lowercase__							) or n < 1:
          return False
     a_									=len(str(lowercase__							)							)  # the power that all digits will be raised to
     # check if sum of each digit multiplied expo times is equal to number
     return n == sum(int(lowercase__							) ** expo for i in str(lowercase__							)							)






def 			UpperCAmelCase_							(		):


     '''simple docstring'''







     a_									=int(input("Enter an integer to see if it is an Armstrong number: "							).strip()							)
     print(F"""{num} is {'' if armstrong_number(lowercase__							) else 'not '}an Armstrong number."""							)
     print(F"""{num} is {'' if narcissistic_number(lowercase__							) else 'not '}an Armstrong number."""							)
     print(F"""{num} is {'' if pluperfect_number(lowercase__							) else 'not '}an Armstrong number."""							)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()


