






from math import sqrt





def      __lowercase  (		lowerCamelCase      :  int			):
 UpperCamelCase_      :		List[Any]				=      [1]
 for i in range(2						,						int(sqrt(lowerCamelCase			)			) + 1						,						1			):
  if number % i == 0:
   values.append(lowerCamelCase			)
   if int(number // i			) != i:
    values.append(int(number // i			)			)
 return sorted(lowerCamelCase			)





def      __lowercase  (		lowerCamelCase      :  int			):
 return sum(factors(lowerCamelCase			)			) > n





def      __lowercase  (		lowerCamelCase      :  int			):
 UpperCamelCase_      :		Tuple				=      factors(lowerCamelCase			)
 UpperCamelCase_      :		List[str]				=      len(lowerCamelCase			)
 UpperCamelCase_      :		str				=      [[0 for i in range(number + 1			)] for j in range(r + 1			)]
 for i in range(r + 1			):
  UpperCamelCase_      :		Dict				=      True

 for i in range(1						,						number + 1			):
  UpperCamelCase_      :		int				=      False

 for i in range(1						,						r + 1			):
  for j in range(1						,						number + 1			):
   if j < values[i - 1]:
    UpperCamelCase_      :		int				=      subset[i - 1][j]
   else:
    UpperCamelCase_      :		Any				=      subset[i - 1][j] or subset[i - 1][j - values[i - 1]]

 return subset[r][number] != 0





def      __lowercase  (		lowerCamelCase      :  int			):
 return abundant(lowerCamelCase			) and not semi_perfect(lowerCamelCase			)


if __name__ == "__main__":
   import doctest

   doctest.testmod(verbose=True)
   for number in (69, 70, 71):
      print(F"""{number} is {'' if weird(number) else 'not '}weird.""")




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


a_					      = logging.get_logger(__name__)

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

a_					      = {
    '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'
        ),
    },
}

a_					      = {
    '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,
}

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





class 	_lowercase		(      snake_case_ ):
 lowercase         =     VOCAB_FILES_NAMES
 lowercase         =     PRETRAINED_VOCAB_FILES_MAP
 lowercase         =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 lowercase         =     ['input_ids', 'attention_mask', 'token_type_ids']
 lowercase         =     DebertaTokenizer

 def __init__(				self			:							Optional[int]     ,   snake_case			:							Any=None     ,   snake_case			:							Union[str, Any]=None     ,   snake_case			:							Optional[Any]=None     ,   snake_case			:							List[str]="replace"     ,   snake_case			:							int="[CLS]"     ,   snake_case			:							Any="[SEP]"     ,   snake_case			:							int="[SEP]"     ,   snake_case			:							Union[str, Any]="[CLS]"     ,   snake_case			:							List[Any]="[UNK]"     ,   snake_case			:							List[str]="[PAD]"     ,   snake_case			:							Tuple="[MASK]"     ,   snake_case			:							int=False     ,   **snake_case			:							List[str]     ,   )     ->		Any:

  """simple docstring"""





  super().__init__(
      snake_case     ,   snake_case     ,   tokenizer_file=snake_case     ,   errors=snake_case     ,   bos_token=snake_case     ,   eos_token=snake_case     ,   unk_token=snake_case     ,   sep_token=snake_case     ,   cls_token=snake_case     ,   pad_token=snake_case     ,   mask_token=snake_case     ,   add_prefix_space=snake_case     ,   **snake_case     ,   )
  UpperCamelCase_      :		str				=      kwargs.pop('add_bos_token'     ,   snake_case    )

  UpperCamelCase_      :		Tuple				=      json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()    )
  if pre_tok_state.get('add_prefix_space'     ,   snake_case    ) != add_prefix_space:
   UpperCamelCase_      :		str				=      getattr(snake_case     ,   pre_tok_state.pop('type'    )    )
   UpperCamelCase_      :		List[Any]				=      add_prefix_space
   UpperCamelCase_      :		List[str]				=      pre_tok_class(**snake_case    )

  UpperCamelCase_      :		int				=      add_prefix_space

 @property
 def 		SCREAMING_SNAKE_CASE__     (				self			:							List[str]    )     ->		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 		SCREAMING_SNAKE_CASE__     (				self			:							Any     ,   snake_case			:							Optional[int]    )     ->		str:

  """simple docstring"""





  UpperCamelCase_      :		List[Any]				=      AddedToken(snake_case     ,   lstrip=snake_case     ,   rstrip=snake_case    ) if isinstance(snake_case     ,   snake_case    ) else value
  UpperCamelCase_      :		Optional[int]				=      value

 def 		SCREAMING_SNAKE_CASE__     (				self			:							str     ,   snake_case			:							List[int]     ,   snake_case			:							Optional[List[int]] = None    )     ->		List[int]:

  """simple docstring"""





  if token_ids_a is None:
   return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
  UpperCamelCase_      :		Dict				=      [self.cls_token_id]
  UpperCamelCase_      :		Tuple				=      [self.sep_token_id]
  return cls + token_ids_a + sep + token_ids_a + sep

 def 		SCREAMING_SNAKE_CASE__     (				self			:							str     ,   snake_case			:							List[int]     ,   snake_case			:							Optional[List[int]] = None    )     ->		List[int]:

  """simple docstring"""





  UpperCamelCase_      :		Optional[int]				=      [self.sep_token_id]
  UpperCamelCase_      :		List[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 		SCREAMING_SNAKE_CASE__     (				self			:							Optional[int]     ,   *snake_case			:							Any     ,   **snake_case			:							Union[str, Any]    )     ->		BatchEncoding:

  """simple docstring"""





  UpperCamelCase_      :		Tuple				=      kwargs.get('is_split_into_words'     ,   snake_case    )
  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(*snake_case     ,   **snake_case    )

 def 		SCREAMING_SNAKE_CASE__     (				self			:							int     ,   *snake_case			:							List[str]     ,   **snake_case			:							Union[str, Any]    )     ->		BatchEncoding:

  """simple docstring"""





  UpperCamelCase_      :		List[Any]				=      kwargs.get('is_split_into_words'     ,   snake_case    )

  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(*snake_case     ,   **snake_case    )

 def 		SCREAMING_SNAKE_CASE__     (				self			:							Optional[Any]     ,   snake_case			:							str     ,   snake_case			:							Optional[str] = None    )     ->		Tuple[str]:

  """simple docstring"""





  UpperCamelCase_      :		Dict				=      self._tokenizer.model.save(snake_case     ,   name=snake_case    )
  return tuple(snake_case    )


 def 		SCREAMING_SNAKE_CASE__     (				self			:							Any     ,   snake_case			:							"Conversation"    )     ->		List[int]:

  """simple docstring"""





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

  if len(snake_case    ) > self.model_max_length:
   UpperCamelCase_      :		Optional[int]				=      input_ids[-self.model_max_length :]
  return input_ids



