






"""simple docstring"""



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

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_gpta import GPTaTokenizer


if TYPE_CHECKING:
    from transformers.pipelines.conversational import Conversation


_UpperCamelCase		:  str						=		logging.get_logger(__name__)

_UpperCamelCase		:  Union[str, Any]						=		{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

_UpperCamelCase		:  Optional[Any]						=		{
    'vocab_file': {
        'gpt2': 'https://huggingface.co/gpt2/resolve/main/vocab.json',
        'gpt2-medium': 'https://huggingface.co/gpt2-medium/resolve/main/vocab.json',
        'gpt2-large': 'https://huggingface.co/gpt2-large/resolve/main/vocab.json',
        'gpt2-xl': 'https://huggingface.co/gpt2-xl/resolve/main/vocab.json',
        'distilgpt2': 'https://huggingface.co/distilgpt2/resolve/main/vocab.json',
    },
    'merges_file': {
        'gpt2': 'https://huggingface.co/gpt2/resolve/main/merges.txt',
        'gpt2-medium': 'https://huggingface.co/gpt2-medium/resolve/main/merges.txt',
        'gpt2-large': 'https://huggingface.co/gpt2-large/resolve/main/merges.txt',
        'gpt2-xl': 'https://huggingface.co/gpt2-xl/resolve/main/merges.txt',
        'distilgpt2': 'https://huggingface.co/distilgpt2/resolve/main/merges.txt',
    },
    'tokenizer_file': {
        'gpt2': 'https://huggingface.co/gpt2/resolve/main/tokenizer.json',
        'gpt2-medium': 'https://huggingface.co/gpt2-medium/resolve/main/tokenizer.json',
        'gpt2-large': 'https://huggingface.co/gpt2-large/resolve/main/tokenizer.json',
        'gpt2-xl': 'https://huggingface.co/gpt2-xl/resolve/main/tokenizer.json',
        'distilgpt2': 'https://huggingface.co/distilgpt2/resolve/main/tokenizer.json',
    },
}

_UpperCamelCase		:  Union[str, Any]						=		{
    'gpt2': 1_0_2_4,
    'gpt2-medium': 1_0_2_4,
    'gpt2-large': 1_0_2_4,
    'gpt2-xl': 1_0_2_4,
    'distilgpt2': 1_0_2_4,
}

class        a     (   a_	):
    UpperCAmelCase_ :     Any	     =VOCAB_FILES_NAMES
    UpperCAmelCase_ :     Any	     =PRETRAINED_VOCAB_FILES_MAP
    UpperCAmelCase_ :     Any	     =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    UpperCAmelCase_ :     int	     =["input_ids", "attention_mask"]
    UpperCAmelCase_ :     Optional[Any]	     =GPTaTokenizer







    def __init__(			self							,		_lowerCamelCase=None							,		_lowerCamelCase=None							,		_lowerCamelCase=None							,		_lowerCamelCase="<|endoftext|>"							,		_lowerCamelCase="<|endoftext|>"							,		_lowerCamelCase="<|endoftext|>"							,		_lowerCamelCase=False							,		**_lowerCamelCase							,		):
      super().__init__(
          _lowerCamelCase							,		_lowerCamelCase							,		tokenizer_file=_lowerCamelCase							,		unk_token=_lowerCamelCase							,		bos_token=_lowerCamelCase							,		eos_token=_lowerCamelCase							,		add_prefix_space=_lowerCamelCase							,		**_lowerCamelCase							,		)

      lowercase							    =       kwargs.pop('add_bos_token'							,		_lowerCamelCase       )

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

      lowercase							    =       add_prefix_space







    def 			UpperCamelCase_      (			self							,		*_lowerCamelCase							,		**_lowerCamelCase       ):
      lowercase							    =       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 			UpperCamelCase_      (			self							,		*_lowerCamelCase							,		**_lowerCamelCase       ):
      lowercase							    =       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 			UpperCamelCase_      (			self							,		_lowerCamelCase							,		_lowerCamelCase = None       ):
      lowercase							    =       self._tokenizer.model.save(_lowerCamelCase							,		name=_lowerCamelCase       )
      return tuple(_lowerCamelCase       )







    def 			UpperCamelCase_      (			self							,		_lowerCamelCase       ):
      lowercase							    =       []
      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:
        lowercase							    =       input_ids[-self.model_max_length :]
      return input_ids










"""simple docstring"""



def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  int    ):





  '''simple docstring'''





  return sum(i for i in range(1		,					number // 2 + 1    ) if number % i == 0    ) == number


if __name__ == "__main__":
    print('Program to check whether a number is a Perfect number or not...')
    _UpperCamelCase		:  Tuple						=		int(input('Enter number: ').strip())
    print(F'''{number} is {'' if perfect(number) else 'not '}a Perfect Number.''')


