
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


__lowerCamelCase							=       logging.get_logger(__name__)

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

__lowerCamelCase							=       {
    """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""",
    },
}

__lowerCamelCase							=       {
    """gpt2""": 10_24,
    """gpt2-medium""": 10_24,
    """gpt2-large""": 10_24,
    """gpt2-xl""": 10_24,
    """distilgpt2""": 10_24,
}






class   UpperCAmelCase		(						__a ):
   A__					:		Optional[Any]						     =     VOCAB_FILES_NAMES
   A__					:		int						     =     PRETRAINED_VOCAB_FILES_MAP
   A__					:		Optional[Any]						     =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   A__					:		Tuple						     =     ["input_ids", "attention_mask"]
   A__					:		List[Any]						     =     GPTaTokenizer



   def __init__(self		:					Any    ,						snake_case__		:					Optional[Any]=None    ,						snake_case__		:					Tuple=None    ,						snake_case__		:					Optional[int]=None    ,						snake_case__		:					Dict="<|endoftext|>"    ,						snake_case__		:					Tuple="<|endoftext|>"    ,						snake_case__		:					Optional[Any]="<|endoftext|>"    ,						snake_case__		:					List[str]=False    ,						**snake_case__		:					Any    ,						)		->	List[Any]:


          '''simple docstring'''


          super().__init__(
              a_    ,						a_    ,						tokenizer_file=a_    ,						unk_token=a_    ,						bos_token=a_    ,						eos_token=a_    ,						add_prefix_space=a_    ,						**a_    ,						)

          snake_case       :   List[Any]							=      kwargs.pop("add_bos_token"    ,						a_ )

          snake_case       :   Any							=      json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
          if pre_tok_state.get("add_prefix_space"    ,						a_ ) != add_prefix_space:
                 snake_case       :   Any							=      getattr(a_    ,						pre_tok_state.pop("type" ) )
                 snake_case       :   Union[str, Any]							=      add_prefix_space
                 snake_case       :   Optional[int]							=      pre_tok_class(**a_ )

          snake_case       :   Any							=      add_prefix_space



   def 				_SCREAMING_SNAKE_CASE   (self		:					Tuple    ,						*snake_case__		:					List[str]    ,						**snake_case__		:					Tuple )		->	Dict:


          '''simple docstring'''


          snake_case       :   Optional[Any]							=      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 				_SCREAMING_SNAKE_CASE   (self		:					Optional[Any]    ,						*snake_case__		:					Optional[Any]    ,						**snake_case__		:					Dict )		->	Optional[Any]:


          '''simple docstring'''


          snake_case       :   Dict							=      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 				_SCREAMING_SNAKE_CASE   (self		:					List[Any]    ,						snake_case__		:					str    ,						snake_case__		:					Optional[str] = None )		->	List[str]:


          '''simple docstring'''


          snake_case       :   Any							=      self._tokenizer.model.save(a_    ,						name=a_ )
          return tuple(a_ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					int    ,						snake_case__		:					"Conversation" )		->	Union[str, Any]:


          '''simple docstring'''


          snake_case       :   Any							=      []
          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:
                 snake_case       :   List[Any]							=      input_ids[-self.model_max_length :]
          return input_ids











"""simple docstring"""


def 					UpperCAmelCase__       (snake_case__							:					list    ):
  """simple docstring"""

  if len(snake_case__    ) <= 1:
    return [tuple(snake_case__    )]

  _snake_case							:		List[Any]								=  []

  def generate(snake_case__							:					int		,   snake_case__							:					list    ):
    if k == 1:
      res.append(tuple(arr[:]    )    )
      return

    generate(k - 1		,   snake_case__    )

    for i in range(k - 1    ):
      if k % 2 == 0:  # k is even
        _snake_case		,					_snake_case							:		Optional[Any]								=  arr[k - 1], arr[i]
      else:  # k is odd
        _snake_case		,					_snake_case							:		List[str]								=  arr[k - 1], arr[0]
      generate(k - 1		,   snake_case__    )

  generate(len(snake_case__    )		,   snake_case__    )
  return res


if __name__ == "__main__":
     A_						=				input('''Enter numbers separated by a comma:\n''').strip()
     A_						=				[int(item) for item in user_input.split(''',''')]
     print(heaps(arr))


