

import json
from typing import Dict, List, Optional, Tuple, Union

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_led import LEDTokenizer


UpperCAmelCase__ :     Optional[int]				    =    logging.get_logger(__name__)


UpperCAmelCase__ :     List[Any]				    =    {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

UpperCAmelCase__ :     List[str]				    =    {
    """vocab_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json""",
    },
    """merges_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt""",
    },
    """tokenizer_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json""",
    },
}

UpperCAmelCase__ :     int				    =    {
    """allenai/led-base-16384""": 16_384,
}







class        a__       (   UpperCAmelCase       ):







  """simple docstring"""




  UpperCAmelCase__      : Dict			       =VOCAB_FILES_NAMES
  UpperCAmelCase__      : Union[str, Any]			       =PRETRAINED_VOCAB_FILES_MAP
  UpperCAmelCase__      : int			       =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  UpperCAmelCase__      : List[Any]			       =LEDTokenizer
  UpperCAmelCase__      : Dict			       =["""input_ids""", """attention_mask"""]


  def __init__(			self    : Dict				,				UpperCAmelCase__    : str=None				,				UpperCAmelCase__    : List[str]=None				,				UpperCAmelCase__    : int=None				,				UpperCAmelCase__    : Any="replace"				,				UpperCAmelCase__    : str="<s>"				,				UpperCAmelCase__    : List[str]="</s>"				,				UpperCAmelCase__    : Any="</s>"				,				UpperCAmelCase__    : Tuple="<s>"				,				UpperCAmelCase__    : Optional[Any]="<unk>"				,				UpperCAmelCase__    : List[str]="<pad>"				,				UpperCAmelCase__    : List[str]="<mask>"				,				UpperCAmelCase__    : Union[str, Any]=False				,				UpperCAmelCase__    : Dict=True				,				**UpperCAmelCase__    : Any				,				)					->Optional[int]:
        """simple docstring"""







        super().__init__(
            UpperCAmelCase__				,				UpperCAmelCase__				,				tokenizer_file=UpperCAmelCase__				,				errors=UpperCAmelCase__				,				bos_token=UpperCAmelCase__				,				eos_token=UpperCAmelCase__				,				sep_token=UpperCAmelCase__				,				cls_token=UpperCAmelCase__				,				unk_token=UpperCAmelCase__				,				pad_token=UpperCAmelCase__				,				mask_token=UpperCAmelCase__				,				add_prefix_space=UpperCAmelCase__				,				trim_offsets=UpperCAmelCase__				,				**UpperCAmelCase__				,				)

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

        SCREAMING_SNAKE_CASE :      Dict       =   add_prefix_space

        # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
        SCREAMING_SNAKE_CASE :      Dict       =   """post_processor"""
        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   getattr(self.backend_tokenizer				,				UpperCAmelCase__				,				UpperCAmelCase__		)
        if tokenizer_component_instance:
              SCREAMING_SNAKE_CASE :      Union[str, Any]       =   json.loads(tokenizer_component_instance.__getstate__()		)

              # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
              if "sep" in state:
                    SCREAMING_SNAKE_CASE :      Tuple       =   tuple(state["""sep"""]		)
              if "cls" in state:
                    SCREAMING_SNAKE_CASE :      Optional[Any]       =   tuple(state["""cls"""]		)

              SCREAMING_SNAKE_CASE :      Optional[int]       =   False

              if state.get("""add_prefix_space"""				,				UpperCAmelCase__		) != add_prefix_space:
                    SCREAMING_SNAKE_CASE :      Optional[int]       =   add_prefix_space
                    SCREAMING_SNAKE_CASE :      Any       =   True

              if state.get("""trim_offsets"""				,				UpperCAmelCase__		) != trim_offsets:
                    SCREAMING_SNAKE_CASE :      int       =   trim_offsets
                    SCREAMING_SNAKE_CASE :      int       =   True

              if changes_to_apply:
                    SCREAMING_SNAKE_CASE :      Dict       =   getattr(UpperCAmelCase__				,				state.pop("""type"""		)		)
                    SCREAMING_SNAKE_CASE :      Tuple       =   component_class(**UpperCAmelCase__		)
                    setattr(self.backend_tokenizer				,				UpperCAmelCase__				,				UpperCAmelCase__		)


  @property
  # Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
  def  _lowercase      (			self    : 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  _lowercase      (			self    : Dict				,				UpperCAmelCase__    : Union[str, Any]		)					->Optional[Any]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      int       =   AddedToken(UpperCAmelCase__				,				lstrip=UpperCAmelCase__				,				rstrip=UpperCAmelCase__		) if isinstance(UpperCAmelCase__				,				UpperCAmelCase__		) else value
        SCREAMING_SNAKE_CASE :      Any       =   value


  def  _lowercase      (			self    : Optional[Any]				,				*UpperCAmelCase__    : int				,				**UpperCAmelCase__    : int		)					->BatchEncoding:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      List[Any]       =   kwargs.get("""is_split_into_words"""				,				UpperCAmelCase__		)

        if is_split_into_words and not self.add_prefix_space:
              raise ValueError(
                  f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
                  """to use it with pretokenized inputs."""		)

        return super()._batch_encode_plus(*UpperCAmelCase__				,				**UpperCAmelCase__		)


  def  _lowercase      (			self    : List[Any]				,				*UpperCAmelCase__    : int				,				**UpperCAmelCase__    : str		)					->BatchEncoding:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Any       =   kwargs.get("""is_split_into_words"""				,				UpperCAmelCase__		)

        if is_split_into_words and not self.add_prefix_space:
              raise ValueError(
                  f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
                  """to use it with pretokenized inputs."""		)

        return super()._encode_plus(*UpperCAmelCase__				,				**UpperCAmelCase__		)


  def  _lowercase      (			self    : List[str]				,				UpperCAmelCase__    : str				,				UpperCAmelCase__    : Optional[str] = None		)					->Tuple[str]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   self._tokenizer.model.save(UpperCAmelCase__				,				name=UpperCAmelCase__		)
        return tuple(UpperCAmelCase__		)


  def  _lowercase      (			self    : List[Any]				,				UpperCAmelCase__    : Optional[Any]				,				UpperCAmelCase__    : Optional[int]=None		)					->Optional[Any]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Tuple       =   [self.bos_token_id] + token_ids_a + [self.eos_token_id]
        if token_ids_a is None:
              return output

        return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]


  def  _lowercase      (			self    : Optional[Any]				,				UpperCAmelCase__    : List[int]				,				UpperCAmelCase__    : Optional[List[int]] = None		)					->List[int]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Tuple       =   [self.sep_token_id]
        SCREAMING_SNAKE_CASE :      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 + sep + token_ids_a + sep		) * [0]




  def  _lowercase      (			self    : int				,				UpperCAmelCase__    : Union[Dict[str, EncodedInput], BatchEncoding]				,				UpperCAmelCase__    : Optional[int] = None				,				UpperCAmelCase__    : PaddingStrategy = PaddingStrategy.DO_NOT_PAD				,				UpperCAmelCase__    : Optional[int] = None				,				UpperCAmelCase__    : Optional[bool] = None				,				)					->dict:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Optional[Any]       =   super()._pad(
            encoded_inputs=UpperCAmelCase__				,				max_length=UpperCAmelCase__				,				padding_strategy=UpperCAmelCase__				,				pad_to_multiple_of=UpperCAmelCase__				,				return_attention_mask=UpperCAmelCase__				,				)

        # Load from model defaults
        if return_attention_mask is None:
              SCREAMING_SNAKE_CASE :      List[str]       =   """attention_mask""" in self.model_input_names

        if return_attention_mask and "global_attention_mask" in encoded_inputs:
              SCREAMING_SNAKE_CASE :      Any       =   encoded_inputs[self.model_input_names[0]]
              # `global_attention_mask` need to have the same length as other (sequential) inputs.
              SCREAMING_SNAKE_CASE :      Optional[Any]       =   len(encoded_inputs["""global_attention_mask"""]		) != len(UpperCAmelCase__		)

              if needs_to_be_padded:
                    SCREAMING_SNAKE_CASE :      Optional[int]       =   len(UpperCAmelCase__		) - len(encoded_inputs["""global_attention_mask"""]		)

                    if self.padding_side == "right":
                          # Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
                          SCREAMING_SNAKE_CASE :      Union[str, Any]       =   (
                              encoded_inputs["""global_attention_mask"""] + [-1] * difference
                          )
                    elif self.padding_side == "left":
                          SCREAMING_SNAKE_CASE :      int       =   [-1] * difference + encoded_inputs[
                              """global_attention_mask"""
                          ]
                    else:
                          raise ValueError("""Invalid padding strategy:""" + str(self.padding_side		)		)

        return encoded_inputs







from math import ceil






def 					__lowercase      (     _A = 1001			)     ->						int:
      SCREAMING_SNAKE_CASE :      Union[str, Any]       =   1

      for i in range(1						,    int(ceil(n / 2.0			)			)			):
            SCREAMING_SNAKE_CASE :      Dict       =   2 * i + 1
            SCREAMING_SNAKE_CASE :      Optional[int]       =   2 * i
            SCREAMING_SNAKE_CASE :      str       =   total + 4 * odd**2 - 6 * even

      return total


if __name__ == "__main__":
  import sys

  if len(sys.argv) == 1:
    print(solution())
  else:
    try:
      UpperCAmelCase__ :     str				    =    int(sys.argv[1])
      print(solution(n))
    except ValueError:
      print("""Invalid entry - please enter a number""")




