


'''simple docstring'''




import os
import re
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


_a   :   str												=   logging.get_logger(__name__)

_a   :   List[Any]												=   {"vocab_file": "spiece.model"}

_a   :   Tuple												=   {
    "vocab_file": {
        "google/bigbird-roberta-base": "https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model",
        "google/bigbird-roberta-large": (
            "https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model"
        ),
        "google/bigbird-base-trivia-itc": (
            "https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model"
        ),
    }
}

_a   :   Optional[Any]												=   {
    "google/bigbird-roberta-base": 4_096,
    "google/bigbird-roberta-large": 4_096,
    "google/bigbird-base-trivia-itc": 4_096,
}




class 			_lowercase (	__lowercase							):
   _SCREAMING_SNAKE_CASE  :  List[Any]          =	VOCAB_FILES_NAMES
   _SCREAMING_SNAKE_CASE  :  List[str]          =	PRETRAINED_VOCAB_FILES_MAP
   _SCREAMING_SNAKE_CASE  :  str          =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _SCREAMING_SNAKE_CASE  :  Optional[int]          =	["input_ids", "attention_mask"]
   _SCREAMING_SNAKE_CASE  :  List[int]          =	[]
   def __init__(						self       :      Union[str, Any]       ,   SCREAMING_SNAKE_CASE_       :      Tuple       ,   SCREAMING_SNAKE_CASE_       :      int="<unk>"       ,   SCREAMING_SNAKE_CASE_       :      Any="<s>"       ,   SCREAMING_SNAKE_CASE_       :      List[str]="</s>"       ,   SCREAMING_SNAKE_CASE_       :      List[Any]="<pad>"       ,   SCREAMING_SNAKE_CASE_       :      List[str]="[SEP]"       ,   SCREAMING_SNAKE_CASE_       :      Any="[MASK]"       ,   SCREAMING_SNAKE_CASE_       :      Dict="[CLS]"       ,   SCREAMING_SNAKE_CASE_       :      Optional[Dict[str, Any]] = None       ,   **SCREAMING_SNAKE_CASE_       :      List[Any]       ,   ) ->							None:
        __snake_case           =						AddedToken(SCREAMING_SNAKE_CASE_       ,   lstrip=SCREAMING_SNAKE_CASE_       ,   rstrip=SCREAMING_SNAKE_CASE_      ) if isinstance(SCREAMING_SNAKE_CASE_       ,   SCREAMING_SNAKE_CASE_      ) else bos_token
        __snake_case           =						AddedToken(SCREAMING_SNAKE_CASE_       ,   lstrip=SCREAMING_SNAKE_CASE_       ,   rstrip=SCREAMING_SNAKE_CASE_      ) if isinstance(SCREAMING_SNAKE_CASE_       ,   SCREAMING_SNAKE_CASE_      ) else eos_token
        __snake_case           =						AddedToken(SCREAMING_SNAKE_CASE_       ,   lstrip=SCREAMING_SNAKE_CASE_       ,   rstrip=SCREAMING_SNAKE_CASE_      ) if isinstance(SCREAMING_SNAKE_CASE_       ,   SCREAMING_SNAKE_CASE_      ) else unk_token
        __snake_case           =						AddedToken(SCREAMING_SNAKE_CASE_       ,   lstrip=SCREAMING_SNAKE_CASE_       ,   rstrip=SCREAMING_SNAKE_CASE_      ) if isinstance(SCREAMING_SNAKE_CASE_       ,   SCREAMING_SNAKE_CASE_      ) else pad_token
        __snake_case           =						AddedToken(SCREAMING_SNAKE_CASE_       ,   lstrip=SCREAMING_SNAKE_CASE_       ,   rstrip=SCREAMING_SNAKE_CASE_      ) if isinstance(SCREAMING_SNAKE_CASE_       ,   SCREAMING_SNAKE_CASE_      ) else cls_token
        __snake_case           =						AddedToken(SCREAMING_SNAKE_CASE_       ,   lstrip=SCREAMING_SNAKE_CASE_       ,   rstrip=SCREAMING_SNAKE_CASE_      ) if isinstance(SCREAMING_SNAKE_CASE_       ,   SCREAMING_SNAKE_CASE_      ) else sep_token

        # Mask token behave like a normal word, i.e. include the space before it
        __snake_case           =						AddedToken(SCREAMING_SNAKE_CASE_       ,   lstrip=SCREAMING_SNAKE_CASE_       ,   rstrip=SCREAMING_SNAKE_CASE_      ) if isinstance(SCREAMING_SNAKE_CASE_       ,   SCREAMING_SNAKE_CASE_      ) else mask_token

        __snake_case           =						{} if sp_model_kwargs is None else sp_model_kwargs

        super().__init__(
            bos_token=SCREAMING_SNAKE_CASE_       ,   eos_token=SCREAMING_SNAKE_CASE_       ,   unk_token=SCREAMING_SNAKE_CASE_       ,   pad_token=SCREAMING_SNAKE_CASE_       ,   sep_token=SCREAMING_SNAKE_CASE_       ,   mask_token=SCREAMING_SNAKE_CASE_       ,   cls_token=SCREAMING_SNAKE_CASE_       ,   sp_model_kwargs=self.sp_model_kwargs       ,   **SCREAMING_SNAKE_CASE_       ,   )

        __snake_case           =						vocab_file

        __snake_case           =						spm.SentencePieceProcessor(**self.sp_model_kwargs      )
        self.sp_model.Load(SCREAMING_SNAKE_CASE_      )
   @property
   def a  (						self       :      List[str]      ) ->							int:
        return self.sp_model.get_piece_size()
   def a  (						self       :      Dict      ) ->							str:
        __snake_case           =						{self.convert_ids_to_tokens(SCREAMING_SNAKE_CASE_      ): i for i in range(self.vocab_size      )}
        vocab.update(self.added_tokens_encoder      )
        return vocab
   def __getstate__(						self       :      Dict      ) ->							str:
        __snake_case           =						self.__dict__.copy()
        __snake_case           =						None
        return state
   def __setstate__(						self       :      Optional[int]       ,   SCREAMING_SNAKE_CASE_       :      Dict      ) ->							Optional[int]:
        __snake_case           =						d

        # for backward compatibility
        if not hasattr(self       ,   'sp_model_kwargs'      ):
             __snake_case           =						{}

        __snake_case           =						spm.SentencePieceProcessor(**self.sp_model_kwargs      )
        self.sp_model.Load(self.vocab_file      )
   def a  (						self       :      List[str]       ,   SCREAMING_SNAKE_CASE_       :      str      ) ->							List[str]:
        return self.sp_model.encode(SCREAMING_SNAKE_CASE_       ,   out_type=SCREAMING_SNAKE_CASE_      )
   def a  (						self       :      str       ,   SCREAMING_SNAKE_CASE_       :      Any      ) ->							str:
        return self.sp_model.piece_to_id(SCREAMING_SNAKE_CASE_      )
   def a  (						self       :      Optional[int]       ,   SCREAMING_SNAKE_CASE_       :      Dict      ) ->							List[str]:
        __snake_case           =						self.sp_model.IdToPiece(SCREAMING_SNAKE_CASE_      )
        return token
   def a  (						self       :      Optional[int]       ,   SCREAMING_SNAKE_CASE_       :      Optional[int]      ) ->							Tuple:
        __snake_case           =						[]
        __snake_case           =						''
        __snake_case           =						False
        for token in tokens:
             # make sure that special tokens are not decoded using sentencepiece model
             if token in self.all_special_tokens:
                  if not prev_is_special:
                       out_string += " "
                  out_string += self.sp_model.decode(SCREAMING_SNAKE_CASE_      ) + token
                  __snake_case           =						True
                  __snake_case           =						[]
             else:
                  current_sub_tokens.append(SCREAMING_SNAKE_CASE_      )
                  __snake_case           =						False
        out_string += self.sp_model.decode(SCREAMING_SNAKE_CASE_      )
        return out_string.strip()
   def a  (						self       :      Union[str, Any]       ,   SCREAMING_SNAKE_CASE_       :      List[int]       ,   SCREAMING_SNAKE_CASE_       :      bool = False       ,   SCREAMING_SNAKE_CASE_       :      bool = None       ,   SCREAMING_SNAKE_CASE_       :      bool = True       ,   **SCREAMING_SNAKE_CASE_       :      int       ,   ) ->							str:
        __snake_case           =						kwargs.pop('use_source_tokenizer'       ,   SCREAMING_SNAKE_CASE_      )

        __snake_case           =						self.convert_ids_to_tokens(SCREAMING_SNAKE_CASE_       ,   skip_special_tokens=SCREAMING_SNAKE_CASE_      )

        # To avoid mixing byte-level and unicode for byte-level BPT
        # we need to build string separately for added tokens and byte-level tokens
        # cf. https://github.com/huggingface/transformers/issues/1133
        __snake_case           =						[]
        __snake_case           =						[]
        for token in filtered_tokens:
             if skip_special_tokens and token in self.all_special_ids:
                  continue
             if token in self.added_tokens_encoder:
                  if current_sub_text:
                       sub_texts.append(self.convert_tokens_to_string(SCREAMING_SNAKE_CASE_      )      )
                       __snake_case           =						[]
                  sub_texts.append(SCREAMING_SNAKE_CASE_      )
             else:
                  current_sub_text.append(SCREAMING_SNAKE_CASE_      )
        if current_sub_text:
             sub_texts.append(self.convert_tokens_to_string(SCREAMING_SNAKE_CASE_      )      )

        # Mimic the behavior of the Rust tokenizer:
        # No space before [MASK] and [SEP]
        if spaces_between_special_tokens:
             __snake_case           =						re.sub(r' (\[(MASK|SEP)\])'       ,   r'\1'       ,   ' '.join(SCREAMING_SNAKE_CASE_      )      )
        else:
             __snake_case           =						''.join(SCREAMING_SNAKE_CASE_      )

        __snake_case           =						(
            clean_up_tokenization_spaces
            if clean_up_tokenization_spaces is not None
            else self.clean_up_tokenization_spaces
        )
        if clean_up_tokenization_spaces:
             __snake_case           =						self.clean_up_tokenization(SCREAMING_SNAKE_CASE_      )
             return clean_text
        else:
             return text
   def a  (						self       :      str       ,   SCREAMING_SNAKE_CASE_       :      str       ,   SCREAMING_SNAKE_CASE_       :      Optional[str] = None      ) ->							Tuple[str]:
        if not os.path.isdir(SCREAMING_SNAKE_CASE_      ):
             logger.error(f'Vocabulary path ({save_directory}) should be a directory'      )
             return
        __snake_case           =						os.path.join(
            SCREAMING_SNAKE_CASE_       ,   (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']      )

        if os.path.abspath(self.vocab_file      ) != os.path.abspath(SCREAMING_SNAKE_CASE_      ) and os.path.isfile(self.vocab_file      ):
             copyfile(self.vocab_file       ,   SCREAMING_SNAKE_CASE_      )
        elif not os.path.isfile(self.vocab_file      ):
             with open(SCREAMING_SNAKE_CASE_       ,   'wb'      ) as fi:
                  __snake_case           =						self.sp_model.serialized_model_proto()
                  fi.write(SCREAMING_SNAKE_CASE_      )

        return (out_vocab_file,)
   def a  (						self       :      Any       ,   SCREAMING_SNAKE_CASE_       :      List[int]       ,   SCREAMING_SNAKE_CASE_       :      Optional[List[int]] = None      ) ->							List[int]:
        if token_ids_a is None:
             return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
        __snake_case           =						[self.cls_token_id]
        __snake_case           =						[self.sep_token_id]
        return cls + token_ids_a + sep + token_ids_a + sep
   def a  (						self       :      List[Any]       ,   SCREAMING_SNAKE_CASE_       :      List[int]       ,   SCREAMING_SNAKE_CASE_       :      Optional[List[int]] = None       ,   SCREAMING_SNAKE_CASE_       :      bool = False      ) ->							List[int]:
        if already_has_special_tokens:
             return super().get_special_tokens_mask(
                 token_ids_a=SCREAMING_SNAKE_CASE_       ,   token_ids_a=SCREAMING_SNAKE_CASE_       ,   already_has_special_tokens=SCREAMING_SNAKE_CASE_      )

        if token_ids_a is None:
             return [1] + ([0] * len(SCREAMING_SNAKE_CASE_      )) + [1]
        return [1] + ([0] * len(SCREAMING_SNAKE_CASE_      )) + [1] + ([0] * len(SCREAMING_SNAKE_CASE_      )) + [1]





   def a  (						self       :      Tuple       ,   SCREAMING_SNAKE_CASE_       :      List[int]       ,   SCREAMING_SNAKE_CASE_       :      Optional[List[int]] = None      ) ->							List[int]:
        __snake_case           =						[self.sep_token_id]
        __snake_case           =						[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]







"""simple docstring"""


from __future__ import annotations



def   _A					(	_a	:						list[float]     ,   _a	:						list[float]				):



      """simple docstring"""






      A				  =		sorted(numsa + numsa				)
      A  ,			A				  =		divmod(len(_a				)     ,   2				)
      if mod == 1:
            return all_numbers[div]
      else:
            return (all_numbers[div] + all_numbers[div - 1]) / 2


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  UpperCAmelCase													=[float(x) for x in input("Enter the elements of first array: ").split()]
  UpperCAmelCase													=[float(x) for x in input("Enter the elements of second array: ").split()]
  print(f"""The median of two arrays is: {median_of_two_arrays(array_a, array_a)}""")




