

import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roberta import RobertaTokenizer


_lowerCAmelCase						=		logging.get_logger(__name__)

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

_lowerCAmelCase						=		{
    "vocab_file": {
        "roberta-base": "https://huggingface.co/roberta-base/resolve/main/vocab.json",
        "roberta-large": "https://huggingface.co/roberta-large/resolve/main/vocab.json",
        "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json",
        "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/vocab.json",
        "roberta-base-openai-detector": "https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json",
        "roberta-large-openai-detector": (
            "https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json"
        ),
    },
    "merges_file": {
        "roberta-base": "https://huggingface.co/roberta-base/resolve/main/merges.txt",
        "roberta-large": "https://huggingface.co/roberta-large/resolve/main/merges.txt",
        "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt",
        "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/merges.txt",
        "roberta-base-openai-detector": "https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt",
        "roberta-large-openai-detector": (
            "https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt"
        ),
    },
    "tokenizer_file": {
        "roberta-base": "https://huggingface.co/roberta-base/resolve/main/tokenizer.json",
        "roberta-large": "https://huggingface.co/roberta-large/resolve/main/tokenizer.json",
        "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/tokenizer.json",
        "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/tokenizer.json",
        "roberta-base-openai-detector": (
            "https://huggingface.co/roberta-base-openai-detector/resolve/main/tokenizer.json"
        ),
        "roberta-large-openai-detector": (
            "https://huggingface.co/roberta-large-openai-detector/resolve/main/tokenizer.json"
        ),
    },
}

_lowerCAmelCase						=		{
    "roberta-base": 5_1_2,
    "roberta-large": 5_1_2,
    "roberta-large-mnli": 5_1_2,
    "distilroberta-base": 5_1_2,
    "roberta-base-openai-detector": 5_1_2,
    "roberta-large-openai-detector": 5_1_2,
}







class 	__UpperCamelCase    (    _UpperCAmelCase						):
  _UpperCAmelCase          =		VOCAB_FILES_NAMES
  _UpperCAmelCase          =		PRETRAINED_VOCAB_FILES_MAP
  _UpperCAmelCase          =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  _UpperCAmelCase          =		['''input_ids''', '''attention_mask''']
  _UpperCAmelCase          =		RobertaTokenizer

  def __init__(		self    ,_A=None    ,_A=None    ,_A=None    ,_A="replace"    ,_A="<s>"    ,_A="</s>"    ,_A="</s>"    ,_A="<s>"    ,_A="<unk>"    ,_A="<pad>"    ,_A="<mask>"    ,_A=False    ,_A=True    ,**_A    ,):
      '''simple docstring'''

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

      _lowerCAmelCase       :   str  =    json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()      )
      if pre_tok_state.get('add_prefix_space'    ,A_      ) != add_prefix_space:
          _lowerCAmelCase       :   Optional[int]  =    getattr(A_    ,pre_tok_state.pop('type'      )      )
          _lowerCAmelCase       :   int  =    add_prefix_space
          _lowerCAmelCase       :   Tuple  =    pre_tok_class(**A_      )

      _lowerCAmelCase       :   Tuple  =    add_prefix_space

      _lowerCAmelCase       :   int  =    'post_processor'
      _lowerCAmelCase       :   Optional[Any]  =    getattr(self.backend_tokenizer    ,A_    ,A_      )
      if tokenizer_component_instance:
          _lowerCAmelCase       :   List[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:
              _lowerCAmelCase       :   Tuple  =    tuple(state['sep']      )
          if "cls" in state:
              _lowerCAmelCase       :   Tuple  =    tuple(state['cls']      )

          _lowerCAmelCase       :   Any  =    False

          if state.get('add_prefix_space'    ,A_      ) != add_prefix_space:
              _lowerCAmelCase       :   Optional[Any]  =    add_prefix_space
              _lowerCAmelCase       :   Optional[Any]  =    True

          if state.get('trim_offsets'    ,A_      ) != trim_offsets:
              _lowerCAmelCase       :   str  =    trim_offsets
              _lowerCAmelCase       :   int  =    True

          if changes_to_apply:
              _lowerCAmelCase       :   int  =    getattr(A_    ,state.pop('type'      )      )
              _lowerCAmelCase       :   Union[str, Any]  =    component_class(**A_      )
              setattr(self.backend_tokenizer    ,A_    ,A_      )

  @property
  def __lowerCamelCase		(		self      ):
      '''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 __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Optional[Any]  =    AddedToken(A_    ,lstrip=A_    ,rstrip=A_      ) if isinstance(A_    ,A_      ) else value
      _lowerCAmelCase       :   Optional[int]  =    value

  def __lowerCamelCase		(		self    ,*_A    ,**_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Optional[int]  =    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 __lowerCamelCase		(		self    ,*_A    ,**_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   str  =    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 __lowerCamelCase		(		self    ,_A    ,_A = None      ):
      '''simple docstring'''

      _lowerCAmelCase       :   str  =    self._tokenizer.model.save(A_    ,name=A_      )
      return tuple(A_      )

  def __lowerCamelCase		(		self    ,_A    ,_A=None      ):
      '''simple docstring'''

      _lowerCAmelCase       :   List[Any]  =    [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 __lowerCamelCase		(		self    ,_A    ,_A = None      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Optional[int]  =    [self.sep_token_id]
      _lowerCAmelCase       :   Optional[Any]  =    [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]








"""simple docstring"""






from __future__ import annotations

from collections.abc import Callable

_lowerCAmelCase						=		list[list[float | int]]

def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase   ):

    '''simple docstring'''



    _lowerCAmelCase       :   int  =    len(_lowerCamelCase   )
    _lowerCAmelCase       :   Matrix  =    [[0 for _ in range(size + 1   )] for _ in range(_lowerCamelCase   )]
    _lowerCAmelCase       :   int
    _lowerCAmelCase       :   int
    _lowerCAmelCase       :   int
    _lowerCAmelCase       :   int
    _lowerCAmelCase       :   int
    _lowerCAmelCase       :   float

    for row in range(_lowerCamelCase   ):
        for col in range(_lowerCamelCase   ):
            _lowerCAmelCase       :   Union[str, Any]  =    matrix[row][col]

        _lowerCAmelCase       :   Tuple  =    vector[row][0]

    _lowerCAmelCase       :   Dict  =    0
    _lowerCAmelCase       :   Any  =    0
    while row < size and col < size:
        # pivoting
        _lowerCAmelCase       :   Optional[int]  =    max((abs(augmented[rowa][col]   ), rowa) for rowa in range(_lowerCamelCase ,   _lowerCamelCase   )   )[
            1
        ]
        if augmented[pivot_row][col] == 0:
            col += 1
            continue
        else:
            _lowerCAmelCase,					_lowerCAmelCase       :   Tuple  =    augmented[pivot_row], augmented[row]

        for rowa in range(row + 1 ,   _lowerCamelCase   ):
            _lowerCAmelCase       :   Dict  =    augmented[rowa][col] / augmented[row][col]
            _lowerCAmelCase       :   Optional[Any]  =    0
            for cola in range(col + 1 ,   size + 1   ):
                augmented[rowa][cola] -= augmented[row][cola] * ratio

        row += 1
        col += 1

    # back substitution
    for col in range(1 ,   _lowerCamelCase   ):
        for row in range(_lowerCamelCase   ):
            _lowerCAmelCase       :   int  =    augmented[row][col] / augmented[col][col]
            for cola in range(_lowerCamelCase ,   size + 1   ):
                augmented[row][cola] -= augmented[col][cola] * ratio

    # round to get rid of numbers like 2.000000000000004
    return [
        [round(augmented[row][size] / augmented[row][row] ,   10   )] for row in range(_lowerCamelCase   )
    ]

def 		lowerCamelCase__		(					_lowerCamelCase   ):

    '''simple docstring'''



    _lowerCAmelCase       :   int  =    len(_lowerCamelCase   )
    _lowerCAmelCase       :   Matrix  =    [[0 for _ in range(_lowerCamelCase   )] for _ in range(_lowerCamelCase   )]
    _lowerCAmelCase       :   Matrix  =    [[0] for _ in range(_lowerCamelCase   )]
    _lowerCAmelCase       :   Matrix
    _lowerCAmelCase       :   int
    _lowerCAmelCase       :   int
    _lowerCAmelCase       :   int

    for x_val, y_val in enumerate(_lowerCamelCase   ):
        for col in range(_lowerCamelCase   ):
            _lowerCAmelCase       :   Union[str, Any]  =    (x_val + 1) ** (size - col - 1)
        _lowerCAmelCase       :   Optional[int]  =    y_val

    _lowerCAmelCase       :   List[Any]  =    solve(_lowerCamelCase ,   _lowerCamelCase   )

    def interpolated_func(_lowerCamelCase   ) -> int:
        return sum(
            round(coeffs[x_val][0]   ) * (var ** (size - x_val - 1))
            for x_val in range(_lowerCamelCase   )   )

    return interpolated_func

def 		lowerCamelCase__		(					_lowerCamelCase   ):

    '''simple docstring'''



    return (
        1
        - variable
        + variable**2
        - variable**3
        + variable**4
        - variable**5
        + variable**6
        - variable**7
        + variable**8
        - variable**9
        + variable**10
    )

def 		lowerCamelCase__		(					_lowerCamelCase = question_function ,   _lowerCamelCase = 10   ):

    '''simple docstring'''



    _lowerCAmelCase       :   list[int]  =    [func(_lowerCamelCase   ) for x_val in range(1 ,   order + 1   )]

    _lowerCAmelCase       :   list[Callable[[int], int]]  =    [
        interpolate(data_points[:max_coeff]   ) for max_coeff in range(1 ,   order + 1   )
    ]

    _lowerCAmelCase       :   int  =    0
    _lowerCAmelCase       :   Callable[[int], int]
    _lowerCAmelCase       :   int

    for poly in polynomials:
        _lowerCAmelCase       :   Any  =    1
        while func(_lowerCamelCase   ) == poly(_lowerCamelCase   ):
            x_val += 1

        ret += poly(_lowerCamelCase   )

    return ret


if __name__ == "__main__":
 print(F'''{solution() = }''')





