



"""simple docstring"""
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


A:   Any								=		logging.get_logger(__name__)

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

A:   Union[str, Any]								=		{
    "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"
        ),
    },
}

A:   Tuple								=		{
    "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       SCREAMING_SNAKE_CASE__  (					UpperCAmelCase__ ):
   __lowerCAmelCase  :   int						=						VOCAB_FILES_NAMES
   __lowerCAmelCase  :   Optional[Any]						=						PRETRAINED_VOCAB_FILES_MAP
   __lowerCAmelCase  :   Optional[int]						=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __lowerCAmelCase  :   str						=						['input_ids', 'attention_mask']
   __lowerCAmelCase  :   List[Any]						=						RobertaTokenizer

   def __init__(   self      ,     _SCREAMING_SNAKE_CASE=None      ,     _SCREAMING_SNAKE_CASE=None      ,     _SCREAMING_SNAKE_CASE=None      ,     _SCREAMING_SNAKE_CASE="replace"      ,     _SCREAMING_SNAKE_CASE="<s>"      ,     _SCREAMING_SNAKE_CASE="</s>"      ,     _SCREAMING_SNAKE_CASE="</s>"      ,     _SCREAMING_SNAKE_CASE="<s>"      ,     _SCREAMING_SNAKE_CASE="<unk>"      ,     _SCREAMING_SNAKE_CASE="<pad>"      ,     _SCREAMING_SNAKE_CASE="<mask>"      ,     _SCREAMING_SNAKE_CASE=False      ,     _SCREAMING_SNAKE_CASE=True      ,     **_SCREAMING_SNAKE_CASE      ,     )  ->      str:





    '''simple docstring'''




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

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

    UpperCAmelCase	:					List[Any]	=	add_prefix_space

    UpperCAmelCase	:					Optional[int]	=	"""post_processor"""
    UpperCAmelCase	:					str	=	getattr(self.backend_tokenizer      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						)
    if tokenizer_component_instance:
     UpperCAmelCase	:					List[str]	=	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:
      UpperCAmelCase	:					int	=	tuple(state["""sep"""]						)
     if "cls" in state:
      UpperCAmelCase	:					Dict	=	tuple(state["""cls"""]						)

     UpperCAmelCase	:					Optional[Any]	=	False

     if state.get("""add_prefix_space"""      ,     _SCREAMING_SNAKE_CASE						) != add_prefix_space:
      UpperCAmelCase	:					Optional[int]	=	add_prefix_space
      UpperCAmelCase	:					Union[str, Any]	=	True

     if state.get("""trim_offsets"""      ,     _SCREAMING_SNAKE_CASE						) != trim_offsets:
      UpperCAmelCase	:					int	=	trim_offsets
      UpperCAmelCase	:					Union[str, Any]	=	True

     if changes_to_apply:
      UpperCAmelCase	:					List[Any]	=	getattr(_SCREAMING_SNAKE_CASE      ,     state.pop("""type"""						)						)
      UpperCAmelCase	:					Optional[int]	=	component_class(**_SCREAMING_SNAKE_CASE						)
      setattr(self.backend_tokenizer      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						)

   @property
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      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 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE						)  ->      Optional[int]:





    '''simple docstring'''




    UpperCAmelCase	:					Union[str, Any]	=	AddedToken(_SCREAMING_SNAKE_CASE      ,     lstrip=_SCREAMING_SNAKE_CASE      ,     rstrip=_SCREAMING_SNAKE_CASE						) if isinstance(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						) else value
    UpperCAmelCase	:					Any	=	value

   def 	SCREAMING_SNAKE_CASE     (   self      ,     *_SCREAMING_SNAKE_CASE      ,     **_SCREAMING_SNAKE_CASE						)  ->      BatchEncoding:





    '''simple docstring'''




    UpperCAmelCase	:					List[str]	=	kwargs.get("""is_split_into_words"""      ,     _SCREAMING_SNAKE_CASE						)
    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(*_SCREAMING_SNAKE_CASE      ,     **_SCREAMING_SNAKE_CASE						)

   def 	SCREAMING_SNAKE_CASE     (   self      ,     *_SCREAMING_SNAKE_CASE      ,     **_SCREAMING_SNAKE_CASE						)  ->      BatchEncoding:





    '''simple docstring'''




    UpperCAmelCase	:					Tuple	=	kwargs.get("""is_split_into_words"""      ,     _SCREAMING_SNAKE_CASE						)

    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(*_SCREAMING_SNAKE_CASE      ,     **_SCREAMING_SNAKE_CASE						)

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE = None						)  ->      Tuple[str]:





    '''simple docstring'''




    UpperCAmelCase	:					str	=	self._tokenizer.model.save(_SCREAMING_SNAKE_CASE      ,     name=_SCREAMING_SNAKE_CASE						)
    return tuple(_SCREAMING_SNAKE_CASE						)

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE=None						)  ->      Union[str, Any]:





    '''simple docstring'''




    UpperCAmelCase	:					Union[str, 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 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE = None						)  ->      List[int]:





    '''simple docstring'''




    UpperCAmelCase	:					Dict	=	[self.sep_token_id]
    UpperCAmelCase	:					Tuple	=	[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]







from __future__ import annotations






def lowerCamelCase__    (							snake_case_	:  list[int]       )      ->				list[int]:  # This function is recursive
   __snake_case							       =   len(snake_case_       )
   # If the array contains only one element, we return it (it's the stop condition of
   # recursion)
   if array_length <= 1:
      return array
      # Else
   __snake_case							       =   array[0]
   __snake_case							       =   False
   __snake_case							       =   1
   __snake_case							       =   []
   while not is_found and i < array_length:
      if array[i] < pivot:
         __snake_case							       =   True
         __snake_case							       =   [element for element in array[i:] if element >= array[i]]
         __snake_case							       =   longest_subsequence(snake_case_       )
         if len(snake_case_       ) > len(snake_case_       ):
            __snake_case							       =   temp_array
      else:
         i += 1

   __snake_case							       =   [element for element in array[1:] if element >= pivot]
   __snake_case							       =   [pivot, *longest_subsequence(snake_case_       )]
   if len(snake_case_       ) > len(snake_case_       ):
      return temp_array
   else:
      return longest_subseq


if __name__ == "__main__":
    import doctest

    doctest.testmod()

