"""simple docstring"""






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


__A			   = logging.get_logger(__name__)


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

__A			   = {
    "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",
    },
}

__A			   = {
    "allenai/led-base-16384": 1_6_3_8_4,
}


class     UpperCAmelCase						(_UpperCAmelCase  ):



    """simple docstring"""

    _UpperCAmelCase	:List[str]						    = VOCAB_FILES_NAMES
    _UpperCAmelCase	:List[str]						    = PRETRAINED_VOCAB_FILES_MAP
    _UpperCAmelCase	:Any						    = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    _UpperCAmelCase	:Optional[int]						    = LEDTokenizer
    _UpperCAmelCase	:int						    = ["input_ids", "attention_mask"]






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

       lowercase__:   Any					=      json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()							)
       if pre_tok_state.get('''add_prefix_space'''    ,			_UpperCAmelCase							) != add_prefix_space:
          lowercase__:   Optional[int]					=      getattr(_UpperCAmelCase    ,			pre_tok_state.pop('''type'''							)							)
          lowercase__:   int					=      add_prefix_space
          lowercase__:   Dict					=      pre_tok_class(**_UpperCAmelCase							)

       lowercase__:   List[Any]					=      add_prefix_space

       # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
       lowercase__:   Optional[Any]					=      '''post_processor'''
       lowercase__:   List[str]					=      getattr(self.backend_tokenizer    ,			_UpperCAmelCase    ,			_UpperCAmelCase							)
       if tokenizer_component_instance:
          lowercase__:   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:
             lowercase__:   Tuple					=      tuple(state['''sep''']							)
          if "cls" in state:
             lowercase__:   int					=      tuple(state['''cls''']							)

          lowercase__:   Optional[Any]					=      False

          if state.get('''add_prefix_space'''    ,			_UpperCAmelCase							) != add_prefix_space:
             lowercase__:   List[str]					=      add_prefix_space
             lowercase__:   str					=      True

          if state.get('''trim_offsets'''    ,			_UpperCAmelCase							) != trim_offsets:
             lowercase__:   Any					=      trim_offsets
             lowercase__:   Any					=      True

          if changes_to_apply:
             lowercase__:   Union[str, Any]					=      getattr(_UpperCAmelCase    ,			state.pop('''type'''							)							)
             lowercase__:   List[Any]					=      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 _snake_case			(   self							):
       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 _snake_case			(   self    ,			_UpperCAmelCase							):
       lowercase__:   Optional[int]					=      AddedToken(_UpperCAmelCase    ,			lstrip=_UpperCAmelCase    ,			rstrip=_UpperCAmelCase							) if isinstance(_UpperCAmelCase    ,			_UpperCAmelCase							) else value
       lowercase__:   Optional[Any]					=      value






    def _snake_case			(   self    ,			*_UpperCAmelCase    ,			**_UpperCAmelCase							):
       lowercase__:   str					=      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 _snake_case			(   self    ,			*_UpperCAmelCase    ,			**_UpperCAmelCase							):
       lowercase__:   Union[str, 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 _snake_case			(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase = None							):
       lowercase__:   List[Any]					=      self._tokenizer.model.save(_UpperCAmelCase    ,			name=_UpperCAmelCase							)
       return tuple(_UpperCAmelCase							)






    def _snake_case			(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase=None							):
       lowercase__:   int					=      [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 _snake_case			(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase = None							):
       lowercase__:   Optional[Any]					=      [self.sep_token_id]
       lowercase__:   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 _snake_case			(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase = None    ,			_UpperCAmelCase = PaddingStrategy.DO_NOT_PAD    ,			_UpperCAmelCase = None    ,			_UpperCAmelCase = None    ,			):
       lowercase__:   Dict					=      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:
          lowercase__:   List[str]					=      '''attention_mask''' in self.model_input_names

       if return_attention_mask and "global_attention_mask" in encoded_inputs:
          lowercase__:   List[str]					=      encoded_inputs[self.model_input_names[0]]
          # `global_attention_mask` need to have the same length as other (sequential) inputs.
          lowercase__:   Union[str, Any]					=      len(encoded_inputs['''global_attention_mask''']							) != len(_UpperCAmelCase							)

          if needs_to_be_padded:
             lowercase__:   Union[str, Any]					=      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`
                lowercase__:   int					=      (
                    encoded_inputs['''global_attention_mask'''] + [-1] * difference
                )
             elif self.padding_side == "left":
                lowercase__:   List[str]					=      [-1] * difference + encoded_inputs[
                    '''global_attention_mask'''
                ]
             else:
                raise ValueError('''Invalid padding strategy:''' + str(self.padding_side							)							)

       return encoded_inputs

"""simple docstring"""






__A			   = [
    (1_0_0_0, "M"),
    (9_0_0, "CM"),
    (5_0_0, "D"),
    (4_0_0, "CD"),
    (1_0_0, "C"),
    (9_0, "XC"),
    (5_0, "L"),
    (4_0, "XL"),
    (1_0, "X"),
    (9, "IX"),
    (5, "V"),
    (4, "IV"),
    (1, "I"),
]



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase )  ->  int:
   lowercase__:   Dict					=      {'''I''': 1, '''V''': 5, '''X''': 1_0, '''L''': 5_0, '''C''': 1_0_0, '''D''': 5_0_0, '''M''': 1_0_0_0}
   lowercase__:   List[str]					=      0
   lowercase__:   List[Any]					=      0
   while place < len(__UpperCAmelCase ):
      if (place + 1 < len(__UpperCAmelCase )) and (vals[roman[place]] < vals[roman[place + 1]]):
         total += vals[roman[place + 1]] - vals[roman[place]]
         place += 2
      else:
         total += vals[roman[place]]
         place += 1
   return total



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase )  ->  str:
   lowercase__:   Optional[Any]					=      []
   for arabic, roman in ROMAN:
      ((lowercase__), (lowercase__)):   Tuple					=      divmod(__UpperCAmelCase       ,    __UpperCAmelCase )
      result.append(roman * factor )
      if number == 0:
         break
   return "".join(__UpperCAmelCase )


if __name__ == "__main__":
       import doctest

       doctest.testmod()
