


'''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


UpperCAmelCase_      :		Optional[Any]          =		logging.get_logger(__name__)


UpperCAmelCase_      :		str          =		{"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_      :		Tuple          =		{
    "allenai/led-base-16384": 1_6384,
}







class 					a			(				snake_case__  ):
 '''simple docstring'''




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




 def __init__(    self		,      lowerCamelCase_=None		,      lowerCamelCase_=None		,      lowerCamelCase_=None		,      lowerCamelCase_="replace"		,      lowerCamelCase_="<s>"		,      lowerCamelCase_="</s>"		,      lowerCamelCase_="</s>"		,      lowerCamelCase_="<s>"		,      lowerCamelCase_="<unk>"		,      lowerCamelCase_="<pad>"		,      lowerCamelCase_="<mask>"		,      lowerCamelCase_=False		,      lowerCamelCase_=True		,      **lowerCamelCase_		,      )      ->    Optional[Any]:
    super().__init__(
        lowerCamelCase_		,      lowerCamelCase_		,      tokenizer_file=lowerCamelCase_		,      errors=lowerCamelCase_		,      bos_token=lowerCamelCase_		,      eos_token=lowerCamelCase_		,      sep_token=lowerCamelCase_		,      cls_token=lowerCamelCase_		,      unk_token=lowerCamelCase_		,      pad_token=lowerCamelCase_		,      mask_token=lowerCamelCase_		,      add_prefix_space=lowerCamelCase_		,      trim_offsets=lowerCamelCase_		,      **lowerCamelCase_		,      )

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

    _a					:  str						=  add_prefix_space

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

       _a					:  Union[str, Any]						=  False

       if state.get('add_prefix_space'		,      lowerCamelCase_      ) != add_prefix_space:
          _a					:  str						=  add_prefix_space
          _a					:  Optional[int]						=  True

       if state.get('trim_offsets'		,      lowerCamelCase_      ) != trim_offsets:
          _a					:  int						=  trim_offsets
          _a					:  Tuple						=  True

       if changes_to_apply:
          _a					:  Union[str, Any]						=  getattr(lowerCamelCase_		,      state.pop('type'      )      )
          _a					:  Dict						=  component_class(**lowerCamelCase_      )
          setattr(self.backend_tokenizer		,      lowerCamelCase_		,      lowerCamelCase_      )




 @property
 # Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
 def   __UpperCamelCase   (    self      )      ->    str:
    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   __UpperCamelCase   (    self		,      lowerCamelCase_      )      ->    Tuple:
    _a					:  int						=  AddedToken(lowerCamelCase_		,      lstrip=lowerCamelCase_		,      rstrip=lowerCamelCase_      ) if isinstance(lowerCamelCase_		,      lowerCamelCase_      ) else value
    _a					:  List[str]						=  value




 def   __UpperCamelCase   (    self		,      *lowerCamelCase_		,      **lowerCamelCase_      )      ->    BatchEncoding:
    _a					:  str						=  kwargs.get('is_split_into_words'		,      lowerCamelCase_      )

    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(*lowerCamelCase_		,      **lowerCamelCase_      )




 def   __UpperCamelCase   (    self		,      *lowerCamelCase_		,      **lowerCamelCase_      )      ->    BatchEncoding:
    _a					:  Optional[Any]						=  kwargs.get('is_split_into_words'		,      lowerCamelCase_      )

    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(*lowerCamelCase_		,      **lowerCamelCase_      )




 def   __UpperCamelCase   (    self		,      lowerCamelCase_		,      lowerCamelCase_ = None      )      ->    Tuple[str]:
    _a					:  List[Any]						=  self._tokenizer.model.save(lowerCamelCase_		,      name=lowerCamelCase_      )
    return tuple(lowerCamelCase_      )




 def   __UpperCamelCase   (    self		,      lowerCamelCase_		,      lowerCamelCase_=None      )      ->    str:
    _a					:  List[str]						=  [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   __UpperCamelCase   (    self		,      lowerCamelCase_		,      lowerCamelCase_ = None      )      ->    List[int]:
    _a					:  Optional[int]						=  [self.sep_token_id]
    _a					:  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]




 def   __UpperCamelCase   (    self		,      lowerCamelCase_		,      lowerCamelCase_ = None		,      lowerCamelCase_ = PaddingStrategy.DO_NOT_PAD		,      lowerCamelCase_ = None		,      lowerCamelCase_ = None		,      )      ->    dict:
    _a					:  Union[str, Any]						=  super()._pad(
        encoded_inputs=lowerCamelCase_		,      max_length=lowerCamelCase_		,      padding_strategy=lowerCamelCase_		,      pad_to_multiple_of=lowerCamelCase_		,      return_attention_mask=lowerCamelCase_		,      )

    # Load from model defaults
    if return_attention_mask is None:
       _a					:  int						=  'attention_mask' in self.model_input_names

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

       if needs_to_be_padded:
          _a					:  Tuple						=  len(lowerCamelCase_      ) - 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`
             _a					:  Optional[Any]						=  (
                 encoded_inputs['global_attention_mask'] + [-1] * difference
             )
          elif self.padding_side == "left":
             _a					:  List[str]						=  [-1] * difference + encoded_inputs[
                 'global_attention_mask'
             ]
          else:
             raise ValueError('Invalid padding strategy:' + str(self.padding_side      )      )

    return encoded_inputs






'''simple docstring'''




def    UpperCAmelCase_  (       A		,	A      ):
   '''simple docstring'''





   return "\n".join(
       f'''{number} * {i} = {number * i}''' for i in range(1		,	number_of_terms + 1      )      )


if __name__ == "__main__":
 print(multiplication_table(number=5, number_of_terms=10))


