



"""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   :							Any										=   logging.get_logger(__name__)


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

__A   :							Dict										=   {
    "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   :							int										=   {
    "allenai/led-base-16384": 16384,
}
class 			_a (					lowercase__):
   """simple docstring"""
   UpperCamelCase__			      =	VOCAB_FILES_NAMES
   UpperCamelCase__			      =	PRETRAINED_VOCAB_FILES_MAP
   UpperCamelCase__			      =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   UpperCamelCase__			      =	LEDTokenizer
   UpperCamelCase__			      =	["""input_ids""", """attention_mask"""]



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

       _UpperCAmelCase					=    json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()   )
       if pre_tok_state.get('''add_prefix_space'''				,   __lowercase   ) != add_prefix_space:
           _UpperCAmelCase					=    getattr(__lowercase				,   pre_tok_state.pop('''type'''   )   )
           _UpperCAmelCase					=    add_prefix_space
           _UpperCAmelCase					=    pre_tok_class(**__lowercase   )

       _UpperCAmelCase					=    add_prefix_space

       # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
       _UpperCAmelCase					=    '''post_processor'''
       _UpperCAmelCase					=    getattr(self.backend_tokenizer				,   __lowercase				,   __lowercase   )
       if tokenizer_component_instance:
           _UpperCAmelCase					=    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					=    tuple(state['''sep''']   )
           if "cls" in state:
               _UpperCAmelCase					=    tuple(state['''cls''']   )

           _UpperCAmelCase					=    False

           if state.get('''add_prefix_space'''				,   __lowercase   ) != add_prefix_space:
               _UpperCAmelCase					=    add_prefix_space
               _UpperCAmelCase					=    True

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

           if changes_to_apply:
               _UpperCAmelCase					=    getattr(__lowercase				,   state.pop('''type'''   )   )
               _UpperCAmelCase					=    component_class(**__lowercase   )
               setattr(self.backend_tokenizer				,   __lowercase				,   __lowercase   )



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



   def  lowercase__						(		self     :    Any				,   *__UpperCamelCase     :    List[Any]				,   **__UpperCamelCase     :    Optional[Any]   )->str:
       _UpperCAmelCase					=    kwargs.get('''is_split_into_words'''				,   __lowercase   )

       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(*__lowercase				,   **__lowercase   )



   def  lowercase__						(		self     :    int				,   *__UpperCamelCase     :    Union[str, Any]				,   **__UpperCamelCase     :    List[str]   )->List[Any]:
       _UpperCAmelCase					=    kwargs.get('''is_split_into_words'''				,   __lowercase   )

       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(*__lowercase				,   **__lowercase   )



   def  lowercase__						(		self     :    Optional[Any]				,   __UpperCamelCase     :    str				,   __UpperCamelCase     :    Optional[str] = None   )->List[str]:
       _UpperCAmelCase					=    self._tokenizer.model.save(__lowercase				,   name=__lowercase   )
       return tuple(__lowercase   )



   def  lowercase__						(		self     :    List[str]				,   __UpperCamelCase     :    int				,   __UpperCamelCase     :    Optional[int]=None   )->Any:
       _UpperCAmelCase					=    [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  lowercase__						(		self     :    int				,   __UpperCamelCase     :    List[int]				,   __UpperCamelCase     :    Optional[List[int]] = None   )->Dict:
       _UpperCAmelCase					=    [self.sep_token_id]
       _UpperCAmelCase					=    [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  lowercase__						(		self     :    Union[str, Any]				,   __UpperCamelCase     :    Union[Dict[str, EncodedInput], BatchEncoding]				,   __UpperCamelCase     :    Optional[int] = None				,   __UpperCamelCase     :    PaddingStrategy = PaddingStrategy.DO_NOT_PAD				,   __UpperCamelCase     :    Optional[int] = None				,   __UpperCamelCase     :    Optional[bool] = None				,   )->Dict:
       _UpperCAmelCase					=    super()._pad(
           encoded_inputs=__lowercase				,   max_length=__lowercase				,   padding_strategy=__lowercase				,   pad_to_multiple_of=__lowercase				,   return_attention_mask=__lowercase				,   )

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

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

           if needs_to_be_padded:
               _UpperCAmelCase					=    len(__lowercase   ) - 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`
                   _UpperCAmelCase					=    (
                       encoded_inputs['''global_attention_mask'''] + [-1] * difference
                   )
               elif self.padding_side == "left":
                   _UpperCAmelCase					=    [-1] * difference + encoded_inputs[
                       '''global_attention_mask'''
                   ]
               else:
                   raise ValueError('''Invalid padding strategy:''' + str(self.padding_side   )   )

       return encoded_inputs








"""simple docstring"""


def  lowercase	(      _SCREAMING_SNAKE_CASE   :						int					,     _SCREAMING_SNAKE_CASE   :						int				):






				'''simple docstring'''



				if a < 0 or b < 0:
								raise ValueError('''the value of both inputs must be positive'''				)

				_UpperCAmelCase					=    str(bin(_SCREAMING_SNAKE_CASE				)				)[2:]  # remove the leading "0b"
				_UpperCAmelCase					=    str(bin(_SCREAMING_SNAKE_CASE				)				)[2:]  # remove the leading "0b"

				_UpperCAmelCase					=    max(len(_SCREAMING_SNAKE_CASE				)					,     len(_SCREAMING_SNAKE_CASE				)				)

				return "0b" + "".join(
				    str(int(char_a == '''1''' and char_b == '''1'''				)				)
				    for char_a, char_b in zip(a_binary.zfill(_SCREAMING_SNAKE_CASE				)					,     b_binary.zfill(_SCREAMING_SNAKE_CASE				)				)				)


if __name__ == "__main__":
					import doctest

					doctest.testmod()



