




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


lowerCamelCase				:		Optional[int]     =   logging.get_logger(__name__)


lowerCamelCase				:		Optional[int]     =   {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

lowerCamelCase				:		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",
    },
}

lowerCamelCase				:		Tuple     =   {
    "allenai/led-base-16384": 16_384,
}







class   A(						UpperCamelCase   ):



 '''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					:  Tuple						,							A_					:  Any=None						,							A_					:  Dict=None						,							A_					:  Any=None						,							A_					:  Optional[int]="replace"						,							A_					:  Union[str, Any]="<s>"						,							A_					:  int="</s>"						,							A_					:  Tuple="</s>"						,							A_					:  List[Any]="<s>"						,							A_					:  Union[str, Any]="<unk>"						,							A_					:  int="<pad>"						,							A_					:  Tuple="<mask>"						,							A_					:  List[str]=False						,							A_					:  Tuple=True						,							**A_					:  Dict						,							)   ->	Any:

       """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_		     =    json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()  )
       if pre_tok_state.get('add_prefix_space'						,							A_  ) != add_prefix_space:
             lowerCamelCase_		     =    getattr(A_						,							pre_tok_state.pop('type'  )  )
             lowerCamelCase_		     =    add_prefix_space
             lowerCamelCase_		     =    pre_tok_class(**A_  )

       lowerCamelCase_		     =    add_prefix_space

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

             lowerCamelCase_		     =    False

             if state.get('add_prefix_space'						,							A_  ) != add_prefix_space:
                   lowerCamelCase_		     =    add_prefix_space
                   lowerCamelCase_		     =    True

             if state.get('trim_offsets'						,							A_  ) != trim_offsets:
                   lowerCamelCase_		     =    trim_offsets
                   lowerCamelCase_		     =    True

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

 @property
 # Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
 def 				a__ (				self					:  Any  )   ->	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 				a__ (				self					:  int						,							A_					:  Any  )   ->	List[Any]:

       """simple docstring"""






       lowerCamelCase_		     =    AddedToken(A_						,							lstrip=A_						,							rstrip=A_  ) if isinstance(A_						,							A_  ) else value
       lowerCamelCase_		     =    value

 def 				a__ (				self					:  int						,							*A_					:  Dict						,							**A_					:  List[Any]  )   ->	BatchEncoding:

       """simple docstring"""






       lowerCamelCase_		     =    kwargs.get('is_split_into_words'						,							A_  )

       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(*A_						,							**A_  )

 def 				a__ (				self					:  Optional[int]						,							*A_					:  Optional[Any]						,							**A_					:  Optional[Any]  )   ->	BatchEncoding:

       """simple docstring"""






       lowerCamelCase_		     =    kwargs.get('is_split_into_words'						,							A_  )

       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(*A_						,							**A_  )

 def 				a__ (				self					:  List[str]						,							A_					:  str						,							A_					:  Optional[str] = None  )   ->	Tuple[str]:

       """simple docstring"""






       lowerCamelCase_		     =    self._tokenizer.model.save(A_						,							name=A_  )
       return tuple(A_  )

 def 				a__ (				self					:  Union[str, Any]						,							A_					:  str						,							A_					:  Optional[int]=None  )   ->	List[Any]:

       """simple docstring"""






       lowerCamelCase_		     =    [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 				a__ (				self					:  Optional[int]						,							A_					:  List[int]						,							A_					:  Optional[List[int]] = None  )   ->	List[int]:

       """simple docstring"""






       lowerCamelCase_		     =    [self.sep_token_id]
       lowerCamelCase_		     =    [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 				a__ (				self					:  Optional[Any]						,							A_					:  Union[Dict[str, EncodedInput], BatchEncoding]						,							A_					:  Optional[int] = None						,							A_					:  PaddingStrategy = PaddingStrategy.DO_NOT_PAD						,							A_					:  Optional[int] = None						,							A_					:  Optional[bool] = None						,							)   ->	dict:

       """simple docstring"""






       lowerCamelCase_		     =    super()._pad(
           encoded_inputs=A_						,							max_length=A_						,							padding_strategy=A_						,							pad_to_multiple_of=A_						,							return_attention_mask=A_						,							)

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

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

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

       return encoded_inputs









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


      '''simple docstring'''



      return int((input_a, input_a).count(1     ) != 0     )

def 			_SCREAMING_SNAKE_CASE			(							):


      '''simple docstring'''



      assert or_gate(0	,							0     ) == 0
      assert or_gate(0	,							1     ) == 1
      assert or_gate(1	,							0     ) == 1
      assert or_gate(1	,							1     ) == 1


if __name__ == "__main__":
 print(or_gate(0, 1))
 print(or_gate(1, 0))
 print(or_gate(0, 0))
 print(or_gate(1, 1))



