





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_				= logging.get_logger(__name__)


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

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

class 					A     (UpperCAmelCase__					):
      _SCREAMING_SNAKE_CASE       =				VOCAB_FILES_NAMES
      _SCREAMING_SNAKE_CASE       =				PRETRAINED_VOCAB_FILES_MAP
      _SCREAMING_SNAKE_CASE       =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _SCREAMING_SNAKE_CASE       =				LEDTokenizer
      _SCREAMING_SNAKE_CASE       =				["""input_ids""", """attention_mask"""]



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



             '''simple docstring'''

             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  ,	)

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

             _snake_case   :				Optional[Any]  					=    add_prefix_space

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

                    _snake_case   :				Union[str, Any]  					=    False

                    if state.get('''add_prefix_space'''  ,	_UpperCAmelCase       ) != add_prefix_space:
                           _snake_case   :				List[Any]  					=    add_prefix_space
                           _snake_case   :				Optional[Any]  					=    True

                    if state.get('''trim_offsets'''  ,	_UpperCAmelCase       ) != trim_offsets:
                           _snake_case   :				Optional[int]  					=    trim_offsets
                           _snake_case   :				Any  					=    True

                    if changes_to_apply:
                           _snake_case   :				Optional[int]  					=    getattr(_UpperCAmelCase  ,	state.pop('''type'''       )       )
                           _snake_case   :				Optional[int]  					=    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  __a			(       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  __a			(       self  ,	lowercase_       )   -> Union[str, Any]:



             '''simple docstring'''

             _snake_case   :				Optional[Any]  					=    AddedToken(_UpperCAmelCase  ,	lstrip=_UpperCAmelCase  ,	rstrip=_UpperCAmelCase       ) if isinstance(_UpperCAmelCase  ,	_UpperCAmelCase       ) else value
             _snake_case   :				Dict  					=    value



      def  __a			(       self  ,	*lowercase_  ,	**lowercase_       )   -> BatchEncoding:



             '''simple docstring'''

             _snake_case   :				List[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()._batch_encode_plus(*_UpperCAmelCase  ,	**_UpperCAmelCase       )



      def  __a			(       self  ,	*lowercase_  ,	**lowercase_       )   -> BatchEncoding:



             '''simple docstring'''

             _snake_case   :				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()._encode_plus(*_UpperCAmelCase  ,	**_UpperCAmelCase       )



      def  __a			(       self  ,	lowercase_  ,	lowercase_ = None       )   -> Tuple[str]:



             '''simple docstring'''

             _snake_case   :				Any  					=    self._tokenizer.model.save(_UpperCAmelCase  ,	name=_UpperCAmelCase       )
             return tuple(_UpperCAmelCase       )



      def  __a			(       self  ,	lowercase_  ,	lowercase_=None       )   -> Optional[Any]:



             '''simple docstring'''

             _snake_case   :				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  __a			(       self  ,	lowercase_  ,	lowercase_ = None       )   -> List[int]:



             '''simple docstring'''

             _snake_case   :				int  					=    [self.sep_token_id]
             _snake_case   :				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  __a			(       self  ,	lowercase_  ,	lowercase_ = None  ,	lowercase_ = PaddingStrategy.DO_NOT_PAD  ,	lowercase_ = None  ,	lowercase_ = None  ,	)   -> dict:



             '''simple docstring'''

             _snake_case   :				str  					=    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:
                    _snake_case   :				List[str]  					=    '''attention_mask''' in self.model_input_names

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

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

             return encoded_inputs














import numpy as np






def      _snake_case     (__lowercase):
   return 1 / (1 + np.exp(-vector))






def      _snake_case     (__lowercase):
   return vector * sigmoid(__lowercase)


if __name__ == "__main__":
 import doctest

 doctest.testmod()





