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_           =				logging.get_logger(__name__)


UpperCAmelCase_           =				{'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

UpperCAmelCase_           =				{
    '''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_           =				{
    '''allenai/led-base-16384''': 1_6_3_8_4,
}




class 			__magic_name__	(		__a     ):





   """simple docstring"""




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



   def __init__(						self       :	List[Any]		,	_lowercase       :	int=None		,	_lowercase       :	Union[str, Any]=None		,	_lowercase       :	Union[str, Any]=None		,	_lowercase       :	Union[str, Any]="replace"		,	_lowercase       :	Optional[Any]="<s>"		,	_lowercase       :	Optional[int]="</s>"		,	_lowercase       :	Tuple="</s>"		,	_lowercase       :	str="<s>"		,	_lowercase       :	Optional[int]="<unk>"		,	_lowercase       :	int="<pad>"		,	_lowercase       :	Optional[Any]="<mask>"		,	_lowercase       :	Optional[int]=False		,	_lowercase       :	List[Any]=True		,	**_lowercase       :	str		,	):



    """simple docstring"""



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

    _UpperCamelCase:  Union[str, Any]    						=			add_prefix_space

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

     _UpperCamelCase:  str    						=			False

     if state.get('''add_prefix_space'''		,	_lowercase				) != add_prefix_space:
      _UpperCamelCase:  Dict    						=			add_prefix_space
      _UpperCamelCase:  List[str]    						=			True

     if state.get('''trim_offsets'''		,	_lowercase				) != trim_offsets:
      _UpperCamelCase:  Optional[Any]    						=			trim_offsets
      _UpperCamelCase:  Tuple    						=			True

     if changes_to_apply:
      _UpperCamelCase:  int    						=			getattr(_lowercase		,	state.pop('''type'''				)				)
      _UpperCamelCase:  Optional[Any]    						=			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 	lowerCAmelCase    (						self       :	Optional[Any]				):



    """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 	lowerCAmelCase    (						self       :	Any		,	_lowercase       :	Tuple				):



    """simple docstring"""



    _UpperCamelCase:  Optional[Any]    						=			AddedToken(_lowercase		,	lstrip=_lowercase		,	rstrip=_lowercase				) if isinstance(_lowercase		,	_lowercase				) else value
    _UpperCamelCase:  List[str]    						=			value



   def 	lowerCAmelCase    (						self       :	Dict		,	*_lowercase       :	List[str]		,	**_lowercase       :	Any				):



    """simple docstring"""



    _UpperCamelCase:  str    						=			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 	lowerCAmelCase    (						self       :	Optional[Any]		,	*_lowercase       :	Optional[Any]		,	**_lowercase       :	Dict				):



    """simple docstring"""



    _UpperCamelCase:  Optional[int]    						=			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 	lowerCAmelCase    (						self       :	str		,	_lowercase       :	str		,	_lowercase       :	Optional[str] = None				):



    """simple docstring"""



    _UpperCamelCase:  str    						=			self._tokenizer.model.save(_lowercase		,	name=_lowercase				)
    return tuple(_lowercase				)



   def 	lowerCAmelCase    (						self       :	Optional[int]		,	_lowercase       :	Union[str, Any]		,	_lowercase       :	Optional[int]=None				):



    """simple docstring"""



    _UpperCamelCase:  Tuple    						=			[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 	lowerCAmelCase    (						self       :	Union[str, Any]		,	_lowercase       :	List[int]		,	_lowercase       :	Optional[List[int]] = None				):



    """simple docstring"""



    _UpperCamelCase:  List[Any]    						=			[self.sep_token_id]
    _UpperCamelCase:  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 	lowerCAmelCase    (						self       :	List[Any]		,	_lowercase       :	Union[Dict[str, EncodedInput], BatchEncoding]		,	_lowercase       :	Optional[int] = None		,	_lowercase       :	PaddingStrategy = PaddingStrategy.DO_NOT_PAD		,	_lowercase       :	Optional[int] = None		,	_lowercase       :	Optional[bool] = None		,	):



    """simple docstring"""



    _UpperCamelCase:  Union[str, Any]    						=			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:  List[Any]    						=			'''attention_mask''' in self.model_input_names

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

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

    return encoded_inputs
import torch




def    lowerCAmelCase_  (		)     ->					int:







 '''simple docstring'''
 if torch.cuda.is_available():
  _UpperCamelCase:  Any    						=			torch.cuda.device_count()
 else:
  _UpperCamelCase:  Union[str, Any]    						=			0
 print(F"""Successfully ran on {num_gpus} GPUs"""		)


if __name__ == "__main__":
  main()