


'''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__						:   Union[str, Any]				=    logging.get_logger(__name__)


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

A__						:   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""",
    },
}

A__						:   Optional[int]				=    {
    """allenai/led-base-16384""": 16384,
}





class   UpperCAmelCase_    (__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     ,	SCREAMING_SNAKE_CASE_=None     ,	SCREAMING_SNAKE_CASE_=None     ,	SCREAMING_SNAKE_CASE_=None     ,	SCREAMING_SNAKE_CASE_="replace"     ,	SCREAMING_SNAKE_CASE_="<s>"     ,	SCREAMING_SNAKE_CASE_="</s>"     ,	SCREAMING_SNAKE_CASE_="</s>"     ,	SCREAMING_SNAKE_CASE_="<s>"     ,	SCREAMING_SNAKE_CASE_="<unk>"     ,	SCREAMING_SNAKE_CASE_="<pad>"     ,	SCREAMING_SNAKE_CASE_="<mask>"     ,	SCREAMING_SNAKE_CASE_=False     ,	SCREAMING_SNAKE_CASE_=True     ,	**SCREAMING_SNAKE_CASE_     ,	)							->	List[str]:
         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     ,	)

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

         __lowerCamelCase  :      Union[str, Any]														=						add_prefix_space

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

              __lowerCamelCase  :      str														=						False

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

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

              if changes_to_apply:
                   __lowerCamelCase  :      int														=						getattr(_lowercase     ,	state.pop('type'      )      )
                   __lowerCamelCase  :      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  lowercase_							(				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  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_      )							->	List[Any]:
         __lowerCamelCase  :      Optional[Any]														=						AddedToken(_lowercase     ,	lstrip=_lowercase     ,	rstrip=_lowercase      ) if isinstance(_lowercase     ,	_lowercase      ) else value
         __lowerCamelCase  :      List[str]														=						value







    def  lowercase_							(				self     ,	*SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_      )							->	List[Any]:
         __lowerCamelCase  :      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  lowercase_							(				self     ,	*SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_      )							->	Optional[Any]:
         __lowerCamelCase  :      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  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_ = None      )							->	List[str]:
         __lowerCamelCase  :      str														=						self._tokenizer.model.save(_lowercase     ,	name=_lowercase      )
         return tuple(_lowercase      )







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_=None      )							->	Optional[Any]:
         __lowerCamelCase  :      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  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_ = None      )							->	List[str]:
         __lowerCamelCase  :      List[Any]														=						[self.sep_token_id]
         __lowerCamelCase  :      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  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_ = None     ,	SCREAMING_SNAKE_CASE_ = PaddingStrategy.DO_NOT_PAD     ,	SCREAMING_SNAKE_CASE_ = None     ,	SCREAMING_SNAKE_CASE_ = None     ,	)							->	List[Any]:
         __lowerCamelCase  :      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:
              __lowerCamelCase  :      List[Any]														=						'''attention_mask''' in self.model_input_names

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

              if needs_to_be_padded:
                   __lowerCamelCase  :      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`
                        __lowerCamelCase  :      str														=						(
                            encoded_inputs['''global_attention_mask'''] + [-1] * difference
                        )
                   elif self.padding_side == "left":
                        __lowerCamelCase  :      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()