






"""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__					: List[str]=	logging.get_logger(__name__)


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

A__					: 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__					: List[Any]=	{
    """allenai/led-base-16384""": 1_63_84,
}
class    __lowerCamelCase    (  lowercase__  ):

   '''simple docstring'''





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

   def __init__(					self ,				snake_case_=None ,				snake_case_=None ,				snake_case_=None ,				snake_case_="replace" ,				snake_case_="<s>" ,				snake_case_="</s>" ,				snake_case_="</s>" ,				snake_case_="<s>" ,				snake_case_="<unk>" ,				snake_case_="<pad>" ,				snake_case_="<mask>" ,				snake_case_=False ,				snake_case_=True ,				**snake_case_ ,				)		->       Optional[int]:
       super().__init__(
           snake_case_ ,				snake_case_ ,				tokenizer_file=snake_case_ ,				errors=snake_case_ ,				bos_token=snake_case_ ,				eos_token=snake_case_ ,				sep_token=snake_case_ ,				cls_token=snake_case_ ,				unk_token=snake_case_ ,				pad_token=snake_case_ ,				mask_token=snake_case_ ,				add_prefix_space=snake_case_ ,				trim_offsets=snake_case_ ,				**snake_case_ ,				)

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

       UpperCamelCase__         =    add_prefix_space

       # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
       UpperCamelCase__         =    'post_processor'
       UpperCamelCase__         =    getattr(self.backend_tokenizer ,				snake_case_ ,				snake_case_      )
       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' ,				snake_case_      ) != add_prefix_space:
               UpperCamelCase__         =    add_prefix_space
               UpperCamelCase__         =    True

           if state.get('trim_offsets' ,				snake_case_      ) != trim_offsets:
               UpperCamelCase__         =    trim_offsets
               UpperCamelCase__         =    True

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

   @property
   # Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Dict:
       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 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_      )		->       int:
       UpperCamelCase__         =    AddedToken(snake_case_ ,				lstrip=snake_case_ ,				rstrip=snake_case_      ) if isinstance(snake_case_ ,				snake_case_      ) else value
       UpperCamelCase__         =    value

   def 						SCREAMING_SNAKE_CASE__ (					self ,				*snake_case_ ,				**snake_case_      )		->       List[Any]:
       UpperCamelCase__         =    kwargs.get('is_split_into_words' ,				snake_case_      )

       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(*snake_case_ ,				**snake_case_      )

   def 						SCREAMING_SNAKE_CASE__ (					self ,				*snake_case_ ,				**snake_case_      )		->       Union[str, Any]:
       UpperCamelCase__         =    kwargs.get('is_split_into_words' ,				snake_case_      )

       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(*snake_case_ ,				**snake_case_      )

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_ = None      )		->       Union[str, Any]:
       UpperCamelCase__         =    self._tokenizer.model.save(snake_case_ ,				name=snake_case_      )
       return tuple(snake_case_      )

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_=None      )		->       int:
       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 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_ = None      )		->       Optional[int]:
       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 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_ = None ,				snake_case_ = PaddingStrategy.DO_NOT_PAD ,				snake_case_ = None ,				snake_case_ = None ,				)		->       Optional[Any]:
       UpperCamelCase__         =    super()._pad(
           encoded_inputs=snake_case_ ,				max_length=snake_case_ ,				padding_strategy=snake_case_ ,				pad_to_multiple_of=snake_case_ ,				return_attention_mask=snake_case_ ,				)

       # 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(snake_case_      )

           if needs_to_be_padded:
               UpperCamelCase__         =    len(snake_case_      ) - 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   lowerCAmelCase_( SCREAMING_SNAKE_CASE = 50_00_00_00					)					->	int:
    """simple docstring"""

    UpperCamelCase__         =    set()
    UpperCamelCase__         =    int((limit - 24) ** (1 / 2)					)

    UpperCamelCase__         =    set(range(3 ,	prime_square_limit + 1 ,	2					)					)
    primes.add(2					)
    for p in range(3 ,	prime_square_limit + 1 ,	2					):
        if p not in primes:
            continue
        primes.difference_update(set(range(p * p ,	prime_square_limit + 1 ,	SCREAMING_SNAKE_CASE					)					)					)

    for primea in primes:
        UpperCamelCase__         =    primea * primea
        for primea in primes:
            UpperCamelCase__         =    primea * primea * primea
            if square + cube >= limit - 16:
                break
            for primea in primes:
                UpperCamelCase__         =    primea * primea * primea * primea
                UpperCamelCase__         =    square + cube + tetr
                if total >= limit:
                    break
                ret.add(SCREAMING_SNAKE_CASE					)

    return len(SCREAMING_SNAKE_CASE					)


if __name__ == "__main__":
  print(F"""{solution() = }""")




