



'''simple docstring'''


import os
from typing import Dict, List, Union

import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs

from .tokenization_gpta import GPTaTokenizer

class  SCREAMING_SNAKE_CASE (  tf.keras.layers.Layer		):






    """simple docstring"""





    def __init__(  self      :					Tuple  ,						UpperCamelCase__      :					Dict[str, int]  ,						UpperCamelCase__      :					List[str]  ,						UpperCamelCase__      :					int = None  ,						UpperCamelCase__      :					int = None ):


       """simple docstring"""







       super().__init__()
       UpperCamelCase					=  pad_token_id
       UpperCamelCase					=  max_length
       UpperCamelCase					=  vocab
       UpperCamelCase					=  merges
       UpperCamelCase					=  BytePairTokenizer(UpperCamelCase__  ,						UpperCamelCase__  ,						sequence_length=UpperCamelCase__ )






    @classmethod
    def  A       (  cls      :					List[str]  ,						UpperCamelCase__      :					GPTaTokenizer  ,						*UpperCamelCase__      :					Union[str, Any]  ,						**UpperCamelCase__      :					Dict ):


       """simple docstring"""







       UpperCamelCase					=  [""" """.join(UpperCamelCase__ ) for m in tokenizer.bpe_ranks.keys()]
       UpperCamelCase					=  tokenizer.get_vocab()
       return cls(UpperCamelCase__  ,						UpperCamelCase__  ,						*UpperCamelCase__  ,						**UpperCamelCase__ )






    @classmethod
    def  A       (  cls      :					int  ,						UpperCamelCase__      :					Union[str, os.PathLike]  ,						*UpperCamelCase__      :					int  ,						**UpperCamelCase__      :					List[str] ):


       """simple docstring"""







       UpperCamelCase					=  GPTaTokenizer.from_pretrained(UpperCamelCase__  ,						*UpperCamelCase__  ,						**UpperCamelCase__ )
       return cls.from_tokenizer(UpperCamelCase__  ,						*UpperCamelCase__  ,						**UpperCamelCase__ )






    @classmethod
    def  A       (  cls      :					str  ,						UpperCamelCase__      :					Union[str, Any] ):


       """simple docstring"""







       return cls(**UpperCamelCase__ )






    def  A       (  self      :					str ):


       """simple docstring"""







       return {
           "vocab": self.vocab,
           "merges": self.merges,
           "max_length": self.max_length,
           "pad_token_id": self.pad_token_id,
       }






    def  A       (  self      :					Tuple  ,						UpperCamelCase__      :					Union[str, Any]  ,						UpperCamelCase__      :					int = None ):


       """simple docstring"""







       UpperCamelCase					=  self.tf_tokenizer(UpperCamelCase__ )
       UpperCamelCase					=  tf.ones_like(UpperCamelCase__ )

       if self.pad_token_id is not None:
          # pad the tokens up to max length
          UpperCamelCase					=  max_length if max_length is not None else self.max_length

          if max_length is not None:
             UpperCamelCase					=  pad_model_inputs(
                 UpperCamelCase__  ,						max_seq_length=UpperCamelCase__  ,						pad_value=self.pad_token_id )

       return {"attention_mask": attention_mask, "input_ids": input_ids}





'''simple docstring'''






def 				UpperCamelCase_			(    A__					:   int = 10_00					):
  '''simple docstring'''





  lowerCAmelCase_			:      Optional[Any]			=   3
  lowerCAmelCase_			:      Dict			=   0
  while a < n:
    if a % 3 == 0 or a % 5 == 0:
      result += a
    elif a % 15 == 0:
      result -= a
    a += 1
  return result


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



