



"""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 							_UpperCAmelCase							( tf.keras.layers.Layer      ):





     '''simple docstring'''



     def __init__(					self       ,	A       ,	A       ,	A = None       ,	A = None		)							->					str:
         super().__init__()
         _UpperCAmelCase							:  Any											=    pad_token_id
         _UpperCAmelCase							:  Dict											=    max_length
         _UpperCAmelCase							:  Union[str, Any]											=    vocab
         _UpperCAmelCase							:  Optional[int]											=    merges
         _UpperCAmelCase							:  Optional[Any]											=    BytePairTokenizer(A       ,	A       ,	sequence_length=A		)





     @classmethod
     def  __lowerCAmelCase	(					cls       ,	A       ,	*A       ,	**A		)							->					Any:
         _UpperCAmelCase							:  Union[str, Any]											=    [''' '''.join(A		) for m in tokenizer.bpe_ranks.keys()]
         _UpperCAmelCase							:  List[Any]											=    tokenizer.get_vocab()
         return cls(A       ,	A       ,	*A       ,	**A		)





     @classmethod
     def  __lowerCAmelCase	(					cls       ,	A       ,	*A       ,	**A		)							->					Union[str, Any]:
         _UpperCAmelCase							:  int											=    GPTaTokenizer.from_pretrained(A       ,	*A       ,	**A		)
         return cls.from_tokenizer(A       ,	*A       ,	**A		)





     @classmethod
     def  __lowerCAmelCase	(					cls       ,	A		)							->					Dict:
         return cls(**A		)





     def  __lowerCAmelCase	(					self		)							->					Any:
         return {
             "vocab": self.vocab,
             "merges": self.merges,
             "max_length": self.max_length,
             "pad_token_id": self.pad_token_id,
         }





     def  __lowerCAmelCase	(					self       ,	A       ,	A = None		)							->					Union[str, Any]:
         _UpperCAmelCase							:  int											=    self.tf_tokenizer(A		)
         _UpperCAmelCase							:  str											=    tf.ones_like(A		)

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

             if max_length is not None:
                 _UpperCAmelCase							,				_UpperCAmelCase							:  Any											=    pad_model_inputs(
                     A       ,	max_seq_length=A       ,	pad_value=self.pad_token_id		)

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







"""simple docstring"""
def  lowerCamelCase_						(UpperCamelCase__		:		int							):
    if not isinstance(UpperCamelCase__     ,       UpperCamelCase__							) or number < 0:
        raise ValueError('''Input must be a non-negative integer'''							)

    _UpperCAmelCase							:  str											=    0
    while number:
        # This way we arrive at next set bit (next 1) instead of looping
        # through each bit and checking for 1s hence the
        # loop won't run 32 times it will only run the number of `1` times
        number &= number - 1
        count += 1
    return count


if __name__ == "__main__":
  import doctest

  doctest.testmod()


