



'''simple docstring'''





def        lowerCamelCase  (				_snake_case  :						list       ):





    '''simple docstring'''


    lowercase__		       =						0
    while len(_snake_case       ) > 1:
        lowercase__		       =						0
        # Consider two files with minimum cost to be merged
        for _ in range(2       ):
            lowercase__		       =						files.index(min(_snake_case       )       )
            temp += files[min_index]
            files.pop(_snake_case       )
        files.append(_snake_case       )
        optimal_merge_cost += temp
    return optimal_merge_cost


if __name__ == "__main__":
 import doctest

 doctest.testmod()









'''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 				snake_case (tf.keras.layers.Layer   ):



       def __init__(				self     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_ = None     ,UpperCAmelCase_ = None      )  ->							List[Any]:
           super().__init__()
           lowercase__		       =						pad_token_id
           lowercase__		       =						max_length
           lowercase__		       =						vocab
           lowercase__		       =						merges
           lowercase__		       =						BytePairTokenizer(UpperCAmelCase_     ,UpperCAmelCase_     ,sequence_length=UpperCAmelCase_      )



       @classmethod
       def 							_a      (				cls     ,UpperCAmelCase_     ,*UpperCAmelCase_     ,**UpperCAmelCase_      )  ->							List[str]:
           lowercase__		       =						[" ".join(UpperCAmelCase_      ) for m in tokenizer.bpe_ranks.keys()]
           lowercase__		       =						tokenizer.get_vocab()
           return cls(UpperCAmelCase_     ,UpperCAmelCase_     ,*UpperCAmelCase_     ,**UpperCAmelCase_      )



       @classmethod
       def 							_a      (				cls     ,UpperCAmelCase_     ,*UpperCAmelCase_     ,**UpperCAmelCase_      )  ->							Optional[Any]:
           lowercase__		       =						GPTaTokenizer.from_pretrained(UpperCAmelCase_     ,*UpperCAmelCase_     ,**UpperCAmelCase_      )
           return cls.from_tokenizer(UpperCAmelCase_     ,*UpperCAmelCase_     ,**UpperCAmelCase_      )



       @classmethod
       def 							_a      (				cls     ,UpperCAmelCase_      )  ->							Optional[Any]:
           return cls(**UpperCAmelCase_      )



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







       def 							_a      (				self     ,UpperCAmelCase_     ,UpperCAmelCase_ = None      )  ->							Tuple:
           lowercase__		       =						self.tf_tokenizer(UpperCAmelCase_      )
           lowercase__		       =						tf.ones_like(UpperCAmelCase_      )

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

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

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




