'''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 				_lowercase	(      tf.keras.layers.Layer   ):
      '''simple docstring'''


      def __init__(				self					,lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_ = None					,lowerCamelCase_ = None							)			->			List[str]:



         '''simple docstring'''



         super().__init__()
         UpperCAmelCase__							:				Union[str, Any]   				=  pad_token_id
         UpperCAmelCase__							:				List[str]   				=  max_length
         UpperCAmelCase__							:				List[str]   				=  vocab
         UpperCAmelCase__							:				str   				=  merges
         UpperCAmelCase__							:				Tuple   				=  BytePairTokenizer(lowerCamelCase_					,lowerCamelCase_					,sequence_length=lowerCamelCase_							)




      @classmethod
      def 		lowerCAmelCase__					(				cls					,lowerCamelCase_					,*lowerCamelCase_					,**lowerCamelCase_							)			->			Union[str, Any]:



         '''simple docstring'''



         UpperCAmelCase__							:				List[str]   				=  [''' '''.join(lowerCamelCase_							) for m in tokenizer.bpe_ranks.keys()]
         UpperCAmelCase__							:				Optional[Any]   				=  tokenizer.get_vocab()
         return cls(lowerCamelCase_					,lowerCamelCase_					,*lowerCamelCase_					,**lowerCamelCase_							)




      @classmethod
      def 		lowerCAmelCase__					(				cls					,lowerCamelCase_					,*lowerCamelCase_					,**lowerCamelCase_							)			->			str:



         '''simple docstring'''



         UpperCAmelCase__							:				Any   				=  GPTaTokenizer.from_pretrained(lowerCamelCase_					,*lowerCamelCase_					,**lowerCamelCase_							)
         return cls.from_tokenizer(lowerCamelCase_					,*lowerCamelCase_					,**lowerCamelCase_							)




      @classmethod
      def 		lowerCAmelCase__					(				cls					,lowerCamelCase_							)			->			Any:



         '''simple docstring'''



         return cls(**lowerCamelCase_							)




      def 		lowerCAmelCase__					(				self							)			->			str:



         '''simple docstring'''



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




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_ = None							)			->			Any:



         '''simple docstring'''



         UpperCAmelCase__							:				Union[str, Any]   				=  self.tf_tokenizer(lowerCamelCase_							)
         UpperCAmelCase__							:				Optional[int]   				=  tf.ones_like(lowerCamelCase_							)

         if self.pad_token_id is not None:
            # pad the tokens up to max length
            UpperCAmelCase__							:				Optional[int]   				=  max_length if max_length is not None else self.max_length

            if max_length is not None:
               UpperCAmelCase__				,      UpperCAmelCase__							:				Tuple   				=  pad_model_inputs(
                   lowerCamelCase_					,max_seq_length=lowerCamelCase_					,pad_value=self.pad_token_id							)

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





'''simple docstring'''

def __UpperCamelCase(   _A					:				int    ):
   '''simple docstring'''

   if length <= 0 or not isinstance(_A  ,			_A    ):
      raise ValueError('''Length must be a positive integer.'''    )
   return [n * (2 * n - 1) for n in range(_A    )]


if __name__ == "__main__":
  print(hexagonal_numbers(length=5))
  print(hexagonal_numbers(length=10))




