



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





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





      @classmethod
      def 					_lowerCamelCase  (  cls    ,						SCREAMING_SNAKE_CASE__    ,						*SCREAMING_SNAKE_CASE__    ,						**SCREAMING_SNAKE_CASE__		)   ->					str:
         lowerCamelCase__						=			[" ".join(snake_case__		) for m in tokenizer.bpe_ranks.keys()]
         lowerCamelCase__						=			tokenizer.get_vocab()
         return cls(snake_case__    ,						snake_case__    ,						*snake_case__    ,						**snake_case__		)





      @classmethod
      def 					_lowerCamelCase  (  cls    ,						SCREAMING_SNAKE_CASE__    ,						*SCREAMING_SNAKE_CASE__    ,						**SCREAMING_SNAKE_CASE__		)   ->					Union[str, Any]:
         lowerCamelCase__						=			GPTaTokenizer.from_pretrained(snake_case__    ,						*snake_case__    ,						**snake_case__		)
         return cls.from_tokenizer(snake_case__    ,						*snake_case__    ,						**snake_case__		)





      @classmethod
      def 					_lowerCamelCase  (  cls    ,						SCREAMING_SNAKE_CASE__		)   ->					List[Any]:
         return cls(**snake_case__		)





      def 					_lowerCamelCase  (  self		)   ->					List[str]:
         return {
             "vocab": self.vocab,
             "merges": self.merges,
             "max_length": self.max_length,
             "pad_token_id": self.pad_token_id,
         }





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__ = None		)   ->					List[str]:
         lowerCamelCase__						=			self.tf_tokenizer(snake_case__		)
         lowerCamelCase__						=			tf.ones_like(snake_case__		)

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

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

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







"""simple docstring"""




def 	UpperCAmelCase__   (				A__   )      ->      list[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))




