




'''simple docstring'''


from __future__ import annotations

from dataclasses import dataclass







@dataclass
class      SCREAMING_SNAKE_CASE    :
      __magic_name__  :       float
      __magic_name__  :       TreeNode | None   							=					None
      __magic_name__  :       TreeNode | None   							=					None



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					TreeNode | None				):




     """simple docstring"""


     def is_valid_tree(UpperCamelCase__    :					TreeNode | None				) -> bool:
          if node is None:
               return True

          if not isinstance(UpperCamelCase__ ,    UpperCamelCase__				):
               return False

          try:
               float(node.data				)
          except (TypeError, ValueError):
               return False

          return is_valid_tree(node.left				) and is_valid_tree(node.right				)

     if not is_valid_tree(UpperCamelCase__				):
          raise ValueError(
              """Each node should be type of TreeNode and data should be float."""				)

     def is_binary_search_tree_recursive_check(
         UpperCamelCase__    :					TreeNode | None ,    UpperCamelCase__    :					float ,    UpperCamelCase__    :					float				) -> bool:

          if node is None:
               return True

          return (
              left_bound < node.data < right_bound
              and is_binary_search_tree_recursive_check(node.left ,    UpperCamelCase__ ,    node.data				)
              and is_binary_search_tree_recursive_check(
                  node.right ,    node.data ,    UpperCamelCase__				)
          )

     return is_binary_search_tree_recursive_check(UpperCamelCase__ ,    -float("""inf"""				) ,    float("""inf"""				)				)


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      SCREAMING_SNAKE_CASE    (						tf.keras.layers.Layer				):






      def __init__( self   :  List[Any]						,						lowercase__   :  Dict[str, int]						,						lowercase__   :  List[str]						,						lowercase__   :  int = None						,						lowercase__   :  int = None       ):


           '''simple docstring'''

           super().__init__()
           a_     :							Optional[Any]     						=  pad_token_id
           a_     :							str     						=  max_length
           a_     :							List[str]     						=  vocab
           a_     :							Union[str, Any]     						=  merges
           a_     :							Union[str, Any]     						=  BytePairTokenizer(lowercase__						,						lowercase__						,						sequence_length=lowercase__       )






      @classmethod
      def 							lowercase_						( cls   :  List[Any]						,						lowercase__   :  GPTaTokenizer						,						*lowercase__   :  Optional[Any]						,						**lowercase__   :  int       ):


           '''simple docstring'''

           a_     :							str     						=  [""" """.join(lowercase__       ) for m in tokenizer.bpe_ranks.keys()]
           a_     :							Any     						=  tokenizer.get_vocab()
           return cls(lowercase__						,						lowercase__						,						*lowercase__						,						**lowercase__       )






      @classmethod
      def 							lowercase_						( cls   :  Dict						,						lowercase__   :  Union[str, os.PathLike]						,						*lowercase__   :  str						,						**lowercase__   :  Optional[int]       ):


           '''simple docstring'''

           a_     :							Optional[int]     						=  GPTaTokenizer.from_pretrained(lowercase__						,						*lowercase__						,						**lowercase__       )
           return cls.from_tokenizer(lowercase__						,						*lowercase__						,						**lowercase__       )






      @classmethod
      def 							lowercase_						( cls   :  Dict						,						lowercase__   :  Optional[Any]       ):


           '''simple docstring'''

           return cls(**lowercase__       )






      def 							lowercase_						( self   :  Optional[Any]       ):


           '''simple docstring'''

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






      def 							lowercase_						( self   :  Dict						,						lowercase__   :  Union[str, Any]						,						lowercase__   :  int = None       ):


           '''simple docstring'''

           a_     :							List[str]     						=  self.tf_tokenizer(lowercase__       )
           a_     :							int     						=  tf.ones_like(lowercase__       )

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

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

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