




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     ):



 '''simple docstring'''


 def __init__(       self      :						Optional[Any]		,							A_      :						Dict[str, int]		,							A_      :						List[str]		,							A_      :						int = None		,							A_      :						int = None						)     ->			str:

       """simple docstring"""






       super().__init__()
       lowerCamelCase_	  =					pad_token_id
       lowerCamelCase_	  =					max_length
       lowerCamelCase_	  =					vocab
       lowerCamelCase_	  =					merges
       lowerCamelCase_	  =					BytePairTokenizer(A_		,							A_		,							sequence_length=A_						)

 @classmethod
 def        a__				(       cls      :						int		,							A_      :						GPTaTokenizer		,							*A_      :						Dict		,							**A_      :						Tuple						)     ->			str:

       """simple docstring"""






       lowerCamelCase_	  =					[' '.join(A_						) for m in tokenizer.bpe_ranks.keys()]
       lowerCamelCase_	  =					tokenizer.get_vocab()
       return cls(A_		,							A_		,							*A_		,							**A_						)

 @classmethod
 def        a__				(       cls      :						Any		,							A_      :						Union[str, os.PathLike]		,							*A_      :						Any		,							**A_      :						int						)     ->			Dict:

       """simple docstring"""






       lowerCamelCase_	  =					GPTaTokenizer.from_pretrained(A_		,							*A_		,							**A_						)
       return cls.from_tokenizer(A_		,							*A_		,							**A_						)

 @classmethod
 def        a__				(       cls      :						List[str]		,							A_      :						List[str]						)     ->			Optional[Any]:

       """simple docstring"""






       return cls(**A_						)

 def        a__				(       self      :						Optional[Any]						)     ->			int:

       """simple docstring"""






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





 def        a__				(       self      :						Optional[int]		,							A_      :						List[str]		,							A_      :						int = None						)     ->			str:

       """simple docstring"""






       lowerCamelCase_	  =					self.tf_tokenizer(A_						)
       lowerCamelCase_	  =					tf.ones_like(A_						)

       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(
                       A_		,							max_seq_length=A_		,							pad_value=self.pad_token_id						)

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









lowerCamelCase       :	Dict									=     "Alexander Joslin"

import operator as op

from .stack import Stack

def     _SCREAMING_SNAKE_CASE    (							lowercase			: str  ):


      '''simple docstring'''



      lowerCamelCase_	  =					{'*': op.mul, '/': op.truediv, '+': op.add, '-': op.sub}

      lowerCamelCase_	  =					Stack()
      lowerCamelCase_	  =					Stack()

      for i in equation:
            if i.isdigit():
                  # RULE 1
                  operand_stack.push(int(lowercase  )  )
            elif i in operators:
                  # RULE 2
                  operator_stack.push(lowercase  )
            elif i == ")":
                  # RULE 4
                  lowerCamelCase_	  =					operator_stack.peek()
                  operator_stack.pop()
                  lowerCamelCase_	  =					operand_stack.peek()
                  operand_stack.pop()
                  lowerCamelCase_	  =					operand_stack.peek()
                  operand_stack.pop()

                  lowerCamelCase_	  =					operators[opr](lowercase			,							lowercase  )
                  operand_stack.push(lowercase  )

    # RULE 5
      return operand_stack.peek()


if __name__ == "__main__":
 lowerCamelCase       :	Any									=     "(5 + ((4 * 2) * (2 + 3)))"
 # answer = 45
 print(F"""{equation} = {dijkstras_two_stack_algorithm(equation)}""")



