






import operator as op

def    UpperCamelCase						(	_A			):






						"""simple docstring"""







						__magic_name__      :       str       						= []
						__magic_name__      :       Optional[Any]       						= lambda _A,   _A			: int(x / y			)  # noqa: E731 integer division operation
						__magic_name__      :       Optional[Any]       						= {
						    """^""": op.pow,
						    """*""": op.mul,
						    """/""": div,
						    """+""": op.add,
						    """-""": op.sub,
						}  # operators & their respective operation

						# print table header
						print("""Symbol""".center(8			),   """Action""".center(12			),   """Stack""",   sep=""" | """			)
						print("""-""" * (30 + len(_A			))			)

						for x in post_fix:
												if x.isdigit():  # if x in digit
																		stack.append(_A			)  # append x to stack
																		# output in tabular format
																		print(x.rjust(8			),   ("""push(""" + x + """)""").ljust(12			),   """,""".join(_A			),   sep=""" | """			)
												else:
																		__magic_name__      :       str       						= stack.pop()  # pop stack
																		# output in tabular format
																		print("""""".rjust(8			),   ("""pop(""" + b + """)""").ljust(12			),   """,""".join(_A			),   sep=""" | """			)

																		__magic_name__      :       Dict       						= stack.pop()  # pop stack
																		# output in tabular format
																		print("""""".rjust(8			),   ("""pop(""" + a + """)""").ljust(12			),   """,""".join(_A			),   sep=""" | """			)

																		stack.append(
																		    str(opr[x](int(_A			),   int(_A			)			)			)			)  # evaluate the 2 values popped from stack & push result to stack
																		# output in tabular format
																		print(
																		    x.rjust(8			),   ("""push(""" + a + x + b + """)""").ljust(12			),   """,""".join(_A			),   sep=""" | """,   )

						return int(stack[0]			)


if __name__ == "__main__":
		__magic_name__:					Dict							=     input("\n\nEnter a Postfix Equation (space separated) = ").split(" ")
		print("\n\tResult = ", solve(Postfix))








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      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__ = None      ,       lowerCAmelCase__ = None			)							->				int:
							super().__init__()
							__magic_name__      :       Any       						= pad_token_id
							__magic_name__      :       Any       						= max_length
							__magic_name__      :       List[str]       						= vocab
							__magic_name__      :       List[Any]       						= merges
							__magic_name__      :       int       						= BytePairTokenizer(lowerCAmelCase__      ,       lowerCAmelCase__      ,       sequence_length=lowerCAmelCase__			)
	@classmethod
	def       __magic_name__    (    cls      ,       lowerCAmelCase__      ,       *lowerCAmelCase__      ,       **lowerCAmelCase__			)							->				Any:
							__magic_name__      :       Union[str, Any]       						= [""" """.join(lowerCAmelCase__			) for m in tokenizer.bpe_ranks.keys()]
							__magic_name__      :       Union[str, Any]       						= tokenizer.get_vocab()
							return cls(lowerCAmelCase__      ,       lowerCAmelCase__      ,       *lowerCAmelCase__      ,       **lowerCAmelCase__			)
	@classmethod
	def       __magic_name__    (    cls      ,       lowerCAmelCase__      ,       *lowerCAmelCase__      ,       **lowerCAmelCase__			)							->				List[str]:
							__magic_name__      :       Optional[Any]       						= GPTaTokenizer.from_pretrained(lowerCAmelCase__      ,       *lowerCAmelCase__      ,       **lowerCAmelCase__			)
							return cls.from_tokenizer(lowerCAmelCase__      ,       *lowerCAmelCase__      ,       **lowerCAmelCase__			)
	@classmethod
	def       __magic_name__    (    cls      ,       lowerCAmelCase__			)							->				List[Any]:
							return cls(**lowerCAmelCase__			)
	def       __magic_name__    (    self			)							->				int:
							return {
							    "vocab": self.vocab,
							    "merges": self.merges,
							    "max_length": self.max_length,
							    "pad_token_id": self.pad_token_id,
							}







	def       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__ = None			)							->				int:
							__magic_name__      :       Dict       						= self.tf_tokenizer(lowerCAmelCase__			)
							__magic_name__      :       Dict       						= tf.ones_like(lowerCAmelCase__			)

							if self.pad_token_id is not None:
													# pad the tokens up to max length
													__magic_name__      :       List[Any]       						= max_length if max_length is not None else self.max_length

													if max_length is not None:
																			__magic_name__						,__magic_name__      :       List[Any]       						= pad_model_inputs(
																			    lowerCAmelCase__      ,       max_seq_length=lowerCAmelCase__      ,       pad_value=self.pad_token_id			)

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