






import operator as op







def 			A__							(	_a  :					Optional[int]	):





 '''simple docstring'''







 snake_case__  :    Optional[Any]							       =[]
 snake_case__  :    List[Any]							       =lambda _a		,  _a	: int(x / y	)  # noqa: E731 integer division operation
 snake_case__  :    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(__lowerCAmelCase	))	)

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

   snake_case__  :    Union[str, Any]							       =stack.pop()  # pop stack
   # output in tabular format
   print("""""".rjust(8	)		,  ("""pop(""" + a + """)""").ljust(12	)		,  """,""".join(__lowerCAmelCase	)		,  sep=""" | """	)

   stack.append(
       str(opr[x](int(__lowerCAmelCase	)		,  int(__lowerCAmelCase	)	)	)	)  # 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(__lowerCAmelCase	)		,  sep=""" | """		,  )

 return int(stack[0]	)


if __name__ == "__main__":
       __lowerCamelCase	:		Optional[Any]										=   input("""\n\nEnter a Postfix Equation (space separated) = """).split(""" """)
       print("""\n\tResult = """, solve(Postfix))













import argparse

from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging


logging.set_verbosity_info()







def 			A__							(      _a  :					int		,  _a  :					Any		,  _a  :					Union[str, Any]		,  _a  :					Tuple	):





 '''simple docstring'''







 snake_case__  :    Any							       =BigBirdConfig.from_json_file(_a	)
 print(f"Building PyTorch model from configuration: {config}"	)

 if is_trivia_qa:
  snake_case__  :    str							       =BigBirdForQuestionAnswering(_a	)
 else:
  snake_case__  :    Optional[int]							       =BigBirdForPreTraining(_a	)

 # Load weights from tf checkpoint
 load_tf_weights_in_big_bird(_a		,  _a		,  is_trivia_qa=_a	)

 # Save pytorch-model
 print(f"Save PyTorch model to {pytorch_dump_path}"	)
 model.save_pretrained(_a	)


if __name__ == "__main__":
       __lowerCamelCase	:		Union[str, Any]										=   argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
       )
       parser.add_argument(
           """--big_bird_config_file""",
           default=None,
           type=str,
           required=True,
           help=(
               """The config json file corresponding to the pre-trained BERT model. \n"""
               """This specifies the model architecture."""
           ),
       )
       parser.add_argument(
           """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
       )
       parser.add_argument(
           """--is_trivia_qa""", action="""store_true""", help="""Whether to convert a model with a trivia_qa head."""
       )
       __lowerCamelCase	:		Tuple										=   parser.parse_args()
       convert_tf_checkpoint_to_pytorch(
           args.tf_checkpoint_path, args.big_bird_config_file, args.pytorch_dump_path, args.is_trivia_qa
       )





