





import operator as op


def 							__snake_case							(    lowerCAmelCase_	)				->						str:
    SCREAMING_SNAKE_CASE__											=			[]
    SCREAMING_SNAKE_CASE__											=			lambda lowerCAmelCase_ ,							lowerCAmelCase_	: int(x / y	)  # noqa: E731 integer division operation
    SCREAMING_SNAKE_CASE__											=			{
        '''^''': op.pow,
        '''*''': op.mul,
        '''/''': div,
        '''+''': op.add,
        '''-''': op.sub,
    }  # operators & their respective operation

    # print table header
    print('''Symbol'''.center(8	) ,							'''Action'''.center(1_2	) ,							'''Stack''' ,							sep=''' | '''	)
    print('''-''' * (3_0 + 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(1_2	) ,							''','''.join(lowerCAmelCase_	) ,							sep=''' | '''	)
        else:
            SCREAMING_SNAKE_CASE__											=			stack.pop()  # pop stack
            # output in tabular format
            print(''''''.rjust(8	) ,							('''pop(''' + b + ''')''').ljust(1_2	) ,							''','''.join(lowerCAmelCase_	) ,							sep=''' | '''	)

            SCREAMING_SNAKE_CASE__											=			stack.pop()  # pop stack
            # output in tabular format
            print(''''''.rjust(8	) ,							('''pop(''' + a + ''')''').ljust(1_2	) ,							''','''.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(1_2	) ,							''','''.join(lowerCAmelCase_	) ,							sep=''' | ''' ,							)

    return int(stack[0]	)


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







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_A					:				Any				=			{"""configuration_xglm""": ["""XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XGLMConfig"""]}

try:
  if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _A					:				Optional[Any]				=			["""XGLMTokenizer"""]

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _A					:				Any				=			["""XGLMTokenizerFast"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _A					:				str				=			[
      """XGLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """XGLMForCausalLM""",
      """XGLMModel""",
      """XGLMPreTrainedModel""",
  ]


try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _A					:				Tuple				=			[
      """FlaxXGLMForCausalLM""",
      """FlaxXGLMModel""",
      """FlaxXGLMPreTrainedModel""",
  ]


try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _A					:				List[Any]				=			[
      """TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """TFXGLMForCausalLM""",
      """TFXGLMModel""",
      """TFXGLMPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_xglm import XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XGLMConfig

  try:
    if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_xglm import XGLMTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_xglm_fast import XGLMTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_xglm import XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, XGLMForCausalLM, XGLMModel, XGLMPreTrainedModel

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel, FlaxXGLMPreTrainedModel

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_xglm import (
        TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFXGLMForCausalLM,
        TFXGLMModel,
        TFXGLMPreTrainedModel,
    )


else:
  import sys

  _A					:				List[Any]				=			_LazyModule(__name__, globals()["""__file__"""], _import_structure)
