


import operator as op
def      snake_case_						( __lowercase       ):
   UpperCAmelCase_							:      Optional[Any]						=   []
   UpperCAmelCase_							:      Optional[Any]						=   lambda __lowercase			,       __lowercase       : int(x / y       )  # noqa: E731 integer division operation
   UpperCAmelCase_							:      List[Any]						=   {
       '''^''': 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(UpperCAmelCase__       ))       )

   for x in post_fix:
      if x.isdigit():  # if x in digit
         stack.append(UpperCAmelCase__       )  # append x to stack
         # output in tabular format
         print(x.rjust(8       )			,       ('''push(''' + x + ''')''').ljust(1_2       )			,       ''','''.join(UpperCAmelCase__       )			,       sep=''' | '''       )
      else:
         UpperCAmelCase_							:      int						=   stack.pop()  # pop stack
         # output in tabular format
         print(''''''.rjust(8       )			,       ('''pop(''' + b + ''')''').ljust(1_2       )			,       ''','''.join(UpperCAmelCase__       )			,       sep=''' | '''       )

         UpperCAmelCase_							:      Union[str, Any]						=   stack.pop()  # pop stack
         # output in tabular format
         print(''''''.rjust(8       )			,       ('''pop(''' + a + ''')''').ljust(1_2       )			,       ''','''.join(UpperCAmelCase__       )			,       sep=''' | '''       )

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

   return int(stack[0]       )


if __name__ == "__main__":
       __UpperCamelCase				:  List[str]      					=							input('\n\nEnter a Postfix Equation (space separated) = ').split(' ')
       print('\n\tResult = ', solve(Postfix))




# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__UpperCamelCase				:  List[Any]      					=							{
    'configuration_vivit': ['VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'VivitConfig'],
}
try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __UpperCamelCase				:  Optional[Any]      					=							['VivitImageProcessor']


try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __UpperCamelCase				:  List[Any]      					=							[
           'VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
           'VivitModel',
           'VivitPreTrainedModel',
           'VivitForVideoClassification',
       ]


if TYPE_CHECKING:
       from .configuration_vivit import VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, VivitConfig

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .image_processing_vivit import VivitImageProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_vivit import (
                  VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  VivitForVideoClassification,
                  VivitModel,
                  VivitPreTrainedModel,
              )


else:
       import sys

       __UpperCamelCase				:  str      					=							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)