code: 
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
  
  
  A			=	{
      '''configuration_realm''': ['''REALM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RealmConfig'''],
      '''tokenization_realm''': ['''RealmTokenizer'''],
  }
  
  try:
         if not is_tokenizers_available():
                raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
         pass
  else:
         A			=	['''RealmTokenizerFast''']
  
  try:
         if not is_torch_available():
                raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
         pass
  else:
         A			=	[
             '''REALM_PRETRAINED_MODEL_ARCHIVE_LIST''',
             '''RealmEmbedder''',
             '''RealmForOpenQA''',
             '''RealmKnowledgeAugEncoder''',
             '''RealmPreTrainedModel''',
             '''RealmReader''',
             '''RealmScorer''',
             '''load_tf_weights_in_realm''',
         ]
         A			=	['''RealmRetriever''']
  
  
  if TYPE_CHECKING:
         from .configuration_realm import REALM_PRETRAINED_CONFIG_ARCHIVE_MAP, RealmConfig
         from .tokenization_realm import RealmTokenizer
  
         try:
                if not is_tokenizers_available():
                       raise OptionalDependencyNotAvailable()
         except OptionalDependencyNotAvailable:
                pass
         else:
                from .tokenization_realm import RealmTokenizerFast
  
         try:
                if not is_torch_available():
                       raise OptionalDependencyNotAvailable()
         except OptionalDependencyNotAvailable:
                pass
         else:
                from .modeling_realm import (
                    REALM_PRETRAINED_MODEL_ARCHIVE_LIST,
                    RealmEmbedder,
                    RealmForOpenQA,
                    RealmKnowledgeAugEncoder,
                    RealmPreTrainedModel,
                    RealmReader,
                    RealmScorer,
                    load_tf_weights_in_realm,
                )
                from .retrieval_realm import RealmRetriever
  
  
  else:
         import sys
  
         A			=	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
  
  
style_context: 
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  lowercase__			=	{
      '''+''': lambda x, y: x + y,
      '''-''': lambda x, y: x - y,
      '''*''': lambda x, y: x * y,
      '''/''': lambda x, y: x / y,
  }
  
  
  def 				UpperCamelCase	(  snake_case   ):
  
  
  
  
     '''simple docstring'''
  
     return c.isdigit()
  
  
  def 				UpperCamelCase	(  snake_case   ):
  
  
  
  
     '''simple docstring'''
  
     SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= []
  
     # iterate over the string in reverse order
     for c in expression.split()[::-1]:
        # push operand to stack
        if is_operand(snake_case   ):
           stack.append(int(snake_case   )   )
  
        else:
           # pop values from stack can calculate the result
           # push the result onto the stack again
           SCREAMING_SNAKE_CASE__			:				Dict       						= stack.pop()
           SCREAMING_SNAKE_CASE__			:				Any       						= stack.pop()
           stack.append(calc[c](snake_case	,     snake_case   )   )
  
     return stack.pop()
  
  
  # Driver code
  if __name__ == "__main__":
         lowercase__			=	'''+ 9 * 2 6'''
         print(evaluate(test_expression))
  
         lowercase__			=	'''/ * 10 2 + 4 1 '''
         print(evaluate(test_expression))
  
  