
'''simple docstring'''





import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin






class 							UpperCAmelCase_							(    A				):

 '''simple docstring'''
 lowercase_					:    int				=							["image_processor", "tokenizer"]
 lowercase_					:    str				=							"AutoImageProcessor"
 lowercase_					:    Optional[int]				=							"AutoTokenizer"







 def __init__(					self		:  Optional[Any]			,				snake_case__		:  Dict=None			,				snake_case__		:  int=None			,				**snake_case__		:  int ):



   '''simple docstring'''




   UpperCAmelCase__      :					List[Any]				       =							None
   if "feature_extractor" in kwargs:
     warnings.warn(
         "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
         " instead."			,				snake_case__			,				)
     UpperCAmelCase__      :					Optional[int]				       =							kwargs.pop("feature_extractor" )

   UpperCAmelCase__      :					Any				       =							image_processor if image_processor is not None else feature_extractor
   if image_processor is None:
     raise ValueError("You need to specify an `image_processor`." )
   if tokenizer is None:
     raise ValueError("You need to specify a `tokenizer`." )

   super().__init__(snake_case__			,				snake_case__ )
   UpperCAmelCase__      :					int				       =							self.image_processor
   UpperCAmelCase__      :					Union[str, Any]				       =							False







 def __call__(					self		:  str			,				*snake_case__		:  Optional[Any]			,				**snake_case__		:  Any ):



   '''simple docstring'''




   if self._in_target_context_manager:
     return self.current_processor(*snake_case__			,				**snake_case__ )

   UpperCAmelCase__      :					List[Any]				       =							kwargs.pop("images"			,				snake_case__ )
   UpperCAmelCase__      :					List[str]				       =							kwargs.pop("text"			,				snake_case__ )
   if len(snake_case__ ) > 0:
     UpperCAmelCase__      :					Tuple				       =							args[0]
     UpperCAmelCase__      :					Optional[Any]				       =							args[1:]

   if images is None and text is None:
     raise ValueError("You need to specify either an `images` or `text` input to process." )

   if images is not None:
     UpperCAmelCase__      :					Optional[int]				       =							self.image_processor(snake_case__			,				*snake_case__			,				**snake_case__ )
   if text is not None:
     UpperCAmelCase__      :					Dict				       =							self.tokenizer(snake_case__			,				**snake_case__ )

   if text is None:
     return inputs
   elif images is None:
     return encodings
   else:
     UpperCAmelCase__      :					str				       =							encodings["input_ids"]
     return inputs







 def 							UpperCamelCase  (					self		:  Union[str, Any]			,				*snake_case__		:  Optional[Any]			,				**snake_case__		:  Union[str, Any] ):



   '''simple docstring'''




   return self.tokenizer.batch_decode(*snake_case__			,				**snake_case__ )







 def 							UpperCamelCase  (					self		:  Optional[Any]			,				*snake_case__		:  List[str]			,				**snake_case__		:  List[Any] ):



   '''simple docstring'''




   return self.tokenizer.decode(*snake_case__			,				**snake_case__ )







 @contextmanager
 def 							UpperCamelCase  (					self		:  Optional[int] ):



   '''simple docstring'''




   warnings.warn(
       "`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your "
       "labels by using the argument `text` of the regular `__call__` method (either in the same call as "
       "your images inputs, or in a separate call." )
   UpperCAmelCase__      :					Any				       =							True
   UpperCAmelCase__      :					List[str]				       =							self.tokenizer
   yield
   UpperCAmelCase__      :					List[Any]				       =							self.image_processor
   UpperCAmelCase__      :					Union[str, Any]				       =							False







 def 							UpperCamelCase  (					self		:  List[str]			,				snake_case__		:  Any			,				snake_case__		:  str=False			,				snake_case__		:  Optional[Any]=None ):



   '''simple docstring'''




   if added_vocab is None:
     UpperCAmelCase__      :					Optional[Any]				       =							self.tokenizer.get_added_vocab()

   UpperCAmelCase__      :					Union[str, Any]				       =							{}

   while tokens:
     UpperCAmelCase__      :					List[Any]				       =							re.search(R"<s_(.*?)>"			,				snake_case__			,				re.IGNORECASE )
     if start_token is None:
       break
     UpperCAmelCase__      :					Tuple				       =							start_token.group(1 )
     UpperCAmelCase__      :					str				       =							re.search(RF"""</s_{key}>"""			,				snake_case__			,				re.IGNORECASE )
     UpperCAmelCase__      :					Any				       =							start_token.group()
     if end_token is None:
       UpperCAmelCase__      :					Dict				       =							tokens.replace(snake_case__			,				"" )
     else:
       UpperCAmelCase__      :					Any				       =							end_token.group()
       UpperCAmelCase__      :					Optional[Any]				       =							re.escape(snake_case__ )
       UpperCAmelCase__      :					Tuple				       =							re.escape(snake_case__ )
       UpperCAmelCase__      :					str				       =							re.search(F"""{start_token_escaped}(.*?){end_token_escaped}"""			,				snake_case__			,				re.IGNORECASE )
       if content is not None:
         UpperCAmelCase__      :					Any				       =							content.group(1 ).strip()
         if r"<s_" in content and r"</s_" in content:  # non-leaf node
           UpperCAmelCase__      :					int				       =							self.tokenajson(snake_case__			,				is_inner_value=snake_case__			,				added_vocab=snake_case__ )
           if value:
             if len(snake_case__ ) == 1:
               UpperCAmelCase__      :					Optional[int]				       =							value[0]
             UpperCAmelCase__      :					Optional[int]				       =							value
         else:  # leaf nodes
           UpperCAmelCase__      :					int				       =							[]
           for leaf in content.split(R"<sep/>" ):
             UpperCAmelCase__      :					str				       =							leaf.strip()
             if leaf in added_vocab and leaf[0] == "<" and leaf[-2:] == "/>":
               UpperCAmelCase__      :					Dict				       =							leaf[1:-2]  # for categorical special tokens
             output[key].append(snake_case__ )
           if len(output[key] ) == 1:
             UpperCAmelCase__      :					List[Any]				       =							output[key][0]

       UpperCAmelCase__      :					Any				       =							tokens[tokens.find(snake_case__ ) + len(snake_case__ ) :].strip()
       if tokens[:6] == r"<sep/>":  # non-leaf nodes
         return [output] + self.tokenajson(tokens[6:]			,				is_inner_value=snake_case__			,				added_vocab=snake_case__ )

   if len(snake_case__ ):
     return [output] if is_inner_value else output
   else:
     return [] if is_inner_value else {"text_sequence": tokens}







 @property
 def 							UpperCamelCase  (					self		:  Any ):



   '''simple docstring'''




   warnings.warn(
       "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."			,				snake_case__			,				)
   return self.image_processor_class







 @property
 def 							UpperCamelCase  (					self		:  Tuple ):



   '''simple docstring'''




   warnings.warn(
       "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."			,				snake_case__			,				)
   return self.image_processor





'''simple docstring'''





import operator as op







def snake_case_     (					lowercase__		):
  UpperCAmelCase__      :					Optional[Any]				       =							[]
  UpperCAmelCase__      :					Any				       =							lambda lowercase__   ,       lowercase__		: int(x / y		)  # noqa: E731 integer division operation
  UpperCAmelCase__      :					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(lowercase__		))		)

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

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

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

  return int(stack[0]		)


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



