

"""simple docstring"""




from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType


class 		_A       (			_lowerCAmelCase					):
      __a								=	['image_processor', 'tokenizer']
      __a								=	'BlipImageProcessor'
      __a								=	'AutoTokenizer'





      def __init__(  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)   ->					List[str]:
         lowerCamelCase__						=			False
         super().__init__(_lowerCAmelCase    ,						_lowerCAmelCase		)
         lowerCamelCase__						=			self.image_processor





      def __call__(  self    ,						SCREAMING_SNAKE_CASE__ = None    ,						SCREAMING_SNAKE_CASE__ = None    ,						SCREAMING_SNAKE_CASE__ = True    ,						SCREAMING_SNAKE_CASE__ = False    ,						SCREAMING_SNAKE_CASE__ = None    ,						SCREAMING_SNAKE_CASE__ = None    ,						SCREAMING_SNAKE_CASE__ = 0    ,						SCREAMING_SNAKE_CASE__ = None    ,						SCREAMING_SNAKE_CASE__ = None    ,						SCREAMING_SNAKE_CASE__ = False    ,						SCREAMING_SNAKE_CASE__ = False    ,						SCREAMING_SNAKE_CASE__ = False    ,						SCREAMING_SNAKE_CASE__ = False    ,						SCREAMING_SNAKE_CASE__ = False    ,						SCREAMING_SNAKE_CASE__ = True    ,						SCREAMING_SNAKE_CASE__ = None    ,						**SCREAMING_SNAKE_CASE__    ,						)   ->					BatchEncoding:







         if images is None and text is None:
            raise ValueError("You have to specify either images or text."		)

         # Get only text
         if images is None:
            lowerCamelCase__						=			self.tokenizer
            lowerCamelCase__						=			self.tokenizer(
                text=_lowerCAmelCase    ,						add_special_tokens=_lowerCAmelCase    ,						padding=_lowerCAmelCase    ,						truncation=_lowerCAmelCase    ,						max_length=_lowerCAmelCase    ,						stride=_lowerCAmelCase    ,						pad_to_multiple_of=_lowerCAmelCase    ,						return_attention_mask=_lowerCAmelCase    ,						return_overflowing_tokens=_lowerCAmelCase    ,						return_special_tokens_mask=_lowerCAmelCase    ,						return_offsets_mapping=_lowerCAmelCase    ,						return_token_type_ids=_lowerCAmelCase    ,						return_length=_lowerCAmelCase    ,						verbose=_lowerCAmelCase    ,						return_tensors=_lowerCAmelCase    ,						**_lowerCAmelCase    ,						)
            return text_encoding

         # add pixel_values
         lowerCamelCase__						=			self.image_processor(_lowerCAmelCase    ,						return_tensors=_lowerCAmelCase		)

         if text is not None:
            lowerCamelCase__						=			self.tokenizer(
                text=_lowerCAmelCase    ,						add_special_tokens=_lowerCAmelCase    ,						padding=_lowerCAmelCase    ,						truncation=_lowerCAmelCase    ,						max_length=_lowerCAmelCase    ,						stride=_lowerCAmelCase    ,						pad_to_multiple_of=_lowerCAmelCase    ,						return_attention_mask=_lowerCAmelCase    ,						return_overflowing_tokens=_lowerCAmelCase    ,						return_special_tokens_mask=_lowerCAmelCase    ,						return_offsets_mapping=_lowerCAmelCase    ,						return_token_type_ids=_lowerCAmelCase    ,						return_length=_lowerCAmelCase    ,						verbose=_lowerCAmelCase    ,						return_tensors=_lowerCAmelCase    ,						**_lowerCAmelCase    ,						)
         else:
            lowerCamelCase__						=			None

         if text_encoding is not None:
            encoding_image_processor.update(_lowerCAmelCase		)

         return encoding_image_processor





      def 					_lowerCamelCase  (  self    ,						*SCREAMING_SNAKE_CASE__    ,						**SCREAMING_SNAKE_CASE__		)   ->					str:
         return self.tokenizer.batch_decode(*_lowerCAmelCase    ,						**_lowerCAmelCase		)





      def 					_lowerCamelCase  (  self    ,						*SCREAMING_SNAKE_CASE__    ,						**SCREAMING_SNAKE_CASE__		)   ->					Optional[Any]:
         return self.tokenizer.decode(*_lowerCAmelCase    ,						**_lowerCAmelCase		)





      @property
      # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
      def 					_lowerCamelCase  (  self		)   ->					Tuple:
         lowerCamelCase__						=			self.tokenizer.model_input_names
         lowerCamelCase__						=			self.image_processor.model_input_names
         return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names		)		)







"""simple docstring"""




import operator as op



def 	UpperCAmelCase__   (				A__   )      ->      Dict:




   """simple docstring"""
   lowerCamelCase__						=			[]
   lowerCamelCase__						=			lambda A__	,    A__   : int(x / y   )  # noqa: E731 integer division operation
   lowerCamelCase__						=			{
       "^": 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(A__   ))   )

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

         lowerCamelCase__						=			stack.pop()  # pop stack
         # output in tabular format
         print("".rjust(8   )	,    ("pop(" + a + ")").ljust(12   )	,    ",".join(A__   )	,    sep=" | "   )

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

   return int(stack[0]   )


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




