
'''simple docstring'''



UpperCamelCase__      :		Optional[int]       							=  "Alexander Joslin"

import operator as op

from .stack import Stack
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_						)     ->     int:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				{"""*""": op.mul, """/""": op.truediv, """+""": op.add, """-""": op.sub}

   _SCREAMING_SNAKE_CASE      =				Stack()
   _SCREAMING_SNAKE_CASE      =				Stack()

   for i in equation:
      if i.isdigit():
         # RULE 1
         operand_stack.push(int(SCREAMING_SNAKE_CASE_						)						)
      elif i in operators:
         # RULE 2
         operator_stack.push(SCREAMING_SNAKE_CASE_						)
      elif i == ")":
         # RULE 4
         _SCREAMING_SNAKE_CASE      =				operator_stack.peek()
         operator_stack.pop()
         _SCREAMING_SNAKE_CASE      =				operand_stack.peek()
         operand_stack.pop()
         _SCREAMING_SNAKE_CASE      =				operand_stack.peek()
         operand_stack.pop()

         _SCREAMING_SNAKE_CASE      =				operators[opr](SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)
         operand_stack.push(SCREAMING_SNAKE_CASE_						)

    # RULE 5
   return operand_stack.peek()


if __name__ == "__main__":
     UpperCamelCase__      :		List[Any]       							=  "(5 + ((4 * 2) * (2 + 3)))"
     # answer = 45
     print(f"""{equation} = {dijkstras_two_stack_algorithm(equation)}""")








'''simple docstring'''



import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding

class    _a				(_lowerCamelCase):





       """simple docstring"""




       SCREAMING_SNAKE_CASE      =  ['image_processor', 'tokenizer']
       SCREAMING_SNAKE_CASE      =  'ChineseCLIPImageProcessor'
       SCREAMING_SNAKE_CASE      =  ('BertTokenizer', 'BertTokenizerFast')






       def __init__(							self       ,							A__=None       ,							A__=None       ,							**A__      )    ->			int:
          _SCREAMING_SNAKE_CASE      =				None
          if "feature_extractor" in kwargs:
             warnings.warn(
                 """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
                 """ instead."""       ,							A__       ,							)
             _SCREAMING_SNAKE_CASE      =				kwargs.pop("""feature_extractor"""      )

          _SCREAMING_SNAKE_CASE      =				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__(A__       ,							A__      )
          _SCREAMING_SNAKE_CASE      =				self.image_processor






       def __call__(							self       ,							A__=None       ,							A__=None       ,							A__=None       ,							**A__      )    ->			Optional[int]:

          if text is None and images is None:
             raise ValueError("""You have to specify either text or images. Both cannot be none."""      )

          if text is not None:
             _SCREAMING_SNAKE_CASE      =				self.tokenizer(A__       ,							return_tensors=A__       ,							**A__      )

          if images is not None:
             _SCREAMING_SNAKE_CASE      =				self.image_processor(A__       ,							return_tensors=A__       ,							**A__      )

          if text is not None and images is not None:
             _SCREAMING_SNAKE_CASE      =				image_features.pixel_values
             return encoding
          elif text is not None:
             return encoding
          else:
             return BatchEncoding(data=dict(**A__      )       ,							tensor_type=A__      )






       def        UpperCamelCase				(							self       ,							*A__       ,							**A__      )    ->			Dict:
          return self.tokenizer.batch_decode(*A__       ,							**A__      )






       def        UpperCamelCase				(							self       ,							*A__       ,							**A__      )    ->			Optional[Any]:
          return self.tokenizer.decode(*A__       ,							**A__      )






       @property
       def        UpperCamelCase				(							self      )    ->			List[Any]:
          _SCREAMING_SNAKE_CASE      =				self.tokenizer.model_input_names
          _SCREAMING_SNAKE_CASE      =				self.image_processor.model_input_names
          return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names      )      )






       @property
       def        UpperCamelCase				(							self      )    ->			Optional[int]:
          warnings.warn(
              """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""       ,							A__       ,							)
          return self.image_processor_class






