


def 		_A		(		_lowercase )					-> Dict:


     """simple docstring"""


     __UpperCamelCase =			[]
     __UpperCamelCase =			set({'(', '[', '{'} )
     __UpperCamelCase =			set({')', ']', '}'} )
     __UpperCamelCase =			{'{': '}', '[': ']', '(': ')'}

     for i in range(len(_lowercase ) ):
          if s[i] in open_brackets:
               stack.append(s[i] )

          elif s[i] in closed_brackets and (
              len(_lowercase ) == 0 or (len(_lowercase ) > 0 and open_to_closed[stack.pop()] != s[i])
          ):
               return False

     return len(_lowercase ) == 0






def 		_A		(		)					-> Tuple:


     """simple docstring"""


     __UpperCamelCase =			input('Enter sequence of brackets: ' )
     if is_balanced(_lowercase ):
          print(_lowercase					,  'is balanced' )
     else:
          print(_lowercase					,  'is not balanced' )


if __name__ == "__main__":
    main()









"""simple docstring"""


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



class   __SCREAMING_SNAKE_CASE     (    _UpperCamelCase ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__				:Optional[Any]						  =       ["image_processor", "tokenizer"]
  SCREAMING_SNAKE_CASE__				:Dict						  =       "AutoImageProcessor"
  SCREAMING_SNAKE_CASE__				:Optional[int]						  =       "AutoTokenizer"

  def __init__(		self :							Dict       , __a :							Tuple       , __a :							str						)		->					Optional[Any]:
        super().__init__(__a       , __a						)
        _UpperCamelCase   :						Tuple    =							self.image_processor

  def __call__(		self :							Tuple       , __a :							Tuple=None       , __a :							str=None       , __a :							Any=None       , **__a :							Optional[Any]						)		->					Union[str, Any]:

        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:
              _UpperCamelCase   :						List[str]    =							self.tokenizer(__a       , return_tensors=__a       , **__a						)

        if images is not None:
              _UpperCamelCase   :						str    =							self.image_processor(__a       , return_tensors=__a       , **__a						)

        if text is not None and images is not None:
              _UpperCamelCase   :						Optional[int]    =							image_features.pixel_values
              return encoding
        elif text is not None:
              return encoding
        else:
              return BatchEncoding(data=dict(**__a						)       , tensor_type=__a						)

  def  __SCREAMING_SNAKE_CASE					(		self :							Optional[int]       , *__a :							Optional[int]       , **__a :							Optional[int]						)		->					List[Any]:
        return self.tokenizer.batch_decode(*__a       , **__a						)

  def  __SCREAMING_SNAKE_CASE					(		self :							int       , *__a :							Any       , **__a :							List[Any]						)		->					Tuple:
        return self.tokenizer.decode(*__a       , **__a						)


  @property
  def  __SCREAMING_SNAKE_CASE					(		self :							str						)		->					Any:
        return ["input_ids", "attention_mask", "pixel_values"]
