




"""simple docstring"""
import qiskit
from qiskit.providers import Backend





def        __magic_name__			(							lowercase							,     lowercase	):
   SCREAMING_SNAKE_CASE_:	str            =bin(lowercase	)[2:], bin(lowercase	)[2:]  # Remove leading '0b'

   # Ensure that both strings are of the same length
   if len(lowercase	) > len(lowercase	):
      SCREAMING_SNAKE_CASE_:	Optional[Any]            =y.zfill(len(lowercase	)	)
   else:
      SCREAMING_SNAKE_CASE_:	List[str]            =x.zfill(len(lowercase	)	)

   # We need (3 * number of bits in the larger number)+1 qBits
   # The second parameter is the number of classical registers, to measure the result
   SCREAMING_SNAKE_CASE_:	Dict            =qiskit.QuantumCircuit((len(lowercase	) * 3) + 1							,     len(lowercase	) + 1	)

   # We are essentially "not-ing" the bits that are 1
   # Reversed because it's easier to perform ops on more significant bits
   for i in range(len(lowercase	)	):
      if x[::-1][i] == "1":
         circuit.x(lowercase	)
   for j in range(len(lowercase	)	):
      if y[::-1][j] == "1":
         circuit.x(len(lowercase	) + j	)

   return circuit, x, y





def        __magic_name__			(							lowercase							,     lowercase							,     lowercase							,     lowercase							,     lowercase							,     ):
   circuit.ccx(lowercase							,     lowercase							,     lowercase	)
   circuit.cx(lowercase							,     lowercase	)
   circuit.ccx(lowercase							,     lowercase							,     lowercase	)
   circuit.cx(lowercase							,     lowercase	)
   circuit.cx(lowercase							,     lowercase	)





def        __magic_name__			(							lowercase							,     lowercase							,     lowercase = qiskit.Aer.get_backend("""aer_simulator"""	)							,     ):

   if vala < 0 or vala < 0:
      raise ValueError("""Both Integers must be positive!"""	)

   # Store the Integers
   SCREAMING_SNAKE_CASE_:	Tuple            =store_two_classics(lowercase							,     lowercase	)
   for i in range(len(lowercase	)	):
      full_adder(lowercase							,     lowercase							,     len(lowercase	) + i							,     len(lowercase	) + len(lowercase	) + i							,     len(lowercase	) + len(lowercase	) + i + 1	)
      circuit.barrier()  # Optional, just for aesthetics

   # Measure the resultant qBits
   for i in range(len(lowercase	) + 1	):
      circuit.measure([(len(lowercase	) * 2) + i]							,     [i]	)

   SCREAMING_SNAKE_CASE_:	Tuple            =qiskit.execute(lowercase							,     lowercase							,     shots=1	).result()

   # The result is in binary. Convert it back to int
   return int(list(res.get_counts()	)[0]							,     2	)


if __name__ == "__main__":
   import doctest

   doctest.testmod()








"""simple docstring"""
import copy

from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging


_UpperCAmelCase       =     logging.get_logger(__name__)
class    a		(					UpperCAmelCase__    ):
  UpperCamelCase		:			int         =   'hybrid-clip'
  UpperCamelCase		:			Optional[int]         =   True



  def __init__(  self	:   List[Any]    ,						lowerCAmelCase	:   Any=512    ,						**lowerCAmelCase	:   Optional[Any]						)       ->   List[str]:


     '''simple docstring'''




     super().__init__(**lowerCAmelCase						)

     if "text_config" not in kwargs:
        raise ValueError("""`text_config` can not be `None`."""						)

     if "vision_config" not in kwargs:
        raise ValueError("""`vision_config` can not be `None`."""						)

     SCREAMING_SNAKE_CASE_:	Optional[int]            =kwargs.pop("""text_config"""						)
     SCREAMING_SNAKE_CASE_:	Optional[Any]            =kwargs.pop("""vision_config"""						)

     SCREAMING_SNAKE_CASE_:	int            =text_config.pop("""model_type"""						)
     SCREAMING_SNAKE_CASE_:	str            =vision_config.pop("""model_type"""						)

     from transformers import AutoConfig

     SCREAMING_SNAKE_CASE_:	Optional[int]            =AutoConfig.for_model(lowerCAmelCase    ,						**lowerCAmelCase						)

     if vision_model_type == "clip":
        SCREAMING_SNAKE_CASE_:	Tuple            =AutoConfig.for_model(lowerCAmelCase    ,						**lowerCAmelCase						).vision_config
     elif vision_model_type == "clip_vision_model":
        from transformers import CLIPVisionConfig

        SCREAMING_SNAKE_CASE_:	List[str]            =CLIPVisionConfig(**lowerCAmelCase						)
     else:
        SCREAMING_SNAKE_CASE_:	Optional[Any]            =AutoConfig.for_model(lowerCAmelCase    ,						**lowerCAmelCase						)

     SCREAMING_SNAKE_CASE_:	Any            =projection_dim
     SCREAMING_SNAKE_CASE_:	int            =1.0



  @classmethod
  def 		lowerCamelCase__    (  cls	:   List[Any]    ,						lowerCAmelCase	:   PretrainedConfig    ,						lowerCAmelCase	:   PretrainedConfig    ,						**lowerCAmelCase	:   str						)       ->   Optional[Any]:


     '''simple docstring'''




     return cls(text_config=text_config.to_dict()    ,						vision_config=vision_config.to_dict()    ,						**lowerCAmelCase						)





  def 		lowerCamelCase__    (  self	:   List[str]						)       ->   Tuple:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	int            =copy.deepcopy(self.__dict__						)
     SCREAMING_SNAKE_CASE_:	List[str]            =self.text_config.to_dict()
     SCREAMING_SNAKE_CASE_:	Optional[int]            =self.vision_config.to_dict()
     SCREAMING_SNAKE_CASE_:	Union[str, Any]            =self.__class__.model_type
     return output



