




"""simple docstring"""
import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute





def        __magic_name__			(							lowercase = np.pi / 2							,     lowercase = np.pi / 2							,     lowercase = np.pi / 2	):
   SCREAMING_SNAKE_CASE_:	Optional[int]            =QuantumRegister(3							,     """qr"""	)  # Define the number of quantum bits
   SCREAMING_SNAKE_CASE_:	Any            =ClassicalRegister(1							,     """cr"""	)  # Define the number of classical bits

   SCREAMING_SNAKE_CASE_:	List[Any]            =QuantumCircuit(lowercase							,     lowercase	)  # Define the quantum circuit.

   # Build the circuit
   quantum_circuit.u(lowercase							,     lowercase							,     lowercase							,     0	)  # Quantum State to teleport
   quantum_circuit.h(1	)  # add hadamard gate
   quantum_circuit.cx(
       1							,     2	)  # add control gate with qubit 1 as control and 2 as target.
   quantum_circuit.cx(0							,     1	)
   quantum_circuit.h(0	)
   quantum_circuit.cz(0							,     2	)  # add control z gate.
   quantum_circuit.cx(1							,     2	)

   quantum_circuit.measure([2]							,     [0]	)  # measure the qubit.

   # Simulate the circuit using qasm simulator
   SCREAMING_SNAKE_CASE_:	Optional[int]            =Aer.get_backend("""aer_simulator"""	)
   SCREAMING_SNAKE_CASE_:	Union[str, Any]            =execute(lowercase							,     lowercase							,     shots=1000	)

   return job.result().get_counts(lowercase	)


if __name__ == "__main__":
   print(
       """Total count for teleported state is: """
       f"""{quantum_teleportation(np.pi/2, np.pi/2, np.pi/2)}"""
   )








"""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



