



'''simple docstring'''




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






class 		_UpperCamelCase						(							lowerCamelCase__					):


			'''simple docstring'''

			_A   :	Dict    					=	['''image_processor''', '''tokenizer''']
			_A   :	Union[str, Any]    					=	'''AutoImageProcessor'''
			_A   :	Union[str, Any]    					=	'''AutoTokenizer'''

			def __init__(							self  :				int							,  lowerCAmelCase__  :				Any							,  lowerCAmelCase__  :				Tuple		):





					"""simple docstring"""






					super().__init__(lowerCAmelCase__							,  lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	Any					 =	self.image_processor

			def __call__(							self  :				Union[str, Any]							,  lowerCAmelCase__  :				Dict=None							,  lowerCAmelCase__  :				List[Any]=None							,  lowerCAmelCase__  :				Tuple=None							,  **lowerCAmelCase__  :				Any		):





					"""simple docstring"""






					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 :	str					 =	self.tokenizer(lowerCAmelCase__							,  return_tensors=lowerCAmelCase__							,  **lowerCAmelCase__		)

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

					if text is not None and images is not None:
							__SCREAMING_SNAKE_CASE :	List[Any]					 =	image_features.pixel_values
							return encoding
					elif text is not None:
							return encoding
					else:
							return BatchEncoding(data=dict(**lowerCAmelCase__		)							,  tensor_type=lowerCAmelCase__		)

			def UpperCamelCase__      (							self  :				Dict							,  *lowerCAmelCase__  :				int							,  **lowerCAmelCase__  :				Tuple		):





					"""simple docstring"""






					return self.tokenizer.batch_decode(*lowerCAmelCase__							,  **lowerCAmelCase__		)

			def UpperCamelCase__      (							self  :				Union[str, Any]							,  *lowerCAmelCase__  :				Any							,  **lowerCAmelCase__  :				Optional[Any]		):





					"""simple docstring"""






					return self.tokenizer.decode(*lowerCAmelCase__							,  **lowerCAmelCase__		)



			@property
			def UpperCamelCase__      (							self  :				List[Any]		):





					"""simple docstring"""






					return ["input_ids", "attention_mask", "pixel_values"]




'''simple docstring'''




import qiskit


def        lowerCAmelCase_     (			_lowerCamelCase:     int = 2      ):
		__SCREAMING_SNAKE_CASE :	Dict					 =	qubits

		# Using Aer's simulator
		__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	qiskit.Aer.get_backend("""aer_simulator"""      )

		# Creating a Quantum Circuit acting on the q register
		__SCREAMING_SNAKE_CASE :	str					 =	qiskit.QuantumCircuit(_lowerCamelCase ,   _lowerCamelCase      )

		# Adding a H gate on qubit 0 (now q0 in superposition)
		circuit.h(0      )

		for i in range(1 ,   _lowerCamelCase      ):
				# Adding CX (CNOT) gate
				circuit.cx(i - 1 ,   _lowerCamelCase      )

		# Mapping the quantum measurement to the classical bits
		circuit.measure(list(range(_lowerCamelCase      )      ) ,   list(range(_lowerCamelCase      )      )      )

		# Now measuring any one qubit would affect other qubits to collapse
		# their super position and have same state as the measured one.

		# Executing the circuit on the simulator
		__SCREAMING_SNAKE_CASE :	str					 =	qiskit.execute(_lowerCamelCase ,   _lowerCamelCase ,   shots=10_00      )

		return job.result().get_counts(_lowerCamelCase      )


if __name__ == "__main__":
						print(f"Total count for various states are: {quantum_entanglement(3)}")