






import torch

from diffusers import StableDiffusionPipeline


lowercase  :							Dict			  =    '''path-to-your-trained-model'''
lowercase  :							Optional[Any]			  =    StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to('''cuda''')

lowercase  :							Dict			  =    '''A photo of sks dog in a bucket'''
lowercase  :							Union[str, Any]			  =    pipe(prompt, num_inference_steps=50, guidance_scale=7.5).images[0]

image.save('''dog-bucket.png''')








import qiskit

def      lowerCAmelCase__	(    _a       :			int  ,      _a       :			int    ):
		snake_case_				:							Tuple						 =       qiskit.Aer.get_backend("aer_simulator"    )

		# Create a Quantum Circuit acting on the q register
		snake_case_				:							Any						 =       qiskit.QuantumCircuit(_a  ,      _a    )

		# Apply X (NOT) Gate to Qubits 0 & 1
		circuit.x(0    )
		circuit.x(1    )

		# Map the quantum measurement to the classical bits
		circuit.measure([0, 1]  ,      [0, 1]    )

		# Execute the circuit on the qasm simulator
		snake_case_				:							Any						 =       qiskit.execute(_a  ,      _a  ,      shots=10_00    )

		# Return the histogram data of the results of the experiment.
		return job.result().get_counts(_a    )


if __name__ == "__main__":
							lowercase  :							List[Any]			  =    single_qubit_measure(2, 2)
							print(F"""Total count for various states are: {counts}""")
