




'''simple docstring'''


import qiskit







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase   ,UpperCamelCase  )    ->    qiskit.result.counts.Counts:
	UpperCAmelCase_		:      Tuple       	=					qiskit.Aer.get_backend('aer_simulator'  )

	UpperCAmelCase_		:      List[str]       	=					qiskit.QuantumCircuit(4   ,2  )
	# encode inputs in qubits 0 and 1
	if bita == 1:
		qc_ha.x(0  )
	if bita == 1:
		qc_ha.x(1  )
	qc_ha.barrier()

	# use cnots to write XOR of the inputs on qubit2
	qc_ha.cx(0   ,2  )
	qc_ha.cx(1   ,2  )

	# use ccx / toffoli gate to write AND of the inputs on qubit3
	qc_ha.ccx(0   ,1   ,3  )
	qc_ha.barrier()

	# extract outputs
	qc_ha.measure(2   ,0  )  # extract XOR value
	qc_ha.measure(3   ,1  )  # extract AND value

	# Execute the circuit on the qasm simulator
	UpperCAmelCase_		:      List[str]       	=					qiskit.execute(UpperCamelCase   ,UpperCamelCase   ,shots=1_0_0_0  )

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


if __name__ == "__main__":
						lowerCAmelCase__										= half_adder(1, 1)
						print(F'Half Adder Output Qubit Counts: {counts}')









'''simple docstring'''


import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
						lowerCAmelCase__										= argparse.ArgumentParser()

						parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")

						parser.add_argument(
						    "--txt2img_unclip",
						    default="kakaobrain/karlo-v1-alpha",
						    type=str,
						    required=False,
						    help="The pretrained txt2img unclip.",
						)

						lowerCAmelCase__										= parser.parse_args()

						lowerCAmelCase__										= UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

						lowerCAmelCase__										= CLIPImageProcessor()
						lowerCAmelCase__										= CLIPVisionModelWithProjection.from_pretrained("openai/clip-vit-large-patch14")

						lowerCAmelCase__										= UnCLIPImageVariationPipeline(
						    decoder=txtaimg.decoder,
						    text_encoder=txtaimg.text_encoder,
						    tokenizer=txtaimg.tokenizer,
						    text_proj=txtaimg.text_proj,
						    feature_extractor=feature_extractor,
						    image_encoder=image_encoder,
						    super_res_first=txtaimg.super_res_first,
						    super_res_last=txtaimg.super_res_last,
						    decoder_scheduler=txtaimg.decoder_scheduler,
						    super_res_scheduler=txtaimg.super_res_scheduler,
						)

						imgaimg.save_pretrained(args.dump_path)






