



import qiskit

def    lowerCamelCase_(lowerCamelCase_    ,   lowerCamelCase_    ) ->       qiskit.result.counts.Counts:
						UpperCAmelCase         =     qiskit.Aer.get_backend("aer_simulator"    )

						# Create a Quantum Circuit acting on the q register
						UpperCAmelCase         =     qiskit.QuantumCircuit(_lowercase    ,   _lowercase    )

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

						# Execute the circuit on the simulator
						UpperCAmelCase         =     qiskit.execute(_lowercase    ,   _lowercase    ,   shots=1_000    )

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


if __name__ == "__main__":
	print(F'''Total count for various states are: {single_qubit_measure(1, 1)}''')








import gc
import unittest

from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
		import jax
		import jax.numpy as jnp
		from flax.jax_utils import replicate
		from flax.training.common_utils import shard





@slow
@require_flax
class  __magic_name__   (							unittest.TestCase						):






		def SCREAMING_SNAKE_CASE_			(						self      :     Optional[int]      )				->					Optional[Any]:
								'''simple docstring'''







								super().tearDown()
								gc.collect()






		def SCREAMING_SNAKE_CASE_			(						self      :     Tuple      )				->					Dict:
								'''simple docstring'''







								UpperCAmelCase       ,		UpperCAmelCase         =     FlaxControlNetModel.from_pretrained(
								    "lllyasviel/sd-controlnet-canny"				,		from_pt=UpperCamelCase__				,		dtype=jnp.bfloataa      )
								UpperCAmelCase       ,		UpperCAmelCase         =     FlaxStableDiffusionControlNetPipeline.from_pretrained(
								    "runwayml/stable-diffusion-v1-5"				,		controlnet=UpperCamelCase__				,		from_pt=UpperCamelCase__				,		dtype=jnp.bfloataa      )
								UpperCAmelCase         =     controlnet_params

								UpperCAmelCase         =     "bird"
								UpperCAmelCase         =     jax.device_count()
								UpperCAmelCase         =     pipe.prepare_text_inputs([prompts] * num_samples      )

								UpperCAmelCase         =     load_image(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"      )
								UpperCAmelCase         =     pipe.prepare_image_inputs([canny_image] * num_samples      )

								UpperCAmelCase         =     jax.random.PRNGKey(0      )
								UpperCAmelCase         =     jax.random.split(UpperCamelCase__				,		jax.device_count()      )

								UpperCAmelCase         =     replicate(UpperCamelCase__      )
								UpperCAmelCase         =     shard(UpperCamelCase__      )
								UpperCAmelCase         =     shard(UpperCamelCase__      )

								UpperCAmelCase         =     pipe(
								    prompt_ids=UpperCamelCase__				,		image=UpperCamelCase__				,		params=UpperCamelCase__				,		prng_seed=UpperCamelCase__				,		num_inference_steps=50				,		jit=UpperCamelCase__				,		).images
								assert images.shape == (jax.device_count(), 1, 7_68, 5_12, 3)

								UpperCAmelCase         =     images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]      )
								UpperCAmelCase         =     images[0, 2_53:2_56, 2_53:2_56, -1]

								UpperCAmelCase         =     jnp.asarray(jax.device_get(image_slice.flatten()      )      )
								UpperCAmelCase         =     jnp.array(
								    [0.16_79_69, 0.11_66_99, 0.08_15_43, 0.15_42_97, 0.13_28_12, 0.10_88_87, 0.16_99_22, 0.16_99_22, 0.20_50_78]      )
								print(F'output_slice: {output_slice}'      )
								assert jnp.abs(output_slice - expected_slice      ).max() < 1e-2







		def SCREAMING_SNAKE_CASE_			(						self      :     Tuple      )				->					Tuple:
								'''simple docstring'''







								UpperCAmelCase       ,		UpperCAmelCase         =     FlaxControlNetModel.from_pretrained(
								    "lllyasviel/sd-controlnet-openpose"				,		from_pt=UpperCamelCase__				,		dtype=jnp.bfloataa      )
								UpperCAmelCase       ,		UpperCAmelCase         =     FlaxStableDiffusionControlNetPipeline.from_pretrained(
								    "runwayml/stable-diffusion-v1-5"				,		controlnet=UpperCamelCase__				,		from_pt=UpperCamelCase__				,		dtype=jnp.bfloataa      )
								UpperCAmelCase         =     controlnet_params

								UpperCAmelCase         =     "Chef in the kitchen"
								UpperCAmelCase         =     jax.device_count()
								UpperCAmelCase         =     pipe.prepare_text_inputs([prompts] * num_samples      )

								UpperCAmelCase         =     load_image(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png"      )
								UpperCAmelCase         =     pipe.prepare_image_inputs([pose_image] * num_samples      )

								UpperCAmelCase         =     jax.random.PRNGKey(0      )
								UpperCAmelCase         =     jax.random.split(UpperCamelCase__				,		jax.device_count()      )

								UpperCAmelCase         =     replicate(UpperCamelCase__      )
								UpperCAmelCase         =     shard(UpperCamelCase__      )
								UpperCAmelCase         =     shard(UpperCamelCase__      )

								UpperCAmelCase         =     pipe(
								    prompt_ids=UpperCamelCase__				,		image=UpperCamelCase__				,		params=UpperCamelCase__				,		prng_seed=UpperCamelCase__				,		num_inference_steps=50				,		jit=UpperCamelCase__				,		).images
								assert images.shape == (jax.device_count(), 1, 7_68, 5_12, 3)

								UpperCAmelCase         =     images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]      )
								UpperCAmelCase         =     images[0, 2_53:2_56, 2_53:2_56, -1]

								UpperCAmelCase         =     jnp.asarray(jax.device_get(image_slice.flatten()      )      )
								UpperCAmelCase         =     jnp.array(
								    [[0.27_14_84, 0.26_17_19, 0.27_53_91, 0.27_73_44, 0.27_92_97, 0.29_10_16, 0.29_49_22, 0.30_27_34, 0.30_27_34]]      )
								print(F'output_slice: {output_slice}'      )
								assert jnp.abs(output_slice - expected_slice      ).max() < 1e-2



