





"""simple docstring"""




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        __UpperCamelCase				(						unittest.TestCase						):






							def     __a   (   self      )	-> List[str]:
								# clean up the VRAM after each test
								super().tearDown()
								gc.collect()






							def     __a   (   self      )	-> Union[str, Any]:
								a,       a      :		Tuple	=				FlaxControlNetModel.from_pretrained(
								    "lllyasviel/sd-controlnet-canny"      ,					from_pt=lowerCAmelCase__      ,					dtype=jnp.bfloataa      )
								a,       a      :		int	=				FlaxStableDiffusionControlNetPipeline.from_pretrained(
								    "runwayml/stable-diffusion-v1-5"      ,					controlnet=lowerCAmelCase__      ,					from_pt=lowerCAmelCase__      ,					dtype=jnp.bfloataa      )
								a      :		str	=				controlnet_params

								a      :		Union[str, Any]	=				"bird"
								a      :		List[str]	=				jax.device_count()
								a      :		Union[str, Any]	=				pipe.prepare_text_inputs([prompts] * num_samples      )

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

								a      :		Union[str, Any]	=				jax.random.PRNGKey(0      )
								a      :		Any	=				jax.random.split(lowerCAmelCase__      ,					jax.device_count()      )

								a      :		Dict	=				replicate(lowerCAmelCase__      )
								a      :		List[str]	=				shard(lowerCAmelCase__      )
								a      :		List[Any]	=				shard(lowerCAmelCase__      )

								a      :		Any	=				pipe(
								    prompt_ids=lowerCAmelCase__      ,					image=lowerCAmelCase__      ,					params=lowerCAmelCase__      ,					prng_seed=lowerCAmelCase__      ,					num_inference_steps=50      ,					jit=lowerCAmelCase__      ,					).images
								assert images.shape == (jax.device_count(), 1, 768, 512, 3)

								a      :		str	=				images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]      )
								a      :		Optional[int]	=				images[0, 253:256, 253:256, -1]

								a      :		str	=				jnp.asarray(jax.device_get(image_slice.flatten()      )      )
								a      :		Dict	=				jnp.array(
								    [0.167_969, 0.116_699, 0.081_543, 0.154_297, 0.132_812, 0.108_887, 0.169_922, 0.169_922, 0.205_078]      )
								print(f"""output_slice: {output_slice}"""      )
								assert jnp.abs(output_slice - expected_slice      ).max() < 1E-2






							def     __a   (   self      )	-> str:
								a,       a      :		Any	=				FlaxControlNetModel.from_pretrained(
								    "lllyasviel/sd-controlnet-openpose"      ,					from_pt=lowerCAmelCase__      ,					dtype=jnp.bfloataa      )
								a,       a      :		Tuple	=				FlaxStableDiffusionControlNetPipeline.from_pretrained(
								    "runwayml/stable-diffusion-v1-5"      ,					controlnet=lowerCAmelCase__      ,					from_pt=lowerCAmelCase__      ,					dtype=jnp.bfloataa      )
								a      :		Dict	=				controlnet_params

								a      :		Any	=				"Chef in the kitchen"
								a      :		Optional[Any]	=				jax.device_count()
								a      :		Union[str, Any]	=				pipe.prepare_text_inputs([prompts] * num_samples      )

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

								a      :		Tuple	=				jax.random.PRNGKey(0      )
								a      :		Union[str, Any]	=				jax.random.split(lowerCAmelCase__      ,					jax.device_count()      )

								a      :		Dict	=				replicate(lowerCAmelCase__      )
								a      :		List[str]	=				shard(lowerCAmelCase__      )
								a      :		Any	=				shard(lowerCAmelCase__      )

								a      :		Any	=				pipe(
								    prompt_ids=lowerCAmelCase__      ,					image=lowerCAmelCase__      ,					params=lowerCAmelCase__      ,					prng_seed=lowerCAmelCase__      ,					num_inference_steps=50      ,					jit=lowerCAmelCase__      ,					).images
								assert images.shape == (jax.device_count(), 1, 768, 512, 3)

								a      :		Union[str, Any]	=				images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]      )
								a      :		Union[str, Any]	=				images[0, 253:256, 253:256, -1]

								a      :		Optional[Any]	=				jnp.asarray(jax.device_get(image_slice.flatten()      )      )
								a      :		List[Any]	=				jnp.array(
								    [[0.271_484, 0.261_719, 0.275_391, 0.277_344, 0.279_297, 0.291_016, 0.294_922, 0.302_734, 0.302_734]]      )
								print(f"""output_slice: {output_slice}"""      )
								assert jnp.abs(output_slice - expected_slice      ).max() < 1E-2









"""simple docstring"""




from __future__ import annotations

from math import pi






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    float    ,       _lowercase						:    float    ,       _lowercase						:    float					) ->dict[str, float]:






	'''simple docstring'''






	if (inductance, frequency, reactance).count(0					) != 1:
		raise ValueError("One and only one argument must be 0"					)
	if inductance < 0:
		raise ValueError("Inductance cannot be negative"					)
	if frequency < 0:
		raise ValueError("Frequency cannot be negative"					)
	if reactance < 0:
		raise ValueError("Inductive reactance cannot be negative"					)
	if inductance == 0:
		return {"inductance": reactance / (2 * pi * frequency)}
	elif frequency == 0:
		return {"frequency": reactance / (2 * pi * inductance)}
	elif reactance == 0:
		return {"reactance": 2 * pi * frequency * inductance}
	else:
		raise ValueError("Exactly one argument must be 0"					)


if __name__ == "__main__":
		import doctest

		doctest.testmod()


