
"""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 				A__ (			unittest.TestCase):

		def 							__lowerCamelCase							(   self				):
			# clean up the VRAM after each test
			super().tearDown()
			gc.collect()

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    ,     __lowerCAmelCase    :		Tuple		    =   FlaxControlNetModel.from_pretrained(
			    'lllyasviel/sd-controlnet-canny' ,	from_pt=_SCREAMING_SNAKE_CASE ,	dtype=jnp.bfloataa				)
			__lowerCAmelCase    ,     __lowerCAmelCase    :		str		    =   FlaxStableDiffusionControlNetPipeline.from_pretrained(
			    'runwayml/stable-diffusion-v1-5' ,	controlnet=_SCREAMING_SNAKE_CASE ,	from_pt=_SCREAMING_SNAKE_CASE ,	dtype=jnp.bfloataa				)
			__lowerCAmelCase    :		Optional[Any]		    =   controlnet_params

			__lowerCAmelCase    :		Optional[int]		    =   'bird'
			__lowerCAmelCase    :		Tuple		    =   jax.device_count()
			__lowerCAmelCase    :		Tuple		    =   pipe.prepare_text_inputs([prompts] * num_samples				)

			__lowerCAmelCase    :		Any		    =   load_image(
			    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png'				)
			__lowerCAmelCase    :		Union[str, Any]		    =   pipe.prepare_image_inputs([canny_image] * num_samples				)

			__lowerCAmelCase    :		Any		    =   jax.random.PRNGKey(0				)
			__lowerCAmelCase    :		Dict		    =   jax.random.split(_SCREAMING_SNAKE_CASE ,	jax.device_count()				)

			__lowerCAmelCase    :		str		    =   replicate(_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		Union[str, Any]		    =   shard(_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		Any		    =   shard(_SCREAMING_SNAKE_CASE				)

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

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

			__lowerCAmelCase    :		Union[str, Any]		    =   jnp.asarray(jax.device_get(image_slice.flatten()				)				)
			__lowerCAmelCase    :		List[Any]		    =   jnp.array(
			    [0.16_7969, 0.11_6699, 0.08_1543, 0.15_4297, 0.13_2812, 0.10_8887, 0.16_9922, 0.16_9922, 0.20_5078]				)
			print(f"output_slice: {output_slice}"				)
			assert jnp.abs(output_slice - expected_slice				).max() < 1E-2







		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    ,     __lowerCAmelCase    :		Any		    =   FlaxControlNetModel.from_pretrained(
			    'lllyasviel/sd-controlnet-openpose' ,	from_pt=_SCREAMING_SNAKE_CASE ,	dtype=jnp.bfloataa				)
			__lowerCAmelCase    ,     __lowerCAmelCase    :		Tuple		    =   FlaxStableDiffusionControlNetPipeline.from_pretrained(
			    'runwayml/stable-diffusion-v1-5' ,	controlnet=_SCREAMING_SNAKE_CASE ,	from_pt=_SCREAMING_SNAKE_CASE ,	dtype=jnp.bfloataa				)
			__lowerCAmelCase    :		Union[str, Any]		    =   controlnet_params

			__lowerCAmelCase    :		Any		    =   'Chef in the kitchen'
			__lowerCAmelCase    :		Any		    =   jax.device_count()
			__lowerCAmelCase    :		str		    =   pipe.prepare_text_inputs([prompts] * num_samples				)

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

			__lowerCAmelCase    :		Tuple		    =   jax.random.PRNGKey(0				)
			__lowerCAmelCase    :		Dict		    =   jax.random.split(_SCREAMING_SNAKE_CASE ,	jax.device_count()				)

			__lowerCAmelCase    :		Union[str, Any]		    =   replicate(_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		Optional[int]		    =   shard(_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		Any		    =   shard(_SCREAMING_SNAKE_CASE				)

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

			__lowerCAmelCase    :		Tuple		    =   images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]				)
			__lowerCAmelCase    :		Union[str, Any]		    =   images[0, 2_53:2_56, 2_53:2_56, -1]

			__lowerCAmelCase    :		Optional[int]		    =   jnp.asarray(jax.device_get(image_slice.flatten()				)				)
			__lowerCAmelCase    :		List[Any]		    =   jnp.array(
			    [[0.27_1484, 0.26_1719, 0.27_5391, 0.27_7344, 0.27_9297, 0.29_1016, 0.29_4922, 0.30_2734, 0.30_2734]]				)
			print(f"output_slice: {output_slice}"				)
			assert jnp.abs(output_slice - expected_slice				).max() < 1E-2

"""simple docstring"""







def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase	):

	if density <= 0:
		raise ValueError('Impossible fluid density'	)
	if bulk_modulus <= 0:
		raise ValueError('Impossible bulk modulus'	)

	return (bulk_modulus / density) ** 0.5


if __name__ == "__main__":
							import doctest

							doctest.testmod()