"""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 lowerCamelCase		(unittest.TestCase      ):






					'''simple docstring'''


					def 			lowerCAmelCase_					(						self			:				int		)							->       Optional[Any]:
									super().tearDown()
									gc.collect()



					def 			lowerCAmelCase_					(						self			:				Dict		)							->       str:
									SCREAMING_SNAKE_CASE__		  =				FlaxControlNetModel.from_pretrained(
									    "lllyasviel/sd-controlnet-canny"   , from_pt=__a   , dtype=jnp.bfloataa		)
									SCREAMING_SNAKE_CASE__		  =				FlaxStableDiffusionControlNetPipeline.from_pretrained(
									    "runwayml/stable-diffusion-v1-5"   , controlnet=__a   , from_pt=__a   , dtype=jnp.bfloataa		)
									SCREAMING_SNAKE_CASE__		  =				controlnet_params

									SCREAMING_SNAKE_CASE__		  =				'bird'
									SCREAMING_SNAKE_CASE__		  =				jax.device_count()
									SCREAMING_SNAKE_CASE__		  =				pipe.prepare_text_inputs([prompts] * num_samples		)

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

									SCREAMING_SNAKE_CASE__		  =				jax.random.PRNGKey(0		)
									SCREAMING_SNAKE_CASE__		  =				jax.random.split(__a   , jax.device_count()		)

									SCREAMING_SNAKE_CASE__		  =				replicate(__a		)
									SCREAMING_SNAKE_CASE__		  =				shard(__a		)
									SCREAMING_SNAKE_CASE__		  =				shard(__a		)

									SCREAMING_SNAKE_CASE__		  =				pipe(
									    prompt_ids=__a   , image=__a   , params=__a   , prng_seed=__a   , num_inference_steps=50   , jit=__a   , ).images
									assert images.shape == (jax.device_count(), 1, 768, 512, 3)

									SCREAMING_SNAKE_CASE__		  =				images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]		)
									SCREAMING_SNAKE_CASE__		  =				images[0, 253:256, 253:256, -1]

									SCREAMING_SNAKE_CASE__		  =				jnp.asarray(jax.device_get(image_slice.flatten()		)		)
									SCREAMING_SNAKE_CASE__		  =				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			:				str		)							->       str:
									SCREAMING_SNAKE_CASE__		  =				FlaxControlNetModel.from_pretrained(
									    "lllyasviel/sd-controlnet-openpose"   , from_pt=__a   , dtype=jnp.bfloataa		)
									SCREAMING_SNAKE_CASE__		  =				FlaxStableDiffusionControlNetPipeline.from_pretrained(
									    "runwayml/stable-diffusion-v1-5"   , controlnet=__a   , from_pt=__a   , dtype=jnp.bfloataa		)
									SCREAMING_SNAKE_CASE__		  =				controlnet_params

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

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

									SCREAMING_SNAKE_CASE__		  =				jax.random.PRNGKey(0		)
									SCREAMING_SNAKE_CASE__		  =				jax.random.split(__a   , jax.device_count()		)

									SCREAMING_SNAKE_CASE__		  =				replicate(__a		)
									SCREAMING_SNAKE_CASE__		  =				shard(__a		)
									SCREAMING_SNAKE_CASE__		  =				shard(__a		)

									SCREAMING_SNAKE_CASE__		  =				pipe(
									    prompt_ids=__a   , image=__a   , params=__a   , prng_seed=__a   , num_inference_steps=50   , jit=__a   , ).images
									assert images.shape == (jax.device_count(), 1, 768, 512, 3)

									SCREAMING_SNAKE_CASE__		  =				images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]		)
									SCREAMING_SNAKE_CASE__		  =				images[0, 253:256, 253:256, -1]

									SCREAMING_SNAKE_CASE__		  =				jnp.asarray(jax.device_get(image_slice.flatten()		)		)
									SCREAMING_SNAKE_CASE__		  =				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"""






import math




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase       )							->   float:

				if initial_intensity < 0:
								raise ValueError("The value of intensity cannot be negative"       )
								# handling of negative values of initial intensity
				if angle < 0 or angle > 360:
								raise ValueError("In Malus Law, the angle is in the range 0-360 degrees"       )
								# handling of values out of allowed range
				return initial_intensity * (math.cos(math.radians(__UpperCAmelCase       )       ) ** 2)


if __name__ == "__main__":
						import doctest

						doctest.testmod(name='malus_law')




