
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			):






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






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

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

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

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

						__lowerCamelCase    					=	replicate(SCREAMING_SNAKE_CASE__  )
						__lowerCamelCase    					=	shard(SCREAMING_SNAKE_CASE__  )
						__lowerCamelCase    					=	shard(SCREAMING_SNAKE_CASE__  )

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

						__lowerCamelCase    					=	jnp.asarray(jax.device_get(image_slice.flatten()  )  )
						__lowerCamelCase    					=	jnp.array(
						    [0.167969, 0.116699, 0.081543, 0.154297, 0.132812, 0.108887, 0.169922, 0.169922, 0.205078]  )
						print(f'''output_slice: {output_slice}'''  )
						assert jnp.abs(output_slice - expected_slice  ).max() < 1e-2







		def  __A       (		self		:							List[str]  )     ->							Optional[Any]:
						__lowerCamelCase					,    __lowerCamelCase    					=	FlaxControlNetModel.from_pretrained(
						    '''lllyasviel/sd-controlnet-openpose'''							,			from_pt=SCREAMING_SNAKE_CASE__							,			dtype=jnp.bfloataa  )
						__lowerCamelCase					,    __lowerCamelCase    					=	FlaxStableDiffusionControlNetPipeline.from_pretrained(
						    '''runwayml/stable-diffusion-v1-5'''							,			controlnet=SCREAMING_SNAKE_CASE__							,			from_pt=SCREAMING_SNAKE_CASE__							,			dtype=jnp.bfloataa  )
						__lowerCamelCase    					=	controlnet_params

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

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

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

						__lowerCamelCase    					=	replicate(SCREAMING_SNAKE_CASE__  )
						__lowerCamelCase    					=	shard(SCREAMING_SNAKE_CASE__  )
						__lowerCamelCase    					=	shard(SCREAMING_SNAKE_CASE__  )

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

						__lowerCamelCase    					=	jnp.asarray(jax.device_get(image_slice.flatten()  )  )
						__lowerCamelCase    					=	jnp.array(
						    [[0.271484, 0.261719, 0.275391, 0.277344, 0.279297, 0.291016, 0.294922, 0.302734, 0.302734]]  )
						print(f'''output_slice: {output_slice}'''  )
						assert jnp.abs(output_slice - expected_slice  ).max() < 1e-2





import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey

class    lowerCAmelCase__     :






		def __init__(		self		:							Any							,			SCREAMING_SNAKE_CASE__		:							List[str]							,			SCREAMING_SNAKE_CASE__		:							int							,			SCREAMING_SNAKE_CASE__		:							int  )     ->							Dict:
						if dst_width < 0 or dst_height < 0:
										raise ValueError('''Destination width/height should be > 0'''  )

						__lowerCamelCase    					=	img
						__lowerCamelCase    					=	img.shape[1]
						__lowerCamelCase    					=	img.shape[0]
						__lowerCamelCase    					=	dst_width
						__lowerCamelCase    					=	dst_height

						__lowerCamelCase    					=	self.src_w / self.dst_w
						__lowerCamelCase    					=	self.src_h / self.dst_h

						__lowerCamelCase    					=	__lowerCamelCase    					=	(
						    np.ones((self.dst_h, self.dst_w, 3)							,			np.uinta  ) * 2_55
						)






		def  __A       (		self		:							List[Any]  )     ->							str:
						for i in range(self.dst_h  ):
										for j in range(self.dst_w  ):
														__lowerCamelCase    					=	self.img[self.get_y(SCREAMING_SNAKE_CASE__  )][self.get_x(SCREAMING_SNAKE_CASE__  )]






		def  __A       (		self		:							str							,			SCREAMING_SNAKE_CASE__		:							int  )     ->							int:
						return int(self.ratio_x * x  )







		def  __A       (		self		:							str							,			SCREAMING_SNAKE_CASE__		:							int  )     ->							int:
						return int(self.ratio_y * y  )


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__   :			List[str]												=    800, 600
					SCREAMING_SNAKE_CASE__   :			int												=    imread("image_data/lena.jpg", 1)
					SCREAMING_SNAKE_CASE__   :			Union[str, Any]												=    NearestNeighbour(im, dst_w, dst_h)
					n.process()

					imshow(
					    F'Image resized from: {im.shape[1]}x{im.shape[0]} to {dst_w}x{dst_h}', n.output
					)
					waitKey(0)
					destroyAllWindows()



