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



					def 			lowerCAmelCase_					(						self			:				Optional[int]		)							->       str:
									SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__		  =				FlaxControlNetModel.from_pretrained(
									    "lllyasviel/sd-controlnet-canny"   , from_pt=_snake_case   , dtype=jnp.bfloataa		)
									SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__		  =				FlaxStableDiffusionControlNetPipeline.from_pretrained(
									    "runwayml/stable-diffusion-v1-5"   , controlnet=_snake_case   , from_pt=_snake_case   , 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(_snake_case   , jax.device_count()		)

									SCREAMING_SNAKE_CASE__		  =				replicate(_snake_case		)
									SCREAMING_SNAKE_CASE__		  =				shard(_snake_case		)
									SCREAMING_SNAKE_CASE__		  =				shard(_snake_case		)

									SCREAMING_SNAKE_CASE__		  =				pipe(
									    prompt_ids=_snake_case   , image=_snake_case   , params=_snake_case   , prng_seed=_snake_case   , num_inference_steps=50   , jit=_snake_case   , ).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			:				Tuple		)							->       Tuple:
									SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__		  =				FlaxControlNetModel.from_pretrained(
									    "lllyasviel/sd-controlnet-openpose"   , from_pt=_snake_case   , dtype=jnp.bfloataa		)
									SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__		  =				FlaxStableDiffusionControlNetPipeline.from_pretrained(
									    "runwayml/stable-diffusion-v1-5"   , controlnet=_snake_case   , from_pt=_snake_case   , 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(_snake_case   , jax.device_count()		)

									SCREAMING_SNAKE_CASE__		  =				replicate(_snake_case		)
									SCREAMING_SNAKE_CASE__		  =				shard(_snake_case		)
									SCREAMING_SNAKE_CASE__		  =				shard(_snake_case		)

									SCREAMING_SNAKE_CASE__		  =				pipe(
									    prompt_ids=_snake_case   , image=_snake_case   , params=_snake_case   , prng_seed=_snake_case   , num_inference_steps=50   , jit=_snake_case   , ).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 sys




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase       )							->   Any:
				SCREAMING_SNAKE_CASE__		  =				len(__UpperCAmelCase       )
				SCREAMING_SNAKE_CASE__		  =				[[0 for x in range(__UpperCAmelCase       )] for x in range(__UpperCAmelCase       )]
				SCREAMING_SNAKE_CASE__		  =				[[0 for x in range(__UpperCAmelCase       )] for x in range(__UpperCAmelCase       )]

				for chain_length in range(2  ,					__UpperCAmelCase       ):
								for a in range(1  ,					n - chain_length + 1       ):
												SCREAMING_SNAKE_CASE__		  =				a + chain_length - 1

												SCREAMING_SNAKE_CASE__		  =				sys.maxsize
												for c in range(__UpperCAmelCase  ,					__UpperCAmelCase       ):
																SCREAMING_SNAKE_CASE__		  =				(
																    matrix[a][c] + matrix[c + 1][b] + array[a - 1] * array[c] * array[b]
																)
																if cost < matrix[a][b]:
																				SCREAMING_SNAKE_CASE__		  =				cost
																				SCREAMING_SNAKE_CASE__		  =				c
				return matrix, sol




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase       )							->   Union[str, Any]:
				if i == j:
								print("A" + str(__UpperCAmelCase       )  ,					end=" "       )
				else:
								print("("  ,					end=" "       )
								print_optiomal_solution(__UpperCAmelCase  ,					__UpperCAmelCase  ,					optimal_solution[i][j]       )
								print_optiomal_solution(__UpperCAmelCase  ,					optimal_solution[i][j] + 1  ,					__UpperCAmelCase       )
								print(")"  ,					end=" "       )




def 						SCREAMING_SNAKE_CASE					(  )							->   Any:
				SCREAMING_SNAKE_CASE__		  =				[30, 35, 15, 5, 10, 20, 25]
				SCREAMING_SNAKE_CASE__		  =				len(__UpperCAmelCase       )
				# Size of matrix created from above array will be
				# 30*35 35*15 15*5 5*10 10*20 20*25
				SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__		  =				matrix_chain_order(__UpperCAmelCase       )

				print("No. of Operation required: " + str(matrix[1][n - 1]       )       )
				print_optiomal_solution(__UpperCAmelCase  ,					1  ,					n - 1       )


if __name__ == "__main__":
						main()




