


from multiprocessing import Lock, Pipe, Process

# lock used to ensure that two processes do not access a pipe at the same time
snake_case							:	Union[str, Any]					      =				Lock()






def 							__lowercase       (			__lowerCAmelCase							:    Optional[int] ,   __lowerCAmelCase							:    int ,   __lowerCAmelCase							:    str ,   __lowerCAmelCase							:    Any ,   __lowerCAmelCase							:    Optional[Any] ,   __lowerCAmelCase							:    str ,   __lowerCAmelCase							:    Union[str, Any]       ):
							global process_lock

							# we perform n swaps since after n swaps we know we are sorted
							# we *could* stop early if we are sorted already, but it takes as long to
							# find out we are sorted as it does to sort the list with this algorithm
							for i in range(0 ,   1_0       ):
														if (i + position) % 2 == 0 and r_send is not None:
																					# send your value to your right neighbor
																					process_lock.acquire()
																					r_send[1].send(__lowerCAmelCase       )
																					process_lock.release()

																					# receive your right neighbor's value
																					process_lock.acquire()
																					a__					=					rr_cv[0].recv()
																					process_lock.release()

																					# take the lower value since you are on the left
																					a__					=					min(__lowerCAmelCase ,   __lowerCAmelCase       )
														elif (i + position) % 2 != 0 and l_send is not None:
																					# send your value to your left neighbor
																					process_lock.acquire()
																					l_send[1].send(__lowerCAmelCase       )
																					process_lock.release()

																					# receive your left neighbor's value
																					process_lock.acquire()
																					a__					=					lr_cv[0].recv()
																					process_lock.release()

																					# take the higher value since you are on the right
																					a__					=					max(__lowerCAmelCase ,   __lowerCAmelCase       )
    # after all swaps are performed, send the values back to main
							result_pipe[1].send(__lowerCAmelCase       )






def 							__lowercase       (			__lowerCAmelCase							:    List[str]       ):
							a__					=					[]
							a__					=					[]
							# initialize the list of pipes where the values will be retrieved
							for _ in arr:
														result_pipe.append(Pipe()       )
							# creates the processes
							# the first and last process only have one neighbor so they are made outside
							# of the loop
							a__					=					Pipe()
							a__					=					Pipe()
							process_array_.append(
							    Process(
							        target=__lowerCAmelCase ,   args=(0, arr[0], None, temp_rs, None, temp_rr, result_pipe[0]) ,   )       )
							a__					=					temp_rs
							a__					=					temp_rr

							for i in range(1 ,   len(__lowerCAmelCase       ) - 1       ):
														a__					=					Pipe()
														a__					=					Pipe()
														process_array_.append(
														    Process(
														        target=__lowerCAmelCase ,   args=(i, arr[i], temp_ls, temp_rs, temp_lr, temp_rr, result_pipe[i]) ,   )       )
														a__					=					temp_rs
														a__					=					temp_rr

							process_array_.append(
							    Process(
							        target=__lowerCAmelCase ,   args=(
							            len(__lowerCAmelCase       ) - 1,
							            arr[len(__lowerCAmelCase       ) - 1],
							            temp_ls,
							            None,
							            temp_lr,
							            None,
							            result_pipe[len(__lowerCAmelCase       ) - 1],
							        ) ,   )       )

							# start the processes
							for p in process_array_:
														p.start()

							# wait for the processes to end and write their values to the list
							for p in range(0 ,   len(__lowerCAmelCase       )       ):
														a__					=					result_pipe[p][0].recv()
														process_array_[p].join()
							return arr






def 							__lowercase       (			):
							a__					=					list(range(1_0 ,   0 ,   -1       )       )
							print('Initial List'       )
							print(*__lowerCAmelCase       )
							a__					=					odd_even_transposition(__lowerCAmelCase       )
							print('Sorted List\n'       )
							print(*__lowerCAmelCase       )


if __name__ == "__main__":
	main()








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 	snake_case_       (unittest.TestCase				):
					def 			lowerCamelCase__(				self       :List[Any]				) ->   str:
												# clean up the VRAM after each test
												super().tearDown()
												gc.collect()
					def 			lowerCamelCase__(				self       :int				) ->   Optional[Any]:
												a__				, a__					=					FlaxControlNetModel.from_pretrained(
												    'lllyasviel/sd-controlnet-canny'      ,from_pt=__snake_case      ,dtype=jnp.bfloataa				)
												a__				, a__					=					FlaxStableDiffusionControlNetPipeline.from_pretrained(
												    'runwayml/stable-diffusion-v1-5'      ,controlnet=__snake_case      ,from_pt=__snake_case      ,dtype=jnp.bfloataa				)
												a__					=					controlnet_params

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

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

												a__					=					jax.random.PRNGKey(0				)
												a__					=					jax.random.split(__snake_case      ,jax.device_count()				)

												a__					=					replicate(__snake_case				)
												a__					=					shard(__snake_case				)
												a__					=					shard(__snake_case				)

												a__					=					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, 7_68, 5_12, 3)

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

												a__					=					jnp.asarray(jax.device_get(image_slice.flatten()				)				)
												a__					=					jnp.array(
												    [0.16_79_69, 0.11_66_99, 0.08_15_43, 0.15_42_97, 0.13_28_12, 0.10_88_87, 0.16_99_22, 0.16_99_22, 0.20_50_78]				)
												print(F'output_slice: {output_slice}'				)
												assert jnp.abs(output_slice - expected_slice				).max() < 1E-2



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

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

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

												a__					=					jax.random.PRNGKey(0				)
												a__					=					jax.random.split(__snake_case      ,jax.device_count()				)

												a__					=					replicate(__snake_case				)
												a__					=					shard(__snake_case				)
												a__					=					shard(__snake_case				)

												a__					=					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, 7_68, 5_12, 3)

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

												a__					=					jnp.asarray(jax.device_get(image_slice.flatten()				)				)
												a__					=					jnp.array(
												    [[0.27_14_84, 0.26_17_19, 0.27_53_91, 0.27_73_44, 0.27_92_97, 0.29_10_16, 0.29_49_22, 0.30_27_34, 0.30_27_34]]				)
												print(F'output_slice: {output_slice}'				)
												assert jnp.abs(output_slice - expected_slice				).max() < 1E-2




