





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       ):
			'''simple docstring'''




			def   a_			(     self  :       List[str]  )      ->      int:







					"""simple docstring"""






					super().tearDown()
					gc.collect()






			def   a_			(     self  :       Optional[int]  )      ->      Optional[int]:







					"""simple docstring"""






					A__ ,     A__     =  FlaxControlNetModel.from_pretrained(
					    """lllyasviel/sd-controlnet-canny"""				,      from_pt=__lowerCAmelCase				,      dtype=jnp.bfloataa  )
					A__ ,     A__     =  FlaxStableDiffusionControlNetPipeline.from_pretrained(
					    """runwayml/stable-diffusion-v1-5"""				,      controlnet=__lowerCAmelCase				,      from_pt=__lowerCAmelCase				,      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(__lowerCAmelCase				,      jax.device_count()  )

					A__     =  replicate(__lowerCAmelCase  )
					A__     =  shard(__lowerCAmelCase  )
					A__     =  shard(__lowerCAmelCase  )

					A__     =  pipe(
					    prompt_ids=__lowerCAmelCase				,      image=__lowerCAmelCase				,      params=__lowerCAmelCase				,      prng_seed=__lowerCAmelCase				,      num_inference_steps=50				,      jit=__lowerCAmelCase				,      ).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(
					    [0.1_6_7_9_6_9, 0.1_1_6_6_9_9, 0.0_8_1_5_4_3, 0.1_5_4_2_9_7, 0.1_3_2_8_1_2, 0.1_0_8_8_8_7, 0.1_6_9_9_2_2, 0.1_6_9_9_2_2, 0.2_0_5_0_7_8]  )
					print(f'output_slice: {output_slice}'  )
					assert jnp.abs(output_slice - expected_slice  ).max() < 1e-2







			def   a_			(     self  :       Tuple  )      ->      Any:







					"""simple docstring"""






					A__ ,     A__     =  FlaxControlNetModel.from_pretrained(
					    """lllyasviel/sd-controlnet-openpose"""				,      from_pt=__lowerCAmelCase				,      dtype=jnp.bfloataa  )
					A__ ,     A__     =  FlaxStableDiffusionControlNetPipeline.from_pretrained(
					    """runwayml/stable-diffusion-v1-5"""				,      controlnet=__lowerCAmelCase				,      from_pt=__lowerCAmelCase				,      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(__lowerCAmelCase				,      jax.device_count()  )

					A__     =  replicate(__lowerCAmelCase  )
					A__     =  shard(__lowerCAmelCase  )
					A__     =  shard(__lowerCAmelCase  )

					A__     =  pipe(
					    prompt_ids=__lowerCAmelCase				,      image=__lowerCAmelCase				,      params=__lowerCAmelCase				,      prng_seed=__lowerCAmelCase				,      num_inference_steps=50				,      jit=__lowerCAmelCase				,      ).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(
					    [[0.2_7_1_4_8_4, 0.2_6_1_7_1_9, 0.2_7_5_3_9_1, 0.2_7_7_3_4_4, 0.2_7_9_2_9_7, 0.2_9_1_0_1_6, 0.2_9_4_9_2_2, 0.3_0_2_7_3_4, 0.3_0_2_7_3_4]]  )
					print(f'output_slice: {output_slice}'  )
					assert jnp.abs(output_slice - expected_slice  ).max() < 1e-2










def   __lowerCamelCase   (				__a  :Optional[Any]       )						->							Tuple:






		"""simple docstring"""

		A__     =  len(__a       )
		while cur > 1:
				# Find the maximum number in arr
				A__     =  arr.index(max(arr[0:cur]       )       )
				# Reverse from 0 to mi
				A__     =  arr[mi::-1] + arr[mi + 1 : len(__a       )]
				# Reverse whole list
				A__     =  arr[cur - 1 :: -1] + arr[cur : len(__a       )]
				cur -= 1
		return arr


if __name__ == "__main__":
					A       :   List[str]					= input('''Enter numbers separated by a comma:\n''').strip()
					A       :   int					= [int(item) for item in user_input.split(''',''')]
					print(pancake_sort(unsorted))



