


'''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 						__UpperCamelCase	(						unittest.TestCase							):





							def 		a__   (  self		:int			):
									# clean up the VRAM after each test
									super().tearDown()
									gc.collect()





							def 		a__   (  self		:Optional[int]			):
									snake_case_       ,      snake_case_		:							Union[str, Any]			       =						FlaxControlNetModel.from_pretrained(
									    """lllyasviel/sd-controlnet-canny"""  ,from_pt=_UpperCamelCase  ,dtype=jnp.bfloataa			)
									snake_case_       ,      snake_case_		:							Tuple			       =						FlaxStableDiffusionControlNetPipeline.from_pretrained(
									    """runwayml/stable-diffusion-v1-5"""  ,controlnet=_UpperCamelCase  ,from_pt=_UpperCamelCase  ,dtype=jnp.bfloataa			)
									snake_case_		:							List[str]			       =						controlnet_params

									snake_case_		:							List[Any]			       =						"""bird"""
									snake_case_		:							Tuple			       =						jax.device_count()
									snake_case_		:							Optional[Any]			       =						pipe.prepare_text_inputs([prompts] * num_samples			)

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

									snake_case_		:							int			       =						jax.random.PRNGKey(0			)
									snake_case_		:							int			       =						jax.random.split(_UpperCamelCase  ,jax.device_count()			)

									snake_case_		:							List[str]			       =						replicate(_UpperCamelCase			)
									snake_case_		:							Tuple			       =						shard(_UpperCamelCase			)
									snake_case_		:							List[str]			       =						shard(_UpperCamelCase			)

									snake_case_		:							str			       =						pipe(
									    prompt_ids=_UpperCamelCase  ,image=_UpperCamelCase  ,params=_UpperCamelCase  ,prng_seed=_UpperCamelCase  ,num_inference_steps=5_0  ,jit=_UpperCamelCase  ,).images
									assert images.shape == (jax.device_count(), 1, 7_6_8, 5_1_2, 3)

									snake_case_		:							List[str]			       =						images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]			)
									snake_case_		:							Dict			       =						images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

									snake_case_		:							Optional[Any]			       =						jnp.asarray(jax.device_get(image_slice.flatten()			)			)
									snake_case_		:							List[str]			       =						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 		a__   (  self		:Union[str, Any]			):
									snake_case_       ,      snake_case_		:							Optional[Any]			       =						FlaxControlNetModel.from_pretrained(
									    """lllyasviel/sd-controlnet-openpose"""  ,from_pt=_UpperCamelCase  ,dtype=jnp.bfloataa			)
									snake_case_       ,      snake_case_		:							List[Any]			       =						FlaxStableDiffusionControlNetPipeline.from_pretrained(
									    """runwayml/stable-diffusion-v1-5"""  ,controlnet=_UpperCamelCase  ,from_pt=_UpperCamelCase  ,dtype=jnp.bfloataa			)
									snake_case_		:							Union[str, Any]			       =						controlnet_params

									snake_case_		:							List[Any]			       =						"""Chef in the kitchen"""
									snake_case_		:							Tuple			       =						jax.device_count()
									snake_case_		:							Tuple			       =						pipe.prepare_text_inputs([prompts] * num_samples			)

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

									snake_case_		:							Tuple			       =						jax.random.PRNGKey(0			)
									snake_case_		:							int			       =						jax.random.split(_UpperCamelCase  ,jax.device_count()			)

									snake_case_		:							Optional[Any]			       =						replicate(_UpperCamelCase			)
									snake_case_		:							Any			       =						shard(_UpperCamelCase			)
									snake_case_		:							str			       =						shard(_UpperCamelCase			)

									snake_case_		:							Dict			       =						pipe(
									    prompt_ids=_UpperCamelCase  ,image=_UpperCamelCase  ,params=_UpperCamelCase  ,prng_seed=_UpperCamelCase  ,num_inference_steps=5_0  ,jit=_UpperCamelCase  ,).images
									assert images.shape == (jax.device_count(), 1, 7_6_8, 5_1_2, 3)

									snake_case_		:							Tuple			       =						images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]			)
									snake_case_		:							str			       =						images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

									snake_case_		:							str			       =						jnp.asarray(jax.device_get(image_slice.flatten()			)			)
									snake_case_		:							Optional[int]			       =						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



'''simple docstring'''






import math





def      UpperCAmelCase      (		lowerCamelCase_  :list				,      lowerCamelCase_  :int ):







		'''simple docstring'''
		snake_case_		:							Union[str, Any]			       =						len(lowerCamelCase_ )
		snake_case_		:							List[Any]			       =						int(math.floor(math.sqrt(lowerCamelCase_ ) ) )
		snake_case_		:							str			       =						0
		while arr[min(lowerCamelCase_				,      lowerCamelCase_ ) - 1] < x:
				snake_case_		:							Any			       =						step
				step += int(math.floor(math.sqrt(lowerCamelCase_ ) ) )
				if prev >= n:
						return -1

		while arr[prev] < x:
				snake_case_		:							Optional[Any]			       =						prev + 1
				if prev == min(lowerCamelCase_				,      lowerCamelCase_ ):
						return -1
		if arr[prev] == x:
				return prev
		return -1


if __name__ == "__main__":
							__A	:	Any     =     input('Enter numbers separated by a comma:\n').strip()
							__A	:	Union[str, Any]     =     [int(item) for item in user_input.split(',')]
							__A	:	Optional[int]     =     int(input('Enter the number to be searched:\n'))
							__A	:	int     =     jump_search(arr, x)
							if res == -1:
														print('Number not found!')
							else:
														print(F'Number {x} is at index {res}')