

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   ):
							"""simple docstring"""







							def 				__lowercase				(			self       )    ->    Any:
												# clean up the VRAM after each test
												super().tearDown()
												gc.collect()


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

												_a	:    Union[str, Any]         =		'''bird'''
												_a	:    Union[str, Any]         =		jax.device_count()
												_a	:    Tuple         =		pipe.prepare_text_inputs([prompts] * num_samples       )

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

												_a	:    Optional[int]         =		jax.random.PRNGKey(0       )
												_a	:    str         =		jax.random.split(_a       ,							jax.device_count()       )

												_a	:    str         =		replicate(_a       )
												_a	:    Dict         =		shard(_a       )
												_a	:    Dict         =		shard(_a       )

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

												_a	:    int         =		images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]       )
												_a	:    Optional[int]         =		images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

												_a	:    Dict         =		jnp.asarray(jax.device_get(image_slice.flatten()       )       )
												_a	:    int         =		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 				__lowercase				(			self       )    ->    str:
												_a      ,					_a	:    Tuple         =		FlaxControlNetModel.from_pretrained(
												    '''lllyasviel/sd-controlnet-openpose'''       ,							from_pt=_a       ,							dtype=jnp.bfloataa       )
												_a      ,					_a	:    Dict         =		FlaxStableDiffusionControlNetPipeline.from_pretrained(
												    '''runwayml/stable-diffusion-v1-5'''       ,							controlnet=_a       ,							from_pt=_a       ,							dtype=jnp.bfloataa       )
												_a	:    Union[str, Any]         =		controlnet_params

												_a	:    Tuple         =		'''Chef in the kitchen'''
												_a	:    Optional[int]         =		jax.device_count()
												_a	:    Optional[Any]         =		pipe.prepare_text_inputs([prompts] * num_samples       )

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

												_a	:    List[Any]         =		jax.random.PRNGKey(0       )
												_a	:    Tuple         =		jax.random.split(_a       ,							jax.device_count()       )

												_a	:    List[str]         =		replicate(_a       )
												_a	:    Dict         =		shard(_a       )
												_a	:    List[str]         =		shard(_a       )

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

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

												_a	:    List[Any]         =		jnp.asarray(jax.device_get(image_slice.flatten()       )       )
												_a	:    List[str]         =		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






from math import ceil



def    __UpperCAmelCase (	__a      :			int = 1_001	)			->     int:

					"""simple docstring"""

					_a	:    List[Any]         =		1

					for i in range(1		,int(ceil(n / 2.0	)	)	):
										_a	:    Optional[Any]         =		2 * i + 1
										_a	:    Optional[int]         =		2 * i
										_a	:    str         =		total + 4 * odd**2 - 6 * even

					return total


if __name__ == "__main__":
			import sys

			if len(sys.argv) == 1:
						print(solution())
			else:
						try:
									a__				=		int(sys.argv[1])
									print(solution(n))
						except ValueError:
									print('''Invalid entry - please enter a number''')



