





"""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 	lowerCamelCase       (					self			:Optional[int]		):
												# clean up the VRAM after each test
												super().tearDown()
												gc.collect()


					def 	lowerCamelCase       (					self			:int		):
												A,	A		=  FlaxControlNetModel.from_pretrained(
												    "lllyasviel/sd-controlnet-canny"     ,       from_pt=__UpperCamelCase     ,       dtype=jnp.bfloataa		)
												A,	A		=  FlaxStableDiffusionControlNetPipeline.from_pretrained(
												    "runwayml/stable-diffusion-v1-5"     ,       controlnet=__UpperCamelCase     ,       from_pt=__UpperCamelCase     ,       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(__UpperCamelCase     ,       jax.device_count()		)

												A		=  replicate(__UpperCamelCase		)
												A		=  shard(__UpperCamelCase		)
												A		=  shard(__UpperCamelCase		)

												A		=  pipe(
												    prompt_ids=__UpperCamelCase     ,       image=__UpperCamelCase     ,       params=__UpperCamelCase     ,       prng_seed=__UpperCamelCase     ,       num_inference_steps=50     ,       jit=__UpperCamelCase     ,       ).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(
												    [0.167_969, 0.116_699, 0.081_543, 0.154_297, 0.132_812, 0.108_887, 0.169_922, 0.169_922, 0.205_078]		)
												print(f"output_slice: {output_slice}"		)
												assert jnp.abs(output_slice - expected_slice		).max() < 1e-2


					def 	lowerCamelCase       (					self			:List[Any]		):
												A,	A		=  FlaxControlNetModel.from_pretrained(
												    "lllyasviel/sd-controlnet-openpose"     ,       from_pt=__UpperCamelCase     ,       dtype=jnp.bfloataa		)
												A,	A		=  FlaxStableDiffusionControlNetPipeline.from_pretrained(
												    "runwayml/stable-diffusion-v1-5"     ,       controlnet=__UpperCamelCase     ,       from_pt=__UpperCamelCase     ,       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(__UpperCamelCase     ,       jax.device_count()		)

												A		=  replicate(__UpperCamelCase		)
												A		=  shard(__UpperCamelCase		)
												A		=  shard(__UpperCamelCase		)

												A		=  pipe(
												    prompt_ids=__UpperCamelCase     ,       image=__UpperCamelCase     ,       params=__UpperCamelCase     ,       prng_seed=__UpperCamelCase     ,       num_inference_steps=50     ,       jit=__UpperCamelCase     ,       ).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(
												    [[0.271_484, 0.261_719, 0.275_391, 0.277_344, 0.279_297, 0.291_016, 0.294_922, 0.302_734, 0.302_734]]		)
												print(f"output_slice: {output_slice}"		)
												assert jnp.abs(output_slice - expected_slice		).max() < 1e-2









"""simple docstring"""
from math import isqrt, loga






def 					A__				(			UpperCamelCase       ):
							A		=  [True] * max_number
							for i in range(2					,  isqrt(max_number - 1       ) + 1       ):
														if is_prime[i]:
																					for j in range(i**2					,  UpperCamelCase					,  UpperCamelCase       ):
																												A		=  False

							return [i for i in range(2					,  UpperCamelCase       ) if is_prime[i]]






def 					A__				(			UpperCamelCase = 800_800					,  UpperCamelCase = 800_800       ):
							A		=  degree * loga(UpperCamelCase       )
							A		=  int(UpperCamelCase       )
							A		=  calculate_prime_numbers(UpperCamelCase       )

							A		=  0
							A		=  0
							A		=  len(UpperCamelCase       ) - 1
							while left < right:
														while (
														    prime_numbers[right] * loga(prime_numbers[left]       )
														    + prime_numbers[left] * loga(prime_numbers[right]       )
														    > upper_bound
														):
																					right -= 1
														hybrid_integers_count += right - left
														left += 1

							return hybrid_integers_count


if __name__ == "__main__":
		print(F"""{solution() = }""")


