




__A        =    [
    "Audio",
    "Array2D",
    "Array3D",
    "Array4D",
    "Array5D",
    "ClassLabel",
    "Features",
    "Sequence",
    "Value",
    "Image",
    "Translation",
    "TranslationVariableLanguages",
]
from .audio import Audio
from .features import ArrayaD, ArrayaD, ArrayaD, ArrayaD, ClassLabel, Features, Sequence, Value
from .image import Image
from .translation import Translation, TranslationVariableLanguages







import gc
import unittest

from diffusers import FlaxStableDiffusionInpaintPipeline
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    UpperCAmelCase__			(       self     :       Tuple):
									# clean up the VRAM after each test
									super().tearDown()
									gc.collect()







							def    UpperCAmelCase__			(       self     :       List[str]):
									lowerCAmelCase_			:     Any									= load_image(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
									    '''/sd2-inpaint/init_image.png''')
									lowerCAmelCase_			:     str									= load_image(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png''')

									lowerCAmelCase_			:     List[Any]									= '''xvjiarui/stable-diffusion-2-inpainting'''
									lowerCAmelCase_					,  lowerCAmelCase_			:     Optional[Any]									= FlaxStableDiffusionInpaintPipeline.from_pretrained(A_				,				safety_checker=A_)

									lowerCAmelCase_			:     List[str]									= '''Face of a yellow cat, high resolution, sitting on a park bench'''

									lowerCAmelCase_			:     List[Any]									= jax.random.PRNGKey(0)
									lowerCAmelCase_			:     str									= 5_0

									lowerCAmelCase_			:     List[Any]									= jax.device_count()
									lowerCAmelCase_			:     Union[str, Any]									= num_samples * [prompt]
									lowerCAmelCase_			:     str									= num_samples * [init_image]
									lowerCAmelCase_			:     Union[str, Any]									= num_samples * [mask_image]
									lowerCAmelCase_					,  lowerCAmelCase_					,  lowerCAmelCase_			:     Optional[Any]									= pipeline.prepare_inputs(A_				,				A_				,				A_)

									# shard inputs and rng
									lowerCAmelCase_			:     str									= replicate(A_)
									lowerCAmelCase_			:     str									= jax.random.split(A_				,				jax.device_count())
									lowerCAmelCase_			:     List[Any]									= shard(A_)
									lowerCAmelCase_			:     str									= shard(A_)
									lowerCAmelCase_			:     Tuple									= shard(A_)

									lowerCAmelCase_			:     int									= pipeline(
									    A_				,				A_				,				A_				,				A_				,				A_				,				A_				,				jit=A_)

									lowerCAmelCase_			:     Optional[int]									= output.images.reshape(A_				,				5_1_2				,				5_1_2				,				3)

									lowerCAmelCase_			:     List[str]									= images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

									lowerCAmelCase_			:     int									= jnp.asarray(jax.device_get(image_slice.flatten()))
									lowerCAmelCase_			:     str									= jnp.array(
									    [0.361_1307, 0.3764_9736, 0.375_7408, 0.3821_3953, 0.3929_5167, 0.384_1631, 0.4155_4978, 0.413_7475, 0.421_7084])
									print(F"""output_slice: {output_slice}""")
									assert jnp.abs(output_slice - expected_slice).max() < 1e-2





