





'''simple docstring'''

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       UpperCAmelCase (						unittest.TestCase  ):




		def 				UpperCAmelCase__   (self      :	Optional[int]				)					-> Optional[Any]:
						# clean up the VRAM after each test
						super().tearDown()
						gc.collect()





		def 				UpperCAmelCase__   (self      :	Any				)					-> Optional[int]:
						lowercase         = load_image(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
						    "/sd2-inpaint/init_image.png"				)
						lowercase         = load_image(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"				)

						lowercase         = "xvjiarui/stable-diffusion-2-inpainting"
						lowercase   ,							lowercase         = FlaxStableDiffusionInpaintPipeline.from_pretrained(A__							,			safety_checker=A__				)

						lowercase         = "Face of a yellow cat, high resolution, sitting on a park bench"

						lowercase         = jax.random.PRNGKey(0				)
						lowercase         = 5_0

						lowercase         = jax.device_count()
						lowercase         = num_samples * [prompt]
						lowercase         = num_samples * [init_image]
						lowercase         = num_samples * [mask_image]
						lowercase   ,							lowercase   ,							lowercase         = pipeline.prepare_inputs(A__							,			A__							,			A__				)

						# shard inputs and rng
						lowercase         = replicate(A__				)
						lowercase         = jax.random.split(A__							,			jax.device_count()				)
						lowercase         = shard(A__				)
						lowercase         = shard(A__				)
						lowercase         = shard(A__				)

						lowercase         = pipeline(
						    A__							,			A__							,			A__							,			A__							,			A__							,			A__							,			jit=A__				)

						lowercase         = output.images.reshape(A__							,			5_1_2							,			5_1_2							,			3				)

						lowercase         = images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

						lowercase         = jnp.asarray(jax.device_get(image_slice.flatten()				)				)
						lowercase         = jnp.array(
						    [0.3_6_1_1_3_0_7, 0.3_7_6_4_9_7_3_6, 0.3_7_5_7_4_0_8, 0.3_8_2_1_3_9_5_3, 0.3_9_2_9_5_1_6_7, 0.3_8_4_1_6_3_1, 0.4_1_5_5_4_9_7_8, 0.4_1_3_7_4_7_5, 0.4_2_1_7_0_8_4]				)
						print(f'output_slice: {output_slice}'				)
						assert jnp.abs(output_slice - expected_slice				).max() < 1e-2











'''simple docstring'''

import warnings

from ...utils import logging
from .image_processing_clip import CLIPImageProcessor


__lowerCamelCase : Union[str, Any]											=			logging.get_logger(__name__)

class       UpperCAmelCase (						_lowercase  ):




		def __init__(self      :	Tuple							,			*A__      :	Union[str, Any]							,			**A__      :	Optional[Any]				)					-> None:
						warnings.warn(
						    "The class CLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
						    " use CLIPImageProcessor instead."							,			A__							,			)
						super().__init__(*A__							,			**A__				)




