


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 			lowerCamelCase__(				self       :List[Any]				) ->   List[str]:
												# clean up the VRAM after each test
												super().tearDown()
												gc.collect()



					def 			lowerCamelCase__(				self       :List[str]				) ->   Optional[int]:
												a__					=					load_image(
												    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
												    '/sd2-inpaint/init_image.png'				)
												a__					=					load_image(
												    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'				)

												a__					=					'xvjiarui/stable-diffusion-2-inpainting'
												a__				, a__					=					FlaxStableDiffusionInpaintPipeline.from_pretrained(lowercase__      ,safety_checker=lowercase__				)

												a__					=					'Face of a yellow cat, high resolution, sitting on a park bench'

												a__					=					jax.random.PRNGKey(0				)
												a__					=					50

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

												# shard inputs and rng
												a__					=					replicate(lowercase__				)
												a__					=					jax.random.split(lowercase__      ,jax.device_count()				)
												a__					=					shard(lowercase__				)
												a__					=					shard(lowercase__				)
												a__					=					shard(lowercase__				)

												a__					=					pipeline(
												    lowercase__      ,lowercase__      ,lowercase__      ,lowercase__      ,lowercase__      ,lowercase__      ,jit=lowercase__				)

												a__					=					output.images.reshape(lowercase__      ,5_12      ,5_12      ,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(
												    [0.3_61_13_07, 0.37_64_97_36, 0.3_75_74_08, 0.38_21_39_53, 0.39_29_51_67, 0.3_84_16_31, 0.41_55_49_78, 0.4_13_74_75, 0.4_21_70_84]				)
												print(F'output_slice: {output_slice}'				)
												assert jnp.abs(output_slice - expected_slice				).max() < 1E-2








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


snake_case							:	Optional[int]					      =				{
    '''configuration_clipseg''': [
        '''CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''CLIPSegConfig''',
        '''CLIPSegTextConfig''',
        '''CLIPSegVisionConfig''',
    ],
    '''processing_clipseg''': ['''CLIPSegProcessor'''],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						snake_case							:	List[str]					      =				[
						    '''CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''CLIPSegModel''',
						    '''CLIPSegPreTrainedModel''',
						    '''CLIPSegTextModel''',
						    '''CLIPSegVisionModel''',
						    '''CLIPSegForImageSegmentation''',
						]

if TYPE_CHECKING:
						from .configuration_clipseg import (
						    CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    CLIPSegConfig,
						    CLIPSegTextConfig,
						    CLIPSegVisionConfig,
						)
						from .processing_clipseg import CLIPSegProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_clipseg import (
												    CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST,
												    CLIPSegForImageSegmentation,
												    CLIPSegModel,
												    CLIPSegPreTrainedModel,
												    CLIPSegTextModel,
												    CLIPSegVisionModel,
												)

else:
						import sys

						snake_case							:	Optional[int]					      =				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




