
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   ):







	'''simple docstring'''





	def   __snake_case				(			self		):
					# clean up the VRAM after each test
					super().tearDown()
					gc.collect()





	def   __snake_case				(			self		):
					A__  :	Tuple         =							load_image(
					    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
					    '''/sd2-inpaint/init_image.png'''		)
					A__  :	Optional[int]         =							load_image(
					    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'''		)

					A__  :	List[Any]         =							'''xvjiarui/stable-diffusion-2-inpainting'''
					A__       ,							A__  :	Dict         =							FlaxStableDiffusionInpaintPipeline.from_pretrained(UpperCamelCase__				,       safety_checker=UpperCamelCase__		)

					A__  :	Union[str, Any]         =							'''Face of a yellow cat, high resolution, sitting on a park bench'''

					A__  :	List[str]         =							jax.random.PRNGKey(0		)
					A__  :	Dict         =							50

					A__  :	Any         =							jax.device_count()
					A__  :	str         =							num_samples * [prompt]
					A__  :	int         =							num_samples * [init_image]
					A__  :	Tuple         =							num_samples * [mask_image]
					A__       ,							A__       ,							A__  :	int         =							pipeline.prepare_inputs(UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__		)

					# shard inputs and rng
					A__  :	Optional[Any]         =							replicate(UpperCamelCase__		)
					A__  :	Any         =							jax.random.split(UpperCamelCase__				,       jax.device_count()		)
					A__  :	Any         =							shard(UpperCamelCase__		)
					A__  :	Optional[int]         =							shard(UpperCamelCase__		)
					A__  :	Union[str, Any]         =							shard(UpperCamelCase__		)

					A__  :	int         =							pipeline(
					    UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__				,       jit=UpperCamelCase__		)

					A__  :	int         =							output.images.reshape(UpperCamelCase__				,       512				,       512				,       3		)

					A__  :	Union[str, Any]         =							images[0, 253:256, 253:256, -1]

					A__  :	Dict         =							jnp.asarray(jax.device_get(image_slice.flatten()		)		)
					A__  :	Union[str, Any]         =							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

from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
					if not (is_transformers_available() and is_torch_available()):
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					from ...utils.dummy_torch_and_transformers_objects import KandinskyPipeline, KandinskyPriorPipeline
else:
					from .pipeline_kandinsky import KandinskyPipeline
					from .pipeline_kandinsky_imgaimg import KandinskyImgaImgPipeline
					from .pipeline_kandinsky_inpaint import KandinskyInpaintPipeline
					from .pipeline_kandinsky_prior import KandinskyPriorPipeline, KandinskyPriorPipelineOutput
					from .text_encoder import MultilingualCLIP