



'''simple docstring'''



import random
import unittest

import torch

from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin





@skip_mps
class _UpperCamelCase		(     A      ,       A      ,       unittest.TestCase		):





				'''simple docstring'''





				lowerCAmelCase__					    =    IFInpaintingPipeline
				lowerCAmelCase__					    =    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"""width""", """height"""}
				lowerCAmelCase__					    =    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
				lowerCAmelCase__					    =    PipelineTesterMixin.required_optional_params - {"""latents"""}

				def 				__lowerCamelCase     (							self  :  Optional[Any]):







											'''simple docstring'''

											return self._get_dummy_components()

				def 				__lowerCamelCase     (							self  :  Union[str, Any]  ,       _lowerCAmelCase  :  List[str]  ,       _lowerCAmelCase  :  Optional[Any]=0):







											'''simple docstring'''

											if str(_lowerCAmelCase).startswith('mps'):
																		__lowercase       	=torch.manual_seed(_lowerCAmelCase)
											else:
																		__lowercase       	=torch.Generator(device=_lowerCAmelCase).manual_seed(_lowerCAmelCase)

											__lowercase       	=floats_tensor((1, 3, 3_2, 3_2)  ,       rng=random.Random(_lowerCAmelCase)).to(_lowerCAmelCase)
											__lowercase       	=floats_tensor((1, 3, 3_2, 3_2)  ,       rng=random.Random(_lowerCAmelCase)).to(_lowerCAmelCase)

											__lowercase       	={
											    'prompt': 'A painting of a squirrel eating a burger',
											    'image': image,
											    'mask_image': mask_image,
											    'generator': generator,
											    'num_inference_steps': 2,
											    'output_type': 'numpy',
											}

											return inputs

				@unittest.skipIf(
				    torch_device != 'cuda' or not is_xformers_available()  ,       reason='XFormers attention is only available with CUDA and `xformers` installed'  ,       )
				def 				__lowerCamelCase     (							self  :  List[str]):







											'''simple docstring'''

											self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3)

				def 				__lowerCamelCase     (							self  :  List[str]):







											'''simple docstring'''

											self._test_save_load_optional_components()

				@unittest.skipIf(torch_device != 'cuda'  ,       reason='float16 requires CUDA')
				def 				__lowerCamelCase     (							self  :  int):







											'''simple docstring'''

											super().test_save_load_floataa(expected_max_diff=1e-1)

				def 				__lowerCamelCase     (							self  :  int):







											'''simple docstring'''

											self._test_attention_slicing_forward_pass(expected_max_diff=1e-2)

				def 				__lowerCamelCase     (							self  :  Any):







											'''simple docstring'''

											self._test_save_load_local()

				def 				__lowerCamelCase     (							self  :  Optional[Any]):







											'''simple docstring'''

											self._test_inference_batch_single_identical(
											    expected_max_diff=1e-2  ,       )








'''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		):





				'''simple docstring'''





				def 				__lowerCamelCase     (							self  :  List[Any]):







											'''simple docstring'''

											super().tearDown()
											gc.collect()

				def 				__lowerCamelCase     (							self  :  List[str]):







											'''simple docstring'''

											__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(_lowerCAmelCase  ,       safety_checker=_lowerCAmelCase)

											__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(_lowerCAmelCase  ,       _lowerCAmelCase  ,       _lowerCAmelCase)

											# shard inputs and rng
											__lowercase       	=replicate(_lowerCAmelCase)
											__lowercase       	=jax.random.split(_lowerCAmelCase  ,       jax.device_count())
											__lowercase       	=shard(_lowerCAmelCase)
											__lowercase       	=shard(_lowerCAmelCase)
											__lowercase       	=shard(_lowerCAmelCase)

											__lowercase       	=pipeline(
											    _lowerCAmelCase  ,       _lowerCAmelCase  ,       _lowerCAmelCase  ,       _lowerCAmelCase  ,       _lowerCAmelCase  ,       _lowerCAmelCase  ,       jit=_lowerCAmelCase)

											__lowercase       	=output.images.reshape(_lowerCAmelCase  ,       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(
											    [0.361_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



