


'''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  lowercase_							(				self      )							->	Optional[Any]:
         super().tearDown()
         gc.collect()







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

         __lowerCamelCase  :      Union[str, Any]														=						'''xvjiarui/stable-diffusion-2-inpainting'''
         __lowerCamelCase  :      Optional[Any]														=						FlaxStableDiffusionInpaintPipeline.from_pretrained(_lowercase     ,	safety_checker=_lowercase      )

         __lowerCamelCase  :      int														=						'''Face of a yellow cat, high resolution, sitting on a park bench'''

         __lowerCamelCase  :      Dict														=						jax.random.PRNGKey(0      )
         __lowerCamelCase  :      Optional[int]														=						50

         __lowerCamelCase  :      List[str]														=						jax.device_count()
         __lowerCamelCase  :      str														=						num_samples * [prompt]
         __lowerCamelCase  :      Union[str, Any]														=						num_samples * [init_image]
         __lowerCamelCase  :      List[str]														=						num_samples * [mask_image]
         __lowerCamelCase  :      int														=						pipeline.prepare_inputs(_lowercase     ,	_lowercase     ,	_lowercase      )

         # shard inputs and rng
         __lowerCamelCase  :      str														=						replicate(_lowercase      )
         __lowerCamelCase  :      Dict														=						jax.random.split(_lowercase     ,	jax.device_count()      )
         __lowerCamelCase  :      Optional[Any]														=						shard(_lowercase      )
         __lowerCamelCase  :      Union[str, Any]														=						shard(_lowercase      )
         __lowerCamelCase  :      List[Any]														=						shard(_lowercase      )

         __lowerCamelCase  :      List[Any]														=						pipeline(
             _lowercase     ,	_lowercase     ,	_lowercase     ,	_lowercase     ,	_lowercase     ,	_lowercase     ,	jit=_lowercase      )

         __lowerCamelCase  :      Tuple														=						output.images.reshape(_lowercase     ,	5_12     ,	5_12     ,	3      )

         __lowerCamelCase  :      List[str]														=						images[0, 2_53:2_56, 2_53:2_56, -1]

         __lowerCamelCase  :      Dict														=						jnp.asarray(jax.device_get(image_slice.flatten()      )      )
         __lowerCamelCase  :      Optional[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







import torch




def    lowerCAmelCase_  (		)     ->					int:







 '''simple docstring'''
 if torch.cuda.is_available():
  _UpperCamelCase:  Any    						=			torch.cuda.device_count()
 else:
  _UpperCamelCase:  Union[str, Any]    						=			0
 print(F"""Successfully ran on {num_gpus} GPUs"""		)


if __name__ == "__main__":
  main()