





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        __magic_name__	(				unittest.TestCase				):





     """simple docstring"""



     def     SCREAMING_SNAKE_CASE		(				self							:Optional[Any]	):





        '''simple docstring'''




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





     def     SCREAMING_SNAKE_CASE		(				self							:int	):





        '''simple docstring'''




        A_       :					int        =      load_image(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
            "/sd2-inpaint/init_image.png"	)
        A_       :					Dict        =      load_image(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"	)

        A_       :					Tuple        =      "xvjiarui/stable-diffusion-2-inpainting"
        A_   ,    A_       :					Dict        =      FlaxStableDiffusionInpaintPipeline.from_pretrained(snake_case      ,   safety_checker=snake_case	)

        A_       :					List[Any]        =      "Face of a yellow cat, high resolution, sitting on a park bench"

        A_       :					Tuple        =      jax.random.PRNGKey(0	)
        A_       :					Any        =      50

        A_       :					Optional[int]        =      jax.device_count()
        A_       :					int        =      num_samples * [prompt]
        A_       :					List[Any]        =      num_samples * [init_image]
        A_       :					str        =      num_samples * [mask_image]
        A_   ,    A_   ,    A_       :					Optional[int]        =      pipeline.prepare_inputs(snake_case      ,   snake_case      ,   snake_case	)

        # shard inputs and rng
        A_       :					Dict        =      replicate(snake_case	)
        A_       :					int        =      jax.random.split(snake_case      ,   jax.device_count()	)
        A_       :					Any        =      shard(snake_case	)
        A_       :					Optional[Any]        =      shard(snake_case	)
        A_       :					Optional[Any]        =      shard(snake_case	)

        A_       :					List[Any]        =      pipeline(
            snake_case      ,   snake_case      ,   snake_case      ,   snake_case      ,   snake_case      ,   snake_case      ,   jit=snake_case	)

        A_       :					Optional[int]        =      output.images.reshape(snake_case      ,   512      ,   512      ,   3	)

        A_       :					int        =      images[0, 253:256, 253:256, -1]

        A_       :					int        =      jnp.asarray(jax.device_get(image_slice.flatten()	)	)
        A_       :					int        =      jnp.array(
            [0.3611307, 0.37649736, 0.3757408, 0.38213953, 0.39295167, 0.3841631, 0.41554978, 0.4137475, 0.4217084]	)
        print(f"output_slice: {output_slice}"	)
        assert jnp.abs(output_slice - expected_slice	).max() < 1e-2







from __future__ import annotations

def   __snake_case    (       _lowerCAmelCase			:			list[int]					,					_lowerCAmelCase			:			int						)			->    list[list[int]]:
   A_       :					list[list[int]]        =      []
   A_       :					list[int]        =      []
   A_       :					Dict        =      0
   A_       :					str        =      sum(_lowerCAmelCase						)
   create_state_space_tree(_lowerCAmelCase					,					_lowerCAmelCase					,					_lowerCAmelCase					,					_lowerCAmelCase					,					_lowerCAmelCase					,					_lowerCAmelCase						)
   return result

def   __snake_case    (       _lowerCAmelCase			:			list[int]					,					_lowerCAmelCase			:			int					,					_lowerCAmelCase			:			int					,					_lowerCAmelCase			:			list[int]					,					_lowerCAmelCase			:			list[list[int]]					,					_lowerCAmelCase			:			int					,					)			->    None:
   if sum(_lowerCAmelCase						) > max_sum or (remaining_nums_sum + sum(_lowerCAmelCase						)) < max_sum:
      return
   if sum(_lowerCAmelCase						) == max_sum:
      result.append(_lowerCAmelCase						)
      return
   for index in range(_lowerCAmelCase					,					len(_lowerCAmelCase						)						):
      create_state_space_tree(
          _lowerCAmelCase					,					_lowerCAmelCase					,					index + 1					,					[*path, nums[index]]					,					_lowerCAmelCase					,					remaining_nums_sum - nums[index]					,					)
_lowerCAmelCase				:   List[Any]						       =						[3, 34, 4, 12, 5, 2]
_lowerCAmelCase				:   Dict						       =						9
_lowerCAmelCase				:   Dict						       =						generate_sum_of_subsets_soln(nums, max_sum)





print(*result)
