
'''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  __A				( unittest.TestCase			):
 def 		lowercase_(   self			:    str       ):



   """simple docstring"""





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






 def 		lowercase_(   self			:    Any       ):



   """simple docstring"""





   __A		:     Optional[int]												=		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		:     List[Any]												=		FlaxStableDiffusionInpaintPipeline.from_pretrained(lowerCamelCase				,   safety_checker=lowerCamelCase       )

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

   __A		:     List[str]												=		jax.random.PRNGKey(0       )
   __A		:     Union[str, Any]												=		50

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

   # shard inputs and rng
   __A		:     str												=		replicate(lowerCamelCase       )
   __A		:     Optional[Any]												=		jax.random.split(lowerCamelCase				,   jax.device_count()       )
   __A		:     List[str]												=		shard(lowerCamelCase       )
   __A		:     Tuple												=		shard(lowerCamelCase       )
   __A		:     Optional[Any]												=		shard(lowerCamelCase       )

   __A		:     Any												=		pipeline(
       lowerCamelCase				,   lowerCamelCase				,   lowerCamelCase				,   lowerCamelCase				,   lowerCamelCase				,   lowerCamelCase				,   jit=lowerCamelCase       )

   __A		:     Optional[int]												=		output.images.reshape(lowerCamelCase				,   5_12				,   5_12				,   3       )

   __A		:     Optional[Any]												=		images[0, 2_53:2_56, 2_53:2_56, -1]

   __A		:     List[Any]												=		jnp.asarray(jax.device_get(image_slice.flatten()       )       )
   __A		:     Any												=		jnp.array(
       [0.3_611_307, 0.37_649_736, 0.3_757_408, 0.38_213_953, 0.39_295_167, 0.3_841_631, 0.41_554_978, 0.4_137_475, 0.4_217_084]       )
   print(f"output_slice: {output_slice}"       )
   assert jnp.abs(output_slice - expected_slice       ).max() < 1e-2





'''simple docstring'''
from __future__ import annotations







def        A_     (						__SCREAMING_SNAKE_CASE						:  list[int] )				->       int:





  """simple docstring"""

  if not nums:
    return 0
  __A		:     List[Any]												=		nums[0]
  __A		:     Union[str, Any]												=		0
  for num in nums[1:]:
    __A							,					__A		:     Union[str, Any]												=		(
        max_excluding + num,
        max(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE ),
    )
  return max(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )


if __name__ == "__main__":
     import doctest

     doctest.testmod()



