"""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 __snake_case    (						unittest.TestCase		):




   def      __a						(   self      :    int				):



     """simple docstring"""






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




   def      __a						(   self      :    str				):



     """simple docstring"""






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

     SCREAMING_SNAKE_CASE__      							=				"""xvjiarui/stable-diffusion-2-inpainting"""
     SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__      							=				FlaxStableDiffusionInpaintPipeline.from_pretrained(_a	,     safety_checker=_a				)

     SCREAMING_SNAKE_CASE__      							=				"""Face of a yellow cat, high resolution, sitting on a park bench"""

     SCREAMING_SNAKE_CASE__      							=				jax.random.PRNGKey(0				)
     SCREAMING_SNAKE_CASE__      							=				50

     SCREAMING_SNAKE_CASE__      							=				jax.device_count()
     SCREAMING_SNAKE_CASE__      							=				num_samples * [prompt]
     SCREAMING_SNAKE_CASE__      							=				num_samples * [init_image]
     SCREAMING_SNAKE_CASE__      							=				num_samples * [mask_image]
     SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__      							=				pipeline.prepare_inputs(_a	,     _a	,     _a				)

     # shard inputs and rng
     SCREAMING_SNAKE_CASE__      							=				replicate(_a				)
     SCREAMING_SNAKE_CASE__      							=				jax.random.split(_a	,     jax.device_count()				)
     SCREAMING_SNAKE_CASE__      							=				shard(_a				)
     SCREAMING_SNAKE_CASE__      							=				shard(_a				)
     SCREAMING_SNAKE_CASE__      							=				shard(_a				)

     SCREAMING_SNAKE_CASE__      							=				pipeline(
         _a	,     _a	,     _a	,     _a	,     _a	,     _a	,     jit=_a				)

     SCREAMING_SNAKE_CASE__      							=				output.images.reshape(_a	,     5_12	,     5_12	,     3				)

     SCREAMING_SNAKE_CASE__      							=				images[0, 2_53:2_56, 2_53:2_56, -1]

     SCREAMING_SNAKE_CASE__      							=				jnp.asarray(jax.device_get(image_slice.flatten()				)				)
     SCREAMING_SNAKE_CASE__      							=				jnp.array(
         [0.3_61_13_07, 0.37_64_97_36, 0.3_75_74_08, 0.38_21_39_53, 0.39_29_51_67, 0.3_84_16_31, 0.41_55_49_78, 0.4_13_74_75, 0.4_21_70_84]				)
     print(f"""output_slice: {output_slice}"""				)
     assert jnp.abs(output_slice - expected_slice				).max() < 1E-2

def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	List[Any]     )							-> Any:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__      							=				[0] * len(__UpperCamelCase     )
  SCREAMING_SNAKE_CASE__      							=				[]
  SCREAMING_SNAKE_CASE__      							=				[1] * len(__UpperCamelCase     )

  for values in graph.values():
    for i in values:
      indegree[i] += 1

  for i in range(len(__UpperCamelCase     )     ):
    if indegree[i] == 0:
      queue.append(__UpperCamelCase     )

  while queue:
    SCREAMING_SNAKE_CASE__      							=				queue.pop(0     )
    for x in graph[vertex]:
      indegree[x] -= 1

      if long_dist[vertex] + 1 > long_dist[x]:
        SCREAMING_SNAKE_CASE__      							=				long_dist[vertex] + 1

      if indegree[x] == 0:
        queue.append(__UpperCamelCase     )

  print(max(__UpperCamelCase     )     )


# Adjacency list of Graph
__lowerCamelCase      :			int							       =		{0: [2, 3, 4], 1: [2, 7], 2: [5], 3: [5, 7], 4: [7], 5: [6], 6: [7], 7: []}






longest_distance(graph)
