
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 					lowerCamelCase		(  unittest.TestCase  ):





     def 		UpperCAmelCase(self				:   Optional[int]       )						->						List[str]:
         super().tearDown()
         gc.collect()





     def 		UpperCAmelCase(self				:   Union[str, Any]       )						->						Tuple:
         snake_case								= load_image(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
             "/sd2-inpaint/init_image.png"       )
         snake_case								= load_image(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"       )

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

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

         snake_case								= jax.random.PRNGKey(0       )
         snake_case								= 5_0

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

         # shard inputs and rng
         snake_case								= replicate(UpperCamelCase__       )
         snake_case								= jax.random.split(UpperCamelCase__    ,   jax.device_count()       )
         snake_case								= shard(UpperCamelCase__       )
         snake_case								= shard(UpperCamelCase__       )
         snake_case								= shard(UpperCamelCase__       )

         snake_case								= pipeline(
             UpperCamelCase__    ,   UpperCamelCase__    ,   UpperCamelCase__    ,   UpperCamelCase__    ,   UpperCamelCase__    ,   UpperCamelCase__    ,   jit=UpperCamelCase__       )

         snake_case								= output.images.reshape(UpperCamelCase__    ,   5_1_2    ,   5_1_2    ,   3       )

         snake_case								= images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

         snake_case								= jnp.asarray(jax.device_get(image_slice.flatten()       )       )
         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



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_A   =			{
    "configuration_lilt": ["LILT_PRETRAINED_CONFIG_ARCHIVE_MAP", "LiltConfig"],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A   =			[
       "LILT_PRETRAINED_MODEL_ARCHIVE_LIST",
       "LiltForQuestionAnswering",
       "LiltForSequenceClassification",
       "LiltForTokenClassification",
       "LiltModel",
       "LiltPreTrainedModel",
   ]

if TYPE_CHECKING:
   from .configuration_lilt import LILT_PRETRAINED_CONFIG_ARCHIVE_MAP, LiltConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_lilt import (
          LILT_PRETRAINED_MODEL_ARCHIVE_LIST,
          LiltForQuestionAnswering,
          LiltForSequenceClassification,
          LiltForTokenClassification,
          LiltModel,
          LiltPreTrainedModel,
      )

else:
   import sys

   _A   =			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

