




"""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 							lowercase_			(	unittest.TestCase				):



 '''simple docstring'''

 def      lowerCAmelCase_				(					self			:			Tuple						):
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()



 def      lowerCAmelCase_				(					self			:			Tuple						):
       _A     =  load_image(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
           '/sd2-inpaint/init_image.png'						)
       _A     =  load_image(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'						)

       _A     =  'xvjiarui/stable-diffusion-2-inpainting'
       _A  ,       _A     =  FlaxStableDiffusionInpaintPipeline.from_pretrained(_UpperCAmelCase       ,				safety_checker=_UpperCAmelCase						)

       _A     =  'Face of a yellow cat, high resolution, sitting on a park bench'

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

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

       # shard inputs and rng
       _A     =  replicate(_UpperCAmelCase						)
       _A     =  jax.random.split(_UpperCAmelCase       ,				jax.device_count()						)
       _A     =  shard(_UpperCAmelCase						)
       _A     =  shard(_UpperCAmelCase						)
       _A     =  shard(_UpperCAmelCase						)

       _A     =  pipeline(
           _UpperCAmelCase       ,				_UpperCAmelCase       ,				_UpperCAmelCase       ,				_UpperCAmelCase       ,				_UpperCAmelCase       ,				_UpperCAmelCase       ,				jit=_UpperCAmelCase						)

       _A     =  output.images.reshape(_UpperCAmelCase       ,				512       ,				512       ,				3						)

       _A     =  images[0, 253:256, 253:256, -1]

       _A     =  jnp.asarray(jax.device_get(image_slice.flatten()						)						)
       _A     =  jnp.array(
           [0.361_1307, 0.3764_9736, 0.375_7408, 0.3821_3953, 0.3929_5167, 0.384_1631, 0.4155_4978, 0.413_7475, 0.421_7084]						)
       print(F'''output_slice: {output_slice}'''						)
       assert jnp.abs(output_slice - expected_slice						).max() < 1E-2




"""simple docstring"""


from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase													={
    "configuration_informer": [
        "INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "InformerConfig",
    ],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase													=[
      "INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
      "InformerForPrediction",
      "InformerModel",
      "InformerPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_informer import INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, InformerConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_informer import (
        INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        InformerForPrediction,
        InformerModel,
        InformerPreTrainedModel,
    )

else:
  import sys

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




