





'''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     __lowerCAmelCase					(   unittest.TestCase			):






       """simple docstring"""





       def snake_case__						(							self       :		Any					)   ->					Dict:

            '''simple docstring'''




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







       def snake_case__						(							self       :		List[Any]					)   ->					Union[str, Any]:

            '''simple docstring'''




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

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

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

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

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

            # shard inputs and rng
            _UpperCamelCase       =	replicate(lowerCAmelCase__					)
            _UpperCamelCase       =	jax.random.split(lowerCAmelCase__	,			jax.device_count()					)
            _UpperCamelCase       =	shard(lowerCAmelCase__					)
            _UpperCamelCase       =	shard(lowerCAmelCase__					)
            _UpperCamelCase       =	shard(lowerCAmelCase__					)

            _UpperCamelCase       =	pipeline(
                lowerCAmelCase__	,			lowerCAmelCase__	,			lowerCAmelCase__	,			lowerCAmelCase__	,			lowerCAmelCase__	,			lowerCAmelCase__	,			jit=lowerCAmelCase__					)

            _UpperCamelCase       =	output.images.reshape(lowerCAmelCase__	,			512	,			512	,			3					)

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

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










'''simple docstring'''

from typing import TYPE_CHECKING

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


lowercase__						:      Optional[Any]	=    {
    'configuration_autoformer': [
        'AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'AutoformerConfig',
    ],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowercase__						:      List[Any]	=    [
       'AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
       'AutoformerForPrediction',
       'AutoformerModel',
       'AutoformerPreTrainedModel',
   ]


if TYPE_CHECKING:
   from .configuration_autoformer import (
       AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
       AutoformerConfig,
   )

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_autoformer import (
          AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          AutoformerForPrediction,
          AutoformerModel,
          AutoformerPreTrainedModel,
      )

else:
   import sys

   lowercase__						:      Tuple	=    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



