


import warnings

from ...utils import logging
from .image_processing_imagegpt import ImageGPTImageProcessor


UpperCAmelCase__   	=							logging.get_logger(__name__)







class       lowerCamelCase__   (       lowerCAmelCase):





     def __init__(self		,     *UpperCAmelCase		,     **UpperCAmelCase       )     ->   None:
      warnings.warn(
          '''The class ImageGPTFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
          ''' Please use ImageGPTImageProcessor instead.'''		,     UpperCAmelCase		,     )
      super().__init__(*UpperCAmelCase		,     **UpperCAmelCase       )





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      __A					(self       )     ->   List[Any]:
      # clean up the VRAM after each test
      super().tearDown()
      gc.collect()





     def      __A					(self       )     ->   Optional[Any]:
      _lowercase							      =load_image(
          '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
          '''/sd2-inpaint/init_image.png'''       )
      _lowercase							      =load_image(
          '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'''       )

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

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

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

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

      # shard inputs and rng
      _lowercase							      =replicate(UpperCAmelCase       )
      _lowercase							      =jax.random.split(UpperCAmelCase		,     jax.device_count()       )
      _lowercase							      =shard(UpperCAmelCase       )
      _lowercase							      =shard(UpperCAmelCase       )
      _lowercase							      =shard(UpperCAmelCase       )

      _lowercase							      =pipeline(
          UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     jit=UpperCAmelCase       )

      _lowercase							      =output.images.reshape(UpperCAmelCase		,     5_1_2		,     5_1_2		,     3       )

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

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

