
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 							A__		(	unittest.TestCase):
      def 						UpperCamelCase__							(      self      ):
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()






      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	List[Any]							=      load_image(
           """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
           """/sd2-inpaint/init_image.png"""      )
       lowerCamelCase		:	Dict							=      load_image(
           """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"""      )

       lowerCamelCase		:	Optional[Any]							=      """xvjiarui/stable-diffusion-2-inpainting"""
       lowerCamelCase			,    lowerCamelCase		:	List[str]							=      FlaxStableDiffusionInpaintPipeline.from_pretrained(__magic_name__    ,  safety_checker=__magic_name__      )

       lowerCamelCase		:	int							=      """Face of a yellow cat, high resolution, sitting on a park bench"""

       lowerCamelCase		:	Optional[Any]							=      jax.random.PRNGKey(0      )
       lowerCamelCase		:	Tuple							=      5_0

       lowerCamelCase		:	Dict							=      jax.device_count()
       lowerCamelCase		:	Union[str, Any]							=      num_samples * [prompt]
       lowerCamelCase		:	Optional[Any]							=      num_samples * [init_image]
       lowerCamelCase		:	Dict							=      num_samples * [mask_image]
       lowerCamelCase			,    lowerCamelCase			,    lowerCamelCase		:	Dict							=      pipeline.prepare_inputs(__magic_name__    ,  __magic_name__    ,  __magic_name__      )

       # shard inputs and rng
       lowerCamelCase		:	List[Any]							=      replicate(__magic_name__      )
       lowerCamelCase		:	str							=      jax.random.split(__magic_name__    ,  jax.device_count()      )
       lowerCamelCase		:	Dict							=      shard(__magic_name__      )
       lowerCamelCase		:	List[Any]							=      shard(__magic_name__      )
       lowerCamelCase		:	int							=      shard(__magic_name__      )

       lowerCamelCase		:	Optional[Any]							=      pipeline(
           __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  jit=__magic_name__      )

       lowerCamelCase		:	List[str]							=      output.images.reshape(__magic_name__    ,  5_1_2    ,  5_1_2    ,  3      )

       lowerCamelCase		:	Optional[Any]							=      images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

       lowerCamelCase		:	Union[str, Any]							=      jnp.asarray(jax.device_get(image_slice.flatten()      )      )
       lowerCamelCase		:	Optional[Any]							=      jnp.array(
           [0.3_611_307, 0.37_649_736, 0.3_757_408, 0.38_213_953, 0.39_295_167, 0.3_841_631, 0.41_554_978, 0.4_137_475, 0.4_217_084]      )
       print(F'''output_slice: {output_slice}'''      )
       assert jnp.abs(output_slice - expected_slice      ).max() < 1e-2






_lowerCamelCase					   ={
    "joule": 1.0,
    "kilojoule": 1_0_0_0,
    "megajoule": 1_0_0_0_0_0_0,
    "gigajoule": 1_0_0_0_0_0_0_0_0_0,
    "wattsecond": 1.0,
    "watthour": 3_6_0_0,
    "kilowatthour": 3_6_0_0_0_0_0,
    "newtonmeter": 1.0,
    "calorie_nutr": 4_1_8_6.8,
    "kilocalorie_nutr": 4_1_8_6_8_0_0.0_0,
    "electronvolt": 1.6_0_2_1_7_6_6_3_4E-1_9,
    "britishthermalunit_it": 1_0_5_5.0_5_5_8_5,
    "footpound": 1.355818,
}





def     _a					(						lowerCamelCase,	lowerCamelCase,	lowerCamelCase							):
 if to_type not in ENERGY_CONVERSION or from_type not in ENERGY_CONVERSION:
  lowerCamelCase		:	Dict							=      (
      F'''Incorrect \'from_type\' or \'to_type\' value: {from_type!r}, {to_type!r}\n'''
      F'''Valid values are: {", ".join(lowerCamelCase							)}'''
  )
  raise ValueError(lowerCamelCase							)
 return value * ENERGY_CONVERSION[from_type] / ENERGY_CONVERSION[to_type]


if __name__ == "__main__":
      import doctest

      doctest.testmod()




