





import gc
import unittest

from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, 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 	_snake_case ( self      )				->  List[str]:
      """simple docstring"""





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





   def 	_snake_case ( self      )				->  Tuple:
      """simple docstring"""





      a__		,     a__	:						str      		=  FlaxStableDiffusionPipeline.from_pretrained(
          "stabilityai/stable-diffusion-2"					,			revision="bf16"					,			dtype=jnp.bfloataa					,			)

      a__	:						List[str]      		=  "A painting of a squirrel eating a burger"
      a__	:						List[Any]      		=  jax.device_count()
      a__	:						Union[str, Any]      		=  num_samples * [prompt]
      a__	:						str      		=  sd_pipe.prepare_inputs(snake_case      )

      a__	:						int      		=  replicate(snake_case      )
      a__	:						List[Any]      		=  shard(snake_case      )

      a__	:						Optional[Any]      		=  jax.random.PRNGKey(0      )
      a__	:						List[Any]      		=  jax.random.split(snake_case					,			jax.device_count()      )

      a__	:						List[Any]      		=  sd_pipe(snake_case					,			snake_case					,			snake_case					,			num_inference_steps=25					,			jit=snake_case      )[0]
      assert images.shape == (jax.device_count(), 1, 768, 768, 3)

      a__	:						Optional[Any]      		=  images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]      )
      a__	:						List[Any]      		=  images[0, 253:256, 253:256, -1]

      a__	:						Dict      		=  jnp.asarray(jax.device_get(image_slice.flatten()      )      )
      a__	:						Tuple      		=  jnp.array([0.4_238, 0.4_414, 0.4_395, 0.4_453, 0.4_629, 0.4_590, 0.4_531, 0.45_508, 0.4_512]      )
      print(F"""output_slice: {output_slice}"""      )
      assert jnp.abs(output_slice - expected_slice      ).max() < 1E-2





   def 	_snake_case ( self      )				->  Optional[int]:
      """simple docstring"""





      a__	:						Tuple      		=  "stabilityai/stable-diffusion-2"
      a__		,     a__	:						str      		=  FlaxDPMSolverMultistepScheduler.from_pretrained(snake_case					,			subfolder="scheduler"      )
      a__		,     a__	:						int      		=  FlaxStableDiffusionPipeline.from_pretrained(
          snake_case					,			scheduler=snake_case					,			revision="bf16"					,			dtype=jnp.bfloataa					,			)
      a__	:						int      		=  scheduler_params

      a__	:						Optional[int]      		=  "A painting of a squirrel eating a burger"
      a__	:						Tuple      		=  jax.device_count()
      a__	:						Optional[int]      		=  num_samples * [prompt]
      a__	:						Any      		=  sd_pipe.prepare_inputs(snake_case      )

      a__	:						Any      		=  replicate(snake_case      )
      a__	:						Union[str, Any]      		=  shard(snake_case      )

      a__	:						str      		=  jax.random.PRNGKey(0      )
      a__	:						str      		=  jax.random.split(snake_case					,			jax.device_count()      )

      a__	:						Any      		=  sd_pipe(snake_case					,			snake_case					,			snake_case					,			num_inference_steps=25					,			jit=snake_case      )[0]
      assert images.shape == (jax.device_count(), 1, 768, 768, 3)

      a__	:						Any      		=  images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]      )
      a__	:						str      		=  images[0, 253:256, 253:256, -1]

      a__	:						Tuple      		=  jnp.asarray(jax.device_get(image_slice.flatten()      )      )
      a__	:						str      		=  jnp.array([0.4_336, 0.42_969, 0.4_453, 0.4_199, 0.4_297, 0.4_531, 0.4_434, 0.4_434, 0.4_297]      )
      print(F"""output_slice: {output_slice}"""      )
      assert jnp.abs(output_slice - expected_slice      ).max() < 1E-2








from PIL import Image






def       _A  (      lowerCamelCase							, lowerCamelCase   ):

   def brightness(lowerCamelCase   ) -> float:
      return 128 + level + (c - 128)

   if not -255.0 <= level <= 255.0:
      raise ValueError("level must be between -255.0 (black) and 255.0 (white)"   )
   return img.point(lowerCamelCase   )


if __name__ == "__main__":
  # Load image
  with Image.open("""image_data/lena.jpg""") as img:
    # Change brightness to 100
    SCREAMING_SNAKE_CASE__       :			List[str]			      =						change_brightness(img, 1_0_0)
    brigt_img.save("""image_data/lena_brightness.png""", format="""png""")

