






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 						UpperCAmelCase__ (							unittest.TestCase							):







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







		def  A__      (					self		):
						_A  ,				_A	:				Tuple   =      FlaxStableDiffusionPipeline.from_pretrained(
						    '''stabilityai/stable-diffusion-2'''       ,revision='''bf16'''       ,dtype=jnp.bfloataa       ,)

						_A	:				Optional[int]   =      '''A painting of a squirrel eating a burger'''
						_A	:				Optional[int]   =      jax.device_count()
						_A	:				List[str]   =      num_samples * [prompt]
						_A	:				int   =      sd_pipe.prepare_inputs(A__		)

						_A	:				int   =      replicate(A__		)
						_A	:				str   =      shard(A__		)

						_A	:				Any   =      jax.random.PRNGKey(0		)
						_A	:				Dict   =      jax.random.split(A__       ,jax.device_count()		)

						_A	:				Union[str, Any]   =      sd_pipe(A__       ,A__       ,A__       ,num_inference_steps=25       ,jit=A__		)[0]
						assert images.shape == (jax.device_count(), 1, 768, 768, 3)

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

						_A	:				List[str]   =      jnp.asarray(jax.device_get(image_slice.flatten()		)		)
						_A	:				Optional[Any]   =      jnp.array([0.42_38, 0.44_14, 0.43_95, 0.44_53, 0.46_29, 0.45_90, 0.45_31, 0.4_55_08, 0.45_12]		)
						print(f"""output_slice: {output_slice}"""		)
						assert jnp.abs(output_slice - expected_slice		).max() < 1E-2







		def  A__      (					self		):
						_A	:				Dict   =      '''stabilityai/stable-diffusion-2'''
						_A  ,				_A	:				Any   =      FlaxDPMSolverMultistepScheduler.from_pretrained(A__       ,subfolder='''scheduler'''		)
						_A  ,				_A	:				Optional[Any]   =      FlaxStableDiffusionPipeline.from_pretrained(
						    A__       ,scheduler=A__       ,revision='''bf16'''       ,dtype=jnp.bfloataa       ,)
						_A	:				Any   =      scheduler_params

						_A	:				Union[str, Any]   =      '''A painting of a squirrel eating a burger'''
						_A	:				Optional[int]   =      jax.device_count()
						_A	:				Optional[int]   =      num_samples * [prompt]
						_A	:				int   =      sd_pipe.prepare_inputs(A__		)

						_A	:				Optional[Any]   =      replicate(A__		)
						_A	:				Union[str, Any]   =      shard(A__		)

						_A	:				Any   =      jax.random.PRNGKey(0		)
						_A	:				Union[str, Any]   =      jax.random.split(A__       ,jax.device_count()		)

						_A	:				List[str]   =      sd_pipe(A__       ,A__       ,A__       ,num_inference_steps=25       ,jit=A__		)[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	:				Optional[int]   =      images[0, 253:256, 253:256, -1]

						_A	:				Dict   =      jnp.asarray(jax.device_get(image_slice.flatten()		)		)
						_A	:				List[Any]   =      jnp.array([0.43_36, 0.4_29_69, 0.44_53, 0.41_99, 0.42_97, 0.45_31, 0.44_34, 0.44_34, 0.42_97]		)
						print(f"""output_slice: {output_slice}"""		)
						assert jnp.abs(output_slice - expected_slice		).max() < 1E-2











from PIL import Image



def  a__ (__lowercase							:Image					,  __lowercase							:float   )		-> Image:

				def brightness(__lowercase							:int   ) -> 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(__lowercase   )


if __name__ == "__main__":
	# Load image
	with Image.open('image_data/lena.jpg') as img:
		# Change brightness to 100
		_UpperCamelCase			:	Any								=change_brightness(img, 100)
		brigt_img.save('image_data/lena_brightness.png', format='png')



