
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 				UpperCAmelCase (    unittest.TestCase    ):




	'''simple docstring'''





	def 							UpperCamelCase_    (			self   :					Union[str, Any]					):
					super().tearDown()
					gc.collect()







	def 							UpperCamelCase_    (			self   :					str					):
					__A       =					load_image(
					    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
					    "/sd2-inpaint/init_image.png"					)
					__A       =					load_image(
					    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"					)

					__A       =					"xvjiarui/stable-diffusion-2-inpainting"
					__A     ,				__A       =					FlaxStableDiffusionInpaintPipeline.from_pretrained(_snake_case				,safety_checker=_snake_case					)

					__A       =					"Face of a yellow cat, high resolution, sitting on a park bench"

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

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

					# shard inputs and rng
					__A       =					replicate(_snake_case					)
					__A       =					jax.random.split(_snake_case				,jax.device_count()					)
					__A       =					shard(_snake_case					)
					__A       =					shard(_snake_case					)
					__A       =					shard(_snake_case					)

					__A       =					pipeline(
					    _snake_case				,_snake_case				,_snake_case				,_snake_case				,_snake_case				,_snake_case				,jit=_snake_case					)

					__A       =					output.images.reshape(_snake_case				,5_12				,5_12				,3					)

					__A       =					images[0, 2_53:2_56, 2_53:2_56, -1]

					__A       =					jnp.asarray(jax.device_get(image_slice.flatten()					)					)
					__A       =					jnp.array(
					    [0.3_61_13_07, 0.37_64_97_36, 0.3_75_74_08, 0.38_21_39_53, 0.39_29_51_67, 0.3_84_16_31, 0.41_55_49_78, 0.4_13_74_75, 0.4_21_70_84]					)
					print(f'''output_slice: {output_slice}'''					)
					assert jnp.abs(output_slice - expected_slice					).max() < 1E-2





def A    (							__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )    ->   int:

			if exponent == 1:
						return base
			if exponent % 2 == 0:
						A__	  =     _modexpt(__UpperCamelCase			,						exponent // 2			,						__UpperCamelCase       ) % modulo_value
						return (x * x) % modulo_value
			else:
						return (base * _modexpt(__UpperCamelCase			,						exponent - 1			,						__UpperCamelCase       )) % modulo_value


def A    (							__UpperCamelCase = 1_777			,						__UpperCamelCase = 1_855			,						__UpperCamelCase = 8       )    ->   int:
			A__	  =     base
			for _ in range(1			,						__UpperCamelCase       ):
						A__	  =     _modexpt(__UpperCamelCase			,						__UpperCamelCase			,						10**digits       )

			return result


if __name__ == "__main__":
							print(f'{solution() = }')




