
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     lowercase (		unittest.TestCase       ):



		def __UpperCamelCase			(			self	)  -> Optional[int]:
				"""simple docstring"""
				# clean up the VRAM after each test
				super().tearDown()
				gc.collect()



		def __UpperCamelCase			(			self	)  -> str:
				"""simple docstring"""
				UpperCamelCase          =					load_image(
				    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
				    '/sd2-inpaint/init_image.png'	)
				UpperCamelCase          =					load_image(
				    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'	)

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

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

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

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

				# shard inputs and rng
				UpperCamelCase          =					replicate(A_	)
				UpperCamelCase          =					jax.random.split(A_    ,				jax.device_count()	)
				UpperCamelCase          =					shard(A_	)
				UpperCamelCase          =					shard(A_	)
				UpperCamelCase          =					shard(A_	)

				UpperCamelCase          =					pipeline(
				    A_    ,				A_    ,				A_    ,				A_    ,				A_    ,				A_    ,				jit=A_	)

				UpperCamelCase          =					output.images.reshape(A_    ,				512    ,				512    ,				3	)

				UpperCamelCase          =					images[0, 253:256, 253:256, -1]

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


import logging
import os

from .state import PartialState


class     lowercase (		logging.LoggerAdapter       ):



		@staticmethod
		def __UpperCamelCase			(			A_	)  -> Optional[Any]:
				"""simple docstring"""
				UpperCamelCase          =					PartialState()
				return not main_process_only or (main_process_only and state.is_main_process)



		def __UpperCamelCase			(			self    ,				A_    ,				A_    ,				*A_    ,				**A_	)  -> Union[str, Any]:
				"""simple docstring"""
				if PartialState._shared_state == {}:
						raise RuntimeError(
						    'You must initialize the accelerate state by calling either `PartialState()` or `Accelerator()` before using the logging utility.'	)
				UpperCamelCase          =					kwargs.pop('main_process_only'    ,				A_	)
				UpperCamelCase          =					kwargs.pop('in_order'    ,				A_	)

				if self.isEnabledFor(A_	):
						if self._should_log(A_	):
								UpperCamelCase     ,  UpperCamelCase          =					self.process(A_    ,				A_	)
								self.logger.log(A_    ,				A_    ,				*A_    ,				**A_	)

						elif in_order:
								UpperCamelCase          =					PartialState()
								for i in range(state.num_processes	):
										if i == state.process_index:
												UpperCamelCase     ,  UpperCamelCase          =					self.process(A_    ,				A_	)
												self.logger.log(A_    ,				A_    ,				*A_    ,				**A_	)
										state.wait_for_everyone()






def 				A	( lowercase   ,	lowercase = None			)       ->  Dict:
		'''simple docstring'''

		if log_level is None:
				UpperCamelCase          =					os.environ.get('ACCELERATE_LOG_LEVEL'   ,	lowercase			)
		UpperCamelCase          =					logging.getLogger(lowercase			)
		if log_level is not None:
				logger.setLevel(log_level.upper()			)
				logger.root.setLevel(log_level.upper()			)
		return MultiProcessAdapter(lowercase   ,	{}			)
