

import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING


_UpperCAmelCase		:   Optional[int]										=       logging.get_logger(__name__)







class  lowercase				(       lowercase_			):
				__SCREAMING_SNAKE_CASE					: Union[str, Any]       			=			'''upernet'''




				def __init__(					self      ,   snake_case=None      ,   snake_case=512      ,   snake_case=0.02      ,   snake_case=[1, 2, 3, 6]      ,   snake_case=True      ,   snake_case=0.4      ,   snake_case=384      ,   snake_case=256      ,   snake_case=1      ,   snake_case=False      ,   snake_case=255      ,   **snake_case      ,   ):
											super().__init__(**snake_case       )

											if backbone_config is None:
																		logger.info('`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'       )
																		snake_case_							=    CONFIG_MAPPING['resnet'](out_features=['stage1', 'stage2', 'stage3', 'stage4']       )
											elif isinstance(snake_case      ,   snake_case       ):
																		snake_case_							=    backbone_config.get('model_type'       )
																		snake_case_							=    CONFIG_MAPPING[backbone_model_type]
																		snake_case_							=    config_class.from_dict(snake_case       )

											snake_case_							=    backbone_config
											snake_case_							=    hidden_size
											snake_case_							=    initializer_range
											snake_case_							=    pool_scales
											snake_case_							=    use_auxiliary_head
											snake_case_							=    auxiliary_loss_weight
											snake_case_							=    auxiliary_in_channels
											snake_case_							=    auxiliary_channels
											snake_case_							=    auxiliary_num_convs
											snake_case_							=    auxiliary_concat_input
											snake_case_							=    loss_ignore_index




				def     a							(					self       ):
											snake_case_							=    copy.deepcopy(self.__dict__       )
											snake_case_							=    self.backbone_config.to_dict()
											snake_case_							=    self.__class__.model_type
											return output







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




				def     a							(					self       ):
											snake_case_							=    load_image(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
											    '/sd2-inpaint/init_image.png'       )
											snake_case_							=    load_image(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'       )

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

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

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

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

											# shard inputs and rng
											snake_case_							=    replicate(snake_case       )
											snake_case_							=    jax.random.split(snake_case      ,   jax.device_count()       )
											snake_case_							=    shard(snake_case       )
											snake_case_							=    shard(snake_case       )
											snake_case_							=    shard(snake_case       )

											snake_case_							=    pipeline(
											    snake_case      ,   snake_case      ,   snake_case      ,   snake_case      ,   snake_case      ,   snake_case      ,   jit=snake_case       )

											snake_case_							=    output.images.reshape(snake_case      ,   512      ,   512      ,   3       )

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

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




