



from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


A__  :  int								=					logging.get_logger(__name__)

A__  :  int								=					{
    '''google/bit-50''': '''https://huggingface.co/google/bit-50/resolve/main/config.json''',
}



class 				__snake_case						(     UpperCamelCase_						,UpperCamelCase_						):
							_a			      = '''bit'''
							_a			      = ['''preactivation''', '''bottleneck''']
							_a			      = ['''SAME''', '''VALID''']




							def __init__(       self     :       Any				,				A_     :       Dict=3				,				A_     :       Optional[Any]=6_4				,				A_     :       Any=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]				,				A_     :       Optional[Any]=[3, 4, 6, 3]				,				A_     :       Tuple="preactivation"				,				A_     :       Tuple="relu"				,				A_     :       List[str]=None				,				A_     :       Dict=3_2				,				A_     :       Union[str, Any]=0.0				,				A_     :       Union[str, Any]=False				,				A_     :       Any=3_2				,				A_     :       List[str]=1				,				A_     :       Any=None				,				A_     :       Tuple=None				,				**A_     :       Tuple				,				):
									super().__init__(**A_)
									if layer_type not in self.layer_types:
											raise ValueError(F"""layer_type={layer_type} is not one of {",".join(self.layer_types)}""")
									if global_padding is not None:
											if global_padding.upper() in self.supported_padding:
													lowerCAmelCase_			:     str									= global_padding.upper()
											else:
													raise ValueError(F"""Padding strategy {global_padding} not supported""")
									lowerCAmelCase_			:     str									= num_channels
									lowerCAmelCase_			:     int									= embedding_size
									lowerCAmelCase_			:     Optional[Any]									= hidden_sizes
									lowerCAmelCase_			:     Any									= depths
									lowerCAmelCase_			:     Optional[int]									= layer_type
									lowerCAmelCase_			:     List[str]									= hidden_act
									lowerCAmelCase_			:     Dict									= global_padding
									lowerCAmelCase_			:     int									= num_groups
									lowerCAmelCase_			:     Tuple									= drop_path_rate
									lowerCAmelCase_			:     Dict									= embedding_dynamic_padding
									lowerCAmelCase_			:     Optional[int]									= output_stride
									lowerCAmelCase_			:     Tuple									= width_factor

									lowerCAmelCase_			:     str									= ['''stem'''] + [F"""stage{idx}""" for idx in range(1				,				len(A_) + 1)]
									lowerCAmelCase_					,  lowerCAmelCase_			:     Any									= get_aligned_output_features_output_indices(
									    out_features=A_				,				out_indices=A_				,				stage_names=self.stage_names)










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 				__snake_case						(     unittest.TestCase						):




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







							def    UpperCAmelCase__			(       self     :       List[str]):
									lowerCAmelCase_			:     Any									= load_image(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
									    '''/sd2-inpaint/init_image.png''')
									lowerCAmelCase_			:     str									= load_image(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png''')

									lowerCAmelCase_			:     List[Any]									= '''xvjiarui/stable-diffusion-2-inpainting'''
									lowerCAmelCase_					,  lowerCAmelCase_			:     Optional[Any]									= FlaxStableDiffusionInpaintPipeline.from_pretrained(A_				,				safety_checker=A_)

									lowerCAmelCase_			:     List[str]									= '''Face of a yellow cat, high resolution, sitting on a park bench'''

									lowerCAmelCase_			:     List[Any]									= jax.random.PRNGKey(0)
									lowerCAmelCase_			:     str									= 5_0

									lowerCAmelCase_			:     List[Any]									= jax.device_count()
									lowerCAmelCase_			:     Union[str, Any]									= num_samples * [prompt]
									lowerCAmelCase_			:     str									= num_samples * [init_image]
									lowerCAmelCase_			:     Union[str, Any]									= num_samples * [mask_image]
									lowerCAmelCase_					,  lowerCAmelCase_					,  lowerCAmelCase_			:     Optional[Any]									= pipeline.prepare_inputs(A_				,				A_				,				A_)

									# shard inputs and rng
									lowerCAmelCase_			:     str									= replicate(A_)
									lowerCAmelCase_			:     str									= jax.random.split(A_				,				jax.device_count())
									lowerCAmelCase_			:     List[Any]									= shard(A_)
									lowerCAmelCase_			:     str									= shard(A_)
									lowerCAmelCase_			:     Tuple									= shard(A_)

									lowerCAmelCase_			:     int									= pipeline(
									    A_				,				A_				,				A_				,				A_				,				A_				,				A_				,				jit=A_)

									lowerCAmelCase_			:     Optional[int]									= output.images.reshape(A_				,				5_1_2				,				5_1_2				,				3)

									lowerCAmelCase_			:     List[str]									= images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

									lowerCAmelCase_			:     int									= jnp.asarray(jax.device_get(image_slice.flatten()))
									lowerCAmelCase_			:     str									= 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





