



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


A__  :  Any								=					logging.get_logger(__name__)

A__  :  List[str]								=					{
    '''shi-labs/dinat-mini-in1k-224''': '''https://huggingface.co/shi-labs/dinat-mini-in1k-224/resolve/main/config.json''',
    # See all Dinat models at https://huggingface.co/models?filter=dinat
}



class 				__snake_case						(     UpperCamelCase_						,UpperCamelCase_						):
							_a			      = '''dinat'''

							_a			      = {
							    '''num_attention_heads''': '''num_heads''',
							    '''num_hidden_layers''': '''num_layers''',
							}




							def __init__(       self     :       Optional[int]				,				A_     :       Union[str, Any]=4				,				A_     :       Optional[Any]=3				,				A_     :       int=6_4				,				A_     :       Optional[int]=[3, 4, 6, 5]				,				A_     :       str=[2, 4, 8, 1_6]				,				A_     :       List[str]=7				,				A_     :       Any=[[1, 8, 1], [1, 4, 1, 4], [1, 2, 1, 2, 1, 2], [1, 1, 1, 1, 1]]				,				A_     :       Any=3.0				,				A_     :       Optional[Any]=True				,				A_     :       Dict=0.0				,				A_     :       Tuple=0.0				,				A_     :       Tuple=0.1				,				A_     :       List[str]="gelu"				,				A_     :       Optional[int]=0.02				,				A_     :       Dict=1e-5				,				A_     :       List[Any]=0.0				,				A_     :       List[str]=None				,				A_     :       Union[str, Any]=None				,				**A_     :       Optional[int]				,				):
									super().__init__(**A_)

									lowerCAmelCase_			:     Optional[Any]									= patch_size
									lowerCAmelCase_			:     Union[str, Any]									= num_channels
									lowerCAmelCase_			:     List[Any]									= embed_dim
									lowerCAmelCase_			:     Dict									= depths
									lowerCAmelCase_			:     List[Any]									= len(A_)
									lowerCAmelCase_			:     Union[str, Any]									= num_heads
									lowerCAmelCase_			:     Dict									= kernel_size
									lowerCAmelCase_			:     str									= dilations
									lowerCAmelCase_			:     Optional[Any]									= mlp_ratio
									lowerCAmelCase_			:     List[Any]									= qkv_bias
									lowerCAmelCase_			:     int									= hidden_dropout_prob
									lowerCAmelCase_			:     Dict									= attention_probs_dropout_prob
									lowerCAmelCase_			:     int									= drop_path_rate
									lowerCAmelCase_			:     List[str]									= hidden_act
									lowerCAmelCase_			:     List[Any]									= layer_norm_eps
									lowerCAmelCase_			:     Optional[Any]									= initializer_range
									# we set the hidden_size attribute in order to make Dinat work with VisionEncoderDecoderModel
									# this indicates the channel dimension after the last stage of the model
									lowerCAmelCase_			:     Tuple									= int(embed_dim * 2 ** (len(A_) - 1))
									lowerCAmelCase_			:     Union[str, Any]									= layer_scale_init_value
									lowerCAmelCase_			:     Dict									= ['''stem'''] + [F"""stage{idx}""" for idx in range(1				,				len(A_) + 1)]
									lowerCAmelCase_					,  lowerCAmelCase_			:     List[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





