
'''simple docstring'''



# This model implementation is heavily based on:

import gc
import random
import tempfile
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    ControlNetModel,
    DDIMScheduler,
    StableDiffusionControlNetInpaintPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet import MultiControlNetModel
from diffusers.utils import floats_tensor, load_image, load_numpy, randn_tensor, slow, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
    TEXT_TO_IMAGE_IMAGE_PARAMS,
)
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
)


enable_full_determinism()





class      A							(    __UpperCAmelCase						,  __UpperCAmelCase						,  __UpperCAmelCase						,  unittest.TestCase			):
			lowerCamelCase							:							Optional[int]    						=					StableDiffusionControlNetInpaintPipeline
			lowerCamelCase							:							List[str]    						=					TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
			lowerCamelCase							:							List[str]    						=					TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
			lowerCamelCase							:							List[Any]    						=					frozenset({"""control_image"""}			)  # skip `image` and `mask` for now, only test for control_image
			lowerCamelCase							:							Optional[Any]    						=					TEXT_TO_IMAGE_IMAGE_PARAMS



			def A__	(   self    )     ->						List[str]:




									'''simple docstring'''



									torch.manual_seed(0    )
									lowercase__								=    UNetaDConditionModel(
									    block_out_channels=(32, 64)    ,       layers_per_block=2    ,       sample_size=32    ,       in_channels=9    ,       out_channels=4    ,       down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")    ,       up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")    ,       cross_attention_dim=32    ,       )
									torch.manual_seed(0    )
									lowercase__								=    ControlNetModel(
									    block_out_channels=(32, 64)    ,       layers_per_block=2    ,       in_channels=4    ,       down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")    ,       cross_attention_dim=32    ,       conditioning_embedding_out_channels=(16, 32)    ,       )
									torch.manual_seed(0    )
									lowercase__								=    DDIMScheduler(
									    beta_start=0.0_00_85    ,       beta_end=0.0_12    ,       beta_schedule="""scaled_linear"""    ,       clip_sample=lowerCamelCase__    ,       set_alpha_to_one=lowerCamelCase__    ,       )
									torch.manual_seed(0    )
									lowercase__								=    AutoencoderKL(
									    block_out_channels=[32, 64]    ,       in_channels=3    ,       out_channels=3    ,       down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]    ,       up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]    ,       latent_channels=4    ,       )
									torch.manual_seed(0    )
									lowercase__								=    CLIPTextConfig(
									    bos_token_id=0    ,       eos_token_id=2    ,       hidden_size=32    ,       intermediate_size=37    ,       layer_norm_eps=1e-05    ,       num_attention_heads=4    ,       num_hidden_layers=5    ,       pad_token_id=1    ,       vocab_size=1_000    ,       )
									lowercase__								=    CLIPTextModel(lowerCamelCase__    )
									lowercase__								=    CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""    )

									lowercase__								=    {
									    """unet""": unet,
									    """controlnet""": controlnet,
									    """scheduler""": scheduler,
									    """vae""": vae,
									    """text_encoder""": text_encoder,
									    """tokenizer""": tokenizer,
									    """safety_checker""": None,
									    """feature_extractor""": None,
									}
									return components



			def A__	(   self    ,       lowerCamelCase__    ,       lowerCamelCase__=0    )     ->						Dict:




									'''simple docstring'''



									if str(lowerCamelCase__    ).startswith("""mps"""    ):
															lowercase__								=    torch.manual_seed(lowerCamelCase__    )
									else:
															lowercase__								=    torch.Generator(device=lowerCamelCase__    ).manual_seed(lowerCamelCase__    )

									lowercase__								=    2
									lowercase__								=    randn_tensor(
									    (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)    ,       generator=lowerCamelCase__    ,       device=torch.device(lowerCamelCase__    )    ,       )
									lowercase__								=    floats_tensor((1, 3, 32, 32)    ,       rng=random.Random(lowerCamelCase__    )    ).to(lowerCamelCase__    )
									lowercase__								=    init_image.cpu().permute(0    ,       2    ,       3    ,       1    )[0]

									lowercase__								=    Image.fromarray(np.uinta(lowerCamelCase__    )    ).convert("""RGB"""    ).resize((64, 64)    )
									lowercase__								=    Image.fromarray(np.uinta(init_image + 4    )    ).convert("""RGB"""    ).resize((64, 64)    )

									lowercase__								=    {
									    """prompt""": """A painting of a squirrel eating a burger""",
									    """generator""": generator,
									    """num_inference_steps""": 2,
									    """guidance_scale""": 6.0,
									    """output_type""": """numpy""",
									    """image""": image,
									    """mask_image""": mask_image,
									    """control_image""": control_image,
									}

									return inputs



			def A__	(   self    )     ->						Any:




									'''simple docstring'''



									return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3    )



			@unittest.skipIf(
			    torch_device != """cuda""" or not is_xformers_available()    ,       reason="""XFormers attention is only available with CUDA and `xformers` installed"""    ,       )
			def A__	(   self    )     ->						Any:




									'''simple docstring'''



									self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3    )



			def A__	(   self    )     ->						List[Any]:




									'''simple docstring'''



									self._test_inference_batch_single_identical(expected_max_diff=2e-3    )





class      A							(    __UpperCAmelCase			):
			lowerCamelCase							:							str    						=					StableDiffusionControlNetInpaintPipeline
			lowerCamelCase							:							List[Any]    						=					TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
			lowerCamelCase							:							List[str]    						=					TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
			lowerCamelCase							:							List[Any]    						=					frozenset([]			)



			def A__	(   self    )     ->						Tuple:




									'''simple docstring'''



									torch.manual_seed(0    )
									lowercase__								=    UNetaDConditionModel(
									    block_out_channels=(32, 64)    ,       layers_per_block=2    ,       sample_size=32    ,       in_channels=4    ,       out_channels=4    ,       down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")    ,       up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")    ,       cross_attention_dim=32    ,       )
									torch.manual_seed(0    )
									lowercase__								=    ControlNetModel(
									    block_out_channels=(32, 64)    ,       layers_per_block=2    ,       in_channels=4    ,       down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")    ,       cross_attention_dim=32    ,       conditioning_embedding_out_channels=(16, 32)    ,       )
									torch.manual_seed(0    )
									lowercase__								=    DDIMScheduler(
									    beta_start=0.0_00_85    ,       beta_end=0.0_12    ,       beta_schedule="""scaled_linear"""    ,       clip_sample=lowerCamelCase__    ,       set_alpha_to_one=lowerCamelCase__    ,       )
									torch.manual_seed(0    )
									lowercase__								=    AutoencoderKL(
									    block_out_channels=[32, 64]    ,       in_channels=3    ,       out_channels=3    ,       down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]    ,       up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]    ,       latent_channels=4    ,       )
									torch.manual_seed(0    )
									lowercase__								=    CLIPTextConfig(
									    bos_token_id=0    ,       eos_token_id=2    ,       hidden_size=32    ,       intermediate_size=37    ,       layer_norm_eps=1e-05    ,       num_attention_heads=4    ,       num_hidden_layers=5    ,       pad_token_id=1    ,       vocab_size=1_000    ,       )
									lowercase__								=    CLIPTextModel(lowerCamelCase__    )
									lowercase__								=    CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""    )

									lowercase__								=    {
									    """unet""": unet,
									    """controlnet""": controlnet,
									    """scheduler""": scheduler,
									    """vae""": vae,
									    """text_encoder""": text_encoder,
									    """tokenizer""": tokenizer,
									    """safety_checker""": None,
									    """feature_extractor""": None,
									}
									return components





class      A							(    __UpperCAmelCase						,  __UpperCAmelCase						,  unittest.TestCase			):
			lowerCamelCase							:							Dict    						=					StableDiffusionControlNetInpaintPipeline
			lowerCamelCase							:							str    						=					TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
			lowerCamelCase							:							List[str]    						=					TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS



			def A__	(   self    )     ->						Any:




									'''simple docstring'''



									torch.manual_seed(0    )
									lowercase__								=    UNetaDConditionModel(
									    block_out_channels=(32, 64)    ,       layers_per_block=2    ,       sample_size=32    ,       in_channels=9    ,       out_channels=4    ,       down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")    ,       up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")    ,       cross_attention_dim=32    ,       )
									torch.manual_seed(0    )

									def init_weights(lowerCamelCase__    ):
															if isinstance(lowerCamelCase__    ,       torch.nn.Convad    ):
																					torch.nn.init.normal(m.weight    )
																					m.bias.data.fill_(1.0    )

									lowercase__								=    ControlNetModel(
									    block_out_channels=(32, 64)    ,       layers_per_block=2    ,       in_channels=4    ,       down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")    ,       cross_attention_dim=32    ,       conditioning_embedding_out_channels=(16, 32)    ,       )
									controlneta.controlnet_down_blocks.apply(lowerCamelCase__    )

									torch.manual_seed(0    )
									lowercase__								=    ControlNetModel(
									    block_out_channels=(32, 64)    ,       layers_per_block=2    ,       in_channels=4    ,       down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")    ,       cross_attention_dim=32    ,       conditioning_embedding_out_channels=(16, 32)    ,       )
									controlneta.controlnet_down_blocks.apply(lowerCamelCase__    )

									torch.manual_seed(0    )
									lowercase__								=    DDIMScheduler(
									    beta_start=0.0_00_85    ,       beta_end=0.0_12    ,       beta_schedule="""scaled_linear"""    ,       clip_sample=lowerCamelCase__    ,       set_alpha_to_one=lowerCamelCase__    ,       )
									torch.manual_seed(0    )
									lowercase__								=    AutoencoderKL(
									    block_out_channels=[32, 64]    ,       in_channels=3    ,       out_channels=3    ,       down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]    ,       up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]    ,       latent_channels=4    ,       )
									torch.manual_seed(0    )
									lowercase__								=    CLIPTextConfig(
									    bos_token_id=0    ,       eos_token_id=2    ,       hidden_size=32    ,       intermediate_size=37    ,       layer_norm_eps=1e-05    ,       num_attention_heads=4    ,       num_hidden_layers=5    ,       pad_token_id=1    ,       vocab_size=1_000    ,       )
									lowercase__								=    CLIPTextModel(lowerCamelCase__    )
									lowercase__								=    CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""    )

									lowercase__								=    MultiControlNetModel([controlneta, controlneta]    )

									lowercase__								=    {
									    """unet""": unet,
									    """controlnet""": controlnet,
									    """scheduler""": scheduler,
									    """vae""": vae,
									    """text_encoder""": text_encoder,
									    """tokenizer""": tokenizer,
									    """safety_checker""": None,
									    """feature_extractor""": None,
									}
									return components



			def A__	(   self    ,       lowerCamelCase__    ,       lowerCamelCase__=0    )     ->						int:




									'''simple docstring'''



									if str(lowerCamelCase__    ).startswith("""mps"""    ):
															lowercase__								=    torch.manual_seed(lowerCamelCase__    )
									else:
															lowercase__								=    torch.Generator(device=lowerCamelCase__    ).manual_seed(lowerCamelCase__    )

									lowercase__								=    2

									lowercase__								=    [
									    randn_tensor(
									        (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)    ,       generator=lowerCamelCase__    ,       device=torch.device(lowerCamelCase__    )    ,       ),
									    randn_tensor(
									        (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)    ,       generator=lowerCamelCase__    ,       device=torch.device(lowerCamelCase__    )    ,       ),
									]
									lowercase__								=    floats_tensor((1, 3, 32, 32)    ,       rng=random.Random(lowerCamelCase__    )    ).to(lowerCamelCase__    )
									lowercase__								=    init_image.cpu().permute(0    ,       2    ,       3    ,       1    )[0]

									lowercase__								=    Image.fromarray(np.uinta(lowerCamelCase__    )    ).convert("""RGB"""    ).resize((64, 64)    )
									lowercase__								=    Image.fromarray(np.uinta(init_image + 4    )    ).convert("""RGB"""    ).resize((64, 64)    )

									lowercase__								=    {
									    """prompt""": """A painting of a squirrel eating a burger""",
									    """generator""": generator,
									    """num_inference_steps""": 2,
									    """guidance_scale""": 6.0,
									    """output_type""": """numpy""",
									    """image""": image,
									    """mask_image""": mask_image,
									    """control_image""": control_image,
									}

									return inputs



			def A__	(   self    )     ->						List[str]:




									'''simple docstring'''



									lowercase__								=    self.get_dummy_components()
									lowercase__								=    self.pipeline_class(**lowerCamelCase__    )
									pipe.to(lowerCamelCase__    )

									lowercase__								=    10.0
									lowercase__								=    4

									lowercase__								=    self.get_dummy_inputs(lowerCamelCase__    )
									lowercase__								=    steps
									lowercase__								=    scale
									lowercase__								=    pipe(**lowerCamelCase__    )[0]

									lowercase__								=    self.get_dummy_inputs(lowerCamelCase__    )
									lowercase__								=    steps
									lowercase__								=    scale
									lowercase__								=    pipe(**lowerCamelCase__    ,       control_guidance_start=0.1    ,       control_guidance_end=0.2    )[0]

									lowercase__								=    self.get_dummy_inputs(lowerCamelCase__    )
									lowercase__								=    steps
									lowercase__								=    scale
									lowercase__								=    pipe(**lowerCamelCase__    ,       control_guidance_start=[0.1, 0.3]    ,       control_guidance_end=[0.2, 0.7]    )[0]

									lowercase__								=    self.get_dummy_inputs(lowerCamelCase__    )
									lowercase__								=    steps
									lowercase__								=    scale
									lowercase__								=    pipe(**lowerCamelCase__    ,       control_guidance_start=0.4    ,       control_guidance_end=[0.5, 0.8]    )[0]

									# make sure that all outputs are different
									assert np.sum(np.abs(output_a - output_a    )    ) > 1e-3
									assert np.sum(np.abs(output_a - output_a    )    ) > 1e-3
									assert np.sum(np.abs(output_a - output_a    )    ) > 1e-3



			def A__	(   self    )     ->						Any:




									'''simple docstring'''



									return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3    )



			@unittest.skipIf(
			    torch_device != """cuda""" or not is_xformers_available()    ,       reason="""XFormers attention is only available with CUDA and `xformers` installed"""    ,       )
			def A__	(   self    )     ->						Optional[int]:




									'''simple docstring'''



									self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3    )



			def A__	(   self    )     ->						List[str]:




									'''simple docstring'''



									self._test_inference_batch_single_identical(expected_max_diff=2e-3    )



			def A__	(   self    )     ->						List[str]:




									'''simple docstring'''



									lowercase__								=    self.get_dummy_components()
									lowercase__								=    self.pipeline_class(**lowerCamelCase__    )
									pipe.to(lowerCamelCase__    )
									pipe.set_progress_bar_config(disable=lowerCamelCase__    )
									with tempfile.TemporaryDirectory() as tmpdir:
															try:
																					# save_pretrained is not implemented for Multi-ControlNet
																					pipe.save_pretrained(lowerCamelCase__    )
															except NotImplementedError:
																					pass





@slow
@require_torch_gpu
class      A							(    unittest.TestCase			):



			def A__	(   self    )     ->						List[str]:




									'''simple docstring'''



									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()



			def A__	(   self    )     ->						List[str]:




									'''simple docstring'''



									lowercase__								=    ControlNetModel.from_pretrained("""lllyasviel/sd-controlnet-canny"""    )

									lowercase__								=    StableDiffusionControlNetInpaintPipeline.from_pretrained(
									    """runwayml/stable-diffusion-inpainting"""    ,       safety_checker=lowerCamelCase__    ,       controlnet=lowerCamelCase__    )
									pipe.enable_model_cpu_offload()
									pipe.set_progress_bar_config(disable=lowerCamelCase__    )

									lowercase__								=    torch.Generator(device="""cpu"""    ).manual_seed(0    )
									lowercase__								=    load_image(
									    """https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png"""    ).resize((512, 512)    )

									lowercase__								=    load_image(
									    """https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"""
									    """/stable_diffusion_inpaint/input_bench_mask.png"""    ).resize((512, 512)    )

									lowercase__								=    """pitch black hole"""

									lowercase__								=    load_image(
									    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"""    ).resize((512, 512)    )

									lowercase__								=    pipe(
									    lowerCamelCase__    ,       image=lowerCamelCase__    ,       mask_image=lowerCamelCase__    ,       control_image=lowerCamelCase__    ,       generator=lowerCamelCase__    ,       output_type="""np"""    ,       num_inference_steps=3    ,       )

									lowercase__								=    output.images[0]

									assert image.shape == (512, 512, 3)

									lowercase__								=    load_numpy(
									    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/inpaint.npy"""    )

									assert np.abs(expected_image - image    ).max() < 9e-2



			def A__	(   self    )     ->						List[str]:




									'''simple docstring'''



									lowercase__								=    ControlNetModel.from_pretrained("""lllyasviel/control_v11p_sd15_inpaint"""    )

									lowercase__								=    StableDiffusionControlNetInpaintPipeline.from_pretrained(
									    """runwayml/stable-diffusion-v1-5"""    ,       safety_checker=lowerCamelCase__    ,       controlnet=lowerCamelCase__    )
									lowercase__								=    DDIMScheduler.from_config(pipe.scheduler.config    )
									pipe.enable_model_cpu_offload()
									pipe.set_progress_bar_config(disable=lowerCamelCase__    )

									lowercase__								=    torch.Generator(device="""cpu"""    ).manual_seed(33    )

									lowercase__								=    load_image(
									    """https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy.png"""    )
									lowercase__								=    init_image.resize((512, 512)    )

									lowercase__								=    load_image(
									    """https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy_mask.png"""    )
									lowercase__								=    mask_image.resize((512, 512)    )

									lowercase__								=    """a handsome man with ray-ban sunglasses"""

									def make_inpaint_condition(lowerCamelCase__    ,       lowerCamelCase__    ):
															lowercase__								=    np.array(image.convert("""RGB"""    )    ).astype(np.floataa    ) / 2_55.0
															lowercase__								=    np.array(image_mask.convert("""L"""    )    ).astype(np.floataa    ) / 2_55.0

															assert image.shape[0:1] == image_mask.shape[0:1], "image and image_mask must have the same image size"
															lowercase__								=    -1.0  # set as masked pixel
															lowercase__								=    np.expand_dims(lowerCamelCase__    ,       0    ).transpose(0    ,       3    ,       1    ,       2    )
															lowercase__								=    torch.from_numpy(lowerCamelCase__    )
															return image

									lowercase__								=    make_inpaint_condition(lowerCamelCase__    ,       lowerCamelCase__    )

									lowercase__								=    pipe(
									    lowerCamelCase__    ,       image=lowerCamelCase__    ,       mask_image=lowerCamelCase__    ,       control_image=lowerCamelCase__    ,       guidance_scale=9.0    ,       eta=1.0    ,       generator=lowerCamelCase__    ,       num_inference_steps=20    ,       output_type="""np"""    ,       )
									lowercase__								=    output.images[0]

									assert image.shape == (512, 512, 3)

									lowercase__								=    load_numpy(
									    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/boy_ray_ban.npy"""    )

									assert np.abs(expected_image - image    ).max() < 9e-2








'''simple docstring'''



import copy
from typing import Dict

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


__A       =      logging.get_logger(__name__)

__A       =      {
    "google/vit-hybrid-base-bit-384": "https://huggingface.co/vit-hybrid-base-bit-384/resolve/main/config.json",
    # See all ViT hybrid models at https://huggingface.co/models?filter=vit
}





class      A							(    __UpperCAmelCase			):
			lowerCamelCase							:							List[str]    						=					"""vit-hybrid"""



			def __init__(   self    ,       lowerCamelCase__=None    ,       lowerCamelCase__=768    ,       lowerCamelCase__=12    ,       lowerCamelCase__=12    ,       lowerCamelCase__=3_072    ,       lowerCamelCase__="gelu"    ,       lowerCamelCase__=0.0    ,       lowerCamelCase__=0.0    ,       lowerCamelCase__=0.02    ,       lowerCamelCase__=1e-12    ,       lowerCamelCase__=224    ,       lowerCamelCase__=1    ,       lowerCamelCase__=3    ,       lowerCamelCase__=[1, 1_024, 24, 24]    ,       lowerCamelCase__=True    ,       **lowerCamelCase__    ,       )     ->						Optional[Any]:




									'''simple docstring'''



									super().__init__(**lowerCamelCase__    )

									if backbone_config is None:
															logger.info("""`backbone_config` is `None`. Initializing the config with a `BiT` backbone."""    )
															lowercase__								=    {
															    """global_padding""": """same""",
															    """layer_type""": """bottleneck""",
															    """depths""": [3, 4, 9],
															    """out_features""": ["""stage3"""],
															    """embedding_dynamic_padding""": True,
															}

									if isinstance(lowerCamelCase__    ,       lowerCamelCase__    ):
															if "model_type" in backbone_config:
																					lowercase__								=    CONFIG_MAPPING[backbone_config["""model_type"""]]
															else:
																					logger.info(
																					    """`model_type` is not found in `backbone_config`. Use `Bit` as the backbone configuration class."""    )
																					lowercase__								=    BitConfig
															lowercase__								=    backbone_config_class(**lowerCamelCase__    )

									lowercase__								=    backbone_featmap_shape
									lowercase__								=    backbone_config
									lowercase__								=    hidden_size
									lowercase__								=    num_hidden_layers
									lowercase__								=    num_attention_heads
									lowercase__								=    intermediate_size
									lowercase__								=    hidden_act
									lowercase__								=    hidden_dropout_prob
									lowercase__								=    attention_probs_dropout_prob
									lowercase__								=    initializer_range
									lowercase__								=    layer_norm_eps
									lowercase__								=    image_size
									lowercase__								=    patch_size
									lowercase__								=    num_channels
									lowercase__								=    qkv_bias



			def A__	(   self    )     ->						Dict[str, any]:




									'''simple docstring'''



									lowercase__								=    copy.deepcopy(self.__dict__    )
									lowercase__								=    self.backbone_config.to_dict()
									lowercase__								=    self.__class__.model_type
									return output






