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	""" Vision Transformer (ViT) in PyTorch

	A PyTorch implement of Vision Transformers as described in:

	'An Image Is Worth 16 x 16 Words: Transformers for Image Recognition at Scale'
	- https://arxiv.org/abs/2010.11929

	`How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers`
	- https://arxiv.org/abs/2106.10270

	The official jax code is released and available at https://github.com/google-research/vision_transformer

	DeiT model defs and weights from https://github.com/facebookresearch/deit,
	paper `DeiT: Data-efficient Image Transformers` - https://arxiv.org/abs/2012.12877

	Acknowledgments:
	* The paper authors for releasing code and weights, thanks!
	* I fixed my class token impl based on Phil Wang's https://github.com/lucidrains/vit-pytorch ... check it out
	for some einops/einsum fun
	* Simple transformer style inspired by Andrej Karpathy's https://github.com/karpathy/minGPT
	* Bert reference code checks against Huggingface Transformers and Tensorflow Bert

	Hacked together by / Copyright 2020, Ross Wightman
	"""
	import math
	import logging
	from functools import partial
	from collections import OrderedDict
	from copy import deepcopy

	import torch
	import torch.nn as nn
	import torch.nn.functional as F

	from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD
	from timm.models.helpers import build_model_with_cfg, named_apply, adapt_input_conv
	from timm.models.layers import PatchEmbed, Mlp, DropPath, trunc_normal_, lecun_normal_
	from timm.models.registry import register_model

	_logger = logging.getLogger(__name__)


	def _cfg(url='', **kwargs):
	return {
	'url': url,
	'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': None,
	'crop_pct': .9, 'interpolation': 'bicubic', 'fixed_input_size': True,
	'mean': IMAGENET_INCEPTION_MEAN, 'std': IMAGENET_INCEPTION_STD,
	'first_conv': 'patch_embed.proj', 'classifier': 'head',
	**kwargs
	}


	default_cfgs = {
	# patch models (weights from official Google JAX impl)
	'vit_tiny_patch16_224': _cfg(
	url='https://storage.googleapis.com/vit_models/augreg/'
	'Ti_16-i21k-300ep-lr_0.001-aug_none-wd_0.03-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.03-res_224.npz'),
	'vit_tiny_patch16_384': _cfg(
	url='https://storage.googleapis.com/vit_models/augreg/'
	'Ti_16-i21k-300ep-lr_0.001-aug_none-wd_0.03-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.03-res_384.npz',
	input_size=(3, 384, 384), crop_pct=1.0),
	'vit_small_patch32_224': _cfg(
	url='https://storage.googleapis.com/vit_models/augreg/'
	'S_32-i21k-300ep-lr_0.001-aug_light1-wd_0.03-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.03-res_224.npz'),
	'vit_small_patch32_384': _cfg(
	url='https://storage.googleapis.com/vit_models/augreg/'
	'S_32-i21k-300ep-lr_0.001-aug_light1-wd_0.03-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.03-res_384.npz',
	input_size=(3, 384, 384), crop_pct=1.0),
	'vit_small_patch16_224': _cfg(
	url='https://storage.googleapis.com/vit_models/augreg/'
	'S_16-i21k-300ep-lr_0.001-aug_light1-wd_0.03-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.03-res_224.npz'),
	'vit_small_patch16_384': _cfg(
	url='https://storage.googleapis.com/vit_models/augreg/'
	'S_16-i21k-300ep-lr_0.001-aug_light1-wd_0.03-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.03-res_384.npz',
	input_size=(3, 384, 384), crop_pct=1.0),
	'vit_base_patch32_224': _cfg(
	url='https://storage.googleapis.com/vit_models/augreg/'
	'B_32-i21k-300ep-lr_0.001-aug_medium1-wd_0.03-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.03-res_224.npz'),
	'vit_base_patch32_384': _cfg(
	url='https://storage.googleapis.com/vit_models/augreg/'
	'B_32-i21k-300ep-lr_0.001-aug_light1-wd_0.1-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.03-res_384.npz',
	input_size=(3, 384, 384), crop_pct=1.0),
	'vit_base_patch16_224': _cfg(
	url='https://storage.googleapis.com/vit_models/augreg/'
	'B_16-i21k-300ep-lr_0.001-aug_medium1-wd_0.1-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.01-res_224.npz'),
	'vit_base_patch16_384': _cfg(
	url='https://storage.googleapis.com/vit_models/augreg/'
	'B_16-i21k-300ep-lr_0.001-aug_medium1-wd_0.1-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.01-res_384.npz',
	input_size=(3, 384, 384), crop_pct=1.0),
	'vit_base_patch8_224': _cfg(
	url='https://storage.googleapis.com/vit_models/augreg/'
	'B_8-i21k-300ep-lr_0.001-aug_medium1-wd_0.1-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.01-res_224.npz'),
	'vit_large_patch32_224': _cfg(
	url='', # no official model weights for this combo, only for in21k
	),
	'vit_large_patch32_384': _cfg(
	url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_large_p32_384-9b920ba8.pth',
	input_size=(3, 384, 384), crop_pct=1.0),
	'vit_large_patch16_224': _cfg(
	url='https://storage.googleapis.com/vit_models/augreg/'
	'L_16-i21k-300ep-lr_0.001-aug_medium1-wd_0.1-do_0.1-sd_0.1--imagenet2012-steps_20k-lr_0.01-res_224.npz'),
	'vit_large_patch16_384': _cfg(
	url='https://storage.googleapis.com/vit_models/augreg/'
	'L_16-i21k-300ep-lr_0.001-aug_medium1-wd_0.1-do_0.1-sd_0.1--imagenet2012-steps_20k-lr_0.01-res_384.npz',
	input_size=(3, 384, 384), crop_pct=1.0),

	'vit_huge_patch14_224': _cfg(url=''),
	'vit_giant_patch14_224': _cfg(url=''),
	'vit_gigantic_patch14_224': _cfg(url=''),

	# patch models, imagenet21k (weights from official Google JAX impl)
	'vit_tiny_patch16_224_in21k': _cfg(
	url='https://storage.googleapis.com/vit_models/augreg/Ti_16-i21k-300ep-lr_0.001-aug_none-wd_0.03-do_0.0-sd_0.0.npz',
	num_classes=21843),
	'vit_small_patch32_224_in21k': _cfg(
	url='https://storage.googleapis.com/vit_models/augreg/S_32-i21k-300ep-lr_0.001-aug_light1-wd_0.03-do_0.0-sd_0.0.npz',
	num_classes=21843),
	'vit_small_patch16_224_in21k': _cfg(
	url='https://storage.googleapis.com/vit_models/augreg/S_16-i21k-300ep-lr_0.001-aug_light1-wd_0.03-do_0.0-sd_0.0.npz',
	num_classes=21843),
	'vit_base_patch32_224_in21k': _cfg(
	url='https://storage.googleapis.com/vit_models/augreg/B_32-i21k-300ep-lr_0.001-aug_medium1-wd_0.03-do_0.0-sd_0.0.npz',
	num_classes=21843),
	'vit_base_patch16_224_in21k': _cfg(
	url='https://storage.googleapis.com/vit_models/augreg/B_16-i21k-300ep-lr_0.001-aug_medium1-wd_0.1-do_0.0-sd_0.0.npz',
	num_classes=21843),
	'vit_base_patch8_224_in21k': _cfg(
	url='https://storage.googleapis.com/vit_models/augreg/B_8-i21k-300ep-lr_0.001-aug_medium1-wd_0.1-do_0.0-sd_0.0.npz',
	num_classes=21843),
	'vit_large_patch32_224_in21k': _cfg(
	url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_large_patch32_224_in21k-9046d2e7.pth',
	num_classes=21843),
	'vit_large_patch16_224_in21k': _cfg(
	url='https://storage.googleapis.com/vit_models/augreg/L_16-i21k-300ep-lr_0.001-aug_medium1-wd_0.1-do_0.1-sd_0.1.npz',
	num_classes=21843),
	'vit_huge_patch14_224_in21k': _cfg(
	url='https://storage.googleapis.com/vit_models/imagenet21k/ViT-H_14.npz',
	hf_hub='timm/vit_huge_patch14_224_in21k',
	num_classes=21843),

	# SAM trained models (https://arxiv.org/abs/2106.01548)
	'vit_base_patch32_sam_224': _cfg(
	url='https://storage.googleapis.com/vit_models/sam/ViT-B_32.npz'),
	'vit_base_patch16_sam_224': _cfg(
	url='https://storage.googleapis.com/vit_models/sam/ViT-B_16.npz'),

	# deit models (FB weights)
	'deit_tiny_patch16_224': _cfg(
	url='https://dl.fbaipublicfiles.com/deit/deit_tiny_patch16_224-a1311bcf.pth',
	mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),
	'deit_small_patch16_224': _cfg(
	url='https://dl.fbaipublicfiles.com/deit/deit_small_patch16_224-cd65a155.pth',
	mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),
	'deit_base_patch16_224': _cfg(
	url='https://dl.fbaipublicfiles.com/deit/deit_base_patch16_224-b5f2ef4d.pth',
	mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),
	'deit_base_patch16_384': _cfg(
	url='https://dl.fbaipublicfiles.com/deit/deit_base_patch16_384-8de9b5d1.pth',
	mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD, input_size=(3, 384, 384), crop_pct=1.0),
	'deit_tiny_distilled_patch16_224': _cfg(
	url='https://dl.fbaipublicfiles.com/deit/deit_tiny_distilled_patch16_224-b40b3cf7.pth',
	mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD, classifier=('head', 'head_dist')),
	'deit_small_distilled_patch16_224': _cfg(
	url='https://dl.fbaipublicfiles.com/deit/deit_small_distilled_patch16_224-649709d9.pth',
	mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD, classifier=('head', 'head_dist')),
	'deit_base_distilled_patch16_224': _cfg(
	url='https://dl.fbaipublicfiles.com/deit/deit_base_distilled_patch16_224-df68dfff.pth',
	mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD, classifier=('head', 'head_dist')),
	'deit_base_distilled_patch16_384': _cfg(
	url='https://dl.fbaipublicfiles.com/deit/deit_base_distilled_patch16_384-d0272ac0.pth',
	mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD, input_size=(3, 384, 384), crop_pct=1.0,
	classifier=('head', 'head_dist')),

	# ViT ImageNet-21K-P pretraining by MILL
	'vit_base_patch16_224_miil_in21k': _cfg(
	url='https://miil-public-eu.oss-eu-central-1.aliyuncs.com/model-zoo/ImageNet_21K_P/models/timm/vit_base_patch16_224_in21k_miil.pth',
	mean=(0, 0, 0), std=(1, 1, 1), crop_pct=0.875, interpolation='bilinear', num_classes=11221,
	),
	'vit_base_patch16_224_miil': _cfg(
	url='https://miil-public-eu.oss-eu-central-1.aliyuncs.com/model-zoo/ImageNet_21K_P/models/timm'
	'/vit_base_patch16_224_1k_miil_84_4.pth',
	mean=(0, 0, 0), std=(1, 1, 1), crop_pct=0.875, interpolation='bilinear',
	),
	}


	class Attention(nn.Module):
	def __init__(self, dim, num_heads=8, qkv_bias=False, attn_drop=0., proj_drop=0.):
	super().__init__()
	self.num_heads = num_heads
	head_dim = dim // num_heads
	self.scale = head_dim ** -0.5

	self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
	self.attn_drop = nn.Dropout(attn_drop)
	self.proj = nn.Linear(dim, dim)
	self.proj_drop = nn.Dropout(proj_drop)

	self.attn_gradients = None
	self.attention_map = None

	def save_attn_gradients(self, attn_gradients):
	self.attn_gradients = attn_gradients

	def get_attn_gradients(self):
	return self.attn_gradients

	def save_attention_map(self, attention_map):
	self.attention_map = attention_map

	def get_attention_map(self):
	return self.attention_map

	def forward(self, x, register_hook=False):
	B, N, C = x.shape
	qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
	q, k, v = qkv.unbind(0) # make torchscript happy (cannot use tensor as tuple)

	attn = (q @ k.transpose(-2, -1)) * self.scale
	attn = attn.softmax(dim=-1)
	attn = self.attn_drop(attn)

	self.save_attention_map(attn)
	if register_hook:
	attn.register_hook(self.save_attn_gradients)

	x = (attn @ v).transpose(1, 2).reshape(B, N, C)
	x = self.proj(x)
	x = self.proj_drop(x)
	return x


	class Block(nn.Module):

	def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, drop=0., attn_drop=0.,
	drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm):
	super().__init__()
	self.norm1 = norm_layer(dim)
	self.attn = Attention(dim, num_heads=num_heads, qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop)
	# NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
	self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
	self.norm2 = norm_layer(dim)
	mlp_hidden_dim = int(dim * mlp_ratio)
	self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)

	def forward(self, x, register_hook=False):
	x = x + self.drop_path(self.attn(self.norm1(x), register_hook=register_hook))
	x = x + self.drop_path(self.mlp(self.norm2(x)))
	return x


	class VisionTransformer(nn.Module):
	""" Vision Transformer

	A PyTorch impl of : `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale`
	- https://arxiv.org/abs/2010.11929

	Includes distillation token & head support for `DeiT: Data-efficient Image Transformers`
	- https://arxiv.org/abs/2012.12877
	"""

	def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, embed_dim=768, depth=12,
	num_heads=12, mlp_ratio=4., qkv_bias=True, representation_size=None, distilled=False,
	drop_rate=0., attn_drop_rate=0., drop_path_rate=0., embed_layer=PatchEmbed, norm_layer=None,
	act_layer=None, weight_init=''):
	"""
	Args:
	img_size (int, tuple): input image size
	patch_size (int, tuple): patch size
	in_chans (int): number of input channels
	num_classes (int): number of classes for classification head
	embed_dim (int): embedding dimension
	depth (int): depth of transformer
	num_heads (int): number of attention heads
	mlp_ratio (int): ratio of mlp hidden dim to embedding dim
	qkv_bias (bool): enable bias for qkv if True
	representation_size (Optional[int]): enable and set representation layer (pre-logits) to this value if set
	distilled (bool): model includes a distillation token and head as in DeiT models
	drop_rate (float): dropout rate
	attn_drop_rate (float): attention dropout rate
	drop_path_rate (float): stochastic depth rate
	embed_layer (nn.Module): patch embedding layer
	norm_layer: (nn.Module): normalization layer
	weight_init: (str): weight init scheme
	"""
	super().__init__()
	self.num_classes = num_classes
	self.num_features = self.embed_dim = embed_dim # num_features for consistency with other models
	self.num_tokens = 2 if distilled else 1
	norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6)
	act_layer = act_layer or nn.GELU

	self.patch_embed = embed_layer(
	img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim)
	num_patches = self.patch_embed.num_patches

	self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
	self.dist_token = nn.Parameter(torch.zeros(1, 1, embed_dim)) if distilled else None
	self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + self.num_tokens, embed_dim))
	self.pos_drop = nn.Dropout(p=drop_rate)

	dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule
	self.blocks = nn.ModuleList([Block(
	dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, drop=drop_rate,
	attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer, act_layer=act_layer)
	for i in range(depth)])
	self.norm = norm_layer(embed_dim)

	# Representation layer
	if representation_size and not distilled:
	self.num_features = representation_size
	self.pre_logits = nn.Sequential(OrderedDict([
	('fc', nn.Linear(embed_dim, representation_size)),
	('act', nn.Tanh())
	]))
	else:
	self.pre_logits = nn.Identity()

	# Classifier head(s)
	self.head = nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity()
	self.head_dist = None
	if distilled:
	self.head_dist = nn.Linear(self.embed_dim, self.num_classes) if num_classes > 0 else nn.Identity()

	self.init_weights(weight_init)

	def init_weights(self, mode=''):
	assert mode in ('jax', 'jax_nlhb', 'nlhb', '')
	head_bias = -math.log(self.num_classes) if 'nlhb' in mode else 0.
	trunc_normal_(self.pos_embed, std=.02)
	if self.dist_token is not None:
	trunc_normal_(self.dist_token, std=.02)
	if mode.startswith('jax'):
	# leave cls token as zeros to match jax impl
	named_apply(partial(_init_vit_weights, head_bias=head_bias, jax_impl=True), self)
	else:
	trunc_normal_(self.cls_token, std=.02)
	self.apply(_init_vit_weights)

	def _init_weights(self, m):
	# this fn left here for compat with downstream users
	_init_vit_weights(m)

	@torch.jit.ignore()
	def load_pretrained(self, checkpoint_path, prefix=''):
	_load_weights(self, checkpoint_path, prefix)

	@torch.jit.ignore
	def no_weight_decay(self):
	return {'pos_embed', 'cls_token', 'dist_token'}

	def get_classifier(self):
	if self.dist_token is None:
	return self.head
	else:
	return self.head, self.head_dist

	def reset_classifier(self, num_classes, global_pool=''):
	self.num_classes = num_classes
	self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()
	if self.num_tokens == 2:
	self.head_dist = nn.Linear(self.embed_dim, self.num_classes) if num_classes > 0 else nn.Identity()

	def forward_features(self, x, register_hook=False):
	x = self.patch_embed(x)
	cls_token = self.cls_token.expand(x.shape[0], -1, -1) # stole cls_tokens impl from Phil Wang, thanks
	if self.dist_token is None:
	x = torch.cat((cls_token, x), dim=1)
	else:
	x = torch.cat((cls_token, self.dist_token.expand(x.shape[0], -1, -1), x), dim=1)
	x = self.pos_drop(x + self.pos_embed)
	# x = self.blocks(x)
	for blk in self.blocks:
	x = blk(x, register_hook=register_hook)
	x = self.norm(x)
	if self.dist_token is None:
	return self.pre_logits(x[:, 0])
	else:
	return x[:, 0], x[:, 1]

	def forward(self, x, register_hook=False):
	x = self.forward_features(x, register_hook=register_hook)
	if self.head_dist is not None:
	x, x_dist = self.head(x[0]), self.head_dist(x[1]) # x must be a tuple
	if self.training and not torch.jit.is_scripting():
	# during inference, return the average of both classifier predictions
	return x, x_dist
	else:
	return (x + x_dist) / 2
	else:
	x = self.head(x)
	return x


	def _init_vit_weights(module: nn.Module, name: str = '', head_bias: float = 0., jax_impl: bool = False):
	""" ViT weight initialization
	* When called without n, head_bias, jax_impl args it will behave exactly the same
	as my original init for compatibility with prev hparam / downstream use cases (ie DeiT).
	* When called w/ valid n (module name) and jax_impl=True, will (hopefully) match JAX impl
	"""
	if isinstance(module, nn.Linear):
	if name.startswith('head'):
	nn.init.zeros_(module.weight)
	nn.init.constant_(module.bias, head_bias)
	elif name.startswith('pre_logits'):
	lecun_normal_(module.weight)
	nn.init.zeros_(module.bias)
	else:
	if jax_impl:
	nn.init.xavier_uniform_(module.weight)
	if module.bias is not None:
	if 'mlp' in name:
	nn.init.normal_(module.bias, std=1e-6)
	else:
	nn.init.zeros_(module.bias)
	else:
	trunc_normal_(module.weight, std=.02)
	if module.bias is not None:
	nn.init.zeros_(module.bias)
	elif jax_impl and isinstance(module, nn.Conv2d):
	# NOTE conv was left to pytorch default in my original init
	lecun_normal_(module.weight)
	if module.bias is not None:
	nn.init.zeros_(module.bias)
	elif isinstance(module, (nn.LayerNorm, nn.GroupNorm, nn.BatchNorm2d)):
	nn.init.zeros_(module.bias)
	nn.init.ones_(module.weight)


	@torch.no_grad()
	def _load_weights(model: VisionTransformer, checkpoint_path: str, prefix: str = ''):
	""" Load weights from .npz checkpoints for official Google Brain Flax implementation
	"""
	import numpy as np

	def _n2p(w, t=True):
	if w.ndim == 4 and w.shape[0] == w.shape[1] == w.shape[2] == 1:
	w = w.flatten()
	if t:
	if w.ndim == 4:
	w = w.transpose([3, 2, 0, 1])
	elif w.ndim == 3:
	w = w.transpose([2, 0, 1])
	elif w.ndim == 2:
	w = w.transpose([1, 0])
	return torch.from_numpy(w)

	w = np.load(checkpoint_path)
	if not prefix and 'opt/target/embedding/kernel' in w:
	prefix = 'opt/target/'

	if hasattr(model.patch_embed, 'backbone'):
	# hybrid
	backbone = model.patch_embed.backbone
	stem_only = not hasattr(backbone, 'stem')
	stem = backbone if stem_only else backbone.stem
	stem.conv.weight.copy_(adapt_input_conv(stem.conv.weight.shape[1], _n2p(w[f'{prefix}conv_root/kernel'])))
	stem.norm.weight.copy_(_n2p(w[f'{prefix}gn_root/scale']))
	stem.norm.bias.copy_(_n2p(w[f'{prefix}gn_root/bias']))
	if not stem_only:
	for i, stage in enumerate(backbone.stages):
	for j, block in enumerate(stage.blocks):
	bp = f'{prefix}block{i + 1}/unit{j + 1}/'
	for r in range(3):
	getattr(block, f'conv{r + 1}').weight.copy_(_n2p(w[f'{bp}conv{r + 1}/kernel']))
	getattr(block, f'norm{r + 1}').weight.copy_(_n2p(w[f'{bp}gn{r + 1}/scale']))
	getattr(block, f'norm{r + 1}').bias.copy_(_n2p(w[f'{bp}gn{r + 1}/bias']))
	if block.downsample is not None:
	block.downsample.conv.weight.copy_(_n2p(w[f'{bp}conv_proj/kernel']))
	block.downsample.norm.weight.copy_(_n2p(w[f'{bp}gn_proj/scale']))
	block.downsample.norm.bias.copy_(_n2p(w[f'{bp}gn_proj/bias']))
	embed_conv_w = _n2p(w[f'{prefix}embedding/kernel'])
	else:
	embed_conv_w = adapt_input_conv(
	model.patch_embed.proj.weight.shape[1], _n2p(w[f'{prefix}embedding/kernel']))
	model.patch_embed.proj.weight.copy_(embed_conv_w)
	model.patch_embed.proj.bias.copy_(_n2p(w[f'{prefix}embedding/bias']))
	model.cls_token.copy_(_n2p(w[f'{prefix}cls'], t=False))
	pos_embed_w = _n2p(w[f'{prefix}Transformer/posembed_input/pos_embedding'], t=False)
	if pos_embed_w.shape != model.pos_embed.shape:
	pos_embed_w = resize_pos_embed( # resize pos embedding when different size from pretrained weights
	pos_embed_w, model.pos_embed, getattr(model, 'num_tokens', 1), model.patch_embed.grid_size)
	model.pos_embed.copy_(pos_embed_w)
	model.norm.weight.copy_(_n2p(w[f'{prefix}Transformer/encoder_norm/scale']))
	model.norm.bias.copy_(_n2p(w[f'{prefix}Transformer/encoder_norm/bias']))
	if isinstance(model.head, nn.Linear) and model.head.bias.shape[0] == w[f'{prefix}head/bias'].shape[-1]:
	model.head.weight.copy_(_n2p(w[f'{prefix}head/kernel']))
	model.head.bias.copy_(_n2p(w[f'{prefix}head/bias']))
	if isinstance(getattr(model.pre_logits, 'fc', None), nn.Linear) and f'{prefix}pre_logits/bias' in w:
	model.pre_logits.fc.weight.copy_(_n2p(w[f'{prefix}pre_logits/kernel']))
	model.pre_logits.fc.bias.copy_(_n2p(w[f'{prefix}pre_logits/bias']))
	for i, block in enumerate(model.blocks.children()):
	block_prefix = f'{prefix}Transformer/encoderblock_{i}/'
	mha_prefix = block_prefix + 'MultiHeadDotProductAttention_1/'
	block.norm1.weight.copy_(_n2p(w[f'{block_prefix}LayerNorm_0/scale']))
	block.norm1.bias.copy_(_n2p(w[f'{block_prefix}LayerNorm_0/bias']))
	block.attn.qkv.weight.copy_(torch.cat([
	_n2p(w[f'{mha_prefix}{n}/kernel'], t=False).flatten(1).T for n in ('query', 'key', 'value')]))
	block.attn.qkv.bias.copy_(torch.cat([
	_n2p(w[f'{mha_prefix}{n}/bias'], t=False).reshape(-1) for n in ('query', 'key', 'value')]))
	block.attn.proj.weight.copy_(_n2p(w[f'{mha_prefix}out/kernel']).flatten(1))
	block.attn.proj.bias.copy_(_n2p(w[f'{mha_prefix}out/bias']))
	for r in range(2):
	getattr(block.mlp, f'fc{r + 1}').weight.copy_(_n2p(w[f'{block_prefix}MlpBlock_3/Dense_{r}/kernel']))
	getattr(block.mlp, f'fc{r + 1}').bias.copy_(_n2p(w[f'{block_prefix}MlpBlock_3/Dense_{r}/bias']))
	block.norm2.weight.copy_(_n2p(w[f'{block_prefix}LayerNorm_2/scale']))
	block.norm2.bias.copy_(_n2p(w[f'{block_prefix}LayerNorm_2/bias']))


	def resize_pos_embed(posemb, posemb_new, num_tokens=1, gs_new=()):
	# Rescale the grid of position embeddings when loading from state_dict. Adapted from
	# https://github.com/google-research/vision_transformer/blob/00883dd691c63a6830751563748663526e811cee/vit_jax/checkpoint.py#L224
	_logger.info('Resized position embedding: %s to %s', posemb.shape, posemb_new.shape)
	ntok_new = posemb_new.shape[1]
	if num_tokens:
	posemb_tok, posemb_grid = posemb[:, :num_tokens], posemb[0, num_tokens:]
	ntok_new -= num_tokens
	else:
	posemb_tok, posemb_grid = posemb[:, :0], posemb[0]
	gs_old = int(math.sqrt(len(posemb_grid)))
	if not len(gs_new): # backwards compatibility
	gs_new = [int(math.sqrt(ntok_new))] * 2
	assert len(gs_new) >= 2
	_logger.info('Position embedding grid-size from %s to %s', [gs_old, gs_old], gs_new)
	posemb_grid = posemb_grid.reshape(1, gs_old, gs_old, -1).permute(0, 3, 1, 2)
	posemb_grid = F.interpolate(posemb_grid, size=gs_new, mode='bicubic', align_corners=False)
	posemb_grid = posemb_grid.permute(0, 2, 3, 1).reshape(1, gs_new[0] * gs_new[1], -1)
	posemb = torch.cat([posemb_tok, posemb_grid], dim=1)
	return posemb


	def checkpoint_filter_fn(state_dict, model):
	""" convert patch embedding weight from manual patchify + linear proj to conv"""
	out_dict = {}
	if 'model' in state_dict:
	# For deit models
	state_dict = state_dict['model']
	for k, v in state_dict.items():
	if 'patch_embed.proj.weight' in k and len(v.shape) < 4:
	# For old models that I trained prior to conv based patchification
	O, I, H, W = model.patch_embed.proj.weight.shape
	v = v.reshape(O, -1, H, W)
	elif k == 'pos_embed' and v.shape != model.pos_embed.shape:
	# To resize pos embedding when using model at different size from pretrained weights
	v = resize_pos_embed(
	v, model.pos_embed, getattr(model, 'num_tokens', 1), model.patch_embed.grid_size)
	out_dict[k] = v
	return out_dict


	def _create_vision_transformer(variant, pretrained=False, default_cfg=None, **kwargs):
	default_cfg = default_cfg or default_cfgs[variant]
	if kwargs.get('features_only', None):
	raise RuntimeError('features_only not implemented for Vision Transformer models.')

	# NOTE this extra code to support handling of repr size for in21k pretrained models
	default_num_classes = default_cfg['num_classes']
	num_classes = kwargs.get('num_classes', default_num_classes)
	repr_size = kwargs.pop('representation_size', None)
	if repr_size is not None and num_classes != default_num_classes:
	# Remove representation layer if fine-tuning. This may not always be the desired action,
	# but I feel better than doing nothing by default for fine-tuning. Perhaps a better interface?
	_logger.warning("Removing representation layer for fine-tuning.")
	repr_size = None

	model = build_model_with_cfg(
	VisionTransformer, variant, pretrained,
	default_cfg=default_cfg,
	representation_size=repr_size,
	pretrained_filter_fn=checkpoint_filter_fn,
	pretrained_custom_load='npz' in default_cfg['url'],
	**kwargs)
	return model


	@register_model
	def vit_tiny_patch16_224(pretrained=False, **kwargs):
	""" ViT-Tiny (Vit-Ti/16)
	"""
	model_kwargs = dict(patch_size=16, embed_dim=192, depth=12, num_heads=3, **kwargs)
	model = _create_vision_transformer('vit_tiny_patch16_224', pretrained=pretrained, **model_kwargs)
	return model


	@register_model
	def vit_tiny_patch16_384(pretrained=False, **kwargs):
	""" ViT-Tiny (Vit-Ti/16) @ 384x384.
	"""
	model_kwargs = dict(patch_size=16, embed_dim=192, depth=12, num_heads=3, **kwargs)
	model = _create_vision_transformer('vit_tiny_patch16_384', pretrained=pretrained, **model_kwargs)
	return model


	@register_model
	def vit_small_patch32_224(pretrained=False, **kwargs):
	""" ViT-Small (ViT-S/32)
	"""
	model_kwargs = dict(patch_size=32, embed_dim=384, depth=12, num_heads=6, **kwargs)
	model = _create_vision_transformer('vit_small_patch32_224', pretrained=pretrained, **model_kwargs)
	return model


	@register_model
	def vit_small_patch32_384(pretrained=False, **kwargs):
	""" ViT-Small (ViT-S/32) at 384x384.
	"""
	model_kwargs = dict(patch_size=32, embed_dim=384, depth=12, num_heads=6, **kwargs)
	model = _create_vision_transformer('vit_small_patch32_384', pretrained=pretrained, **model_kwargs)
	return model


	@register_model
	def vit_small_patch16_224(pretrained=False, **kwargs):
	""" ViT-Small (ViT-S/16)
	NOTE I've replaced my previous 'small' model definition and weights with the small variant from the DeiT paper
	"""
	model_kwargs = dict(patch_size=16, embed_dim=384, depth=12, num_heads=6, **kwargs)
	model = _create_vision_transformer('vit_small_patch16_224', pretrained=pretrained, **model_kwargs)
	return model


	@register_model
	def vit_small_patch16_384(pretrained=False, **kwargs):
	""" ViT-Small (ViT-S/16)
	NOTE I've replaced my previous 'small' model definition and weights with the small variant from the DeiT paper
	"""
	model_kwargs = dict(patch_size=16, embed_dim=384, depth=12, num_heads=6, **kwargs)
	model = _create_vision_transformer('vit_small_patch16_384', pretrained=pretrained, **model_kwargs)
	return model


	@register_model
	def vit_base_patch32_224(pretrained=False, **kwargs):
	""" ViT-Base (ViT-B/32) from original paper (https://arxiv.org/abs/2010.11929).
	ImageNet-1k weights fine-tuned from in21k, source https://github.com/google-research/vision_transformer.
	"""
	model_kwargs = dict(patch_size=32, embed_dim=768, depth=12, num_heads=12, **kwargs)
	model = _create_vision_transformer('vit_base_patch32_224', pretrained=pretrained, **model_kwargs)
	return model


	@register_model
	def vit_base_patch32_384(pretrained=False, **kwargs):
	""" ViT-Base model (ViT-B/32) from original paper (https://arxiv.org/abs/2010.11929).
	ImageNet-1k weights fine-tuned from in21k @ 384x384, source https://github.com/google-research/vision_transformer.
	"""
	model_kwargs = dict(patch_size=32, embed_dim=768, depth=12, num_heads=12, **kwargs)
	model = _create_vision_transformer('vit_base_patch32_384', pretrained=pretrained, **model_kwargs)
	return model


	@register_model
	def vit_base_patch16_224(pretrained=False, **kwargs):
	""" ViT-Base (ViT-B/16) from original paper (https://arxiv.org/abs/2010.11929).
	ImageNet-1k weights fine-tuned from in21k @ 224x224, source https://github.com/google-research/vision_transformer.
	"""
	model_kwargs = dict(patch_size=16, embed_dim=768, depth=12, num_heads=12, **kwargs)
	model = _create_vision_transformer('vit_base_patch16_224', pretrained=pretrained, **model_kwargs)
	return model


	@register_model
	def vit_base_patch16_384(pretrained=False, **kwargs):
	""" ViT-Base model (ViT-B/16) from original paper (https://arxiv.org/abs/2010.11929).
	ImageNet-1k weights fine-tuned from in21k @ 384x384, source https://github.com/google-research/vision_transformer.
	"""
	model_kwargs = dict(patch_size=16, embed_dim=768, depth=12, num_heads=12, **kwargs)
	model = _create_vision_transformer('vit_base_patch16_384', pretrained=pretrained, **model_kwargs)
	return model


	@register_model
	def vit_base_patch8_224(pretrained=False, **kwargs):
	""" ViT-Base (ViT-B/8) from original paper (https://arxiv.org/abs/2010.11929).
	ImageNet-1k weights fine-tuned from in21k @ 224x224, source https://github.com/google-research/vision_transformer.
	"""
	model_kwargs = dict(patch_size=8, embed_dim=768, depth=12, num_heads=12, **kwargs)
	model = _create_vision_transformer('vit_base_patch8_224', pretrained=pretrained, **model_kwargs)
	return model


	@register_model
	def vit_large_patch32_224(pretrained=False, **kwargs):
	""" ViT-Large model (ViT-L/32) from original paper (https://arxiv.org/abs/2010.11929). No pretrained weights.
	"""
	model_kwargs = dict(patch_size=32, embed_dim=1024, depth=24, num_heads=16, **kwargs)
	model = _create_vision_transformer('vit_large_patch32_224', pretrained=pretrained, **model_kwargs)
	return model


	@register_model
	def vit_large_patch32_384(pretrained=False, **kwargs):
	""" ViT-Large model (ViT-L/32) from original paper (https://arxiv.org/abs/2010.11929).
	ImageNet-1k weights fine-tuned from in21k @ 384x384, source https://github.com/google-research/vision_transformer.
	"""
	model_kwargs = dict(patch_size=32, embed_dim=1024, depth=24, num_heads=16, **kwargs)
	model = _create_vision_transformer('vit_large_patch32_384', pretrained=pretrained, **model_kwargs)
	return model


	@register_model
	def vit_large_patch16_224(pretrained=False, **kwargs):
	""" ViT-Large model (ViT-L/32) from original paper (https://arxiv.org/abs/2010.11929).
	ImageNet-1k weights fine-tuned from in21k @ 224x224, source https://github.com/google-research/vision_transformer.
	"""
	model_kwargs = dict(patch_size=16, embed_dim=1024, depth=24, num_heads=16, **kwargs)
	model = _create_vision_transformer('vit_large_patch16_224', pretrained=pretrained, **model_kwargs)
	return model


	@register_model
	def vit_large_patch16_384(pretrained=False, **kwargs):
	""" ViT-Large model (ViT-L/16) from original paper (https://arxiv.org/abs/2010.11929).
	ImageNet-1k weights fine-tuned from in21k @ 384x384, source https://github.com/google-research/vision_transformer.
	"""
	model_kwargs = dict(patch_size=16, embed_dim=1024, depth=24, num_heads=16, **kwargs)
	model = _create_vision_transformer('vit_large_patch16_384', pretrained=pretrained, **model_kwargs)
	return model


	@register_model
	def vit_base_patch16_sam_224(pretrained=False, **kwargs):
	""" ViT-Base (ViT-B/16) w/ SAM pretrained weights. Paper: https://arxiv.org/abs/2106.01548
	"""
	# NOTE original SAM weights release worked with representation_size=768
	model_kwargs = dict(patch_size=16, embed_dim=768, depth=12, num_heads=12, representation_size=0, **kwargs)
	model = _create_vision_transformer('vit_base_patch16_sam_224', pretrained=pretrained, **model_kwargs)
	return model


	@register_model
	def vit_base_patch32_sam_224(pretrained=False, **kwargs):
	""" ViT-Base (ViT-B/32) w/ SAM pretrained weights. Paper: https://arxiv.org/abs/2106.01548
	"""
	# NOTE original SAM weights release worked with representation_size=768
	model_kwargs = dict(patch_size=32, embed_dim=768, depth=12, num_heads=12, representation_size=0, **kwargs)
	model = _create_vision_transformer('vit_base_patch32_sam_224', pretrained=pretrained, **model_kwargs)
	return model


	@register_model
	def vit_huge_patch14_224(pretrained=False, **kwargs):
	""" ViT-Huge model (ViT-H/14) from original paper (https://arxiv.org/abs/2010.11929).
	"""
	model_kwargs = dict(patch_size=14, embed_dim=1280, depth=32, num_heads=16, **kwargs)
	model = _create_vision_transformer('vit_huge_patch14_224', pretrained=pretrained, **model_kwargs)
	return model


	@register_model
	def vit_giant_patch14_224(pretrained=False, **kwargs):
	""" ViT-Giant model (ViT-g/14) from `Scaling Vision Transformers` - https://arxiv.org/abs/2106.04560
	"""
	model_kwargs = dict(patch_size=14, embed_dim=1408, mlp_ratio=48/11, depth=40, num_heads=16, **kwargs)
	model = _create_vision_transformer('vit_giant_patch14_224', pretrained=pretrained, **model_kwargs)
	return model


	@register_model
	def vit_gigantic_patch14_224(pretrained=False, **kwargs):
	""" ViT-Gigantic model (ViT-G/14) from `Scaling Vision Transformers` - https://arxiv.org/abs/2106.04560
	"""
	model_kwargs = dict(patch_size=14, embed_dim=1664, mlp_ratio=64/13, depth=48, num_heads=16, **kwargs)
	model = _create_vision_transformer('vit_gigantic_patch14_224', pretrained=pretrained, **model_kwargs)
	return model


	@register_model
	def vit_tiny_patch16_224_in21k(pretrained=False, **kwargs):
	""" ViT-Tiny (Vit-Ti/16).
	ImageNet-21k weights @ 224x224, source https://github.com/google-research/vision_transformer.
	NOTE: this model has valid 21k classifier head and no representation (pre-logits) layer
	"""
	model_kwargs = dict(patch_size=16, embed_dim=192, depth=12, num_heads=3, **kwargs)
	model = _create_vision_transformer('vit_tiny_patch16_224_in21k', pretrained=pretrained, **model_kwargs)
	return model


	@register_model
	def vit_small_patch32_224_in21k(pretrained=False, **kwargs):
	""" ViT-Small (ViT-S/16)
	ImageNet-21k weights @ 224x224, source https://github.com/google-research/vision_transformer.
	NOTE: this model has valid 21k classifier head and no representation (pre-logits) layer
	"""
	model_kwargs = dict(patch_size=32, embed_dim=384, depth=12, num_heads=6, **kwargs)
	model = _create_vision_transformer('vit_small_patch32_224_in21k', pretrained=pretrained, **model_kwargs)
	return model


	@register_model
	def vit_small_patch16_224_in21k(pretrained=False, **kwargs):
	""" ViT-Small (ViT-S/16)
	ImageNet-21k weights @ 224x224, source https://github.com/google-research/vision_transformer.
	NOTE: this model has valid 21k classifier head and no representation (pre-logits) layer
	"""
	model_kwargs = dict(patch_size=16, embed_dim=384, depth=12, num_heads=6, **kwargs)
	model = _create_vision_transformer('vit_small_patch16_224_in21k', pretrained=pretrained, **model_kwargs)
	return model


	@register_model
	def vit_base_patch32_224_in21k(pretrained=False, **kwargs):
	""" ViT-Base model (ViT-B/32) from original paper (https://arxiv.org/abs/2010.11929).
	ImageNet-21k weights @ 224x224, source https://github.com/google-research/vision_transformer.
	NOTE: this model has valid 21k classifier head and no representation (pre-logits) layer
	"""
	model_kwargs = dict(
	patch_size=32, embed_dim=768, depth=12, num_heads=12, **kwargs)
	model = _create_vision_transformer('vit_base_patch32_224_in21k', pretrained=pretrained, **model_kwargs)
	return model


	@register_model
	def vit_base_patch16_224_in21k(pretrained=False, **kwargs):
	""" ViT-Base model (ViT-B/16) from original paper (https://arxiv.org/abs/2010.11929).
	ImageNet-21k weights @ 224x224, source https://github.com/google-research/vision_transformer.
	NOTE: this model has valid 21k classifier head and no representation (pre-logits) layer
	"""
	model_kwargs = dict(
	patch_size=16, embed_dim=768, depth=12, num_heads=12, **kwargs)
	model = _create_vision_transformer('vit_base_patch16_224_in21k', pretrained=pretrained, **model_kwargs)
	return model


	@register_model
	def vit_base_patch8_224_in21k(pretrained=False, **kwargs):
	""" ViT-Base model (ViT-B/8) from original paper (https://arxiv.org/abs/2010.11929).
	ImageNet-21k weights @ 224x224, source https://github.com/google-research/vision_transformer.
	NOTE: this model has valid 21k classifier head and no representation (pre-logits) layer
	"""
	model_kwargs = dict(
	patch_size=8, embed_dim=768, depth=12, num_heads=12, **kwargs)
	model = _create_vision_transformer('vit_base_patch8_224_in21k', pretrained=pretrained, **model_kwargs)
	return model


	@register_model
	def vit_large_patch32_224_in21k(pretrained=False, **kwargs):
	""" ViT-Large model (ViT-L/32) from original paper (https://arxiv.org/abs/2010.11929).
	ImageNet-21k weights @ 224x224, source https://github.com/google-research/vision_transformer.
	NOTE: this model has a representation layer but the 21k classifier head is zero'd out in original weights
	"""
	model_kwargs = dict(
	patch_size=32, embed_dim=1024, depth=24, num_heads=16, representation_size=1024, **kwargs)
	model = _create_vision_transformer('vit_large_patch32_224_in21k', pretrained=pretrained, **model_kwargs)
	return model


	@register_model
	def vit_large_patch16_224_in21k(pretrained=False, **kwargs):
	""" ViT-Large model (ViT-L/16) from original paper (https://arxiv.org/abs/2010.11929).
	ImageNet-21k weights @ 224x224, source https://github.com/google-research/vision_transformer.
	NOTE: this model has valid 21k classifier head and no representation (pre-logits) layer
	"""
	model_kwargs = dict(
	patch_size=16, embed_dim=1024, depth=24, num_heads=16, **kwargs)
	model = _create_vision_transformer('vit_large_patch16_224_in21k', pretrained=pretrained, **model_kwargs)
	return model


	@register_model
	def vit_huge_patch14_224_in21k(pretrained=False, **kwargs):
	""" ViT-Huge model (ViT-H/14) from original paper (https://arxiv.org/abs/2010.11929).
	ImageNet-21k weights @ 224x224, source https://github.com/google-research/vision_transformer.
	NOTE: this model has a representation layer but the 21k classifier head is zero'd out in original weights
	"""
	model_kwargs = dict(
	patch_size=14, embed_dim=1280, depth=32, num_heads=16, representation_size=1280, **kwargs)
	model = _create_vision_transformer('vit_huge_patch14_224_in21k', pretrained=pretrained, **model_kwargs)
	return model


	@register_model
	def deit_tiny_patch16_224(pretrained=False, **kwargs):
	""" DeiT-tiny model @ 224x224 from paper (https://arxiv.org/abs/2012.12877).
	ImageNet-1k weights from https://github.com/facebookresearch/deit.
	"""
	model_kwargs = dict(patch_size=16, embed_dim=192, depth=12, num_heads=3, **kwargs)
	model = _create_vision_transformer('deit_tiny_patch16_224', pretrained=pretrained, **model_kwargs)
	return model


	@register_model
	def deit_small_patch16_224(pretrained=False, **kwargs):
	""" DeiT-small model @ 224x224 from paper (https://arxiv.org/abs/2012.12877).
	ImageNet-1k weights from https://github.com/facebookresearch/deit.
	"""
	model_kwargs = dict(patch_size=16, embed_dim=384, depth=12, num_heads=6, **kwargs)
	model = _create_vision_transformer('deit_small_patch16_224', pretrained=pretrained, **model_kwargs)
	return model


	@register_model
	def deit_base_patch16_224(pretrained=False, **kwargs):
	""" DeiT base model @ 224x224 from paper (https://arxiv.org/abs/2012.12877).
	ImageNet-1k weights from https://github.com/facebookresearch/deit.
	"""
	model_kwargs = dict(patch_size=16, embed_dim=768, depth=12, num_heads=12, **kwargs)
	model = _create_vision_transformer('deit_base_patch16_224', pretrained=pretrained, **model_kwargs)
	return model


	@register_model
	def deit_base_patch16_384(pretrained=False, **kwargs):
	""" DeiT base model @ 384x384 from paper (https://arxiv.org/abs/2012.12877).
	ImageNet-1k weights from https://github.com/facebookresearch/deit.
	"""
	model_kwargs = dict(patch_size=16, embed_dim=768, depth=12, num_heads=12, **kwargs)
	model = _create_vision_transformer('deit_base_patch16_384', pretrained=pretrained, **model_kwargs)
	return model


	@register_model
	def deit_tiny_distilled_patch16_224(pretrained=False, **kwargs):
	""" DeiT-tiny distilled model @ 224x224 from paper (https://arxiv.org/abs/2012.12877).
	ImageNet-1k weights from https://github.com/facebookresearch/deit.
	"""
	model_kwargs = dict(patch_size=16, embed_dim=192, depth=12, num_heads=3, **kwargs)
	model = _create_vision_transformer(
	'deit_tiny_distilled_patch16_224', pretrained=pretrained, distilled=True, **model_kwargs)
	return model


	@register_model
	def deit_small_distilled_patch16_224(pretrained=False, **kwargs):
	""" DeiT-small distilled model @ 224x224 from paper (https://arxiv.org/abs/2012.12877).
	ImageNet-1k weights from https://github.com/facebookresearch/deit.
	"""
	model_kwargs = dict(patch_size=16, embed_dim=384, depth=12, num_heads=6, **kwargs)
	model = _create_vision_transformer(
	'deit_small_distilled_patch16_224', pretrained=pretrained, distilled=True, **model_kwargs)
	return model


	@register_model
	def deit_base_distilled_patch16_224(pretrained=False, **kwargs):
	""" DeiT-base distilled model @ 224x224 from paper (https://arxiv.org/abs/2012.12877).
	ImageNet-1k weights from https://github.com/facebookresearch/deit.
	"""
	model_kwargs = dict(patch_size=16, embed_dim=768, depth=12, num_heads=12, **kwargs)
	model = _create_vision_transformer(
	'deit_base_distilled_patch16_224', pretrained=pretrained, distilled=True, **model_kwargs)
	return model


	@register_model
	def deit_base_distilled_patch16_384(pretrained=False, **kwargs):
	""" DeiT-base distilled model @ 384x384 from paper (https://arxiv.org/abs/2012.12877).
	ImageNet-1k weights from https://github.com/facebookresearch/deit.
	"""
	model_kwargs = dict(patch_size=16, embed_dim=768, depth=12, num_heads=12, **kwargs)
	model = _create_vision_transformer(
	'deit_base_distilled_patch16_384', pretrained=pretrained, distilled=True, **model_kwargs)
	return model


	@register_model
	def vit_base_patch16_224_miil_in21k(pretrained=False, **kwargs):
	""" ViT-Base (ViT-B/16) from original paper (https://arxiv.org/abs/2010.11929).
	Weights taken from: https://github.com/Alibaba-MIIL/ImageNet21K
	"""
	model_kwargs = dict(patch_size=16, embed_dim=768, depth=12, num_heads=12, qkv_bias=False, **kwargs)
	model = _create_vision_transformer('vit_base_patch16_224_miil_in21k', pretrained=pretrained, **model_kwargs)
	return model


	@register_model
	def vit_base_patch16_224_miil(pretrained=False, **kwargs):
	""" ViT-Base (ViT-B/16) from original paper (https://arxiv.org/abs/2010.11929).
	Weights taken from: https://github.com/Alibaba-MIIL/ImageNet21K
	"""
	model_kwargs = dict(patch_size=16, embed_dim=768, depth=12, num_heads=12, qkv_bias=False, **kwargs)
	model = _create_vision_transformer('vit_base_patch16_224_miil', pretrained=pretrained, **model_kwargs)
	return model