text: """ 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
  
  The official jax code is released and available at https://github.com/google-research/vision_transformer
  
  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
  
  DeiT model defs and weights from https://github.com/facebookresearch/deit,
  paper `DeiT: Data-efficient Image Transformers` - https://arxiv.org/abs/2012.12877
  
  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
  from .helpers import build_model_with_cfg, overlay_external_default_cfg
  from .layers import StdConv2dSame, DropPath, to_2tuple, trunc_normal_
  from .resnet import resnet26d, resnet50d
  from .resnetv2 import ResNetV2
  from .registry import register_model
  from einops import rearrange
  
  
  _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',
          'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
          'first_conv': 'patch_embed.proj', 'classifier': 'head',
          **kwargs
      }
  
  
  default_cfgs = {
      # patch models (my experiments)
      'vit_small_patch16_224': _cfg(
          url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/vit_small_p16_224-15ec54c9.pth',
      ),
  
      # patch models (weights ported from official Google JAX impl)
      'vit_base_patch16_224': _cfg(
          url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_p16_224-80ecf9dd.pth',
          mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5),
      ),
      'vit_base_patch32_224': _cfg(
          url='',  # no official model weights for this combo, only for in21k
          mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)),
      'vit_base_patch16_384': _cfg(
          url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_p16_384-83fb41ba.pth',
          input_size=(3, 384, 384), mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0),
      'vit_base_patch32_384': _cfg(
          url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_p32_384-830016f5.pth',
          input_size=(3, 384, 384), mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0),
      'vit_large_patch16_224': _cfg(
          url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_large_p16_224-4ee7a4dc.pth',
          mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)),
      'vit_large_patch32_224': _cfg(
          url='',  # no official model weights for this combo, only for in21k
          mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)),
      'vit_large_patch16_384': _cfg(
          url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_large_p16_384-b3be5167.pth',
          input_size=(3, 384, 384), mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0),
      '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), mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0),
  
  
      # patch models, imagenet21k (weights ported from official Google JAX impl)
      'vit_base_patch16_224_in21k': _cfg(
          url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_patch16_224_in21k-e5005f0a.pth',
          num_classes=21843, mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)),
      'vit_base_patch32_224_in21k': _cfg(
          url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_patch32_224_in21k-8db57226.pth',
          num_classes=21843, mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)),
      'vit_large_patch16_224_in21k': _cfg(
          url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_large_patch16_224_in21k-606da67d.pth',
          num_classes=21843, mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)),
      '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, mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)),
      'vit_huge_patch14_224_in21k': _cfg(
          hf_hub='timm/vit_huge_patch14_224_in21k',
          num_classes=21843, mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)),
  
      # hybrid models (weights ported from official Google JAX impl)
      'vit_base_resnet50_224_in21k': _cfg(
          url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_resnet50_224_in21k-6f7c7740.pth',
          num_classes=21843, mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=0.9, first_conv='patch_embed.backbone.stem.conv'),
      'vit_base_resnet50_384': _cfg(
          url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_resnet50_384-9fd3c705.pth',
          input_size=(3, 384, 384), mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0, first_conv='patch_embed.backbone.stem.conv'),
  
      # hybrid models (my experiments)
      'vit_small_resnet26d_224': _cfg(),
      'vit_small_resnet50d_s3_224': _cfg(),
      'vit_base_resnet26d_224': _cfg(),
      'vit_base_resnet50d_224': _cfg(),
  
      # deit models (FB weights)
      'vit_deit_tiny_patch16_224': _cfg(
          url='https://dl.fbaipublicfiles.com/deit/deit_tiny_patch16_224-a1311bcf.pth'),
      'vit_deit_small_patch16_224': _cfg(
          url='https://dl.fbaipublicfiles.com/deit/deit_small_patch16_224-cd65a155.pth'),
      'vit_deit_base_patch16_224': _cfg(
          url='https://dl.fbaipublicfiles.com/deit/deit_base_patch16_224-b5f2ef4d.pth',),
      'vit_deit_base_patch16_384': _cfg(
          url='https://dl.fbaipublicfiles.com/deit/deit_base_patch16_384-8de9b5d1.pth',
          input_size=(3, 384, 384), crop_pct=1.0),
      'vit_deit_tiny_distilled_patch16_224': _cfg(
          url='https://dl.fbaipublicfiles.com/deit/deit_tiny_distilled_patch16_224-b40b3cf7.pth'),
      'vit_deit_small_distilled_patch16_224': _cfg(
          url='https://dl.fbaipublicfiles.com/deit/deit_small_distilled_patch16_224-649709d9.pth'),
      'vit_deit_base_distilled_patch16_224': _cfg(
          url='https://dl.fbaipublicfiles.com/deit/deit_base_distilled_patch16_224-df68dfff.pth', ),
      'vit_deit_base_distilled_patch16_384': _cfg(
          url='https://dl.fbaipublicfiles.com/deit/deit_base_distilled_patch16_384-d0272ac0.pth',
          input_size=(3, 384, 384), crop_pct=1.0),
  }
  
  
  class Mlp(nn.Module):
      def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
          super().__init__()
          out_features = out_features or in_features
          hidden_features = hidden_features or in_features
          self.fc1 = nn.Linear(in_features, hidden_features)
          self.act = act_layer()
          self.fc2 = nn.Linear(hidden_features, out_features)
          self.drop = nn.Dropout(drop)
  
      def forward(self, x):
          x = self.fc1(x)
          x = self.act(x)
          x = self.drop(x)
          x = self.fc2(x)
          x = self.drop(x)
          return x
  
  
  class Attention(nn.Module):
      def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.):
          super().__init__()
          self.num_heads = num_heads
          head_dim = dim // num_heads
          # NOTE scale factor was wrong in my original version, can set manually to be compat with prev weights
          self.scale = qk_scale or 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)
  
      def forward(self, x):
          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[0], qkv[1], qkv[2]   # 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)
  
          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, qk_scale=None, 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, qk_scale=qk_scale, 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):
          x = x + self.drop_path(self.attn(self.norm1(x)))
          x = x + self.drop_path(self.mlp(self.norm2(x)))
          return x
  
  
  class PatchEmbed(nn.Module):
      """ Image to Patch Embedding
      """
      def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768):
          super().__init__()
          img_size = to_2tuple(img_size)
          patch_size = to_2tuple(patch_size)
          num_patches = (img_size[1] // patch_size[1]) * (img_size[0] // patch_size[0])
          self.img_size = img_size
          self.patch_size = patch_size
          self.num_patches = num_patches
  
          self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
  
      def forward(self, x):
          B, C, H, W = x.shape
          # FIXME look at relaxing size constraints
          assert H == self.img_size[0] and W == self.img_size[1], \
              f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
          x = self.proj(x).flatten(2).transpose(1, 2)
          return x
  
  
  class HybridEmbed(nn.Module):
      """ CNN Feature Map Embedding
      Extract feature map from CNN, flatten, project to embedding dim.
      """
      def __init__(self, backbone, img_size=224, feature_size=None, in_chans=3, embed_dim=768):
          super().__init__()
          assert isinstance(backbone, nn.Module)
          img_size = to_2tuple(img_size)
          self.img_size = img_size
          self.backbone = backbone
          if feature_size is None:
              with torch.no_grad():
                  # FIXME this is hacky, but most reliable way of determining the exact dim of the output feature
                  # map for all networks, the feature metadata has reliable channel and stride info, but using
                  # stride to calc feature dim requires info about padding of each stage that isn't captured.
                  training = backbone.training
                  if training:
                      backbone.eval()
                  o = self.backbone(torch.zeros(1, in_chans, img_size[0], img_size[1]))
                  if isinstance(o, (list, tuple)):
                      o = o[-1]  # last feature if backbone outputs list/tuple of features
                  feature_size = o.shape[-2:]
                  feature_dim = o.shape[1]
                  backbone.train(training)
          else:
              feature_size = to_2tuple(feature_size)
              if hasattr(self.backbone, 'feature_info'):
                  feature_dim = self.backbone.feature_info.channels()[-1]
              else:
                  feature_dim = self.backbone.num_features
          self.num_patches = feature_size[0] * feature_size[1]
          self.proj = nn.Conv2d(feature_dim, embed_dim, 1)
  
      def forward(self, x):
          x = self.backbone(x)
          if isinstance(x, (list, tuple)):
              x = x[-1]  # last feature if backbone outputs list/tuple of features
          x = self.proj(x).flatten(2).transpose(1, 2)
          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
      """
      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, qk_scale=None, representation_size=None,
                   drop_rate=0., attn_drop_rate=0., drop_path_rate=0., hybrid_backbone=None, norm_layer=None):
          """
          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
              qk_scale (float): override default qk scale of head_dim ** -0.5 if set
              representation_size (Optional[int]): enable and set representation layer (pre-logits) to this value if set
              drop_rate (float): dropout rate
              attn_drop_rate (float): attention dropout rate
              drop_path_rate (float): stochastic depth rate
              hybrid_backbone (nn.Module): CNN backbone to use in-place of PatchEmbed module
              norm_layer: (nn.Module): normalization layer
          """
          super().__init__()
          self.num_classes = num_classes
          self.num_features = self.embed_dim = embed_dim  # num_features for consistency with other models
          norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6)
  
          if hybrid_backbone is not None:
              self.patch_embed = HybridEmbed(
                  hybrid_backbone, img_size=img_size, in_chans=in_chans, embed_dim=embed_dim)
          else:
              self.patch_embed = PatchEmbed(
                  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.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, 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, qk_scale=qk_scale,
                  drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer)
              for i in range(depth)])
          self.norm = norm_layer(embed_dim)
  
          self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
  
  
          # Representation layer
          if representation_size:
              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
          self.head = nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity()
  
          trunc_normal_(self.pos_embed, std=.02)
          trunc_normal_(self.cls_token, std=.02)
          self.apply(self._init_weights)
  
      def _init_weights(self, m):
          if isinstance(m, nn.Linear):
              trunc_normal_(m.weight, std=.02)
              if isinstance(m, nn.Linear) and m.bias is not None:
                  nn.init.constant_(m.bias, 0)
          elif isinstance(m, nn.LayerNorm):
              nn.init.constant_(m.bias, 0)
              nn.init.constant_(m.weight, 1.0)
  
      @torch.jit.ignore
      def no_weight_decay(self):
          return {'pos_embed', 'cls_token'}
  
      def get_classifier(self):
          return self.head
  
      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()
  
      def forward_features(self, x):
          B = x.shape[0]
          x = self.patch_embed(x)
  
          cls_tokens = self.cls_token.expand(B, -1, -1)  # stole cls_tokens impl from Phil Wang, thanks
          x = torch.cat((cls_tokens, x), dim=1)
          x = x + self.pos_embed
          x = self.pos_drop(x)
  
          for blk in self.blocks:
              x = blk(x)
              # print(x.shape)
  
          # x = self.norm(x)[:, 0]
          # # print(x.shape)
          # x = self.pre_logits(x)
          return x
  
      def forward(self, inp, patch=16):
  
          x = self.forward_features(inp)
          x = torch.transpose(x, 1, 2).unsqueeze(2)
          x = self.avgpool(x)
          x = torch.flatten(x, 1)
          return x
  
  class DistilledVisionTransformer(VisionTransformer):
      """ Vision Transformer with distillation token.
  
      Paper: `Training data-efficient image transformers & distillation through attention` -
          https://arxiv.org/abs/2012.12877
  
      This impl of distilled ViT is taken from https://github.com/facebookresearch/deit
      """
      def __init__(self, *args, **kwargs):
          super().__init__(*args, **kwargs)
          self.dist_token = nn.Parameter(torch.zeros(1, 1, self.embed_dim))
          num_patches = self.patch_embed.num_patches
          self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 2, self.embed_dim))
          self.head_dist = nn.Linear(self.embed_dim, self.num_classes) if self.num_classes > 0 else nn.Identity()
  
          trunc_normal_(self.dist_token, std=.02)
          trunc_normal_(self.pos_embed, std=.02)
          self.head_dist.apply(self._init_weights)
  
      def forward_features(self, x):
          B = x.shape[0]
          x = self.patch_embed(x)
  
          cls_tokens = self.cls_token.expand(B, -1, -1)  # stole cls_tokens impl from Phil Wang, thanks
          dist_token = self.dist_token.expand(B, -1, -1)
          x = torch.cat((cls_tokens, dist_token, x), dim=1)
  
          x = x + self.pos_embed
          x = self.pos_drop(x)
  
          for blk in self.blocks:
              x = blk(x)
  
          # x = self.norm(x)[:, 0]
          return x # x[:, 0], x[:, 1]
  
      def forward(self, inp, patch=16):
          # print(inp.shape)
          x = self.forward_features(inp)
          x = torch.transpose(x, 1, 2).unsqueeze(2)
          x = self.avgpool(x)
          return x
  
  
  def resize_pos_embed(posemb, posemb_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 True:
          posemb_tok, posemb_grid = posemb[:, :1], posemb[0, 1:]
          ntok_new -= 1
      else:
          posemb_tok, posemb_grid = posemb[:, :0], posemb[0]
      gs_old = int(math.sqrt(len(posemb_grid)))
      gs_new = int(math.sqrt(ntok_new))
      _logger.info('Position embedding grid-size from %s to %s', 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, gs_new), mode='bilinear')
      posemb_grid = posemb_grid.permute(0, 2, 3, 1).reshape(1, gs_new * gs_new, -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)
          out_dict[k] = v
      return out_dict
  
  
  def _create_vision_transformer(variant, pretrained=False, distilled=False, **kwargs):
      default_cfg = deepcopy(default_cfgs[variant])
      overlay_external_default_cfg(default_cfg, kwargs)
      default_num_classes = default_cfg['num_classes']
      default_img_size = default_cfg['input_size'][-2:]
  
      num_classes = kwargs.pop('num_classes', default_num_classes)
      img_size = kwargs.pop('img_size', default_img_size)
      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
  
      if kwargs.get('features_only', None):
          raise RuntimeError('features_only not implemented for Vision Transformer models.')
  
      model_cls = DistilledVisionTransformer if distilled else VisionTransformer
      model = build_model_with_cfg(
          model_cls, variant, pretrained,
          default_cfg=default_cfg,
          img_size=img_size,
          num_classes=num_classes,
          representation_size=repr_size,
          pretrained_filter_fn=checkpoint_filter_fn,
          **kwargs)
  
      return model
  
  
  @register_model
  def vit_small_patch16_224(pretrained=False, **kwargs):
      """ My custom 'small' ViT model. Depth=8, heads=8= mlp_ratio=3."""
      model_kwargs = dict(
          patch_size=16, embed_dim=768, depth=8, num_heads=8, mlp_ratio=3.,
          qkv_bias=False, norm_layer=nn.LayerNorm, **kwargs)
      if pretrained:
          # NOTE my scale was wrong for original weights, leaving this here until I have better ones for this model
          model_kwargs.setdefault('qk_scale', 768 ** -0.5)
      model = _create_vision_transformer('vit_small_patch16_224', 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_patch32_224(pretrained=False, **kwargs):
      """ ViT-Base (ViT-B/32) from original paper (https://arxiv.org/abs/2010.11929). No pretrained weights.
      """
      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_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_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_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_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_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_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_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.
      """
      model_kwargs = dict(
          patch_size=16, embed_dim=768, depth=12, num_heads=12, representation_size=768, **kwargs)
      model = _create_vision_transformer('vit_base_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.
      """
      model_kwargs = dict(
          patch_size=32, embed_dim=768, depth=12, num_heads=12, representation_size=768, **kwargs)
      model = _create_vision_transformer('vit_base_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.
      """
      model_kwargs = dict(
          patch_size=16, embed_dim=1024, depth=24, num_heads=16, representation_size=1024, **kwargs)
      model = _create_vision_transformer('vit_large_patch16_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.
      """
      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_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: converted weights not currently available, too large for github release hosting.
      """
      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 vit_base_resnet50_224_in21k(pretrained=False, **kwargs):
      """ R50+ViT-B/16 hybrid model from original paper (https://arxiv.org/abs/2010.11929).
      ImageNet-21k weights @ 224x224, source https://github.com/google-research/vision_transformer.
      """
      # create a ResNetV2 w/o pre-activation, that uses StdConv and GroupNorm and has 3 stages, no head
      backbone = ResNetV2(
          layers=(3, 4, 9), num_classes=0, global_pool='', in_chans=kwargs.get('in_chans', 3),
          preact=False, stem_type='same', conv_layer=StdConv2dSame)
      model_kwargs = dict(
          embed_dim=768, depth=12, num_heads=12, hybrid_backbone=backbone,
          representation_size=768, **kwargs)
      model = _create_vision_transformer('vit_base_resnet50_224_in21k', pretrained=pretrained, **model_kwargs)
      return model
  
  
  @register_model
  def vit_base_resnet50_384(pretrained=False, **kwargs):
      """ R50+ViT-B/16 hybrid 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.
      """
      # create a ResNetV2 w/o pre-activation, that uses StdConv and GroupNorm and has 3 stages, no head
      backbone = ResNetV2(
          layers=(3, 4, 9), num_classes=0, global_pool='', in_chans=kwargs.get('in_chans', 3),
          preact=False, stem_type='same', conv_layer=StdConv2dSame)
      model_kwargs = dict(embed_dim=768, depth=12, num_heads=12, hybrid_backbone=backbone, **kwargs)
      model = _create_vision_transformer('vit_base_resnet50_384', pretrained=pretrained, **model_kwargs)
      return model
  
  
  @register_model
  def vit_small_resnet26d_224(pretrained=False, **kwargs):
      """ Custom ViT small hybrid w/ ResNet26D stride 32. No pretrained weights.
      """
      backbone = resnet26d(pretrained=pretrained, in_chans=kwargs.get('in_chans', 3), features_only=True, out_indices=[4])
      model_kwargs = dict(embed_dim=768, depth=8, num_heads=8, mlp_ratio=3, hybrid_backbone=backbone, **kwargs)
      model = _create_vision_transformer('vit_small_resnet26d_224', pretrained=pretrained, **model_kwargs)
      return model
  
  
  @register_model
  def vit_small_resnet50d_s3_224(pretrained=False, **kwargs):
      """ Custom ViT small hybrid w/ ResNet50D 3-stages, stride 16. No pretrained weights.
      """
      backbone = resnet50d(pretrained=pretrained, in_chans=kwargs.get('in_chans', 3), features_only=True, out_indices=[3])
      model_kwargs = dict(embed_dim=768, depth=8, num_heads=8, mlp_ratio=3, hybrid_backbone=backbone, **kwargs)
      model = _create_vision_transformer('vit_small_resnet50d_s3_224', pretrained=pretrained, **model_kwargs)
      return model
  
  
  @register_model
  def vit_base_resnet26d_224(pretrained=False, **kwargs):
      """ Custom ViT base hybrid w/ ResNet26D stride 32. No pretrained weights.
      """
      backbone = resnet26d(pretrained=pretrained, in_chans=kwargs.get('in_chans', 3), features_only=True, out_indices=[4])
      model_kwargs = dict(embed_dim=768, depth=12, num_heads=12, hybrid_backbone=backbone, **kwargs)
      model = _create_vision_transformer('vit_base_resnet26d_224', pretrained=pretrained, **model_kwargs)
      return model
  
  
  @register_model
  def vit_base_resnet50d_224(pretrained=False, **kwargs):
      """ Custom ViT base hybrid w/ ResNet50D stride 32. No pretrained weights.
      """
      backbone = resnet50d(pretrained=pretrained, in_chans=kwargs.get('in_chans', 3), features_only=True, out_indices=[4])
      model_kwargs = dict(embed_dim=768, depth=12, num_heads=12, hybrid_backbone=backbone, **kwargs)
      model = _create_vision_transformer('vit_base_resnet50d_224', pretrained=pretrained, **model_kwargs)
      return model
  
  
  @register_model
  def vit_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('vit_deit_tiny_patch16_224', pretrained=pretrained, **model_kwargs)
      return model
  
  
  @register_model
  def vit_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('vit_deit_small_patch16_224', pretrained=pretrained, **model_kwargs)
      return model
  
  
  @register_model
  def vit_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('vit_deit_base_patch16_224', pretrained=pretrained, **model_kwargs)
      return model
  
  
  @register_model
  def vit_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('vit_deit_base_patch16_384', pretrained=pretrained, **model_kwargs)
      return model
  
  
  @register_model
  def vit_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(
          'vit_deit_tiny_distilled_patch16_224', pretrained=pretrained,  distilled=True, **model_kwargs)
      return model
  
  
  @register_model
  def vit_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(
          'vit_deit_small_distilled_patch16_224', pretrained=pretrained,  distilled=True, **model_kwargs)
      return model
  
  
  @register_model
  def vit_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(
          'vit_deit_base_distilled_patch16_224', pretrained=pretrained,  distilled=True, **model_kwargs)
      return model
  
  
  @register_model
  def vit_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(
          'vit_deit_base_distilled_patch16_384', pretrained=pretrained, distilled=True, **model_kwargs)
      return model