hunk.code_window:     """
       def __init__(
               self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, global_pool='avg',
               embed_dim=768, depth=12, num_heads=12, mlp_ratio=4., qkv_bias=True, init_values=None,
               drop_rate=0., attn_drop_rate=0., drop_path_rate=0., weight_init='skip', class_token=False,
               rel_pos_type='mlp', shared_rel_pos=False, fc_norm=False,
               embed_layer=PatchEmbed, norm_layer=None, act_layer=None, block_fn=RelPosBlock):
           """
           Args:
               img_size (int, tuple): input image size
  
hunk.labels: keep keep keep keep replace replace replace keep keep keep keep
hunk.after_edit:             embed_dim=768, depth=12, num_heads=12, mlp_ratio=4., qkv_bias=True, init_values=1e-5,
               class_token=False, rel_pos_type='mlp', shared_rel_pos=False, fc_norm=False,
               drop_rate=0., attn_drop_rate=0., drop_path_rate=0., weight_init='skip',
  
hunk.file_path: timm/models/vision_transformer_relpos.py
hunk.type: replace
file: """ 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
  
  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 typing import Optional
  
  import torch
  import torch.nn as nn
  import torch.nn.functional as F
  import torch.utils.checkpoint
  
  from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD
  from .helpers import build_model_with_cfg, resolve_pretrained_cfg, named_apply, adapt_input_conv, checkpoint_seq
  from .layers import PatchEmbed, Mlp, DropPath, trunc_normal_, lecun_normal_
  from .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_large_patch14_224': _cfg(url=''),
      '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_id='timm/vit_huge_patch14_224_in21k',
          num_classes=21843),
  
      # SAM trained models (https://arxiv.org/abs/2106.01548)
      'vit_base_patch32_224_sam': _cfg(
          url='https://storage.googleapis.com/vit_models/sam/ViT-B_32.npz'),
      'vit_base_patch16_224_sam': _cfg(
          url='https://storage.googleapis.com/vit_models/sam/ViT-B_16.npz'),
  
      # DINO pretrained - https://arxiv.org/abs/2104.14294 (no classifier head, for fine-tune only)
      'vit_small_patch16_224_dino': _cfg(
          url='https://dl.fbaipublicfiles.com/dino/dino_deitsmall16_pretrain/dino_deitsmall16_pretrain.pth',
          mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD, num_classes=0),
      'vit_small_patch8_224_dino': _cfg(
          url='https://dl.fbaipublicfiles.com/dino/dino_deitsmall8_pretrain/dino_deitsmall8_pretrain.pth',
          mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD, num_classes=0),
      'vit_base_patch16_224_dino': _cfg(
          url='https://dl.fbaipublicfiles.com/dino/dino_vitbase16_pretrain/dino_vitbase16_pretrain.pth',
          mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD, num_classes=0),
      'vit_base_patch8_224_dino': _cfg(
          url='https://dl.fbaipublicfiles.com/dino/dino_vitbase8_pretrain/dino_vitbase8_pretrain.pth',
          mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD, num_classes=0),
  
  
      # 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',
      ),
  
      'vit_base_patch16_rpn_224': _cfg(
          url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tpu-weights/vit_base_patch16_rpn_224-sw-3b07e89d.pth'),
  
      # experimental (may be removed)
      'vit_base_patch32_plus_256': _cfg(url='', input_size=(3, 256, 256), crop_pct=0.95),
      'vit_base_patch16_plus_240': _cfg(url='', input_size=(3, 240, 240), crop_pct=0.95),
      'vit_small_patch16_36x1_224': _cfg(url=''),
      'vit_small_patch16_18x2_224': _cfg(url=''),
      'vit_base_patch16_18x2_224': _cfg(url=''),
  }
  
  
  class Attention(nn.Module):
      def __init__(self, dim, num_heads=8, qkv_bias=False, attn_drop=0., proj_drop=0.):
          super().__init__()
          assert dim % num_heads == 0, 'dim should be divisible by num_heads'
          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)
  
      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.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)
  
          x = (attn @ v).transpose(1, 2).reshape(B, N, C)
          x = self.proj(x)
          x = self.proj_drop(x)
          return x
  
  
  class LayerScale(nn.Module):
      def __init__(self, dim, init_values=1e-5, inplace=False):
          super().__init__()
          self.inplace = inplace
          self.gamma = nn.Parameter(init_values * torch.ones(dim))
  
      def forward(self, x):
          return x.mul_(self.gamma) if self.inplace else x * self.gamma
  
  
  class Block(nn.Module):
  
      def __init__(
              self, dim, num_heads, mlp_ratio=4., qkv_bias=False, drop=0., attn_drop=0., init_values=None,
              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)
          self.ls1 = LayerScale(dim, init_values=init_values) if init_values else nn.Identity()
          # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
          self.drop_path1 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
  
          self.norm2 = norm_layer(dim)
          self.mlp = Mlp(in_features=dim, hidden_features=int(dim * mlp_ratio), act_layer=act_layer, drop=drop)
          self.ls2 = LayerScale(dim, init_values=init_values) if init_values else nn.Identity()
          self.drop_path2 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
  
      def forward(self, x):
          x = x + self.drop_path1(self.ls1(self.attn(self.norm1(x))))
          x = x + self.drop_path2(self.ls2(self.mlp(self.norm2(x))))
          return x
  
  
  class ResPostBlock(nn.Module):
  
      def __init__(
              self, dim, num_heads, mlp_ratio=4., qkv_bias=False, drop=0., attn_drop=0., init_values=None,
              drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm):
          super().__init__()
          self.init_values = init_values
  
          self.attn = Attention(dim, num_heads=num_heads, qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop)
          self.norm1 = norm_layer(dim)
          self.drop_path1 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
  
          self.mlp = Mlp(in_features=dim, hidden_features=int(dim * mlp_ratio), act_layer=act_layer, drop=drop)
          self.norm2 = norm_layer(dim)
          self.drop_path2 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
  
          self.init_weights()
  
      def init_weights(self):
          # NOTE this init overrides that base model init with specific changes for the block type
          if self.init_values is not None:
              nn.init.constant_(self.norm1.weight, self.init_values)
              nn.init.constant_(self.norm2.weight, self.init_values)
  
      def forward(self, x):
          x = x + self.drop_path1(self.norm1(self.attn(x)))
          x = x + self.drop_path2(self.norm2(self.mlp(x)))
          return x
  
  
  class ParallelBlock(nn.Module):
  
      def __init__(
              self, dim, num_heads, num_parallel=2, mlp_ratio=4., qkv_bias=False, init_values=None,
              drop=0., attn_drop=0., drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm):
          super().__init__()
          self.num_parallel = num_parallel
          self.attns = nn.ModuleList()
          self.ffns = nn.ModuleList()
          for _ in range(num_parallel):
              self.attns.append(nn.Sequential(OrderedDict([
                  ('norm', norm_layer(dim)),
                  ('attn', Attention(dim, num_heads=num_heads, qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop)),
                  ('ls', LayerScale(dim, init_values=init_values) if init_values else nn.Identity()),
                  ('drop_path', DropPath(drop_path) if drop_path > 0. else nn.Identity())
              ])))
              self.ffns.append(nn.Sequential(OrderedDict([
                  ('norm', norm_layer(dim)),
                  ('mlp', Mlp(dim, hidden_features=int(dim * mlp_ratio), act_layer=act_layer, drop=drop)),
                  ('ls', LayerScale(dim, init_values=init_values) if init_values else nn.Identity()),
                  ('drop_path', DropPath(drop_path) if drop_path > 0. else nn.Identity())
              ])))
  
      def _forward_jit(self, x):
          x = x + torch.stack([attn(x) for attn in self.attns]).sum(dim=0)
          x = x + torch.stack([ffn(x) for ffn in self.ffns]).sum(dim=0)
          return x
  
      @torch.jit.ignore
      def _forward(self, x):
          x = x + sum(attn(x) for attn in self.attns)
          x = x + sum(ffn(x) for ffn in self.ffns)
          return x
  
      def forward(self, x):
          if torch.jit.is_scripting() or torch.jit.is_tracing():
              return self._forward_jit(x)
          else:
              return self._forward(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, global_pool='token',
              embed_dim=768, depth=12, num_heads=12, mlp_ratio=4., qkv_bias=True, init_values=None,
              drop_rate=0., attn_drop_rate=0., drop_path_rate=0., weight_init='', class_token=True,
              fc_norm=None, embed_layer=PatchEmbed, norm_layer=None, act_layer=None, block_fn=Block):
          """
          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
              global_pool (str): type of global pooling for final sequence (default: 'token')
              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
              init_values: (float): layer-scale init values
              drop_rate (float): dropout rate
              attn_drop_rate (float): attention dropout rate
              drop_path_rate (float): stochastic depth rate
              weight_init (str): weight init scheme
              class_token (bool): use class token
              fc_norm (Optional[bool]): pre-fc norm after pool, set if global_pool == 'avg' if None (default: None)
              embed_layer (nn.Module): patch embedding layer
              norm_layer: (nn.Module): normalization layer
              act_layer: (nn.Module): MLP activation layer
          """
          super().__init__()
          assert global_pool in ('', 'avg', 'token')
          assert class_token or global_pool != 'token'
          use_fc_norm = global_pool == 'avg' if fc_norm is None else fc_norm
          norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6)
          act_layer = act_layer or nn.GELU
  
          self.num_classes = num_classes
          self.global_pool = global_pool
          self.num_features = self.embed_dim = embed_dim  # num_features for consistency with other models
          self.num_tokens = 1 if class_token else 0
          self.grad_checkpointing = False
  
          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)) if self.num_tokens > 0 else None
          self.pos_embed = nn.Parameter(torch.randn(1, num_patches + self.num_tokens, embed_dim) * .02)
          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.Sequential(*[
              block_fn(
                  dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, init_values=init_values,
                  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) if not use_fc_norm else nn.Identity()
  
          # Classifier Head
          self.fc_norm = norm_layer(embed_dim) if use_fc_norm else nn.Identity()
          self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()
  
          if weight_init != 'skip':
              self.init_weights(weight_init)
  
      def init_weights(self, mode=''):
          assert mode in ('jax', 'jax_nlhb', 'moco', '')
          head_bias = -math.log(self.num_classes) if 'nlhb' in mode else 0.
          trunc_normal_(self.pos_embed, std=.02)
          if self.cls_token is not None:
              nn.init.normal_(self.cls_token, std=1e-6)
          named_apply(get_init_weights_vit(mode, head_bias), self)
  
      def _init_weights(self, m):
          # this fn left here for compat with downstream users
          init_weights_vit_timm(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'}
  
      @torch.jit.ignore
      def group_matcher(self, coarse=False):
          return dict(
              stem=r'^cls_token|pos_embed|patch_embed',  # stem and embed
              blocks=[(r'^blocks\.(\d+)', None), (r'^norm', (99999,))]
          )
  
      @torch.jit.ignore
      def set_grad_checkpointing(self, enable=True):
          self.grad_checkpointing = enable
  
      @torch.jit.ignore
      def get_classifier(self):
          return self.head
  
      def reset_classifier(self, num_classes: int, global_pool=None):
          self.num_classes = num_classes
          if global_pool is not None:
              assert global_pool in ('', 'avg', 'token')
              self.global_pool = global_pool
          self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()
  
      def forward_features(self, x):
          x = self.patch_embed(x)
          if self.cls_token is not None:
              x = torch.cat((self.cls_token.expand(x.shape[0], -1, -1), x), dim=1)
          x = self.pos_drop(x + self.pos_embed)
          if self.grad_checkpointing and not torch.jit.is_scripting():
              x = checkpoint_seq(self.blocks, x)
          else:
              x = self.blocks(x)
          x = self.norm(x)
          return x
  
      def forward_head(self, x, pre_logits: bool = False):
          if self.global_pool:
              x = x[:, self.num_tokens:].mean(dim=1) if self.global_pool == 'avg' else x[:, 0]
          x = self.fc_norm(x)
          return x if pre_logits else self.head(x)
  
      def forward(self, x):
          x = self.forward_features(x)
          x = self.forward_head(x)
          return x
  
  
  def init_weights_vit_timm(module: nn.Module, name: str = ''):
      """ ViT weight initialization, original timm impl (for reproducibility) """
      if isinstance(module, nn.Linear):
          trunc_normal_(module.weight, std=.02)
          if module.bias is not None:
              nn.init.zeros_(module.bias)
      elif hasattr(module, 'init_weights'):
          module.init_weights()
  
  
  def init_weights_vit_jax(module: nn.Module, name: str = '', head_bias: float = 0.):
      """ ViT weight initialization, matching JAX (Flax) impl """
      if isinstance(module, nn.Linear):
          if name.startswith('head'):
              nn.init.zeros_(module.weight)
              nn.init.constant_(module.bias, head_bias)
          else:
              nn.init.xavier_uniform_(module.weight)
              if module.bias is not None:
                  nn.init.normal_(module.bias, std=1e-6) if 'mlp' in name else nn.init.zeros_(module.bias)
      elif isinstance(module, nn.Conv2d):
          lecun_normal_(module.weight)
          if module.bias is not None:
              nn.init.zeros_(module.bias)
      elif hasattr(module, 'init_weights'):
          module.init_weights()
  
  
  def init_weights_vit_moco(module: nn.Module, name: str = ''):
      """ ViT weight initialization, matching moco-v3 impl minus fixed PatchEmbed """
      if isinstance(module, nn.Linear):
          if 'qkv' in name:
              # treat the weights of Q, K, V separately
              val = math.sqrt(6. / float(module.weight.shape[0] // 3 + module.weight.shape[1]))
              nn.init.uniform_(module.weight, -val, val)
          else:
              nn.init.xavier_uniform_(module.weight)
          if module.bias is not None:
              nn.init.zeros_(module.bias)
      elif hasattr(module, 'init_weights'):
          module.init_weights()
  
  
  def get_init_weights_vit(mode='jax', head_bias: float = 0.):
      if 'jax' in mode:
          return partial(init_weights_vit_jax, head_bias=head_bias)
      elif 'moco' in mode:
          return init_weights_vit_moco
      else:
          return init_weights_vit_timm
  
  
  @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']))
      # NOTE representation layer has been removed, not used in latest 21k/1k pretrained weights
      # 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)
          elif 'pre_logits' in k:
              # NOTE representation layer removed as not used in latest 21k/1k pretrained weights
              continue
          out_dict[k] = v
      return out_dict
  
  
  def _create_vision_transformer(variant, pretrained=False, **kwargs):
      if kwargs.get('features_only', None):
          raise RuntimeError('features_only not implemented for Vision Transformer models.')
  
      pretrained_cfg = resolve_pretrained_cfg(variant, kwargs=kwargs)
      model = build_model_with_cfg(
          VisionTransformer, variant, pretrained,
          pretrained_cfg=pretrained_cfg,
          pretrained_filter_fn=checkpoint_filter_fn,
          pretrained_custom_load='npz' in pretrained_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/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=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_large_patch14_224(pretrained=False, **kwargs):
      """ ViT-Large model (ViT-L/14)
      """
      model_kwargs = dict(patch_size=14, embed_dim=1024, depth=24, num_heads=16, **kwargs)
      model = _create_vision_transformer('vit_large_patch14_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, **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, **kwargs)
      model = _create_vision_transformer('vit_huge_patch14_224_in21k', pretrained=pretrained, **model_kwargs)
      return model
  
  
  @register_model
  def vit_base_patch16_224_sam(pretrained=False, **kwargs):
      """ ViT-Base (ViT-B/16) w/ SAM pretrained weights. Paper: https://arxiv.org/abs/2106.01548
      """
      model_kwargs = dict(patch_size=16, embed_dim=768, depth=12, num_heads=12, **kwargs)
      model = _create_vision_transformer('vit_base_patch16_224_sam', pretrained=pretrained, **model_kwargs)
      return model
  
  
  @register_model
  def vit_base_patch32_224_sam(pretrained=False, **kwargs):
      """ ViT-Base (ViT-B/32) w/ SAM pretrained weights. Paper: https://arxiv.org/abs/2106.01548
      """
      model_kwargs = dict(patch_size=32, embed_dim=768, depth=12, num_heads=12, **kwargs)
      model = _create_vision_transformer('vit_base_patch32_224_sam', pretrained=pretrained, **model_kwargs)
      return model
  
  
  @register_model
  def vit_small_patch16_224_dino(pretrained=False, **kwargs):
      """ ViT-Small (ViT-S/16) w/ DINO pretrained weights (no head) - https://arxiv.org/abs/2104.14294
      """
      model_kwargs = dict(patch_size=16, embed_dim=384, depth=12, num_heads=6, **kwargs)
      model = _create_vision_transformer('vit_small_patch16_224_dino', pretrained=pretrained, **model_kwargs)
      return model
  
  
  @register_model
  def vit_small_patch8_224_dino(pretrained=False, **kwargs):
      """ ViT-Small (ViT-S/8) w/ DINO pretrained weights (no head) - https://arxiv.org/abs/2104.14294
      """
      model_kwargs = dict(patch_size=8, embed_dim=384, depth=12, num_heads=6, **kwargs)
      model = _create_vision_transformer('vit_small_patch8_224_dino', pretrained=pretrained, **model_kwargs)
      return model
  
  
  @register_model
  def vit_base_patch16_224_dino(pretrained=False, **kwargs):
      """ ViT-Base (ViT-B/16) /w DINO pretrained weights (no head) - https://arxiv.org/abs/2104.14294
      """
      model_kwargs = dict(patch_size=16, embed_dim=768, depth=12, num_heads=12, **kwargs)
      model = _create_vision_transformer('vit_base_patch16_224_dino', pretrained=pretrained, **model_kwargs)
      return model
  
  
  @register_model
  def vit_base_patch8_224_dino(pretrained=False, **kwargs):
      """ ViT-Base (ViT-B/8) w/ DINO pretrained weights (no head) - https://arxiv.org/abs/2104.14294
      """
      model_kwargs = dict(patch_size=8, embed_dim=768, depth=12, num_heads=12, **kwargs)
      model = _create_vision_transformer('vit_base_patch8_224_dino', pretrained=pretrained, **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
  
  
  # Experimental models below
  
  @register_model
  def vit_base_patch32_plus_256(pretrained=False, **kwargs):
      """ ViT-Base (ViT-B/32+)
      """
      model_kwargs = dict(patch_size=32, embed_dim=896, depth=12, num_heads=14, init_values=1e-5, **kwargs)
      model = _create_vision_transformer('vit_base_patch32_plus_256', pretrained=pretrained, **model_kwargs)
      return model
  
  
  @register_model
  def vit_base_patch16_plus_240(pretrained=False, **kwargs):
      """ ViT-Base (ViT-B/16+)
      """
      model_kwargs = dict(patch_size=16, embed_dim=896, depth=12, num_heads=14, init_values=1e-5, **kwargs)
      model = _create_vision_transformer('vit_base_patch16_plus_240', pretrained=pretrained, **model_kwargs)
      return model
  
  
  @register_model
  def vit_base_patch16_rpn_224(pretrained=False, **kwargs):
      """ ViT-Base (ViT-B/16) w/ residual post-norm
      """
      model_kwargs = dict(
          patch_size=16, embed_dim=768, depth=12, num_heads=12, qkv_bias=False, init_values=1e-5, class_token=False,
          block_fn=ResPostBlock, global_pool=kwargs.pop('global_pool', 'avg'), **kwargs)
      model = _create_vision_transformer('vit_base_patch16_rpn_224', pretrained=pretrained, **model_kwargs)
      return model
  
  
  @register_model
  def vit_small_patch16_36x1_224(pretrained=False, **kwargs):
      """ ViT-Base w/ LayerScale + 36 x 1 (36 block serial) config. Experimental, may remove.
      Based on `Three things everyone should know about Vision Transformers` - https://arxiv.org/abs/2203.09795
      Paper focuses on 24x2 + 48x1 for 'Small' width but those are extremely slow.
      """
      model_kwargs = dict(patch_size=16, embed_dim=384, depth=36, num_heads=6, init_values=1e-5, **kwargs)
      model = _create_vision_transformer('vit_small_patch16_36x1_224', pretrained=pretrained, **model_kwargs)
      return model
  
  
  @register_model
  def vit_small_patch16_18x2_224(pretrained=False, **kwargs):
      """ ViT-Small w/ LayerScale + 18 x 2 (36 block parallel) config. Experimental, may remove.
      Based on `Three things everyone should know about Vision Transformers` - https://arxiv.org/abs/2203.09795
      Paper focuses on 24x2 + 48x1 for 'Small' width but those are extremely slow.
      """
      model_kwargs = dict(
          patch_size=16, embed_dim=384, depth=18, num_heads=6, init_values=1e-5, block_fn=ParallelBlock, **kwargs)
      model = _create_vision_transformer('vit_small_patch16_18x2_224', pretrained=pretrained, **model_kwargs)
      return model
  
  
  @register_model
  def vit_base_patch16_18x2_224(pretrained=False, **kwargs):
      """ ViT-Base w/ LayerScale + 18 x 2 (36 block parallel) config. Experimental, may remove.
      Based on `Three things everyone should know about Vision Transformers` - https://arxiv.org/abs/2203.09795
      """
      model_kwargs = dict(
          patch_size=16, embed_dim=768, depth=18, num_heads=12, init_values=1e-5, block_fn=ParallelBlock, **kwargs)
      model = _create_vision_transformer('vit_base_patch16_18x2_224', pretrained=pretrained, **model_kwargs)
      return model
  