""" 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
# ------------------------------------------
# Modification:
# Added code for dualprompt implementation
# -- Jaeho Lee, dlwogh9344@khu.ac.kr
# ------------------------------------------
"""
import math
import logging
from functools import partial
from collections import OrderedDict
from typing import Optional
from copy import deepcopy
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 timm.models.helpers import build_model_with_cfg, resolve_pretrained_cfg, named_apply, adapt_input_conv, \
    checkpoint_seq
from timm.models.layers import PatchEmbed, Mlp, DropPath, trunc_normal_, lecun_normal_
from timm.models.registry import register_model

from peft.prompt.dp_prompt import EPrompt
from attention import PreT_Attention

_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_224': _cfg(
        url='https://storage.googleapis.com/vit_models/imagenet21k/ViT-B_16.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, *args):
        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, attn_layer=Attention):
        super().__init__()
        self.norm1 = norm_layer(dim)
        self.attn = attn_layer(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, prompt=None):
        x = x + self.drop_path1(self.ls1(self.attn(self.norm1(x), prompt)))
        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,
            class_token=True, no_embed_class=False, fc_norm=None, drop_rate=0., attn_drop_rate=0., drop_path_rate=0.,
            weight_init='', embed_layer=PatchEmbed, norm_layer=None, act_layer=None, block_fn=Block,
            prompt_length=None, embedding_key='cls', prompt_init='uniform', prompt_pool=False, prompt_key=False, pool_size=None,
            top_k=None, batchwise_prompt=False, prompt_key_init='uniform', head_type='token', use_prompt_mask=False,
            use_g_prompt=False, g_prompt_length=None, g_prompt_layer_idx=None, use_prefix_tune_for_g_prompt=False,
            use_e_prompt=False, e_prompt_layer_idx=None, use_prefix_tune_for_e_prompt=False, same_key_value=False,):
        """
        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
            class_token (bool): use class token
            fc_norm (Optional[bool]): pre-fc norm after pool, set if global_pool == 'avg' if None (default: None)
            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
            embed_layer (nn.Module): patch embedding layer
            norm_layer: (nn.Module): normalization layer
            act_layer: (nn.Module): MLP activation layer
            block_fn: (nn.Module): transformer block
            prompt_pool (bool): use prompt pool or not
        """
        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.img_size = img_size
        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.class_token = class_token
        self.num_prefix_tokens = 1 if class_token else 0
        self.no_embed_class = no_embed_class
        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 class_token else None
        embed_len = num_patches if no_embed_class else num_patches + self.num_prefix_tokens
        self.pos_embed = nn.Parameter(torch.randn(1, embed_len, embed_dim) * .02)
        self.pos_drop = nn.Dropout(p=drop_rate)

        self.prompt_pool = prompt_pool
        self.head_type = head_type
        self.use_prompt_mask = use_prompt_mask

        self.use_g_prompt = use_g_prompt
        self.g_prompt_layer_idx = g_prompt_layer_idx
        # num_g_prompt : The actual number of layers to which g-prompt is attached.
        # In official code, create as many layers as the total number of layers and select them based on the index
        num_g_prompt = len(self.g_prompt_layer_idx) if self.g_prompt_layer_idx is not None else 0
        self.use_prefix_tune_for_g_prompt = use_prefix_tune_for_g_prompt
        
        self.use_e_prompt = use_e_prompt
        self.e_prompt_layer_idx = e_prompt_layer_idx
        num_e_prompt = len(self.e_prompt_layer_idx) if self.e_prompt_layer_idx is not None else 0
        self.use_prefix_tune_for_e_prompt = use_prefix_tune_for_e_prompt

        # if not self.use_prefix_tune_for_g_prompt and not self.use_g_prompt:
        #     self.use_g_prompt = False
        #     self.g_prompt_layer_idx = []

        if use_g_prompt and g_prompt_length is not None and len(g_prompt_layer_idx) != 0:
            if not use_prefix_tune_for_g_prompt:
                g_prompt_shape = (num_g_prompt, g_prompt_length, embed_dim)
                if prompt_init == 'zero':
                    self.g_prompt = nn.Parameter(torch.zeros(g_prompt_shape))
                elif prompt_init == 'uniform':
                    self.g_prompt = nn.Parameter(torch.randn(g_prompt_shape))
                    nn.init.uniform_(self.g_prompt, -1, 1)
            else:
                if same_key_value:
                    g_prompt_shape = (num_g_prompt, 1, g_prompt_length, num_heads, embed_dim // num_heads)
                    if prompt_init == 'zero':
                        self.g_prompt = nn.Parameter(torch.zeros(g_prompt_shape))
                    elif prompt_init == 'uniform':
                        self.g_prompt = nn.Parameter(torch.randn(g_prompt_shape))
                        nn.init.uniform_(self.g_prompt, -1, 1)
                    self.g_prompt = self.g_prompt.repeat(1, 2, 1, 1, 1)
                else:
                    g_prompt_shape = (num_g_prompt, 2, g_prompt_length, num_heads, embed_dim // num_heads)
                    if prompt_init == 'zero':
                        self.g_prompt = nn.Parameter(torch.zeros(g_prompt_shape))
                    elif prompt_init == 'uniform':
                        self.g_prompt = nn.Parameter(torch.randn(g_prompt_shape))
                        nn.init.uniform_(self.g_prompt, -1, 1)
        else:
            self.g_prompt = None
        
        if use_e_prompt and e_prompt_layer_idx is not None:
            self.e_prompt = EPrompt(length=prompt_length, embed_dim=embed_dim, embedding_key=embedding_key,
                                    prompt_init=prompt_init,
                                    prompt_pool=prompt_pool, prompt_key=prompt_key, pool_size=pool_size, top_k=top_k,
                                    batchwise_prompt=batchwise_prompt,
                                    prompt_key_init=prompt_key_init, num_layers=num_e_prompt,
                                    use_prefix_tune_for_e_prompt=use_prefix_tune_for_e_prompt,
                                    num_heads=num_heads, same_key_value=same_key_value)
        
        if not (use_g_prompt or use_e_prompt):
            attn_layer = Attention
        elif not (use_prefix_tune_for_g_prompt or use_prefix_tune_for_e_prompt):
            # Prompt tunning
            attn_layer = Attention
        else:
            # Prefix tunning
            attn_layer = PreT_Attention
        
        self.total_prompt_len = 0
        if self.prompt_pool:
            if not self.use_prefix_tune_for_g_prompt:
                self.total_prompt_len += g_prompt_length * len(self.g_prompt_layer_idx)
            if not self.use_prefix_tune_for_e_prompt:
                self.total_prompt_len += prompt_length * top_k * len(self.e_prompt_layer_idx)
        
        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,
                attn_layer=attn_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)

        # self.act = OrderedDict()

    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, task_id=-1, cls_features=None, train=False, 
                         layer_feat=False, available_mini_model_list=None, enhance_id=None):
        x = self.patch_embed(x)
        # self.act['rep'] = 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:
            feats_l = []
            if self.use_g_prompt or self.use_e_prompt:
                if self.use_prompt_mask and train:
                    start = task_id * self.e_prompt.top_k
                    end = (task_id + 1) * self.e_prompt.top_k
                    single_prompt_mask = torch.arange(start, end).to(x.device)
                    prompt_mask = single_prompt_mask.unsqueeze(0).expand(x.shape[0], -1)
                    if end > self.e_prompt.pool_size:
                        prompt_mask = None
                else:
                    prompt_mask = None
                g_prompt_counter = -1
                e_prompt_counter = -1

                res = self.e_prompt(
                    x, prompt_mask=prompt_mask, cls_features=cls_features, 
                    available_mini_model_list=available_mini_model_list, training=train,
                    enhance_id=enhance_id
                    )
                e_prompt = res['batched_prompt']

                self.g_prefix_feature = {} 
                self.e_prefix_feature = {}

                for i, block in enumerate(self.blocks):
                    if i in self.g_prompt_layer_idx:
                        if self.use_prefix_tune_for_g_prompt:
                            g_prompt_counter += 1
                            # Prefix tunning, [B, 2, g_prompt_length, num_heads, embed_dim // num_heads]
                            idx = torch.tensor([g_prompt_counter] * x.shape[0]).to(x.device)
                            g_prompt = self.g_prompt[idx]
                        else:
                            g_prompt=None
                        x = block(x, prompt=g_prompt)
                        self.g_prefix_feature[i] = {"key": block.attn.key_}
                    elif i in self.e_prompt_layer_idx:
                        e_prompt_counter += 1
                        if self.use_prefix_tune_for_e_prompt:
                            # Prefix tunning, [B, 2, top_k * e_prompt_length, num_heads, embed_dim // num_heads]
                            x = block(x, prompt=e_prompt[e_prompt_counter])
                            self.e_prefix_feature[i] = {"key": block.attn.key_}
                        else:
                            # Pommpt tunning, [B, top_k * e_prompt_length, embed_dim]
                            prompt = e_prompt[e_prompt_counter]
                            x = torch.cat([prompt, x], dim=1)
                            x = block(x)
                    else:
                        x = block(x)
                    feats_l.append(x.detach())
            else:
                for i, block in enumerate(self.blocks):
                    x = block(x)
                    feats_l.append(x.detach())
                res = dict()
                res['layer_feats'] = feats_l

        if layer_feat:
            return {'layer_feats': torch.stack(feats_l, dim=0)}

        x = self.norm(x)
        res['x'] = x

        return res

    def forward_head(self, res, pre_logits: bool=False, layer_feat=False):
        if layer_feat:
            x = res['layer_feats']
            print(x.shape)
            if self.class_token and self.head_type == 'token':
                if self.prompt_pool:
                    x = x[:, :, self.total_prompt_len]
                else:
                    x = x[:, :, 0]
            elif self.head_type == 'gap' and self.global_pool == 'avg':
                x = x.mean(dim=2)
            elif self.head_type == 'prompt' and self.prompt_pool:
                x = x[:, :, 1:(1 + self.total_prompt_len)] if self.class_token else x[:, :, 0:self.total_prompt_len]
                x = x.mean(dim=2)
            elif self.head_type == 'token+prompt' and self.prompt_pool and self.class_token:
                x = x[:, :, 0:self.total_prompt_len + 1]
                x = x.mean(dim=2)
            else:
                raise ValueError(f'Invalid classifier={self.classifier}')
            return {'layer_feats': x}

        x = res['x']
        if self.class_token and self.head_type == 'token':
            if self.prompt_pool:
                x = x[:, self.total_prompt_len]
            else:
                x = x[:, 0]
        elif self.head_type == 'gap' and self.global_pool == 'avg':
            x = x[:, (1 + self.total_prompt_len):].mean(dim=1)
        elif self.head_type == 'prompt' and self.prompt_pool:
            x = x[:, 1:(1 + self.total_prompt_len)] if self.class_token else x[:, 0:self.total_prompt_len]
            x = x.mean(dim=1)
        elif self.head_type == 'token+prompt' and self.prompt_pool and self.class_token:
            x = x[:, 0:self.total_prompt_len + 1]
            x = x.mean(dim=1)
        else:
            raise ValueError(f'Invalid classifier={self.classifier}')

        res['pre_logits'] = x

        x = self.fc_norm(x)

        res['logits'] = self.head(x)

        return res

    def forward(self, x, task_id=-1, cls_features=None, train=False, layer_feat=False, 
                fc_only=False, available_mini_model_list=None,
                enhance_id=None):
        if fc_only:
            res = dict()
            x = self.fc_norm(x)
            res['logits'] = self.head(x)
            return res
        # if layer_feat:
        #     res = self.forward_features(x, task_id=task_id, cls_features=cls_features, train=True, layer_feat=layer_feat)
        #     return self.forward_head(res, layer_feat=True)

        res = self.forward_features(
            x, task_id=task_id, cls_features=cls_features, train=train,
            available_mini_model_list=available_mini_model_list,
            enhance_id=enhance_id,
            )
        res = self.forward_head(res)
        return res


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_prefix_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_prefix_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
    # modify
    _logger.info('Resized position embedding: %s to %s', posemb.shape, posemb_new.shape)
    ntok_new = posemb_new.shape[1]
    if num_prefix_tokens:
        posemb_prefix, posemb_grid = posemb[:, :num_prefix_tokens], posemb[0, num_prefix_tokens:]
        # ntok_new -= num_prefix_tokens
    else:
        posemb_prefix, posemb_grid = posemb[:, :0], posemb[0]
    gs_old = int(math.sqrt(len(posemb_grid)))
    if ntok_new > gs_old ** 2:
        ntok_new -= gs_old ** 2
        # expand cls's pos embedding for prompt tokens
        posemb_prefix = posemb_prefix.expand(-1, ntok_new, -1)
    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_prefix, posemb_grid], dim=1)
    return posemb


def checkpoint_filter_fn(state_dict, model, adapt_layer_scale=False):
    """ convert patch embedding weight from manual patchify + linear proj to conv"""
    import re
    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[1] != model.pos_embed.shape[1]:
            # To resize pos embedding when using model at different size from pretrained weights
            v = resize_pos_embed(
                v,
                model.pos_embed,
                0 if getattr(model, 'no_embed_class') else getattr(model, 'num_prefix_tokens', 1),
                model.patch_embed.grid_size
            )
        elif adapt_layer_scale and 'gamma_' in k:
            # remap layer-scale gamma into sub-module (deit3 models)
            k = re.sub(r'gamma_([0-9])', r'ls\1.gamma', k)
        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, pretrained_cfg=kwargs.pop('pretrained_cfg', None))
    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


@register_model
def vit_base_patch16_224_deit(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=False, **model_kwargs)
    #del model.head
    state_dict = model.state_dict()
    ckpt = torch.load('./checkpoints/deit_base_patch16_224-b5f2ef4d.pth', map_location='cpu')['model']
    not_in_k = [k for k in ckpt.keys() if k not in state_dict.keys()]
    head = [k for k in ckpt.keys() if 'head' in k]
    for k in not_in_k:
        del ckpt[k]
    for k in head:
        del ckpt[k]
    state_dict.update(ckpt)
    model.load_state_dict(state_dict)
    # del model.norm
    # model.norm = nn.LayerNorm(768)
    return model


# add dino
@register_model
def vit_base_patch16_224_dino(pretrained=False, **kwargs):
    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=False, **model_kwargs)
    #del model.head
    state_dict = model.state_dict()
    #ckpt = torch.load('/home/work/xiejingyi/SLCA_code/convs/dino_vitbase16_pretrain.pth', map_location='cpu')
    ckpt = torch.load('./checkpoints/dino_vitbase16_pretrain.pth', map_location='cpu')
    not_in_k = [k for k in ckpt.keys() if k not in state_dict.keys()]
    for k in not_in_k:
        del ckpt[k]
    state_dict.update(ckpt)
    model.load_state_dict(state_dict)
    # del model.norm
    # model.norm = nn.LayerNorm(768)
    return model


# add ibot
@register_model
def vit_base_patch16_224_ibot(pretrained=False, **kwargs):
    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=False, **model_kwargs)
    #del model.head
    state_dict = model.state_dict()
    #ckpt = torch.load('/home/work/xiejingyi/SLCA_code/convs/ibot_vitbase16_pretrain.pth', map_location='cpu')['state_dict']
    ckpt = torch.load('./checkpoints/ibot_vitbase16_pretrain.pth', map_location='cpu')['state_dict']
    not_in_k = [k for k in ckpt.keys() if k not in state_dict.keys()]
    for k in not_in_k:
        del ckpt[k]
    state_dict.update(ckpt)
    model.load_state_dict(state_dict)
    # del model.norm
    # model.norm = nn.LayerNorm(768)
    return model

# add ibot
@register_model
def vit_base_patch16_224_21k_ibot(pretrained=False, **kwargs):
    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=False, **model_kwargs)
    # del model.head
    state_dict = model.state_dict()
    #s_ckpt = torch.load('/home/work/xiejingyi/SLCA_code/convs/checkpoint.pth', map_location='cpu')['teacher']
    s_ckpt = torch.load('./checkpoints/checkpoint.pth', map_location='cpu')['teacher']
    ckpt = {}
    for key, val in s_ckpt.items():
        new_key = key.replace('backbone.', '')
        ckpt[new_key] = val
    not_in_k = [k for k in ckpt.keys() if k not in state_dict.keys()]
    for k in not_in_k:
        del ckpt[k]
    state_dict.update(ckpt)
    model.load_state_dict(state_dict)
    # del model.norm
    # model.norm = nn.LayerNorm(768)
    return model

@register_model
def vit_base_patch16_224_mocov3(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, fc_norm=True, **kwargs)
    model = _create_vision_transformer('vit_base_patch16_224_in21k', pretrained=False, **model_kwargs)
    # del model.head
    #ckpt = torch.load('/home/work/xiejingyi/SLCA_code/convs/mocov3-vit-base-300ep.pth', map_location='cpu')['model']
    ckpt = torch.load('./checkpoints/mocov3-vit-base-300ep.pth', map_location='cpu')['model']
    state_dict = model.state_dict()
    not_in_k = [k for k in ckpt.keys() if k not in state_dict.keys()]
    for k in not_in_k:
        del ckpt[k]
    state_dict.update(ckpt)
    model.load_state_dict(state_dict)
    # del model.norm
    # model.norm = nn.Identity() # nn.Identity()
    # if adapter:
    #    del model.adp_layer1
    #    model.adp_layer1 = deepcopy(model.patch_embed)
    return model

@register_model
def vit_base_patch16_224_mae(pretrained=False, adapter=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, global_pool='avg', **kwargs)
    model = _create_vision_transformer('vit_base_patch16_224_in21k', pretrained=False, **model_kwargs)
    #del model.head
    last_block = deepcopy(model.blocks[-1])
    ckpt = torch.load('./checkpoints/mae_pretrain_vit_base.pth', map_location='cpu')['model']
    state_dict = model.state_dict()
    not_in_k = [k for k in ckpt.keys() if k not in state_dict.keys()]
    for k in not_in_k:
        del ckpt[k]
    #print(ckpt.keys())
    state_dict.update(ckpt)
    model.load_state_dict(state_dict)
    # model.load_state_dict(torch.load('mae_pretrain_vit_base.pth', map_location='cpu')['model'])
    # del model.norm
    # model.norm = nn.LayerNorm(768)
    # model.blocks[-1] = last_block
    # if adapter:
    #    del model.adp_layer1
    #    model.adp_layer1 = deepcopy(model.patch_embed)
    return model


@register_model
def vit_small_patch16_224_ims(pretrained=False, **kwargs):
    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=False, **model_kwargs)
    #del model.head
    state_dict = model.state_dict()
    ckpt = torch.load('./checkpoints/best_checkpoint.pth', map_location='cpu')['model']
    ckpt_keys = ckpt.keys()
    not_in_k = [k for k in ckpt.keys() if k not in state_dict.keys()]
    head = [k for k in ckpt.keys() if 'head' in k]
    for k in head:
        del ckpt[k]
    for k in not_in_k:
        del ckpt[k]
    state_dict.update(ckpt)
    model.load_state_dict(state_dict)
    # del model.norm
    # model.norm = nn.LayerNorm(768)
    return model
