""" 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 os

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint

from data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD,\
    OPENAI_CLIP_MEAN, OPENAI_CLIP_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 getPi(mask, another_encryption=False):
    dim=mask.shape[0]
    if another_encryption:
        p=torch.rand(size=(dim,dim))
        ip=torch.linalg.inv(p)   
        return p,ip
    p=torch.zeros([dim,dim],dtype=torch.float)
    for i in range(dim):
        p[i][mask[i]]=1
    ip = torch.linalg.inv (p)    
    return p,ip
def getPi_Random(dim=197):
    mask = torch.randperm(dim)
    p,ip=getPi(mask,False)
    p,ip=p.to("cuda"),ip.to("cuda")
    return p,ip

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://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tresnet/vit_base_patch16_224_in21k_miil-887286df.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://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tresnet/vit_base_patch16_224_1k_miil_84_4-2deb18e3.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=''),

    'vit_base_patch32_224_clip_laion2b': _cfg(
        hf_hub_id='laion/CLIP-ViT-B-32-laion2B-s34B-b79K',
        hf_hub_filename='open_clip_pytorch_model.bin',
        mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, num_classes=512),
    'vit_large_patch14_224_clip_laion2b': _cfg(
        hf_hub_id='laion/CLIP-ViT-L-14-laion2B-s32B-b82K',
        hf_hub_filename='open_clip_pytorch_model.bin',
        mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD, num_classes=768),
    'vit_huge_patch14_224_clip_laion2b': _cfg(
        hf_hub_id='laion/CLIP-ViT-H-14-laion2B-s32B-b79K',
        hf_hub_filename='open_clip_pytorch_model.bin',
        mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, num_classes=1024),
    'vit_giant_patch14_224_clip_laion2b': _cfg(
        hf_hub_id='CLIP-ViT-g-14-laion2B-s12B-b42K',
        hf_hub_filename='open_clip_pytorch_model.bin',
        mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, num_classes=1024),

}


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,
            class_token=True,
            no_embed_class=False,
            pre_norm=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,
    ):
        """
        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
        """
        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_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,
            bias=not pre_norm,  # disable bias if pre-norm is used (e.g. CLIP)
        )
        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.norm_pre = norm_layer(embed_dim) if pre_norm else nn.Identity()

        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)
        #keys
        self.mask = torch.randperm(num_patches+1)
        self.p,self.ip=getPi(self.mask,False)
        #self.p,self.ip=self.p.to("cuda"),self.ip.to("cuda")
        #self.pc,self.ipc=getPi(self.maskc)
        file_dir = os.path.dirname(__file__)
        key_dir = os.path.join(file_dir, "key_768.pt")
        unkey_dir = os.path.join(file_dir, "unkey_768.pt")
        self.pc=torch.load(key_dir)
        self.ipc=torch.load(unkey_dir)
        self.pc,self.ipc=self.pc.to("cuda"),self.ipc.to("cuda")
        
        
    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 _pos_embed(self, x):
        if self.no_embed_class:
            # deit-3, updated JAX (big vision)
            # position embedding does not overlap with class token, add then concat
            # x = x + self.pos_embed
            if self.cls_token is not None:
                x = torch.cat((self.cls_token.expand(x.shape[0], -1, -1), x), dim=1)
        else:
            # original timm, JAX, and deit vit impl
            # pos_embed has entry for class token, concat then add
            if self.cls_token is not None:
                x = torch.cat((self.cls_token.expand(x.shape[0], -1, -1), x), dim=1)
            # x = x + self.pos_embed
        return self.pos_drop(x)

    def forward_features(self, x):
        x = self.patch_embed(x)
        x = self._pos_embed(x)
        x = self.norm_pre(x)
        if self.grad_checkpointing and not torch.jit.is_scripting():
            x = checkpoint_seq(self.blocks, x)
        else:
            self.p,self.ip = getPi_Random(197)
            # self.p,self.ip=self.p.to("cuda"),self.ip.to("cuda")
            x=torch.matmul(self.p,x)
            x=torch.matmul(x,self.ipc)
            x = self.blocks(x)
            x=torch.matmul(self.ip,x)
            x=torch.matmul(x,self.pc)
        x = self.norm(x)
        return x

    def forward_head(self, x, pre_logits: bool = False):
        if self.global_pool:
            x = x[:, self.num_prefix_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_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
    _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 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 _convert_openai_clip(state_dict, model):
    out_dict = {}
    swaps = [
        ('visual.', ''), ('conv1', 'patch_embed.proj'), ('positional_embedding', 'pos_embed'),
        ('transformer.resblocks.', 'blocks.'), ('ln_pre', 'norm_pre'), ('ln_post', 'norm'), ('ln_', 'norm'),
        ('in_proj_', 'qkv.'), ('out_proj', 'proj'), ('mlp.c_fc', 'mlp.fc1'), ('mlp.c_proj', 'mlp.fc2'),
    ]
    for k, v in state_dict.items():
        if not k.startswith('visual.'):
            continue
        for sp in swaps:
            k = k.replace(sp[0], sp[1])

        if k == 'proj':
            k = 'head.weight'
            v = v.transpose(0, 1)
            out_dict['head.bias'] = torch.zeros(v.shape[0])
        elif k == 'class_embedding':
            k = 'cls_token'
            v = v.unsqueeze(0).unsqueeze(1)
        elif k == 'pos_embed':
            v = v.unsqueeze(0)
            if 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
                )
        out_dict[k] = v
    return out_dict


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']

    if 'visual.class_embedding' in state_dict:
        return _convert_openai_clip(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 (little-g) 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 (big-G) 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_patch32_224_clip_laion2b(pretrained=False, **kwargs):
    """ ViT-B/32
    Pretrained weights from CLIP image tower trained on LAION-2B image-text pairs.
    """
    model_kwargs = dict(patch_size=32, embed_dim=768, depth=12, num_heads=12, pre_norm=True, **kwargs)
    model = _create_vision_transformer('vit_base_patch32_224_clip_laion2b', pretrained=pretrained, **model_kwargs)
    return model


@register_model
def vit_large_patch14_224_clip_laion2b(pretrained=False, **kwargs):
    """ ViT-Large model (ViT-L/14)
    Pretrained weights from CLIP image tower trained on LAION-2B image-text pairs.
    """
    model_kwargs = dict(patch_size=14, embed_dim=1024, depth=24, num_heads=16, pre_norm=True, **kwargs)
    model = _create_vision_transformer('vit_large_patch14_224_clip_laion2b', pretrained=pretrained, **model_kwargs)
    return model


@register_model
def vit_huge_patch14_224_clip_laion2b(pretrained=False, **kwargs):
    """ ViT-Huge model (ViT-H/14) from original paper (https://arxiv.org/abs/2010.11929).
    Pretrained weights from CLIP image tower trained on LAION-2B image-text pairs.
    """
    model_kwargs = dict(patch_size=14, embed_dim=1280, depth=32, num_heads=16, pre_norm=True, **kwargs)
    model = _create_vision_transformer('vit_huge_patch14_224_clip_laion2b', pretrained=pretrained, **model_kwargs)
    return model


@register_model
def vit_giant_patch14_224_clip_laion2b(pretrained=False, **kwargs):
    """ ViT-Giant (little-g) model (ViT-g/14) from `Scaling Vision Transformers` - https://arxiv.org/abs/2106.04560
    Pretrained weights from CLIP image tower trained on LAION-2B image-text pairs.
    """
    model_kwargs = dict(
        patch_size=14, embed_dim=1408, mlp_ratio=48/11, depth=40, num_heads=16, pre_norm=True, **kwargs)
    model = _create_vision_transformer('vit_giant_patch14_224_clip_laion2b', pretrained=pretrained, **model_kwargs)
    return model