import torch
import torch.nn as nn
import timm
from timm.models.vision_transformer import PatchEmbed
from functools import partial


class vit(timm.models.vision_transformer.VisionTransformer):
    def __init__(self, global_pool=False, **kwargs):
        super(vit, self).__init__()
        self.global_pool = global_pool
        embed_dim = kwargs['embed_dim']
        num_classes = kwargs['num_classes']
        self.head = nn.Linear(embed_dim, num_classes, bias=True)
        if self.global_pool:
            norm_layer = kwargs['norm_layer']
            embed_dim = kwargs['embed_dim']
            self.fc_norm = norm_layer(embed_dim)

            del self.norm  

        for param in self.parameters():
            param.requires_grad = False

        for param in self.head.parameters():
            param.requires_grad = True

    def forward_features(self, x):
        B = x.shape[0]
        x = self.patch_embed(x)

        cls_tokens = self.cls_token.expand(B, -1, -1) 
        x = torch.cat((cls_tokens, x), dim=1)
        x = x + self.pos_embed
        x = self.pos_drop(x)

        for blk in self.blocks:
            x = blk(x)

        if self.global_pool:
            x = x[:, 1:, :].mean(dim=1) 
            outcome = self.fc_norm(x)
        else:
            x = self.norm(x)
            outcome = x[:, 0]

        return outcome
    
    def forward(self, x):
        x = self.forward_features(x)
        x = self.head(x)
        return x 


def vit_base_patch16(**kwargs):
    model = vit(img_size=224, patch_size=16, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4, qkv_bias=True,
                 norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
    return model


def vit_large_patch16(**kwargs):
    model = vit(patch_size=16, embed_dim=1024, depth=24, num_heads=16, mlp_ratio=4, qkv_bias=True,
                norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
    return model


def vit_huge_patch14(**kwargs):
    model = vit(patch_size=14, embed_dim=1280, depth=32, num_heads=16, mlp_ratio=4, qkv_bias=True,
                norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
    return model