""" Vision Transformer (ViT) in PyTorch

A PyTorch implement of Vision Transformers as described in
'An Image Is Worth 16 x 16 Words: Transformers for Image Recognition at Scale' - https://arxiv.org/abs/2010.11929

The official jax code is released and available at https://github.com/google-research/vision_transformer

Status/TODO:
* Models updated to be compatible with official impl. Args added to support backward compat for old PyTorch weights.
* Weights ported from official jax impl for 384x384 base and small models, 16x16 and 32x32 patches.
* Trained (supervised on ImageNet-1k) my custom 'small' patch model to 77.9, 'base' to 79.4 top-1 with this code.
* Hopefully find time and GPUs for SSL or unsupervised pretraining on OpenImages w/ ImageNet fine-tune in future.

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 torch
import torch.nn as nn
import warnings
from functools import partial

from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.models.helpers import load_pretrained
from timm.models.layers import DropPath, to_2tuple, trunc_normal_
from timm.models.resnet import resnet26d, resnet50d
from timm.models.registry import register_model
from torch.nn.modules.activation import ReLU
from .lsq_layer import  QuantLinear, QuantAct, QuantConv2d
from .binary_layer import BinaryActivation, BinaryLinear



def _cfg(url='', **kwargs):
    return {
        'url': url,
        'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': None,
        'crop_pct': .9, 'interpolation': 'bicubic',
        'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
        'first_conv': 'patch_embed.proj', 'classifier': 'head',
        **kwargs
    }


default_cfgs = {
    # patch models
    'vit_small_patch16_224': _cfg(
        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/vit_small_p16_224-15ec54c9.pth',
    ),
    'vit_base_patch16_224': _cfg(
        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_p16_224-80ecf9dd.pth',
        mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5),
    ),
    'vit_base_patch16_384': _cfg(
        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_p16_384-83fb41ba.pth',
        input_size=(3, 384, 384), mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0),
    'vit_base_patch32_384': _cfg(
        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_p32_384-830016f5.pth',
        input_size=(3, 384, 384), mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0),
    'vit_large_patch16_224': _cfg(
        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_large_p16_224-4ee7a4dc.pth',
        mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)),
    'vit_large_patch16_384': _cfg(
        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_large_p16_384-b3be5167.pth',
        input_size=(3, 384, 384), mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0),
    'vit_large_patch32_384': _cfg(
        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_large_p32_384-9b920ba8.pth',
        input_size=(3, 384, 384), mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0),
    'vit_huge_patch16_224': _cfg(),
    'vit_huge_patch32_384': _cfg(input_size=(3, 384, 384)),
    # hybrid models
    'vit_small_resnet26d_224': _cfg(),
    'vit_small_resnet50d_s3_224': _cfg(),
    'vit_base_resnet26d_224': _cfg(),
    'vit_base_resnet50d_224': _cfg(),
}


class Mlp(nn.Module):
    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.,
                wbits=-1, abits=-1, offset=False, learned=True):
        super().__init__()
        out_features = out_features or in_features
        hidden_features = hidden_features or in_features

        self.quant1 = BinaryActivation() if abits == 1 else QuantAct(nbits=abits, offset=offset, learned=learned)
        self.fc1 = BinaryLinear(in_features, hidden_features) if wbits == 1 else QuantLinear(in_features, hidden_features, nbits=wbits, learned=learned)
        self.act = act_layer(inplace=True) if isinstance(act_layer, nn.ReLU) else act_layer()
        self.quant2 = BinaryActivation() if abits == 1 else QuantAct(nbits=abits, signed=False, offset=True, learned=learned)
        self.fc2 = BinaryLinear(hidden_features, out_features) if wbits == 1 else QuantLinear(hidden_features, out_features, nbits=wbits, learned=learned)
        self.drop = nn.Dropout(drop)

    def forward(self, x):
        x = self.quant1(x)
        x = self.fc1(x)
        x = self.act(x)
        x = self.drop(x)
        x = self.quant2(x)
        x = self.fc2(x)
        x = self.drop(x)
        # print(self.quant2.alpha[:4], self.quant2.beta)
        return x


class Attention(nn.Module):
    def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.,
                wbits=-1, abits=-1, offset=False, learned=True):
        super().__init__()
        self.num_heads = num_heads
        head_dim = dim // num_heads
        # NOTE scale factor was wrong in my original version, can set manually to be compat with prev weights
        self.scale = qk_scale or head_dim ** -0.5

        self.quant_input = BinaryActivation() if abits == 1 else QuantAct(nbits=abits, offset=offset, learned=learned)
        # self.qkv = BinaryLinear(dim, dim * 3, bias=qkv_bias) if wbits == 1 else QuantLinear(dim, dim * 3, bias=qkv_bias, nbits=wbits)
        self.proj_q = BinaryLinear(dim, dim, bias=qkv_bias) if wbits == 1 else QuantLinear(dim, dim, bias=qkv_bias, nbits=wbits, learned=learned)
        self.proj_k = BinaryLinear(dim, dim, bias=qkv_bias) if wbits == 1 else QuantLinear(dim, dim, bias=qkv_bias, nbits=wbits, learned=learned)
        self.proj_v = BinaryLinear(dim, dim, bias=qkv_bias) if wbits == 1 else QuantLinear(dim, dim, bias=qkv_bias, nbits=wbits, learned=learned)
        self.attn_drop = nn.Dropout(attn_drop)

        self.quant_q = BinaryActivation() if abits == 1 else QuantAct(nbits=abits, offset=offset, learned=learned)
        self.quant_k = BinaryActivation() if abits == 1 else QuantAct(nbits=abits, offset=offset, learned=learned)
        self.quant_v = BinaryActivation() if abits == 1 else QuantAct(nbits=abits, offset=offset, learned=learned)
        self.quant_attn = BinaryActivation() if abits == 1 else QuantAct(nbits=abits, signed=False, offset=False, learned=learned)

        self.quant_output = BinaryActivation() if abits == 1 else QuantAct(nbits=abits, offset=offset, learned=learned)
        self.proj = BinaryLinear(dim, dim) if wbits == 1 else QuantLinear(dim, dim, nbits=wbits, learned=learned)
        self.proj_drop = nn.Dropout(proj_drop)

    def forward(self, x):
        B, N, C = x.shape
        x = self.quant_input(x)
        # qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
        # q.shape : B H N D
        q = self.proj_q(x).reshape(B, N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)
        k = self.proj_k(x).reshape(B, N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)
        v = self.proj_v(x).reshape(B, N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)
        # q, k, v = qkv[0], qkv[1], qkv[2]
        q, k, v = self.quant_q(q), self.quant_k(k), self.quant_v(v)

        attn = (q @ k.transpose(-2, -1)) * self.scale
        attn = attn.softmax(dim=-1)
        attn = self.attn_drop(attn)
        attn = self.quant_attn(attn)

        x = (attn @ v).transpose(1, 2).reshape(B, N, C)
        x = self.quant_output(x)
        x = self.proj(x)
        x = self.proj_drop(x)
        return x


class Block(nn.Module):

    def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
                 drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm,
                 wbits=-1, abits=-1, offset=False, learned=True):
        super().__init__()
        self.norm1 = norm_layer(dim)
        self.attn = Attention(
            dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop,
            proj_drop=drop, wbits=wbits, abits=abits, offset=offset, learned=learned)
        # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
        self.norm2 = norm_layer(dim)
        mlp_hidden_dim = int(dim * mlp_ratio)
        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer,
        drop=drop, wbits=wbits, abits=abits, offset=offset, learned=learned)

    def forward(self, x):
        x = x + self.drop_path(self.attn(self.norm1(x)))
        x = x + self.drop_path(self.mlp(self.norm2(x)))
        return x


class PatchEmbed(nn.Module):
    """ Image to Patch Embedding
    """
    def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768, learned=True):
        super().__init__()
        img_size = to_2tuple(img_size)
        patch_size = to_2tuple(patch_size)
        num_patches = (img_size[1] // patch_size[1]) * (img_size[0] // patch_size[0])
        self.img_size = img_size
        self.patch_size = patch_size
        self.num_patches = num_patches

        self.quant = QuantAct(nbits=8, signed=True, learned=learned)
        self.proj = QuantConv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size, nbits=8, learned=learned)

    def forward(self, x):
        B, C, H, W = x.shape
        # FIXME look at relaxing size constraints
        assert H == self.img_size[0] and W == self.img_size[1], \
            f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
        # x = self.quant(x - 0.456) + 0.456
        x = self.quant(x)
        x = self.proj(x).flatten(2).transpose(1, 2)
        return x


class HybridEmbed(nn.Module):
    """ CNN Feature Map Embedding
    Extract feature map from CNN, flatten, project to embedding dim.
    """
    def __init__(self, backbone, img_size=224, feature_size=None, in_chans=3, embed_dim=768):
        super().__init__()
        assert isinstance(backbone, nn.Module)
        img_size = to_2tuple(img_size)
        self.img_size = img_size
        self.backbone = backbone
        if feature_size is None:
            with torch.no_grad():
                # FIXME this is hacky, but most reliable way of determining the exact dim of the output feature
                # map for all networks, the feature metadata has reliable channel and stride info, but using
                # stride to calc feature dim requires info about padding of each stage that isn't captured.
                training = backbone.training
                if training:
                    backbone.eval()
                o = self.backbone(torch.zeros(1, in_chans, img_size[0], img_size[1]))[-1]
                feature_size = o.shape[-2:]
                feature_dim = o.shape[1]
                backbone.train(training)
        else:
            feature_size = to_2tuple(feature_size)
            feature_dim = self.backbone.feature_info.channels()[-1]
        self.num_patches = feature_size[0] * feature_size[1]
        self.proj = nn.Linear(feature_dim, embed_dim)

    def forward(self, x):
        x = self.backbone(x)[-1]
        x = x.flatten(2).transpose(1, 2)
        x = self.proj(x)
        return x


class QuantVisionTransformer(nn.Module):
    """ Vision Transformer with support for patch or hybrid CNN input stage
    """
    def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, embed_dim=768, depth=12,
                 num_heads=12, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop_rate=0., attn_drop_rate=0.,
                 drop_path_rate=0., hybrid_backbone=None, norm_layer=nn.LayerNorm,
                 wbits=-1, abits=-1, act_layer=nn.GELU, offset=False, learned=True):
        super().__init__()
        if wbits == -1:
            print("Use float weights.")
        else:
            print(f"Use {wbits} bit weights.")
        if abits == -1:
            print("Use float activations.")
        else:
            print(f"Use {abits} bit activations.")
            
        self.num_classes = num_classes
        self.num_features = self.embed_dim = embed_dim  # num_features for consistency with other models

        if hybrid_backbone is not None:
            self.patch_embed = HybridEmbed(
                hybrid_backbone, img_size=img_size, in_chans=in_chans, embed_dim=embed_dim)
        else:
            self.patch_embed = PatchEmbed(
                img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim, learned=learned)
        num_patches = self.patch_embed.num_patches

        self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
        self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dim))
        self.pos_drop = nn.Dropout(p=drop_rate)

        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)]  # stochastic depth decay rule
        if act_layer == nn.ReLU:
            print('using relu nonlinearity')
        self.blocks = nn.ModuleList([
            Block(
                dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,
                drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer,
                wbits=wbits, abits=abits, act_layer=act_layer, offset=offset, learned=learned)
            for i in range(depth)])
        self.norm = norm_layer(embed_dim)

        # NOTE as per official impl, we could have a pre-logits representation dense layer + tanh here
        #self.repr = nn.Linear(embed_dim, representation_size)
        #self.repr_act = nn.Tanh()

        # Classifier head
        self.quant = QuantAct(nbits=8, offset=offset, learned=learned)
        self.head = QuantLinear(embed_dim, num_classes, nbits=8, learned=learned) if num_classes > 0 else nn.Identity()

        trunc_normal_(self.pos_embed, std=.02)
        trunc_normal_(self.cls_token, std=.02)
        self.apply(self._init_weights)

    def _init_weights(self, m):
        if isinstance(m, nn.Linear):
            trunc_normal_(m.weight, std=.02)
            if isinstance(m, nn.Linear) and m.bias is not None:
                nn.init.constant_(m.bias, 0)
        elif isinstance(m, nn.LayerNorm):
            nn.init.constant_(m.bias, 0)
            nn.init.constant_(m.weight, 1.0)

    @torch.jit.ignore
    def no_weight_decay(self):
        return {'pos_embed', 'cls_token'}

    def get_classifier(self):
        return self.head

    def reset_classifier(self, num_classes, global_pool=''):
        self.num_classes = num_classes
        self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()

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

        cls_tokens = self.cls_token.expand(B, -1, -1)  # stole cls_tokens impl from Phil Wang, thanks
        x = torch.cat((cls_tokens, x), dim=1)
        x = x + self.pos_embed
        x = self.pos_drop(x)

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

        x = self.norm(x)
        return x[:, 0]
        # return x.mean(dim=1)

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


def _conv_filter(state_dict, patch_size=16):
    """ convert patch embedding weight from manual patchify + linear proj to conv"""
    out_dict = {}
    for k, v in state_dict.items():
        if 'patch_embed.proj.weight' in k:
            v = v.reshape((v.shape[0], 3, patch_size, patch_size))
        out_dict[k] = v
    return out_dict

@register_model
def deit_tiny_patch16_224(pretrained=False, **kwargs):
    model = QuantVisionTransformer(
        patch_size=16, embed_dim=192, depth=12, num_heads=3, mlp_ratio=4, qkv_bias=True,
        norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
    model.default_cfg = _cfg()
    if pretrained:
        checkpoint = torch.hub.load_state_dict_from_url(
            url="https://dl.fbaipublicfiles.com/deit/deit_tiny_patch16_224-a1311bcf.pth",
            map_location="cpu", check_hash=True
        )
        model.load_state_dict(checkpoint["model"])
    return model



@register_model
def deit_small_patch16_224(pretrained=False, **kwargs):
    model = QuantVisionTransformer(
        patch_size=16, embed_dim=384, depth=12, num_heads=6, mlp_ratio=4, qkv_bias=True,
        norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
    model.default_cfg = _cfg()
    if pretrained:
        checkpoint = torch.hub.load_state_dict_from_url(
            url="https://dl.fbaipublicfiles.com/deit/deit_small_patch16_224-cd65a155.pth",
            map_location="cpu", check_hash=True
        )
        model.load_state_dict(checkpoint["model"])
    return model

@register_model
def deit_base_patch16_224(pretrained=False, **kwargs):
    model = QuantVisionTransformer(
        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)
    model.default_cfg = _cfg()
    if pretrained:
        checkpoint = torch.hub.load_state_dict_from_url(
            url="https://dl.fbaipublicfiles.com/deit/deit_base_patch16_224-b5f2ef4d.pth",
            map_location="cpu", check_hash=True
        )
        model.load_state_dict(checkpoint["model"])
    return model