"""These modules are adapted from those of timm, see
https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
"""

import torch
import torch.nn as nn
from functools import partial
import torch.nn.functional as F
from .utils.utils import *

import torch
import torch.nn as nn

from ..registry import register_model

__all__ = ["convit_tiny", "convit_small", "convit_base"]


class PatchEmbed(nn.Module):
    """2D Image to Patch Embedding"""

    def __init__(
        self,
        img_size=32,
        patch_size=16,
        in_chans=3,
        embed_dim=768,
        norm_layer=None,
        flatten=True,
    ):
        super().__init__()
        img_size = (img_size, img_size)
        patch_size = (patch_size, patch_size)
        self.img_size = img_size
        self.patch_size = patch_size
        self.grid_size = (img_size[0] // patch_size[0], img_size[1] // patch_size[1])
        self.num_patches = self.grid_size[0] * self.grid_size[1]
        self.flatten = flatten

        self.proj = nn.Conv2d(
            in_chans, embed_dim, kernel_size=patch_size, stride=patch_size
        )
        self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity()

    def forward(self, x):
        B, C, H, W = x.shape
        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.proj(x)
        if self.flatten:
            x = x.flatten(2).transpose(1, 2)  # BCHW -> BNC
        x = self.norm(x)
        return x


class GPSA(nn.Module):
    def __init__(
        self,
        dim,
        num_heads=8,
        qkv_bias=False,
        attn_drop=0.0,
        proj_drop=0.0,
        locality_strength=1.0,
    ):
        super().__init__()
        self.num_heads = num_heads
        self.dim = dim
        head_dim = dim // num_heads
        self.scale = head_dim ** -0.5
        self.locality_strength = locality_strength

        self.qk = nn.Linear(dim, dim * 2, bias=qkv_bias)
        self.v = nn.Linear(dim, dim, bias=qkv_bias)

        self.attn_drop = nn.Dropout(attn_drop)
        self.proj = nn.Linear(dim, dim)
        self.pos_proj = nn.Linear(3, num_heads)
        self.proj_drop = nn.Dropout(proj_drop)
        self.gating_param = nn.Parameter(torch.ones(self.num_heads))
        self.rel_indices: torch.Tensor = torch.zeros(
            1, 1, 1, 3
        )  # silly torchscript hack, won't work with None

    def forward(self, x):
        B, N, C = x.shape
        if self.rel_indices is None or self.rel_indices.shape[1] != N:
            self.rel_indices = self.get_rel_indices(N)
        attn = self.get_attention(x)
        v = (
            self.v(x)
            .reshape(B, N, self.num_heads, C // self.num_heads)
            .permute(0, 2, 1, 3)
        )
        x = (attn @ v).transpose(1, 2).reshape(B, N, C)
        x = self.proj(x)
        x = self.proj_drop(x)
        return x

    def get_attention(self, x):
        B, N, C = x.shape
        qk = (
            self.qk(x)
            .reshape(B, N, 2, self.num_heads, C // self.num_heads)
            .permute(2, 0, 3, 1, 4)
        )
        q, k = qk[0], qk[1]
        pos_score = self.rel_indices.expand(B, -1, -1, -1).to(x.device)
        pos_score = self.pos_proj(pos_score).permute(0, 3, 1, 2)
        patch_score = (q @ k.transpose(-2, -1)) * self.scale
        patch_score = patch_score.softmax(dim=-1)
        pos_score = pos_score.softmax(dim=-1)

        gating = self.gating_param.view(1, -1, 1, 1)
        attn = (1.0 - torch.sigmoid(gating)) * patch_score + torch.sigmoid(
            gating
        ) * pos_score
        attn /= attn.sum(dim=-1).unsqueeze(-1)
        attn = self.attn_drop(attn)
        return attn

    def get_attention_map(self, x, return_map=False):
        attn_map = self.get_attention(x).mean(0)  # average over batch
        distances = self.rel_indices.squeeze()[:, :, -1] ** 0.5
        dist = torch.einsum("nm,hnm->h", (distances, attn_map)) / distances.size(0)
        if return_map:
            return dist, attn_map
        else:
            return dist

    def local_init(self):
        self.v.weight.data.copy_(torch.eye(self.dim))
        locality_distance = 1  # max(1,1/locality_strength**.5)

        kernel_size = int(self.num_heads ** 0.5)
        center = (kernel_size - 1) / 2 if kernel_size % 2 == 0 else kernel_size // 2
        for h1 in range(kernel_size):
            for h2 in range(kernel_size):
                position = h1 + kernel_size * h2
                self.pos_proj.weight.data[position, 2] = -1
                self.pos_proj.weight.data[position, 1] = (
                    2 * (h1 - center) * locality_distance
                )
                self.pos_proj.weight.data[position, 0] = (
                    2 * (h2 - center) * locality_distance
                )
        self.pos_proj.weight.data *= self.locality_strength

    def get_rel_indices(self, num_patches: int) -> torch.Tensor:
        img_size = int(num_patches ** 0.5)
        rel_indices = torch.zeros(1, num_patches, num_patches, 3)
        ind = torch.arange(img_size).view(1, -1) - torch.arange(img_size).view(-1, 1)
        indx = ind.repeat(img_size, img_size)
        indy = ind.repeat_interleave(img_size, dim=0).repeat_interleave(img_size, dim=1)
        indd = indx ** 2 + indy ** 2
        rel_indices[:, :, :, 2] = indd.unsqueeze(0)
        rel_indices[:, :, :, 1] = indy.unsqueeze(0)
        rel_indices[:, :, :, 0] = indx.unsqueeze(0)
        device = self.qk.weight.device
        return rel_indices.to(device)


class HybridEmbed(nn.Module):
    """CNN Feature Map Embedding
    Extract feature map from CNN, flatten, project to embedding dim.
    """

    def __init__(
        self,
        backbone,
        img_size=32,
        patch_size=1,
        feature_size=None,
        in_chans=3,
        embed_dim=768,
    ):
        super().__init__()
        assert isinstance(backbone, nn.Module)
        img_size = (img_size, img_size)
        patch_size = (patch_size, patch_size)
        self.img_size = img_size
        self.patch_size = patch_size
        self.backbone = backbone
        if feature_size is None:
            with torch.no_grad():
                # NOTE Most reliable way of determining output dims is to run forward pass
                training = backbone.training
                if training:
                    backbone.eval()
                o = self.backbone(torch.zeros(1, in_chans, img_size[0], img_size[1]))
                if isinstance(o, (list, tuple)):
                    o = o[-1]  # last feature if backbone outputs list/tuple of features
                feature_size = o.shape[-2:]
                feature_dim = o.shape[1]
                backbone.train(training)
        else:
            feature_size = (feature_size, feature_size)
            if hasattr(self.backbone, "feature_info"):
                feature_dim = self.backbone.feature_info.channels()[-1]
            else:
                feature_dim = self.backbone.num_features
        assert (
            feature_size[0] % patch_size[0] == 0
            and feature_size[1] % patch_size[1] == 0
        )
        self.grid_size = (
            feature_size[0] // patch_size[0],
            feature_size[1] // patch_size[1],
        )
        self.num_patches = self.grid_size[0] * self.grid_size[1]
        self.proj = nn.Conv2d(
            feature_dim, embed_dim, kernel_size=patch_size, stride=patch_size
        )

    def forward(self, x):
        x = self.backbone(x)
        if isinstance(x, (list, tuple)):
            x = x[-1]  # last feature if backbone outputs list/tuple of features
        x = self.proj(x).flatten(2).transpose(1, 2)
        return x


class MHSA(nn.Module):
    def __init__(self, dim, num_heads=8, qkv_bias=False, attn_drop=0.0, proj_drop=0.0):
        super().__init__()
        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 get_attention_map(self, x, return_map=False):
        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[0], qkv[1], qkv[2]
        attn_map = (q @ k.transpose(-2, -1)) * self.scale
        attn_map = attn_map.softmax(dim=-1).mean(0)

        img_size = int(N ** 0.5)
        ind = torch.arange(img_size).view(1, -1) - torch.arange(img_size).view(-1, 1)
        indx = ind.repeat(img_size, img_size)
        indy = ind.repeat_interleave(img_size, dim=0).repeat_interleave(img_size, dim=1)
        indd = indx ** 2 + indy ** 2
        distances = indd ** 0.5
        distances = distances.to("cuda")

        dist = torch.einsum("nm,hnm->h", (distances, attn_map)) / N
        if return_map:
            return dist, attn_map
        else:
            return dist

    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[0], qkv[1], qkv[2]

        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 Block(nn.Module):
    def __init__(
        self,
        dim,
        num_heads,
        mlp_ratio=4.0,
        qkv_bias=False,
        drop=0.0,
        attn_drop=0.0,
        drop_path=0.0,
        act_layer=nn.GELU,
        norm_layer=nn.LayerNorm,
        use_gpsa=True,
        **kwargs,
    ):
        super().__init__()
        self.norm1 = norm_layer(dim)
        self.use_gpsa = use_gpsa
        if self.use_gpsa:
            self.attn = GPSA(
                dim,
                num_heads=num_heads,
                qkv_bias=qkv_bias,
                attn_drop=attn_drop,
                proj_drop=drop,
                **kwargs,
            )
        else:
            self.attn = MHSA(
                dim,
                num_heads=num_heads,
                qkv_bias=qkv_bias,
                attn_drop=attn_drop,
                proj_drop=drop,
            )
        self.drop_path = DropPath(drop_path) if drop_path > 0.0 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,
        )

    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 ConViT(nn.Module):
    """Vision Transformer with support for patch or hybrid CNN input stage"""

    def __init__(
        self,
        img_size=32,
        patch_size=16,
        in_chans=3,
        num_classes=10,
        embed_dim=768,
        depth=12,
        num_heads=12,
        mlp_ratio=4.0,
        qkv_bias=False,
        drop_rate=0.0,
        attn_drop_rate=0.0,
        drop_path_rate=0.0,
        hybrid_backbone=None,
        norm_layer=nn.LayerNorm,
        global_pool=None,
        local_up_to_layer=3,
        locality_strength=1.0,
        use_pos_embed=True,
    ):
        super().__init__()
        embed_dim *= num_heads
        self.num_classes = num_classes
        self.local_up_to_layer = local_up_to_layer
        self.num_features = (
            self.embed_dim
        ) = embed_dim  # num_features for consistency with other models
        self.locality_strength = locality_strength
        self.use_pos_embed = use_pos_embed

        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,
            )
        num_patches = self.patch_embed.num_patches
        self.num_patches = num_patches

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

        if self.use_pos_embed:
            self.pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim))
            trunc_normal_(self.pos_embed, std=0.02)

        dpr = [
            x.item() for x in torch.linspace(0, drop_path_rate, depth)
        ]  # stochastic depth decay rule
        self.blocks = nn.ModuleList(
            [
                Block(
                    dim=embed_dim,
                    num_heads=num_heads,
                    mlp_ratio=mlp_ratio,
                    qkv_bias=qkv_bias,
                    drop=drop_rate,
                    attn_drop=attn_drop_rate,
                    drop_path=dpr[i],
                    norm_layer=norm_layer,
                    use_gpsa=True,
                    locality_strength=locality_strength,
                )
                if i < local_up_to_layer
                else Block(
                    dim=embed_dim,
                    num_heads=num_heads,
                    mlp_ratio=mlp_ratio,
                    qkv_bias=qkv_bias,
                    drop=drop_rate,
                    attn_drop=attn_drop_rate,
                    drop_path=dpr[i],
                    norm_layer=norm_layer,
                    use_gpsa=False,
                )
                for i in range(depth)
            ]
        )
        self.norm = norm_layer(embed_dim)

        # Classifier head
        self.feature_info = [dict(num_chs=embed_dim, reduction=0, module="head")]
        self.head = (
            nn.Linear(embed_dim, num_classes) if num_classes > 0 else nn.Identity()
        )

        trunc_normal_(self.cls_token, std=0.02)
        self.apply(self._init_weights)
        for n, m in self.named_modules():
            if hasattr(m, "local_init"):
                m.local_init()

    def _init_weights(self, m):
        if isinstance(m, nn.Linear):
            trunc_normal_(m.weight, std=0.02)
            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)

        if self.use_pos_embed:
            x = x + self.pos_embed
        x = self.pos_drop(x)

        for u, blk in enumerate(self.blocks):
            if u == self.local_up_to_layer:
                x = torch.cat((cls_tokens, x), dim=1)
            x = blk(x)

        x = self.norm(x)
        return x[:, 0]

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


@register_model
def convit_tiny(num_classes=10, **kwargs):
    model = ConViT(
        local_up_to_layer=10,
        locality_strength=1.0,
        embed_dim=48,
        num_heads=4,
        norm_layer=partial(nn.LayerNorm, eps=1e-6),
        num_classes=num_classes,
        **kwargs,
    )

    return model


@register_model
def convit_small(num_classes=10, **kwargs):
    model = ConViT(
        local_up_to_layer=10,
        locality_strength=1.0,
        embed_dim=48,
        num_heads=9,
        norm_layer=partial(nn.LayerNorm, eps=1e-6),
        num_classes=num_classes,
        **kwargs,
    )
    return model


@register_model
def convit_base(num_classes=10, **kwargs):
    model = ConViT(
        local_up_to_layer=10,
        locality_strength=1.0,
        embed_dim=48,
        num_heads=16,
        norm_layer=partial(nn.LayerNorm, eps=1e-6),
        num_classes=num_classes,
        **kwargs,
    )
    return model


if __name__ == "__main__":
    m = convit_small(10)
    x = torch.ones(6, 3, 32, 32)
    print(m(x).shape)
