import torch
import torch.nn as nn
from torch.nn.init import trunc_normal_
from timm.models.layers import DropPath, Mlp, PatchEmbed
import math

class Attention(nn.Module):
    def __init__(
            self,
            dim: int, 
            num_heads: int = 8, 
            qkv_bias: bool = False, 
            qk_scale = None,
            attn_drop: float = 0., 
            proj_drop: float = 0.
        ):
        super().__init__()
        self.num_heads = num_heads
        head_dim = dim // num_heads
        self.scale = qk_scale or head_dim ** -0.5

        self.q = nn.Linear(dim, dim, bias=qkv_bias)
        self.k = nn.Linear(dim, dim, 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.proj_drop = nn.Dropout(proj_drop)

    def forward(
            self, 
            q, 
            k,
            v,
            attn_mask=None
        ):
        """
        q: batch_size x target_len x d_model
        k: batch_size x source_len x d_model
        v: batch_size x source_len x d_model
        attn_mask: target_len x source_len
        return: batch_size x target_len x d_model
        """
        B, T, _ = q.shape
        _, S, _ = k.shape
        
        q = self.q(q).view(B, T, self.num_heads, -1).transpose(1, 2)
        k = self.k(k).view(B, S, self.num_heads, -1).transpose(1, 2)
        v = self.v(v).view(B, S, self.num_heads, -1).transpose(1, 2)
        
        attn = (q @ k.transpose(-2, -1)) * self.scale
        
        if attn_mask is not None:
            attn = attn.masked_fill(attn_mask, float('-inf'))
            
        attn = attn.softmax(dim=-1)
        attn = self.attn_drop(attn)

        x = (attn @ v).transpose(1, 2).reshape(B, T, -1)
        x = self.proj(x)
        x = self.proj_drop(x)
        return x, attn

class SelfAttention(nn.Module):
    def __init__(
            self,
            dim: int, 
            num_heads: int = 8, 
            qkv_bias: bool = False, 
            qk_scale = None,
            attn_drop: float = 0., 
            proj_drop: float =0.
        ):
        super().__init__()
        self.num_heads = num_heads
        head_dim = dim // num_heads
        self.scale = qk_scale or 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, 
            qkv,
            attn_mask=None
        ):
        """
        qkv: batch_size x len x d_model
        attn_mask: len x len
        return: batch_size x len x d_model
        """
        B, N, _ = qkv.shape
        
        qkv = self.qkv(qkv).reshape(B, N, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
        q, k, v = qkv[0], qkv[1], qkv[2]

        attn = (q @ k.transpose(-2, -1)) * self.scale
        
        if attn_mask is not None:
            attn = attn.masked_fill(attn_mask, float('-inf'))
            
        attn = attn.softmax(dim=-1)
        attn = self.attn_drop(attn)

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

# TODO : adapt both blocks to handle time and space
class SpatialBlock(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
        ):
        super().__init__()
        self.norm1 = norm_layer(dim)
        self.attn = SelfAttention(
            dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
        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)

    def forward(self, x, return_attention=False):
        y = self.norm1(x)
        y, attn = self.attn(y)
        if return_attention:
            return attn
        x = x + self.drop_path(y)
        x = x + self.drop_path(self.mlp(self.norm2(x)))
        return x
    
class TemporalBlock(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
        ):
        super().__init__()
        self.norm1 = norm_layer(dim)
        self.attn = SelfAttention(
            dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
        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)

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

# TODO : adapt the different parts to have a spatially and temporally aware model (right now it is a simple Vision Transformer aka "Spatial Transformer")
class SpatioTemporalTransformer(nn.Module):
    """ """
    def __init__(
            self, 
            img_size=[224], 
            patch_size=16, 
            in_chans=3, 
            num_classes=0, 
            embed_dim=768, 
            depth=12,
            num_heads=12, 
            mlp_ratio=4., 
            num_frames=8,
            qkv_bias=False, 
            qk_scale=None, 
            drop_rate=0.,
            attn_drop_rate=0.,
            drop_path_rate=0., 
            norm_layer=nn.LayerNorm, 
            return_all_tokens=False,
            **kwargs
        ):
        super().__init__()
        self.num_features = self.embed_dim = embed_dim
        self.num_heads = num_heads
        self.depth = depth 
        self.num_frames = num_frames
        self.patch_embed = PatchEmbed(
            img_size=img_size[0], patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim)
        self.num_patches = self.patch_embed.num_patches

        # TODO : it would requiere attention on whether to use the cls_token or not for the transformer
        # self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
        self.pos_embed = nn.Parameter(torch.zeros(1, self.num_patches, 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

        # TODO : in the paper they remove
        # Spatial Transformer Blocks
        self.spatial_blocks = nn.ModuleList([
            SpatialBlock(
                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)
            for i in range(depth)])
        # Temporal Transformer Blocks
        self.temporal_blocks = nn.ModuleList([
            TemporalBlock(
                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)
            for i in range(depth)])

        self.norm = norm_layer(embed_dim)
        self.head = nn.Linear(embed_dim, num_classes) 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)

        self.return_all_tokens = return_all_tokens

    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)

    def interpolate_pos_encoding(self, x, w, h, use_cls_token=True):
        npatch = x.shape[1] - 1
        N = self.pos_embed.shape[1] - 1
        if npatch == N and w == h:
            return self.pos_embed
        class_pos_embed = self.pos_embed[:, 0]
        patch_pos_embed = self.pos_embed[:, 1:]
        dim = x.shape[-1]
        w0 = w // self.patch_embed.patch_size
        h0 = h // self.patch_embed.patch_size
        # we add a small number to avoid floating point error in the interpolation
        # see discussion at https://github.com/facebookresearch/dino/issues/8
        w0, h0 = w0 + 0.1, h0 + 0.1
        patch_pos_embed = nn.functional.interpolate(
            patch_pos_embed.reshape(1, int(math.sqrt(N)), int(math.sqrt(N)), dim).permute(0, 3, 1, 2),
            scale_factor=(w0 / math.sqrt(N), h0 / math.sqrt(N)),
            mode='bicubic',
        )
        assert int(w0) == patch_pos_embed.shape[-2] and int(h0) == patch_pos_embed.shape[-1]
        patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim)
        if use_cls_token:
            return torch.cat((class_pos_embed.unsqueeze(0), patch_pos_embed), dim=1)
        else:
            return patch_pos_embed

    def prepare_tokens(self, x):
        B, nc, w, h = x.shape
        x = self.patch_embed(x)
        x = x.flatten(2).transpose(1, 2)

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

        x = x + self.interpolate_pos_encoding(x, w, h)

        return self.pos_drop(x)

    def forward(self, x, return_all_tokens=None):
        x = self.prepare_tokens(x)

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

        return_all_tokens = self.return_all_tokens if \
            return_all_tokens is None else return_all_tokens
        if return_all_tokens:
            return x
        return x[:, 0]

    def get_num_layers(self):
        return len(self.blocks)
    
    def get_last_selfattention(self, x):
        x  = self.prepare_tokens(x)
        for i, blk in enumerate(self.blocks):
            if i < len(self.blocks) - 1:
                x = blk(x)
            else:
                return blk(x, return_attention=True)

    def get_intermediate_layers(self, x, n=1):
        x = self.prepare_tokens(x)
        output = []
        for i, blk in enumerate(self.blocks):
            x = blk(x)
            if len(self.blocks) - i <= n:
                output.append(self.norm(x))
        return output
