import torch
import torch.nn as nn
import torch.nn.functional as F
import timm
from timm.models.layers import DropPath, trunc_normal_
import numpy as np
from utils import misc
from knn_cuda import KNN

class Encoder(nn.Module):   ## Embedding module
    def __init__(self, encoder_channel):
        super().__init__()
        self.encoder_channel = encoder_channel
        self.first_conv = nn.Sequential(
            nn.Conv1d(3, 128, 1),
            nn.BatchNorm1d(128),
            nn.ReLU(inplace=True),
            nn.Conv1d(128, 256, 1)
        )
        self.second_conv = nn.Sequential(
            nn.Conv1d(512, 512, 1),
            nn.BatchNorm1d(512),
            nn.ReLU(inplace=True),
            nn.Conv1d(512, self.encoder_channel, 1)
        )

    def forward(self, point_groups):
        '''
            point_groups : B G N 3
            -----------------
            feature_global : B G C
        '''
        bs, g, n , _ = point_groups.shape
        point_groups = point_groups.reshape(bs * g, n, 3)
        # encoder
        feature = self.first_conv(point_groups.transpose(2,1))  # BG 256 n
        feature_global = torch.max(feature,dim=2,keepdim=True)[0]  # BG 256 1
        feature = torch.cat([feature_global.expand(-1,-1,n), feature], dim=1)# BG 512 n
        feature = self.second_conv(feature) # BG 1024 n
        feature_global = torch.max(feature, dim=2, keepdim=False)[0] # BG 1024
        return feature_global.reshape(bs, g, self.encoder_channel)


class Group(nn.Module):  # FPS + KNN
    def __init__(self, num_group, group_size, axis=np.array([0.0, 1.0, 0.0]), p=1):
        super().__init__()
        self.num_group = num_group
        self.group_size = group_size
        self.knn = KNN(k=self.group_size, transpose_mode=True)
        self.axis = axis
        self.p = p

    def forward(self, xyz):
        '''
            input: B N 3
            ---------------------------
            output: B G M 3
            center : B G 3
        '''
        batch_size, num_points, _ = xyz.shape
        # fps the centers out
        center = misc.fps(xyz, self.num_group) # B G 3
        # knn to get the neighborhood
        _, idx = self.knn(xyz, center) # B G M
        assert idx.size(1) == self.num_group
        assert idx.size(2) == self.group_size
        idx_base = torch.arange(0, batch_size, device=xyz.device).view(-1, 1, 1) * num_points
        idx = idx + idx_base
        idx = idx.view(-1)
        neighborhood = xyz.view(batch_size * num_points, -1)[idx, :]
        neighborhood = neighborhood.view(batch_size, self.num_group, self.group_size, 3).contiguous()
        # normalize
        neighborhood = neighborhood - center.unsqueeze(2)
        
        return neighborhood, center

## Transformers
class Mlp(nn.Module):
    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
        super().__init__()
        out_features = out_features or in_features
        hidden_features = hidden_features or in_features
        self.fc1 = nn.Linear(in_features, hidden_features)
        self.act = act_layer()
        self.fc2 = nn.Linear(hidden_features, out_features)
        self.drop = nn.Dropout(drop)

    def forward(self, x):
        x = self.fc1(x)
        x = self.act(x)
        x = self.drop(x)
        x = self.fc2(x)
        x = self.drop(x)
        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.):
        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.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[0], qkv[1], qkv[2]   # 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 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):
        super().__init__()
        self.norm1 = norm_layer(dim)

        # 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)

        self.attn = Attention(
            dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_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 TransformerEncoder(nn.Module):
    def __init__(self, embed_dim=768, depth=4, num_heads=12, mlp_ratio=4., qkv_bias=False, qk_scale=None,
                 drop_rate=0., attn_drop_rate=0., drop_path_rate=0.):
        super().__init__()
        
        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 = drop_path_rate[i] if isinstance(drop_path_rate, list) else drop_path_rate
                )
            for i in range(depth)])

    def forward(self, x, pos):
        for _, block in enumerate(self.blocks):
            x = block(x + pos)
        return x
    

class TransformerDecoder(nn.Module):
    def __init__(self, embed_dim=384, depth=4, num_heads=6, mlp_ratio=4., qkv_bias=False, qk_scale=None,
                 drop_rate=0., attn_drop_rate=0., drop_path_rate=0.1, norm_layer=nn.LayerNorm):
        super().__init__()
        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=drop_path_rate[i] if isinstance(drop_path_rate, list) else drop_path_rate
            )
            for i in range(depth)])
        self.norm = norm_layer(embed_dim)
        self.head = nn.Identity()

        self.apply(self._init_weights)

    def _init_weights(self, m):
        if isinstance(m, nn.Linear):
            nn.init.xavier_uniform_(m.weight)
            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 forward(self, x, pos, return_token_num):
        for _, block in enumerate(self.blocks):
            x = block(x + pos)

        x_rec = self.head(self.norm(x[:, -return_token_num:]))  # only return the mask tokens predict pixel

        return x_rec, x