import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as checkpoint
from functools import partial
import numpy as np

#from modeling_finetune import Block, _cfg, PatchEmbed, get_sinusoid_encoding_table
from timm.models.registry import register_model
from timm.models.layers import trunc_normal_ as __call_trunc_normal_
from timm.models.layers import drop_path, to_2tuple



def trunc_normal_(tensor, mean=0., std=1.):
    __call_trunc_normal_(tensor, mean=mean, std=std, a=-std, b=std)


__all__ = [
    'pretrain_videomae_small_patch16_224',
    'pretrain_videomae_base_patch16_224', 
    'pretrain_videomae_large_patch16_224', 
    'pretrain_videomae_huge_patch16_224',
]


class PretrainVisionTransformerEncoder(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=0, 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., norm_layer=nn.LayerNorm, init_values=None, tubelet_size=2, use_checkpoint=False,
                 use_learnable_pos_emb=False):
        super().__init__()
        self.num_classes = num_classes
        self.num_features = self.embed_dim = embed_dim  # num_features for consistency with other models
        self.patch_embed = PatchEmbed(
            img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim,tubelet_size=tubelet_size)
        num_patches = self.patch_embed.num_patches
        self.use_checkpoint = use_checkpoint


        # TODO: Add the cls token
        if use_learnable_pos_emb:
            self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dim))
        else:
            # sine-cosine positional embeddings 
            self.pos_embed = get_sinusoid_encoding_table(num_patches, embed_dim)

        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, qk_scale=qk_scale,
                drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer,
                init_values=init_values)
            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()

        if use_learnable_pos_emb:
            trunc_normal_(self.pos_embed, std=.02)

        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 get_num_layers(self):
        return len(self.blocks)

    @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, mask):
        _, _, T, _, _ = x.shape
        x = self.patch_embed(x)
        
        x = x + self.pos_embed.type_as(x).to(x.device).clone().detach()

        B, _, C = x.shape
        x_vis = x[~mask].reshape(B, -1, C) # ~mask means visible

        if self.use_checkpoint:
            for blk in self.blocks:
                x_vis = checkpoint.checkpoint(blk, x_vis)
        else:   
            for blk in self.blocks:
                x_vis = blk(x_vis)

        x_vis = self.norm(x_vis)
        return x_vis

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

class PretrainVisionTransformerDecoder(nn.Module):
    """ Vision Transformer with support for patch or hybrid CNN input stage
    """
    def __init__(self, patch_size=16, num_classes=768, 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.,
                 norm_layer=nn.LayerNorm, init_values=None, num_patches=196, tubelet_size=2, use_checkpoint=False
                 ):
        super().__init__()
        self.num_classes = num_classes
        assert num_classes == 3 * tubelet_size * patch_size ** 2 
        self.num_features = self.embed_dim = embed_dim  # num_features for consistency with other models
        self.patch_size = patch_size
        self.use_checkpoint = use_checkpoint


        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, qk_scale=qk_scale,
                drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer,
                init_values=init_values)
            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()

        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 get_num_layers(self):
        return len(self.blocks)

    @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(self, x, return_token_num,return_block=-1):
        if self.use_checkpoint:
            for i,blk in enumerate(self.blocks):
                x = checkpoint.checkpoint(blk, x)
                if i+1 == return_block:
                    if return_token_num > 0:
                        return x[:, -return_token_num:]
                    else:
                        return x
        else:   
            for i,blk in enumerate(self.blocks):
                x = blk(x)
                if i+1 == return_block:
                    if return_token_num > 0:
                        return x[:, -return_token_num:]
                    else:
                        return x

        if return_token_num > 0:
            x = self.head(self.norm(x[:, -return_token_num:])) # only return the mask tokens predict pixels
        else:
            x = self.head(self.norm(x))

        return x

class PretrainVisionTransformer(nn.Module):
    """ Vision Transformer with support for patch or hybrid CNN input stage
    """
    def __init__(self,
                 img_size=224, 
                 patch_size=16, 
                 encoder_in_chans=3, 
                 encoder_num_classes=0, 
                 encoder_embed_dim=768, 
                 encoder_depth=12,
                 encoder_num_heads=12, 
                 decoder_num_classes=1536, #  decoder_num_classes=768, 
                 decoder_embed_dim=512, 
                 decoder_depth=8,
                 decoder_num_heads=8, 
                 mlp_ratio=4., 
                 qkv_bias=False, 
                 qk_scale=None, 
                 drop_rate=0., 
                 attn_drop_rate=0.,
                 drop_path_rate=0., 
                 norm_layer=nn.LayerNorm, 
                 init_values=0.,
                 use_learnable_pos_emb=False,
                 use_checkpoint=False,
                 tubelet_size=2,
                 num_classes=0, # avoid the error from create_fn in timm
                 in_chans=0, # avoid the error from create_fn in timm
                 ):
        super().__init__()
        self.encoder = PretrainVisionTransformerEncoder(
            img_size=img_size, 
            patch_size=patch_size, 
            in_chans=encoder_in_chans, 
            num_classes=encoder_num_classes, 
            embed_dim=encoder_embed_dim, 
            depth=encoder_depth,
            num_heads=encoder_num_heads, 
            mlp_ratio=mlp_ratio, 
            qkv_bias=qkv_bias, 
            qk_scale=qk_scale, 
            drop_rate=drop_rate, 
            attn_drop_rate=attn_drop_rate,
            drop_path_rate=drop_path_rate, 
            norm_layer=norm_layer, 
            init_values=init_values,
            tubelet_size=tubelet_size,
            use_checkpoint=use_checkpoint,
            use_learnable_pos_emb=use_learnable_pos_emb)

        self.decoder = PretrainVisionTransformerDecoder(
            patch_size=patch_size, 
            num_patches=self.encoder.patch_embed.num_patches,
            num_classes=decoder_num_classes, 
            embed_dim=decoder_embed_dim, 
            depth=decoder_depth,
            num_heads=decoder_num_heads, 
            mlp_ratio=mlp_ratio, 
            qkv_bias=qkv_bias, 
            qk_scale=qk_scale, 
            drop_rate=drop_rate, 
            attn_drop_rate=attn_drop_rate,
            drop_path_rate=drop_path_rate, 
            norm_layer=norm_layer, 
            init_values=init_values,
            tubelet_size=tubelet_size,
            use_checkpoint=use_checkpoint)

        self.encoder_to_decoder = nn.Linear(encoder_embed_dim, decoder_embed_dim, bias=False)

        self.mask_token = nn.Parameter(torch.zeros(1, 1, decoder_embed_dim))

        self.pos_embed = get_sinusoid_encoding_table(self.encoder.patch_embed.num_patches, decoder_embed_dim)

        trunc_normal_(self.mask_token, std=.02)


    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 get_num_layers(self):
        return len(self.blocks)

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

    def forward(self, x, mask, decoder_blocks=-1):
        _, _, T, _, _ = x.shape
        x_vis = self.encoder(x, mask) # [B, N_vis, C_e]
        x_vis = self.encoder_to_decoder(x_vis) # [B, N_vis, C_d]
        B, N, C = x_vis.shape
        # we don't unshuffle the correct visible token order, 
        # but shuffle the pos embedding accorddingly.
        expand_pos_embed = self.pos_embed.expand(B, -1, -1).type_as(x).to(x.device).clone().detach()
        pos_emd_vis = expand_pos_embed[~mask].reshape(B, -1, C)
        pos_emd_mask = expand_pos_embed[mask].reshape(B, -1, C)
        x_full = torch.cat([x_vis + pos_emd_vis, self.mask_token + pos_emd_mask], dim=1) # [B, N, C_d]
        x = self.decoder(x_full, pos_emd_mask.shape[1],return_block=decoder_blocks) # [B, N_mask, 3 * 16 * 16]

        return x

@register_model
def pretrain_videomae_small_patch16_224(pretrained=False, **kwargs):
    model = PretrainVisionTransformer(
        img_size=224,
        patch_size=16,
        encoder_embed_dim=384,
        encoder_depth=12,
        encoder_num_heads=6,
        encoder_num_classes=0,
        decoder_num_classes=1536, 
        decoder_embed_dim=192, 
        decoder_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.load(
            kwargs["init_ckpt"], map_location="cpu"
        )
        model.load_state_dict(checkpoint["model"])
    return model

@register_model
def pretrain_videomae_base_patch16_224(pretrained=False, **kwargs):
    model = PretrainVisionTransformer(
        img_size=224,
        patch_size=16, 
        encoder_embed_dim=768, 
        encoder_depth=12, 
        encoder_num_heads=12,
        encoder_num_classes=0,
        decoder_num_classes=1536,
        decoder_embed_dim=384,
        decoder_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.load(
            kwargs["init_ckpt"], map_location="cpu"
        )
        model.load_state_dict(checkpoint["model"])
    return model
 
@register_model
def pretrain_videomae_large_patch16_224(pretrained=False, **kwargs):
    model = PretrainVisionTransformer(
        img_size=224,
        patch_size=16, 
        encoder_embed_dim=1024, 
        encoder_depth=24, 
        encoder_num_heads=16,
        encoder_num_classes=0,
        decoder_num_classes=1536, 
        decoder_embed_dim=512,
        decoder_num_heads=8,
        mlp_ratio=4, 
        qkv_bias=True,
        norm_layer=partial(nn.LayerNorm, eps=1e-6), 
        **kwargs)
    model.default_cfg = _cfg()
    if pretrained:
        checkpoint = torch.load(
            kwargs["init_ckpt"], map_location="cpu"
        )
        model.load_state_dict(checkpoint["model"])
    return model

@register_model
def pretrain_videomae_huge_patch16_224(pretrained=False, **kwargs):
    model = PretrainVisionTransformer(
        img_size=224,
        patch_size=16, 
        encoder_embed_dim=1280, 
        encoder_depth=32, 
        encoder_num_heads=16,
        encoder_num_classes=0,
        decoder_num_classes=1536, 
        decoder_embed_dim=640,
        decoder_num_heads=8,
        mlp_ratio=4, 
        qkv_bias=True,
        norm_layer=partial(nn.LayerNorm, eps=1e-6), 
        **kwargs)
    model.default_cfg = _cfg()
    if pretrained:
        checkpoint = torch.load(
            kwargs["init_ckpt"], map_location="cpu"
        )
        model.load_state_dict(checkpoint["model"])
    return model

@register_model
def pretrain_videomae_giant_patch14_224(pretrained=False, **kwargs):
    model = PretrainVisionTransformer(
        img_size=224,
        patch_size=14,
        encoder_embed_dim=1408,
        encoder_depth=40,
        encoder_num_heads=16,
        encoder_num_classes=0,
        decoder_depth=4,
        decoder_num_classes=1176,  # 14 * 14 * 3 * 2,
        decoder_embed_dim=512,
        decoder_num_heads=8,
        mlp_ratio=48 / 11,
        qkv_bias=True,
        norm_layer=partial(nn.LayerNorm, eps=1e-6),
        **kwargs)
    model.default_cfg = _cfg()
    if pretrained:
        checkpoint = torch.load(kwargs["init_ckpt"], map_location="cpu")
        model.load_state_dict(checkpoint["model"])
    return model





def _cfg(url='', **kwargs):
    return {
        'url': url,
        'num_classes': 400, 'input_size': (3, 224, 224), 'pool_size': None,
        'crop_pct': .9, 'interpolation': 'bicubic',
        'mean': (0.5, 0.5, 0.5), 'std': (0.5, 0.5, 0.5),
        **kwargs
    }


class DropPath(nn.Module):
    """Drop paths (Stochastic Depth) per sample  (when applied in main path of residual blocks).
    """
    def __init__(self, drop_prob=None):
        super(DropPath, self).__init__()
        self.drop_prob = drop_prob

    def forward(self, x):
        return drop_path(x, self.drop_prob, self.training)
    
    def extra_repr(self) -> str:
        return 'p={}'.format(self.drop_prob)


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)
        # commit this for the orignal BERT implement 
        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., attn_head_dim=None):
        super().__init__()
        self.num_heads = num_heads
        head_dim = dim // num_heads
        if attn_head_dim is not None:
            head_dim = attn_head_dim
        all_head_dim = head_dim * self.num_heads
        self.scale = qk_scale or head_dim ** -0.5

        self.qkv = nn.Linear(dim, all_head_dim * 3, bias=False)
        if qkv_bias:
            self.q_bias = nn.Parameter(torch.zeros(all_head_dim))
            self.v_bias = nn.Parameter(torch.zeros(all_head_dim))
        else:
            self.q_bias = None
            self.v_bias = None

        self.attn_drop = nn.Dropout(attn_drop)
        self.proj = nn.Linear(all_head_dim, dim)
        self.proj_drop = nn.Dropout(proj_drop)

    def forward(self, x):
        B, N, C = x.shape
        qkv_bias = None
        if self.q_bias is not None:
            qkv_bias = torch.cat((self.q_bias, torch.zeros_like(self.v_bias, requires_grad=False), self.v_bias))
        # qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
        qkv = F.linear(input=x, weight=self.qkv.weight, bias=qkv_bias)
        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]   # make torchscript happy (cannot use tensor as tuple)

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

        
        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


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., init_values=None, act_layer=nn.GELU, norm_layer=nn.LayerNorm,
                 attn_head_dim=None):
        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, attn_head_dim=attn_head_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)

        if init_values > 0:
            self.gamma_1 = nn.Parameter(init_values * torch.ones((dim)),requires_grad=True)
            self.gamma_2 = nn.Parameter(init_values * torch.ones((dim)),requires_grad=True)
        else:
            self.gamma_1, self.gamma_2 = None, None

    def forward(self, x):
        if self.gamma_1 is None:
            x = x + self.drop_path(self.attn(self.norm1(x)))
            x = x + self.drop_path(self.mlp(self.norm2(x)))
        else:
            x = x + self.drop_path(self.gamma_1 * self.attn(self.norm1(x)))
            x = x + self.drop_path(self.gamma_2 * 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, num_frames=16, tubelet_size=2):
        super().__init__()
        img_size = to_2tuple(img_size)
        patch_size = to_2tuple(patch_size)
        self.tubelet_size = int(tubelet_size)
        num_patches = (img_size[1] // patch_size[1]) * (img_size[0] // patch_size[0]) * (num_frames // self.tubelet_size)
        self.img_size = img_size
        self.patch_size = patch_size
        self.num_patches = num_patches
        self.proj = nn.Conv3d(in_channels=in_chans, out_channels=embed_dim, 
                            kernel_size = (self.tubelet_size,  patch_size[0],patch_size[1]), 
                            stride=(self.tubelet_size,  patch_size[0],  patch_size[1]))

    def forward(self, x, **kwargs):
        B, C, T, 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.proj(x).flatten(2).transpose(1, 2)
        return x
    
# sin-cos position encoding
# https://github.com/jadore801120/attention-is-all-you-need-pytorch/blob/master/transformer/Models.py#L31
def get_sinusoid_encoding_table(n_position, d_hid): 
    ''' Sinusoid position encoding table ''' 
    # TODO: make it with torch instead of numpy 
    def get_position_angle_vec(position): 
        return [position / np.power(10000, 2 * (hid_j // 2) / d_hid) for hid_j in range(d_hid)] 

    sinusoid_table = np.array([get_position_angle_vec(pos_i) for pos_i in range(n_position)]) 
    sinusoid_table[:, 0::2] = np.sin(sinusoid_table[:, 0::2]) # dim 2i 
    sinusoid_table[:, 1::2] = np.cos(sinusoid_table[:, 1::2]) # dim 2i+1 

    return  torch.tensor(sinusoid_table,dtype=torch.float, requires_grad=False).unsqueeze(0) 
