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

from timm.models.layers import drop_path, to_2tuple, trunc_normal_

logger = logging.getLogger(__name__)


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 MLP(nn.Module):
    """Very simple multi-layer perceptron (also called FFN)"""

    def __init__(self, input_dim, hidden_dim, output_dim, num_layers, dropout=0):
        super().__init__()
        self.num_layers = num_layers
        h = [hidden_dim] * (num_layers - 1)
        self.layers = nn.ModuleList(
            nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim])
        )
        self.dropout = dropout
        if dropout:
            self.dropout = nn.Dropout(dropout)

    def forward(self, x):
        for i, layer in enumerate(self.layers):
            x = F.relu(layer(x)) if i < self.num_layers - 1 else layer(x)
            if self.dropout and i < self.num_layers:
                x = self.dropout(x)
        return x

class PostProcess(nn.Module):
    """ This module converts the model's output into the format expected by the coco api"""
    
    @torch.no_grad()
    def forward(self, out_sted, frames_id):
        """Perform the computation for inference evaluation
        """
        # import pdb; pdb.set_trace()

        b, t, _ = out_sted.shape
        device = out_sted.device
        temp_prob_map = torch.zeros(b,t,t).to(device)
        inf = -1e32
        for i_b in range(len(frames_id)): 
            duration = len(frames_id[0])
            sted_prob = (torch.ones(t, t) * inf).tril(0).to(device)
            sted_prob[duration:,:] = inf
            sted_prob[:,duration:] = inf
            temp_prob_map[i_b,:,:] = sted_prob
        
        temp_prob_map += F.log_softmax(out_sted[:, :, 0], dim=1).unsqueeze(2) + \
                F.log_softmax(out_sted[:, :, 1], dim=1).unsqueeze(1)
        
        pred_steds = []
        for i_b in range(b):
            prob_map = temp_prob_map[i_b]  # [T * T]
            frame_id_seq = frames_id[i_b]
            prob_seq = prob_map.flatten(0)  
            max_tstamp = prob_seq.max(dim=0)[1].item()
            start_idx = max_tstamp // t
            end_idx = max_tstamp % t
            pred_sted = [frame_id_seq[start_idx], frame_id_seq[end_idx]+1]
            pred_steds.append(pred_sted)
    
        return pred_steds

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)
        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])
        )
        logger.info(f'Num of patches: {num_patches}')

    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, ckpt_num_frame=-1, cur_frame=12): 
    ''' 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)] 
    
    if ckpt_num_frame != -1 and ckpt_num_frame != cur_frame:
        logger.info(f"Interpolate position embedding")
        logger.info(f"Testing frame: {cur_frame}")
        logger.info(f"Checkpoint frame: {ckpt_num_frame}")

        T = ckpt_num_frame # checkpoint frame
        new_T = cur_frame # testing frame
        n_position = n_position // new_T * T # generate checkpoint position embedding
        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 
        sinusoid_table = torch.tensor(sinusoid_table, dtype=torch.float, requires_grad=False).unsqueeze(0)
        # interpolate
        P = int((n_position // T) ** 0.5)
        C = d_hid
        sinusoid_table = sinusoid_table.reshape(-1, T, P, P, C)
        sinusoid_table = sinusoid_table.permute(0, 2, 3, 4, 1).reshape(-1, C, T)  # BHW, C, T
        sinusoid_table = torch.nn.functional.interpolate(sinusoid_table, size=new_T, mode='linear')
        sinusoid_table = sinusoid_table.reshape(1, P, P, C, new_T).permute(0, 4, 1, 2, 3) # B, T, H, W, C
        sinusoid_table = sinusoid_table.flatten(1, 3)
        return sinusoid_table
    else:
        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) 
    

def get_sinusoid_encoding_table2(n_position=784, d_hid=1024, cur_frame=8, ckpt_num_frame=4, pre_n_position=784): 
    ''' 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)] 
    
    # generate checkpoint position embedding
    sinusoid_table = np.array([get_position_angle_vec(pos_i) for pos_i in range(pre_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 
    sinusoid_table = torch.tensor(sinusoid_table, dtype=torch.float, requires_grad=False).unsqueeze(0)
    
    print(f"n_position: {n_position}")
    print(f"pre_n_position: {pre_n_position}")
    
    if n_position != pre_n_position:
        T = ckpt_num_frame # checkpoint frame
        P = 14 # checkpoint size
        C = d_hid
        new_P = int((n_position // cur_frame) ** 0.5) # testing size
        print(f'Pretraining uses 14x14, but current version is {new_P}x{new_P}')
        print(f'Interpolate the position embedding')
        sinusoid_table = sinusoid_table.reshape(-1, T, P, P, C)
        sinusoid_table = sinusoid_table.reshape(-1, P, P, C).permute(0, 3, 1, 2)
        sinusoid_table = torch.nn.functional.interpolate(
            sinusoid_table, size=(new_P, new_P), mode='bicubic', align_corners=False)
        # BT, C, H, W -> BT, H, W, C ->  B, T, H, W, C
        sinusoid_table = sinusoid_table.permute(0, 2, 3, 1).reshape(-1, T, new_P, new_P, C)
        sinusoid_table = sinusoid_table.flatten(1, 3)  # B, THW, C
    
    if cur_frame != ckpt_num_frame:
        print(f'Pretraining uses 4 frames, but current frame is {cur_frame}')
        print(f'Interpolate the position embedding')
        T = ckpt_num_frame # checkpoint frame
        new_T = cur_frame # testing frame
        # interpolate
        P = int((n_position // cur_frame) ** 0.5) # testing size
        C = d_hid
        sinusoid_table = sinusoid_table.reshape(-1, T, P, P, C)
        sinusoid_table = sinusoid_table.permute(0, 2, 3, 4, 1).reshape(-1, C, T)  # BHW, C, T
        sinusoid_table = torch.nn.functional.interpolate(sinusoid_table, size=new_T, mode='linear')
        sinusoid_table = sinusoid_table.reshape(1, P, P, C, new_T).permute(0, 4, 1, 2, 3) # B, T, H, W, C
        sinusoid_table = sinusoid_table.flatten(1, 3)  # B, THW, C
        
    return sinusoid_table


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, 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_frames=8, tubelet_size=1,
                 use_learnable_pos_emb=False,
                 use_checkpoint=False, checkpoint_num=0, 
                 ckpt_num_frame=-1, with_ln=True, return_index=-1
                 ):
        super().__init__()
        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, 
            num_frames=num_frames, tubelet_size=tubelet_size
        )
        num_patches = self.patch_embed.num_patches
        self.depth = depth + return_index + 1
        self.use_checkpoint = use_checkpoint
        self.checkpoint_num = checkpoint_num
        logger.info(f"Use checkpoint: {use_checkpoint}")
        logger.info(f"Checkpoint number: {checkpoint_num}")
        logger.info(f"Real runing depth: {self.depth}")

        # TODO: Add the cls token
        if use_learnable_pos_emb:
            self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dim))
            self.img_pos_embed = nn.Parameter(torch.zeros(1, num_patches//(num_frames//tubelet_size) + 1, embed_dim))
        else:
            # sine-cosine positional embeddings 
            if img_size != 224:
                self.pos_embed = get_sinusoid_encoding_table2(num_patches, embed_dim, ckpt_num_frame=ckpt_num_frame, cur_frame=num_frames//tubelet_size)
                self.img_pos_embed = get_sinusoid_encoding_table2(num_patches//(num_frames//tubelet_size), embed_dim, cur_frame=1, ckpt_num_frame=1, pre_n_position=14*14)
            else:
                self.pos_embed = get_sinusoid_encoding_table(num_patches, embed_dim, ckpt_num_frame=ckpt_num_frame, cur_frame=num_frames//tubelet_size)
                self.img_pos_embed = get_sinusoid_encoding_table(num_patches//(num_frames//tubelet_size), 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(self.depth)])
        
        if with_ln:
            self.norm = norm_layer(embed_dim)
        else:
            self.norm = nn.Identity()

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

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

    def forward_features(self, x, use_image=False):
        x = self.patch_embed(x)
        
        if use_image:
            x = x + self.img_pos_embed.type_as(x).to(x.device).clone().detach()
        else:
            x = x + self.pos_embed.type_as(x).to(x.device).clone().detach()

        B, _, C = x.shape
        x_vis = x

        for idx, blk in enumerate(self.blocks):
            if self.use_checkpoint and idx < self.checkpoint_num:
                x_vis = checkpoint.checkpoint(blk, x_vis)
            else:
                x_vis = blk(x_vis)

        # with ln ot not
        x_vis = self.norm(x_vis)
        return x_vis

    def forward(self, x, use_image=False):
        x_vis = self.forward_features(x, use_image)
        return x_vis


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_embed_dim=768, 
                 encoder_depth=12,
                 encoder_num_heads=12, 
                 mlp_ratio=4., 
                 qkv_bias=True, 
                 qk_scale=None, 
                 drop_rate=0., 
                 attn_drop_rate=0.,
                 drop_path_rate=0., 
                 norm_layer=partial(nn.LayerNorm, eps=1e-6), 
                 init_values=0.,
                 use_learnable_pos_emb=False,
                 num_frames=8,
                 tubelet_size=1,
                 use_checkpoint=False,
                 checkpoint_num=0,
                 ckpt_num_frame=4, # the pretrained model uses 4 frames
                 return_index=-1,
                 with_ln=False
                ):
        super().__init__()

        self.encoder = PretrainVisionTransformerEncoder(
            img_size=img_size, 
            patch_size=patch_size, 
            in_chans=encoder_in_chans, 
            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,
            num_frames=num_frames,
            tubelet_size=tubelet_size,
            use_learnable_pos_emb=use_learnable_pos_emb,
            use_checkpoint=use_checkpoint,
            checkpoint_num=checkpoint_num,
            ckpt_num_frame=ckpt_num_frame,
            with_ln=with_ln,
            return_index=return_index
        )
        logger.info(f'With LN: {with_ln}')
        logger.info(f'Total {encoder_depth} layer')
        logger.info(f'Return {encoder_depth+return_index+1}-th layer')

        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)

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

    def forward(self, x, use_image=False):
        T = x.shape[2]
        x_vis = self.encoder(x, use_image) # [B, N_vis, C_e]
        B, TL, C = x_vis.shape
        x_vis = x_vis.view(B, T, TL // T, C)

        return x_vis


def build_vit(config):
    model = PretrainVisionTransformer(
        img_size=config.vision_encoder.img_size, 
        patch_size=config.vision_encoder.patch_size, 
        encoder_embed_dim=config.vision_encoder.encoder_embed_dim, 
        encoder_depth=config.vision_encoder.encoder_depth,
        encoder_num_heads=config.vision_encoder.encoder_num_heads, 
        drop_path_rate=config.vision_encoder.drop_path_rate, 
        num_frames=config.vision_encoder.num_frames,
        tubelet_size=config.vision_encoder.tubelet_size,
        use_checkpoint=config.vision_encoder.use_checkpoint,
        checkpoint_num=config.vision_encoder.checkpoint_num,
        return_index=config.vision_encoder.get('return_index', -1),
        with_ln=config.vision_encoder.get('with_ln', False),
    )
    model.default_cfg = _cfg()
    if config.vision_encoder.pretrained:
        logger.info(f"Loading pretrained weights from {config.vision_encoder.pretrained}")
        state_dict = torch.load(config.vision_encoder.pretrained, map_location='cpu')
        model.load_state_dict(state_dict, strict=False)
    else:
        logger.info("No pretrained weights!!!")
    return model