from functools import partial
import torch
import torch.nn as nn
import numpy as np
import torch.utils.checkpoint
from timm.models.swin_transformer import SwinTransformerBlock
from timm.models.vision_transformer import Block
from timm.models.layers import to_2tuple


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

    def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768):
        super().__init__()
        img_size = to_2tuple(img_size)
        patch_size = to_2tuple(patch_size)
        num_patches = (img_size[1] // patch_size[1]) * (img_size[0] // patch_size[0])
        self.patch_hw = (img_size[1] // patch_size[1], img_size[0] // patch_size[0])
        self.img_size = img_size
        self.patch_size = patch_size
        self.num_patches = num_patches

        self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)

    def forward(self, x):
        B, C, H, W = x.shape
        x = self.proj(x).flatten(2).transpose(1, 2)
        return x


def get_1d_sincos_pos_embed_from_grid(embed_dim, pos):
    """
    embed_dim: output dimension for each position
    pos: a list of positions to be encoded: size (M,)
    out: (M, D)
    """
    assert embed_dim % 2 == 0
    omega = np.arange(embed_dim // 2, dtype=float)
    omega /= embed_dim / 2.
    omega = 1. / 10000 ** omega  # (D/2,)

    pos = pos.reshape(-1)  # (M,)
    out = np.einsum('m,d->md', pos, omega)  # (M, D/2), outer product

    emb_sin = np.sin(out)  # (M, D/2)
    emb_cos = np.cos(out)  # (M, D/2)

    emb = np.concatenate([emb_sin, emb_cos], axis=1)  # (M, D)
    return emb


def get_2d_sincos_pos_embed_from_grid(embed_dim, grid):
    assert embed_dim % 2 == 0
    # use half of dimensions to encode grid_h
    emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0])  # (H*W, D/2)
    emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1])  # (H*W, D/2)
    emb = np.concatenate([emb_h, emb_w], axis=1)  # (H*W, D)
    return emb


def get_2d_sincos_pos_embed(embed_dim, grid_size, cls_token=False):
    """
    grid_size: int of the grid height and width
    return:
    pos_embed: [grid_size*grid_size, embed_dim] or [1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token)
    """
    grid_h = np.arange(grid_size, dtype=np.float32)
    grid_w = np.arange(grid_size, dtype=np.float32)
    grid = np.meshgrid(grid_w, grid_h)  # here w goes first
    grid = np.stack(grid, axis=0)

    grid = grid.reshape([2, 1, grid_size, grid_size])
    pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid)
    if cls_token:
        pos_embed = np.concatenate([np.zeros([1, embed_dim]), pos_embed], axis=0)
    return pos_embed


def get_2d_sincos_pos_embed_flexible(embed_dim, grid_size, cls_token=False):
    """
    grid_size: int of the grid height and width
    return:
    pos_embed: [grid_size*grid_size, embed_dim] or [1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token)
    """
    grid_h = np.arange(grid_size[0], dtype=np.float32)
    grid_w = np.arange(grid_size[1], dtype=np.float32)
    grid = np.meshgrid(grid_w, grid_h)  # here w goes first
    grid = np.stack(grid, axis=0)

    grid = grid.reshape([2, 1, grid_size[0], grid_size[1]])
    pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid)
    if cls_token:
        pos_embed = np.concatenate([np.zeros([1, embed_dim]), pos_embed], axis=0)
    return pos_embed


class SwinTransformerBlockWrapper(torch.nn.Module):
    """
    Wrap SwinTransformerBlock to fit the input shape of [B, N, C] like TransformerBlock.

    The SwinTransformerBlock takes the input shape of [B, H, W, C], and TransformerBlock
    takes the input shape of [B, N, C].
    """

    def __init__(self, block: SwinTransformerBlock):
        super().__init__()
        self.block = block
        self.input_resolution = block.input_resolution

    def forward(self, x):
        """
        :param x: [B, N, C]
        :return: [B, N, C]
        """
        B, N, C = x.shape
        x = x.reshape(B, *self.input_resolution, C)
        x = self.block(x)
        x = x.reshape(B, N, C)
        return x


class MaskedAutoencoderViT(nn.Module):
    """ Masked Autoencoder with VisionTransformer backbone
    """

    def __init__(
            self,
            img_size=224,
            patch_size=16,
            in_chans=3,  # input channels. 1 for audio, 3 for image
            embed_dim=1024,
            depth=24,  # transformer depth
            num_heads=16,
            decoder_mode=0,  # 0: transformer (global attn), 1: swin-transformer (swined local attn)
            no_shift=False,  # invalid when decoder_mode=0. swin-transformer. shift patch or not
            decoder_embed_dim=512,
            decoder_depth=8,  # invalid when decoder_mode=1. It will be fixed to 16 when decoder_mode=1.
            decoder_num_heads=16,  # invalid when decoder_mode=1. It will be fixed to 16 when decoder_mode=1.
            mlp_ratio=4.,  # hidden dimension / embed dimension in feedforward layer of transformer
            norm_layer=nn.LayerNorm,
            norm_pix_loss=False,  # use (per-patch) normalized pixels as targets for computing loss
            pos_trainable=False,
    ):
        super().__init__()

        self.img_size = to_2tuple(img_size)

        self.embed_dim = embed_dim
        self.decoder_embed_dim = decoder_embed_dim
        # MAE encoder specifics
        self.patch_embed = PatchEmbed(img_size, patch_size, in_chans, embed_dim)
        num_patches = self.patch_embed.num_patches

        self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))

        self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dim),
                                      requires_grad=pos_trainable)  # fixed sin-cos embedding

        self.encoder_depth = depth
        self.blocks = nn.ModuleList([
            Block(embed_dim, num_heads, mlp_ratio, qkv_bias=True, norm_layer=norm_layer) for _ in range(depth)])
        self.norm = norm_layer(embed_dim)

        # --------------------------------------------------------------------------
        # MAE decoder specifics
        self.decoder_embed = nn.Linear(embed_dim, decoder_embed_dim, bias=True)

        self.mask_token = nn.Parameter(torch.zeros(1, 1, decoder_embed_dim))
        self.decoder_pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, decoder_embed_dim),
                                              requires_grad=pos_trainable)  # fixed sin-cos embedding

        self.no_shift = no_shift

        self.decoder_mode = decoder_mode

        window_size = (4, 4)
        feat_size = (self.img_size[0] // patch_size, 8)

        if self.decoder_mode == 1:
            decoder_modules = []
            for index in range(16):
                if self.no_shift:
                    shift_size = (0, 0)
                else:
                    if (index % 2) == 0:
                        shift_size = (0, 0)
                    else:
                        shift_size = (2, 0)
                    # shift_size = tuple([0 if ((index % 2) == 0) else w // 2 for w in window_size])
                decoder_modules.append(
                    SwinTransformerBlockWrapper(
                        SwinTransformerBlock(
                            dim=decoder_embed_dim,
                            input_resolution=feat_size,
                            num_heads=16,
                            window_size=window_size,
                            shift_size=shift_size,
                            mlp_ratio=mlp_ratio,
                            proj_drop=0.0,
                            attn_drop=0.0,
                            drop_path=0.0,
                            norm_layer=norm_layer,
                        )
                    )
                )
            self.decoder_blocks = nn.ModuleList(decoder_modules)
        else:
            # Transformer
            self.decoder_blocks = nn.ModuleList([
                Block(decoder_embed_dim, decoder_num_heads, mlp_ratio, qkv_bias=True, norm_layer=norm_layer)
                for _ in range(decoder_depth)])

        self.decoder_norm = norm_layer(decoder_embed_dim)
        self.decoder_pred = nn.Linear(decoder_embed_dim, patch_size ** 2 * in_chans, bias=True)  # decoder to patch

        self.norm_pix_loss = norm_pix_loss

        self.patch_size = patch_size

        self.initialize_weights()

    def initialize_weights(self):
        # initialize (and freeze) pos_embed by sin-cos embedding
        pos_embed = get_2d_sincos_pos_embed_flexible(self.pos_embed.shape[-1], self.patch_embed.patch_hw,
                                                     cls_token=True)
        self.pos_embed.data.copy_(torch.from_numpy(pos_embed).float().unsqueeze(0))

        decoder_pos_embed = get_2d_sincos_pos_embed_flexible(self.decoder_pos_embed.shape[-1],
                                                             self.patch_embed.patch_hw, cls_token=True)
        self.decoder_pos_embed.data.copy_(torch.from_numpy(decoder_pos_embed).float().unsqueeze(0))

        # initialize patch_embed like nn.Linear (instead of nn.Conv2d)
        w = self.patch_embed.proj.weight.data
        torch.nn.init.xavier_uniform_(w.view([w.shape[0], -1]))

        # timm's trunc_normal_(std=.02) is effectively normal_(std=0.02) as cutoff is too big (2.)
        torch.nn.init.normal_(self.cls_token, std=.02)
        torch.nn.init.normal_(self.mask_token, std=.02)

        # initialize nn.Linear and nn.LayerNorm
        self.apply(self._init_weights)

    def _init_weights(self, m):
        if isinstance(m, nn.Linear):
            # we use xavier_uniform following official JAX ViT:
            torch.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 patchify(self, imgs):
        """
        imgs: (N, 3, H, W)
        x: (N, L, patch_size**2 *3)
        L = (H/p)*(W/p)
        """
        p = self.patch_embed.patch_size[0]

        h = imgs.shape[2] // p
        w = imgs.shape[3] // p
        # h,w = self.patch_embed.patch_hw
        x = imgs.reshape(shape=(imgs.shape[0], 1, h, p, w, p))
        x = torch.einsum('nchpwq->nhwpqc', x)
        x = x.reshape(imgs.shape[0], h * w, p ** 2 * 1)

        return x

    def unpatchify(self, x):
        """
        x: (N, L, patch_size**2 *3)
        specs: (N, 1, H, W)
        """
        p = self.patch_embed.patch_size[0]
        h = self.img_size[0] // p
        w = 128 // p
        x = x.reshape(shape=(x.shape[0], h, w, p, p, 1))
        x = torch.einsum('nhwpqc->nchpwq', x)
        specs = x.reshape(x.shape[0], 1, h * p, w * p)
        return specs

    def random_masking(self, x, mask_ratio):
        """
        Perform per-sample random masking by per-sample shuffling.
        Per-sample shuffling is done by argsort random noise.
        x: [N, L, D], sequence
        """
        N, L, D = x.shape  # batch, length, dim
        len_keep = int(L * (1 - mask_ratio))

        noise = torch.rand(N, L, device=x.device)  # noise in [0, 1]

        # sort noise for each sample
        ids_shuffle = torch.argsort(noise, dim=1)  # ascend: small is keep, large is remove
        ids_restore = torch.argsort(ids_shuffle, dim=1)

        # keep the first subset
        ids_keep = ids_shuffle[:, :len_keep]
        x_masked = torch.gather(x, dim=1, index=ids_keep.unsqueeze(-1).repeat(1, 1, D))

        # generate the binary mask: 0 is keep, 1 is remove
        mask = torch.ones([N, L], device=x.device)
        mask[:, :len_keep] = 0
        # unshuffle to get the binary mask
        mask = torch.gather(mask, dim=1, index=ids_restore)

        return x_masked, mask, ids_restore

    def forward_encoder(self, x, mask_ratio):
        """
        :param x: [N, C, H, W]
        :param mask_ratio: float. ratio of masked patches
        :return: tuple. x: [N, L', D], mask: [N, L], ids_restore: [N, L], None
        """
        # embed patches
        x = self.patch_embed(x)

        B, L, D = x.shape

        # add pos embed w/o cls token
        x = x + self.pos_embed[:, 1:L + 1, :]

        # masking: length -> length * mask_ratio
        x, mask, ids_restore = self.random_masking(x, mask_ratio)

        # append cls token
        cls_token = self.cls_token + self.pos_embed[:, :1, :]
        cls_tokens = cls_token.expand(x.shape[0], -1, -1)
        x = torch.cat((cls_tokens, x), dim=1)

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

        return x, mask, ids_restore

    def forward_encoder_no_mask(
            self,
            x,
            header='mean'
    ):
        """
        :param x: [N, C, H, W]
        :param header: str. 'cls' or 'mean'
        :param key_padding_mask: [N, L], 0 is keep, 1 is remove
        :return: contextual_emb: [N, L, D]
        """
        # embed patches
        x = self.patch_embed(x)

        B, L, D = x.shape

        # add pos embed w/o cls token
        x = x + self.pos_embed[:, 1:L + 1, :]

        # append cls token
        cls_token = self.cls_token + self.pos_embed[:, :1, :]
        cls_tokens = cls_token.expand(x.shape[0], -1, -1)
        x = torch.cat((cls_tokens, x), dim=1)

        # apply Transformer blocks
        for n, blk in enumerate(self.blocks):
            x = blk(x)

        x = self.norm(x)

        if header == 'cls':
            emb = x[:, 0, :]
        elif header == 'mean':
            emb = x[:, 1:, :].mean(dim=1)
        else:
            raise NotImplementedError

        return emb

    def forward_decoder(self, x, ids_restore):
        """
        :param x: [N, L, D]
        :param ids_restore: [N, L]
        :return: pred: [N, L, p*p*3], None, None
        """
        # embed tokens
        x = self.decoder_embed(x)  # [N, L, D] -> [N, L, D']

        # append mask tokens to sequence
        mask_tokens = self.mask_token.repeat(x.shape[0], ids_restore.shape[1] + 1 - x.shape[1], 1)
        x_ = torch.cat([x[:, 1:, :], mask_tokens], dim=1)  # no cls token
        x_ = torch.gather(x_, dim=1, index=ids_restore.unsqueeze(-1).repeat(1, 1, x.shape[2]))  # unshuffle
        x = torch.cat([x[:, :1, :], x_], dim=1)  # append cls token

        B, L, D = x.shape

        # add pos embed
        x = x + self.decoder_pos_embed[:, :L, :]

        if self.decoder_mode != 0:
            B, L, D = x.shape
            x = x[:, 1:, :]

        if self.decoder_mode > 3:  # mvit
            x = self.decoder_blocks(x)
        else:
            # apply Transformer blocks
            for blk in self.decoder_blocks:
                x = blk(x)

        x = self.decoder_norm(x)

        # predictor projection
        pred = self.decoder_pred(x)

        # remove cls token
        if self.decoder_mode == 0:
            pred = pred[:, 1:, :]

        return pred

    def forward_loss(self, imgs, pred, mask, norm_pix_loss=False):
        """
        imgs: [N, 3, H, W]
        pred: [N, L, p*p*3]
        mask: [N, L], 0 is keep, 1 is remove,
        """
        target = self.patchify(imgs)
        if norm_pix_loss:
            mean = target.mean(dim=-1, keepdim=True)
            var = target.var(dim=-1, keepdim=True)
            target = (target - mean) / (var + 1.e-6) ** .5

        loss = (pred - target) ** 2
        loss = loss.mean(dim=-1)  # [N, L], mean loss per patch

        loss = (loss * mask).sum() / mask.sum()  # mean loss on removed patches
        return loss

    def forward(self, imgs, mask_ratio=0.8):
        """

        :param imgs: [N, C, H, W]
        :param mask_ratio: float. ratio of masked patches
        :return: tuple. loss_recon: float, pred: [N, L, p*p*3], mask: [N, L], None
        """
        emb_enc, mask, ids_restore = self.forward_encoder(imgs, mask_ratio)
        pred = self.forward_decoder(emb_enc, ids_restore)  # [N, L, p*p*3]
        loss_recon = self.forward_loss(imgs, pred, mask, norm_pix_loss=self.norm_pix_loss)
        return loss_recon, pred, mask


if __name__ == '__main__':
    device = 'cpu'
    # device = 'cuda'

    # Model
    audio_mae = MaskedAutoencoderViT(
        img_size=(2048, 128),
        patch_size=16,
        in_chans=1,
        embed_dim=768,
        depth=12,
        num_heads=12,
        decoder_mode=1,
        no_shift=False,
        decoder_embed_dim=512,
        norm_layer=partial(nn.LayerNorm, eps=1e-6),
        norm_pix_loss=False,
        pos_trainable=False,
    )

    # Load pre-trained weights
    ckpt_path = 'music-mae-32kHz.pth'
    audio_mae.load_state_dict(torch.load(ckpt_path, map_location='cpu'))
    audio_mae.to(device)

    # Generate a batch of random inputs: (N, C, H, W), N=4 (batch size), C=1 (channel), H=2048, W=128
    # Each input is a mel spectrogram with shape (2048, 128)
    x = torch.randn(4, 1, 2048, 128).to(device)

    # Compute the representation of the input batch
    emb = audio_mae.forward_encoder_no_mask(x, header='mean')  # torch.Size([4, 768])