import math
import warnings
from functools import partial
import torch
from torch import nn

from .transformer import Block


def _no_grad_trunc_normal_(tensor, mean, std, a, b):
    # Cut & paste from PyTorch official master until it's in a few official releases - RW
    # Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf
    def norm_cdf(x):
        # Computes standard normal cumulative distribution function
        return (1. + math.erf(x / math.sqrt(2.))) / 2.

    if (mean < a - 2 * std) or (mean > b + 2 * std):
        warnings.warn("mean is more than 2 std from [a, b] in nn.init.trunc_normal_. "
                      "The distribution of values may be incorrect.",
                      stacklevel=2)

    with torch.no_grad():
        # Values are generated by using a truncated uniform distribution and
        # then using the inverse CDF for the normal distribution.
        # Get upper and lower cdf values
        l = norm_cdf((a - mean) / std)
        u = norm_cdf((b - mean) / std)

        # Uniformly fill tensor with values from [l, u], then translate to
        # [2l-1, 2u-1].
        tensor.uniform_(2 * l - 1, 2 * u - 1)

        # Use inverse cdf transform for normal distribution to get truncated
        # standard normal
        tensor.erfinv_()

        # Transform to proper mean, std
        tensor.mul_(std * math.sqrt(2.))
        tensor.add_(mean)

        # Clamp to ensure it's in the proper range
        tensor.clamp_(min=a, max=b)
        return tensor


def trunc_normal_(tensor, mean=0., std=1., a=-2., b=2.):
    return _no_grad_trunc_normal_(tensor, mean, std, a, b)


def get_num_patches(height=64, width=1001, patch_height=16, patch_width=16):
    return (height // patch_height) * (width // patch_width)


from einops.layers.torch import Rearrange


class PatchEmbed_v2(nn.Module):
    def __init__(self, patch_height=64, patch_width=4, embed_dim=768, input_dim=1):
        super().__init__()
        self.patch_height = patch_height
        self.patch_width = patch_width
        self.patch_maker = Rearrange('b c (h p1) (w p2) -> b (w h) (p1 p2 c)', p1=patch_height, p2=patch_width)
        self.patch_embed = nn.Linear(patch_height * patch_width * input_dim, embed_dim)

    def forward(self, melspec, length=None):
        height = melspec.shape[2] - melspec.shape[2] % self.patch_height
        width = melspec.shape[3] - melspec.shape[3] % self.patch_width
        patch = self.patch_maker(melspec[:, :, :height, :width])
        patch_embed = self.patch_embed(patch)

        if length is not None:
            patch_length = (torch.div(height, self.patch_height, rounding_mode='trunc')) * torch.div(
                (length - length % self.patch_width), self.patch_width, rounding_mode='trunc')
        else:
            patch_length = None

        return patch, patch_embed, patch_length


class FrameAST(nn.Module):
    """ Vision Transformer """

    def __init__(self, nprompt=0, spec_h=64, spec_w=1001, patch_w=16, patch_h=16, pos_type="cut", in_chans=1,
                 num_classes=0, embed_dim=768, depth=12,
                 num_heads=12, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop_rate=0.0, attn_drop_rate=0.,
                 drop_path_rate=0.0, norm_layer=nn.LayerNorm, **kwargs):
        super().__init__()
        self.num_features = self.embed_dim = embed_dim
        self.spec_w = spec_w
        self.spec_h = spec_h
        self.embed_dim = embed_dim
        self.patch_w = patch_w
        self.patch_h = patch_h

        self.pos_type = pos_type

        self.patch_embed = PatchEmbed_v2(patch_h, patch_w, embed_dim)
        self.mask_embed = nn.Parameter(torch.zeros(1, 1, self.embed_dim))

        # hack
        self.nprompt = nprompt
        if self.nprompt > 0:
            self.prompt_embed = nn.Parameter(torch.zeros(1, self.nprompt, self.embed_dim))
            trunc_normal_(self.prompt_embed, std=.02)

        num_patches = get_num_patches(spec_h, spec_w, patch_h, patch_w)
        self.num_patches = num_patches

        self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, 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
        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)
            for i in range(depth)])
        self.norm_frame = norm_layer(embed_dim)

        trunc_normal_(self.pos_embed, std=.02)
        trunc_normal_(self.mask_embed, std=.02)
        self.apply(self._init_weights)

    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 prepare_tokens(self, x, mask_index, length, mask=True):
        B, nc, h, w = x.shape
        mel_patches, x, patch_length = self.patch_embed(x, length)  # patch linear embedding
        B, T, C = x.shape

        if (mask_index is not None) and mask:
            mask_index_expand = mask_index.unsqueeze(2).expand(B, T, self.embed_dim).float()
            x = (1 - mask_index_expand) * x + mask_index_expand * self.mask_embed.expand(B, T, C)

        # add positional encoding to each token
        if self.pos_type == "cut":
            pos = self.pos_embed[:, 1:T + 1, :].expand(B, -1, -1)
            x = x + pos
        else:
            pos = self.interpolate_pos_encoding(x, h, w)
            x = x + pos[:, 1:]

        # pos = self.pos_embed[:,1:T+1,:].expand(B,-1,-1)
        # x = x + pos

        return self.pos_drop(x), pos, mel_patches, h, w, patch_length

    def forward(self, x, mask_index=None, mask_input=True, length=None):
        x, pos, mel_patches, h, w, patch_length = self.prepare_tokens(x, mask_index, length, mask_input)

        length_mask = torch.arange(mel_patches.shape[1]).to(x.device) < patch_length.unsqueeze(1)
        length_mask = length_mask.to(x.device)
        mask_index = mask_index & length_mask

        if self.nprompt > 0:
            x = torch.cat([self.prompt_embed.expand(x.shape[0], -1, -1), x], dim=1)

        for i, blk in enumerate(self.blocks):
            x = blk(x, patch_length + self.nprompt)

        frame_repr = self.norm_frame(x)

        return frame_repr[:, self.nprompt:][mask_index]

    def interpolate_pos_encoding(self, x, h, w):
        npatch = x.shape[1] - 1
        N = self.pos_embed.shape[1] - 1
        if npatch == N and w == self.spec_w and h == self.spec_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_width
        h0 = h // self.patch_embed.patch_height
        # 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, self.spec_h // self.patch_h, self.spec_w // self.patch_w, dim).permute(0, 3, 1,
                                                                                                              2),
            scale_factor=(h0 / (self.spec_h // self.patch_h), w0 / (self.spec_w // self.patch_w)),
            mode='bicubic',
        )
        assert int(h0) == patch_pos_embed.shape[-2] and int(w0) == patch_pos_embed.shape[-1]
        patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim)
        return torch.cat((class_pos_embed.unsqueeze(0), patch_pos_embed), dim=1)

    def get_last_selfattention(self, x):
        x, _, _, _, _, _ = self.prepare_tokens(x, mask_index=None, length=None, mask=False)
        atts = []
        for i, blk in enumerate(self.blocks):
            if i < len(self.blocks) - 1:
                x, att = blk(x, return_attention=True)
                atts.append(att)
            else:
                x, att = blk(x, return_attention=True)
                atts.append(att)
                return atts
                # return attention of the last block

    def get_intermediate_layers(self, x, length, n=1, scene=True, other_emb=None):
        x, _, _, _, _, patch_length = self.prepare_tokens(x, mask_index=None, length=length, mask=False)
        # we return the output tokens from the `n` last blocks
        if other_emb is not None:
            x = torch.cat([other_emb, x], dim=1)
        output = []
        if self.nprompt > 0:
            x = torch.cat([self.prompt_embed.expand(x.shape[0], -1, -1), x], dim=1)
        for i, blk in enumerate(self.blocks):
            x = blk(x, patch_length + self.nprompt)
            if len(self.blocks) - i <= n:
                norm_x = self.norm_frame(x)
                if scene:
                    length_mask = torch.arange(x.shape[1] - self.nprompt).to(x.device) < patch_length.unsqueeze(1)
                    avg = torch.sum(norm_x[:, self.nprompt:] * length_mask.unsqueeze(-1), dim=1) / (
                            patch_length.unsqueeze(-1) + 1e-6)
                    negative = (~length_mask) * -1e10
                    # max = torch.max(norm_x[:,self.nprompt:]+negative.unsqueeze(-1),1).values
                    output.append(avg)
                    if self.nprompt > 0:
                        output.append(torch.mean(norm_x[:, :self.nprompt], dim=1))
                else:
                    output.append(norm_x[:, self.nprompt:])

        return torch.cat(output, dim=-1)


def get_cls_avg(output_i, cur_len, use_cls):
    length_mask = torch.arange(output_i[0].shape[1]).to(output_i[0].device) < cur_len.unsqueeze(1)
    cls = [torch.zeros_like(x[:, 0]) for x in output_i]
    avg = [torch.sum(x * length_mask.unsqueeze(-1), dim=1) / (cur_len.unsqueeze(1) + 1e-6) for x in output_i]
    return cls, avg


def FrameASTModel(patch_h=64, patch_w=4, atst_dropout=0.1, **kwargs):
    return FrameAST(
        patch_h=patch_h,
        patch_w=patch_w,
        embed_dim=768,
        depth=12,
        num_heads=12,
        qkv_bias=False,
        norm_layer=partial(nn.LayerNorm, eps=1e-6),
        drop_path_rate=atst_dropout,
        drop_rate=atst_dropout,
        **kwargs)