import math
import torch
import julius

import typing as tp

from torch import nn

from torch.nn import functional as F

from .utils import center_trim
from .states import capture_init


def unfold(a, kernel_size, stride):
    *shape, length = a.shape
    n_frames = math.ceil(length / stride)
    
    tgt_length = (n_frames - 1) * stride + kernel_size
    a = F.pad(a, (0, tgt_length - length))
    strides = list(a.stride())

    assert strides[-1] == 1

    strides = strides[:-1] + [stride, 1]

    return a.as_strided([*shape, n_frames, kernel_size], strides)

def rescale_conv(conv, reference):
    scale = (conv.weight.std().detach() / reference) ** 0.5
    conv.weight.data /= scale

    if conv.bias is not None: conv.bias.data /= scale

def rescale_module(module, reference):
    for sub in module.modules():
        if isinstance(sub, (nn.Conv1d, nn.ConvTranspose1d, nn.Conv2d, nn.ConvTranspose2d)): rescale_conv(sub, reference)

class BLSTM(nn.Module):
    def __init__(self, dim, layers=1, max_steps=None, skip=False):
        super().__init__()
        assert max_steps is None or max_steps % 4 == 0
        self.max_steps = max_steps
        self.lstm = nn.LSTM(bidirectional=True, num_layers=layers, hidden_size=dim, input_size=dim)
        self.linear = nn.Linear(2 * dim, dim)
        self.skip = skip

    def forward(self, x):
        B, C, T = x.shape
        y = x
        framed = False

        if self.max_steps is not None and T > self.max_steps:
            width = self.max_steps
            stride = width // 2
            frames = unfold(x, width, stride)
            nframes = frames.shape[2]
            framed = True
            x = frames.permute(0, 2, 1, 3).reshape(-1, C, width)

        x = x.permute(2, 0, 1)

        x = self.lstm(x)[0]
        x = self.linear(x)
        x = x.permute(1, 2, 0)

        if framed:
            out = []
            frames = x.reshape(B, -1, C, width)
            limit = stride // 2

            for k in range(nframes):
                if k == 0: out.append(frames[:, k, :, :-limit])
                elif k == nframes - 1: out.append(frames[:, k, :, limit:])
                else: out.append(frames[:, k, :, limit:-limit])

            out = torch.cat(out, -1)
            out = out[..., :T]
            x = out

        if self.skip: x = x + y

        return x

class LayerScale(nn.Module):
    def __init__(self, channels: int, init: float = 0):
        super().__init__()
        self.scale = nn.Parameter(torch.zeros(channels, requires_grad=True))
        self.scale.data[:] = init

    def forward(self, x):
        return self.scale[:, None] * x

class DConv(nn.Module):
    def __init__(self, channels: int, compress: float = 4, depth: int = 2, init: float = 1e-4, norm=True, attn=False, heads=4, ndecay=4, lstm=False, gelu=True, kernel=3, dilate=True):
        super().__init__()
        assert kernel % 2 == 1
        self.channels = channels
        self.compress = compress
        self.depth = abs(depth)
        dilate = depth > 0

        norm_fn: tp.Callable[[int], nn.Module]
        norm_fn = lambda d: nn.Identity()  

        if norm: norm_fn = lambda d: nn.GroupNorm(1, d)  

        hidden = int(channels / compress)

        act: tp.Type[nn.Module]
        act = nn.GELU if gelu else nn.ReLU
            
        self.layers = nn.ModuleList([])

        for d in range(self.depth):
            dilation = 2**d if dilate else 1
            padding = dilation * (kernel // 2)

            mods = [
                nn.Conv1d(channels, hidden, kernel, dilation=dilation, padding=padding),
                norm_fn(hidden),
                act(),
                nn.Conv1d(hidden, 2 * channels, 1),
                norm_fn(2 * channels),
                nn.GLU(1),
                LayerScale(channels, init),
            ]

            if attn: mods.insert(3, LocalState(hidden, heads=heads, ndecay=ndecay))
            if lstm: mods.insert(3, BLSTM(hidden, layers=2, max_steps=200, skip=True))

            layer = nn.Sequential(*mods)
            self.layers.append(layer)

    def forward(self, x):
        for layer in self.layers:
            x = x + layer(x)

        return x

class LocalState(nn.Module):
    def __init__(self, channels: int, heads: int = 4, nfreqs: int = 0, ndecay: int = 4):
        super().__init__()

        assert channels % heads == 0, (channels, heads)

        self.heads = heads
        self.nfreqs = nfreqs
        self.ndecay = ndecay
        self.content = nn.Conv1d(channels, channels, 1)
        self.query = nn.Conv1d(channels, channels, 1)
        self.key = nn.Conv1d(channels, channels, 1)

        if nfreqs: self.query_freqs = nn.Conv1d(channels, heads * nfreqs, 1)

        if ndecay:
            self.query_decay = nn.Conv1d(channels, heads * ndecay, 1)
            self.query_decay.weight.data *= 0.01

            assert self.query_decay.bias is not None  

            self.query_decay.bias.data[:] = -2

        self.proj = nn.Conv1d(channels + heads * nfreqs, channels, 1)

    def forward(self, x):
        B, C, T = x.shape
        heads = self.heads
        indexes = torch.arange(T, device=x.device, dtype=x.dtype)
        delta = indexes[:, None] - indexes[None, :]

        queries = self.query(x).view(B, heads, -1, T)
        keys = self.key(x).view(B, heads, -1, T)

        dots = torch.einsum("bhct,bhcs->bhts", keys, queries)
        dots /= keys.shape[2] ** 0.5


        if self.nfreqs:
            periods = torch.arange(1, self.nfreqs + 1, device=x.device, dtype=x.dtype)
            freq_kernel = torch.cos(2 * math.pi * delta / periods.view(-1, 1, 1))
            freq_q = self.query_freqs(x).view(B, heads, -1, T) / self.nfreqs**0.5
            dots += torch.einsum("fts,bhfs->bhts", freq_kernel, freq_q)

        if self.ndecay:
            decays = torch.arange(1, self.ndecay + 1, device=x.device, dtype=x.dtype)
            decay_q = self.query_decay(x).view(B, heads, -1, T)
            decay_q = torch.sigmoid(decay_q) / 2
            decay_kernel = -decays.view(-1, 1, 1) * delta.abs() / self.ndecay**0.5
            dots += torch.einsum("fts,bhfs->bhts", decay_kernel, decay_q)


        dots.masked_fill_(torch.eye(T, device=dots.device, dtype=torch.bool), -100)
        weights = torch.softmax(dots, dim=2)

        content = self.content(x).view(B, heads, -1, T)
        result = torch.einsum("bhts,bhct->bhcs", weights, content)

        if self.nfreqs:
            time_sig = torch.einsum("bhts,fts->bhfs", weights, freq_kernel)
            result = torch.cat([result, time_sig], 2)

        result = result.reshape(B, -1, T)
        return x + self.proj(result)

class Demucs(nn.Module):
    @capture_init
    def __init__(self, sources, audio_channels=2, channels=64, growth=2.0, depth=6, rewrite=True, lstm_layers=0, kernel_size=8, stride=4, context=1, gelu=True, glu=True, norm_starts=4, norm_groups=4, dconv_mode=1, dconv_depth=2, dconv_comp=4, dconv_attn=4, dconv_lstm=4, dconv_init=1e-4, normalize=True, resample=True, rescale=0.1, samplerate=44100, segment=4 * 10):
        super().__init__()
        self.audio_channels = audio_channels
        self.sources = sources
        self.kernel_size = kernel_size
        self.context = context
        self.stride = stride
        self.depth = depth
        self.resample = resample
        self.channels = channels
        self.normalize = normalize
        self.samplerate = samplerate
        self.segment = segment

        self.encoder = nn.ModuleList()
        self.decoder = nn.ModuleList()
        self.skip_scales = nn.ModuleList()

        if glu:
            activation = nn.GLU(dim=1)
            ch_scale = 2
        else:
            activation = nn.ReLU()
            ch_scale = 1


        act2 = nn.GELU if gelu else nn.ReLU

        in_channels = audio_channels
        padding = 0


        for index in range(depth):
            norm_fn = lambda d: nn.Identity()  

            if index >= norm_starts: norm_fn = lambda d: nn.GroupNorm(norm_groups, d)  

            encode = []
            encode += [nn.Conv1d(in_channels, channels, kernel_size, stride), norm_fn(channels), act2()]

            attn = index >= dconv_attn
            lstm = index >= dconv_lstm

            if dconv_mode & 1: encode += [DConv(channels, depth=dconv_depth, init=dconv_init, compress=dconv_comp, attn=attn, lstm=lstm)]
            if rewrite: encode += [nn.Conv1d(channels, ch_scale * channels, 1), norm_fn(ch_scale * channels), activation]

            self.encoder.append(nn.Sequential(*encode))

            decode = []

            out_channels = in_channels if index > 0 else len(self.sources) * audio_channels
                
            if rewrite: decode += [nn.Conv1d(channels, ch_scale * channels, 2 * context + 1, padding=context), norm_fn(ch_scale * channels), activation]
            if dconv_mode & 2: decode += [DConv(channels, depth=dconv_depth, init=dconv_init, compress=dconv_comp, attn=attn, lstm=lstm)]

            decode += [nn.ConvTranspose1d(channels, out_channels, kernel_size, stride, padding=padding)]

            if index > 0: decode += [norm_fn(out_channels), act2()]

            self.decoder.insert(0, nn.Sequential(*decode))
            in_channels = channels
            channels = int(growth * channels)


        channels = in_channels

        self.lstm = BLSTM(channels, lstm_layers) if lstm_layers else None


        if rescale: rescale_module(self, reference=rescale)

    def valid_length(self, length):
        if self.resample: length *= 2

        for _ in range(self.depth):
            length = math.ceil((length - self.kernel_size) / self.stride) + 1
            length = max(1, length)

        for _ in range(self.depth):
            length = (length - 1) * self.stride + self.kernel_size

        if self.resample: length = math.ceil(length / 2)

        return int(length)

    def forward(self, mix):
        x = mix
        length = x.shape[-1]

        if self.normalize:
            mono = mix.mean(dim=1, keepdim=True)
            mean = mono.mean(dim=-1, keepdim=True)
            std = mono.std(dim=-1, keepdim=True)
            x = (x - mean) / (1e-5 + std)
        else:
            mean = 0
            std = 1

        delta = self.valid_length(length) - length
        x = F.pad(x, (delta // 2, delta - delta // 2))

        if self.resample: x = julius.resample_frac(x, 1, 2)

        saved = []

        for encode in self.encoder:
            x = encode(x)
            saved.append(x)

        if self.lstm: x = self.lstm(x)

        for decode in self.decoder:
            skip = saved.pop(-1)
            skip = center_trim(skip, x)
            x = decode(x + skip)

        if self.resample: x = julius.resample_frac(x, 2, 1)

        x = x * std + mean
        x = center_trim(x, length)
        x = x.view(x.size(0), len(self.sources), self.audio_channels, x.size(-1))

        return x

    def load_state_dict(self, state, strict=True):
        for idx in range(self.depth):
            for a in ["encoder", "decoder"]:
                for b in ["bias", "weight"]:
                    new = f"{a}.{idx}.3.{b}"
                    old = f"{a}.{idx}.2.{b}"

                    if old in state and new not in state: state[new] = state.pop(old)
        super().load_state_dict(state, strict=strict)