from functools import partial

import torch
import torch.nn.functional as F
from torch import nn
from vocos.spectral_ops import ISTFT


def drop_path(
    x, drop_prob: float = 0.0, training: bool = False, scale_by_keep: bool = True
):
    """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).

    This is the same as the DropConnect impl I created for EfficientNet, etc networks, however,
    the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper...
    See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for
    changing the layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use
    'survival rate' as the argument.

    """  # noqa: E501

    if drop_prob == 0.0 or not training:
        return x
    keep_prob = 1 - drop_prob
    shape = (x.shape[0],) + (1,) * (
        x.ndim - 1
    )  # work with diff dim tensors, not just 2D ConvNets
    random_tensor = x.new_empty(shape).bernoulli_(keep_prob)
    if keep_prob > 0.0 and scale_by_keep:
        random_tensor.div_(keep_prob)
    return x * random_tensor


class DropPath(nn.Module):
    """Drop paths (Stochastic Depth) per sample  (when applied in main path of residual blocks)."""  # noqa: E501

    def __init__(self, drop_prob: float = 0.0, scale_by_keep: bool = True):
        super(DropPath, self).__init__()
        self.drop_prob = drop_prob
        self.scale_by_keep = scale_by_keep

    def forward(self, x):
        return drop_path(x, self.drop_prob, self.training, self.scale_by_keep)

    def extra_repr(self):
        return f"drop_prob={round(self.drop_prob,3):0.3f}"


class LayerNorm(nn.Module):
    r"""LayerNorm that supports two data formats: channels_last (default) or channels_first.
    The ordering of the dimensions in the inputs. channels_last corresponds to inputs with
    shape (batch_size, height, width, channels) while channels_first corresponds to inputs
    with shape (batch_size, channels, height, width).
    """  # noqa: E501

    def __init__(self, normalized_shape, eps=1e-6, data_format="channels_last"):
        super().__init__()
        self.weight = nn.Parameter(torch.ones(normalized_shape))
        self.bias = nn.Parameter(torch.zeros(normalized_shape))
        self.eps = eps
        self.data_format = data_format
        if self.data_format not in ["channels_last", "channels_first"]:
            raise NotImplementedError
        self.normalized_shape = (normalized_shape,)

    def forward(self, x):
        if self.data_format == "channels_last":
            return F.layer_norm(
                x, self.normalized_shape, self.weight, self.bias, self.eps
            )
        elif self.data_format == "channels_first":
            u = x.mean(1, keepdim=True)
            s = (x - u).pow(2).mean(1, keepdim=True)
            x = (x - u) / torch.sqrt(s + self.eps)
            x = self.weight[:, None] * x + self.bias[:, None]
            return x


class ConvNeXtBlock(nn.Module):
    r"""ConvNeXt Block. There are two equivalent implementations:
    (1) DwConv -> LayerNorm (channels_first) -> 1x1 Conv -> GELU -> 1x1 Conv; all in (N, C, H, W)
    (2) DwConv -> Permute to (N, H, W, C); LayerNorm (channels_last) -> Linear -> GELU -> Linear; Permute back
    We use (2) as we find it slightly faster in PyTorch

    Args:
        dim (int): Number of input channels.
        drop_path (float): Stochastic depth rate. Default: 0.0
        layer_scale_init_value (float): Init value for Layer Scale. Default: 1e-6.
        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.0.
        kernel_size (int): Kernel size for depthwise conv. Default: 7.
        dilation (int): Dilation for depthwise conv. Default: 1.
    """  # noqa: E501

    def __init__(
        self,
        dim: int,
        drop_path: float = 0.0,
        layer_scale_init_value: float = 1e-6,
        mlp_ratio: float = 4.0,
        kernel_size: int = 7,
        dilation: int = 1,
    ):
        super().__init__()

        self.dwconv = nn.Conv1d(
            dim,
            dim,
            kernel_size=kernel_size,
            padding=int(dilation * (kernel_size - 1) / 2),
            groups=dim,
        )  # depthwise conv
        self.norm = LayerNorm(dim, eps=1e-6)
        self.pwconv1 = nn.Linear(
            dim, int(mlp_ratio * dim)
        )  # pointwise/1x1 convs, implemented with linear layers
        self.act = nn.GELU()
        self.pwconv2 = nn.Linear(int(mlp_ratio * dim), dim)
        self.gamma = (
            nn.Parameter(layer_scale_init_value * torch.ones((dim)), requires_grad=True)
            if layer_scale_init_value > 0
            else None
        )
        self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()

    def forward(self, x, apply_residual: bool = True):
        input = x

        x = self.dwconv(x)
        x = x.permute(0, 2, 1)  # (N, C, L) -> (N, L, C)
        x = self.norm(x)
        x = self.pwconv1(x)
        x = self.act(x)
        x = self.pwconv2(x)

        if self.gamma is not None:
            x = self.gamma * x

        x = x.permute(0, 2, 1)  # (N, L, C) -> (N, C, L)
        x = self.drop_path(x)

        if apply_residual:
            x = input + x

        return x


class ParallelConvNeXtBlock(nn.Module):
    def __init__(self, kernel_sizes: list[int], *args, **kwargs):
        super().__init__()
        self.blocks = nn.ModuleList(
            [
                ConvNeXtBlock(kernel_size=kernel_size, *args, **kwargs)
                for kernel_size in kernel_sizes
            ]
        )

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        return torch.stack(
            [block(x, apply_residual=False) for block in self.blocks] + [x],
            dim=1,
        ).sum(dim=1)


class ConvNeXt(nn.Module):
    def __init__(
        self,
        input_channels: int = 3,
        depths: list[int] = [3, 3, 9, 3],
        dims: list[int] = [96, 192, 384, 768],
        drop_path_rate: float = 0.0,
        layer_scale_init_value: float = 1e-6,
        kernel_sizes: tuple[int] = (7,),
    ):
        super().__init__()
        assert len(depths) == len(dims)

        self.channel_layers = nn.ModuleList()
        stem = nn.Sequential(
            nn.Conv1d(
                input_channels,
                dims[0],
                kernel_size=7,
                padding=3,
                # padding_mode="replicate",
                padding_mode="zeros",
            ),
            LayerNorm(dims[0], eps=1e-6, data_format="channels_first"),
        )
        self.channel_layers.append(stem)

        for i in range(len(depths) - 1):
            mid_layer = nn.Sequential(
                LayerNorm(dims[i], eps=1e-6, data_format="channels_first"),
                nn.Conv1d(dims[i], dims[i + 1], kernel_size=1),
            )
            self.channel_layers.append(mid_layer)

        block_fn = (
            partial(ConvNeXtBlock, kernel_size=kernel_sizes[0])
            if len(kernel_sizes) == 1
            else partial(ParallelConvNeXtBlock, kernel_sizes=kernel_sizes)
        )

        self.stages = nn.ModuleList()
        drop_path_rates = [
            x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))
        ]

        cur = 0
        for i in range(len(depths)):
            stage = nn.Sequential(
                *[
                    block_fn(
                        dim=dims[i],
                        drop_path=drop_path_rates[cur + j],
                        layer_scale_init_value=layer_scale_init_value,
                    )
                    for j in range(depths[i])
                ]
            )
            self.stages.append(stage)
            cur += depths[i]

        self.apply(self._init_weights)

    def _init_weights(self, m):
        if isinstance(m, (nn.Conv1d, nn.Linear)):
            nn.init.trunc_normal_(m.weight, std=0.02)
            nn.init.constant_(m.bias, 0)

    def forward(
        self,
        x: torch.Tensor,
        return_features: bool = False,
    ) -> torch.Tensor:
        features = []

        for channel_layer, stage in zip(self.channel_layers, self.stages):
            x = channel_layer(x)
            x = stage(x)

            if return_features:
                features.append(x)

        if return_features:
            return features

        return x


class ISTFTHead(nn.Module):
    """
    ISTFT Head module for predicting STFT complex coefficients.

    Args:
        dim (int): Hidden dimension of the model.
        n_fft (int): Size of Fourier transform.
        hop_length (int): The distance between neighboring sliding window frames, which should align with
                          the resolution of the input features.
        win_length (int): The size of window frame and STFT filter.
        padding (str, optional): Type of padding. Options are "center" or "same". Defaults to "same".
    """  # noqa: E501

    def __init__(
        self,
        dim: int,
        n_fft: int,
        hop_length: int,
        win_length: int,
        padding: str = "same",
    ):
        super().__init__()

        self.n_fft = n_fft
        self.hop_length = hop_length
        self.win_length = win_length

        self.istft = ISTFT(
            n_fft=n_fft,
            hop_length=hop_length,
            win_length=win_length,
            padding=padding,
        )

        out_dim = n_fft * 2
        self.out = nn.Conv1d(dim, out_dim, 1)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        """
        Forward pass of the ISTFTHead module.

        Args:
            x (Tensor): Input tensor of shape (B, H, L), where B is the batch size,
                        L is the sequence length, and H denotes the model dimension.

        Returns:
            Tensor: Reconstructed time-domain audio signal of shape (B, T), where T is the length of the output signal.
        """  # noqa: E501

        x = self.out(x)

        mag, p = x.chunk(2, dim=1)
        mag = torch.exp(mag)
        mag = torch.clip(
            mag, max=1e2
        )  # safeguard to prevent excessively large magnitudes

        # wrapping happens here. These two lines produce real and imaginary value
        x = torch.cos(p)
        y = torch.sin(p)

        S = mag * (x + 1j * y)

        x = self.istft(S)
        return x.unsqueeze(1)
