import torch
import torch.nn as nn
from einops import pack, rearrange

from .unit_nn import (
    SinusoidalPosEmb,
    TimestepEmbedding,
    ResnetBlock1D,
    Block1D,
)

from .transformer import BasicTransformerBlock


# ---------------------------------------------------------------------------
# Small helpers to keep block construction readable
# ---------------------------------------------------------------------------


def _make_resnet(dim_in, dim_out, time_emb_dim, cond_dim):
    return ResnetBlock1D(
        dim=dim_in,
        dim_out=dim_out,
        time_emb_dim=time_emb_dim,
        film_dim=cond_dim,
    )


def _make_transformer(dim, n_heads, head_dim, dropout, act_fn):
    return BasicTransformerBlock(
        dim=dim,
        num_attention_heads=n_heads,
        attention_head_dim=head_dim,
        dropout=dropout,
        activation_fn=act_fn,
    )


# ---------------------------------------------------------------------------
# Building-block containers
# ---------------------------------------------------------------------------


class DownBlock(nn.Module):
    """
    ResNet -> n Transformer blocks -> channel-mixing Conv1d.
    T is never halved; the conv is purely for channel interaction.
    """

    def __init__(
        self,
        dim_in,
        dim_out,
        time_emb_dim,
        cond_dim,
        n_transformer,
        n_heads,
        head_dim,
        dropout,
        act_fn,
    ):
        super().__init__()
        self.resnet = _make_resnet(dim_in, dim_out, time_emb_dim, cond_dim)
        self.transformers = nn.ModuleList(
            [
                _make_transformer(dim_out, n_heads, head_dim, dropout, act_fn)
                for _ in range(n_transformer)
            ]
        )
        self.mix = nn.Conv1d(dim_out, dim_out, 3, padding=1)

    def forward(self, x, t, cond=None):
        x = self.resnet(x, t, film_cond=cond)
        x = rearrange(x, "b c t -> b t c")
        for block in self.transformers:
            x = block(hidden_states=x, attention_mask=None, timestep=t)
        x = rearrange(x, "b t c -> b c t")
        return self.mix(x)


class MidBlock(nn.Module):
    """ResNet -> n Transformer blocks. No spatial change."""

    def __init__(
        self,
        dim,
        time_emb_dim,
        cond_dim,
        n_transformer,
        n_heads,
        head_dim,
        dropout,
        act_fn,
    ):
        super().__init__()
        self.resnet = _make_resnet(dim, dim, time_emb_dim, cond_dim)
        self.transformers = nn.ModuleList(
            [
                _make_transformer(dim, n_heads, head_dim, dropout, act_fn)
                for _ in range(n_transformer)
            ]
        )

    def forward(self, x, t, cond=None):
        x = self.resnet(x, t, film_cond=cond)
        x = rearrange(x, "b c t -> b t c")
        for block in self.transformers:
            x = block(hidden_states=x, attention_mask=None, timestep=t)
        return rearrange(x, "b t c -> b c t")


class UpBlock(nn.Module):
    """
    Skip-connection concat -> ResNet -> n Transformer blocks -> Conv1d.
    dim_in is the channel dim of the skip, not the doubled dim;
    the doubling is handled internally via ResnetBlock1D(dim=2*dim_in).
    """

    def __init__(
        self,
        dim_in,
        dim_out,
        time_emb_dim,
        cond_dim,
        n_transformer,
        n_heads,
        head_dim,
        dropout,
        act_fn,
    ):
        super().__init__()
        self.resnet = _make_resnet(2 * dim_in, dim_out, time_emb_dim, cond_dim)
        self.transformers = nn.ModuleList(
            [
                _make_transformer(dim_out, n_heads, head_dim, dropout, act_fn)
                for _ in range(n_transformer)
            ]
        )
        self.mix = nn.Conv1d(dim_out, dim_out, 3, padding=1)

    def forward(self, x, skip, t, cond=None):
        x = self.resnet(pack([x, skip], "b * t")[0], t, film_cond=cond)
        x = rearrange(x, "b c t -> b t c")
        for block in self.transformers:
            x = block(hidden_states=x, attention_mask=None, timestep=t)
        x = rearrange(x, "b t c -> b c t")
        return self.mix(x)


# ---------------------------------------------------------------------------
# Full Decoder
# ---------------------------------------------------------------------------


class Decoder(nn.Module):
    """
    1D U-Net decoder for Matcha-TTS CFM.

    Time dimension T is held constant throughout (no spatial downsampling).
    The "U-Net" structure provides multi-scale channel mixing and
    skip connections for gradient flow, not resolution pyramids.

    Input channels to the first block are 2 * in_channels because
    mu is channel-concatenated with x before entering the U-Net.

    Args:
        in_channels:    feat_dim (mel bins or latent dim)
        out_channels:   feat_dim (same; predicts residual vector field)
        channels:       channel widths at each down/up level
        n_mid_blocks:   number of mid blocks at the bottleneck
        n_transformer:  transformer blocks per down/mid/up stage
        n_heads:        attention heads
        head_dim:       dimension per head
        dropout:        dropout in transformers
        act_fn:         activation in transformer FFN ('snake' or 'silu')
        cond_dim:       optional semantic conditioning dim (pooled mu etc.)
    """

    def __init__(
        self,
        in_channels: int = None,
        out_channels: int = None,
        channels: tuple = (256, 256),
        n_mid_blocks: int = 2,
        n_transformer: int = 1,
        n_heads: int = 4,
        head_dim: int = 64,
        dropout: float = 0.05,
        act_fn: str = "snakebeta",
        cond_dim: int = None,
        in_c: int = None,  # TODO need to fix these dumbass details
        out_c: int = None, # TODO need to fix these dumbass details
    ):
        super().__init__()

        if in_channels is None:
            in_channels = in_c
        elif in_c is not None and in_c != in_channels:
            raise ValueError("Received conflicting values for in_channels and in_c.")

        if out_channels is None:
            out_channels = out_c
        elif out_c is not None and out_c != out_channels:
            raise ValueError("Received conflicting values for out_channels and out_c.")

        if in_channels is None or out_channels is None:
            raise ValueError(
                "Decoder requires in_channels/out_channels (or aliases in_c/out_c)."
            )

        # Time conditioning
        time_emb_dim = channels[0] * 4
        self.time_emb = SinusoidalPosEmb(channels[0])
        self.time_mlp = TimestepEmbedding(
            in_channels=channels[0],
            time_embed_dim=time_emb_dim,
            act_fn="silu",
            cond_proj_dim=cond_dim,
        )

        # mu is concatenated into x before the U-Net, so first dim_in = 2 * in_channels
        dims_in = (2 * in_channels,) + channels[:-1]
        dims_out = channels

        self.down_blocks = nn.ModuleList(
            [
                DownBlock(
                    di,
                    do,
                    time_emb_dim,
                    cond_dim,
                    n_transformer,
                    n_heads,
                    head_dim,
                    dropout,
                    act_fn,
                )
                for di, do in zip(dims_in, dims_out)
            ]
        )

        self.mid_blocks = nn.ModuleList(
            [
                MidBlock(
                    channels[-1],
                    time_emb_dim,
                    cond_dim,
                    n_transformer,
                    n_heads,
                    head_dim,
                    dropout,
                    act_fn,
                )
                for _ in range(n_mid_blocks)
            ]
        )

        # Up path: channel dims mirror the down path in reverse
        up_dims_in = channels[::-1]
        up_dims_out = channels[::-1][1:] + (channels[0],)

        self.up_blocks = nn.ModuleList(
            [
                UpBlock(
                    di,
                    do,
                    time_emb_dim,
                    cond_dim,
                    n_transformer,
                    n_heads,
                    head_dim,
                    dropout,
                    act_fn,
                )
                for di, do in zip(up_dims_in, up_dims_out)
            ]
        )

        self.final_block = Block1D(channels[0], channels[0])
        self.final_proj = nn.Conv1d(channels[0], out_channels, 1)

        self._init_weights()

    def _init_weights(self):
        for m in self.modules():
            if isinstance(m, (nn.Conv1d, nn.Linear)):
                nn.init.kaiming_normal_(m.weight, nonlinearity="relu")
                if m.bias is not None:
                    nn.init.constant_(m.bias, 0)
            elif isinstance(m, nn.GroupNorm):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)

    def forward(
        self,
        x: torch.Tensor,  # (B, feat_dim, T)  noisy interpolant
        mu: torch.Tensor,  # (B, feat_dim, T)  encoder conditioning
        t: torch.Tensor,  # (B,)              flow timestep
        cond: torch.Tensor = None,  # (B, cond_dim)     optional semantic cond
    ) -> torch.Tensor:  # (B, feat_dim, T)

        # Time embedding
        t = t.reshape(x.shape[0])
        t = self.time_mlp(self.time_emb(t), condition=cond)

        # Condition on mu via channel concatenation
        x = pack([x, mu], "b * t")[0]  # (B, 2*feat_dim, T)

        # Down path - save hiddens for skip connections
        hiddens = []
        for block in self.down_blocks:
            x = block(x, t, cond)
            hiddens.append(x)

        # Bottleneck
        for block in self.mid_blocks:
            x = block(x, t, cond)

        # Up path - skip connections from corresponding down blocks
        for block in self.up_blocks:
            x = block(x, hiddens.pop(), t, cond)

        x = self.final_block(x)
        return self.final_proj(x)