import torch
import torch.nn as nn
import torch.nn.functional as F
from abc import ABC
from typing import Optional

from .decoder import Decoder
from .source_generator import SourceGenerator

class BASECFM(nn.Module, ABC):
    def __init__(self, feat_dim: int, cfm_params, embed_dim: int = 256):
        super().__init__()
        self.feat_dim = feat_dim
        self.embed_dim = embed_dim
        self.sigma_min = cfm_params.sigma_min
        self.estimator: Optional[nn.Module] = None
        self.src_gen: Optional[nn.Module] = None
        self.cond_proj: nn.Linear = nn.Linear(embed_dim, feat_dim)

    # ---- inference -----------------------------------------------------------

    @torch.inference_mode()
    def forward(
        self,
        src_cond: torch.Tensor,  # (B, feat_dim, L)
        mu_fusion: torch.Tensor,  # (B, embed_dim, L)
        n_timesteps: int,
        temperature: float = 1.0,
    ) -> torch.Tensor:
        mean_c, logvar_c = self.src_gen(src_cond)
        eps = torch.randn_like(mean_c) * temperature
        z = mean_c + torch.exp(0.5 * logvar_c) * eps
        t_span = torch.linspace(0, 1, n_timesteps + 1, device=src_cond.device)
        return self.solve_euler(z, t_span, mu_fusion)

    def solve_euler(
        self,
        x: torch.Tensor,  # (B, feat_dim, L)
        t_span: torch.Tensor,  # (n_steps+1,)
        mu: torch.Tensor,  # (B, embed_dim, L)
    ) -> torch.Tensor:
        t = t_span[0]
        dt = t_span[1] - t_span[0]
        B = x.shape[0]
        
        # Project mu_fusion to feat_dim for estimator
        # mu is (B, embed_dim, L) -> cond_proj requires (B, L, embed_dim)
        mu_proj = self.cond_proj(mu.transpose(1, 2)).transpose(1, 2) # (B, feat_dim, L)

        for step in range(1, len(t_span)):
            t_batch = t.expand(B)  # (B,)
            dphi_dt = self.estimator(x, mu_proj, t_batch)
            x = x + dt * dphi_dt
            t = t + dt
            if step < len(t_span) - 1:
                dt = t_span[step + 1] - t

        return x

    # ---- training ------------------------------------------------------------

    def compute_loss(
        self,
        x1: torch.Tensor,  # (B, feat_dim, L)
        src_cond: torch.Tensor,  # (B, feat_dim, L)
        mu_fusion: torch.Tensor,  # (B, embed_dim, L)
        lambda_var: float = 0.5,  # Hyperparameters from the paper
        lambda_align: float = 0.5,
    ) -> tuple:
        B = x1.shape[0]

        # t sampled per sample, broadcast-ready for interpolation
        t = torch.rand(B, 1, 1, device=src_cond.device, dtype=src_cond.dtype)  # (B,1,1)
        mean_c, logvar_c = self.src_gen(src_cond)  # (B, C, L)
        eps = torch.randn_like(mean_c)
        z = mean_c + torch.exp(0.5 * logvar_c) * eps

        y = (1 - (1 - self.sigma_min) * t) * z + t * x1  # interpolant
        u = x1 - (1 - self.sigma_min) * z  # target velocity

        # Project mu_fusion to feat_dim for estimator
        mu_proj = self.cond_proj(mu_fusion.transpose(1, 2)).transpose(1, 2) # (B, feat_dim, L)

        # estimator expects t as (B,)
        t_batch = t.reshape(B)
        pred = self.estimator(y, mu_proj, t_batch)

        # 4. Standard Flow Matching Loss
        loss_fm = F.mse_loss(pred, u)

        # 5. Variance Regularization Loss [Eq. 9 in paper]
        # D_KL( N(mu_c, sigma_c^2) || N(mu_c, I) ) = 0.5 * (sigma^2 - log(sigma^2) - 1)
        loss_var = 0.5 * (torch.exp(logvar_c) - logvar_c - 1).mean()

        # 6. Cosine Alignment Loss [Eq. 10 in paper]
        sim = F.cosine_similarity(z.flatten(1), x1.flatten(1), dim=1)
        loss_align = (1.0 - sim).mean()

        # 7. Total Loss [Eq. 11 in paper]
        loss_total = loss_fm + lambda_var * loss_var + lambda_align * loss_align

        # Return total loss, and a dictionary for logging
        loss_dict = {
            "fm": loss_fm.item(),
            "var": loss_var.item(),
            "align": loss_align.item(),
        }

        return loss_total, loss_dict


class CFM(BASECFM):
    def __init__(
        self, feat_dim: int, cfm_params, decoder_params: dict, embed_dim: int = 256
    ):
        super().__init__(feat_dim=feat_dim, cfm_params=cfm_params, embed_dim=embed_dim)
        self.estimator = Decoder(in_c=feat_dim, out_c=feat_dim, **decoder_params)
        self.src_gen = SourceGenerator(feat_dim=feat_dim)