"""Stage 2 flow-matching training and evaluation loops."""

import time

import numpy as np
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
from timm.utils import AverageMeter

from .training_utils import (
    compute_subject_metrics,
    format_subject_accuracies,
    validate_finite_loss,
)


def train_one_epoch_flow_matching(
    *,
    epoch: int,
    stage1_model: torch.nn.Module,
    stage2_models: nn.ModuleDict,
    train_loader: DataLoader,
    optimizers: dict[str, torch.optim.Optimizer],
    device: torch.device,
    subjects: list[int],
) -> float:
    """Train all per-subject Stage 2 CFM models for one epoch."""
    stage1_model.eval()
    for model in stage2_models.values():
        model.train()

    use_cuda = device.type == "cuda"
    if use_cuda:
        torch.cuda.empty_cache()
        torch.cuda.reset_peak_memory_stats()

    loss_m = AverageMeter()
    data_time_m = AverageMeter()
    step_time_m = AverageMeter()

    end = time.monotonic()

    for batch_idx, batch in enumerate(train_loader):
        feats = [f.to(device) for f in batch["features"]]
        fmri = batch["fmri"].to(device)
        batch_size = fmri.size(0)
        data_time = time.monotonic() - end

        with torch.no_grad():
            mu_anchor = stage1_model(feats)

        batch_loss = 0.0

        for i, sub in enumerate(subjects):
            sub_key = str(sub)
            cfm = stage2_models[sub_key]
            optimizer = optimizers[sub_key]

            x1 = fmri[:, i].transpose(1, 2)
            mu = mu_anchor[:, i].transpose(1, 2)

            loss, _ = cfm.compute_loss(x1, mu)

            optimizer.zero_grad()
            loss.backward()
            optimizer.step()

            batch_loss += float(loss.item())

        loss_item = batch_loss / len(subjects)
        validate_finite_loss(loss_item, batch_idx)

        if use_cuda:
            torch.cuda.synchronize()
        step_time = time.monotonic() - end

        loss_m.update(loss_item, batch_size)
        data_time_m.update(data_time, batch_size)
        step_time_m.update(step_time, batch_size)

        if (batch_idx + 1) % 20 == 0:
            tput = batch_size / step_time_m.avg
            if use_cuda:
                alloc_mem_gb = torch.cuda.max_memory_allocated() / 1e9
                res_mem_gb = torch.cuda.max_memory_reserved() / 1e9
            else:
                alloc_mem_gb = 0.0
                res_mem_gb = 0.0

            print(
                f"Stage 2 Train: {epoch:>3d} [{batch_idx:>3d}]"
                f"  Loss: {loss_m.val:#.3g} ({loss_m.avg:#.3g})"
                f"  Time: {data_time_m.avg:.3f},{step_time_m.avg:.3f} {tput:.0f}/s"
                f"  Mem: {alloc_mem_gb:.2f},{res_mem_gb:.2f} GB"
            )

        end = time.monotonic()

    return float(loss_m.avg)


@torch.no_grad()
def evaluate_stage2(
    *,
    epoch: int,
    stage1_model: torch.nn.Module,
    stage2_models: nn.ModuleDict,
    val_loader: DataLoader,
    device: torch.device,
    subjects: list[int],
    ds_name: str = "val",
    n_timesteps: int = 10,
) -> tuple[float, dict[str, np.ndarray | float]]:
    """Evaluate Stage 2 model stack on one validation split."""
    stage1_model.eval()
    for model in stage2_models.values():
        model.eval()

    samples = []
    outputs = []

    for batch in val_loader:
        feats = [f.to(device) for f in batch["features"]]
        fmri = batch["fmri"].to(device)

        mu_anchor = stage1_model(feats)

        batch_preds = []
        for i, sub in enumerate(subjects):
            cfm = stage2_models[str(sub)]
            mu = mu_anchor[:, i].transpose(1, 2)

            pred = cfm(mu, n_timesteps=n_timesteps)
            pred = pred.transpose(1, 2).unsqueeze(1)
            batch_preds.append(pred)

        pred_combined = torch.cat(batch_preds, dim=1)

        n_samples, n_subjects, seq_len, channels = fmri.shape
        if n_subjects != len(subjects):
            raise ValueError(
                f"Expected {len(subjects)} subjects in batch, got {n_subjects}."
            )

        outputs.append(
            pred_combined.cpu().numpy().swapaxes(0, 1).reshape((n_subjects, n_samples * seq_len, channels))
        )
        samples.append(
            fmri.cpu().numpy().swapaxes(0, 1).reshape((n_subjects, n_samples * seq_len, channels))
        )

    outputs_np = np.concatenate(outputs, axis=1)
    samples_np = np.concatenate(samples, axis=1)

    acc, metrics = compute_subject_metrics(samples_np, outputs_np, subjects)
    accs_fmt = format_subject_accuracies(metrics)

    print(f"Evaluate Stage 2 ({ds_name}): {epoch:>3d}  Acc: {accs_fmt} ({acc:.3f})")

    return acc, metrics