import time
import math
import numpy as np
import torch
import torch.nn as nn
from typing import Dict
from torch.utils.data import DataLoader
from timm.utils import AverageMeter

def train_one_epoch_condition(
    *,
    epoch: int,
    model: torch.nn.Module,
    train_loader: DataLoader,
    optimizer: torch.optim.Optimizer,
    device: torch.device,
):
    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

        pred, _ = model(feats)

        loss = nn.MSELoss()(pred, fmri)
        loss_item = loss.item()

        if math.isnan(loss_item) or math.isinf(loss_item):
            raise RuntimeError(
                f"NaN/Inf loss encountered on step {batch_idx + 1}; exiting"
            )

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

        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 = res_mem_gb = 0.0

            print(
                f"Stage 1 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 loss_m.avg

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,
):
    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, embed_anchor = stage1_model(feats)

        batch_loss = 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)
            src_cond = mu_anchor[:, i].transpose(1, 2)
            mu_fusion = embed_anchor.transpose(1, 2)

            loss, _ = cfm.compute_loss(x1, src_cond, mu_fusion)

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

            batch_loss += loss.item()

        loss_item = batch_loss / len(subjects)

        if math.isnan(loss_item) or math.isinf(loss_item):
            raise RuntimeError(
                f"NaN/Inf loss encountered on step {batch_idx + 1}; exiting"
            )

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

        loss_m.update(loss_item, fmri.size(0))
        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 = 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 loss_m.avg

def train_one_epoch_jointly(
    *,
    epoch: int,
    stage1_model: torch.nn.Module,
    stage2_models: nn.ModuleDict,
    train_loader: DataLoader,
    stage1_optimizer: torch.optim.Optimizer,
    stage2_optimizers: Dict[str, torch.optim.Optimizer],
    device: torch.device,
    subjects: list,
):
    stage1_model.train()
    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

        mu_anchor, embed_anchor = stage1_model(feats)
        stage1_loss = nn.MSELoss()(mu_anchor, fmri)

        stage2_loss = 0.0
        for i, sub in enumerate(subjects):
            sub_key = str(sub)
            cfm = stage2_models[sub_key]

            x1 = fmri[:, i].transpose(1, 2)
            src_cond = mu_anchor[:, i].transpose(1, 2)
            mu_fusion = embed_anchor.transpose(1, 2)

            loss, _ = cfm.compute_loss(x1, src_cond, mu_fusion)
            stage2_loss += loss

        stage2_loss = stage2_loss / len(subjects)
        total_loss = stage1_loss + stage2_loss
        loss_item = total_loss.item()

        if math.isnan(loss_item) or math.isinf(loss_item):
            raise RuntimeError(
                f"NaN/Inf loss encountered on step {batch_idx + 1}; exiting"
            )

        stage1_optimizer.zero_grad()
        for opt in stage2_optimizers.values():
            opt.zero_grad()

        total_loss.backward()

        stage1_optimizer.step()
        for opt in stage2_optimizers.values():
            opt.step()

        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 = res_mem_gb = 0.0

            print(
                f"Joint 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 loss_m.avg

@torch.no_grad()
def run_inference(
    *,
    stage1_model: nn.Module,
    stage2_models: nn.ModuleDict,
    test_loader,
    fmri_num_samples: dict[str, dict[str, int]],
    subjects: list[int],
    device: torch.device,
    n_timesteps: int = 25,
) -> dict[str, dict[str, np.ndarray]]:
    stage1_model.eval()
    stage2_models.eval()

    submission = {f"sub-{sub:02d}": {} for sub in subjects}

    for batch_idx, batch in enumerate(test_loader):
        feats = [f.to(device) for f in batch["features"]]
        episodes = batch["episode"]

        mu_anchor, embed_anchor = stage1_model(feats)

        N, S, T, V = mu_anchor.shape
        assert N == 1, "Batch size must be 1 for submission"

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

            src_cond = mu_anchor[:, i].transpose(1, 2)
            mu_fusion = embed_anchor.transpose(1, 2)

            pred = cfm(src_cond, mu_fusion, n_timesteps=n_timesteps)
            pred = pred.transpose(1, 2)
            batch_preds.append(pred)

        for ii, episode in enumerate(episodes):
            for jj, sub_id in enumerate(subjects):
                sub = f"sub-{sub_id:02d}"
                pred = batch_preds[jj][ii].cpu().numpy()

                num_samples = fmri_num_samples[sub].get(episode, len(pred))
                pred = pred[:num_samples].astype(np.float32)

                submission[sub][episode] = pred

        if (batch_idx + 1) % 10 == 0:
            print(f"  Processed {batch_idx + 1} episodes...")

    return submission