"""
Quick test: Credit Bridge on CIFAR-10 with s=deltaL conditioning.
deltaL = grad_{h_L} CE(out_head(h_L), y) -- output-layer-local, dim=d_hidden.
This gives 512-dim conditioning instead of 10-dim e_T.
"""
import os
import sys
import json
import argparse
import time
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.utils.data import DataLoader
import torchvision
import torchvision.transforms as transforms

sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))

from models.residual_mlp import ResidualMLP
from models.value_net import ValueNet, SinusoidalTimeEmbed, create_ema_model, update_ema
from metrics.credit_metrics import (
    cosine_similarity_batch, perturbation_correlation, nudging_test
)


class ValueNetLargeS(nn.Module):
    """Value net with larger s_dim (for deltaL conditioning)."""
    def __init__(self, d_hidden, s_dim, time_embed_dim=32, hidden_dim=256, num_layers=3):
        super().__init__()
        self.ln = nn.LayerNorm(d_hidden)
        self.time_embed = SinusoidalTimeEmbed(time_embed_dim)
        # Compress s to a fixed dim to keep value net manageable
        self.s_compress = nn.Linear(s_dim, 64)
        input_dim = d_hidden + time_embed_dim + 64
        layers = []
        for i in range(num_layers):
            in_d = input_dim if i == 0 else hidden_dim
            layers.append(nn.Linear(in_d, hidden_dim))
            layers.append(nn.GELU())
        layers.append(nn.Linear(hidden_dim, 1))
        self.net = nn.Sequential(*layers)

    def forward(self, h, t, s):
        h_normed = self.ln(h)
        t_emb = self.time_embed(t)
        s_compressed = self.s_compress(s)
        inp = torch.cat([h_normed, t_emb, s_compressed], dim=-1)
        return self.net(inp).squeeze(-1)


def get_cifar10(batch_size=128):
    transform_train = transforms.Compose([
        transforms.RandomCrop(32, padding=4),
        transforms.RandomHorizontalFlip(),
        transforms.ToTensor(),
        transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2470, 0.2435, 0.2616)),
    ])
    transform_test = transforms.Compose([
        transforms.ToTensor(),
        transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2470, 0.2435, 0.2616)),
    ])
    trainset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transform_train)
    testset = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=transform_test)
    train_loader = DataLoader(trainset, batch_size=batch_size, shuffle=True, num_workers=4, pin_memory=True)
    test_loader = DataLoader(testset, batch_size=batch_size, shuffle=False, num_workers=4, pin_memory=True)
    return train_loader, test_loader


def evaluate(model, test_loader, device):
    model.eval()
    correct, total = 0, 0
    with torch.no_grad():
        for x, y in test_loader:
            x = x.view(x.size(0), -1).to(device)
            y = y.to(device)
            logits = model(x)
            correct += (logits.argmax(1) == y).sum().item()
            total += x.size(0)
    return correct / total


def compute_deltaL(model, hL_det, y):
    """Compute delta_L = grad_{h_L} CE(out_head(out_ln(h_L)), y). Output-layer-local."""
    hL_req = hL_det.clone().requires_grad_(True)
    logits_local = model.out_head(model.out_ln(hL_req))
    loss_local = F.cross_entropy(logits_local, y, reduction='sum')
    delta_L = torch.autograd.grad(loss_local, hL_req, create_graph=False)[0].detach()
    return delta_L


def train_cb_deltaL(model, train_loader, test_loader, device, args):
    """Credit bridge with s=deltaL conditioning."""
    d = model.d_hidden
    L = model.num_blocks
    C = 10
    warmup_epochs = max(1, args.epochs // 5)

    value_net = ValueNetLargeS(d_hidden=d, s_dim=d, time_embed_dim=32,
                                hidden_dim=256, num_layers=3).to(device)
    value_net_ema = create_ema_model(value_net)

    Bs_fallback = [torch.randn(d, C, device=device) / np.sqrt(C) for _ in range(L)]

    block_opts = [optim.AdamW(block.parameters(), lr=args.lr, weight_decay=args.wd)
                  for block in model.blocks]
    embed_opt = optim.AdamW(model.embed.parameters(), lr=args.lr, weight_decay=args.wd)
    head_opt = optim.AdamW(
        list(model.out_head.parameters()) + list(model.out_ln.parameters()),
        lr=args.lr, weight_decay=args.wd
    )
    value_opt = optim.Adam(value_net.parameters(), lr=args.lr_fb)

    all_schedulers = ([optim.lr_scheduler.CosineAnnealingLR(o, T_max=args.epochs) for o in block_opts]
                      + [optim.lr_scheduler.CosineAnnealingLR(embed_opt, T_max=args.epochs),
                         optim.lr_scheduler.CosineAnnealingLR(head_opt, T_max=args.epochs)])

    log = {'train_loss': [], 'train_acc': [], 'test_acc': [], 'value_loss': []}

    for epoch in range(1, args.epochs + 1):
        model.train()
        value_net.train()
        total_loss, correct, total = 0, 0, 0
        total_vloss = 0

        if epoch <= warmup_epochs:
            credit_blend = 0.0
        else:
            credit_blend = min(1.0, (epoch - warmup_epochs) / max(1, warmup_epochs))

        for x, y in train_loader:
            x = x.view(x.size(0), -1).to(device)
            y = y.to(device)
            batch = x.size(0)

            with torch.no_grad():
                logits, hiddens = model(x, return_hidden=True)
                loss_val = F.cross_entropy(logits, y)
                e_T = logits.softmax(dim=-1)
                e_T[torch.arange(batch), y] -= 1
                true_loss = F.cross_entropy(logits, y, reduction='none').detach()

            hL_det = hiddens[-1].detach()

            # Compute s = deltaL (output-layer-local gradient)
            s = compute_deltaL(model, hL_det, y)

            # Train value net
            t_L = torch.ones(batch, device=device)
            V_terminal = value_net(hL_det, t_L, s)
            loss_term = ((V_terminal - true_loss) ** 2).mean()

            # Terminal gradient matching
            loss_tgrad = torch.tensor(0.0, device=device)
            if args.term_grad_weight > 0:
                hL_req = hL_det.clone().requires_grad_(True)
                V_at_L = value_net(hL_req, t_L, s)
                grad_V_L = torch.autograd.grad(V_at_L.sum(), hL_req, create_graph=True)[0]
                # a_L_exact is just s (deltaL) itself
                a_L_exact = s
                loss_tgrad = ((grad_V_L - a_L_exact) ** 2).sum(dim=-1).mean()

            # Bridge consistency
            loss_bridge = 0.0
            for l in range(L):
                h_l_det = hiddens[l].detach()
                t_l = torch.full((batch,), l / L, device=device)
                t_l_next = torch.full((batch,), (l + 1) / L, device=device)
                V_l = value_net(h_l_det, t_l, s)
                with torch.no_grad():
                    h_next_det = hiddens[l + 1].detach()
                    log_terms = []
                    for k in range(args.K):
                        noise = args.sigma_bridge * torch.randn_like(h_next_det)
                        V_next = value_net_ema(h_next_det + noise, t_l_next, s)
                        log_terms.append(-V_next / args.lam)
                    log_stack = torch.stack(log_terms, dim=-1)
                    V_target = -args.lam * (torch.logsumexp(log_stack, dim=-1) - np.log(args.K))
                loss_bridge = loss_bridge + ((V_l - V_target.detach()) ** 2).mean()
            loss_bridge = loss_bridge / L

            value_loss = loss_term + loss_bridge + args.term_grad_weight * loss_tgrad
            value_opt.zero_grad()
            value_loss.backward()
            torch.nn.utils.clip_grad_norm_(value_net.parameters(), 1.0)
            value_opt.step()
            update_ema(value_net, value_net_ema, args.ema_momentum)
            total_vloss += value_loss.item() * batch

            # Compute credits
            cb_credits = []
            for l in range(L):
                h_l_det = hiddens[l].detach().requires_grad_(True)
                t_l = torch.full((batch,), l / L, device=device)
                V_l = value_net(h_l_det, t_l, s)
                a_l = torch.autograd.grad(V_l.sum(), h_l_det, create_graph=False)[0]
                cb_credits.append(a_l.detach())

            dfa_credits = [(e_T @ Bs_fallback[l].T).detach() for l in range(L)]

            credits = []
            for l in range(L):
                if credit_blend >= 1.0:
                    a = cb_credits[l]
                elif credit_blend <= 0.0:
                    a = dfa_credits[l]
                else:
                    cb_rms = (cb_credits[l] ** 2).mean(dim=-1, keepdim=True).sqrt() + 1e-6
                    dfa_rms = (dfa_credits[l] ** 2).mean(dim=-1, keepdim=True).sqrt() + 1e-6
                    a = credit_blend * (cb_credits[l] / cb_rms) + (1 - credit_blend) * (dfa_credits[l] / dfa_rms)
                credits.append(a)

            # Update output head
            logits_out = model.out_head(model.out_ln(hL_det))
            loss_out = F.cross_entropy(logits_out, y)
            head_opt.zero_grad()
            loss_out.backward()
            head_opt.step()

            # Update blocks
            for l in range(L):
                h_l = hiddens[l].detach()
                a = credits[l]
                rms = (a ** 2).mean(dim=-1, keepdim=True).sqrt() + 1e-6
                a_norm = a / rms
                f_l = model.blocks[l](h_l)
                local_loss = (f_l * a_norm).sum(dim=-1).mean()
                block_opts[l].zero_grad()
                local_loss.backward()
                torch.nn.utils.clip_grad_norm_(model.blocks[l].parameters(), 1.0)
                block_opts[l].step()

            # Update embedding
            a_0 = credits[0]
            rms_0 = (a_0 ** 2).mean(dim=-1, keepdim=True).sqrt() + 1e-6
            a_0_norm = a_0 / rms_0
            h0 = model.embed(x)
            embed_loss = (h0 * a_0_norm).sum(dim=-1).mean()
            embed_opt.zero_grad()
            embed_loss.backward()
            embed_opt.step()

            total_loss += loss_val.item() * batch
            correct += (logits.argmax(1) == y).sum().item()
            total += batch

        for sch in all_schedulers:
            sch.step()

        log['train_loss'].append(total_loss / total)
        log['train_acc'].append(correct / total)
        log['test_acc'].append(evaluate(model, test_loader, device))
        log['value_loss'].append(total_vloss / total)
        if epoch % 10 == 0 or epoch == 1:
            phase = "warmup" if epoch <= warmup_epochs else f"blend={credit_blend:.2f}"
            print(f"  [CB-deltaL] Ep {epoch} ({phase}): loss={log['train_loss'][-1]:.4f} "
                  f"train={log['train_acc'][-1]:.4f} test={log['test_acc'][-1]:.4f} "
                  f"vloss={log['value_loss'][-1]:.6f}")
    return log, value_net


def compute_diagnostics(model, value_net, test_loader, device, args):
    model.eval()
    value_net.eval()
    d = model.d_hidden
    L = model.num_blocks

    for x, y in test_loader:
        x = x.view(x.size(0), -1).to(device)
        y = y.to(device)
        break

    batch = x.size(0)

    # BP gradients
    logits_bp, hiddens_bp = model(x, return_hidden=True)
    for l in range(L + 1):
        hiddens_bp[l].retain_grad()
    loss_bp = F.cross_entropy(logits_bp, y)
    loss_bp.backward()
    bp_grads = {l: hiddens_bp[l].grad.detach().clone() for l in range(L + 1)}

    with torch.no_grad():
        logits, hiddens = model(x, return_hidden=True)

    hL_det = hiddens[-1].detach()
    s = compute_deltaL(model, hL_det, y)

    results = {'bp_cosine': [], 'perturbation_rho': [], 'nudging': {'0.01': []}}

    for l in range(L):
        h_l = hiddens[l].detach()
        t_l = torch.full((batch,), l / L, device=device)

        h_l_req = h_l.clone().requires_grad_(True)
        V_l = value_net(h_l_req, t_l, s)
        a_l = torch.autograd.grad(V_l.sum(), h_l_req, create_graph=False)[0].detach()

        bp_cos = cosine_similarity_batch(a_l, bp_grads[l])
        results['bp_cosine'].append(bp_cos)

        def make_fwd_fn(start_l):
            def fwd_fn(h):
                with torch.no_grad():
                    curr = h
                    for i in range(start_l, L):
                        curr = curr + model.blocks[i](curr)
                    out = model.out_head(model.out_ln(curr))
                    return F.cross_entropy(out, y, reduction='none')
            return fwd_fn

        fwd_fn = make_fwd_fn(l)
        rho = perturbation_correlation(h_l, a_l, fwd_fn, epsilon=1e-3, M=16)
        results['perturbation_rho'].append(rho)

        nud = nudging_test(h_l, a_l, fwd_fn, eta=0.01)
        results['nudging']['0.01'].append(nud)

    return results


def main():
    parser = argparse.ArgumentParser()
    parser.add_argument('--d_hidden', type=int, default=512)
    parser.add_argument('--num_blocks', type=int, default=4)
    parser.add_argument('--batch_size', type=int, default=128)
    parser.add_argument('--epochs', type=int, default=100)
    parser.add_argument('--lr', type=float, default=1e-3)
    parser.add_argument('--lr_fb', type=float, default=1e-3)
    parser.add_argument('--wd', type=float, default=0.01)
    parser.add_argument('--lam', type=float, default=0.1)
    parser.add_argument('--K', type=int, default=4)
    parser.add_argument('--sigma_bridge', type=float, default=0.05)
    parser.add_argument('--ema_momentum', type=float, default=0.995)
    parser.add_argument('--term_grad_weight', type=float, default=1.0)
    parser.add_argument('--seed', type=int, default=42)
    parser.add_argument('--gpu', type=int, default=1)
    parser.add_argument('--output_dir', type=str, default='results/cifar_deltaL')
    args = parser.parse_args()

    device = torch.device(f'cuda:{args.gpu}' if torch.cuda.is_available() else 'cpu')
    print(f"Device: {device}")
    os.makedirs(args.output_dir, exist_ok=True)

    torch.manual_seed(args.seed)
    np.random.seed(args.seed)
    torch.cuda.manual_seed_all(args.seed)

    train_loader, test_loader = get_cifar10(args.batch_size)
    input_dim = 32 * 32 * 3

    model = ResidualMLP(input_dim, args.d_hidden, 10, args.num_blocks).to(device)
    print(f"Model: d={args.d_hidden}, L={args.num_blocks}")
    print(f"Conditioning: s=deltaL (dim={args.d_hidden})")

    t0 = time.time()
    log, vnet = train_cb_deltaL(model, train_loader, test_loader, device, args)
    elapsed = time.time() - t0

    diag = compute_diagnostics(model, vnet, test_loader, device, args)

    mean_gamma = np.mean(diag['bp_cosine'])
    mean_rho = np.mean(diag['perturbation_rho'])
    mean_nudge = np.mean(diag['nudging']['0.01'])

    print(f"\nDone in {elapsed:.0f}s")
    print(f"Test acc: {log['test_acc'][-1]:.4f}")
    print(f"Mean Gamma: {mean_gamma:.4f}")
    print(f"Mean rho: {mean_rho:.4f}")
    print(f"Mean nudge: {mean_nudge:.6f}")
    print(f"Gamma per layer: {[round(g, 4) for g in diag['bp_cosine']]}")
    print(f"rho per layer: {[round(r, 4) for r in diag['perturbation_rho']]}")

    result = {
        'test_acc': log['test_acc'][-1],
        'mean_gamma': float(mean_gamma),
        'mean_rho': float(mean_rho),
        'mean_nudge': float(mean_nudge),
        'gamma_per_layer': [float(g) for g in diag['bp_cosine']],
        'rho_per_layer': [float(r) for r in diag['perturbation_rho']],
        'log': log,
    }

    out_path = os.path.join(args.output_dir, f'cb_deltaL_d{args.d_hidden}_L{args.num_blocks}_s{args.seed}.json')
    with open(out_path, 'w') as f:
        json.dump(result, f, indent=2)
    print(f"Saved to {out_path}")


if __name__ == '__main__':
    main()