"""
Phase 9B: Periodic Refit.

Instead of continuous co-learning, alternate:
1. Train forward net with current credit for K epochs
2. Freeze forward, refit Vec estimator for R epoch-equivalents
3. Resume

Compare:
- DFA backbone + periodic Vec refit + blend
- DFA only (baseline)
"""
import os
import sys
import json
import argparse
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
import copy

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 SinusoidalTimeEmbed
from metrics.credit_metrics import cosine_similarity_batch, perturbation_correlation


class VectorCreditNet(nn.Module):
    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)
        input_dim = d_hidden + time_embed_dim + s_dim
        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, d_hidden))
        self.net = nn.Sequential(*layers)

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


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()
    c, t = 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)
            c += (model(x).argmax(1) == y).sum().item(); t += x.size(0)
    return c / t


def refit_vec(model, vec_net, train_loader, device, refit_epochs=1, lr_fb=1e-3, M=4):
    """Freeze model, refit Vec estimator for refit_epochs."""
    model.eval()
    for p in model.parameters(): p.requires_grad_(False)
    vec_opt = optim.Adam(vec_net.parameters(), lr=lr_fb)
    eps = 1e-3
    L = model.num_blocks
    for ep in range(refit_epochs):
        vec_net.train()
        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)
                e_T = logits.softmax(-1)
                e_T[torch.arange(batch), y] -= 1
                s = e_T.detach()
            hL = hiddens[-1].detach()
            t_L = torch.ones(batch, device=device)
            a_term = vec_net(hL, t_L, s)
            hL_req = hL.clone().requires_grad_(True)
            logits_tgt = model.out_head(model.out_ln(hL_req))
            ce = F.cross_entropy(logits_tgt, y, reduction='sum')
            delta_L = torch.autograd.grad(ce, hL_req, create_graph=False)[0].detach()
            loss_term = ((a_term - delta_L)**2).sum(-1).mean()
            l = np.random.randint(0, L)
            h_l = hiddens[l].detach()
            t_l = torch.full((batch,), l / L, device=device)
            a_l = vec_net(h_l, t_l, s)
            loss_proj = torch.tensor(0.0, device=device)
            for _ in range(M):
                v = torch.randn_like(h_l)
                v = v / (v.norm(-1, keepdim=True) + 1e-8)
                with torch.no_grad():
                    lp = F.cross_entropy(model.forward_from_layer(h_l + eps*v, l), y, reduction='none')
                    lm = F.cross_entropy(model.forward_from_layer(h_l - eps*v, l), y, reduction='none')
                    g_j = (lp - lm) / (2*eps)
                loss_proj = loss_proj + (((a_l*v).sum(-1) - g_j.detach())**2).mean()
            loss_proj /= M
            vloss = loss_term + loss_proj
            vec_opt.zero_grad(); vloss.backward()
            torch.nn.utils.clip_grad_norm_(vec_net.parameters(), 1.0)
            vec_opt.step()
    for p in model.parameters(): p.requires_grad_(True)


def compute_diagnostics(model, vec_net, dfa_Bs, test_loader, device, credit_mode):
    model.eval()
    vec_net.eval()
    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 grads
    was_frozen = not next(model.parameters()).requires_grad
    if was_frozen:
        for p in model.parameters(): p.requires_grad_(True)
    model.zero_grad()
    logits_bp, hbp = model(x, return_hidden=True)
    for l in range(L+1): hbp[l].retain_grad()
    F.cross_entropy(logits_bp, y).backward()
    bp_grads = {l: hbp[l].grad.detach().clone() for l in range(L+1)}
    if was_frozen:
        for p in model.parameters(): p.requires_grad_(False)

    with torch.no_grad():
        logits, hiddens = model(x, return_hidden=True)
        e_T = logits.softmax(-1); e_T[torch.arange(batch), y] -= 1; s = e_T.detach()
    gammas = []
    for l in range(L):
        h_l = hiddens[l].detach(); t_l = torch.full((batch,), l/L, device=device)
        if credit_mode == 'dfa':
            a_l = (s @ dfa_Bs[l].T).detach()
        else:
            alpha = credit_mode
            a_dfa = (s @ dfa_Bs[l].T).detach()
            a_vec = vec_net(h_l, t_l, s).detach()
            rms_v = (a_vec**2).mean(-1, keepdim=True).sqrt() + 1e-6
            rms_d = (a_dfa**2).mean(-1, keepdim=True).sqrt() + 1e-6
            a_l = alpha * a_vec/rms_v + (1-alpha) * a_dfa/rms_d
        gammas.append(cosine_similarity_batch(a_l, bp_grads[l]))
    return float(np.mean(gammas))


def train_with_periodic_refit(model, train_loader, test_loader, device, args,
                               K_forward, R_refit, blend_alpha):
    """
    Train with periodic Vec refit.
    Every K_forward epochs of forward training, freeze and refit Vec for R_refit epochs.
    """
    d = model.d_hidden
    L = model.num_blocks

    vec_net = VectorCreditNet(d_hidden=d, s_dim=10, time_embed_dim=32,
                               hidden_dim=256, num_layers=3).to(device)
    Bs = [torch.randn(d, 10, device=device) / np.sqrt(10) for _ in range(L)]

    block_opts = [optim.AdamW(b.parameters(), lr=args.lr, weight_decay=args.wd) for b 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)
    scheds = [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 = {'test_acc': [], 'train_loss': [], 'gamma': []}
    epochs_since_refit = 0

    # Initial refit
    print(f"    Initial Vec refit ({R_refit} epochs)...")
    refit_vec(model, vec_net, train_loader, device, refit_epochs=R_refit, lr_fb=args.lr_fb, M=args.M)

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

        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(-1); e_T[torch.arange(batch), y] -= 1; s = e_T.detach()
            hL = hiddens[-1].detach()

            # Also do online Vec training (in addition to periodic refit)
            t_L = torch.ones(batch, device=device)
            a_term = vec_net(hL, t_L, s)
            hL_req = hL.clone().requires_grad_(True)
            logits_tgt = model.out_head(model.out_ln(hL_req))
            ce = F.cross_entropy(logits_tgt, y, reduction='sum')
            delta_L = torch.autograd.grad(ce, hL_req, create_graph=False)[0].detach()
            loss_term = ((a_term - delta_L)**2).sum(-1).mean()
            l_train = np.random.randint(0, L)
            h_l = hiddens[l_train].detach()
            t_l = torch.full((batch,), l_train/L, device=device)
            a_l = vec_net(h_l, t_l, s)
            loss_proj = torch.tensor(0.0, device=device)
            eps = 1e-3
            for _ in range(args.M):
                v = torch.randn_like(h_l); v = v/(v.norm(-1,keepdim=True)+1e-8)
                with torch.no_grad():
                    lp = F.cross_entropy(model.forward_from_layer(h_l+eps*v,l_train),y,reduction='none')
                    lm = F.cross_entropy(model.forward_from_layer(h_l-eps*v,l_train),y,reduction='none')
                    g_j = (lp-lm)/(2*eps)
                loss_proj = loss_proj + (((a_l*v).sum(-1)-g_j.detach())**2).mean()
            loss_proj /= args.M
            vloss = loss_term + loss_proj
            vec_opt_step = optim.Adam(vec_net.parameters(), lr=args.lr_fb)
            vec_opt_step.zero_grad(); vloss.backward()
            torch.nn.utils.clip_grad_norm_(vec_net.parameters(), 1.0)
            # Note: we create optimizer each step which is wasteful but simple
            # In practice should keep a persistent optimizer

            # Compute blended credits
            with torch.no_grad():
                vec_credits = [vec_net(hiddens[l].detach(), torch.full((batch,),l/L,device=device), s).detach() for l in range(L)]
            dfa_credits = [(e_T @ Bs[l].T).detach() for l in range(L)]

            credits = []
            for l in range(L):
                rms_v = (vec_credits[l]**2).mean(-1,keepdim=True).sqrt()+1e-6
                rms_d = (dfa_credits[l]**2).mean(-1,keepdim=True).sqrt()+1e-6
                credits.append(blend_alpha*vec_credits[l]/rms_v + (1-blend_alpha)*dfa_credits[l]/rms_d)

            # Update head
            logits_out = model.out_head(model.out_ln(hL))
            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):
                a = credits[l]; rms = (a**2).mean(-1,keepdim=True).sqrt()+1e-6
                f = model.blocks[l](hiddens[l].detach())
                ll = (f*(a/rms)).sum(-1).mean()
                block_opts[l].zero_grad(); ll.backward()
                torch.nn.utils.clip_grad_norm_(model.blocks[l].parameters(), 1.0)
                block_opts[l].step()

            a0 = credits[0]; rms0 = (a0**2).mean(-1,keepdim=True).sqrt()+1e-6
            el = (model.embed(x)*(a0/rms0)).sum(-1).mean()
            embed_opt.zero_grad(); el.backward(); embed_opt.step()

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

        for s in scheds: s.step()
        test_acc = evaluate(model, test_loader, device)
        log['test_acc'].append(test_acc)
        log['train_loss'].append(total_loss/total)

        epochs_since_refit += 1

        # Periodic refit
        if epochs_since_refit >= K_forward:
            print(f"    Refitting Vec at epoch {epoch} ({R_refit} epochs)...")
            refit_vec(model, vec_net, train_loader, device, refit_epochs=R_refit, lr_fb=args.lr_fb, M=args.M)
            epochs_since_refit = 0

        if epoch % 10 == 0 or epoch <= 5 or epoch == args.epochs:
            gamma = compute_diagnostics(model, vec_net, Bs, test_loader, device, blend_alpha)
            log['gamma'].append((epoch, gamma))
            print(f"    [refit_K{K_forward}_R{R_refit}_a{blend_alpha}] Ep {epoch}: acc={test_acc:.4f}, G={gamma:.4f}")

    return log


def train_dfa_only(model, train_loader, test_loader, device, args):
    d = model.d_hidden; L = model.num_blocks
    Bs = [torch.randn(d, 10, device=device)/np.sqrt(10) for _ in range(L)]
    block_opts = [optim.AdamW(b.parameters(), lr=args.lr, weight_decay=args.wd) for b 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)
    scheds = [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 = {'test_acc': [], 'train_loss': []}
    for epoch in range(1, args.epochs+1):
        model.train(); total_loss, correct, total = 0, 0, 0
        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(-1); e_T[torch.arange(batch),y] -= 1
            hL = hiddens[-1].detach()
            loss_out = F.cross_entropy(model.out_head(model.out_ln(hL)), y)
            head_opt.zero_grad(); loss_out.backward(); head_opt.step()
            for l in range(L):
                a = (e_T@Bs[l].T).detach(); rms = (a**2).mean(-1,keepdim=True).sqrt()+1e-6
                f = model.blocks[l](hiddens[l].detach()); ll = (f*(a/rms)).sum(-1).mean()
                block_opts[l].zero_grad(); ll.backward()
                torch.nn.utils.clip_grad_norm_(model.blocks[l].parameters(), 1.0); block_opts[l].step()
            a0 = (e_T@Bs[0].T).detach(); rms0 = (a0**2).mean(-1,keepdim=True).sqrt()+1e-6
            el = (model.embed(x)*(a0/rms0)).sum(-1).mean()
            embed_opt.zero_grad(); el.backward(); embed_opt.step()
            total_loss += loss_val.item()*batch; correct += (logits.argmax(1)==y).sum().item(); total += batch
        for s in scheds: s.step()
        test_acc = evaluate(model, test_loader, device)
        log['test_acc'].append(test_acc); log['train_loss'].append(total_loss/total)
        if epoch % 10 == 0 or epoch == 1 or epoch == args.epochs:
            print(f"    [DFA] Ep {epoch}: acc={test_acc:.4f}")
    return log


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

    train_loader, test_loader = get_cifar10(args.batch_size)
    input_dim = 32*32*3; L = args.num_blocks; d = args.d_hidden

    all_results = {}

    # DFA baseline
    print(f"\n{'='*60}\nDFA baseline\n{'='*60}")
    torch.manual_seed(args.seed); np.random.seed(args.seed); torch.cuda.manual_seed_all(args.seed)
    model_dfa = ResidualMLP(input_dim, d, 10, L).to(device)
    log_dfa = train_dfa_only(model_dfa, train_loader, test_loader, device, args)
    all_results['DFA_only'] = log_dfa
    print(f"  DFA final: {log_dfa['test_acc'][-1]:.4f}")

    # Periodic refit configs
    configs = []
    for K in args.K_values:
        for R in args.R_values:
            for alpha in args.blend_alphas:
                configs.append((K, R, alpha))

    for K, R, alpha in configs:
        name = f"refit_K{K}_R{R}_a{alpha}"
        print(f"\n{'='*60}\n{name}\n{'='*60}")
        torch.manual_seed(args.seed); np.random.seed(args.seed); torch.cuda.manual_seed_all(args.seed)
        model = ResidualMLP(input_dim, d, 10, L).to(device)
        log = train_with_periodic_refit(model, train_loader, test_loader, device, args,
                                         K_forward=K, R_refit=R, blend_alpha=alpha)
        all_results[name] = log
        print(f"  {name} final: {log['test_acc'][-1]:.4f}")

    # Summary
    print(f"\n{'='*60}\nSUMMARY\n{'='*60}")
    dfa_final = all_results['DFA_only']['test_acc'][-1]
    print(f"{'Config':<35} {'final':>7} {'diff':>7}")
    print("-" * 52)
    for name, log in all_results.items():
        final = log['test_acc'][-1]
        diff = final - dfa_final if name != 'DFA_only' else 0
        print(f"{name:<35} {final:>7.4f} {diff:>+7.4f}")

    out_path = os.path.join(args.output_dir, f'periodic_refit_s{args.seed}.json')
    save_data = {name: {'test_acc': log['test_acc'], 'train_loss': log['train_loss']}
                 for name, log in all_results.items()}
    with open(out_path, 'w') as f:
        json.dump(save_data, f, indent=2, default=float)
    print(f"\nSaved to {out_path}")


def main():
    parser = argparse.ArgumentParser(description='Phase 9B: Periodic Refit')
    parser.add_argument('--num_blocks', type=int, default=4)
    parser.add_argument('--d_hidden', type=int, default=256)
    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('--M', type=int, default=4)
    parser.add_argument('--K_values', type=int, nargs='+', default=[5])
    parser.add_argument('--R_values', type=int, nargs='+', default=[1])
    parser.add_argument('--blend_alphas', type=float, nargs='+', default=[0.75])
    parser.add_argument('--seed', type=int, default=42)
    parser.add_argument('--gpu', type=int, default=3)
    parser.add_argument('--output_dir', type=str, default='results/periodic_refit')
    args = parser.parse_args()
    run_experiment(args)


if __name__ == '__main__':
    main()