"""Claims 2, 3 and 6.

Claim 2 (Thm 1 parameter regime).  The theorem promises F^tr = o_d(1) under
    n = Theta~(d^2 K),  m = Omega~(d^8 K^4),  eta*T = Theta(d^2).
m = d^8 K^4 is numerically unreachable (d=32, K=3 -> 8.7e13 neurons), so we
test the *asymptotic statement* instead: hold the prescribed n and eta*T
scalings, push d up, and check the forgetting decreases towards 0.  We use the
exact linear-loss solver so that the width can be set large enough for the
third (width) term of Thm 1 to be numerically negligible, isolating the
d-dependence the theorem predicts.  Controls relax each condition in turn.

Claim 3 (Thm 2).  After KT GD iterations the misclassification *train* error
and train loss are o_d(1) uniformly over all K tasks.  Checked with the hinge
loss the theorem assumes.

Claim 6 (decomposition).  Test-time forgetting <= train-time forgetting +
delayed generalization gap, and the *joint* (not individual) control by n and
m: a 2-D grid where neither large n alone nor large m alone drives forgetting
down.
"""

import json
import os
import sys
import time
from multiprocessing import Pool

import numpy as np

sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
import clcore as C  # noqa: E402
from exp2_mechanism import exact_linear_run  # noqa: E402

OUT = os.path.join(os.path.dirname(os.path.abspath(__file__)), "results")
os.makedirs(OUT, exist_ok=True)

DIMS = [12, 16, 24, 32, 48, 64]
SEEDS = list(range(5))
M_BIG = 20_000            # large enough that the 1/sqrt(m) term is negligible
K = 3
C_N = 1.0                # n = C_N * d^2 * K
C_T = 0.15               # eta*T = C_T * d^2   (eta = 2 fixed, T = C_T d^2 / eta)


def regime_job(job):
    tag, d, seed = job
    n = int(round(C_N * d * d * K))
    eta = 2.0
    T = max(5, int(round(C_T * d * d / eta)))
    m = M_BIG
    if tag == "prescribed":
        pass
    elif tag == "fixed_n":            # violate n = Theta~(d^2 K): n stays small
        n = int(round(C_N * 12 * 12 * K))
    elif tag == "long_train":         # violate eta*T = Theta(d^2): eta*T ~ d^3
        T = max(5, int(round(C_T * d ** 3 / (12 * eta))))
    elif tag == "small_m":            # violate the width condition
        m = 300
    r = exact_linear_run(d=d, m=m, K=K, n=n, T=T, eta=eta, sigma_c=0.1, seed=seed)
    r["sweep"] = "regime"
    r["variant"] = tag
    return r


def claim3_job(job):
    """Hinge-loss GD; record per-task train loss / misclassification error."""
    d, m, n, K3, T, eta, seed = job
    res = C.continual_run(d=d, m=m, K=K3, n=n, T=T, eta=eta, sigma_c=0.1,
                          loss_name="hinge", seed=seed, n_test=2000)
    return dict(
        sweep="claim3", d=d, m=m, n=n, K=K3, T=T, eta=eta, seed=seed,
        loss_at=res["loss_at"].tolist(),
        err_at=res["err_at"].tolist(),
        test_loss_at=res["test_loss_at"].tolist(),
        test_err_at=res["test_err_at"].tolist(),
        forget=[C.train_forgetting(res, k) for k in range(K3)],
        test_forget=[C.test_forgetting(res, k) for k in range(K3)],
        gen_gap=[C.gen_gap(res, k) for k in range(K3)],
        dist=list(res["dist"]),
    )


def claim6_job(job):
    """(n, m) grid: joint control of forgetting."""
    n, m, seed = job
    res = C.continual_run(d=50, m=m, K=3, n=n, T=200, eta=8.0, sigma_c=0.1,
                          loss_name="hinge", seed=seed, n_test=3000)
    return dict(
        sweep="claim6", n=n, m=m, seed=seed, d=50, K=3, T=200, eta=8.0,
        loss_at=res["loss_at"].tolist(),
        test_loss_at=res["test_loss_at"].tolist(),
        forget=[C.train_forgetting(res, k) for k in range(3)],
        test_forget=[C.test_forgetting(res, k) for k in range(3)],
        gen_gap=[C.gen_gap(res, k) for k in range(3)],
        err_at=res["err_at"].tolist(),
        test_err_at=res["test_err_at"].tolist(),
    )


if __name__ == "__main__":
    t0 = time.time()
    recs = []

    jobs = [(tag, d, s)
            for tag in ["prescribed", "fixed_n", "long_train", "small_m"]
            for d in DIMS for s in SEEDS]
    with Pool(C.NPROC) as p:
        recs += list(p.imap_unordered(regime_job, jobs))
    print("regime done", f"{time.time()-t0:.0f}s", flush=True)

    # Claim 3: hinge loss, K = 6 tasks.  eta*T = 1600 = 0.64 d^2 puts the
    # network in the interpolating regime the theorem assumes; eta*T = 400 is
    # the authors' own Fig.-1 horizon and is reported for comparison.
    j3 = [(50, m, n, 6, 200, eta, s)
          for eta in [8.0, 2.0]
          for m in [500, 2000] for n in [500, 2000] for s in range(4)]
    with Pool(C.NPROC) as p:
        recs += list(p.imap_unordered(claim3_job, j3))
    print("claim3 done", f"{time.time()-t0:.0f}s", flush=True)

    # Claim 6: (n, m) grid.  The n=8000 x m=5000 corner alone costs more than
    # the rest of the grid put together (cost ~ n*m per GD step), and the
    # claim being tested is qualitative -- that neither axis alone drives
    # forgetting down -- so the grid is capped at n=4000 and 3 seeds.
    j6 = [(n, m, s)
          for n in [125, 500, 2000, 4000]
          for m in [50, 200, 1000, 5000]
          for s in range(3)]
    with Pool(C.NPROC) as p:
        recs += list(p.imap_unordered(claim6_job, j6))
    print("claim6 done", f"{time.time()-t0:.0f}s", flush=True)

    with open(os.path.join(OUT, "exp3_regime.json"), "w") as f:
        json.dump(recs, f)
    print("wrote exp3_regime.json", f"{time.time()-t0:.0f}s")