#!/usr/bin/env python3

"""
Ablation script: loads a checkpoint and the next iteration's training data,
then trains copies of the agent with different hyperparameter configs
(number of updates, learning rate, batch size, etc.) and evaluates each.

Usage:
    python ablate_updates.py

Edit the ABLATION_CONFIGS list below to define the hyperparameter grid.
"""

import os
import io
import copy
import json
import random
import datetime
import argparse

import torch
import wandb
from tqdm import tqdm

# ---------------------------------------------------------------------------
# Configuration
# ---------------------------------------------------------------------------

# Checkpoint to start from (all ablations fork from this).
CHECKPOINT_PATH = "/datadrive/ayush/home/minimoX/learning/outputs/bootstrap_bs_800_para_8000updates/2026-03-10_21-21-05/0.pt"

# Training examples generated in the *same* iteration (i.e. examples_0.json).
EXAMPLES_PATH = "/datadrive/ayush/home/minimoX/learning/outputs/bootstrap_bs_800_para_8000updates/2026-03-10_21-21-05/examples_0.json"

# Where to write ablation outputs (default: alongside the checkpoint).
OUTPUT_ROOT = None  # If None, writes to <checkpoint_dir>/ablations/

# GPU device index (set to None for CPU).
CUDA_DEVICE = 0

# wandb project (set to None to disable).
WANDB_PROJECT = "peano-ablation"

# Each dict specifies overrides.  Keys:
#   mode          – "sample" (original: sample with replacement, char-budget batches)
#                   "epoch"  (deduplicate, shuffle, epoch-based, fixed example-count batches)
#   n_steps       – gradient steps (only for mode="sample"; default: 8000)
#   n_epochs      – number of epochs (only for mode="epoch"; default: 1)
#   batch_size    – for mode="sample": character-token budget per batch (default: 10000)
#                   for mode="epoch":  number of examples per batch (default: 64)
#   lr            – learning rate for AdamW  (default: 1e-4)
#   tag           – human-readable name used in output dir & wandb run name
ABLATION_CONFIGS = [
    # --- Mode A: original sampling (with replacement, char-budget batches) ---
    # Dir names auto-generated: updates_{ckpt}_{mode}_{params}
    #{"mode": "sample", "n_steps": 2000, "batch_size": 10000},
    #{"mode": "sample", "n_steps": 4000, "batch_size": 10000},

    # --- Mode B: epoch training (deduplicate, shuffle, re-shuffle each epoch) ---
    #{"mode": "epoch", "batch_size": 32, "n_epochs": 4, "save_every": 1},  # also save intermediate checkpoints at epoch 2
    {"mode": "epoch", "batch_size": 64, "n_epochs": 2, "save_every": 1},  # save per-epoch ckpts: epoch_1.pt (mid) + epoch_2.pt/1.pt (final)
    #{"mode": "epoch", "batch_size": 128, "n_epochs": 4, "save_every": 1},  # also save intermediate checkpoints at epoch 2

    # --- optional: epoch with different lr ---
    # {"mode": "epoch", "batch_size": 64, "lr": 5e-5},
    # {"mode": "epoch", "batch_size": 64, "lr": 3e-4},
]

# ---------------------------------------------------------------------------
# Helpers
# ---------------------------------------------------------------------------

def now() -> str:
    return "[" + datetime.datetime.now().isoformat() + "]"


def load_examples(path: str) -> list[str]:
    """Load training examples (list of strings) from a JSON file."""
    with open(path) as f:
        examples = json.load(f)
    # examples can be plain strings or dicts with a 'str' key
    out = []
    for e in examples:
        if isinstance(e, str):
            out.append(e)
        elif isinstance(e, dict) and "str" in e:
            out.append(e["str"])
        else:
            raise ValueError(f"Unexpected example format: {type(e)}")
    return out


def ckpt_num(path: str) -> str:
    """Extract checkpoint number from path, e.g. '/foo/0.pt' -> '0'."""
    return os.path.splitext(os.path.basename(path))[0]


def make_run_name(cfg: dict, checkpoint_path: str) -> str:
    """Build directory name: updates_{ckpt_num}_{mode}_{params}."""
    cn = ckpt_num(checkpoint_path)
    mode = cfg.get("mode", "sample")
    parts = [f"updates_{cn}", mode]
    if mode == "sample":
        parts.append(f"{cfg.get('n_steps', 8000)}steps")
        bs = cfg.get('batch_size') or 10000
        parts.append(f"bs{bs}")
    elif mode == "epoch":
        parts.append(f"{cfg.get('n_epochs', 1)}ep")
        bs = cfg.get('batch_size') or 64
        parts.append(f"bs{bs}")
    if cfg.get("lr") is not None:
        parts.append(f"lr{cfg['lr']}")
    return "_".join(parts)


def deep_copy_agent(agent):
    """Deep-copy an agent via serialize/deserialize (handles CUDA tensors)."""
    buf = io.BytesIO()
    torch.save(agent, buf)
    buf.seek(0)
    return torch.load(buf, weights_only=False)


def set_lr(optimizer, lr: float):
    """Override learning rate on all param groups."""
    for pg in optimizer.param_groups:
        pg["lr"] = lr


def train_agent_sample(agent, examples: list[str], n_steps: int,
                       batch_size: int | None = None,
                       lr: float | None = None,
                       verbose: bool = True):
    """
    Original training mode: sample batches with replacement.

      - n_steps:    number of gradient steps
      - batch_size: total character-token budget per batch (default from agent)
      - lr:         learning rate override
    """
    lm_policy = agent._policy          # LMPolicy
    lm = lm_policy._lm                 # TransformerLMPolicy

    bs = batch_size if batch_size is not None else lm_policy._batch_size

    if lr is not None:
        set_lr(lm._optimizer, lr)

    lm.fit(examples, bs, n_steps, verbose=verbose)
    lm.eval()
    return n_steps


def train_agent_epoch(agent, examples: list[str],
                      batch_size: int = 64,
                      n_epochs: int = 1,
                      lr: float | None = None,
                      save_every: int | None = None,
                      run_dir: str | None = None,
                      verbose: bool = True):
    """
    Epoch-based training: deduplicate, then train for n_epochs epochs
    with re-shuffling each epoch and fixed example-count batches.

      - batch_size:  number of examples per batch
      - n_epochs:    number of passes over the dataset
      - lr:          learning rate override
      - save_every:  save a checkpoint every N epochs (None = only final)
      - run_dir:     directory to save intermediate checkpoints into
    """
    lm = agent._policy._lm             # TransformerLMPolicy

    if lr is not None:
        set_lr(lm._optimizer, lr)

    # Deduplicate
    unique_examples = list(dict.fromkeys(examples))  # preserves first occurrence order
    print(f"  Dedup: {len(examples)} -> {len(unique_examples)} unique examples")

    lm._lm.train()
    batches_per_epoch = (len(unique_examples) + batch_size - 1) // batch_size
    total_steps = 0
    saved_epochs = set()

    for epoch in range(n_epochs):
        # Re-shuffle each epoch
        random.shuffle(unique_examples)

        rng = range(batches_per_epoch)
        if verbose:
            rng = tqdm(rng, desc=f"Epoch {epoch+1}/{n_epochs}")

        for i in rng:
            batch = unique_examples[i * batch_size : (i + 1) * batch_size]
            lm._optimizer.zero_grad()
            loss = lm.get_loss(batch)
            loss.backward()
            wandb.log({"train_loss": loss, "epoch": epoch + 1})
            lm._optimizer.step()
            total_steps += 1

        # Intermediate checkpoint
        if save_every and run_dir and (epoch + 1) % save_every == 0:
            lm._lm.eval()
            ckpt_path = os.path.join(run_dir, f"epoch_{epoch+1}.pt")
            torch.save(agent, ckpt_path)
            print(f"  {now()} Saved checkpoint at epoch {epoch+1} -> {ckpt_path}")
            saved_epochs.add(epoch + 1)
            lm._lm.train()

    lm._lm.eval()

    # Save final if not already saved by save_every
    if run_dir and n_epochs not in saved_epochs:
        final_path = os.path.join(run_dir, f"epoch_{n_epochs}.pt")
        torch.save(agent, final_path)
        print(f"  {now()} Saved final checkpoint -> {final_path}")

    print(f"  Done: {n_epochs} epoch(s), {total_steps} total steps, "
          f"{len(unique_examples)} unique examples (batch_size={batch_size})")
    return total_steps


# ---------------------------------------------------------------------------
# Main
# ---------------------------------------------------------------------------

def run_ablation(cfg: dict, base_agent, examples: list[str], output_root: str,
                 checkpoint_path: str, examples_path: str = None):
    tag = cfg.get("tag") or make_run_name(cfg, checkpoint_path)
    mode = cfg.get("mode", "sample")
    n_steps = cfg.get("n_steps", 8000)
    batch_size = cfg.get("batch_size", None)
    lr = cfg.get("lr", None)

    run_dir = os.path.join(output_root, tag)
    os.makedirs(run_dir, exist_ok=True)

    # Save config (include checkpoint + examples paths for reproducibility)
    saved_cfg = {**cfg, "checkpoint": checkpoint_path, "examples": examples_path}
    with open(os.path.join(run_dir, "ablation_config.json"), "w") as f:
        json.dump(saved_cfg, f, indent=2)

    # Deep-copy agent so each ablation starts from the same checkpoint
    print(f"\n{'='*60}")
    print(f"{now()} Starting ablation: {tag}  (mode={mode})")
    print(f"  n_steps={n_steps}, batch_size={batch_size}, lr={lr}")
    print(f"  output -> {run_dir}")
    print(f"{'='*60}")

    agent = deep_copy_agent(base_agent)

    # Init wandb run for this ablation
    wandb_config = {
        "checkpoint": CHECKPOINT_PATH,
        "examples": EXAMPLES_PATH,
        "mode": mode,
        "batch_size": batch_size or agent._policy._batch_size,
        "lr": lr or agent._policy._lm._optimizer.param_groups[0]["lr"],
        "tag": tag,
    }
    if mode == "sample":
        wandb_config["n_steps"] = n_steps
    elif mode == "epoch":
        wandb_config["n_epochs"] = cfg.get("n_epochs", 1)
    if WANDB_PROJECT:
        wandb.init(
            project=WANDB_PROJECT,
            name=tag,
            config=wandb_config,
            reinit=True,
        )

    if mode == "sample":
        total_steps = train_agent_sample(agent, examples, n_steps=n_steps,
                                         batch_size=batch_size, lr=lr)
    elif mode == "epoch":
        total_steps = train_agent_epoch(agent, examples,
                                        batch_size=batch_size or 64,
                                        n_epochs=cfg.get("n_epochs", 1), lr=lr,
                                        save_every=cfg.get("save_every"),
                                        run_dir=run_dir)
    else:
        raise ValueError(f"Unknown training mode: {mode}")

    # Save the trained agent as 1.pt (next iteration checkpoint)
    out_path = os.path.join(run_dir, "1.pt")
    torch.save(agent, out_path)
    print(f"{now()} Saved trained agent to {out_path}")

    # Save training stats
    stats = {"total_gradient_steps": total_steps}
    with open(os.path.join(run_dir, "train_stats.json"), "w") as f:
        json.dump(stats, f, indent=2)

    if WANDB_PROJECT:
        wandb.finish()

    return out_path, total_steps


def main():
    global WANDB_PROJECT
    parser = argparse.ArgumentParser(description="Ablation over training hyperparameters")
    parser.add_argument("--checkpoint", default=CHECKPOINT_PATH,
                        help="Path to the base .pt checkpoint")
    parser.add_argument("--examples", default=EXAMPLES_PATH,
                        help="Path to examples JSON file")
    parser.add_argument("--output", default=None,
                        help="Root output directory (default: <checkpoint_dir>/ablations/)")
    parser.add_argument("--device", type=int, default=CUDA_DEVICE,
                        help="CUDA device index (use -1 for CPU)")
    parser.add_argument("--seed", type=int, default=None,
                        help="Random seed for reproducible shuffling/training (default: unseeded).")
    parser.add_argument("--wandb-project", default=WANDB_PROJECT,
                        help="W&B project name (empty string to disable)")
    parser.add_argument("--configs", nargs="+", default=None,
                        help="Run only ablations whose tags match these (default: all)")
    args = parser.parse_args()

    # Set device
    if args.device >= 0 and torch.cuda.is_available():
        os.environ["CUDA_VISIBLE_DEVICES"] = str(args.device)

    # Seed for reproducible shuffles / training stochasticity (dropout, etc.)
    if args.seed is not None:
        random.seed(args.seed)
        torch.manual_seed(args.seed)
        torch.cuda.manual_seed_all(args.seed)
        print(f"{now()} Seeded RNGs with seed={args.seed}")

    WANDB_PROJECT = args.wandb_project or None

    # If wandb is disabled, make wandb.log a no-op (policy.py's fit() calls it directly)
    if not WANDB_PROJECT:
        wandb.log = lambda *args, **kwargs: None

    # Default output dir: <checkpoint_dir>/ablations/<timestamp>/
    ckpt_dir = os.path.dirname(os.path.abspath(args.checkpoint))
    timestamp = datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
    output_root = os.path.join(args.output or os.path.join(ckpt_dir, "ablations"), timestamp)
    os.makedirs(output_root, exist_ok=True)

    print(f"{now()} Loading checkpoint from {args.checkpoint}")
    base_agent = torch.load(args.checkpoint, weights_only=False)

    print(f"{now()} Loading examples from {args.examples}")
    examples = load_examples(args.examples)
    print(f"  {len(examples)} training examples loaded.")

    configs = ABLATION_CONFIGS
    if args.configs:
        configs = [c for c in configs if
                   (c.get("tag") or make_run_name(c, args.checkpoint)) in args.configs]
        print(f"  Running subset: {[c.get('tag') or make_run_name(c, args.checkpoint) for c in configs]}")

    results = {}

    for cfg in configs:
        agent_path, total_steps = run_ablation(cfg, base_agent, examples, output_root,
                                                args.checkpoint,
                                                examples_path=os.path.abspath(args.examples))
        run_name = cfg.get("tag") or make_run_name(cfg, args.checkpoint)
        results[run_name] = {"path": agent_path, "total_gradient_steps": total_steps}

    # Summary
    print(f"\n{'='*60}")
    print(f"{now()} All ablations complete.")
    print(f"{'='*60}")
    summary_path = os.path.join(output_root, "summary.json")
    with open(summary_path, "w") as f:
        json.dump({
            "checkpoint": os.path.abspath(args.checkpoint),
            "examples": os.path.abspath(args.examples),
            "configs": ABLATION_CONFIGS,
            "results": results,
        }, f, indent=2)
    print(f"Summary saved to {summary_path}")

    for tag, path in results.items():
        print(f"  {tag}: {path}")


if __name__ == "__main__":
    main()


#command to run this script:    

# python ablate_updates.py --checkpoint /datadrive/ayush/home/minimoX/learning/outputs/bootstrap_bs_parallel/2026-01-19_12-06-16/0.pt --examples /datadrive/ayush/home/minimoX/learning/outputs/bootstrap_bs_parallel/2026-01-19_12-06-16/examples_0.json --wandb-project peano-ablation

# python ablate_updates.py --checkpoint /datadrive/ayush/home/minimoX/learning/outputs/bootstrap_bs_800_para_8000updates/2026-03-10_21-21-05/0.pt --examples /datadrive/ayush/home/minimoX/learning/outputs/bootstrap_bs_800_para_8000updates/2026-03-10_21-21-05/examples_0.json --wandb-project peano-ablation

# python ablate_updates.py --checkpoint /datadrive/ayush/home/minimoX/learning/outputs/bootstrap_itlearn_fresh_parallel/2026-01-25_12-18-11/0.pt --examples /datadrive/ayush/home/minimoX/learning/outputs/bootstrap_itlearn_fresh_parallel/2026-01-25_12-18-11/examples_5.json --wandb-project peano-ablation