#!/usr/bin/env python3
"""Evaluate cheap frozen-weight gate interventions on validation data."""

from __future__ import annotations

import argparse
import hashlib
import json
import math
import sys
import time
from pathlib import Path
from typing import Any

import torch
from torch.nn import functional as F
from torch.utils.data import DataLoader, Subset
from torchvision import datasets, transforms
from torchvision.transforms import InterpolationMode

REPO_ROOT = Path(__file__).resolve().parents[1]
if str(REPO_ROOT) not in sys.path:
    sys.path.insert(0, str(REPO_ROOT))

from gmnet.analysis import (
    INTERVENTION_MODES,
    install_gate_interventions,
    load_model_checkpoint,
    set_intervention_mode,
    summarize_intervention_coherence,
)


DEFAULT_CHECKPOINT = Path("/nfs/ywang29/GmNet/gmnet_s3.npy")
DEFAULT_OUTPUT_DIR = Path("/tmp/gmnet_runs/e4_interventions")
DEFAULT_DATA_ROOTS = {
    "imagenet": Path("/s3-code/ywang29/datasets/imagenet-1k"),
    "cifar100": Path("/tmp/gmnet_data/cifar-100"),
}
ECE_BINS = 15


def parse_args() -> argparse.Namespace:
    parser = argparse.ArgumentParser(description=__doc__)
    parser.add_argument("--checkpoint", type=Path, default=DEFAULT_CHECKPOINT)
    parser.add_argument("--data-root", type=Path)
    parser.add_argument(
        "--data-source-uri",
        help="canonical upstream dataset URI recorded in result provenance",
    )
    parser.add_argument(
        "--data-staging-manifest",
        type=Path,
        help="JSON manifest proving how a local dataset cache was staged",
    )
    parser.add_argument(
        "--dataset",
        choices=("auto", "imagenet", "cifar100"),
        default="auto",
    )
    parser.add_argument("--output-dir", type=Path, default=DEFAULT_OUTPUT_DIR)
    parser.add_argument("--batch-size", type=int, default=128)
    parser.add_argument("--workers", type=int, default=8)
    parser.add_argument(
        "--max-samples",
        type=int,
        default=5_000,
        help="deterministic random validation subset size; 0 evaluates all samples",
    )
    parser.add_argument("--seed", type=int, default=20260712)
    parser.add_argument(
        "--device",
        default="cuda:0" if torch.cuda.is_available() else "cpu",
    )
    parser.add_argument(
        "--modes",
        nargs="+",
        choices=INTERVENTION_MODES,
        default=list(INTERVENTION_MODES),
    )
    return parser.parse_args()


def build_validation_loader(
    args: argparse.Namespace,
    checkpoint_audit: dict[str, Any],
) -> tuple[DataLoader, dict[str, Any]]:
    checkpoint_dataset = str(
        checkpoint_audit.get("data_config", {}).get("dataset", "imagenet")
    ).lower()
    dataset_name = args.dataset if args.dataset != "auto" else checkpoint_dataset
    dataset_name = dataset_name.replace("-", "")
    if dataset_name not in DEFAULT_DATA_ROOTS:
        raise ValueError(f"unsupported validation dataset: {dataset_name}")
    data_root = (
        args.data_root
        if args.data_root is not None
        else DEFAULT_DATA_ROOTS[dataset_name]
    ).expanduser().resolve()

    if dataset_name == "imagenet":
        validation_root = data_root / "val"
        if not validation_root.is_dir():
            raise FileNotFoundError(
                f"ImageNet validation split not found: {validation_root}"
            )
        transform = transforms.Compose(
            [
                transforms.Resize(256, interpolation=InterpolationMode.BICUBIC),
                transforms.CenterCrop(224),
                transforms.ToTensor(),
                transforms.Normalize(
                    mean=(0.485, 0.456, 0.406),
                    std=(0.229, 0.224, 0.225),
                ),
            ]
        )
        dataset = datasets.ImageFolder(validation_root, transform=transform)
        expected_classes = 1_000
        preprocess = "resize256_bicubic_centercrop224_imagenet_normalization"
    else:
        data_config = checkpoint_audit.get("data_config", {})
        mean = tuple(data_config.get("mean", (0.5071, 0.4867, 0.4408)))
        std = tuple(data_config.get("std", (0.2675, 0.2565, 0.2761)))
        transform = transforms.Compose(
            [transforms.ToTensor(), transforms.Normalize(mean=mean, std=std)]
        )
        dataset = datasets.CIFAR100(
            data_root,
            train=False,
            transform=transform,
            download=False,
        )
        validation_root = data_root
        expected_classes = 100
        preprocess = "tensor_cifar100_paper_normalization"
    if len(dataset.classes) != expected_classes:
        raise ValueError(
            f"expected {expected_classes} classes, found {len(dataset.classes)}"
        )

    total_samples = len(dataset)
    requested = total_samples if args.max_samples == 0 else args.max_samples
    sample_count = min(requested, total_samples)
    if sample_count == total_samples:
        indices = list(range(total_samples))
        subset_method = "full_validation_set_in_canonical_order"
    else:
        generator = torch.Generator().manual_seed(args.seed)
        indices = torch.randperm(
            total_samples, generator=generator
        )[:sample_count].tolist()
        subset_method = "torch_randperm_without_replacement"
    subset = Subset(dataset, indices)
    loader = DataLoader(
        subset,
        batch_size=args.batch_size,
        shuffle=False,
        num_workers=args.workers,
        pin_memory=args.device.startswith("cuda"),
        persistent_workers=args.workers > 0,
    )
    index_digest = hashlib.sha256(
        ",".join(str(index) for index in indices).encode("ascii")
    ).hexdigest()
    provenance: dict[str, Any] = {
        "source_uri": args.data_source_uri or str(validation_root),
        "access_path": str(validation_root),
        "access_mode": "direct_path",
    }
    if args.data_staging_manifest is not None:
        manifest_path = args.data_staging_manifest.expanduser().resolve()
        if not manifest_path.is_file():
            raise FileNotFoundError(
                f"data staging manifest not found: {manifest_path}"
            )
        manifest_bytes = manifest_path.read_bytes()
        manifest = json.loads(manifest_bytes)
        if not isinstance(manifest, dict):
            raise TypeError("data staging manifest must be a JSON mapping")
        manifest_source = manifest.get("source")
        if not isinstance(manifest_source, str) or not manifest_source:
            raise ValueError("data staging manifest must identify its source")
        expected_manifest_values = {
            "classes": len(dataset.classes),
            "images": total_samples,
        }
        for key, expected_value in expected_manifest_values.items():
            if key in manifest and int(manifest[key]) != expected_value:
                raise ValueError(
                    f"staging manifest {key}={manifest[key]} does not match "
                    f"loaded dataset value {expected_value}"
                )
        provenance.update(
            {
                "access_mode": "local_staging_cache",
                "staging_manifest_path": str(manifest_path),
                "staging_manifest_sha256": hashlib.sha256(
                    manifest_bytes
                ).hexdigest(),
                "staging_manifest": manifest,
            }
        )
    return loader, {
        "root": str(validation_root),
        "dataset": dataset_name,
        "full_validation_samples": total_samples,
        "evaluated_samples": sample_count,
        "subset_method": subset_method,
        "subset_seed": args.seed,
        "subset_indices_sha256": index_digest,
        "class_count": len(dataset.classes),
        "preprocess": preprocess,
        "provenance": provenance,
    }


def _empty_metrics() -> dict[str, Any]:
    return {
        "samples": 0.0,
        "top1_correct": 0.0,
        "top5_correct": 0.0,
        "baseline_agreement": 0.0,
        "kl_from_baseline_sum": 0.0,
        "logit_squared_error_sum": 0.0,
        "logit_values": 0.0,
        "nll_sum": 0.0,
        "ece_counts": [0.0] * ECE_BINS,
        "ece_confidence_sums": [0.0] * ECE_BINS,
        "ece_correct_sums": [0.0] * ECE_BINS,
    }


def _update_metrics(
    metrics: dict[str, Any],
    logits: torch.Tensor,
    targets: torch.Tensor,
    baseline_logits: torch.Tensor,
) -> None:
    batch_size = targets.numel()
    predictions = logits.argmax(dim=1)
    baseline_predictions = baseline_logits.argmax(dim=1)
    top5 = logits.topk(5, dim=1).indices
    probabilities = F.softmax(logits, dim=1)
    confidence, _ = probabilities.max(dim=1)
    metrics["samples"] += batch_size
    metrics["top1_correct"] += (predictions == targets).sum().item()
    metrics["top5_correct"] += top5.eq(targets[:, None]).any(dim=1).sum().item()
    metrics["baseline_agreement"] += (
        predictions == baseline_predictions
    ).sum().item()
    metrics["kl_from_baseline_sum"] += F.kl_div(
        F.log_softmax(logits, dim=1),
        F.softmax(baseline_logits, dim=1),
        reduction="sum",
    ).item()
    metrics["logit_squared_error_sum"] += (
        logits - baseline_logits
    ).square().sum().item()
    metrics["logit_values"] += logits.numel()
    metrics["nll_sum"] += F.cross_entropy(
        logits, targets, reduction="sum"
    ).item()
    bin_indices = torch.clamp((confidence * ECE_BINS).long(), max=ECE_BINS - 1)
    correct = predictions.eq(targets).float()
    for bin_index in range(ECE_BINS):
        mask = bin_indices.eq(bin_index)
        if mask.any():
            metrics["ece_counts"][bin_index] += mask.sum().item()
            metrics["ece_confidence_sums"][bin_index] += confidence[mask].sum().item()
            metrics["ece_correct_sums"][bin_index] += correct[mask].sum().item()


def _finalize_metrics(metrics: dict[str, Any]) -> dict[str, float | int]:
    samples = int(metrics["samples"])
    if samples == 0:
        raise ValueError("no samples were evaluated")
    ece = 0.0
    for count, confidence_sum, correct_sum in zip(
        metrics["ece_counts"],
        metrics["ece_confidence_sums"],
        metrics["ece_correct_sums"],
        strict=True,
    ):
        if count:
            ece += count / samples * abs(confidence_sum / count - correct_sum / count)
    return {
        "samples": samples,
        "top1_percent": 100.0 * metrics["top1_correct"] / samples,
        "top5_percent": 100.0 * metrics["top5_correct"] / samples,
        "nll": metrics["nll_sum"] / samples,
        "ece_percent": 100.0 * ece,
        "prediction_agreement_with_baseline_percent": (
            100.0 * metrics["baseline_agreement"] / samples
        ),
        "mean_kl_from_baseline": max(
            metrics["kl_from_baseline_sum"] / samples, 0.0
        ),
        "logit_rmse_from_baseline": math.sqrt(
            metrics["logit_squared_error_sum"] / metrics["logit_values"]
        ),
    }


def render_markdown(result: dict[str, Any]) -> str:
    rows = []
    for mode, metrics in result["interventions"].items():
        coherence = metrics["intervention_reference_coherence"][
            "mean_gate_input_pearson"
        ]
        rows.append(
            f"| {mode} | {metrics['top1_percent']:.3f} | "
            f"{metrics['top5_percent']:.3f} | "
            f"{metrics['nll']:.5f} | {metrics['ece_percent']:.3f} | "
            f"{metrics['prediction_agreement_with_baseline_percent']:.3f} | "
            f"{metrics['mean_kl_from_baseline']:.6f} | "
            f"{coherence!s} |"
        )
    audit = result["checkpoint"]
    data = result["data"]
    return "\n".join(
        [
            "# E4 Frozen Gate Intervention Audit",
            "",
            "## Scope",
            "",
            f"- Checkpoint: `{audit['path']}` (`{audit['sha256']}`).",
            f"- Selected topology: `{audit['selected_topology']}`.",
            f"- Data: deterministic random {data['evaluated_samples']}-image subset "
            f"of {data['full_validation_samples']} {data['dataset']} validation "
            "images.",
            f"- Data source: `{data['provenance']['source_uri']}` via "
            f"`{data['provenance']['access_mode']}` at "
            f"`{data['provenance']['access_path']}`.",
            "- This is a frozen-checkpoint sensitivity diagnostic. It is not a "
            "matched-retraining causal accuracy result.",
            "",
            "## Results",
            "",
            "| Intervention | Top-1 (%) | Top-5 (%) | NLL | ECE (%) | "
            "Baseline agreement (%) | KL from baseline | Reference Pearson |",
            "|---|---:|---:|---:|---:|---:|---:|---:|",
            *rows,
            "",
            "`stop_gradient` is intentionally excluded: it is forward-identical "
            "during inference and can only be evaluated through retraining. "
            "`mean_gate` uses the per-channel mean gate value over batch and "
            "spatial dimensions at every block.",
            "",
        ]
    )


def main() -> int:
    args = parse_args()
    if "baseline" not in args.modes:
        raise ValueError("baseline must be included to compute paired diagnostics")
    device = torch.device(args.device)
    if device.type == "cuda":
        torch.cuda.set_device(device)

    model, checkpoint_audit = load_model_checkpoint(args.checkpoint, device=device)
    installed_gates = install_gate_interventions(model, seed=args.seed)
    loader, data_audit = build_validation_loader(args, checkpoint_audit)
    accumulators = {mode: _empty_metrics() for mode in args.modes}

    started = time.time()
    model.eval()
    with torch.inference_mode():
        for images, targets in loader:
            images = images.to(device, non_blocking=True)
            targets = targets.to(device, non_blocking=True)

            set_intervention_mode(model, "baseline")
            baseline_logits = model(images)
            for mode in args.modes:
                if mode == "baseline":
                    logits = baseline_logits
                else:
                    set_intervention_mode(model, mode)
                    logits = model(images)
                _update_metrics(
                    accumulators[mode], logits, targets, baseline_logits
                )

    if device.type == "cuda":
        torch.cuda.synchronize(device)
    interventions = {}
    for mode, accumulator in accumulators.items():
        metrics = _finalize_metrics(accumulator)
        metrics["intervention_reference_coherence"] = (
            summarize_intervention_coherence(model, mode)
        )
        interventions[mode] = metrics

    result = {
        "schema_version": 1,
        "experiment": "E4",
        "status": "completed_frozen_checkpoint_diagnostic",
        "claim_scope": (
            "Frozen-checkpoint forward intervention only. Accuracy differences "
            "do not establish matched-retraining causality."
        ),
        "device": str(device),
        "torch_version": torch.__version__,
        "elapsed_seconds": time.time() - started,
        "installed_gate_count": installed_gates,
        "intervention_scope": "all_self_gate_blocks_jointly",
        "intervention_definitions": {
            "batch_shuffle": "fixed seeded batch permutation per gate block",
            "spatial_shuffle": "fixed seeded spatial permutation per gate block",
            "channel_shuffle": "fixed seeded channel permutation per gate block",
            "mean_gate": (
                "per-channel gate-value mean over batch and spatial dimensions"
            ),
            "stop_gradient": "training-only; deliberately not evaluated",
        },
        "checkpoint": checkpoint_audit,
        "data": data_audit,
        "interventions": interventions,
    }
    args.output_dir.mkdir(parents=True, exist_ok=True)
    json_path = args.output_dir / "results.json"
    markdown_path = args.output_dir / "RESULTS.md"
    json_path.write_text(json.dumps(result, indent=2, sort_keys=True), encoding="utf-8")
    markdown_path.write_text(render_markdown(result), encoding="utf-8")
    print(json.dumps({"results": str(json_path), "markdown": str(markdown_path)}))
    return 0


if __name__ == "__main__":
    raise SystemExit(main())