"""Probe residual activation scale for a saved TaoTrain checkpoint."""

from __future__ import annotations

import argparse
import json
import re
import sys
from pathlib import Path
from typing import Any

import torch

REPO_ROOT = Path(__file__).resolve().parents[2]
SRC_ROOT = REPO_ROOT / "src"
if str(SRC_ROOT) not in sys.path:
    sys.path.insert(0, str(SRC_ROOT))

from taoTrain.checkpointing.checkpoint import CheckpointManager
from taoTrain.config import ModelConfig
from taoTrain.models import get_model


def load_sentencepiece(path: Path):
    import sentencepiece as spm

    processor = spm.SentencePieceProcessor()
    processor.load(str(path))
    return processor


def load_tokens(args: argparse.Namespace) -> tuple[torch.Tensor, int]:
    tokenizer = load_sentencepiece(Path(args.tokenizer_path))
    tokens: list[int] = []
    with Path(args.data_path).open("r", encoding="utf-8", errors="replace") as handle:
        for line in handle:
            if len(tokens) >= args.max_tokens:
                break
            line = line.strip()
            if not line:
                continue
            try:
                record = json.loads(line)
            except json.JSONDecodeError:
                continue
            text = record.get(args.text_field)
            if not isinstance(text, str) or not text:
                continue
            ids = list(tokenizer.encode(text, out_type=int))
            eos_id = tokenizer.eos_id()
            if eos_id >= 0:
                ids.append(eos_id)
            tokens.extend(ids)
    if len(tokens) < args.seq_len + 2:
        raise ValueError(f"Need at least {args.seq_len + 2} tokens, got {len(tokens)}")
    return torch.tensor(tokens[: args.max_tokens], dtype=torch.long), int(tokenizer.vocab_size())


def sample_batch(tokens: torch.Tensor, *, batch_size: int, seq_len: int, device: torch.device) -> tuple[torch.Tensor, torch.Tensor]:
    max_start = tokens.numel() - seq_len - 1
    starts = torch.linspace(0, max_start - 1, steps=batch_size).long()
    rows = [tokens[int(start) : int(start) + seq_len + 1] for start in starts]
    batch = torch.stack(rows, dim=0).to(device=device)
    return batch[:, :-1].contiguous(), batch[:, 1:].contiguous()


def tensor_stats(value: torch.Tensor) -> dict[str, float | int]:
    data = value.detach().float()
    finite = torch.isfinite(data)
    finite_count = int(finite.sum().cpu())
    numel = data.numel()
    if finite_count:
        finite_data = data[finite]
        rms = float(torch.sqrt(torch.mean(finite_data * finite_data)).cpu())
        max_abs = float(finite_data.abs().max().cpu())
    else:
        rms = float("inf")
        max_abs = float("inf")
    return {
        "numel": numel,
        "finite": finite_count,
        "rms": rms,
        "max_abs": max_abs,
    }


def main() -> None:
    parser = argparse.ArgumentParser()
    parser.add_argument("--checkpoint", required=True)
    parser.add_argument("--tokenizer-path", required=True)
    parser.add_argument("--data-path", required=True)
    parser.add_argument("--text-field", default="text")
    parser.add_argument("--output", required=True)
    parser.add_argument("--batch-size", type=int, default=2)
    parser.add_argument("--seq-len", type=int, default=512)
    parser.add_argument("--max-tokens", type=int, default=200_000)
    parser.add_argument("--device", default="cuda")
    parser.add_argument("--dtype", choices=["float32", "bfloat16", "float16"], default="bfloat16")
    args = parser.parse_args()

    device = torch.device(args.device if args.device == "cpu" or torch.cuda.is_available() else "cpu")
    dtype = {
        "float32": torch.float32,
        "bfloat16": torch.bfloat16,
        "float16": torch.float16,
    }[args.dtype]

    tokens, _ = load_tokens(args)
    input_ids, labels = sample_batch(tokens, batch_size=args.batch_size, seq_len=args.seq_len, device=device)
    attention_mask = torch.ones_like(input_ids)

    checkpoint_path = Path(args.checkpoint)
    checkpoint = CheckpointManager(checkpoint_path.parent).load(checkpoint_path, device=device)
    config_dict = checkpoint.get("config", {})
    model_config = ModelConfig(**config_dict.get("model", {}))
    model = get_model(model_config, device=device)
    model.load_state_dict(checkpoint["model_state"], strict=False)
    model.eval()

    layer_stats: dict[str, dict[str, float | int]] = {}
    handles = []
    layer_pattern = re.compile(r"^(?:model\.)?(?:layers|blocks)\.\d+$")

    def make_hook(name: str):
        def hook(_module, _inputs, output):
            value = output[0] if isinstance(output, tuple) else output
            if torch.is_tensor(value):
                layer_stats[name] = tensor_stats(value)

        return hook

    for name, module in model.named_modules():
        if layer_pattern.match(name):
            handles.append(module.register_forward_hook(make_hook(name)))

    device_type = "cuda" if device.type == "cuda" else "cpu"
    autocast_enabled = device.type == "cuda" and dtype in {torch.float16, torch.bfloat16}
    with torch.no_grad(), torch.autocast(device_type=device_type, dtype=dtype, enabled=autocast_enabled):
        outputs = model(input_ids=input_ids, attention_mask=attention_mask, labels=labels)

    for handle in handles:
        handle.remove()

    result: dict[str, Any] = {
        "checkpoint": str(checkpoint_path),
        "loss": float(outputs["loss"].detach().cpu()),
        "batch_size": args.batch_size,
        "seq_len": args.seq_len,
        "device": str(device),
        "dtype": str(dtype),
        "layers": layer_stats,
    }
    output_path = Path(args.output)
    output_path.parent.mkdir(parents=True, exist_ok=True)
    output_path.write_text(json.dumps(result, indent=2), encoding="utf-8")
    print(json.dumps(result, indent=2))


if __name__ == "__main__":
    main()