"""Simplified dump comparator — a self-contained single-file script for comparing
two dump directories tensor-by-tensor.

For advanced features (unshard, token alignment, per-dimension annotations), see the
full ``comparator/`` package: ``python -m sglang.srt.debug_utils.comparator``.
"""

import argparse
import functools
import re
from dataclasses import dataclass
from pathlib import Path
from typing import Callable, List, Optional

import torch

from sglang.srt.debug_utils.dumper import get_truncated_value


def main(args):
    import polars as pl

    from sglang.srt.debug_utils.dump_loader import find_row, read_meta

    df_target = read_meta(args.target_path)
    df_target = df_target.filter(
        (pl.col("step") >= args.start_step) & (pl.col("step") <= args.end_step)
    )
    if args.filter:
        df_target = df_target.filter(pl.col("filename").str.contains(args.filter))
    assert all(c in df_target.columns for c in ["rank", "step", "dump_index", "name"])

    df_baseline = read_meta(args.baseline_path)
    print("df_target", df_target)
    print("df_baseline", df_baseline)

    tensor_dim_descs: List[TensorDimDesc] = _get_tensor_dim_descs()

    for row in df_target.iter_rows(named=True):
        path_target = Path(args.target_path) / row["filename"]

        tensor_dim_desc: Optional[TensorDimDesc] = None
        if tensor_dim_descs:
            matched: list[TensorDimDesc] = [
                desc
                for desc in tensor_dim_descs
                if re.search(desc.pattern, row["filename"]) is not None
            ]
            if matched:
                tensor_dim_desc = matched[0]

        row_baseline = find_row(
            df_baseline,
            conditions=dict(
                step=row["step"],
                **{
                    k: v
                    for k, v in row.items()
                    if k not in ["step", "dump_index", "filename"]
                },
            ),
        )

        if row_baseline is None:
            print(f"Skip: target={str(path_target)} since no baseline")
            x_target = _load_object(path_target)
            if x_target is not None:
                print(f"x_target(sample)={get_truncated_value(x_target)}")
            continue

        path_baseline = Path(args.baseline_path) / row_baseline["filename"]
        print(
            f"Check:\n"
            f"target={str(path_target)} (duplicate_index={row['duplicate_index']})\n"
            f"baseline={str(path_baseline)} (duplicate_index={row_baseline['duplicate_index']})"
        )
        check_tensor_pair(
            path_baseline=path_baseline,
            path_target=path_target,
            diff_threshold=args.diff_threshold,
            name=row["name"],
            tensor_dim_desc=tensor_dim_desc,
        )
        print()


def check_tensor_pair(
    path_baseline,
    path_target,
    diff_threshold: float = 1e-3,
    name="",
    tensor_dim_desc: Optional["TensorDimDesc"] = None,
):
    x_baseline = _load_object(path_baseline)
    x_target = _load_object(path_target)

    if x_baseline is None or x_target is None:
        print(
            f"Skip comparison because of None: x_baseline={x_baseline}, x_target={x_target}"
        )
        return

    print(
        f"Raw "
        f"[shape] {x_baseline.shape} vs {x_target.shape}\t"
        f"[{'' if x_baseline.dtype == x_target.dtype else '🟠'}dtype] {x_baseline.dtype} vs {x_target.dtype}"
    )

    if tensor_dim_desc is not None:
        import einops

        x_baseline = einops.rearrange(
            x_baseline,
            tensor_dim_desc.baseline_desc + " -> " + tensor_dim_desc.target_desc,
        )
        if tensor_dim_desc.baseline_cropper is not None:
            print("Apply baseline_cropper")
            x_baseline = tensor_dim_desc.baseline_cropper(x_baseline)

    x_baseline, x_target = _comparison_preprocessor(x_baseline, x_target, name=name)
    x_baseline = _try_unify_shape(x_baseline, target_shape=x_target.shape)

    print(
        f"After preprocessor "
        f"[shape] {x_baseline.shape} vs {x_target.shape}\t"
        f"[dtype] {x_baseline.dtype} vs {x_target.dtype}"
    )

    x_baseline_original_dtype = x_baseline.dtype
    x_target_original_dtype = x_target.dtype

    x_target = x_target.float()
    x_baseline = x_baseline.float()

    for name, fn in [
        ("mean", torch.mean),
        ("std", torch.std),
        ("min", torch.min),
        ("max", torch.max),
        *(
            [
                ("p1", functools.partial(torch.quantile, q=0.01)),
                ("p5", functools.partial(torch.quantile, q=0.05)),
                ("p95", functools.partial(torch.quantile, q=0.95)),
                ("p99", functools.partial(torch.quantile, q=0.99)),
            ]
            if x_baseline.numel() < 10_000_000
            else []
        ),
    ]:
        value_baseline = fn(x_baseline).item()
        value_target = fn(x_target).item()
        print(
            f"[{name}] {value_baseline :.4f} vs {value_target:.4f} (diff: {value_target - value_baseline:.4f})"
        )

    if x_baseline.shape != x_target.shape:
        print(f"⚠️ Shape mismatch")
        return

    diff_info = _compute_and_print_diff(
        x_baseline=x_baseline,
        x_target=x_target,
        diff_threshold=diff_threshold,
    )
    needs_print = diff_info["max_abs_diff"] > 1e-3

    if (x_baseline_original_dtype != x_target_original_dtype) and (
        (
            downcast_dtype := _compute_smaller_dtype(
                x_baseline_original_dtype, x_target_original_dtype
            )
        )
        is not None
    ):
        _compute_and_print_diff(
            x_baseline=x_baseline.to(downcast_dtype),
            x_target=x_target.to(downcast_dtype),
            diff_threshold=diff_threshold,
            prefix_text=f"When downcast to {downcast_dtype}: ",
        )

    if needs_print:
        print(f"x_baseline(sample)={get_truncated_value(x_baseline)}")
        print(f"x_target(sample)={get_truncated_value(x_target)}")


def _compute_and_print_diff(
    x_baseline, x_target, diff_threshold: float, prefix_text=""
):
    raw_abs_diff = (x_target - x_baseline).abs()

    max_abs_diff = raw_abs_diff.max().item()
    mean_abs_diff = raw_abs_diff.mean().item()
    rel_diff = _calc_rel_diff(x_target, x_baseline)

    rel_diff_marker: str = "❌" if rel_diff > diff_threshold else "✅"
    print(
        prefix_text
        + f"{rel_diff_marker} rel_diff={rel_diff}\t"
        + f"max_abs_diff={max_abs_diff}\t"
        + f"mean_abs_diff={mean_abs_diff}"
    )

    max_diff_coord = _argmax_coord(raw_abs_diff)
    print(
        f"max_abs_diff happens at coord={max_diff_coord} with "
        f"baseline={x_baseline[max_diff_coord].item()} "
        f"target={x_target[max_diff_coord].item()}"
    )

    return dict(max_abs_diff=max_abs_diff)


def _argmax_coord(x: torch.Tensor) -> tuple:
    flat_idx = x.argmax()
    return tuple(idx.item() for idx in torch.unravel_index(flat_idx, x.shape))


def _compute_smaller_dtype(dtype_a, dtype_b):
    info_dict = {
        (torch.float32, torch.bfloat16): torch.bfloat16,
        # ... add more ...
    }
    return info_dict.get((dtype_a, dtype_b)) or info_dict.get((dtype_b, dtype_a))


def _try_unify_shape(x: torch.Tensor, target_shape):
    x_shape = x.shape
    num_dim_to_remove = len(x_shape) - len(target_shape)
    if (x_shape[num_dim_to_remove:] == target_shape) and all(
        val == 1 for val in x_shape[:num_dim_to_remove]
    ):
        out = functools.reduce(lambda a, _: a.squeeze(0), range(num_dim_to_remove), x)
        print(f"Unify shape: {x_shape} -> {out.shape} (to match {target_shape})")
        return out

    return x


# Copied from DeepGEMM
def _calc_rel_diff(x: torch.Tensor, y: torch.Tensor):
    x, y = x.double(), y.double()
    denominator = (x * x + y * y).sum()
    sim = 2 * (x * y).sum() / denominator
    return 1 - sim


def _load_object(path):
    try:
        x = torch.load(path, weights_only=False)
    except Exception as e:
        print(f"Skip load {path} since error {e}")
        return None

    if isinstance(x, dict) and "value" in x:
        x = x["value"]

    if not isinstance(x, torch.Tensor):
        print(f"Skip load {path} since {type(x)=} is not a Tensor ({x=})")
        return None
    return x.cuda()


def _comparison_preprocessor(x_baseline, x_target, name):
    """Customization endpoint. Can insert arbitrary adhoc postprocessing logic here."""
    return x_baseline, x_target


@dataclass
class TensorDimDesc:
    pattern: str
    baseline_desc: str
    target_desc: str
    baseline_cropper: Optional[Callable[[torch.Tensor], torch.Tensor]] = None


def _get_tensor_dim_descs() -> List[TensorDimDesc]:
    """Customization endpoint. Return a list of TensorDimDesc to rearrange baseline
    dimensions to match target layout via einops before comparison."""
    return []


if __name__ == "__main__":
    # python -m sglang.srt.debug_utils.dump_comparator --baseline-path ... --target-path ...
    parser = argparse.ArgumentParser()
    parser.add_argument("--baseline-path", type=str)
    parser.add_argument("--target-path", type=str)
    parser.add_argument("--start-step", type=int, default=0)
    parser.add_argument("--end-step", type=int, default=1000000)
    parser.add_argument("--diff-threshold", type=float, default=1e-3)
    parser.add_argument(
        "--filter", type=str, default=None, help="Regex to filter filenames"
    )
    args = parser.parse_args()
    main(args)
