# pyright: basic

from __future__ import annotations

from dataclasses import dataclass
from pathlib import Path
from typing import Any

import numpy as np
import pandas as pd
import xarray as xr
from bayesian_torch.utils.util import predictive_entropy


@dataclass
class BackendEvaluation:
    backend: str
    source_file: Path
    image_ids: np.ndarray
    y_true: np.ndarray
    y_prob: np.ndarray
    uncertainty_confidence: np.ndarray
    uncertainty_std: np.ndarray
    uncertainty_metric: str


def _resolve_dataset_path(model_output_dir: Path) -> Path:
    primary = model_output_dir / "model_evaluation_results.nc"
    if primary.exists():
        return primary

    candidates = sorted(model_output_dir.glob("*.nc"))
    if not candidates:
        raise FileNotFoundError(f"No netCDF file found under {model_output_dir}")

    return candidates[0]


def _positive_probability(
    predictions: xr.DataArray,
    class_index: int,
) -> tuple[np.ndarray, np.ndarray, np.ndarray, str]:
    if "img_class" not in predictions.dims:
        raise ValueError("predictions is missing required dim: img_class")

    if class_index >= predictions.sizes["img_class"]:
        raise ValueError(
            f"positive class index {class_index} is out of bounds for img_class size {predictions.sizes['img_class']}"
        )

    if "model" in predictions.dims:
        class_probs = predictions.isel(img_class=class_index)
        prob_mean = class_probs.mean(dim="model").values
        # Confidence is the direct model output probability for the predicted class.
        prob_std = class_probs.std(dim="model").values
        return (
            np.asarray(prob_mean, dtype=float),
            np.asarray(prob_mean, dtype=float),
            np.asarray(prob_std, dtype=float),
            "std",
        )

    sample_like = [d for d in predictions.dims if d in {"sample", "mc_sample", "draw"}]
    if sample_like:
        dim = str(sample_like[0])
        class_probs = predictions.isel(img_class=class_index)
        prob_mean = class_probs.mean(dim=dim).values

        # For Bayesian MC predictions, uncertainty should come from predictive
        # entropy of the predictive distribution rather than classwise std.
        mc_preds = predictions.transpose(dim, "img_id", "img_class").values
        entropy_uncertainty = predictive_entropy(np.asarray(mc_preds, dtype=float))
        return (
            np.asarray(prob_mean, dtype=float),
            np.asarray(prob_mean, dtype=float),
            np.asarray(entropy_uncertainty, dtype=float),
            "predictive_entropy",
        )

    prob = predictions.isel(img_class=class_index).values
    return (
        np.asarray(prob, dtype=float),
        np.asarray(prob, dtype=float),
        np.full_like(np.asarray(prob, dtype=float), np.nan),
        "unknown",
    )


def _labels_to_binary(labels: xr.DataArray, class_index: int) -> np.ndarray:
    if "label" in labels.dims:
        if class_index >= labels.sizes["label"]:
            raise ValueError(
                f"positive class index {class_index} is out of bounds for label size {labels.sizes['label']}"
            )

        # One-hot labels expected in this repository.
        binary = labels.argmax(dim="label").values == class_index
        return np.asarray(binary, dtype=int)

    # Fallback if labels are already binary.
    return np.asarray(labels.values, dtype=int)


def load_backend_evaluation(
    config: dict[str, Any],
    backend: str,
    class_index: int,
) -> BackendEvaluation:
    output_key = f"{backend}_path"
    if output_key not in config["output"]:
        raise KeyError(f"Missing output path key in config: output.{output_key}")

    model_output_dir = Path(config["output"][output_key]).expanduser().resolve()
    ds_path = _resolve_dataset_path(model_output_dir)
    ds = xr.open_dataset(ds_path)

    if "predictions" not in ds or "labels" not in ds:
        raise ValueError(
            f"Dataset {ds_path} must contain predictions and labels variables"
        )

    predictions = ds["predictions"]
    labels = ds["labels"]

    if "img_id" in predictions.coords:
        image_ids = np.asarray(predictions.coords["img_id"].values)
    elif "img_id" in labels.coords:
        image_ids = np.asarray(labels.coords["img_id"].values)
    else:
        length = predictions.sizes.get("img_id", labels.sizes.get("img_id"))
        if length is None:
            raise ValueError("Could not infer img_id length from predictions/labels")
        image_ids = np.arange(length)

    y_true = _labels_to_binary(labels, class_index=class_index)
    y_prob, conf, y_std, uncertainty_metric = _positive_probability(
        predictions, class_index=class_index
    )

    if len(y_true) != len(y_prob):
        raise ValueError(
            f"Length mismatch after loading backend {backend}: labels={len(y_true)}, probs={len(y_prob)}"
        )

    return BackendEvaluation(
        backend=backend,
        source_file=ds_path,
        image_ids=image_ids,
        y_true=y_true,
        y_prob=y_prob,
        uncertainty_confidence=conf,
        uncertainty_std=y_std,
        uncertainty_metric=uncertainty_metric,
    )


def load_clinical_table(config: dict[str, Any], root_dir: Path) -> pd.DataFrame:
    csv_path = (root_dir / config["data"]["xls_file_path"]).resolve()
    df = pd.read_csv(csv_path)
    df.columns = df.columns.str.strip()
    return df


def physician_column(df: pd.DataFrame) -> str:
    exact = "DXCONFID"
    if exact in df.columns:
        return exact

    for col in df.columns:
        if "dxconfid" in col.lower():
            return col

    raise KeyError(
        "No physician confidence column with DXCONFID found in clinical table"
    )