"""Tools for visualising embedding spaces using UMAP."""

from __future__ import annotations

import argparse
from pathlib import Path
from typing import List

import matplotlib.pyplot as plt
import numpy as np
from matplotlib.lines import Line2D
from mpl_toolkits.mplot3d import Axes3D  # noqa: F401 - needed for 3D projections
from umap import UMAP

from pipeline.storage import load_corpus, load_embeddings

DEFAULT_INDEX_DIR = Path("index")
DEFAULT_CORPUS_PATH = DEFAULT_INDEX_DIR / "corpus.json"
DEFAULT_EMBEDDINGS_PATH = DEFAULT_INDEX_DIR / "embeddings.npy"


def parse_args(argv: List[str] | None = None) -> argparse.Namespace:
    """Parse command-line options for the visualiser.

    Parameters
    ----------
    argv: List[str] | None, default None
        Optional argument list override for testing.

    Returns
    -------
    argparse.Namespace
        Parsed CLI arguments.
    """

    parser = argparse.ArgumentParser(description="Visualise SPECTER2 embeddings in 2D or 3D.")
    parser.add_argument(
        "--embeddings",
        type=Path,
        default=DEFAULT_EMBEDDINGS_PATH,
        help="Path to embeddings.npy (default: index/embeddings.npy)",
    )
    parser.add_argument(
        "--corpus",
        type=Path,
        default=DEFAULT_CORPUS_PATH,
        help="Path to corpus.json metadata (default: index/corpus.json)",
    )
    parser.add_argument(
        "--dims",
        type=int,
        choices=(2, 3),
        default=2,
        help="Number of UMAP dimensions (2 or 3, default: 2)",
    )
    parser.add_argument(
        "--output",
        type=Path,
        default=None,
        help="Optional output path for the generated plot (default: derived from embeddings path)",
    )
    parser.add_argument(
        "--show",
        action="store_true",
        help="Display the plot interactively after saving",
    )
    return parser.parse_args(argv)


def plot_embeddings(
    embeddings_path: Path,
    corpus_path: Path,
    dims: int = 2,
    output_path: Path | None = None,
    show: bool = False,
) -> Path:
    """Create a UMAP projection and save the resulting plot.

    Parameters
    ----------
    embeddings_path: Path
        Location of the `embeddings.npy` file.
    corpus_path: Path
        Location of the `corpus.json` file.
    dims: int, default 2
        Number of UMAP dimensions (2 or 3).
    output_path: Path | None, default None
        Optional destination for the saved figure. Uses a default if not provided.
    show: bool, default False
        Whether to display the plot after saving.

    Returns
    -------
    Path
        The path to the saved figure.
    """

    embeddings = load_embeddings(embeddings_path)
    corpus = load_corpus(corpus_path)

    if embeddings.shape[0] != len(corpus):
        raise ValueError(
            "Embeddings and corpus lengths do not match. Ensure the inputs originate from the same build run."
        )

    reducer = UMAP(n_components=dims, n_neighbors=15, min_dist=0.1, random_state=42)
    coordinates = reducer.fit_transform(embeddings)

    categories = [metadata.get("categories", []) for metadata in corpus]
    primary_labels = [category[0] if category else "unknown" for category in categories]
    label_to_index = {label: idx for idx, label in enumerate(sorted(set(primary_labels)))}
    colour_indices = np.array([label_to_index[label] for label in primary_labels])

    fig = _create_figure(coordinates, colour_indices, primary_labels, label_to_index, dims)

    derived_output = embeddings_path.with_name(f"embedding_plot_{dims}d.png")
    output = output_path or derived_output
    output.parent.mkdir(parents=True, exist_ok=True)
    fig.savefig(output, dpi=200, bbox_inches="tight")
    if show:
        plt.show()
    else:
        plt.close(fig)

    print(f"Saved {dims}D embedding visualisation to {output}")
    return output


def _create_figure(
    coordinates: np.ndarray,
    colour_indices: np.ndarray,
    labels: List[str],
    label_to_index: dict[str, int],
    dims: int,
) -> plt.Figure:
    """Create a matplotlib figure for the requested dimensionality.

    Parameters
    ----------
    coordinates: np.ndarray
        UMAP-reduced coordinates of shape (n_samples, dims).
    colour_indices: np.ndarray
        Integer indices representing colour assignments per sample.
    labels: List[str]
        Primary category labels aligned with the coordinates.
    label_to_index: dict[str, int]
        Mapping from label names to integer colour indices.
    dims: int
        Dimensionality of the embedding visualisation (2 or 3).

    Returns
    -------
    plt.Figure
        The generated matplotlib figure.
    """

    plt.rcdefaults()
    fig = plt.figure(figsize=(10, 8))

    if dims == 2:
        ax = fig.add_subplot(111)
        scatter = ax.scatter(
            coordinates[:, 0],
            coordinates[:, 1],
            c=colour_indices,
            cmap="tab20",
            s=20,
            alpha=0.85,
        )
        ax.set_xlabel("UMAP 1")
        ax.set_ylabel("UMAP 2")
    else:
        ax = fig.add_subplot(111, projection="3d")
        scatter = ax.scatter(
            coordinates[:, 0],
            coordinates[:, 1],
            coordinates[:, 2],
            c=colour_indices,
            cmap="tab20",
            s=20,
            alpha=0.85,
        )
        ax.set_xlabel("UMAP 1")
        ax.set_ylabel("UMAP 2")
        ax.set_zlabel("UMAP 3")

    ax.set_title(f"SPECTER2 Embeddings ({dims}D UMAP)")

    # Build a small legend using the primary labels.
    unique_labels = sorted(set(labels))
    handles = []
    for label in unique_labels:
        colour_value = label_to_index[label]
        rgba = scatter.cmap(scatter.norm(colour_value))
        handle = Line2D([0], [0], marker="o", color="w", label=label, markerfacecolor=rgba, markersize=8)
        handles.append(handle)

    if len(handles) <= 12:
        ax.legend(handles=handles, title="Primary Category", bbox_to_anchor=(1.05, 1), loc="upper left")

    return fig


def main(argv: List[str] | None = None) -> None:
    """Entry point for the visualisation CLI.

    Parameters
    ----------
    argv: List[str] | None, default None
        Optional argument override when invoking programmatically.
    """

    args = parse_args(argv)
    plot_embeddings(
        embeddings_path=args.embeddings,
        corpus_path=args.corpus,
        dims=args.dims,
        output_path=args.output,
        show=args.show,
    )


if __name__ == "__main__":  # pragma: no cover - CLI entry point
    main()