"""Portable event-level artifacts used by the parity campaign."""

from __future__ import annotations

import hashlib
import json
from dataclasses import dataclass, field
from pathlib import Path
from typing import Any

import numpy as np

SCHEMA_VERSION = 1


@dataclass
class ValidationArtifact:
    """Normalized representation of one extraction run.

    Variable-sized node and edge arrays are stored flattened with offsets.
    ``sample_id`` is the join key and is never inferred from row position.
    """

    sample_id: np.ndarray
    labels: np.ndarray
    folds: np.ndarray
    weights: np.ndarray
    globals: np.ndarray
    node_features_flat: np.ndarray
    node_offsets: np.ndarray
    edge_src_flat: np.ndarray
    edge_dst_flat: np.ndarray
    edge_features_flat: np.ndarray
    edge_offsets: np.ndarray
    logits: np.ndarray | None = None
    scores: np.ndarray | None = None
    predictions: np.ndarray | None = None
    manifest: dict[str, Any] = field(default_factory=dict)

    @property
    def event_count(self) -> int:
        return int(self.sample_id.shape[0])

    def validate(self) -> None:
        n = self.event_count
        for name in ("labels", "folds", "weights", "globals"):
            if getattr(self, name).shape[0] != n:
                raise ValueError(f"{name} does not contain one row per sample")
        for name in ("node_offsets", "edge_offsets"):
            offsets = getattr(self, name)
            if offsets.shape != (n + 1,) or offsets[0] != 0:
                raise ValueError(f"{name} must have shape ({n + 1},) and start at zero")
            if np.any(offsets[1:] < offsets[:-1]):
                raise ValueError(f"{name} must be monotonic")
        if self.node_offsets[-1] != len(self.node_features_flat):
            raise ValueError("node offsets do not describe node_features_flat")
        if self.edge_offsets[-1] != len(self.edge_src_flat):
            raise ValueError("edge offsets do not describe edge_src_flat")
        if len(self.edge_src_flat) != len(self.edge_dst_flat):
            raise ValueError("edge source and destination arrays differ in length")
        if self.edge_offsets[-1] != len(self.edge_features_flat):
            raise ValueError("edge offsets do not describe edge_features_flat")
        if len(np.unique(self.sample_id)) != n:
            raise ValueError("sample_id contains duplicates")

    def event_nodes(self, index: int) -> np.ndarray:
        start, stop = self.node_offsets[index : index + 2]
        return self.node_features_flat[start:stop]

    def event_edges(self, index: int) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
        start, stop = self.edge_offsets[index : index + 2]
        return (
            self.edge_src_flat[start:stop],
            self.edge_dst_flat[start:stop],
            self.edge_features_flat[start:stop],
        )


def _optional_arrays(artifact: ValidationArtifact) -> dict[str, np.ndarray]:
    return {
        name: value
        for name in ("logits", "scores", "predictions")
        if (value := getattr(artifact, name)) is not None
    }


def save_artifact(artifact: ValidationArtifact, directory: str | Path) -> Path:
    artifact.validate()
    directory = Path(directory)
    directory.mkdir(parents=True, exist_ok=True)
    arrays = {
        "sample_id": artifact.sample_id,
        "labels": artifact.labels,
        "folds": artifact.folds,
        "weights": artifact.weights,
        "globals": artifact.globals,
        "node_features_flat": artifact.node_features_flat,
        "node_offsets": artifact.node_offsets,
        "edge_src_flat": artifact.edge_src_flat,
        "edge_dst_flat": artifact.edge_dst_flat,
        "edge_features_flat": artifact.edge_features_flat,
        "edge_offsets": artifact.edge_offsets,
        **_optional_arrays(artifact),
    }
    np.savez_compressed(directory / "artifact.npz", **arrays)
    manifest = {
        "schema_version": SCHEMA_VERSION,
        "event_count": artifact.event_count,
        "arrays": sorted(arrays),
        **artifact.manifest,
    }
    (directory / "manifest.json").write_text(
        json.dumps(manifest, indent=2, sort_keys=True) + "\n"
    )
    return directory


def load_artifact(directory: str | Path) -> ValidationArtifact:
    directory = Path(directory)
    manifest = json.loads((directory / "manifest.json").read_text())
    if manifest.get("schema_version") != SCHEMA_VERSION:
        raise ValueError(
            f"unsupported validation artifact schema: {manifest.get('schema_version')}"
        )
    with np.load(directory / "artifact.npz", allow_pickle=False) as data:
        values = {key: data[key] for key in data.files}
    optional = {
        name: values.pop(name, None) for name in ("logits", "scores", "predictions")
    }
    artifact = ValidationArtifact(**values, **optional, manifest=manifest)
    artifact.validate()
    return artifact


def file_sha256(path: str | Path) -> str:
    digest = hashlib.sha256()
    with Path(path).open("rb") as stream:
        for chunk in iter(lambda: stream.read(1024 * 1024), b""):
            digest.update(chunk)
    return digest.hexdigest()