"""Legacy evidence audit; all operations are read-only until an output is written."""

from __future__ import annotations

import re
from pathlib import Path
from typing import Any

import h5py
import pandas as pd

from .data import FORBIDDEN_MODEL_INPUTS


MIBIG_PATTERN = re.compile(r"^BGC\d{7}$")


def audit_legacy_data(atlas_csv: str | Path, genes_csv: str | Path, embeddings_h5: str | Path) -> dict[str, Any]:
    atlas = pd.read_csv(atlas_csv)
    genes = pd.read_csv(genes_csv)
    labeled = atlas.dropna(subset=["compound_family"]).copy()
    target_by_bgc = labeled.set_index("bgc_id")["compound_family"].astype(str).to_dict()

    direct_hits: dict[str, int] = {}
    if "mibig_id" in genes:
        gene_targets = genes["mibig_id"].fillna("").astype(str).str.extract(r"^(BGC\d{7})", expand=False)
        matches = gene_targets == genes["bgc_id"].map(target_by_bgc)
        direct_hits = matches.groupby(genes["bgc_id"]).sum().astype(int).to_dict()

    with h5py.File(embeddings_h5, "r") as handle:
        embedding_keys = set(handle.keys())
    gene_ids = set(genes["gene_id"].astype(str))

    group_sizes = labeled.groupby("compound_family")["bgc_id"].nunique()
    return {
        "schema_version": 1,
        "atlas": {
            "rows": int(len(atlas)),
            "unique_bgcs": int(atlas["bgc_id"].nunique()),
            "unique_genomes": int(atlas["genome_id"].nunique()),
            "labeled_bgcs": int(labeled["bgc_id"].nunique()),
            "reference_ids": int(labeled["compound_family"].nunique()),
            "eligible_groups_at_least_5": int((group_sizes >= 5).sum()),
            "labels_matching_mibig_accession": int(
                labeled["compound_family"].astype(str).map(lambda value: bool(MIBIG_PATTERN.match(value))).sum()
            ),
            "labels_with_at_least_3_direct_target_hits": int(
                sum(direct_hits.get(str(identifier), 0) >= 3 for identifier in labeled["bgc_id"])
            ),
        },
        "genes": {
            "rows": int(len(genes)),
            "unique_gene_ids": int(genes["gene_id"].nunique()),
            "duplicate_gene_ids_across_rows": int(len(genes) - genes["gene_id"].nunique()),
            "duplicate_composite_keys": int(genes.duplicated(["bgc_id", "gene_id"]).sum()),
        },
        "embeddings": {
            "keys": len(embedding_keys),
            "unique_gene_coverage": (len(gene_ids & embedding_keys) / len(gene_ids)) if gene_ids else 0.0,
            "missing_unique_gene_ids": len(gene_ids - embedding_keys),
        },
        "leakage": {
            "label_generation_columns_present": sorted(
                {"mibig_id", "pident", "qcovs", "evalue"}.intersection(genes.columns)
            ),
            "forbidden_atlas_feature_columns_present": sorted(FORBIDDEN_MODEL_INPUTS.intersection(atlas.columns)),
            "finding": "Legacy labels are MIBiG-reference pseudo-labels and label-generating alignment fields must not be model inputs.",
        },
    }