"""Strict, release-only loader for live linguistic knowledge."""

from __future__ import annotations

import csv
from pathlib import Path

import pandas as pd

from src.consent_lineage import (
    CONSENT_LINEAGE_COLUMNS,
    CROWDSOURCING_RECORD_COLUMNS,
    DATASET_GOVERNANCE_COLUMNS,
    DATASET_PROVENANCE_COLUMNS,
    consent_lineage_is_verified,
)


_KNOWLEDGE_COLUMNS = (
    "Utterance",
    "Clarification",
    "Dialect",
    "Tone_Category",
    "Linguistic_Context",
    "Pragmatic_Analysis",
)
_REQUIRED_COLUMNS = set(
    (*_KNOWLEDGE_COLUMNS[:3], *CROWDSOURCING_RECORD_COLUMNS,
     *CONSENT_LINEAGE_COLUMNS, *DATASET_GOVERNANCE_COLUMNS)
)


def _read_governed_csv_strict(path: Path) -> pd.DataFrame:
    """Parse one CSV without silently dropping malformed or ambiguous rows."""
    with path.open("r", encoding="utf-8-sig", newline="") as source:
        reader = csv.reader(source, strict=True)
        try:
            header = next(reader)
        except StopIteration as exc:
            raise ValueError("empty CSV") from exc
        if any(not str(column).strip() for column in header):
            raise ValueError("blank CSV header")
        if len(header) != len(set(header)):
            raise ValueError("duplicate CSV headers")
        missing = sorted(_REQUIRED_COLUMNS - set(header))
        if missing:
            raise ValueError("missing governance columns: " + ", ".join(missing))
        for row_number, row in enumerate(reader, start=2):
            if len(row) != len(header):
                raise ValueError(f"row {row_number} has a width mismatch")

    frame = pd.read_csv(
        path,
        dtype=str,
        encoding="utf-8-sig",
        keep_default_na=False,
    )
    if list(frame.columns) != header:
        raise ValueError("CSV schema changed while reading")
    return frame


def load_verified_release_knowledge(dataset_dir) -> pd.DataFrame:
    """Load only active, consent-verified rows approved for public reuse.

    Only ``<language>/data/<dialect>.csv`` files are dataset candidates; root
    audit files, rejection/graveyard files, pending queues, exports, histories,
    and profiles are outside this topology and cannot enter live search.
    """
    root = Path(dataset_dir).expanduser().resolve()
    released = []
    for path in sorted(root.glob("*/data/*.csv")):
        try:
            frame = _read_governed_csv_strict(path)
        except Exception as exc:
            print(f"Governed knowledge file rejected ({type(exc).__name__}); details redacted.")
            continue

        mask = frame.apply(
            lambda row: consent_lineage_is_verified(row.to_dict()),
            axis=1,
        )
        mask &= frame["Release_Eligible"].str.strip().str.casefold().eq("true")
        mask &= frame["Ethics_Review_Status"].str.strip().str.casefold().eq(
            "approved_verified_consent"
        )
        mask &= frame["License_Compatibility_Status"].str.strip().str.casefold().eq(
            "exact_match"
        )
        mask &= frame[list(DATASET_PROVENANCE_COLUMNS)].apply(
            lambda row: all(str(value).strip() for value in row),
            axis=1,
        )
        eligible = frame.loc[mask].copy()
        if not eligible.empty:
            released.append(eligible)

    if not released:
        return pd.DataFrame(columns=list(_KNOWLEDGE_COLUMNS))
    return pd.concat(released, ignore_index=True)