import click

from layer_helpers import read_csv_rows, representative_value, stable_id, write_csv_rows


CANONICAL_FIELDS = [
    "canonical_product_id",
    "canonical_name",
    "product_type",
    "brand",
    "variant",
    "size_value",
    "size_unit",
    "pack_qty",
    "measure_type",
    "normalized_quantity",
    "normalized_quantity_unit",
    "notes",
    "created_at",
    "updated_at",
]

LINK_FIELDS = [
    "observed_product_id",
    "canonical_product_id",
    "link_method",
    "link_confidence",
    "review_status",
    "reviewed_by",
    "reviewed_at",
    "link_notes",
]


def to_float(value):
    try:
        return float(value)
    except (TypeError, ValueError):
        return None


def normalized_quantity(row):
    size_value = to_float(row.get("representative_size_value"))
    pack_qty = to_float(row.get("representative_pack_qty")) or 1.0
    size_unit = row.get("representative_size_unit", "")
    measure_type = row.get("representative_measure_type", "")

    if size_value is not None and size_unit:
        return format(size_value * pack_qty, "g"), size_unit

    if row.get("representative_pack_qty") and measure_type == "count":
        return row["representative_pack_qty"], "count"

    if measure_type == "each":
        return "1", "each"

    return "", ""


def auto_link_rule(observed_row):
    if observed_row.get("is_fee") == "true":
        return "", "", ""

    if observed_row.get("representative_upc"):
        return (
            "exact_upc",
            f"upc={observed_row['representative_upc']}",
            "high",
        )

    if (
        observed_row.get("representative_name_norm")
        and observed_row.get("representative_size_value")
        and observed_row.get("representative_size_unit")
    ):
        return (
            "exact_name_size",
            "|".join(
                [
                    f"name={observed_row['representative_name_norm']}",
                    f"size={observed_row['representative_size_value']}",
                    f"unit={observed_row['representative_size_unit']}",
                    f"pack={observed_row['representative_pack_qty']}",
                    f"measure={observed_row['representative_measure_type']}",
                ]
            ),
            "high",
        )

    if (
        observed_row.get("representative_name_norm")
        and not observed_row.get("representative_size_value")
        and not observed_row.get("representative_size_unit")
        and not observed_row.get("representative_pack_qty")
    ):
        return (
            "exact_name",
            "|".join(
                [
                    f"name={observed_row['representative_name_norm']}",
                    f"measure={observed_row['representative_measure_type']}",
                ]
            ),
            "medium",
        )

    return "", "", ""


def canonical_row_for_group(canonical_product_id, group_rows, link_method):
    quantity_value, quantity_unit = normalized_quantity(
        {
            "representative_size_value": representative_value(
                group_rows, "representative_size_value"
            ),
            "representative_size_unit": representative_value(
                group_rows, "representative_size_unit"
            ),
            "representative_pack_qty": representative_value(
                group_rows, "representative_pack_qty"
            ),
            "representative_measure_type": representative_value(
                group_rows, "representative_measure_type"
            ),
        }
    )
    return {
        "canonical_product_id": canonical_product_id,
        "canonical_name": representative_value(group_rows, "representative_name_norm"),
        "product_type": "",
        "brand": representative_value(group_rows, "representative_brand"),
        "variant": representative_value(group_rows, "representative_variant"),
        "size_value": representative_value(group_rows, "representative_size_value"),
        "size_unit": representative_value(group_rows, "representative_size_unit"),
        "pack_qty": representative_value(group_rows, "representative_pack_qty"),
        "measure_type": representative_value(group_rows, "representative_measure_type"),
        "normalized_quantity": quantity_value,
        "normalized_quantity_unit": quantity_unit,
        "notes": f"auto-linked via {link_method}",
        "created_at": "",
        "updated_at": "",
    }


def build_canonical_layer(observed_rows):
    canonical_rows = []
    link_rows = []
    groups = {}

    for observed_row in sorted(observed_rows, key=lambda row: row["observed_product_id"]):
        link_method, group_key, confidence = auto_link_rule(observed_row)
        if not group_key:
            continue

        canonical_product_id = stable_id("gcan", f"{link_method}|{group_key}")
        groups.setdefault(canonical_product_id, {"method": link_method, "rows": []})
        groups[canonical_product_id]["rows"].append(observed_row)
        link_rows.append(
            {
                "observed_product_id": observed_row["observed_product_id"],
                "canonical_product_id": canonical_product_id,
                "link_method": link_method,
                "link_confidence": confidence,
                "review_status": "",
                "reviewed_by": "",
                "reviewed_at": "",
                "link_notes": "",
            }
        )

    for canonical_product_id, group in sorted(groups.items()):
        canonical_rows.append(
            canonical_row_for_group(
                canonical_product_id, group["rows"], group["method"]
            )
        )

    return canonical_rows, link_rows


@click.command()
@click.option(
    "--observed-csv",
    default="giant_output/products_observed.csv",
    show_default=True,
    help="Path to observed product rows.",
)
@click.option(
    "--canonical-csv",
    default="giant_output/products_canonical.csv",
    show_default=True,
    help="Path to canonical product output.",
)
@click.option(
    "--links-csv",
    default="giant_output/product_links.csv",
    show_default=True,
    help="Path to observed-to-canonical link output.",
)
def main(observed_csv, canonical_csv, links_csv):
    observed_rows = read_csv_rows(observed_csv)
    canonical_rows, link_rows = build_canonical_layer(observed_rows)
    write_csv_rows(canonical_csv, canonical_rows, CANONICAL_FIELDS)
    write_csv_rows(links_csv, link_rows, LINK_FIELDS)
    click.echo(
        f"wrote {len(canonical_rows)} canonical rows to {canonical_csv} and "
        f"{len(link_rows)} links to {links_csv}"
    )


if __name__ == "__main__":
    main()