import csv

import click

from layer_helpers import read_csv_rows, 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 build_canonical_layer(observed_rows):
    canonical_rows = []
    link_rows = []

    for observed_row in sorted(observed_rows, key=lambda row: row["observed_product_id"]):
        canonical_product_id = stable_id(
            "gcan", f"seed|{observed_row['observed_product_id']}"
        )
        quantity_value, quantity_unit = normalized_quantity(observed_row)

        canonical_rows.append(
            {
                "canonical_product_id": canonical_product_id,
                "canonical_name": observed_row["representative_name_norm"],
                "product_type": "",
                "brand": observed_row["representative_brand"],
                "variant": observed_row["representative_variant"],
                "size_value": observed_row["representative_size_value"],
                "size_unit": observed_row["representative_size_unit"],
                "pack_qty": observed_row["representative_pack_qty"],
                "measure_type": observed_row["representative_measure_type"],
                "normalized_quantity": quantity_value,
                "normalized_quantity_unit": quantity_unit,
                "notes": f"seeded from {observed_row['observed_product_id']}",
                "created_at": "",
                "updated_at": "",
            }
        )
        link_rows.append(
            {
                "observed_product_id": observed_row["observed_product_id"],
                "canonical_product_id": canonical_product_id,
                "link_method": "seed_observed_product",
                "link_confidence": "",
                "review_status": "",
                "reviewed_by": "",
                "reviewed_at": "",
                "link_notes": "",
            }
        )

    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()