from collections import defaultdict

import click

from layer_helpers import (
    compact_join,
    distinct_values,
    first_nonblank,
    read_csv_rows,
    representative_value,
    stable_id,
    write_csv_rows,
)


OUTPUT_FIELDS = [
    "observed_product_id",
    "retailer",
    "observed_key",
    "representative_upc",
    "representative_item_name",
    "representative_name_norm",
    "representative_brand",
    "representative_variant",
    "representative_size_value",
    "representative_size_unit",
    "representative_pack_qty",
    "representative_measure_type",
    "representative_image_url",
    "is_store_brand",
    "is_fee",
    "first_seen_date",
    "last_seen_date",
    "times_seen",
    "example_order_id",
    "example_item_name",
    "raw_name_examples",
    "normalized_name_examples",
    "example_prices",
    "distinct_item_names_count",
    "distinct_upcs_count",
]


def build_observed_key(row):
    if row.get("upc"):
        return "|".join(
            [
                row["retailer"],
                f"upc={row['upc']}",
                f"name={row['item_name_norm']}",
            ]
        )

    return "|".join(
        [
            row["retailer"],
            f"name={row['item_name_norm']}",
            f"size={row['size_value']}",
            f"unit={row['size_unit']}",
            f"pack={row['pack_qty']}",
            f"measure={row['measure_type']}",
            f"store_brand={row['is_store_brand']}",
            f"fee={row['is_fee']}",
        ]
    )


def build_observed_products(rows):
    grouped = defaultdict(list)
    for row in rows:
        grouped[build_observed_key(row)].append(row)

    observed_rows = []
    for observed_key, group_rows in sorted(grouped.items()):
        ordered = sorted(
            group_rows,
            key=lambda row: (row["order_date"], row["order_id"], int(row["line_no"])),
        )
        observed_rows.append(
            {
                "observed_product_id": stable_id("gobs", observed_key),
                "retailer": ordered[0]["retailer"],
                "observed_key": observed_key,
                "representative_upc": representative_value(ordered, "upc"),
                "representative_item_name": representative_value(ordered, "item_name"),
                "representative_name_norm": representative_value(
                    ordered, "item_name_norm"
                ),
                "representative_brand": representative_value(ordered, "brand_guess"),
                "representative_variant": representative_value(ordered, "variant"),
                "representative_size_value": representative_value(ordered, "size_value"),
                "representative_size_unit": representative_value(ordered, "size_unit"),
                "representative_pack_qty": representative_value(ordered, "pack_qty"),
                "representative_measure_type": representative_value(
                    ordered, "measure_type"
                ),
                "representative_image_url": first_nonblank(ordered, "image_url"),
                "is_store_brand": representative_value(ordered, "is_store_brand"),
                "is_fee": representative_value(ordered, "is_fee"),
                "first_seen_date": ordered[0]["order_date"],
                "last_seen_date": ordered[-1]["order_date"],
                "times_seen": str(len(ordered)),
                "example_order_id": ordered[0]["order_id"],
                "example_item_name": ordered[0]["item_name"],
                "raw_name_examples": compact_join(
                    distinct_values(ordered, "item_name"), limit=4
                ),
                "normalized_name_examples": compact_join(
                    distinct_values(ordered, "item_name_norm"), limit=4
                ),
                "example_prices": compact_join(
                    distinct_values(ordered, "line_total"), limit=4
                ),
                "distinct_item_names_count": str(
                    len(distinct_values(ordered, "item_name"))
                ),
                "distinct_upcs_count": str(len(distinct_values(ordered, "upc"))),
            }
        )

    observed_rows.sort(key=lambda row: row["observed_product_id"])
    return observed_rows


@click.command()
@click.option(
    "--items-enriched-csv",
    default="giant_output/items_enriched.csv",
    show_default=True,
    help="Path to enriched Giant item rows.",
)
@click.option(
    "--output-csv",
    default="giant_output/products_observed.csv",
    show_default=True,
    help="Path to observed product output.",
)
def main(items_enriched_csv, output_csv):
    rows = read_csv_rows(items_enriched_csv)
    observed_rows = build_observed_products(rows)
    write_csv_rows(output_csv, observed_rows, OUTPUT_FIELDS)
    click.echo(f"wrote {len(observed_rows)} rows to {output_csv}")


if __name__ == "__main__":
    main()