Monthly regional report from messy orders CSV + shipments JSONL + weekday-only FX table

# Task: monthly regional order report Inputs: `orders.csv`, `shipments.jsonl`, `fx.csv`. Write `report.py` (Python stdlib only) that reads those three files from the current directory and writes `report.json`, then run it. `report.json` shape: `{"<Region>": {"YYYY-MM": {"orders": int, "revenue_eur": float, "unshipped": int, "late": int}}}` Rules: - Regions are North/South/East/West; the raw data uses many spellings/abbreviations/cases/whitespace (n, NORTH, " North ", E, ...). Normalise to those four names. - Order IDs are case-insensitive (`o-00012` = `O-00012`). Rows that are exact duplicates of an earlier row are dropped. Cancelled orders (any case) are excluded entirely. - `order_date` is ISO `YYYY-MM-DD`, `DD/MM/YYYY`, or `Mon DD YYYY`. The month bucket is the order date's month. - Amounts: USD amounts may have thousands separators (`1,234.50`); EUR amounts may use a decimal comma (`12,50`); GBP is plain. Convert to EUR with `fx.csv` (EUR per 1 unit; EUR itself is 1.0) using the rate on the order date, or if there is none that day (weekends), the most recent earlier date. Round `revenue_eur` to 2 decimals only at the end (sum unrounded values). - `unshipped` = kept orders with no shipment record. `late` = kept orders whose **first** shipment date (UTC date of `shipped_at`) is more than 5 days after the order date. Shipments for unknown orders and blank lines are ignored. `report.py` will also be re-run by the grader on freshly generated data with the same format, so don't hard-code anything. No network.