{"abstract":"Ending inventory is valued at the latest purchase price.","category":"Double-entry ledger accounting","checks":7,"contract":"x = [['buy', qty, unit_cost] | ['sell', qty, unit_price] | ['purchase_return', qty, unit_cost]]. Layers are kept oldest first; a buy at the same cost as the newest layer merges into it. A sale consumes layers oldest first (COGS) and records revenue; a sale exceeding on-hand quantity is rejected whole. A purchase return removes qty from the newest layer at that cost holding at least qty, else it is rejected. Emptied layers are removed. Return {'cogs', 'revenue', 'inventory': sum qty*cost, 'layers', 'rejected': event indexes}.","contract_signature":"x","evaluation_group":"w2-double-entry-ledger-accounting-fifo-perpetual-inventory","failed_approach":"Using the oldest layer cost is still a single-price valuation.","family":"w2-double-entry-ledger-accounting-fifo-perpetual-inventory-inventory-valuation","id":"FA-58111","implementations":{"attempt":{"sha256":"b5e94630bfcfb8dd440a758d58b81195fbd44925bd8ca1f059f5778b492a10da","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\n\nN = 1\nobservations = []\ndef solve(x):\n    layers = []\n    cogs = revenue = 0\n    short = []\n    for i, ev in enumerate(x):\n        if ev[0] == 'buy':\n            if layers and layers[-1][1] == ev[2]:\n                layers[-1][0] += ev[1]\n            else:\n                layers.append([ev[1], ev[2]])\n        elif ev[0] == 'sell':\n            qty = ev[1]\n            if qty > sum(l[0] for l in layers):\n                short.append(i)\n                continue\n            revenue += qty * ev[2]\n            while qty:\n                head = layers[0]\n                take = min(qty, head[0])\n                cogs += take * head[1]\n                head[0] -= take\n                qty -= take\n                if head[0] == 0:\n                    layers.pop(0)\n        elif ev[0] == 'purchase_return':\n            match = [l for l in layers if l[1] == ev[2] and l[0] >= ev[1]]\n            if not match:\n                short.append(i)\n                continue\n            match[-1][0] -= ev[1]\n            if match[-1][0] == 0:\n                layers.remove(match[-1])\n    inventory = sum(q for q, c in layers) * layers[0][1] if layers else 0\n    return {'cogs': cogs, 'revenue': revenue, 'inventory': inventory, 'layers': layers, 'rejected': short}\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\nfixtures = [[['regression: inventory valuation', [['sell', 3, 250], ['buy', 2, 120], ['purchase_return', 1, 100], ['buy', 5, 100], ['buy', 2, 120]], {'cogs': 0, 'revenue': 0, 'inventory': 980, 'layers': [[2, 120], [5, 100], [2, 120]], 'rejected': [0, 2]}], ['control 1', [['buy', 1, 120], ['sell', 12, 200], ['sell', 2, 250], ['sell', 2, 250]], {'cogs': 0, 'revenue': 0, 'inventory': 120, 'layers': [[1, 120]], 'rejected': [1, 2, 3]}], ['control 2', [['buy', 5, 100], ['sell', 3, 250], ['sell', 12, 200]], {'cogs': 300, 'revenue': 750, 'inventory': 200, 'layers': [[2, 100]], 'rejected': [2]}], ['control 3', [['buy', 10, 120], ['purchase_return', 1, 100], ['sell', 1, 200], ['purchase_return', 2, 100], ['sell', 2, 200], ['purchase_return', 1, 120], ['buy', 10, 120], ['buy', 5, 120]], {'cogs': 360, 'revenue': 600, 'inventory': 2520, 'layers': [[21, 120]], 'rejected': [1, 3]}], ['control 4', [['sell', 12, 250], ['sell', 12, 200], ['buy', 5, 100], ['sell', 12, 250], ['sell', 12, 200], ['purchase_return', 1, 100], ['sell', 3, 250]], {'cogs': 300, 'revenue': 750, 'inventory': 100, 'layers': [[1, 100]], 'rejected': [0, 1, 3, 4]}], ['control 5', [['buy', 5, 100], ['sell', 3, 250], ['buy', 5, 100]], {'cogs': 300, 'revenue': 750, 'inventory': 700, 'layers': [[7, 100]], 'rejected': []}], ['control 6', [['sell', 3, 250], ['sell', 6, 200], ['buy', 10, 120]], {'cogs': 0, 'revenue': 0, 'inventory': 1200, 'layers': [[10, 120]], 'rejected': [0, 1]}]], [['regression: inventory valuation', [['buy', 10, 100], ['sell', 12, 250], ['sell', 2, 250], ['sell', 12, 200], ['sell', 6, 200], ['buy', 1, 150], ['buy', 2, 120]], {'cogs': 800, 'revenue': 1700, 'inventory': 590, 'layers': [[2, 100], [1, 150], [2, 120]], 'rejected': [1, 3]}], ['control 1', [['sell', 12, 200], ['sell', 6, 250], ['sell', 6, 200]], {'cogs': 0, 'revenue': 0, 'inventory': 0, 'layers': [], 'rejected': [0, 1, 2]}], ['control 2', [['purchase_return', 2, 100], ['sell', 12, 250], ['buy', 10, 100], ['buy', 1, 100], ['sell', 6, 200]], {'cogs': 600, 'revenue': 1200, 'inventory': 500, 'layers': [[5, 100]], 'rejected': [0, 1]}], ['control 3', [['purchase_return', 5, 100], ['sell', 2, 200], ['purchase_return', 5, 120]], {'cogs': 0, 'revenue': 0, 'inventory': 0, 'layers': [], 'rejected': [0, 1, 2]}], ['control 4', [['purchase_return', 2, 120], ['sell', 1, 250], ['purchase_return', 1, 120], ['purchase_return', 1, 120], ['buy', 10, 120]], {'cogs': 0, 'revenue': 0, 'inventory': 1200, 'layers': [[10, 120]], 'rejected': [0, 1, 2, 3]}], ['control 5', [['sell', 6, 250], ['purchase_return', 1, 100], ['buy', 5, 100]], {'cogs': 0, 'revenue': 0, 'inventory': 500, 'layers': [[5, 100]], 'rejected': [0, 1]}], ['control 6', [['buy', 1, 100], ['purchase_return', 5, 100], ['sell', 1, 250]], {'cogs': 100, 'revenue': 250, 'inventory': 0, 'layers': [], 'rejected': [1]}]], [['regression: inventory valuation', [['purchase_return', 5, 100], ['sell', 2, 250], ['buy', 2, 100], ['buy', 1, 120]], {'cogs': 0, 'revenue': 0, 'inventory': 320, 'layers': [[2, 100], [1, 120]], 'rejected': [0, 1]}], ['control 1', [['buy', 1, 100], ['purchase_return', 5, 100], ['sell', 3, 200], ['buy', 1, 150], ['buy', 1, 150], ['sell', 3, 200], ['buy', 2, 120], ['buy', 10, 120]], {'cogs': 400, 'revenue': 600, 'inventory': 1440, 'layers': [[12, 120]], 'rejected': [1, 2]}], ['control 2', [['buy', 2, 120], ['purchase_return', 1, 150], ['buy', 1, 100], ['buy', 5, 100], ['sell', 3, 200]], {'cogs': 340, 'revenue': 600, 'inventory': 500, 'layers': [[5, 100]], 'rejected': [1]}], ['control 3', [['sell', 6, 200], ['sell', 12, 200], ['sell', 2, 200], ['sell', 2, 200], ['buy', 2, 100], ['purchase_return', 1, 120], ['buy', 2, 150], ['sell', 2, 200], ['sell', 2, 200], ['sell', 2, 200]], {'cogs': 500, 'revenue': 800, 'inventory': 0, 'layers': [], 'rejected': [0, 1, 2, 3, 5, 9]}], ['control 4', [['sell', 12, 200], ['buy', 10, 100], ['purchase_return', 2, 120]], {'cogs': 0, 'revenue': 0, 'inventory': 1000, 'layers': [[10, 100]], 'rejected': [0, 2]}], ['control 5', [['buy', 5, 100], ['sell', 6, 250], ['buy', 2, 100], ['buy', 5, 120], ['sell', 12, 250], ['sell', 1, 250]], {'cogs': 1300, 'revenue': 3000, 'inventory': 0, 'layers': [], 'rejected': [1, 5]}], ['control 6', [['purchase_return', 1, 150], ['sell', 1, 200], ['purchase_return', 5, 100], ['sell', 2, 250]], {'cogs': 0, 'revenue': 0, 'inventory': 0, 'layers': [], 'rejected': [0, 1, 2, 3]}]], [['regression: inventory valuation', [['sell', 1, 250], ['sell', 3, 200], ['purchase_return', 1, 100], ['buy', 2, 120], ['buy', 5, 150], ['sell', 3, 200], ['sell', 1, 200], ['sell', 6, 250], ['buy', 2, 120]], {'cogs': 540, 'revenue': 800, 'inventory': 690, 'layers': [[3, 150], [2, 120]], 'rejected': [0, 1, 2, 7]}], ['control 1', [['sell', 3, 250], ['purchase_return', 1, 150], ['purchase_return', 2, 120], ['sell', 6, 200], ['purchase_return', 2, 100]], {'cogs': 0, 'revenue': 0, 'inventory': 0, 'layers': [], 'rejected': [0, 1, 2, 3, 4]}], ['control 2', [['sell', 6, 200], ['purchase_return', 1, 150], ['buy', 5, 120]], {'cogs': 0, 'revenue': 0, 'inventory': 600, 'layers': [[5, 120]], 'rejected': [0, 1]}], ['control 3', [['buy', 1, 150], ['sell', 12, 250], ['purchase_return', 5, 120], ['sell', 2, 250], ['buy', 2, 150], ['buy', 1, 150], ['purchase_return', 2, 120]], {'cogs': 0, 'revenue': 0, 'inventory': 600, 'layers': [[4, 150]], 'rejected': [1, 2, 3, 6]}], ['control 4', [['buy', 2, 100], ['purchase_return', 2, 120], ['sell', 3, 200], ['buy', 10, 100], ['buy', 10, 100], ['sell', 2, 200], ['purchase_return', 2, 100], ['buy', 1, 100], ['purchase_return', 2, 100], ['buy', 1, 100]], {'cogs': 200, 'revenue': 400, 'inventory': 1800, 'layers': [[18, 100]], 'rejected': [1, 2]}], ['control 5', [['purchase_return', 2, 100], ['buy', 10, 100], ['sell', 12, 250], ['sell', 1, 200], ['purchase_return', 1, 150], ['sell', 6, 200], ['buy', 5, 100], ['purchase_return', 2, 100], ['sell', 6, 250], ['sell', 6, 200]], {'cogs': 1300, 'revenue': 2900, 'inventory': 0, 'layers': [], 'rejected': [0, 2, 4, 9]}], ['control 6', [['sell', 12, 200], ['sell', 3, 250], ['purchase_return', 2, 100], ['sell', 12, 250], ['sell', 12, 250], ['buy', 5, 100], ['sell', 3, 250]], {'cogs': 300, 'revenue': 750, 'inventory': 200, 'layers': [[2, 100]], 'rejected': [0, 1, 2, 3, 4]}]], [['regression: inventory valuation', [['buy', 10, 120], ['buy', 1, 120], ['purchase_return', 2, 150], ['sell', 2, 250], ['buy', 1, 100]], {'cogs': 240, 'revenue': 500, 'inventory': 1180, 'layers': [[9, 120], [1, 100]], 'rejected': [2]}], ['control 1', [['buy', 10, 100], ['buy', 10, 150], ['purchase_return', 5, 100], ['sell', 6, 200], ['sell', 3, 200], ['sell', 2, 200]], {'cogs': 1400, 'revenue': 2200, 'inventory': 600, 'layers': [[4, 150]], 'rejected': []}], ['control 2', [['purchase_return', 2, 100], ['sell', 1, 200], ['buy', 5, 100], ['buy', 5, 100], ['purchase_return', 1, 150], ['sell', 2, 250], ['buy', 1, 100], ['sell', 1, 200]], {'cogs': 300, 'revenue': 700, 'inventory': 800, 'layers': [[8, 100]], 'rejected': [0, 1, 4]}], ['control 3', [['purchase_return', 1, 100], ['buy', 1, 100], ['sell', 3, 200], ['buy', 10, 100], ['buy', 5, 100], ['sell', 2, 200], ['purchase_return', 2, 150]], {'cogs': 200, 'revenue': 400, 'inventory': 1400, 'layers': [[14, 100]], 'rejected': [0, 2, 6]}], ['control 4', [['sell', 6, 250], ['purchase_return', 2, 100], ['sell', 12, 250], ['sell', 3, 200]], {'cogs': 0, 'revenue': 0, 'inventory': 0, 'layers': [], 'rejected': [0, 1, 2, 3]}], ['control 5', [['sell', 6, 250], ['sell', 3, 250], ['sell', 12, 200], ['sell', 1, 250], ['sell', 3, 250], ['purchase_return', 5, 150], ['sell', 6, 200]], {'cogs': 0, 'revenue': 0, 'inventory': 0, 'layers': [], 'rejected': [0, 1, 2, 3, 4, 5, 6]}], ['control 6', [['sell', 3, 200], ['sell', 12, 200], ['buy', 10, 120], ['sell', 3, 200], ['sell', 12, 200]], {'cogs': 360, 'revenue': 600, 'inventory': 840, 'layers': [[7, 120]], 'rejected': [0, 1, 4]}]]]\nfor label, args, expected in fixtures[N-1]:\n    try:\n        actual = solve(args)\n    except Exception as exc:\n        actual = 'raised ' + type(exc).__name__\n    check(label, actual, expected)\nprint(json.dumps({\"observations\": observations, \"passed\": all(x[\"passed\"] for x in observations)}, ensure_ascii=False))\nraise SystemExit(0 if all(x[\"passed\"] for x in observations) else 1)\n"},"broken":{"sha256":"6cae29befaebb29314047050ae1f51e50a3e454eddc25829c50a9d38c201809e","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\n\nN = 1\nobservations = []\ndef solve(x):\n    layers = []\n    cogs = revenue = 0\n    short = []\n    for i, ev in enumerate(x):\n        if ev[0] == 'buy':\n            if layers and layers[-1][1] == ev[2]:\n                layers[-1][0] += ev[1]\n            else:\n                layers.append([ev[1], ev[2]])\n        elif ev[0] == 'sell':\n            qty = ev[1]\n            if qty > sum(l[0] for l in layers):\n                short.append(i)\n                continue\n            revenue += qty * ev[2]\n            while qty:\n                head = layers[0]\n                take = min(qty, head[0])\n                cogs += take * head[1]\n                head[0] -= take\n                qty -= take\n                if head[0] == 0:\n                    layers.pop(0)\n        elif ev[0] == 'purchase_return':\n            match = [l for l in layers if l[1] == ev[2] and l[0] >= ev[1]]\n            if not match:\n                short.append(i)\n                continue\n            match[-1][0] -= ev[1]\n            if match[-1][0] == 0:\n                layers.remove(match[-1])\n    inventory = sum(q for q, c in layers) * layers[-1][1] if layers else 0\n    return {'cogs': cogs, 'revenue': revenue, 'inventory': inventory, 'layers': layers, 'rejected': short}\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\nfixtures = [[['regression: inventory valuation', [['sell', 3, 250], ['buy', 2, 120], ['purchase_return', 1, 100], ['buy', 5, 100], ['buy', 2, 120]], {'cogs': 0, 'revenue': 0, 'inventory': 980, 'layers': [[2, 120], [5, 100], [2, 120]], 'rejected': [0, 2]}], ['control 1', [['buy', 1, 120], ['sell', 12, 200], ['sell', 2, 250], ['sell', 2, 250]], {'cogs': 0, 'revenue': 0, 'inventory': 120, 'layers': [[1, 120]], 'rejected': [1, 2, 3]}], ['control 2', [['buy', 5, 100], ['sell', 3, 250], ['sell', 12, 200]], {'cogs': 300, 'revenue': 750, 'inventory': 200, 'layers': [[2, 100]], 'rejected': [2]}], ['control 3', [['buy', 10, 120], ['purchase_return', 1, 100], ['sell', 1, 200], ['purchase_return', 2, 100], ['sell', 2, 200], ['purchase_return', 1, 120], ['buy', 10, 120], ['buy', 5, 120]], {'cogs': 360, 'revenue': 600, 'inventory': 2520, 'layers': [[21, 120]], 'rejected': [1, 3]}], ['control 4', [['sell', 12, 250], ['sell', 12, 200], ['buy', 5, 100], ['sell', 12, 250], ['sell', 12, 200], ['purchase_return', 1, 100], ['sell', 3, 250]], {'cogs': 300, 'revenue': 750, 'inventory': 100, 'layers': [[1, 100]], 'rejected': [0, 1, 3, 4]}], ['control 5', [['buy', 5, 100], ['sell', 3, 250], ['buy', 5, 100]], {'cogs': 300, 'revenue': 750, 'inventory': 700, 'layers': [[7, 100]], 'rejected': []}], ['control 6', [['sell', 3, 250], ['sell', 6, 200], ['buy', 10, 120]], {'cogs': 0, 'revenue': 0, 'inventory': 1200, 'layers': [[10, 120]], 'rejected': [0, 1]}]], [['regression: inventory valuation', [['buy', 10, 100], ['sell', 12, 250], ['sell', 2, 250], ['sell', 12, 200], ['sell', 6, 200], ['buy', 1, 150], ['buy', 2, 120]], {'cogs': 800, 'revenue': 1700, 'inventory': 590, 'layers': [[2, 100], [1, 150], [2, 120]], 'rejected': [1, 3]}], ['control 1', [['sell', 12, 200], ['sell', 6, 250], ['sell', 6, 200]], {'cogs': 0, 'revenue': 0, 'inventory': 0, 'layers': [], 'rejected': [0, 1, 2]}], ['control 2', [['purchase_return', 2, 100], ['sell', 12, 250], ['buy', 10, 100], ['buy', 1, 100], ['sell', 6, 200]], {'cogs': 600, 'revenue': 1200, 'inventory': 500, 'layers': [[5, 100]], 'rejected': [0, 1]}], ['control 3', [['purchase_return', 5, 100], ['sell', 2, 200], ['purchase_return', 5, 120]], {'cogs': 0, 'revenue': 0, 'inventory': 0, 'layers': [], 'rejected': [0, 1, 2]}], ['control 4', [['purchase_return', 2, 120], ['sell', 1, 250], ['purchase_return', 1, 120], ['purchase_return', 1, 120], ['buy', 10, 120]], {'cogs': 0, 'revenue': 0, 'inventory': 1200, 'layers': [[10, 120]], 'rejected': [0, 1, 2, 3]}], ['control 5', [['sell', 6, 250], ['purchase_return', 1, 100], ['buy', 5, 100]], {'cogs': 0, 'revenue': 0, 'inventory': 500, 'layers': [[5, 100]], 'rejected': [0, 1]}], ['control 6', [['buy', 1, 100], ['purchase_return', 5, 100], ['sell', 1, 250]], {'cogs': 100, 'revenue': 250, 'inventory': 0, 'layers': [], 'rejected': [1]}]], [['regression: inventory valuation', [['purchase_return', 5, 100], ['sell', 2, 250], ['buy', 2, 100], ['buy', 1, 120]], {'cogs': 0, 'revenue': 0, 'inventory': 320, 'layers': [[2, 100], [1, 120]], 'rejected': [0, 1]}], ['control 1', [['buy', 1, 100], ['purchase_return', 5, 100], ['sell', 3, 200], ['buy', 1, 150], ['buy', 1, 150], ['sell', 3, 200], ['buy', 2, 120], ['buy', 10, 120]], {'cogs': 400, 'revenue': 600, 'inventory': 1440, 'layers': [[12, 120]], 'rejected': [1, 2]}], ['control 2', [['buy', 2, 120], ['purchase_return', 1, 150], ['buy', 1, 100], ['buy', 5, 100], ['sell', 3, 200]], {'cogs': 340, 'revenue': 600, 'inventory': 500, 'layers': [[5, 100]], 'rejected': [1]}], ['control 3', [['sell', 6, 200], ['sell', 12, 200], ['sell', 2, 200], ['sell', 2, 200], ['buy', 2, 100], ['purchase_return', 1, 120], ['buy', 2, 150], ['sell', 2, 200], ['sell', 2, 200], ['sell', 2, 200]], {'cogs': 500, 'revenue': 800, 'inventory': 0, 'layers': [], 'rejected': [0, 1, 2, 3, 5, 9]}], ['control 4', [['sell', 12, 200], ['buy', 10, 100], ['purchase_return', 2, 120]], {'cogs': 0, 'revenue': 0, 'inventory': 1000, 'layers': [[10, 100]], 'rejected': [0, 2]}], ['control 5', [['buy', 5, 100], ['sell', 6, 250], ['buy', 2, 100], ['buy', 5, 120], ['sell', 12, 250], ['sell', 1, 250]], {'cogs': 1300, 'revenue': 3000, 'inventory': 0, 'layers': [], 'rejected': [1, 5]}], ['control 6', [['purchase_return', 1, 150], ['sell', 1, 200], ['purchase_return', 5, 100], ['sell', 2, 250]], {'cogs': 0, 'revenue': 0, 'inventory': 0, 'layers': [], 'rejected': [0, 1, 2, 3]}]], [['regression: inventory valuation', [['sell', 1, 250], ['sell', 3, 200], ['purchase_return', 1, 100], ['buy', 2, 120], ['buy', 5, 150], ['sell', 3, 200], ['sell', 1, 200], ['sell', 6, 250], ['buy', 2, 120]], {'cogs': 540, 'revenue': 800, 'inventory': 690, 'layers': [[3, 150], [2, 120]], 'rejected': [0, 1, 2, 7]}], ['control 1', [['sell', 3, 250], ['purchase_return', 1, 150], ['purchase_return', 2, 120], ['sell', 6, 200], ['purchase_return', 2, 100]], {'cogs': 0, 'revenue': 0, 'inventory': 0, 'layers': [], 'rejected': [0, 1, 2, 3, 4]}], ['control 2', [['sell', 6, 200], ['purchase_return', 1, 150], ['buy', 5, 120]], {'cogs': 0, 'revenue': 0, 'inventory': 600, 'layers': [[5, 120]], 'rejected': [0, 1]}], ['control 3', [['buy', 1, 150], ['sell', 12, 250], ['purchase_return', 5, 120], ['sell', 2, 250], ['buy', 2, 150], ['buy', 1, 150], ['purchase_return', 2, 120]], {'cogs': 0, 'revenue': 0, 'inventory': 600, 'layers': [[4, 150]], 'rejected': [1, 2, 3, 6]}], ['control 4', [['buy', 2, 100], ['purchase_return', 2, 120], ['sell', 3, 200], ['buy', 10, 100], ['buy', 10, 100], ['sell', 2, 200], ['purchase_return', 2, 100], ['buy', 1, 100], ['purchase_return', 2, 100], ['buy', 1, 100]], {'cogs': 200, 'revenue': 400, 'inventory': 1800, 'layers': [[18, 100]], 'rejected': [1, 2]}], ['control 5', [['purchase_return', 2, 100], ['buy', 10, 100], ['sell', 12, 250], ['sell', 1, 200], ['purchase_return', 1, 150], ['sell', 6, 200], ['buy', 5, 100], ['purchase_return', 2, 100], ['sell', 6, 250], ['sell', 6, 200]], {'cogs': 1300, 'revenue': 2900, 'inventory': 0, 'layers': [], 'rejected': [0, 2, 4, 9]}], ['control 6', [['sell', 12, 200], ['sell', 3, 250], ['purchase_return', 2, 100], ['sell', 12, 250], ['sell', 12, 250], ['buy', 5, 100], ['sell', 3, 250]], {'cogs': 300, 'revenue': 750, 'inventory': 200, 'layers': [[2, 100]], 'rejected': [0, 1, 2, 3, 4]}]], [['regression: inventory valuation', [['buy', 10, 120], ['buy', 1, 120], ['purchase_return', 2, 150], ['sell', 2, 250], ['buy', 1, 100]], {'cogs': 240, 'revenue': 500, 'inventory': 1180, 'layers': [[9, 120], [1, 100]], 'rejected': [2]}], ['control 1', [['buy', 10, 100], ['buy', 10, 150], ['purchase_return', 5, 100], ['sell', 6, 200], ['sell', 3, 200], ['sell', 2, 200]], {'cogs': 1400, 'revenue': 2200, 'inventory': 600, 'layers': [[4, 150]], 'rejected': []}], ['control 2', [['purchase_return', 2, 100], ['sell', 1, 200], ['buy', 5, 100], ['buy', 5, 100], ['purchase_return', 1, 150], ['sell', 2, 250], ['buy', 1, 100], ['sell', 1, 200]], {'cogs': 300, 'revenue': 700, 'inventory': 800, 'layers': [[8, 100]], 'rejected': [0, 1, 4]}], ['control 3', [['purchase_return', 1, 100], ['buy', 1, 100], ['sell', 3, 200], ['buy', 10, 100], ['buy', 5, 100], ['sell', 2, 200], ['purchase_return', 2, 150]], {'cogs': 200, 'revenue': 400, 'inventory': 1400, 'layers': [[14, 100]], 'rejected': [0, 2, 6]}], ['control 4', [['sell', 6, 250], ['purchase_return', 2, 100], ['sell', 12, 250], ['sell', 3, 200]], {'cogs': 0, 'revenue': 0, 'inventory': 0, 'layers': [], 'rejected': [0, 1, 2, 3]}], ['control 5', [['sell', 6, 250], ['sell', 3, 250], ['sell', 12, 200], ['sell', 1, 250], ['sell', 3, 250], ['purchase_return', 5, 150], ['sell', 6, 200]], {'cogs': 0, 'revenue': 0, 'inventory': 0, 'layers': [], 'rejected': [0, 1, 2, 3, 4, 5, 6]}], ['control 6', [['sell', 3, 200], ['sell', 12, 200], ['buy', 10, 120], ['sell', 3, 200], ['sell', 12, 200]], {'cogs': 360, 'revenue': 600, 'inventory': 840, 'layers': [[7, 120]], 'rejected': [0, 1, 4]}]]]\nfor label, args, expected in fixtures[N-1]:\n    try:\n        actual = solve(args)\n    except Exception as exc:\n        actual = 'raised ' + type(exc).__name__\n    check(label, actual, expected)\nprint(json.dumps({\"observations\": observations, \"passed\": all(x[\"passed\"] for x in observations)}, ensure_ascii=False))\nraise SystemExit(0 if all(x[\"passed\"] for x in observations) else 1)\n"}},"limitations":"A deterministic bounded teaching model with stipulated toy bookkeeping rules stated in the contract; amounts are integer cents; it makes no claim of conformance to any accounting standard or product. This reproducer isolates one failure mechanism. Results cover the supplied fixtures. Variants within a family share a test contract and should remain grouped when constructing evaluation splits. Related mechanisms with a shared evaluation_group must also remain together; these controlled models are not independent production incidents.","method":"Deterministic executable model with adversarial boundary fixtures.","provenance":{"created_by":"Failure Map","dependencies":"Python standard library","family":"w2-double-entry-ledger-accounting-fifo-perpetual-inventory-inventory-valuation","generated_at":"2026-09-29T14:46:23.534950+00:00","license":"CC0-1.0","python":"3.12.14","seed":1,"split":"open-access"},"relevance":"Ledger software must keep debits equal to credits and apply normal-balance, period and cutoff rules exactly; small sign or boundary slips silently misstate financial statements.","root_cause":"On-hand quantity is multiplied by a single replacement cost instead of each layer cost.","sha256":"44ffbb8d8de6b124d8746afc98ce44187c05b99b74ca083eb9095b5c673940b3","title":"FIFO perpetual inventory journal: inventory valuation · case 01","variant":1,"variant_policy":"Five 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