#!/usr/bin/env python3
"""
Add hard negative/positive examples that the current model gets wrong.
"""

import json
from pathlib import Path

HARD_EXAMPLES = [
    # Current model gets these WRONG - needs correction
    {
        "text": "BTC breaks 100k! New ATH, institutional buying surging",
        "sentiment": "Bullish",
        "entities": ["BTC"],
        "source": "hard_positive",
    },
    {
        "text": "Major hack on DeFi protocol, 50M drained from liquidity pools",
        "sentiment": "Bearish",
        "entities": ["DeFi"],
        "source": "hard_negative",
    },
    {
        "text": "Rug pull suspected, dev wallet drained liquidity",
        "sentiment": "Bearish",
        "entities": ["token"],
        "source": "hard_negative",
    },
    {
        "text": "SEC sues exchange for unregistered securities",
        "sentiment": "Bearish",
        "entities": ["SEC", "exchange"],
        "source": "hard_negative",
    },
    {
        "text": "Major hack on DeFi protocol drains $50M from liquidity pools",
        "sentiment": "Bearish",
        "entities": ["DeFi"],
        "source": "hard_negative",
    },
    {
        "text": "Panic selling BTC at 50k, liquidation cascade",
        "sentiment": "Bearish",
        "entities": ["BTC"],
        "source": "hard_negative",
    },
    {
        "text": "HODL strong hands, diamond hands win",
        "sentiment": "Bullish",
        "entities": ["BTC"],
        "source": "hard_positive",
    },
    {
        "text": "ETF approval sends Bitcoin to new highs",
        "sentiment": "Bullish",
        "entities": ["BTC"],
        "source": "hard_positive",
    },
    {
        "text": "Whale accumulation pushes ETH above 3k",
        "sentiment": "Bullish",
        "entities": ["ETH"],
        "source": "hard_positive",
    },
    {
        "text": "SEC sues exchange for unregistered securities, regulatory crackdown",
        "sentiment": "Bearish",
        "entities": ["SEC", "exchange"],
        "source": "hard_negative",
    },
    {
        "text": "Rug pull suspected on new memecoin, dev wallet drains liquidity",
        "sentiment": "Bearish",
        "entities": ["memecoin"],
        "source": "hard_negative",
    },
    {
        "text": "Panic selling as Bitcoin drops below $50K support",
        "sentiment": "Bearish",
        "entities": ["BTC"],
        "source": "hard_negative",
    },
    {
        "text": "FOMO buying drives PEPE to new ATH, experts warn of correction",
        "sentiment": "Bullish",
        "entities": ["PEPE"],
        "source": "hard_positive",
    },
    {
        "text": "New ETF approved for Solana, price surges 20%",
        "sentiment": "Bullish",
        "entities": ["SOL"],
        "source": "hard_positive",
    },
    {
        "text": "Rug pull suspected on new memecoin, dev wallet drained liquidity",
        "sentiment": "Bearish",
        "entities": ["memecoin"],
        "source": "hard_negative",
    },
    {
        "text": "HODL strategy pays off as long-term holders profit",
        "sentiment": "Bullish",
        "entities": ["BTC"],
        "source": "hard_positive",
    },
    # More hard examples for boundary cases
    {
        "text": "Major exchange hack suspected, $100M in BTC moved to unknown wallets",
        "sentiment": "Bearish",
        "entities": ["BTC"],
        "source": "hard_negative",
    },
    {
        "text": "Coinbase to delist 5 tokens including REN, BAND, MANA, CVC, ALGO",
        "sentiment": "Bearish",
        "entities": ["REN", "BAND", "MANA", "CVC", "ALGO"],
        "source": "hard_negative",
    },
    {
        "text": "Ethereum ETF outflows hit $280M as Grayscale ETHE bleeds",
        "sentiment": "Bearish",
        "entities": ["ETH", "Grayscale"],
        "source": "hard_negative",
    },
    {
        "text": "MicroStrategy adds 12,000 BTC, total holdings exceed 252,000 BTC",
        "sentiment": "Bullish",
        "entities": ["BTC", "MicroStrategy"],
        "source": "hard_positive",
    },
    {
        "text": "Binance deal gives Circle a boost in stablecoin race with Tether",
        "sentiment": "Bullish",
        "entities": ["Circle", "Tether", "Binance"],
        "source": "hard_positive",
    },
    {
        "text": "Major DeFi hack drains $50M from liquidity pools, users panic",
        "sentiment": "Bearish",
        "entities": ["DeFi"],
        "source": "hard_negative",
    },
    {
        "text": "Ethereum Layer 2 adoption hits record high, Arbitrum and Optimism lead",
        "sentiment": "Bullish",
        "entities": ["ETH", "Arbitrum", "Optimism"],
        "source": "hard_positive",
    },
    {
        "text": "SEC sues Binance for unregistered securities, BNB drops 15%",
        "sentiment": "Bearish",
        "entities": ["BNB", "Binance", "SEC"],
        "source": "hard_negative",
    },
    {
        "text": "Solana outage halts network for 5 hours, SOL drops 10%",
        "sentiment": "Bearish",
        "entities": ["SOL", "Solana"],
        "source": "hard_negative",
    },
    {
        "text": "New ETF approved for Solana, price surges 20% on launch",
        "sentiment": "Bullish",
        "entities": ["SOL"],
        "source": "hard_positive",
    },
    {
        "text": "Rug pull suspected on new memecoin, dev wallet drains liquidity",
        "sentiment": "Bearish",
        "entities": ["memecoin"],
        "source": "hard_negative",
    },
]

# Load existing augmented
with open("/mnt/dolphinng5_predict/sentiment_engine/data/final_augmented_set.jsonl") as f:
    data = [json.loads(line) for line in open("/mnt/dolphinng5_predict/sentiment_engine/data/final_augmented_set.jsonl")]

# Add hard examples
existing_texts = set(d["text"] for d in data)
added = 0
for ex in HARD_EXAMPLES:
    if ex["text"] not in [d["text"] for d in data]:
        data.append({
            "text": ex["text"],
            "sentiment": ex["sentiment"],
            "event_type": "price_action",
            "entities": ex["entities"],
            "source": ex["source"],
        })
        added += 1

print(f"Added {added} hard examples")
print(f"Total: {len(data)} samples")

# Save
with open("/mnt/dolphinng5_predict/sentiment_engine/data/final_labeled_complete.jsonl", "w") as f:
    for d in data:
        json.dump(d, f)
        f.write("\n")

# Stats
from collections import Counter
dist = Counter(d["sentiment"] for d in data)
print(f"Final distribution: {dict(dist)}")