src/dedup_quality.py

"""
Deduplication, contamination check, and quality filter for SFT traces.

Pipeline:
  1. Load all traces from data/sft_traces_v2/ + existing sft_traces.jsonl
  2. Deduplicate by query hash (exact match) and by fuzzy similarity (near-dupes)
  3. Contamination check: remove traces whose queries appear in gold_traces.jsonl
  4. Quality filter: remove traces that are too short, have empty reasoning,
     or have malformed structure
  5. Shuffle and write final dataset

Usage:
  python src/dedup_quality.py --input data/sft_traces_v2/ --output data/sft_traces_final.jsonl
"""

import argparse
import hashlib
import json
import os
import random
import re
import sys
from collections import defaultdict
from difflib import SequenceMatcher

PROJECT_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))

def load_traces_from_dir(dir_path: str) -> list[dict]:
    """Load all .jsonl files from a directory."""
    traces = []
    if not os.path.exists(dir_path):
        return traces
    for fname in sorted(os.listdir(dir_path)):
        if fname.endswith('.jsonl'):
            fpath = os.path.join(dir_path, fname)
            with open(fpath, 'r', encoding='utf-8') as f:
                for line in f:
                    try:
                        trace = json.loads(line)
                        if trace and 'query' in trace and 'trace' in trace:
                            trace['_source'] = fname
                            traces.append(trace)
                    except json.JSONDecodeError:
                        continue
    return traces

def load_traces_from_file(fpath: str) -> list[dict]:
    """Load traces from a single .jsonl file."""
    traces = []
    if not os.path.exists(fpath):
        return traces
    with open(fpath, 'r', encoding='utf-8') as f:
        for line in f:
            try:
                trace = json.loads(line)
                if trace and 'query' in trace and 'trace' in trace:
                    trace['_source'] = os.path.basename(fpath)
                    traces.append(trace)
            except json.JSONDecodeError:
                continue
    return traces

def query_hash(trace: dict) -> str:
    """Hash the query for exact dedup."""
    return hashlib.md5(trace['query'].strip().lower().encode()).hexdigest()

def normalize_query(query: str) -> str:
    """Normalize query for fuzzy matching."""
    # Remove punctuation, lowercase, collapse whitespace
    q = re.sub(r'[^\w\s]', '', query.lower())
    q = ' '.join(q.split())
    return q

def fuzzy_similarity(q1: str, q2: str) -> float:
    """Compute similarity between two queries."""
    n1, n2 = normalize_query(q1), normalize_query(q2)
    if n1 == n2:
        return 1.0
    # Quick length check
    if abs(len(n1) - len(n2)) > max(len(n1), len(n2)) * 0.5:
        return 0.0
    return SequenceMatcher(None, n1, n2).ratio()

def check_trace_quality(trace: dict) -> tuple[bool, str]:
    """Check if a trace meets quality standards.
    
    Returns (is_valid, reason_if_rejected)
    """
    query = trace.get('query', '')
    trace_msgs = trace.get('trace', [])
    
    # Must have system, user, and at least 2 assistant turns
    if len(trace_msgs) < 4:
        return False, "too_few_messages"
    
    # Query must be substantial
    if len(query) < 15:
        return False, "query_too_short"
    
    # Check assistant turns have real content
    assistant_turns = [m for m in trace_msgs if m['role'] == 'assistant']
    if len(assistant_turns) < 2:
        return False, "too_few_assistant_turns"
    
    for turn in assistant_turns:
        content = turn.get('content', '')
        if len(content) < 50:
            return False, "assistant_turn_too_short"
        # Must contain at least one special token
        if not any(tok in content for tok in ['<|reasoning|>', '<|search|>', '<|evidence|>', '<|finish|>']):
            return False, "no_special_tokens"
    
    # Must have at least one search
    has_search = any('<|search|>' in m.get('content', '') for m in assistant_turns)
    if not has_search:
        return False, "no_search_action"
    
    # Must have evidence or finish
    has_evidence = any('<|evidence|>' in m.get('content', '') for m in assistant_turns)
    has_finish = any('<|finish|>' in m.get('content', '') for m in assistant_turns)
    if not has_evidence and not has_finish:
        return False, "no_evidence_or_finish"
    
    # Check for reasoning density — at least one turn should have substantial reasoning
    max_reasoning_len = 0
    for turn in assistant_turns:
        content = turn.get('content', '')
        # Extract reasoning sections
        reasoning_sections = re.findall(r'<\|reasoning\|>(.*?)<\|end\|>', content, re.DOTALL)
        for r in reasoning_sections:
            max_reasoning_len = max(max_reasoning_len, len(r.strip()))
    
    if max_reasoning_len < 30:
        return False, "reasoning_too_thin"
    
    return True, "ok"

def deduplicate(traces: list[dict], similarity_threshold: float = 0.85) -> tuple[list[dict], dict]:
    """Remove duplicate and near-duplicate traces.
    
    Returns (deduplicated_traces, stats)
    """
    stats = {
        'exact_dups_removed': 0,
        'fuzzy_dups_removed': 0,
        'total_input': len(traces),
    }
    
    # Phase 1: Exact dedup by query hash
    seen_hashes = set()
    exact_deduped = []
    for trace in traces:
        h = query_hash(trace)
        if h not in seen_hashes:
            seen_hashes.add(h)
            exact_deduped.append(trace)
        else:
            stats['exact_dups_removed'] += 1
    
    # Phase 2: Fuzzy dedup by query similarity
    # Group by first word for efficiency
    groups = defaultdict(list)
    for trace in exact_deduped:
        first_word = normalize_query(trace['query']).split()[0] if normalize_query(trace['query']).split() else ''
        groups[first_word].append(trace)
    
    fuzzy_deduped = []
    for first_word, group in groups.items():
        if len(group) == 1:
            fuzzy_deduped.extend(group)
            continue
        
        # Compare within group
        kept = []
        for trace in group:
            is_dup = False
            for kept_trace in kept:
                sim = fuzzy_similarity(trace['query'], kept_trace['query'])
                if sim >= similarity_threshold:
                    is_dup = True
                    stats['fuzzy_dups_removed'] += 1
                    break
            if not is_dup:
                kept.append(trace)
        fuzzy_deduped.extend(kept)
    
    stats['total_output'] = len(fuzzy_deduped)
    return fuzzy_deduped, stats

def check_contamination(traces: list[dict], gold_traces: list[dict]) -> tuple[list[dict], dict]:
    """Remove traces whose queries match gold trace queries.
    
    Returns (clean_traces, stats)
    """
    gold_queries = set()
    for gt in gold_traces:
        gold_queries.add(normalize_query(gt['query']))
    
    clean = []
    removed = 0
    for trace in traces:
        nq = normalize_query(trace['query'])
        if nq in gold_queries:
            removed += 1
        else:
            clean.append(trace)
    
    return clean, {'contamination_removed': removed, 'gold_queries': len(gold_queries)}

def main():
    parser = argparse.ArgumentParser(description="Dedup and quality filter SFT traces")
    parser.add_argument("--input", type=str, default=os.path.join(PROJECT_DIR, "data", "sft_traces_v2"),
                       help="Input directory with .jsonl files")
    parser.add_argument("--existing", type=str, default=os.path.join(PROJECT_DIR, "data", "sft_traces.jsonl"),
                       help="Existing traces to merge with")
    parser.add_argument("--gold", type=str, default=os.path.join(PROJECT_DIR, "data", "gold_traces.jsonl"),
                       help="Gold traces for contamination check")
    parser.add_argument("--output", type=str, default=os.path.join(PROJECT_DIR, "data", "sft_traces_final.jsonl"),
                       help="Output file")
    parser.add_argument("--seed", type=int, default=42)
    parser.add_argument("--similarity", type=float, default=0.85,
                       help="Fuzzy dedup similarity threshold")
    args = parser.parse_args()

    print("=" * 60)
    print("SFT TRACE DEDUPLICATION & QUALITY PIPELINE")
    print("=" * 60)

    # 1. Load all traces
    print("\n1. Loading traces...")
    new_traces = load_traces_from_dir(args.input)
    existing_traces = load_traces_from_file(args.existing)
    gold_traces = load_traces_from_file(args.gold)
    
    print(f"   New traces:      {len(new_traces):,}")
    print(f"   Existing traces: {len(existing_traces):,}")
    print(f"   Gold traces:     {len(gold_traces):,}")
    
    all_traces = new_traces + existing_traces
    print(f"   Total to process: {len(all_traces):,}")

    # 2. Quality filter
    print("\n2. Quality filtering...")
    quality_stats = defaultdict(int)
    quality_passed = []
    for trace in all_traces:
        is_valid, reason = check_trace_quality(trace)
        if is_valid:
            quality_passed.append(trace)
        else:
            quality_stats[reason] += 1
    
    print(f"   Passed: {len(quality_passed):,}")
    print(f"   Rejected: {sum(quality_stats.values()):,}")
    for reason, count in sorted(quality_stats.items(), key=lambda x: -x[1]):
        print(f"     {reason}: {count}")

    # 3. Contamination check
    print("\n3. Contamination check (vs gold traces)...")
    clean_traces, contam_stats = check_contamination(quality_passed, gold_traces)
    print(f"   Removed: {contam_stats['contamination_removed']}")
    print(f"   Remaining: {len(clean_traces):,}")

    # 4. Deduplication
    print("\n4. Deduplication...")
    deduped_traces, dedup_stats = deduplicate(clean_traces, args.similarity)
    print(f"   Exact dups removed: {dedup_stats['exact_dups_removed']:,}")
    print(f"   Fuzzy dups removed: {dedup_stats['fuzzy_dups_removed']:,}")
    print(f"   Final count: {dedup_stats['total_output']:,}")

    # 5. Shuffle and write
    print(f"\n5. Writing to {args.output}...")
    rng = random.Random(args.seed)
    rng.shuffle(deduped_traces)
    
    # Remove internal fields
    for trace in deduped_traces:
        trace.pop('_source', None)
    
    with open(args.output, 'w', encoding='utf-8') as f:
        for trace in deduped_traces:
            f.write(json.dumps(trace, ensure_ascii=False) + '\n')
    
    print(f"   Written: {len(deduped_traces):,} traces")
    
    # Summary
    print("\n" + "=" * 60)
    print("SUMMARY")
    print("=" * 60)
    print(f"  Input traces:        {len(all_traces):,}")
    print(f"  Quality rejected:    {sum(quality_stats.values()):,}")
    print(f"  Contamination removed: {contam_stats['contamination_removed']}")
    print(f"  Exact dups removed:  {dedup_stats['exact_dups_removed']:,}")
    print(f"  Fuzzy dups removed:  {dedup_stats['fuzzy_dups_removed']:,}")
    print(f"  Final dataset:       {len(deduped_traces):,}")
    
    # Category distribution
    cat_counts = defaultdict(int)
    for trace in deduped_traces:
        # Try to infer category from query pattern
        q = trace['query'].lower()
        if any(w in q for w in ['walk me through', 'implementation', 'step by step', 'control flow']):
            cat_counts['implementation'] += 1
        elif any(w in q for w in ['trace how data', 'data flow', 'data path', 'interact']):
            cat_counts['cross_file'] += 1
        elif any(w in q for w in ['architecture', 'module', 'structure of', 'map out']):
            cat_counts['architecture'] += 1
        elif any(w in q for w in ['where is', 'used across', 'usage', 'called from']):
            cat_counts['usage'] += 1
        elif any(w in q for w in ['error', 'failure', 'fail', 'debug']):
            cat_counts['error/debug'] += 1
        elif any(w in q for w in ['api', 'contract', 'interface', 'parameters']):
            cat_counts['api'] += 1
        elif any(w in q for w in ['depend', 'dependency', 'blast radius', 'impact']):
            cat_counts['dependency/impact'] += 1
        elif any(w in q for w in ['fields', 'data structure', 'layout', 'memory']):
            cat_counts['data_structure'] += 1
        elif any(w in q for w in ['compare', 'tradeoff', 'vs', 'contrast']):
            cat_counts['comparison'] += 1
        elif any(w in q for w in ['security', 'validation', 'vulnerability']):
            cat_counts['security'] += 1
        elif any(w in q for w in ['performance', 'bottleneck', 'hot', 'optimize']):
            cat_counts['performance'] += 1
        elif any(w in q for w in ['pattern', 'design']):
            cat_counts['design_patterns'] += 1
        else:
            cat_counts['other'] += 1
    
    print(f"\n  Category distribution:")
    for cat, count in sorted(cat_counts.items(), key=lambda x: -x[1]):
        print(f"    {cat:20s}: {count:5d}")


if __name__ == "__main__":
    main()