#!/usr/bin/env bash
# =============================================================================
# evolve.sh — Push3 evolution orchestrator
#
# Outer evolutionary loop: generate candidates → score → select → repeat.
#
# Usage:
#   ./tools/push3-evolution/evolve.sh \
#     --seed optimizer_v3.push3 \
#     --population 10 \
#     --generations 5 \
#     --mutation-rate 2 \
#     --elites 2 \
#     [--output evolved/] \
#     [--diverse-seeds] \
#     [--run-id <N>]
#
# --diverse-seeds  Initialise gen_0 with diverse candidates.  When the
#                  persistent seeds pool (tools/push3-evolution/seeds/) is
#                  non-empty, a random sample from the pool is used (crossover
#                  between hand-written and evolved programs).  When the pool is
#                  empty, falls back to the parametric seed-gen-cli variants.
#                  Any shortfall (pool or variants < --population) is filled by
#                  mutating the main seed.
#
# --run-id <N>     Integer identifier for this run, used to name candidates
#                  admitted to the seeds pool (e.g. run003_gen2_c005.push3).
#                  Auto-incremented from the highest existing run in the pool
#                  manifest when omitted.
#
# Algorithm:
#   1. Initialize population: N copies of seed, each with M random mutations.
#   2. For each generation:
#      a. Score all candidates via the configured fitness backend
#         (batch-eval.sh for revm [default], fitness.sh for anvil)
#      b. Log generation stats (min/max/mean fitness, best candidate)
#      c. Select k survivors via tournament selection (k = population/2)
#      d. Elitism: copy top N candidates unchanged into next generation
#      e. Generate next population: mutate survivors + crossover pairs
#   3. Output best candidate as Push3 file.
#   4. Show diff: original vs evolved (which constants changed, by how much).
#
# Output:
#   <output>/run_NNN/        NNN auto-increments from the highest existing run dir
#     generation_0.jsonl   {candidate_id, fitness, mutations_applied}
#     generation_1.jsonl
#     ...
#     best.push3           highest-fitness program
#     diff.txt             parameter changes vs original
#     evolution.log        full run log
#
# Environment:
#   BASE_RPC_URL    Required when EVAL_MODE=revm (the default). Base network RPC endpoint.
#   ANVIL_FORK_URL  Passed through to fitness.sh when EVAL_MODE=anvil.
#
# TSX resolution order: tsx in PATH → node_modules/.bin/tsx → npx tsx.
# =============================================================================

set -euo pipefail

# Foundry tools (forge, cast, anvil)
export PATH="${HOME}/.foundry/bin:${PATH}"

SCRIPT_DIR="$(cd "$(dirname "$0")" && pwd)"
FITNESS_SH="$SCRIPT_DIR/fitness.sh"
BATCH_EVAL_SH="$SCRIPT_DIR/revm-evaluator/batch-eval.sh"
MUTATE_CLI="$SCRIPT_DIR/mutate-cli.ts"
SEED_GEN_CLI="$SCRIPT_DIR/seed-gen-cli.ts"

# EVAL_MODE controls which fitness backend is used:
#   revm   (default) — in-process revm via FitnessEvaluator.t.sol (batch-eval.sh)
#                      Requires BASE_RPC_URL env var.  10-100× faster at scale.
#   anvil            — per-candidate Anvil+forge-script pipeline (fitness.sh)
EVAL_MODE="${EVAL_MODE:-revm}"

# =============================================================================
# Argument parsing
# =============================================================================

SEED=""
POPULATION=10
GENERATIONS=5
MUTATION_RATE=2
ELITES=2
OUTPUT_DIR="evolved"
DIVERSE_SEEDS=false
RUN_ID=""

while [[ $# -gt 0 ]]; do
  case $1 in
    --seed)          SEED="$2";          shift 2 ;;
    --population)    POPULATION="$2";    shift 2 ;;
    --generations)   GENERATIONS="$2";   shift 2 ;;
    --mutation-rate) MUTATION_RATE="$2"; shift 2 ;;
    --elites)        ELITES="$2";        shift 2 ;;
    --output)        OUTPUT_DIR="$2";    shift 2 ;;
    --diverse-seeds) DIVERSE_SEEDS=true; shift   ;;
    --run-id)        RUN_ID="$2";        shift 2 ;;
    *) echo "Unknown option: $1" >&2; exit 2 ;;
  esac
done

if [ -z "$SEED" ];   then echo "Error: --seed required" >&2;              exit 2; fi
if [ ! -f "$SEED" ]; then echo "Error: seed file not found: $SEED" >&2; exit 2; fi

# Validate numeric args
for _name_val in "population:$POPULATION" "generations:$GENERATIONS" "mutation-rate:$MUTATION_RATE"; do
  _name="${_name_val%%:*}"
  _val="${_name_val##*:}"
  if ! [[ "$_val" =~ ^[0-9]+$ ]] || [ "$_val" -lt 1 ]; then
    echo "Error: --${_name} must be a positive integer (got: $_val)" >&2
    exit 2
  fi
done

if ! [[ "$ELITES" =~ ^[0-9]+$ ]]; then
  echo "Error: --elites must be a non-negative integer (got: $ELITES)" >&2
  exit 2
fi

# Canonicalize paths
SEED="$(cd "$(dirname "$SEED")" && pwd)/$(basename "$SEED")"

# Resolve base output dir and create run_NNN subdirectory
mkdir -p "$OUTPUT_DIR"
BASE_DIR="$(cd "$OUTPUT_DIR" && pwd)"

# Auto-increment: find the highest run_NNN directory under BASE_DIR and add 1
RUN_NUM=$(python3 - "$BASE_DIR" <<'PYEOF'
import sys, os, re
base = sys.argv[1]
max_n = 0
if os.path.isdir(base):
    for name in os.listdir(base):
        m = re.fullmatch(r'run_(\d+)', name)
        if m and os.path.isdir(os.path.join(base, name)):
            max_n = max(max_n, int(m.group(1)))
print(f"{max_n + 1:03d}")
PYEOF
)

OUTPUT_DIR="$BASE_DIR/run_${RUN_NUM}"
mkdir -p "$OUTPUT_DIR"
LOG="$OUTPUT_DIR/evolution.log"

# Seeds pool — persistent candidate pool across all runs
SEEDS_DIR="$SCRIPT_DIR/seeds"
POOL_MANIFEST="$SEEDS_DIR/manifest.jsonl"
ADMISSION_THRESHOLD=6000000000000000000000  # 6e21 wei

# Validate/auto-compute RUN_ID
if [ -n "$RUN_ID" ]; then
  if ! [[ "$RUN_ID" =~ ^[0-9]+$ ]] || [ "$RUN_ID" -lt 1 ]; then
    echo "Error: --run-id must be a positive integer (got: $RUN_ID)" >&2
    exit 2
  fi
  RUN_ID=$(printf '%03d' "$RUN_ID")
else
  # Auto-increment: find the highest run ID in the manifest and add 1
  if [ -f "$POOL_MANIFEST" ]; then
    RUN_ID=$(python3 - "$POOL_MANIFEST" <<'PYEOF'
import json, sys
max_run = 0
with open(sys.argv[1]) as f:
    for line in f:
        line = line.strip()
        if not line:
            continue
        try:
            d = json.loads(line)
            r = d.get("run")
            if r is not None:
                max_run = max(max_run, int(r))
        except (json.JSONDecodeError, ValueError, TypeError):
            pass
print(f"{max_run + 1:03d}")
PYEOF
) || RUN_ID="001"
  else
    RUN_ID="001"
  fi
fi

# =============================================================================
# Helpers
# =============================================================================

log() {
  local msg="[evolve] $*"
  echo "$msg" >&2
  echo "$msg" >> "$LOG"
}

fail() {
  log "ERROR: $*"
  exit 2
}

# Locate a tsx runner (TypeScript executor for mutate-cli.ts).
# Tries: tsx in PATH → local node_modules → npx tsx.
find_tsx_cmd() {
  if command -v tsx &>/dev/null; then
    echo "tsx"
  elif [ -x "$SCRIPT_DIR/node_modules/.bin/tsx" ]; then
    echo "$SCRIPT_DIR/node_modules/.bin/tsx"
  elif command -v npx &>/dev/null; then
    echo "npx tsx"
  else
    return 1
  fi
}

# Run the mutate-cli.ts with the given arguments.
# All mutation operations run from SCRIPT_DIR so relative TS imports resolve.
run_mutate_cli() {
  (cd "$SCRIPT_DIR" && $TSX_CMD "$MUTATE_CLI" "$@")
}

# Run the seed-gen-cli.ts with the given arguments.
run_seed_gen_cli() {
  (cd "$SCRIPT_DIR" && $TSX_CMD "$SEED_GEN_CLI" "$@")
}

# Integer min/max/mean via python3 (bash arithmetic overflows on wei values).
py_stats() {
  python3 -c "import sys; nums = [int(x) for x in sys.stdin.read().split()]; print(min(nums) if nums else 0, max(nums) if nums else 0, round(sum(nums)/len(nums)) if nums else 0)"
}

# Top-N selection: return filepaths of the N highest-scoring candidates (descending).
py_top_n() {
  local n="$1"
  local scores_file="$2"
  python3 - "$n" "$scores_file" <<'PYEOF'
import sys
n = int(sys.argv[1])
entries = []
with open(sys.argv[2]) as f:
    for line in f:
        parts = line.rstrip('\n').split('\t')
        if len(parts) >= 3:
            entries.append((int(parts[1]), parts[2]))
entries.sort(key=lambda x: x[0], reverse=True)
for _, path in entries[:n]:
    print(path)
PYEOF
}

# Tournament selection: given a scores file (one "idx score filepath" per line),
# run k tournaments of size 2 and return winner filepaths (one per line).
py_tournament() {
  local k="$1"
  local scores_file="$2"
  python3 - "$k" "$scores_file" <<'PYEOF'
import sys, random
k = int(sys.argv[1])
entries = []
with open(sys.argv[2]) as f:
    for line in f:
        parts = line.rstrip('\n').split('\t')
        if len(parts) >= 3:
            entries.append((int(parts[0]), int(parts[1]), parts[2]))
if not entries:
    sys.exit(1)
for _ in range(k):
    a = random.choice(entries)
    b = random.choice(entries)
    winner = a if a[1] >= b[1] else b
    print(winner[2])
PYEOF
}

# =============================================================================
# Tool checks
# =============================================================================

for _tool in python3 node; do
  command -v "$_tool" &>/dev/null || fail "$_tool not found in PATH"
done

[ -f "$FITNESS_SH" ] || fail "fitness.sh not found at $FITNESS_SH"
[ -f "$MUTATE_CLI" ] || fail "mutate-cli.ts not found at $MUTATE_CLI"
[ -x "$FITNESS_SH" ] || chmod +x "$FITNESS_SH"

if [ "$EVAL_MODE" = "revm" ]; then
  [ -f "$BATCH_EVAL_SH" ] || fail "batch-eval.sh not found at $BATCH_EVAL_SH"
  [ -x "$BATCH_EVAL_SH" ] || chmod +x "$BATCH_EVAL_SH"
  [ -n "${BASE_RPC_URL:-}" ] || fail "EVAL_MODE=revm requires BASE_RPC_URL env var (Base network RPC)"
  command -v forge &>/dev/null || fail "forge not found in PATH (required for EVAL_MODE=revm)"
elif [ "$EVAL_MODE" != "anvil" ]; then
  fail "Unknown EVAL_MODE '$EVAL_MODE' — must be 'anvil' or 'revm'"
fi

TSX_CMD="$(find_tsx_cmd)" || fail \
  "No TypeScript runner found. Install tsx (npm install -g tsx) or ensure npx is in PATH."

# =============================================================================
# Work directory — holds all candidate .push3 files across generations
#
# Fixed path under BASE_DIR so stale /tmp/tmp.* dirs from killed runs can
# never interfere with a new run.  Cleaned at startup; run outputs are
# already preserved in BASE_DIR/run_NNN/ before this directory is wiped.
# =============================================================================

WORK_DIR="$BASE_DIR/.work"
rm -rf "$WORK_DIR"
mkdir -p "$WORK_DIR"

# =============================================================================
# Log run header
# =============================================================================

log "========================================================"
log "Push3 Evolution — $(date -u '+%Y-%m-%dT%H:%M:%SZ')"
log "  Seed:           $SEED"
log "  Population:     $POPULATION"
log "  Generations:    $GENERATIONS"
log "  Mutation rate:  $MUTATION_RATE"
log "  Elites:         $ELITES"
log "  Diverse seeds:  $DIVERSE_SEEDS"
log "  Run ID:         $RUN_ID"
log "  Base dir:       $BASE_DIR"
log "  Output:         $OUTPUT_DIR"
log "  TSX:            $TSX_CMD"
log "  Eval mode:      $EVAL_MODE"
log "========================================================"

# =============================================================================
# Step 1 — Initialize generation 0
#
# N copies of the seed, each independently mutated MUTATION_RATE times.
# =============================================================================

log ""
log "=== Initializing population ==="

GEN_DIR="$WORK_DIR/gen_0"
mkdir -p "$GEN_DIR"

if [ "$DIVERSE_SEEDS" = "true" ]; then
  # --- Diverse-seeds mode: prefer persistent pool; fall back to seed-gen-cli ---
  VARIANT_IDX=0

  # Build a random sample list from the pool in one pass (also determines if
  # the pool has any usable entries, avoiding a second manifest parse).
  POOL_SAMPLE_LIST="$WORK_DIR/pool_sample.txt"
  POOL_COUNT=0
  if [ -f "$POOL_MANIFEST" ]; then
    python3 - "$POOL_MANIFEST" "$SEEDS_DIR" "$POPULATION" > "$POOL_SAMPLE_LIST" <<'PYEOF'
import json, sys, os, random
manifest_path, seeds_dir, n = sys.argv[1], sys.argv[2], int(sys.argv[3])
entries = []
with open(manifest_path) as f:
    for line in f:
        line = line.strip()
        if not line:
            continue
        try:
            d = json.loads(line)
            fpath = os.path.join(seeds_dir, d.get('file', ''))
            if os.path.exists(fpath):
                entries.append(fpath)
        except json.JSONDecodeError:
            pass
random.shuffle(entries)
for path in entries[:n]:
    print(path)
PYEOF
    POOL_COUNT=$(wc -l < "$POOL_SAMPLE_LIST" 2>/dev/null || echo 0)
  fi

  if [ "$POOL_COUNT" -gt 0 ]; then
    # --- Pool mode: random sample from the seeds pool ---
    log "  diverse-seeds: sampling up to $POPULATION candidates from pool ($POOL_COUNT available)"

    while IFS= read -r POOL_FILE && [ "$VARIANT_IDX" -lt "$POPULATION" ]; do
      CAND_FILE="$GEN_DIR/candidate_$(printf '%03d' $VARIANT_IDX).push3"
      cp "$POOL_FILE" "$CAND_FILE"
      printf '0\n' > "${CAND_FILE%.push3}.ops"
      VARIANT_IDX=$((VARIANT_IDX + 1))
    done < "$POOL_SAMPLE_LIST"

    log "  diverse-seeds: seeded $VARIANT_IDX candidate(s) from pool"
  else
    # --- Fallback: parametric variants from seed-gen-cli (pool is empty) ---
    log "  diverse-seeds: pool empty, falling back to seed-gen-cli parametric variants"
    [ -f "$SEED_GEN_CLI" ] || fail "seed-gen-cli.ts not found at $SEED_GEN_CLI"
    SEED_VARIANTS_DIR="$WORK_DIR/seed_variants"
    SEED_VARIANTS_LIST="$WORK_DIR/seed_variants_list.txt"

    # Run seed-gen-cli as a direct command (not inside <(...)) so its exit code is
    # checked by the parent shell and fail() aborts the entire script on error.
    # Stderr goes to the log file for diagnostics rather than being discarded.
    run_seed_gen_cli --count "$POPULATION" --output-dir "$SEED_VARIANTS_DIR" \
      > "$SEED_VARIANTS_LIST" 2>>"$LOG" \
      || fail "seed-gen-cli.ts failed to generate variants"

    while IFS= read -r VARIANT_FILE && [ "$VARIANT_IDX" -lt "$POPULATION" ]; do
      CAND_FILE="$GEN_DIR/candidate_$(printf '%03d' $VARIANT_IDX).push3"
      cp "$VARIANT_FILE" "$CAND_FILE"
      printf '0\n' > "${CAND_FILE%.push3}.ops"
      VARIANT_IDX=$((VARIANT_IDX + 1))
    done < "$SEED_VARIANTS_LIST"
  fi

  # Fill any remaining slots with mutations of the seed
  while [ "$VARIANT_IDX" -lt "$POPULATION" ]; do
    CAND_FILE="$GEN_DIR/candidate_$(printf '%03d' $VARIANT_IDX).push3"
    MUTATED=$(run_mutate_cli mutate "$SEED" "$MUTATION_RATE") \
      || fail "Failed to mutate seed for fallback candidate $VARIANT_IDX"
    printf '%s\n' "$MUTATED" > "$CAND_FILE"
    printf '%d\n' "$MUTATION_RATE" > "${CAND_FILE%.push3}.ops"
    VARIANT_IDX=$((VARIANT_IDX + 1))
  done

  log "Initialized ${POPULATION} candidates in gen_0 (diverse-seeds, pool=$POOL_COUNT)"
else
  # --- Default mode: N copies of the seed, each independently mutated ---
  for i in $(seq 0 $((POPULATION - 1))); do
    CAND_FILE="$GEN_DIR/candidate_$(printf '%03d' $i).push3"
    MUTATED=$(run_mutate_cli mutate "$SEED" "$MUTATION_RATE") \
      || fail "Failed to mutate seed for initial candidate $i"
    printf '%s\n' "$MUTATED" > "$CAND_FILE"
    printf '%d\n' "$MUTATION_RATE" > "${CAND_FILE%.push3}.ops"
  done

  log "Initialized ${POPULATION} candidates in gen_0"
fi

# =============================================================================
# Step 2 — Evolution loop
# =============================================================================

GLOBAL_BEST_FITNESS=-1
GLOBAL_BEST_GEN=-1
GLOBAL_BEST_CAND=""

CURRENT_GEN_DIR="$GEN_DIR"

for gen in $(seq 0 $((GENERATIONS - 1))); do

  log ""
  log "=== Generation $((gen + 1)) / $GENERATIONS ==="

  JSONL_FILE="$OUTPUT_DIR/generation_${gen}.jsonl"
  SCORES_FILE="$WORK_DIR/scores_gen_${gen}.txt"

  # --- a. Score all candidates ---

  SCORE_VALUES=""
  CAND_COUNT=0

  # In revm mode, batch-score all candidates in one forge test invocation before
  # the per-candidate loop.  Scores are written to a temp JSONL file that the
  # loop reads with a fast Python lookup.
  BATCH_SCORES_FILE="$WORK_DIR/batch_scores_gen_${gen}.jsonl"

  if [ "$EVAL_MODE" = "revm" ]; then
    declare -a _BATCH_FILES=()
    for _CF in "$CURRENT_GEN_DIR"/candidate_*.push3; do
      [ -f "$_CF" ] && _BATCH_FILES+=("$_CF")
    done

    if [ "${#_BATCH_FILES[@]}" -gt 0 ]; then
      BATCH_EC=0
      BATCH_EVAL_LOG="$WORK_DIR/batch-eval-gen-${gen}.log"
      bash "$BATCH_EVAL_SH" "${_BATCH_FILES[@]}" > "$BATCH_SCORES_FILE" 2>"$BATCH_EVAL_LOG" \
        || BATCH_EC=$?

      if [ "$BATCH_EC" -ne 0 ]; then
        echo "--- last 20 lines of $BATCH_EVAL_LOG ---" >&2
        tail -n 20 "$BATCH_EVAL_LOG" >&2
        echo "--- end batch-eval.log ---" >&2
      fi

      if [ "$BATCH_EC" -eq 2 ]; then
        fail "batch-eval.sh reported an infrastructure error (exit 2) — aborting evolution"
      fi
      log "  revm batch scoring complete (exit $BATCH_EC)"
    fi
  fi

  for CAND_FILE in "$CURRENT_GEN_DIR"/candidate_*.push3; do
    [ -f "$CAND_FILE" ] || continue

    CAND_IDX="${CAND_FILE##*candidate_}"
    CAND_IDX="${CAND_IDX%.push3}"
    # Canonical candidate_id format: "candidate_XXX" (matches source filename and batch-eval IDs).
    CID="candidate_${CAND_IDX}"

    # Read mutations_applied from sidecar; default 0 if missing.
    OPS_FILE="${CAND_FILE%.push3}.ops"
    MUTATIONS_APPLIED=0
    [ -f "$OPS_FILE" ] && MUTATIONS_APPLIED=$(cat "$OPS_FILE")

    SCORE=0
    FITNESS_EC=0

    if [ "$EVAL_MODE" = "revm" ] && [ -f "$BATCH_SCORES_FILE" ]; then
      # Look up pre-computed score from batch-eval.sh output.
      # batch-eval uses filename as candidate_id (e.g. "candidate_000")
      BATCH_CID="$(basename "$CAND_FILE" .push3)"
      SCORE=$(python3 - "$BATCH_CID" "$BATCH_SCORES_FILE" <<'PYEOF'
import json, sys
cid = sys.argv[1]
with open(sys.argv[2]) as f:
    for line in f:
        try:
            d = json.loads(line)
            if d.get("candidate_id") == cid:
                print(d["fitness"])
                sys.exit(0)
        except (json.JSONDecodeError, KeyError):
            pass
print(0)
PYEOF
) || SCORE=0
    else
      # Anvil mode (or revm fallback): score candidate individually.
      SCORE=$(bash "$FITNESS_SH" "$CAND_FILE") || FITNESS_EC=$?

      # Exit 2 = infrastructure error (Anvil down, missing tools): abort immediately.
      if [ "$FITNESS_EC" -eq 2 ]; then
        fail "fitness.sh reported an infrastructure error (exit 2) — aborting evolution"
      fi
    fi

    # Validate that score is a non-negative integer; treat any other output as invalid.
    if ! [[ "$SCORE" =~ ^[0-9]+$ ]]; then
      log "  $CID: invalid/missing score, using 0"
      SCORE=0
    else
      log "  $CID: fitness=$SCORE"
    fi

    # Append to JSONL — use the actual operations recorded for this candidate.
    printf '{"candidate_id":"%s","fitness":%s,"mutations_applied":%d}\n' \
      "$CID" "$SCORE" "$MUTATIONS_APPLIED" >> "$JSONL_FILE"

    # Record index, score, and filepath for selection (tab-delimited so paths with spaces are safe).
    printf '%d\t%s\t%s\n' "$CAND_COUNT" "$SCORE" "$CAND_FILE" >> "$SCORES_FILE"

    SCORE_VALUES="$SCORE_VALUES $SCORE"
    CAND_COUNT=$((CAND_COUNT + 1))
  done

  if [ "$CAND_COUNT" -eq 0 ]; then
    fail "No candidates found in $CURRENT_GEN_DIR"
  fi

  # --- b. Log generation stats ---

  read -r MIN MAX MEAN < <(printf '%s' "$SCORE_VALUES" | py_stats)
  log "  Stats: min=$MIN  max=$MAX  mean=$MEAN  candidates=$CAND_COUNT"

  # Find best candidate for this generation (filepath returned directly).
  BEST_FILE_THIS_GEN=$(python3 - "$SCORES_FILE" <<'PYEOF'
import sys
entries = []
with open(sys.argv[1]) as f:
    for line in f:
        parts = line.rstrip('\n').split('\t')
        if len(parts) >= 3:
            entries.append((int(parts[1]), parts[2]))
if not entries:
    sys.exit(1)
print(max(entries, key=lambda x: x[0])[1])
PYEOF
) || fail "Could not determine best candidate from $SCORES_FILE"

  if python3 -c "import sys; sys.exit(0 if int(sys.argv[1]) > int(sys.argv[2]) else 1)" "$MAX" "$GLOBAL_BEST_FITNESS" || [ "$GLOBAL_BEST_FITNESS" = "-1" ]; then
    GLOBAL_BEST_FITNESS="$MAX"
    GLOBAL_BEST_GEN="$gen"
    GLOBAL_BEST_CAND="$BEST_FILE_THIS_GEN"
    log "  New global best: gen=$gen fitness=$GLOBAL_BEST_FITNESS file=$(basename "$BEST_FILE_THIS_GEN")"
  fi

  # Skip next-generation creation after the final generation
  [ "$gen" -eq "$((GENERATIONS - 1))" ] && break

  # --- c. Tournament selection (k = population / 2) ---

  K=$((POPULATION / 2))
  [ "$K" -lt 1 ] && K=1

  SURVIVOR_FILES=()
  while IFS= read -r WIN_FILE; do
    SURVIVOR_FILES+=("$WIN_FILE")
  done < <(py_tournament "$K" "$SCORES_FILE")

  log "  Selected ${#SURVIVOR_FILES[@]} survivors via tournament"

  # --- d/e. Generate next population (elitism + offspring) ---

  NEXT_GEN_DIR="$WORK_DIR/gen_$((gen + 1))"
  mkdir -p "$NEXT_GEN_DIR"

  NEXT_IDX=0

  # --- d. Elitism: copy top ELITES candidates unchanged ---

  if [ "$ELITES" -gt 0 ]; then
    ELITE_FILES=()
    while IFS= read -r ELITE_FILE; do
      [ -f "$ELITE_FILE" ] && ELITE_FILES+=("$ELITE_FILE")
    done < <(py_top_n "$ELITES" "$SCORES_FILE")

    for ELITE_FILE in "${ELITE_FILES[@]}"; do
      DEST="$NEXT_GEN_DIR/candidate_$(printf '%03d' $NEXT_IDX).push3"
      cp "$ELITE_FILE" "$DEST"
      printf '0\n' > "${DEST%.push3}.ops"
      NEXT_IDX=$((NEXT_IDX + 1))
    done

    log "  Elitism: carried over ${#ELITE_FILES[@]} top candidate(s) unchanged"
  fi

  # --- e. Fill remaining slots with mutation and crossover offspring ---

  NON_ELITE=$((POPULATION - NEXT_IDX))
  HALF=$((NON_ELITE / 2))

  # First half of remaining: mutate random survivors
  for _i in $(seq 1 $HALF); do
    SUR="${SURVIVOR_FILES[$((RANDOM % ${#SURVIVOR_FILES[@]}))]}"
    DEST="$NEXT_GEN_DIR/candidate_$(printf '%03d' $NEXT_IDX).push3"
    if MUTATED=$(run_mutate_cli mutate "$SUR" "$MUTATION_RATE" 2>/dev/null); then
      printf '%s\n' "$MUTATED" > "$DEST"
      printf '%d\n' "$MUTATION_RATE" > "${DEST%.push3}.ops"
    else
      # Fallback: copy the survivor as-is to keep population size stable
      cp "$SUR" "$DEST"
      printf '0\n' > "${DEST%.push3}.ops"
    fi
    NEXT_IDX=$((NEXT_IDX + 1))
  done

  # Second half of remaining: crossover random survivor pairs
  REMAINING=$((POPULATION - NEXT_IDX))
  for _i in $(seq 1 $REMAINING); do
    SUR_A="${SURVIVOR_FILES[$((RANDOM % ${#SURVIVOR_FILES[@]}))]}"
    SUR_B="${SURVIVOR_FILES[$((RANDOM % ${#SURVIVOR_FILES[@]}))]}"
    DEST="$NEXT_GEN_DIR/candidate_$(printf '%03d' $NEXT_IDX).push3"
    if CROSSED=$(run_mutate_cli crossover "$SUR_A" "$SUR_B" 2>/dev/null); then
      printf '%s\n' "$CROSSED" > "$DEST"
      printf '0\n' > "${DEST%.push3}.ops"
    else
      # Fallback: mutate one survivor
      if MUTATED=$(run_mutate_cli mutate "$SUR_A" "$MUTATION_RATE" 2>/dev/null); then
        printf '%s\n' "$MUTATED" > "$DEST"
        printf '%d\n' "$MUTATION_RATE" > "${DEST%.push3}.ops"
      else
        cp "$SUR_A" "$DEST"
        printf '0\n' > "${DEST%.push3}.ops"
      fi
    fi
    NEXT_IDX=$((NEXT_IDX + 1))
  done

  log "  Generated ${NEXT_IDX} candidates for generation $((gen + 1))"
  CURRENT_GEN_DIR="$NEXT_GEN_DIR"

done

# =============================================================================
# Step 3 — Output best candidate
# =============================================================================

if [ -z "$GLOBAL_BEST_CAND" ] || [ ! -f "$GLOBAL_BEST_CAND" ]; then
  fail "No valid best candidate recorded — evolution produced no scorable output"
fi

BEST_OUTPUT="$OUTPUT_DIR/best.push3"
cp "$GLOBAL_BEST_CAND" "$BEST_OUTPUT"
log ""
log "Best candidate → $BEST_OUTPUT"
log "  Fitness: $GLOBAL_BEST_FITNESS  (generation $GLOBAL_BEST_GEN)"

# =============================================================================
# Step 4 — Diff: original vs evolved constants
# =============================================================================

DIFF_OUTPUT="$OUTPUT_DIR/diff.txt"

python3 - "$SEED" "$BEST_OUTPUT" > "$DIFF_OUTPUT" <<'PYEOF'
import sys, re

def extract_ints(path):
    """Extract all large integer literals (≥6 digits) from a Push3 file."""
    text = open(path).read()
    text = re.sub(r';;[^\n]*', '', text)   # strip comments
    return [int(m) for m in re.findall(r'\b(\d{6,})\b', text)]

seed_path, best_path = sys.argv[1], sys.argv[2]
orig = extract_ints(seed_path)
best = extract_ints(best_path)

print(f"=== Push3 Evolution Diff ===")
print(f"Seed: {seed_path}")
print(f"Best: {best_path}")
print()

changed = 0
for i, (o, b) in enumerate(zip(orig, best)):
    if o != b:
        pct = (b - o) / o * 100 if o != 0 else float('inf')
        print(f"  const[{i:3d}]: {o:>25d}  →  {b:>25d}  (Δ={b - o:+d}, {pct:+.2f}%)")
        changed += 1

if len(orig) != len(best):
    added = len(best) - len(orig)
    if added > 0:
        for i, val in enumerate(best[len(orig):]):
            print(f"  const[{len(orig) + i:3d}]: {'(new)':>25s}  →  {val:>25d}")
    else:
        print(f"  ({-added} constant(s) removed from end)")

print()
if changed == 0 and len(orig) == len(best):
    print("No constant changes — evolution applied structural mutations only.")
else:
    total = min(len(orig), len(best))
    print(f"Summary: {changed} of {total} constant(s) changed.")
PYEOF

log "Diff written to $DIFF_OUTPUT"
log ""
cat "$DIFF_OUTPUT" >&2

log "========================================================"
log "Evolution complete."
log "  Generations run:  $GENERATIONS"
log "  Best fitness:     $GLOBAL_BEST_FITNESS"
log "  Best from gen:    $GLOBAL_BEST_GEN"
log "  Output directory: $OUTPUT_DIR"
log "========================================================"

# =============================================================================
# Step 5 — Seed pool admission
#
# Scan all generation JSONL files for candidates that scored above the
# admission threshold (6e21).  Deduplicate by Push3 content hash against the
# existing pool.  Admit qualifying candidates into seeds/ and rewrite
# manifest.jsonl, keeping at most the top-100 by fitness.
# =============================================================================

log ""
log "=== Seed pool admission (run=$RUN_ID, threshold=$ADMISSION_THRESHOLD) ==="

mkdir -p "$SEEDS_DIR"

_ADMISSION_OUT="$WORK_DIR/admission_output.txt"
_ADMISSION_RC=0

python3 - "$OUTPUT_DIR" "$WORK_DIR" "$SEEDS_DIR" \
  "$ADMISSION_THRESHOLD" "$RUN_ID" "$(date -u '+%Y-%m-%d')" \
  > "$_ADMISSION_OUT" 2>&1 <<'PYEOF' || _ADMISSION_RC=$?
import json, sys, os, hashlib, shutil, tempfile

output_dir, work_dir, seeds_dir = sys.argv[1], sys.argv[2], sys.argv[3]
threshold  = int(sys.argv[4])
run_id     = sys.argv[5]
today      = sys.argv[6]
MAX_EVOLVED = 100  # cap applies to evolved entries only; hand-written are always pinned

manifest_path = os.path.join(seeds_dir, 'manifest.jsonl')

# ── 1. Read existing manifest ─────────────────────────────────────────────────
existing = []
if os.path.exists(manifest_path):
    with open(manifest_path) as f:
        for line in f:
            line = line.strip()
            if line:
                try:
                    existing.append(json.loads(line))
                except json.JSONDecodeError:
                    pass

# ── 2. Hash existing pool files for deduplication ────────────────────────────
def file_hash(path):
    with open(path, 'rb') as fh:
        return hashlib.sha256(fh.read()).hexdigest()

existing_hashes = set()
for entry in existing:
    fpath = os.path.join(seeds_dir, entry.get('file', ''))
    if os.path.exists(fpath):
        existing_hashes.add(file_hash(fpath))

# ── 3. Collect qualifying candidates from generation JSONL files ──────────────
qualifying = []  # (fitness, push3_path, gen_idx, cand_str)

for fname in sorted(os.listdir(output_dir)):
    if not (fname.startswith('generation_') and fname.endswith('.jsonl')):
        continue
    try:
        gen_idx = int(fname[len('generation_'):-len('.jsonl')])  # validate integer suffix
    except ValueError:
        continue
    with open(os.path.join(output_dir, fname)) as f:
        for line in f:
            try:
                d       = json.loads(line)
                cid     = d.get('candidate_id', '')
                fitness = int(d.get('fitness', 0))
                if fitness < threshold:
                    continue
                # cid format: "candidate_XXX" (gen_idx derived from enclosing filename)
                if not cid.startswith('candidate_'):
                    continue
                cand_str  = cid[len('candidate_'):]      # numeric suffix, e.g. "001"
                push3_path = os.path.join(
                    work_dir, f'gen_{gen_idx}',
                    f'candidate_{int(cand_str):03d}.push3'
                )
                if os.path.exists(push3_path):
                    qualifying.append((fitness, push3_path, gen_idx, cand_str))
            except (json.JSONDecodeError, ValueError, TypeError, AttributeError):
                pass

qualifying.sort(key=lambda x: x[0], reverse=True)

# ── 4. Deduplicate and assign filenames (resolve --run-id reuse collisions) ───
new_items = []     # (fitness, push3_path, manifest_entry)
seen      = set(existing_hashes)

for fitness, push3_path, gen_idx, cand_str in qualifying:
    h = file_hash(push3_path)
    if h in seen:
        continue
    seen.add(h)
    # Canonical name: run{run_id}_gen{gen_idx:03d}_c{cand_str}.push3
    # If a different file already occupies that name (same run-id reused), add
    # a counter suffix (_r2, _r3, …) until we find an unused or same-content slot.
    base     = f'run{run_id}_gen{gen_idx:03d}_c{cand_str}'
    filename = f'{base}.push3'
    dest     = os.path.join(seeds_dir, filename)
    if os.path.exists(dest) and file_hash(dest) != h:
        counter = 2
        while True:
            filename = f'{base}_r{counter}.push3'
            dest     = os.path.join(seeds_dir, filename)
            if not os.path.exists(dest) or file_hash(dest) == h:
                break
            counter += 1
    entry = {
        'file':       filename,
        'fitness':    fitness,
        'origin':     'evolved',
        'run':        run_id,
        'generation': gen_idx,
        'date':       today,
    }
    new_items.append((fitness, push3_path, entry))

if not new_items:
    print(f'No new qualifying candidates from run {run_id} '
          f'(threshold={threshold}, scanned {len(qualifying)} above-threshold hits)')
    sys.exit(0)

# ── 5. Separate pinned (hand-written) from evolved; top-100 cap on evolved only
#
# NOTE: raw fitness values are only comparable within the same evaluation run.
# Entries with fitness_flags='token_value_inflation' (or other flags) are ranked
# as fitness=0 so that inflated scores do not bias pool admission or eviction.
def effective_fitness(entry):
    flags = entry.get('fitness_flags') or ''
    if 'token_value_inflation' in flags:
        return 0
    return int(entry.get('fitness') or 0)

pinned  = [(effective_fitness(e), e, None) for e in existing
           if e.get('origin') != 'evolved']
evolved = [(effective_fitness(e), e, None) for e in existing
           if e.get('origin') == 'evolved']
for fitness, push3_path, entry in new_items:
    evolved.append((fitness, entry, push3_path))

evolved.sort(key=lambda x: x[0], reverse=True)
admitted_evolved = evolved[:MAX_EVOLVED]
evicted_evolved  = evolved[MAX_EVOLVED:]

# ── 6. Copy admitted new files; remove evicted evolved files ─────────────────
admitted_count = 0
for _, entry, src_path in admitted_evolved:
    if src_path is not None:          # new candidate
        dest = os.path.join(seeds_dir, entry['file'])
        shutil.copy2(src_path, dest)
        print(f'  admitted: {entry["file"]}  fitness={entry["fitness"]}')
        admitted_count += 1

for _, entry, src_path in evicted_evolved:
    if src_path is not None:          # rejected before being copied
        print(f'  rejected (below pool floor): {entry["file"]}  fitness={entry["fitness"]}')
    else:                              # existing evolved entry pushed out
        fpath = os.path.join(seeds_dir, entry.get('file', ''))
        if os.path.exists(fpath):
            os.remove(fpath)
            print(f'  evicted from pool: {entry["file"]}  fitness={entry["fitness"]}')

# Warn if any pinned (hand-written) entry ranks below the current pool floor
if evicted_evolved and pinned:
    pool_floor = evicted_evolved[0][0]
    for fit, entry, _ in pinned:
        if fit <= pool_floor:
            print(f'  WARNING: pinned seed "{entry.get("file")}" (fitness={fit}) '
                  f'ranks below evolved pool floor ({pool_floor}) — kept in manifest regardless')

# ── 7. Rewrite manifest.jsonl atomically via temp-file + rename ──────────────
admitted = admitted_evolved + pinned
admitted.sort(key=lambda x: x[0], reverse=True)

manifest_dir = os.path.dirname(manifest_path)
with tempfile.NamedTemporaryFile('w', dir=manifest_dir, delete=False, suffix='.tmp') as tmp:
    tmp_path = tmp.name
    for _, entry, _ in admitted:
        tmp.write(json.dumps(entry) + '\n')
os.replace(tmp_path, manifest_path)

print(f'Pool updated: {len(admitted)} entries total '
      f'({len(admitted_evolved)} evolved + {len(pinned)} pinned), '
      f'+{admitted_count} from run {run_id}')
PYEOF

while IFS= read -r _line; do log "  $_line"; done < "$_ADMISSION_OUT"
if [ "$_ADMISSION_RC" -ne 0 ]; then
  log "  WARNING: seed pool admission failed (exit $_ADMISSION_RC) — pool unchanged"
fi