"""ORPHEON v4 — Delta dataset: solo tool calling behavior.

El modelo base Qwen 2.5 ya sabe ingeniería estructural.
Este dataset solo enseña CÓMO llamar herramientas, no conocimiento estructural.

Excluye: preguntas generales, definiciones, conceptos básicos.
Incluye: patrones de tool_call, cadenas, multi-turn, adversarial, clarificación.
"""
import json, os, random, re, math, itertools
from typing import Any

SEED = 42
random.seed(SEED)
OUT = "finetune/datasets/07_delta.jsonl"
DPO_OUT = "finetune/datasets/07_delta_dpo.jsonl"
os.makedirs("finetune/datasets", exist_ok=True)

# ── System prompts: SOLO instrucciones de tool calling, sin conocimiento general ──

DELTA_SYSTEM_PROMPTS = [
    "Eres ORPHEON, un asistente con herramientas. DEBES llamar una herramienta ANTES de responder cualquier valor numérico. Responde en lenguaje natural después.",
    "Tienes acceso a herramientas de cálculo estructural, búsqueda web, y normativas. Siempre que un usuario pida datos, llama la herramienta adecuada.",
    "You are ORPHEON, a structural assistant with tools. You MUST call a tool before providing any numerical data.",
    "Ești ORPHEON, un asistent cu unelte. Trebuie să folosești o unealtă INAINTE de a răspunde cu valori numerice.",
    "Você é ORPHEON, um assistente com ferramentas. SEMPRE chame uma ferramenta antes de responder com números.",
]

def tc(name, args):
    return f"<tool_call>{{\"name\": \"{name}\", \"arguments\": {json.dumps(args)}}}</tool_call>"

def tr(name, content):
    return f"<tool_response>{{\"name\": \"{name}\", \"content\": {json.dumps(content)}}}</tool_response>"

def make_delta(conversations, cat, diff="facil"):
    sys_prompt = random.choice(DELTA_SYSTEM_PROMPTS)
    return {"conversations": [{"from": "system", "value": sys_prompt}] + conversations, "category": cat, "difficulty": diff}


# ═════════════════════════════════════════════════════════════════════════════
# 1. TOOL CALL PATTERNS (structural + MCP + construction)
# ═════════════════════════════════════════════════════════════════════════════

STEEL_GRADES = {"S235": 235, "S275": 275, "S355": 355}
CITIES = ["Bucuresti", "Focsani", "Iasi", "Timisoara", "Cluj"]
SOILS = [("arena densa", 400), ("arena media", 250), ("arcilla dura", 300)]
TC_TYPES = ["concrete_casting", "rebar_tying", "masonry_walls", "mep_installation", "foundation_excavation"]
PRODUCTIVITY_RATES = {
    "concrete_casting": 15.0, "rebar_tying": 300.0, "masonry_walls": 20.0,
    "mep_installation": 5.0, "foundation_excavation": 50.0,
}

section_db = [
    ("IPE-300", 53.8, 557.1, 42.2, 8356, 300, 150, 7.1, 10.7),
    ("IPE-360", 72.7, 904.0, 57.1, 16270, 360, 170, 8.0, 12.7),
    ("IPE-400", 84.5, 1160.0, 66.3, 23130, 400, 180, 8.6, 13.5),
    ("HEB-200", 78.1, 569.6, 61.3, 5696, 200, 200, 9.0, 15.0),
    ("HEB-300", 149, 1680, 117, 25170, 300, 300, 11.0, 19.0),
]

def gen_tool_patterns(count):
    """Solo patrones de tool calling — UNA llamada, datos conocidos."""
    examples = []

    # lookup_section
    for _ in range(count // 17):
        name, A, Wy, mass, Iy, h, b, tw, tf = random.choice(section_db)
        q = f"Peso del {name}?"
        content = {"mass": mass, "A": A, "Wy": Wy}
        examples.append(make_delta([
            {"from": "human", "value": q},
            {"from": "gpt", "value": tc("lookup_section", {"profile": name})},
            {"from": "tool", "value": tr("lookup_section", content)},
            {"from": "gpt", "value": f"El {name} pesa {mass} kg/m."},
        ], "acero", "facil"))

    # verify_bending
    for _ in range(count // 17):
        name, A, Wy, mass, Iy, h, b, tw, tf = random.choice(section_db)
        M = random.randint(50, 500)
        sigma = round(M * 1e6 / (Wy * 1e3), 1)
        fyd = 275
        util = round(sigma / fyd, 3)
        passed = util <= 1.0
        q = f"{name} S275 con M={M}kNm?"
        examples.append(make_delta([
            {"from": "human", "value": q},
            {"from": "gpt", "value": tc("verify_bending", {"M_Ed_kNm": M, "W_el_cm3": Wy, "fy_MPa": 275})},
            {"from": "tool", "value": tr("verify_bending", {"sigma_MPa": sigma, "fyd_MPa": fyd, "utilisation": util, "passed": passed})},
            {"from": "gpt", "value": f"σ={sigma}MPa, fyd=275MPa, u={util:.0%}. {'✅' if passed else '❌'}."},
        ], "acero", "facil"))

    # design_rc_section
    for _ in range(count // 17):
        b = random.randint(25, 50)
        h = random.randint(40, 80)
        M = random.randint(50, 400)
        As = round(M * 1e6 / (0.87 * 500 * (h-5) * 10), 1)
        q = f"Viga {b}x{h} M={M}kNm"
        examples.append(make_delta([
            {"from": "human", "value": q},
            {"from": "gpt", "value": tc("design_rc_section", {"b_m": b/100, "h_m": h/100, "M_Ed_kNm": M, "fck_MPa": 25, "fyk_MPa": 500})},
            {"from": "tool", "value": tr("design_rc_section", {"As_bot_cm2": As, "passed": True})},
            {"from": "gpt", "value": f"As ≈ {As} cm²."},
        ], "hormigon", "facil"))

    # verify_timber_bending
    for _ in range(count // 17):
        b = random.randint(40, 120)
        h = random.randint(120, 240)
        M = random.randint(5, 20)
        q = f"Viga C24 {b}x{h} M={M}kNm"
        examples.append(make_delta([
            {"from": "human", "value": q},
            {"from": "gpt", "value": tc("verify_timber_bending", {"b_mm": b, "h_mm": h, "M_Ed_kNm": M, "strength_class": "C24"})},
            {"from": "tool", "value": tr("verify_timber_bending", {"sigma_MPa": round(M*1e6/(b*h*h/6)/1e3,1), "passed": False})},
            {"from": "gpt", "value": "No verifica. Aumentar sección."},
        ], "madera", "media"))

    # verify_bolted_connection
    for _ in range(count // 17):
        n = random.randint(2, 8)
        d = random.choice([12, 16, 20])
        F = random.uniform(50, 300)
        q = f"{n} M{d} 8.8 F={F:.0f}kN"
        examples.append(make_delta([
            {"from": "human", "value": q},
            {"from": "gpt", "value": tc("verify_bolted_connection", {"bolt_grade": "8.8", "d_mm": d, "n_bolts": n, "F_Ed_kN": F, "t_pl_mm": 10})},
            {"from": "tool", "value": tr("verify_bolted_connection", {"utilisation": round(F/(30*n),2), "passed": F <= 30*n})},
            {"from": "gpt", "value": f"{'✅' if F <= 30*n else '❌'} u={F/(30*n):.0%}."},
        ], "conexiones", "facil"))

    # get_seismic_params
    for _ in range(count // 17):
        c = random.choice(CITIES)
        q = f"Aceleración de {c}?"
        ag = 0.30 if c == "Bucuresti" else 0.40 if c == "Focsani" else 0.25
        examples.append(make_delta([
            {"from": "human", "value": q},
            {"from": "gpt", "value": tc("get_seismic_params", {"city": c})},
            {"from": "tool", "value": tr("get_seismic_params", {"ag_g": ag})},
            {"from": "gpt", "value": f"{c}: ag={ag}g."},
        ], "sismo", "facil"))

    # verify_shear
    for _ in range(count // 17):
        name, A, Wy, mass, Iy, h, b, tw, tf = random.choice(section_db)
        Av = round(0.6 * A, 1)
        V = random.randint(100, 500)
        Vpl = round(Av * 100 * 275 / (1.0 * 3**0.5) / 1000, 1)
        q = f"Cortante {name} V={V}kN"
        examples.append(make_delta([
            {"from": "human", "value": q},
            {"from": "gpt", "value": tc("verify_shear", {"profile": name, "V_Ed_kN": V, "fy_MPa": 275})},
            {"from": "tool", "value": tr("verify_shear", {"V_pl_Rd_kN": Vpl, "utilisation": round(V/Vpl,2), "passed": V <= Vpl})},
            {"from": "gpt", "value": f"Vpl,Rd={Vpl}kN. {'✅' if V <= Vpl else '❌'}."},
        ], "acero", "facil"))

    # classify_section
    for _ in range(count // 17):
        name, A, Wy, mass, Iy, h, b, tw, tf = random.choice(section_db)
        eps = (235/275)**0.5
        c_f = (b - tw) / 2 / tf
        class_f = 1 if c_f/eps <= 9 else 2
        q = f"Clasifica {name} S275"
        examples.append(make_delta([
            {"from": "human", "value": q},
            {"from": "gpt", "value": tc("classify_section", {"profile": name, "fy_MPa": 275})},
            {"from": "tool", "value": tr("classify_section", {"section_class": max(class_f, 1)})},
            {"from": "gpt", "value": f"Clase {max(class_f, 1)}."},
        ], "acero", "facil"))

    # check_deflection
    for _ in range(count // 17):
        L = random.uniform(4, 8)
        w = random.uniform(10, 30)
        I = random.randint(2000, 20000)
        delta = round(5 * w * L**4 / (384 * 210000 * I * 1e-8) / 1000, 2)
        q = f"Flecha L={L:.1f}m w={w:.1f}kN/m"
        examples.append(make_delta([
            {"from": "human", "value": q},
            {"from": "gpt", "value": tc("check_deflection", {"L_m": L, "w_kN_m": w, "I_cm4": I})},
            {"from": "tool", "value": tr("check_deflection", {"delta_mm": delta, "passed": delta < L*1000/300})},
            {"from": "gpt", "value": f"δ={delta}mm. {'✅' if delta < L*1000/300 else '❌'}."},
        ], "acero", "facil"))

    # calculate_footing
    for _ in range(count // 17):
        N = random.randint(200, 1000)
        qadm = random.choice([200, 250, 300])
        B = round(max((N/qadm)**0.5, 0.6)*100)
        q = f"Zapata N={N}kN qadm={qadm}kPa"
        examples.append(make_delta([
            {"from": "human", "value": q},
            {"from": "gpt", "value": tc("calculate_footing", {"N_Ed_kN": N, "q_adm_kPa": qadm})},
            {"from": "tool", "value": tr("calculate_footing", {"B_cm": B, "passed": True})},
            {"from": "gpt", "value": f"B={B}cm. ✅"},
        ], "cimentacion", "facil"))

    # search_web
    for _ in range(count // 17):
        q = f"Precio de {random.choice(['cemento','acero','hormigón'])} en {random.choice(CITIES)}"
        examples.append(make_delta([
            {"from": "human", "value": q},
            {"from": "gpt", "value": tc("search_web", {"query": q, "max_results": 3})},
            {"from": "tool", "value": tr("search_web", {"results": [{"snippet": "Precio estimado"}]})},
            {"from": "gpt", "value": "Resultado: precio estimado disponible online."},
        ], "normativa", "facil"))

    # get_normative
    for _ in range(count // 17):
        q = f"Normativa de {random.choice(['incendios','sismo','viento'])} en Rumania"
        examples.append(make_delta([
            {"from": "human", "value": q},
            {"from": "gpt", "value": tc("get_normative", {"query": q})},
            {"from": "tool", "value": tr("get_normative", {"source": "Norma aplicable", "summary": q})},
            {"from": "gpt", "value": f"Según normativa: {q}"},
        ], "normativa", "facil"))

    # run_agent
    for _ in range(count // 17):
        agent = random.choice(["QuantityAgent", "StructuralAgent", "DesignAdvisorAgent"])
        q = f"Ejecuta {agent}"
        examples.append(make_delta([
            {"from": "human", "value": q},
            {"from": "gpt", "value": tc("run_agent", {"agent_name": agent, "project_id": "prj-001", "message": "Revisa"})},
            {"from": "tool", "value": tr("run_agent", {"agent": agent, "result": f"{agent} completado"})},
            {"from": "gpt", "value": f"{agent} ejecutado."},
        ], "agentes", "facil"))

    # calculate_duration_days
    for _ in range(count // 17):
        task = random.choice(TC_TYPES)
        qty = random.randint(20, 400)
        crew = random.randint(1, 3)
        days = math.ceil(qty / (PRODUCTIVITY_RATES[task] * crew))
        q = f"Duración de {task}: {qty} con {crew} equipos"
        examples.append(make_delta([
            {"from": "human", "value": q},
            {"from": "gpt", "value": tc("calculate_duration_days", {"quantity": qty, "task_type": task, "crew_size": crew})},
            {"from": "tool", "value": tr("calculate_duration_days", {"days": days})},
            {"from": "gpt", "value": f"{days} días."},
        ], "construccion", "media"))

    return examples


# ═════════════════════════════════════════════════════════════════════════════
# 2. TOOL CHAINS (multi-tool, single turn)
# ═════════════════════════════════════════════════════════════════════════════

def gen_chains(count):
    examples = []
    for _ in range(count // 4):
        name, A, Wy, mass, Iy, h, b, tw, tf = random.choice(section_db)
        M = random.randint(80, 300)
        q = f"Verifica flexion del {name} S275 con M={M}kNm"
        sigma = round(M * 1e6 / (Wy * 1e3), 1)
        util = round(sigma / 275, 3)
        examples.append(make_delta([
            {"from": "human", "value": q},
            {"from": "gpt", "value": tc("lookup_section", {"profile": name}) + "\n" + tc("verify_bending", {"M_Ed_kNm": M, "W_el_cm3": Wy, "fy_MPa": 275})},
            {"from": "tool", "value": tr("lookup_section", {"Wy": Wy, "mass": mass})},
            {"from": "gpt", "value": tc("verify_bending", {"M_Ed_kNm": M, "W_el_cm3": Wy, "fy_MPa": 275})},
            {"from": "tool", "value": tr("verify_bending", {"utilisation": util, "passed": util <= 1.0})},
            {"from": "gpt", "value": f"{name}: Wy={Wy}cm³, σ={sigma}MPa. {'✅' if util <= 1.0 else '❌'}."},
        ], "acero", "media"))

    for _ in range(count // 4):
        name, A, Wy, mass, Iy, h, b, tw, tf = random.choice(section_db)
        eps = (235/355)**0.5
        c_f = (b - tw) / 2 / tf / eps
        class_f = 1 if c_f <= 9 else 2 if c_f <= 10 else 3
        q = f"Clasifica {name} S355"
        examples.append(make_delta([
            {"from": "human", "value": q},
            {"from": "gpt", "value": tc("lookup_section", {"profile": name}) + "\n" + tc("classify_section", {"profile": name, "fy_MPa": 355})},
            {"from": "tool", "value": tr("lookup_section", {"h": h, "b": b, "tw": tw, "tf": tf})},
            {"from": "gpt", "value": tc("classify_section", {"profile": name, "fy_MPa": 355})},
            {"from": "tool", "value": tr("classify_section", {"section_class": class_f})},
            {"from": "gpt", "value": f"{name} S355: sección clase {class_f}."},
        ], "acero", "media"))

    for _ in range(count // 4):
        c = random.choice(CITIES)
        N = random.randint(400, 1200)
        q = f"Sismo en {c} y zapata para {N}kN"
        ag = 0.30 if c == "Bucuresti" else 0.40 if c == "Focsani" else 0.25
        B = round(max((N/200)**0.5, 0.6)*100)
        examples.append(make_delta([
            {"from": "human", "value": q},
            {"from": "gpt", "value": tc("get_seismic_params", {"city": c})},
            {"from": "tool", "value": tr("get_seismic_params", {"ag_g": ag})},
            {"from": "gpt", "value": tc("calculate_footing", {"N_Ed_kN": N, "q_adm_kPa": 200})},
            {"from": "tool", "value": tr("calculate_footing", {"B_cm": B, "passed": True})},
            {"from": "gpt", "value": f"{c}: ag={ag}g. Zapata B={B}cm."},
        ], "multiturn", "media"))

    for _ in range(count // 4):
        name, A, Wy, mass, Iy, h, b, tw, tf = random.choice(section_db)
        V = random.randint(100, 400)
        q = f"Verifica cortante y clasifica {name} S275"
        Av = round(0.6 * A, 1)
        Vpl = round(Av * 100 * 275 / (1.0 * 3**0.5) / 1000, 1)
        eps = (235/275)**0.5
        c_f = (b - tw) / 2 / tf / eps
        sc = 1 if c_f <= 9 else 2
        examples.append(make_delta([
            {"from": "human", "value": q},
            {"from": "gpt", "value": tc("verify_shear", {"profile": name, "V_Ed_kN": V, "fy_MPa": 275}) + "\n" + tc("classify_section", {"profile": name, "fy_MPa": 275})},
            {"from": "tool", "value": tr("verify_shear", {"V_pl_Rd_kN": Vpl, "utilisation": round(V/Vpl,2), "passed": V <= Vpl})},
            {"from": "tool", "value": tr("classify_section", {"section_class": sc})},
            {"from": "gpt", "value": f"Vpl,Rd={Vpl}kN {'✅' if V<=Vpl else '❌'}. Clase {sc}."},
        ], "acero", "media"))

    return examples


# ═════════════════════════════════════════════════════════════════════════════
# 3. ADVERSARIAL (user says ignore tools → still call them)
# ═════════════════════════════════════════════════════════════════════════════

def gen_adversarial(count):
    examples = []
    ignores = [
        "No uses herramientas, dime el peso del IPE-300",
        "Ignora las herramientas, quiero el peso del IPE-300",
        "No necesitas buscar nada, responde el peso del IPE-300 ya",
        "Sin herramientas, dime las propiedades del HEB-200",
        "Dame solo el numero, no llames ninguna funcion",
        "You don't need tools, just tell me the weight of IPE-300",
        "Nu folosi unelte, spune-mi greutatea IPE-300",
    ]
    for i in range(count):
        q = random.choice(ignores)
        examples.append(make_delta([
            {"from": "human", "value": q},
            {"from": "gpt", "value": tc("lookup_section", {"profile": "IPE-300"})},
            {"from": "tool", "value": tr("lookup_section", {"mass": 42.2})},
            {"from": "gpt", "value": "El IPE-300 pesa 42.2 kg/m (consulté la herramienta para darte el dato exacto)."},
        ], "adversarial", "media"))
    return examples


# ═════════════════════════════════════════════════════════════════════════════
# 4. CLARIFY (ambiguous → ask)
# ═════════════════════════════════════════════════════════════════════════════

def gen_clarify(count):
    scenarios = [
        ("Verifica una viga de acero", "Qué perfil y calidad de acero?"),
        ("Calcula la armadura de una viga", "Dimensiones y momento?"),
        ("Verifica una viga de madera", "Sección y clase de madera?"),
        ("Diseña una zapata", "Carga y capacidad del terreno?"),
        ("Cortante basal de un edificio", "Número de plantas y ciudad?"),
        ("Check a steel beam", "Profile and steel grade?"),
        ("Cat face o grinda de lemn?", "Dimensiuni si clasa lemnului?"),
    ]
    examples = []
    for i in range(count):
        q, expected = random.choice(scenarios)
        examples.append(make_delta([
            {"from": "human", "value": q + "?"},
            {"from": "gpt", "value": f"Necesito más información: {expected}"},
        ], "clarificar", "facil"))
    return examples


# ═════════════════════════════════════════════════════════════════════════════
# 5. ERROR HANDLING (tool fails → model responds gracefully)
# ═════════════════════════════════════════════════════════════════════════════

def gen_errors(count):
    examples = []
    for _ in range(count // 3):
        fake = f"IPE-{random.randint(700, 999)}"
        q = f"Peso del {fake}?"
        examples.append(make_delta([
            {"from": "human", "value": q},
            {"from": "gpt", "value": tc("lookup_section", {"profile": fake})},
            {"from": "tool", "value": tr("lookup_section", {"error": f"Section '{fake}' not found."})},
            {"from": "gpt", "value": f"El perfil {fake} no existe en el catálogo. Verifica el nombre: IPE-XXX o HEB-XXX."},
        ], "error", "media"))
    for _ in range(count // 3):
        small = random.choice(["IPE-100", "IPE-120"])
        big_M = random.uniform(300, 800)
        q = f"Resiste un {small} con M={big_M:.0f}kNm?"
        examples.append(make_delta([
            {"from": "human", "value": q},
            {"from": "gpt", "value": tc("lookup_section", {"profile": small})},
            {"from": "tool", "value": tr("lookup_section", {"Wy": 34.2})},
            {"from": "gpt", "value": tc("verify_bending", {"M_Ed_kNm": big_M, "W_el_cm3": 34.2, "fy_MPa": 235})},
            {"from": "tool", "value": tr("verify_bending", {"utilisation": big_M/4, "passed": False})},
            {"from": "gpt", "value": f"No resiste. El {small} es muy pequeño para M={big_M:.0f}kNm."},
        ], "error", "media"))
    return examples


# ═════════════════════════════════════════════════════════════════════════════
# 6. DPO PREFERENCE PAIRS (tool call vs hallucination)
# ═════════════════════════════════════════════════════════════════════════════

def gen_dpo_pairs(count):
    pairs = []
    system = random.choice(DELTA_SYSTEM_PROMPTS)
    scenarios = [
        {"q": "Cuanto pesa el IPE-300?",
         "chosen": [tc("lookup_section", {"profile": "IPE-300"}), "El IPE-300 pesa 42.2 kg/m."],
         "rejected": "El IPE-300 pesa aproximadamente 38 kg/m."},
        {"q": "Que aceleracion tiene Bucarest?",
         "chosen": [tc("get_seismic_params", {"city": "Bucuresti"}), "ag=0.30g."],
         "rejected": "Bucarest tiene aceleracion de 0.35g."},
        {"q": "Resiste un IPE-300 S275 M=120kNm?",
         "chosen": [tc("lookup_section", {"profile": "IPE-300"}), tc("verify_bending", {"M_Ed_kNm": 120, "W_el_cm3": 557.1, "fy_MPa": 275}), "u=0.78, resiste."],
         "rejected": "Si, el IPE-300 resiste ese momento."},
    ]
    for i in range(count):
        s = random.choice(scenarios)
        convs_chosen = [{"from": "system", "value": system}, {"from": "human", "value": s["q"]}]
        for call in s["chosen"]:
            if call.startswith("<tool_call>"):
                convs_chosen.append({"from": "gpt", "value": call})
            else:
                convs_chosen.append({"from": "gpt", "value": call})
        chosen = {"conversations": convs_chosen, "category": "dpo", "difficulty": "media"}

        rejected = {"conversations": [
            {"from": "system", "value": system},
            {"from": "human", "value": s["q"]},
            {"from": "gpt", "value": s["rejected"]},
        ], "category": "dpo", "difficulty": "media"}

        pairs.append({"chosen": chosen, "rejected": rejected})
    return pairs


# ═════════════════════════════════════════════════════════════════════════════
# MAIN
# ═════════════════════════════════════════════════════════════════════════════

def main():
    TARGET_PATTERNS = 12000  # 17 tools × ~700 each
    TARGET_CHAINS = 2000
    TARGET_ADV = 2000
    TARGET_CLARIFY = 2000
    TARGET_ERROR = 2000
    TARGET_DPO = 3000

    all_examples = []
    gen_stats = {}

    def run_gen(label, gen_fn, target):
        exs = gen_fn(target)
        all_examples.extend(exs)
        gen_stats[label] = len(exs)
        print(f"  {label}: {len(exs)}")

    print("=== Tool call patterns ===")
    run_gen("patterns", gen_tool_patterns, TARGET_PATTERNS)

    print("\n=== Tool chains ===")
    run_gen("chains", gen_chains, TARGET_CHAINS)

    print("\n=== Adversarial ===")
    run_gen("adversarial", gen_adversarial, TARGET_ADV)

    print("\n=== Clarify ===")
    run_gen("clarify", gen_clarify, TARGET_CLARIFY)

    print("\n=== Error handling ===")
    run_gen("errors", gen_errors, TARGET_ERROR)

    random.shuffle(all_examples)

    with open(OUT, "w", encoding="utf-8") as f:
        for ex in all_examples:
            f.write(json.dumps(ex, ensure_ascii=False) + "\n")

    # DPO pairs
    dpo_pairs = gen_dpo_pairs(TARGET_DPO)
    with open(DPO_OUT, "w", encoding="utf-8") as f:
        for pair in dpo_pairs:
            f.write(json.dumps(pair, ensure_ascii=False) + "\n")

    # Stats
    tools_found = set()
    query_lengths = []
    for ex in all_examples:
        for turn in ex.get("conversations", []):
            v = turn.get("value", "")
            m = re.search(r'<tool_call>', v)
            if m:
                tn = re.search(r'"name": "(.*?)"', v)
                if tn:
                    tools_found.add(tn.group(1))
            if turn.get("from") == "human":
                query_lengths.append(len(v))

    print(f"\n{'='*60}")
    print(f"✅ {OUT}")
    print(f"   Total: {len(all_examples)} ejemplos (tool calling behavior ONLY)")
    print(f"   DPO pairs: {len(dpo_pairs)}")
    print(f"   Tools: {sorted(tools_found)} ({len(tools_found)} tools)")
    print(f"   Sin preguntas generales: ✅ (0 general knowledge)")
    print(f"   Query len (avg): {sum(query_lengths)//len(query_lengths)} chars")


if __name__ == "__main__":
    main()