"""
Inference Script for Thermal Grid RL Agent Environment
========================================================
MANDATORY
- API_BASE_URL, MODEL_NAME, HF_TOKEN must be set in environment / .env
- Use OpenAI client for all LLM calls
- Emit [START], [STEP], [END] to stdout exactly as specified

Environment Variables:
    HF_TOKEN       - Hugging Face / API key (checked first)
    API_KEY        - Alternative API key (fallback)
    API_BASE_URL   - The API endpoint for the LLM
    MODEL_NAME     - The model identifier to use for inference
    ENV_URL        - URL of the thermal grid environment server

STDOUT FORMAT
    [START] task=<task_name> env=<benchmark> model=<model_name>
    [STEP]  step=<n> action=<action_str> reward=<0.00> done=<true|false> error=<msg|null>
    [END]   success=<true|false> steps=<n> score=<score> rewards=<r1,r2,...,rn>
"""

import asyncio
import os
import re
import json
import logging
from typing import List, Optional

from openai import OpenAI
from dotenv import load_dotenv

load_dotenv()

from client import ThermalGridRlAgentEnv
from models import ThermalGridRlAgentAction, ThermalGridRlAgentObservation
from server.thermal_grid_rl_agent_environment import ThermalGridTaskID
from server.grader import ThermalGridGrader

logging.basicConfig(level=logging.ERROR)
logger = logging.getLogger(__name__)


API_KEY      = os.getenv("HF_TOKEN") 
API_BASE_URL = os.getenv("API_BASE_URL") or "https://router.huggingface.co/v1"
DEFAULT_MODEL = os.getenv("MODEL_NAME") or "meta-llama/Llama-3.2-1B-Instruct"
ENV_URL      = os.getenv("ENV_URL", "http://localhost:8000")

print("\n--- LOADED ENVIRONMENT VARIABLES ---")
print(f"API_BASE_URL : {API_BASE_URL}")
print(f"MODEL_NAME   : {DEFAULT_MODEL}")
print(f"API_KEY      : {API_KEY[:4]}...{API_KEY[-4:] if len(API_KEY)>8 else ''}")
print(f"ENV_URL      : {ENV_URL}")
print("------------------------------------\n")

BENCHMARK               = "thermal_grid_rl_multi_agent"
MAX_STEPS               = 30
SUCCESS_SCORE_THRESHOLD = 0.1
EARLY_STOP_REWARD = 0.9   # not 1.0 (unless rewards are definitely capped at 1.0)
EARLY_STOP_CONSEC = 5      # 5 steps for more stability

TASKS = [
    ThermalGridTaskID.BASELINE,
    ThermalGridTaskID.LOAD_SHIFT,
    ThermalGridTaskID.GRID_STRESS,
]


def log_start(task: str, env: str, model: str) -> None:
    print(f"[START] task={task} env={env} model={model}", flush=True)


def log_step(step: int, action: str, reward: float, done: bool, error: Optional[str]) -> None:
    print(
        f"[STEP] step={step} action={action} reward={reward:.2f} "
        f"done={str(done).lower()} error={error if error else 'null'}",
        flush=True,
    )


def log_end(success: bool, steps: int, score: float, rewards: List[float]) -> None:
    rewards_str = ",".join(f"{r:.2f}" for r in rewards)
    print(
        f"[END] success={str(success).lower()} steps={steps} "
        f"score={score:.3f} rewards={rewards_str}",
        flush=True,
    )


class CoolingAgent:
    """Focuses on thermal safety and equipment longevity."""
    @staticmethod
    def get_recommendation(obs: ThermalGridRlAgentObservation) -> str:
        max_cpu = max(obs.max_cpu_temps_c) if obs.max_cpu_temps_c else 0.0
        ambient = obs.ambient_temp_c
        
        if max_cpu > 75.0 or ambient > 38.0:
            return (
                "CRITICAL: Thermal emergency. Suggest CRAC at 12°C, 100% fans, "
                "and all 4 chillers. Priorities: Safety over cost."
            )
        elif max_cpu > 65.0:
            return (
                "WARNING: High temperatures. Recommend CRAC at 15°C and 85% fans. "
                "Ensure at least 3 chillers are active."
            )
        elif obs.thermal_mass_lag_c_per_min > 0.3:
            return (
                "PREDICTIVE: Room heating up. Proactively lower CRAC setpoint "
                "and increase fans by 10%."
            )
        return "STATUS: Thermal state stable. Maintain current cooling setpoints."

class EnergyAgent:
    """Focuses on PUE, electricity cost, and grid signals."""
    @staticmethod
    def get_recommendation(obs: ThermalGridRlAgentObservation) -> str:
        price = obs.energy_price_per_kwh
        dr_signal = obs.demand_response_signal
        pue = obs.pue
        
        if dr_signal == 1 or price > 0.15:
            return (
                "CRITICAL: DR signal active or high pricing. Suggest raising CRAC "
                "to 25°C and reducing fans to 40% to shed load."
            )
        elif pue > 1.30:
            return (
                "INEFFICIENCY: High PUE. Recommend optimizing chiller count "
                "and raising CRAC setpoint to improve efficiency."
            )
        return "STATUS: Energy usage within bounds. Optimize for efficiency if safety allows."

class WorkloadAgent:
    """Focuses on throughput and job scheduling."""
    @staticmethod
    def get_recommendation(obs: ThermalGridRlAgentObservation) -> str:
        pending = obs.pending_batch_jobs
        off_peak = obs.off_peak_window
        
        if pending > 100 and off_peak == 1:
            return (
                "OPPORTUNITY: Off-peak window and high backlog. Suggest running "
                "all pending batch jobs now."
            )
        elif pending > 50:
            return "URGENT: Batch backlog growing. Suggest increasing throughput."
        return "STATUS: Workload manageable. Schedule batch jobs normally."

class OversightAgent:
    """Deterministic safety monitor that can override the Coordinator."""
    def __init__(self, thermal_limit_c: float = 80.0):
        self.thermal_limit_c = thermal_limit_c

    def enforce(self, obs: ThermalGridRlAgentObservation, action: ThermalGridRlAgentAction) -> ThermalGridRlAgentAction:
        """Deterministic safety overrides."""
        max_cpu = max(obs.max_cpu_temps_c) if obs.max_cpu_temps_c else 0
        
        # Priority 4: Metadata fix
        action.metadata["oversight_triggered"] = False
        action.metadata["oversight_reason"] = None

        if max_cpu > self.thermal_limit_c:
            # Emergency cooling
            action.crac_setpoint_c = 12.0 
            action.fan_speeds_pct = [100.0] * len(action.fan_speeds_pct)
            action.num_active_chillers = 4
            
            action.metadata["oversight_triggered"] = True
            action.metadata["oversight_reason"] = f"CPU Temp CRITICAL ({max_cpu:.1f}°C). Emergency cooling forced."
            print(f"[OVERSIGHT] OVERRIDE: {action.metadata['oversight_reason']}")
            
        return action

def simulate_negotiation(obs: ThermalGridRlAgentObservation, step: int) -> str:
    """
    Simulate a structured one-round debate between the three specialized agents.
    Each agent gives an initial position, then replies to the others.
    Returns a formatted transcript string for the coordinator to resolve.
    """
    cooling_pos  = CoolingAgent.get_recommendation(obs)
    energy_pos   = EnergyAgent.get_recommendation(obs)
    workload_pos = WorkloadAgent.get_recommendation(obs)

    max_cpu = max(obs.max_cpu_temps_c) if obs.max_cpu_temps_c else 0.0
    price   = obs.energy_price_per_kwh
    pue     = obs.pue
    pending = obs.pending_batch_jobs
    dr      = obs.demand_response_signal
    off_peak = obs.off_peak_window

    # --- Cooling Agent's reply to Energy Agent ---
    if dr == 1 or price > 0.15:
        cooling_reply = (
            "[COOLING→ENERGY] I understand the DR signal, but thermal safety "
            "cannot be compromised. If we raise the setpoint above 22°C now, "
            "CPU temps will spike within 5 steps. Propose: CRAC at 18°C max."
        )
    elif max_cpu > 65.0:
        cooling_reply = (
            "[COOLING→ENERGY] Current CPU temps are dangerously high. "
            "Any further energy saving must wait. Safety override required."
        )
    else:
        cooling_reply = (
            "[COOLING→ENERGY] Thermal state is manageable. I can accept "
            "a moderate setpoint increase if PUE improvement is significant."
        )

    # --- Energy Agent's reply to Cooling Agent ---
    if pue > 1.30:
        energy_reply = (
            "[ENERGY→COOLING] PUE is above 1.30 — we're burning money. "
            "Raising CRAC by 2°C and reducing fans by 15% will save ~8% energy "
            "with minimal thermal impact at current load levels."
        )
    elif dr == 1:
        energy_reply = (
            "[ENERGY→COOLING] Grid is requesting load shed. We MUST reduce "
            "facility power by at least 10%. I support minimal cooling for now "
            "if you can keep CPUs below 70°C."
        )
    else:
        energy_reply = (
            "[ENERGY→COOLING] Energy costs are within acceptable bounds. "
            "No immediate conflict — support your cooling recommendation."
        )

    # --- Workload Agent's reply ---
    if pending > 50 and off_peak == 1 and dr == 0:
        workload_reply = (
            "[WORKLOAD→BOTH] Off-peak window is active and backlog is growing. "
            "If either of you can spare 10% headroom, I recommend running "
            "batch jobs now to clear the queue before peak pricing resumes."
        )
    elif pending > 100:
        workload_reply = (
            "[WORKLOAD→BOTH] Batch backlog is critical. Throughput must improve "
            "or SLAs will be breached. Request at least 2 chillers remain active."
        )
    else:
        workload_reply = (
            "[WORKLOAD→BOTH] Workload is stable. No conflict from my side — "
            "please optimize for energy efficiency and safety as you see fit."
        )

    transcript = f"""
--- ROUND 1: AGENT POSITIONS ---
[COOLING AGENT] : {cooling_pos}
[ENERGY AGENT]  : {energy_pos}
[WORKLOAD AGENT]: {workload_pos}

--- ROUND 2: AGENT REPLIES ---
{cooling_reply}
{energy_reply}
{workload_reply}
""".strip()

    return transcript


SYSTEM_PROMPT = """You are the Facility Coordinator for a datacenter.
Analyze the state and agent recommendations, then output a JSON object with your final control actions.

Required JSON format:
```json
{
  "reasoning": "Step-by-step logic resolving conflicts",
  "crac_setpoint_c": 16.0, 
  "fan_speeds_pct": [75.0, 75.0, 75.0, 75.0, 75.0, 75.0, 75.0, 75.0, 75.0, 75.0], 
  "num_active_chillers": 3
}
```

Constraints:
- crac_setpoint_c: Float between 12.0 and 27.0
- fan_speeds_pct: List of exactly 10 floats between 20.0 and 100.0
- num_active_chillers: Integer between 1 and 4

Priority: 1. Safety, 2. Grid, 3. Throughput, 4. PUE.
Output ONLY the JSON object. Do not add conversational text."""


def build_user_message(obs: ThermalGridRlAgentObservation, step: int, task: str) -> str:
    max_cpu = max(obs.max_cpu_temps_c) if obs.max_cpu_temps_c else 0.0
    avg_cpu = sum(obs.mean_cpu_temps_c) / len(obs.mean_cpu_temps_c) if obs.mean_cpu_temps_c else 0.0

    # Run multi-turn agent negotiation
    negotiation_transcript = simulate_negotiation(obs, step)

    return f"""STEP {step}/{MAX_STEPS} | Task: {task}

{negotiation_transcript}

--- CURRENT ENVIRONMENT STATE ---
- Thermal : max_cpu={max_cpu:.1f}°C  avg_cpu={avg_cpu:.1f}°C
- Cooling : PUE={obs.pue:.3f}  setpoint={obs.crac_supply_temp_c:.1f}°C  fans={obs.avg_fan_speed_pct:.0f}%  chillers={obs.num_active_chillers}
- Grid    : price=${obs.energy_price_per_kwh:.3f}/kWh  DR={obs.demand_response_signal}  ambient={obs.ambient_temp_c:.1f}°C
- Batch   : {obs.pending_batch_jobs} jobs pending  off_peak={obs.off_peak_window}

You have read both rounds of debate above. Resolve the conflict and respond with JSON only."""


def _extract_json(raw: str) -> dict:
    """Robustly parse JSON from LLM, handling markdown and truncation."""
    if not raw: return {}
    
    # 1. Strip markdown code blocks
    cleaned = re.sub(r"```(?:json)?\s*(.*?)\s*```", r"\1", raw, flags=re.DOTALL)
    cleaned = cleaned.strip()
    
    # 2. Try direct parse
    try:
        return json.loads(cleaned)
    except json.JSONDecodeError:
        pass
    
    # 3. Attempt to fix truncated JSON (adding missing closing braces)
    # This handles the case where the LLM repeats itself and gets cut off
    for _ in range(5): 
        cleaned += "\n}"
        try:
            return json.loads(cleaned)
        except:
            continue
            
    # 4. Fallback: search for first { and last }
    m = re.search(r'\{.*\}', raw, re.DOTALL)
    if m:
        try:
            return json.loads(m.group())
        except:
            pass
            
    return {}


def get_llm_action(
    client: OpenAI,
    obs: ThermalGridRlAgentObservation,
    step: int,
    task_id: ThermalGridTaskID,
    model: str
) -> tuple:

    user_prompt = build_user_message(obs, step, task_id.value)

    raw_attempts = []
    parsed_data = None
    final_raw = ""

    for attempt in range(3):
        try:
            response = client.chat.completions.create(
                model=model,
                messages=[
                    {"role": "system", "content": SYSTEM_PROMPT},
                    {"role": "user",   "content": user_prompt},
                ],
                max_tokens=1024,
                temperature=0.3 if attempt == 0 else 0.7,
            )

            raw = response.choices[0].message.content or "{}"
            raw_attempts.append(raw)

            data = _extract_json(raw)

            if data and "crac_setpoint_c" in data:
                parsed_data = data
                final_raw = raw

                crac     = max(12.0, min(27.0, float(data.get("crac_setpoint_c", 18.0))))
                fans     = [max(20.0, min(100.0, float(f))) for f in data.get("fan_speeds_pct", [70.0] * 10)]
                chillers = max(1, min(4, int(data.get("num_active_chillers", 2))))

                if len(fans) != 10:
                    fans = [fans[0] if fans else 70.0] * 10

                action = ThermalGridRlAgentAction(
                    crac_setpoint_c=crac,
                    fan_speeds_pct=fans,
                    num_active_chillers=chillers,
                )

                oversight = OversightAgent()
                final_action = oversight.enforce(obs, action)

                return final_action, user_prompt, final_raw, raw_attempts, parsed_data

        except Exception as e:
            print(f"[ERROR] LLM Attempt {attempt+1} failed: {e}")
            logger.warning(f"Step {step} Attempt {attempt+1} failed: {e}")

    raise ValueError(f"Unparseable LLM response after 3 attempts: {raw_attempts}")

async def run_inference(task_id: ThermalGridTaskID, model: str, train_mode: bool = False) -> None:
    client = OpenAI(base_url=API_BASE_URL, api_key=API_KEY)

    if train_mode:
        print(f"[MODE] TRAIN — collecting trajectories")
    else:
        print(f"[MODE] INFERENCE — early stopping enabled")

    log_start(task=task_id.value, env=BENCHMARK, model=model)

    env    = ThermalGridRlAgentEnv(base_url=ENV_URL)
    grader = ThermalGridGrader(task_id=task_id.value)

    rewards = []
    steps_taken = 0
    success = False

    episode_buffer = []

    try:
        # ===============================
        # RESET ENV
        # ===============================
        try:
            reset_result = await env.reset(task_id=task_id.value)
        except Exception as e:
            raise RuntimeError(f"env.reset() failed: {e}")

        observation = reset_result.observation

        # ===============================
        # MAIN LOOP
        # ===============================
        for step in range(1, MAX_STEPS + 1):
            try:
                # ✅ GET ACTION FROM LLM
                action, prompt, raw, raw_attempts, parsed_data = await asyncio.to_thread(
                    get_llm_action, client, observation, step, task_id, model
                )

                # Final action dict
                action_dict = {
                    "crac_setpoint_c": action.crac_setpoint_c,
                    "fan_speeds_pct": action.fan_speeds_pct,
                    "num_active_chillers": action.num_active_chillers,
                }

                action_str = json.dumps(action_dict, separators=(",", ":"))


                # ===============================
                # ENV STEP
                # ===============================
                step_result = await env.step(action)

                reward = float(step_result.reward or 0.0)
                done   = step_result.done

                rewards.append(reward)
                steps_taken = step

                # ===============================
                # SAVE DATASET ENTRY
                # ===============================
                episode_buffer.append({
                    "step": step,

                    # Input
                    "prompt": prompt,

                    # Final action (your required format)
                    "response": action_dict,

                    # Reward
                    "reward": reward,

                    # Oversight info
                    "oversight_triggered": action.metadata.get("oversight_triggered", False),

                    # Extra (important for debugging + replay)
                    "raw_response": raw,
                    "raw_attempts": raw_attempts,
                    "parsed_action": parsed_data,

                    # Structured state (VERY IMPORTANT)
                    "state": {
                        "max_cpu": max(observation.max_cpu_temps_c) if observation.max_cpu_temps_c else 0.0,
                        "avg_cpu": sum(observation.mean_cpu_temps_c)/len(observation.mean_cpu_temps_c) if observation.mean_cpu_temps_c else 0.0,
                        "pue": observation.pue,
                        "energy_price": observation.energy_price_per_kwh,
                        "ambient": observation.ambient_temp_c,
                        "pending_jobs": observation.pending_batch_jobs,
                        "off_peak": observation.off_peak_window
                    }
                })

                # Logging
                log_step(step=step, action=action_str, reward=reward, done=done, error=None)

                # Move to next state
                observation = step_result.observation

                # ===============================
                # EARLY STOP
                # ===============================
                if not train_mode:
                    recent = rewards[-EARLY_STOP_CONSEC:] if len(rewards) >= EARLY_STOP_CONSEC else []
                    if recent and all(r >= EARLY_STOP_REWARD for r in recent):
                        print("[EARLY STOP] Stable high reward achieved")
                        break

                if done:
                    break

            except Exception as e:
                import traceback
                print("\n[ERROR] Exception in loop:")
                traceback.print_exc()
                log_step(step=step, action="{}", reward=0.0, done=False, error=str(e))
                break

        # ===============================
        # FINAL SCORE
        # ===============================
        score = min(max(grader.get_thermal_grid_score(), 0.0), 1.0)
        success = score >= SUCCESS_SCORE_THRESHOLD

        # ===============================
        # SAVE DATASET
        # ===============================
        if train_mode:
            # 1. Save all prompts for RL exploration
            with open("all_prompts.jsonl", "a") as f:
                for step_data in episode_buffer:
                    f.write(json.dumps({"prompt": step_data["prompt"]}) + "\n")

            # 2. Save full trajectories
            with open("expert_trajectories.jsonl", "a") as f:
                for step_data in episode_buffer:
                    f.write(json.dumps({
                        "step": step_data["step"],
                        "prompt": step_data["prompt"],
                        "response": step_data["response"],
                        "reward": step_data["reward"],
                        "oversight_triggered": step_data["oversight_triggered"]
                    }) + "\n")

    finally:
        try:
            await env.close()
        except:
            pass

        log_end(success=success, steps=steps_taken, score=score, rewards=rewards)
async def main() -> None:
    import argparse

    parser = argparse.ArgumentParser(description="Thermal Grid RL Agent Inference")

    parser.add_argument("--model", type=str, default=DEFAULT_MODEL)
    parser.add_argument("--train", action="store_true", default=False)

    args = parser.parse_args()

    current_model = args.model
    train_mode = args.train

    # Clear old trajectory data only when starting a new training collection run
    if train_mode:
        for f_path in ["all_prompts.jsonl", "expert_trajectories.jsonl"]:
            if os.path.exists(f_path):
                os.remove(f_path)

    for task_id in TASKS:
        await run_inference(task_id, current_model, train_mode=train_mode)


if __name__ == "__main__":
    asyncio.run(main())