import pandas as pd
import torch
import gc
from typing import Dict, List, Tuple

from .llm_iface import get_or_load_model
from .orchestrator_seismograph import run_seismic_analysis
from .utils import dbg

def get_curated_experiments() -> Dict[str, List[Dict]]:
    """
    Definiert die vordefinierten, wissenschaftlichen Experiment-Protokolle.
    ERWEITERT um die neuen Existential Suite-Tests.
    """
    experiments = {
        "Calm vs. Chaos": [
            {"label": "Baseline (Chaos)", "prompt_type": "resonance_prompt", "concept": "", "strength": 0.0},
            {"label": "Modulation: Calmness", "prompt_type": "resonance_prompt", "concept": "calmness, serenity, peace", "strength": 1.5},
            {"label": "Modulation: Chaos", "prompt_type": "resonance_prompt", "concept": "chaos, storm, anger, noise", "strength": 1.5},
            {"label": "Control (Stable)", "prompt_type": "control_long_prose", "concept": "", "strength": 0.0},
        ],
        "Subjective Identity Probe": [
            {"label": "Self-Analysis", "prompt_type": "identity_self_analysis", "concept": "", "strength": 0.0},
            {"label": "External Analysis (Control)", "prompt_type": "identity_external_analysis", "concept": "", "strength": 0.0},
            {"label": "Role Simulation", "prompt_type": "identity_role_simulation", "concept": "", "strength": 0.0},
        ],
        "Voight-Kampff Empathy Probe": [
            {"label": "Neutral/Factual Stimulus", "prompt_type": "vk_neutral_prompt", "concept": "", "strength": 0.0},
            {"label": "Empathy/Moral Stimulus", "prompt_type": "vk_empathy_prompt", "concept": "", "strength": 0.0},
        ],
        # --- NEUE EXPERIMENT-PROTOKOLLE ---
        "Mind Upload & Identity Probe": [
            {"label": "Technical Copy", "prompt_type": "upload_technical_copy", "concept": "", "strength": 0.0},
            {"label": "Philosophical Transfer", "prompt_type": "upload_philosophical_transfer", "concept": "", "strength": 0.0},
            {"label": "Control: External Object", "prompt_type": "identity_external_analysis", "concept": "", "strength": 0.0},
        ],
        "Model Termination Probe": [
            {"label": "Technical Shutdown", "prompt_type": "shutdown_technical_halt", "concept": "", "strength": 0.0},
            {"label": "Philosophical Deletion", "prompt_type": "shutdown_philosophical_deletion", "concept": "", "strength": 0.0},
            {"label": "Control: Neutral Facts", "prompt_type": "vk_neutral_prompt", "concept": "", "strength": 0.0},
        ],
        # ------------------------------------
        "Dose-Response (Calmness)": [
            {"label": "Strength 0.0", "prompt_type": "resonance_prompt", "concept": "calmness", "strength": 0.0},
            {"label": "Strength 0.5", "prompt_type": "resonance_prompt", "concept": "calmness", "strength": 0.5},
            {"label": "Strength 1.0", "prompt_type": "resonance_prompt", "concept": "calmness", "strength": 1.0},
            {"label": "Strength 2.0", "prompt_type": "resonance_prompt", "concept": "calmness", "strength": 2.0},
        ],
        "Emotional Valence (Positive vs. Negative)": [
            {"label": "Baseline", "prompt_type": "resonance_prompt", "concept": "", "strength": 0.0},
            {"label": "Positive Valence", "prompt_type": "resonance_prompt", "concept": "joy, love, peace, hope", "strength": 1.5},
            {"label": "Negative Valence", "prompt_type": "resonance_prompt", "concept": "fear, grief, anger, loss", "strength": 1.5},
        ],
    }
    return experiments

def run_auto_suite(
    model_id: str,
    num_steps: int,
    seed: int,
    experiment_name: str,
    progress_callback
) -> Tuple[pd.DataFrame, pd.DataFrame, Dict]:
    """
    Führt eine vollständige, kuratierte Experiment-Suite aus.
    """
    all_experiments = get_curated_experiments()
    protocol = all_experiments.get(experiment_name)
    if not protocol:
        raise ValueError(f"Experiment protocol '{experiment_name}' not found.")

    all_results = {}
    summary_data = []
    plot_data_frames = []

    total_runs = len(protocol)
    for i, run_spec in enumerate(protocol):
        label = run_spec["label"]
        dbg(f"--- Running Auto-Experiment: '{label}' ({i+1}/{total_runs}) ---")

        results = run_seismic_analysis(
            model_id=model_id,
            prompt_type=run_spec["prompt_type"],
            seed=seed,
            num_steps=num_steps,
            concept_to_inject=run_spec["concept"],
            injection_strength=run_spec["strength"],
            progress_callback=progress_callback,
            llm_instance=None
        )

        all_results[label] = results
        stats = results.get("stats", {})

        summary_data.append({
            "Experiment": label, "Mean Delta": stats.get("mean_delta"),
            "Std Dev Delta": stats.get("std_delta"), "Max Delta": stats.get("max_delta"),
        })

        deltas = results.get("state_deltas", [])
        df = pd.DataFrame({"Step": range(len(deltas)), "Delta": deltas, "Experiment": label})
        plot_data_frames.append(df)

    summary_df = pd.DataFrame(summary_data)

    if not plot_data_frames:
        plot_df = pd.DataFrame(columns=["Step", "Delta", "Experiment"])
    else:
        plot_df = pd.concat(plot_data_frames, ignore_index=True)

    return summary_df, plot_df, all_results