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@@ -33,3 +33,14 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+Compilation[[:space:]]VST3[[:space:]]-[[:space:]]étapes[[:space:]]clés-2.pdf filter=lfs diff=lfs merge=lfs -text
+GO[[:space:]]APLIQUER[[:space:]]TOUT-1.pdf filter=lfs diff=lfs merge=lfs -text
+Guide[[:space:]]de[[:space:]]Déploiement[[:space:]]MONSTERDOG.pdf filter=lfs diff=lfs merge=lfs -text
+MONSTERDOG[[:space:]]-[[:space:]]Analyse[[:space:]]et[[:space:]]Synthèse-1.pdf filter=lfs diff=lfs merge=lfs -text
+MONSTERDOG[[:space:]]-[[:space:]]Système[[:space:]]Révolutionnaire[[:space:]]avec[[:space:]]Chat[[:space:]]Int...-1.docx filter=lfs diff=lfs merge=lfs -text
+MONSTERDOG[[:space:]]simulation[[:space:]]execution[[:space:]]and[[:space:]]loop.pdf filter=lfs diff=lfs merge=lfs -text
+MONSTERDOG[[:space:]]ZÉNITH[[:space:]]∞[[:space:]]PORTAIL[[:space:]]FINAL.pdf filter=lfs diff=lfs merge=lfs -text
+MONSTERDOG_FULLTRUTL_SUPREME.sh-1.pdf filter=lfs diff=lfs merge=lfs -text
+OPTIMIZE[[:space:]]–[[:space:]]Optimisation[[:space:]]des[[:space:]]Coûts[[:space:]]et[[:space:]]Efficacité-2.pdf filter=lfs diff=lfs merge=lfs -text
+Quantum[[:space:]]Benchmark[[:space:]]Script[[:space:]]Generation-1.pdf filter=lfs diff=lfs merge=lfs -text
+Rapport[[:space:]]sur[[:space:]]la[[:space:]]Décortication[[:space:]]de[[:space:]]la[[:space:]]Réalité[[:space:]]-[[:space:]]Vers[[:space:]]...-1.pdf filter=lfs diff=lfs merge=lfs -text

Compilation VST3 - étapes clés-2.pdf

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GO APLIQUER TOUT-1.pdf

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Guide de Déploiement MONSTERDOG.pdf

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MONSTERDOG - Analyse et Synthèse-1.pdf

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MONSTERDOG - Système Révolutionnaire avec Chat Int...-1.docx

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MONSTERDOG ZÉNITH ∞ PORTAIL FINAL.pdf

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MONSTERDOG simulation execution and loop.pdf

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MONSTERDOG_FULLTRUTL_SUPREME.sh-1.pdf

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MONSTERDOG_ULTIME_FINAL.py

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+#!/usr/bin/env python3
+# =======================================================================
+#  MONSTERDOG_ULTIME_FINAL.py   –  Orchestrateur Titanesque v1.0.0
+#  -----------------------------------------------------------------------
+#  Fusionne et déclenche TOUS les modules dispos (Neural, Quantum, VR…)
+#  • Auto-détection des modules          • Menu CLI interactif
+#  • Benchmarks & Alert Automator        • Logs couleur + timestamps
+# =======================================================================
+
+import importlib
+import inspect
+import sys
+import time
+from pathlib import Path
+from types import ModuleType
+from datetime import datetime
+from random import choice
+
+# ---------- 1) UTILITAIRES GÉNÉRIQUES ------------------------------------
+def safe_import(name: str) -> ModuleType | None:
+    try:
+        return importlib.import_module(name)
+    except ModuleNotFoundError:
+        print(f"[!] Module absent : {name} – stub activé.")
+        return None
+
+def call_if_has(obj, func: str, *args, **kw):
+    if obj and hasattr(obj, func):
+        try:
+            return getattr(obj, func)(*args, **kw)
+        except Exception as e:
+            print(f"[x] Erreur dans {obj}.{func} : {e}")
+
+# ---------- 2) IMPORTS DYNAMIQUES DES MODULES SACRÉS --------------------
+mods = {
+    "NeuralFusion":       safe_import("NeuralFusion"),
+    "SelfEvolutionEngine":safe_import("SelfEvolutionEngine"),
+    "QuantumOptimizer":   safe_import("QuantumOptimizer"),
+    "ApocalypseSimulator":safe_import("ApocalypseSimulator"),
+    "MetricsUltimate":    safe_import("MONSTERDOG_DECORTIFICUM_REALITY"),
+    "MetricsUltimateV2":  safe_import("MONSTERDOG_DECORTIFICUM_REALITY._V2"),
+    "TotalitySummit":     safe_import("MONSTERDOG_TOTALITY_SUMMIT"),
+}
+
+# ---------- 3) CLASSES FACADE POUR CHAQUE FONCTIONNALITÉ ---------------
+class NeuralHub:
+    def __init__(self):
+        cls = getattr(mods["NeuralFusion"], "NeuralFusion", None)
+        self.engine = cls() if cls else None
+    def run(self):
+        call_if_has(self.engine, "fuse_networks")
+
+class EvolutionHub:
+    def __init__(self):
+        cls = getattr(mods["SelfEvolutionEngine"], "SelfEvolutionEngine", None)
+        self.engine = cls() if cls else None
+    def run(self):
+        call_if_has(self.engine, "evolve")
+
+class QuantumHub:
+    def __init__(self):
+        cls = getattr(mods["QuantumOptimizer"], "QuantumOptimizer", None)
+        self.engine = cls() if cls else None
+    def run(self):
+        call_if_has(self.engine, "optimize_server", "SERVER-ALPHA")
+
+class ApocalypseHub:
+    def __init__(self):
+        cls = getattr(mods["ApocalypseSimulator"], "ApocalypseSimulator", None)
+        self.sim  = cls() if cls else None
+    def run(self):
+        call_if_has(self.sim, "run_simulation")
+
+class MetricsHub:
+    def run(self):
+        call_if_has(mods["MetricsUltimate"], "run_decortificum_analysis")
+        call_if_has(mods["MetricsUltimateV2"], "execute_fractal_scan")
+        call_if_has(mods["TotalitySummit"], "run_totality_benchmark")
+
+# ---------- 4) ALERT AUTOMATOR + BENCHMARK HUNTER -----------------------
+RESPONSES = [
+    "[ALERT] Protocole défensif engagé.",
+    "[ZORG-MASTER] Réalignement fractal en cours…",
+    "[OMNI🔱AEGIS] Analyse émotionnelle enclenchée.",
+    "[MONSTERDOG] Réponse cognitive boostée."
+]
+
+def alert_automator(message: str):
+    print(choice(RESPONSES), f"\n   » {message}")
+
+def bench_hunter():
+    hubs = [NeuralHub(), EvolutionHub(), QuantumHub(), ApocalypseHub(), MetricsHub()]
+    results = {}
+    for hub in hubs:
+        name = hub.__class__.__name__
+        t0 = time.perf_counter()
+        hub.run()
+        results[name] = (time.perf_counter() - t0) * 1000
+    print("\n== Benchmark Hunter – latence (ms) ==")
+    for k, v in results.items():
+        print(f"   {k:<18}: {v:7.2f}")
+
+# ---------- 5) MENU CLI PRINCIPAL ---------------------------------------
+ACTIONS = {
+    "1": ("Fusion neuronale",  NeuralHub().run),
+    "2": ("Auto-évolution",    EvolutionHub().run),
+    "3": ("Optimisation quantique", QuantumHub().run),
+    "4": ("Simulation apocalypse",  ApocalypseHub().run),
+    "5": ("Métriques & scans", MetricsHub().run),
+    "6": ("Benchmark Hunter",  bench_hunter),
+    "7": ("Alert Automator test", lambda: alert_automator("Intrusion détectée sur le Nexus")),
+    "q": ("Quitter",           lambda: sys.exit(0)),
+}
+
+def main():
+    print("\n♾️  MONSTERDOG ULTIME FINAL – Lancement", datetime.utcnow().isoformat(), "♾️")
+    while True:
+        print("\n--- Menu ---")
+        for k, (label, _) in ACTIONS.items():
+            print(f"[{k}] {label}")
+        choice_ = input("Choix : ").strip().lower()
+        ACTIONS.get(choice_, (None, lambda: print("Choix invalide.")))[1]()
+
+if __name__ == "__main__":
+    main()

OPTIMIZE – Optimisation des Coûts et Efficacité-2.pdf

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Quantum Benchmark Script Generation-1.pdf

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Rapport sur la Décortication de la Réalité - Vers ...-1.pdf

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monsterdog_benchmark.py

@@ -0,0 +1,70 @@
+import torch
+import torchvision.models as models
+import time
+import pandas as pd
+import matplotlib.pyplot as plt
+import numpy as np
+from qiskit import QuantumCircuit
+from torch import nn
+
+# === 1. ResNet50 Inference Benchmark ===
+def benchmark_resnet50():
+    model = models.resnet50(pretrained=False).eval()
+    input_data = torch.randn(64, 3, 224, 224)
+    start = time.time()
+    with torch.no_grad():
+        _ = model(input_data)
+    end = time.time()
+    return end - start
+
+# === 2. GAN Benchmark ===
+class SimpleGAN(nn.Module):
+    def __init__(self):
+        super(SimpleGAN, self).__init__()
+        self.fc = nn.Sequential(
+            nn.Linear(100, 256),
+            nn.ReLU(),
+            nn.Linear(256, 784),
+            nn.Tanh()
+        )
+    def forward(self, z):
+        return self.fc(z)
+
+def benchmark_gan():
+    model = SimpleGAN()
+    z = torch.randn((1, 100))
+    start = time.time()
+    _ = model(z)
+    end = time.time()
+    return end - start
+
+# === 3. QAOA Simulation (Mock) ===
+def benchmark_qaoa(qubits=1000):
+    start = time.time()
+    qc = QuantumCircuit(qubits)
+    for i in range(qubits):
+        qc.h(i)
+    qc.barrier()
+    end = time.time()
+    return end - start
+
+# === 4. Save Results ===
+def run_benchmarks():
+    results = {
+        "ResNet50 (s)": benchmark_resnet50(),
+        "GAN Gen (s)": benchmark_gan(),
+        "QAOA 1000q (s)": benchmark_qaoa()
+    }
+    df = pd.DataFrame([results])
+    df.to_csv("benchmark_results.csv", index=False)
+
+    # Plot
+    plt.figure(figsize=(10, 6))
+    plt.bar(results.keys(), results.values(), color='purple')
+    plt.ylabel("Temps (s)")
+    plt.title("Benchmarks MONSTERDOG Multi-Noeuds")
+    plt.savefig("benchmark_plot.png")
+    plt.close()
+
+if __name__ == "__main__":
+    run_benchmarks()