"""
Tests unitaires pour FAISSManager avec property-based testing.

Property 11: FAISS Index Consistency
- L'index FAISS doit maintenir la cohérence entre vecteurs et métadonnées
- Validates: Requirements 4.8, 12.3, 12.6
"""

import pytest
import numpy as np
import tempfile
import shutil
import sys
from pathlib import Path

sys.path.insert(0, str(Path(__file__).parent.parent.parent))
from core.embedding.faiss_manager import FAISSManager


class TestFAISSManager:
    """Tests pour FAISSManager."""
    
    def setup_method(self):
        """Setup avant chaque test."""
        self.temp_dir = tempfile.mkdtemp()
        self.index_path = Path(self.temp_dir) / "test_index"
        self.manager = FAISSManager(dimensions=512)
    
    def teardown_method(self):
        """Cleanup après chaque test."""
        shutil.rmtree(self.temp_dir, ignore_errors=True)
    
    def test_add_single_embedding(self):
        """Test ajout d'un seul embedding."""
        vector = np.random.randn(512).astype(np.float32)
        vector = vector / np.linalg.norm(vector)
        
        metadata = {
            'state_id': 'state_001',
            'timestamp': '2024-11-22T10:00:00'
        }
        
        idx = self.manager.add_embedding('emb_001', vector, metadata)
        
        assert idx == 0
        assert self.manager.index.ntotal == 1
    
    def test_property_index_consistency(self):
        """Property 11: Cohérence entre index et métadonnées."""
        n_vectors = 20
        
        for i in range(n_vectors):
            vector = np.random.randn(512).astype(np.float32)
            vector = vector / np.linalg.norm(vector)
            metadata = {'state_id': f'state_{i:03d}', 'index': i}
            self.manager.add_embedding(f'emb_{i:03d}', vector, metadata)
        
        assert self.manager.index.ntotal == n_vectors
        assert len(self.manager.metadata_store) == n_vectors
    
    def test_search_similar(self):
        """Test recherche de similarité."""
        vectors = []
        for i in range(5):
            vector = np.random.randn(512).astype(np.float32)
            vector = vector / np.linalg.norm(vector)
            vectors.append(vector)
            self.manager.add_embedding(f'emb_{i}', vector, {'state_id': f'state_{i}'})
        
        query = vectors[0]
        results = self.manager.search_similar(query, k=3)
        
        assert len(results) == 3
        assert results[0].similarity > 0.99
    
    def test_save_and_load(self):
        """Test sauvegarde et chargement."""
        n_vectors = 10
        original_vectors = []
        
        for i in range(n_vectors):
            vector = np.random.randn(512).astype(np.float32)
            vector = vector / np.linalg.norm(vector)
            original_vectors.append(vector)
            self.manager.add_embedding(f'emb_{i}', vector, {'state_id': f'state_{i}'})
        
        index_path = self.index_path / "index.faiss"
        metadata_path = self.index_path / "metadata.pkl"
        self.manager.save(index_path, metadata_path)
        
        new_manager = FAISSManager.load(index_path, metadata_path)
        
        assert new_manager.index.ntotal == n_vectors
        assert len(new_manager.metadata_store) == n_vectors


if __name__ == '__main__':
    pytest.main([__file__, '-v'])