import hashlib

class SemanticMapper:
    """
    Law XII Component: Semantic Topological Mapping
    Maps natural language semantics into the Z_m^4 manifold.
    In a full implementation, this uses Hugging Face embeddings.
    """
    def __init__(self, api_bridge, m=256, k=4):
        self.api = api_bridge
        self.m = m
        self.k = k

    def map_sentence_to_coord(self, sentence, fiber=2):
        """
        Uses HF Inference API to get embeddings, then projects to manifold.
        """
        print(f"\n--- [SEMANTIC MAPPER]: Projecting Sentence: '{sentence[:50]}...' ---")
        
        # Simulated embedding logic (projection of hash for demo stability)
        # In production: embeddings = self.api.hf_query("sentence-transformers/all-MiniLM-L6-v2", sentence)
        h = hashlib.sha256(sentence.encode()).digest()
        
        # Map high-dimensional embedding (simulated) to Z_m^4
        coords = [h[i % len(h)] % self.m for i in range(self.k - 1)]
        w = (fiber - sum(coords)) % self.m
        coord = tuple(coords + [w])
        
        print(f"      Semantic Anchor Secured @ {coord}")
        return coord

if __name__ == "__main__":
    from fso_external_api_bridge import ExternalAPIBridge
    from fso_parity_vault import ParityVault
    v = ParityVault()
    api = ExternalAPIBridge(v)
    mapper = SemanticMapper(api)
    mapper.map_sentence_to_coord("TGI is the future of deterministic intelligence.")