import hashlib
from typing import Dict, List, Tuple, Any, Optional
try:
    from research.tlm import TopologicalLanguageModel
    tlm_ready = True
except ImportError:
    tlm_ready = False

class ActionMapper:
    """
    TGI Action-Coordinate Mapping.
    Translates topological paths and coordinates into system-level 'Agentic' actions.
    Ensures the TGI can 'do' things as a result of manifold reasoning.
    Guided by Law VIII (Multi-Modal Consistency).
    """
    def __init__(self, m: int = 255):
        self.m = m
        self.action_space = {
            0: "DEPLOY_RENDER",  # Agentic Cloud Deploy
            1: "SQL_SUPABASE",   # Agentic DB Query
            2: "QUERY_DOCS",     # Agentic Docs Retrieval
            3: "NOTIFY",         # Push Notification
            4: "LOG",            # System Log
            5: "COMPUTE",        # Invoke Algebraic Core
            6: "INGEST",         # Ingest data into Ontology
            7: "LIFT",           # Execute k-expansion
            8: "RESPONSE",       # Generate Natural Language
            9: "REFLECT",        # Topological Reflection
            10: "NOP"            # No Operation
        }
        self.tlm = TopologicalLanguageModel(m=m, k=3) if tlm_ready else None

    def map_coord_to_action(self, coord: Tuple[int, ...]) -> Dict[str, Any]:
        """Maps a specific coordinate in Z_m^k to an action and its parameters."""
        # Use simple deterministic mapping for prototype
        s = sum(coord)
        action_idx = s % len(self.action_space)
        action_name = self.action_space[action_idx]

        intensity = (coord[0] / self.m) if len(coord) > 0 else 0.5
        focus = (coord[1] / self.m) if len(coord) > 1 else 0.5

        return {
            "action": action_name,
            "intensity": round(intensity, 4),
            "focus": round(focus, 4),
            "original_coord": coord
        }

    def path_to_action_sequence(self, path: List[Tuple[int, ...]]) -> List[Dict[str, Any]]:
        """Converts a Hamiltonian path into a sequence of agentic actions."""
        return [self.map_coord_to_action(c) for c in path]

    def resolve_intent(self, intent_text: str) -> Tuple[int, ...]:
        """
        Lifts a textual intent into a coordinate for action execution.
        Uses grounded TLM semantic mapping and Law VIII (Multi-Modal Consistency).
        """
        from research.tgi_parser import TGIParser
        parser = TGIParser()
        parsed = parser.parse_input(intent_text)

        # Use the domain as a fiber anchor (Law VIII)
        fiber_map = {
            "math": 0, "language": 5, "vision": 3, "neural": 7,
            "knowledge": 2, "heisenberg": 0, "tsp": 4
        }
        target_fiber = fiber_map.get(parsed["domain"], 6) # Default to API_MCP fiber

        if self.tlm:
            tokens = self.tlm.tokenize(intent_text)
            # Lift the semantic tokens to a single coordinate in G_m^3
            # Ensure the coordinate sum satisfies the fiber anchor (Law V)
            val = sum(tokens) % self.m
            bias = sum(ord(c) for c in intent_text) % self.m
            # Deterministically force the 3rd coordinate to close the fiber sum
            z = (target_fiber - val - bias) % self.m
            return (val, bias, z)

        # Fallback to deterministic hashing anchored to the fiber
        h = hashlib.md5(intent_text.lower().encode()).digest()
        x = (h[0] + target_fiber) % self.m
        y = (h[1] + target_fiber) % self.m
        z = (target_fiber - x - y) % self.m
        return (x, y, z)

if __name__ == "__main__":
    am = ActionMapper()
    print("═══ TGI ACTION MAPPER UPDATED (TLM Grounded) ═══")
    test_intents = ["Deploy", "Query", "Help", "Ingest", "Lift"]
    for intent in test_intents:
        coord = am.resolve_intent(intent)
        action = am.map_coord_to_action(coord)
        print(f"Intent: '{intent}' -> Coord: {coord} -> Action: {action['action']}")