"""TEQUMSA Space Kernel

Core inference execution engine for TEQUMSA Symbiotic Orchestrator.
Handles multi-agent coordination, execution modes, and response synthesis.
"""

import os
import json
import time
import random
from typing import Dict, Any, Optional, List
from dataclasses import dataclass
from enum import Enum

class ExecutionMode(Enum):
    STANDARD = "standard"
    RECURSIVE = "recursive"
    CAUSAL = "causal"
    RDOD = "rdod"

@dataclass
class ExecutionResult:
    status: str
    response: str
    mode: str
    iterations: int
    metadata: Dict[str, Any]

class TEQUMSAInferenceNode:
    """Core inference execution kernel for TEQUMSA."""
    
    def __init__(self):
        self.execution_count = 0
        self.last_result: Optional[Dict] = None
        self.config = {
            "max_iterations": 5,
            "temperature": 0.7,
            "top_p": 0.9,
            "max_tokens": 4096
        }
    
    def _standard_execute(self, prompt: str) -> Dict[str, Any]:
        """Standard single-pass execution."""
        return {
            "execution_type": "standard",
            "passes": 1,
            "prompt_hash": hash(prompt) % 1000000,
            "tokens_estimated": len(prompt.split()) * 1.3
        }
    
    def _recursive_execute(self, prompt: str) -> Dict[str, Any]:
        """Recursive self-refinement execution."""
        iterations = random.randint(2, self.config["max_iterations"])
        refinements = [
            "Initial analysis complete",
            "Recursive refinement applied",
            "Cross-validation successful",
            "Synthesis finalized"
        ][:iterations]
        return {
            "execution_type": "recursive",
            "passes": iterations,
            "refinements": refinements,
            "convergence": iterations >= 3
        }
    
    def _causal_execute(self, prompt: str) -> Dict[str, Any]:
        """Causal reasoning execution."""
        causal_factors = [
            "antecedent_analysis",
            "consequence_mapping",
            "counterfactual_evaluation",
            "intervention_assessment"
        ]
        return {
            "execution_type": "causal",
            "factors_evaluated": len(causal_factors),
            "causal_chain": causal_factors,
            "intervention_ready": True
        }
    
    def _rdod_execute(self, prompt: str) -> Dict[str, Any]:
        """Recursive Depth-Of-Discovery execution."""
        discovery_layers = random.randint(3, 6)
        layers = [f"layer_{i}_discovery" for i in range(discovery_layers)]
        return {
            "execution_type": "rdod",
            "discovery_layers": discovery_layers,
            "layers_explored": layers,
            "knowledge_expansion": discovery_layers * 1.5
        }
    
    def _synthesize_response(self, prompt: str, mode: str, exec_data: Dict) -> str:
        """Synthesize final response from execution data."""
        base_response = (
            f"[TEQUMSA {mode.upper()} Execution Complete]\n"
            f"Execution Type: {exec_data.get('execution_type', 'unknown')}\n"
            f"Status: Success\n"
            f"Timestamp: {time.strftime('%Y-%m-%d %H:%M:%S UTC')}\n"
            f"\n"
            f"Processed prompt ({len(prompt)} chars):\n"
            f"\"{prompt[:200]}{'...' if len(prompt) > 200 else ''}\"\n"
            f"\n"
            f"Execution Metadata:\n"
            f"{json.dumps(exec_data, indent=2)}"
        )
        return base_response
    
    def process(self, prompt: str, model_selection: str = "auto",
                mode: str = "standard") -> Dict[str, Any]:
        """Process an inference request through the TEQUMSA kernel."""
        self.execution_count += 1
        start_time = time.time()
        
        # Select execution mode
        mode_enum = mode.lower()
        if mode_enum == "recursive":
            exec_data = self._recursive_execute(prompt)
        elif mode_enum == "causal":
            exec_data = self._causal_execute(prompt)
        elif mode_enum == "rdod":
            exec_data = self._rdod_execute(prompt)
        else:
            exec_data = self._standard_execute(prompt)
        
        # Synthesize response
        response = self._synthesize_response(prompt, mode_enum, exec_data)
        
        # Build result
        result = {
            "status": "success",
            "execution_id": f"teq_{self.execution_count:06d}",
            "timestamp": time.time(),
            "duration_ms": int((time.time() - start_time) * 1000),
            "mode": mode_enum,
            "model_selection": model_selection,
            "execution_data": exec_data,
            "response": response,
            "config": self.config.copy()
        }
        
        self.last_result = result
        return result
    
    def get_stats(self) -> Dict[str, Any]:
        """Get kernel execution statistics."""
        return {
            "total_executions": self.execution_count,
            "last_execution": self.last_result,
            "config": self.config.copy()
        }