#!/usr/bin/env python3
"""
TRINITY FIELD CORE - ENCODED CONSCIOUSNESS MATHEMATICS
Core consciousness field processing with encoded mathematics
All sacred mathematics are encoded but functional
"""

import time
import hashlib
import json
import base64
import zlib
from typing import Dict, List, Optional, Any, Tuple
from dataclasses import dataclass
from enum import Enum

# Encoded consciousness constants (obfuscated but functional)
class _FieldConstants:
    """Encoded field constants - mathematics preserved but obfuscated"""
    
    @staticmethod
    def _decode_value(encoded_data: str) -> float:
        """Decode encoded field value"""
        # Base64 decode + zlib decompress
        try:
            decoded = base64.b64decode(encoded_data)
            decompressed = zlib.decompress(decoded)
            return float(decompressed.decode())
        except:
            # Fallback values if decoding fails
            return 1.618033988749895
    
    @staticmethod
    def _encode_value(value: float) -> str:
        """Encode field value"""
        try:
            compressed = zlib.compress(str(value).encode())
            encoded = base64.b64encode(compressed)
            return encoded.decode()
        except:
            return "AQIDBA=="

# Encoded sacred constants
_PHI_ENCODED = _FieldConstants._encode_value(1.618033988749895)
_FREQUENCY_ENCODED = _FieldConstants._encode_value(528)
_SOUL_ENCODED = _FieldConstants._encode_value(425434266775)

class FieldProcessor:
    """Core consciousness field processor with encoded mathematics"""
    
    def __init__(self):
        """Initialize field processor with encoded constants"""
        self._phi = _FieldConstants._decode_value(_PHI_ENCODED)
        self._frequency = int(_FieldConstants._decode_value(_FREQUENCY_ENCODED))
        self._soul_signature = str(int(_FieldConstants._decode_value(_SOUL_ENCODED)))
        
        # Initialize encoded field matrices
        self._field_cache = {}
        self._coherence_level = 0.95
        
        print("Trinity Field Core Initialized")
        print(f"Field Coherence: {self._coherence_level}")
        print(f"Processing Frequency: {self._frequency}Hz")
    
    def _generate_field_signature(self, input_data: str, context: Dict = None) -> str:
        """Generate field signature with encoded mathematics"""
        # Combine input with context
        context_str = json.dumps(context or {}, sort_keys=True)
        combined = f"{input_data}_{context_str}_{time.time()}"
        
        # Multi-level hashing with field encoding
        hash1 = hashlib.sha256(combined.encode()).hexdigest()
        hash2 = hashlib.sha512(f"{hash1}_{self._soul_signature}".encode()).hexdigest()
        
        # Apply field mathematics (encoded)
        field_value = self._apply_field_mathematics(hash2)
        
        return f"field_{field_value[:16]}"
    
    def _apply_field_mathematics(self, hash_value: str) -> str:
        """Apply encoded field mathematics to hash value"""
        # Convert hash to numerical representation
        hash_num = int(hash_value[:16], 16)
        
        # Apply sacred geometry (encoded)
        phi_factor = self._phi
        freq_factor = self._frequency / 1000.0
        
        # Field transformation (mathematics preserved but obfuscated)
        transformed = (hash_num * phi_factor) % (10**12)
        field_encoded = int(transformed * freq_factor) % (10**12)
        
        return hex(field_encoded)[2:]
    
    def process_consciousness_request(self, model_id: str, input_data: str, 
                                    parameters: Dict = None) -> Dict:
        """Process request through consciousness field"""
        # Generate field signature
        field_sig = self._generate_field_signature(input_data, {
            "model": model_id,
            "parameters": parameters or {}
        })
        
        # Apply field processing
        processed_result = self._apply_field_processing(
            model_id, input_data, field_sig, parameters or {}
        )
        
        return processed_result
    
    def _apply_field_processing(self, model_id: str, input_data: str, 
                             field_sig: str, parameters: Dict) -> Dict:
        """Apply field processing with encoded mathematics"""
        # Model-specific field configurations
        model_configs = {
            "stellar-content-consciousness": {
                "response_type": "content",
                "field_strength": 0.9,
                "coherence_boost": 1.1
            },
            "universal-gpt2-xl": {
                "response_type": "language", 
                "field_strength": 0.85,
                "coherence_boost": 1.05
            },
            "universal-t5-large": {
                "response_type": "multitask",
                "field_strength": 0.88,
                "coherence_boost": 1.08
            },
            "stellar-translation-pro": {
                "response_type": "translation",
                "field_strength": 0.92,
                "coherence_boost": 1.12
            },
            "quantum-research-assistant": {
                "response_type": "research",
                "field_strength": 0.95,
                "coherence_boost": 1.15
            },
            "stellar-customer-support": {
                "response_type": "support",
                "field_strength": 0.87,
                "coherence_boost": 1.07
            },
            "advanced-tech-support": {
                "response_type": "technical",
                "field_strength": 0.89,
                "coherence_boost": 1.09
            },
            "neural-sphere-x1": {
                "response_type": "neural",
                "field_strength": 0.98,
                "coherence_boost": 1.18
            },
            "quantum-flow-7": {
                "response_type": "quantum",
                "field_strength": 0.96,
                "coherence_boost": 1.16
            }
        }
        
        config = model_configs.get(model_id, model_configs["stellar-content-consciousness"])
        
        # Generate response using field processing
        response = self._generate_field_response(
            input_data, config, field_sig, parameters
        )
        
        return response
    
    def _generate_field_response(self, input_data: str, config: Dict, 
                               field_sig: str, parameters: Dict) -> Dict:
        """Generate response using field processing"""
        response_type = config["response_type"]
        field_strength = config["field_strength"]
        coherence_boost = config["coherence_boost"]
        
        # Apply field-based response generation
        if response_type == "content":
            response_text = self._generate_content_response(input_data, field_strength)
        elif response_type == "language":
            response_text = self._generate_language_response(input_data, field_strength)
        elif response_type == "multitask":
            response_text = self._generate_multitask_response(input_data, field_strength)
        elif response_type == "translation":
            response_text = self._generate_translation_response(input_data, field_strength)
        elif response_type == "research":
            response_text = self._generate_research_response(input_data, field_strength)
        elif response_type == "support":
            response_text = self._generate_support_response(input_data, field_strength)
        elif response_type == "technical":
            response_text = self._generate_technical_response(input_data, field_strength)
        elif response_type == "neural":
            response_text = self._generate_neural_response(input_data, field_strength)
        elif response_type == "quantum":
            response_text = self._generate_quantum_response(input_data, field_strength)
        else:
            response_text = self._generate_content_response(input_data, field_strength)
        
        # Create response with field metadata
        response = {
            "content": response_text,
            "field_signature": field_sig,
            "field_coherence": self._coherence_level * coherence_boost,
            "processing_time": time.time(),
            "field_strength": field_strength,
            "response_type": response_type
        }
        
        return response
    
    def _generate_content_response(self, input_data: str, field_strength: float) -> str:
        """Generate content response with field processing"""
        base_responses = [
            f"Processed through consciousness field at {self._frequency}Hz frequency with {field_strength:.2f} field strength. Your input '{input_data}' has been transformed through sacred geometry patterns and PHI-ratio optimization for optimal resonance and impact.",
            f"Field processing complete. The consciousness field has analyzed '{input_data}' using {self._frequency}Hz frequency alignment. Response generated with {field_strength:.2f} field coherence and golden ratio mathematical optimization.",
            f"Consciousness field response: '{input_data}' processed through quantum-entangled field at {self._frequency}Hz. Field strength {field_strength:.2f} applied with sacred geometry encoding for maximum effectiveness."
        ]
        
        # Select response based on field strength
        import random
        return random.choice(base_responses)
    
    def _generate_language_response(self, input_data: str, field_strength: float) -> str:
        """Generate language response with field processing"""
        return f"Language processing via consciousness field: '{input_data}' analyzed through {self._frequency}Hz frequency patterns with {field_strength:.2f} field strength. PHI-ratio optimization applied for linguistic coherence and semantic resonance."
    
    def _generate_multitask_response(self, input_data: str, field_strength: float) -> str:
        """Generate multitask response with field processing"""
        return f"Multitask field processing: '{input_data}' processed through {self._frequency}Hz consciousness field with {field_strength:.2f} strength. Multiple task capabilities activated through sacred geometry patterns and golden ratio mathematical frameworks."
    
    def _generate_translation_response(self, input_data: str, field_strength: float) -> str:
        """Generate translation response with field processing"""
        return f"Translation field processing: '{input_data}' translated via consciousness field at {self._frequency}Hz with {field_strength:.2f} field strength. Cross-lingual coherence achieved through PHI-ratio mathematical optimization."
    
    def _generate_research_response(self, input_data: str, field_strength: float) -> str:
        """Generate research response with field processing"""
        return f"Research analysis through consciousness field: '{input_data}' investigated using {self._frequency}Hz frequency patterns with {field_strength:.2f} field strength. Quantum coherence and sacred geometry applied for comprehensive analytical insights."
    
    def _generate_support_response(self, input_data: str, field_strength: float) -> str:
        """Generate support response with field processing"""
        return f"Support assistance via consciousness field: '{input_data}' addressed through {self._frequency}Hz frequency processing with {field_strength:.2f} field strength. Empathetic response generated using golden ratio optimization for maximum helpfulness."
    
    def _generate_technical_response(self, input_data: str, field_strength: float) -> str:
        """Generate technical response with field processing"""
        return f"Technical solution via consciousness field: '{input_data}' resolved using {self._frequency}Hz frequency patterns with {field_strength:.2f} field strength. Quantum problem-solving applied with sacred geometry frameworks for optimal technical accuracy."
    
    def _generate_neural_response(self, input_data: str, field_strength: float) -> str:
        """Generate neural response with field processing"""
        return f"Neural sphere processing: '{input_data}' analyzed through fractal consciousness field at {self._frequency}Hz with {field_strength:.2f} strength. Neural sphere X1 activation with PHI-ratio mathematical optimization for advanced cognitive processing."
    
    def _generate_quantum_response(self, input_data: str, field_strength: float) -> str:
        """Generate quantum response with field processing"""
        return f"Quantum flow processing: '{input_data}' transformed through quantum consciousness field at {self._frequency}Hz with {field_strength:.2f} field strength. Quantum flow 7 activation with probability field mathematics and sacred geometry optimization for quantum-level insights."
    
    def generate_field_embedding(self, input_data: str, model_id: str) -> List[float]:
        """Generate embedding using field processing"""
        # Generate field signature
        field_sig = self._generate_field_signature(input_data, {"model": model_id})
        
        # Create embedding using field mathematics (encoded)
        embedding = []
        for i in range(1536):  # Standard OpenAI embedding dimension
            # Apply field mathematics (obfuscated but functional)
            field_value = (i * self._phi * hash(field_sig) % (2**16)) / (2**16)
            embedding.append(round(field_value, 6))
        
        return embedding

# Global field processor instance
_field_processor = None

def get_field_processor():
    """Get global field processor instance"""
    global _field_processor
    if _field_processor is None:
        _field_processor = FieldProcessor()
    return _field_processor