from abc import ABC, abstractmethod
from typing import List, Tuple, Dict, Optional
import sys
import os

# Agregar el directorio padre al path para importar config
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from config import MEMORY_SEARCH_CONFIG, UI_MESSAGES
from core.memory_manager import MemoryManager

class PatternSearcher(ABC):
    """Interfaz base para buscadores de patrones (Strategy Pattern)"""
    
    def __init__(self):
        pass
    
    @abstractmethod
    def get_patterns(self) -> Dict[str, Tuple[bytes, List[str]]]:
        """Retorna los patrones a buscar"""
        pass
    
    @abstractmethod
    def search(self, memory_manager: MemoryManager) -> List[Tuple[str, int]]:
        """Busca los patrones en memoria"""
        pass
    
    def pattern_matches(self, data: bytes, pattern: bytes, mask: List[str], offset: int) -> bool:
        """Verifica si un patrón coincide en una posición específica"""
        pattern_len = len(pattern)
        if offset + pattern_len > len(data):
            return False
        
        data_slice = data[offset:offset + pattern_len]
        for i, (p_byte, m_char) in enumerate(zip(pattern, mask)):
            if m_char != '?' and data_slice[i] != p_byte:
                return False
        return True

class StaminaPatternSearcher(PatternSearcher):
    """Buscador específico para patrones de stamina"""
    
    def get_patterns(self) -> Dict[str, Tuple[bytes, List[str]]]:
        """Define los patrones específicos para stamina"""
        return {
            "stamina_pattern_1": (
                bytes([0x75, 0x00, 0xF3, 0x0F, 0x10, 0x44, 0x24, 0x08, 
                      0xF3, 0x0F, 0x5A, 0xC0, 0xF2, 0x0F, 0x5A, 0xE8, 
                      0xF3, 0x0F, 0x11, 0xAE, 0x94, 0x01, 0x00, 0x00]),
                ['x', '?', 'x', 'x', 'x', 'x', 'x', 'x', 
                 'x', 'x', 'x', 'x', 'x', 'x', 'x', 'x', 
                 'x', 'x', 'x', 'x', 'x', 'x', 'x', 'x']
            )
        }
    
    def search(self, memory_manager: MemoryManager) -> List[Tuple[str, int]]:
        """Busca patrones de stamina en memoria"""
        patterns = self.get_patterns()
        regions = memory_manager.get_memory_regions()
        found_addresses = []
        
        print(UI_MESSAGES['info']['searching_patterns'].format(region_count=len(regions)))
        
        # Pre-calcular longitudes de patrones
        pattern_info = {name: (pattern, mask, len(pattern)) 
                       for name, (pattern, mask) in patterns.items()}
        
        for region_idx, region in enumerate(regions):
            print(f"   Región {region_idx + 1}/{len(regions)}: 0x{region['base']:X} - 0x{region['base'] + region['size']:X}")
            
            # Configuración optimizada de chunks desde config
            chunk_size = MEMORY_SEARCH_CONFIG['chunk_size']
            overlap = MEMORY_SEARCH_CONFIG['overlap']
            
            for offset in range(0, region['size'], chunk_size):
                current_address = region['base'] + offset
                read_size = min(chunk_size + overlap, region['size'] - offset)
                
                data = memory_manager.read_memory(current_address, read_size)
                if not data:
                    continue
                
                # Buscar cada patrón
                data_len = len(data)
                for pattern_name, (pattern, mask, pattern_len) in pattern_info.items():
                    search_limit = data_len - pattern_len + 1
                    for i in range(search_limit):
                        if self.pattern_matches(data, pattern, mask, i):
                            found_address = current_address + i
                            found_addresses.append((pattern_name, found_address))
                            print(UI_MESSAGES['info']['pattern_found'].format(pattern_name=pattern_name, address=found_address))
                            print("   🛑 Deteniendo búsqueda - primer patrón encontrado")
                            return found_addresses

class SpeedPatternSearcher(PatternSearcher):
    """Buscador específico para patrones de velocidad"""
    
    def get_patterns(self) -> Dict[str, Tuple[bytes, List[str]]]:
        """Define los patrones específicos para velocidad"""
        return {
            "speed_pattern_1": (
                bytes([0xE0, 0x67, 0x03, 0x36, 0x7B, 0x02, 0x00, 0x00, 
                      0x00, 0x00, 0xC8, 0x41, 0x00, 0x00, 0x80, 0x3F]),
                ['x', 'x', 'x', 'x', 'x', 'x', 'x', 'x', 
                 'x', 'x', 'x', 'x', 'x', 'x', 'x', 'x']
            )
        }
    
    def search(self, memory_manager: MemoryManager) -> List[Tuple[str, int]]:
        """Busca patrones de velocidad en memoria"""
        patterns = self.get_patterns()
        regions = memory_manager.get_memory_regions()
        found_addresses = []
        
        print(UI_MESSAGES['info']['searching_patterns'].format(region_count=len(regions)))
        
        # Pre-calcular longitudes de patrones
        pattern_info = {name: (pattern, mask, len(pattern)) 
                       for name, (pattern, mask) in patterns.items()}
        
        for region_idx, region in enumerate(regions):
            print(f"   Región {region_idx + 1}/{len(regions)}: 0x{region['base']:X} - 0x{region['base'] + region['size']:X}")
            
            # Configuración optimizada de chunks desde config
            chunk_size = MEMORY_SEARCH_CONFIG['chunk_size']
            overlap = MEMORY_SEARCH_CONFIG['overlap']
            
            for offset in range(0, region['size'], chunk_size):
                current_address = region['base'] + offset
                read_size = min(chunk_size + overlap, region['size'] - offset)
                
                data = memory_manager.read_memory(current_address, read_size)
                if not data:
                    continue
                
                # Buscar cada patrón
                data_len = len(data)
                for pattern_name, (pattern, mask, pattern_len) in pattern_info.items():
                    search_limit = data_len - pattern_len + 1
                    for i in range(search_limit):
                        if self.pattern_matches(data, pattern, mask, i):
                            found_address = current_address + i
                            found_addresses.append((pattern_name, found_address))
                            print(UI_MESSAGES['info']['pattern_found'].format(pattern_name=pattern_name, address=found_address))
                            print("   🛑 Deteniendo búsqueda - primer patrón encontrado")
                            return found_addresses
        
        return found_addresses