from difflib import SequenceMatcher
import os
from ..services.service_registry import ServiceRegistry
from ..config import config
import asyncio

def get_lora_info(lora_name):
    """Get the lora path and trigger words from cache"""
    async def _get_lora_info_async():
        scanner = await ServiceRegistry.get_lora_scanner()
        cache = await scanner.get_cached_data()
        
        for item in cache.raw_data:
            if item.get('file_name') == lora_name:
                file_path = item.get('file_path')
                if file_path:
                    for root in config.loras_roots:
                        root = root.replace(os.sep, '/')
                        if file_path.startswith(root):
                            relative_path = os.path.relpath(file_path, root).replace(os.sep, '/')
                            # Get trigger words from civitai metadata
                            civitai = item.get('civitai', {})
                            trigger_words = civitai.get('trainedWords', []) if civitai else []
                            return relative_path, trigger_words
        return lora_name, []
    
    try:
        # Check if we're already in an event loop
        loop = asyncio.get_running_loop()
        # If we're in a running loop, we need to use a different approach
        # Create a new thread to run the async code
        import concurrent.futures
        
        def run_in_thread():
            new_loop = asyncio.new_event_loop()
            asyncio.set_event_loop(new_loop)
            try:
                return new_loop.run_until_complete(_get_lora_info_async())
            finally:
                new_loop.close()
        
        with concurrent.futures.ThreadPoolExecutor() as executor:
            future = executor.submit(run_in_thread)
            return future.result()
            
    except RuntimeError:
        # No event loop is running, we can use asyncio.run()
        return asyncio.run(_get_lora_info_async())

def fuzzy_match(text: str, pattern: str, threshold: float = 0.7) -> bool:
        """
        Check if text matches pattern using fuzzy matching.
        Returns True if similarity ratio is above threshold.
        """
        if not pattern or not text:
            return False
        
        # Convert both to lowercase for case-insensitive matching
        text = text.lower()
        pattern = pattern.lower()
        
        # Split pattern into words
        search_words = pattern.split()
        
        # Check each word
        for word in search_words:
            # First check if word is a substring (faster)
            if word in text:
                continue
            
            # If not found as substring, try fuzzy matching
            # Check if any part of the text matches this word
            found_match = False
            for text_part in text.split():
                ratio = SequenceMatcher(None, text_part, word).ratio()
                if ratio >= threshold:
                    found_match = True
                    break
                
            if not found_match:
                return False
        
        # All words found either as substrings or fuzzy matches
        return True

def calculate_recipe_fingerprint(loras):
    """
    Calculate a unique fingerprint for a recipe based on its LoRAs.
    
    The fingerprint is created by sorting LoRA hashes, filtering invalid entries,
    normalizing strength values to 2 decimal places, and joining in format:
    hash1:strength1|hash2:strength2|...
    
    Args:
        loras (list): List of LoRA dictionaries with hash and strength values
        
    Returns:
        str: The calculated fingerprint
    """
    if not loras:
        return ""
    
    # Filter valid entries and extract hash and strength
    valid_loras = []
    for lora in loras:
        # Skip excluded loras
        if lora.get("exclude", False):
            continue
            
        # Get the hash - use modelVersionId as fallback if hash is empty
        hash_value = lora.get("hash", "").lower()
        if not hash_value and lora.get("isDeleted", False) and lora.get("modelVersionId"):
            hash_value = str(lora.get("modelVersionId"))
            
        # Skip entries without a valid hash
        if not hash_value:
            continue
            
        # Normalize strength to 2 decimal places (check both strength and weight fields)
        strength = round(float(lora.get("strength", lora.get("weight", 1.0))), 2)
        
        valid_loras.append((hash_value, strength))
    
    # Sort by hash
    valid_loras.sort()
    
    # Join in format hash1:strength1|hash2:strength2|...
    fingerprint = "|".join([f"{hash_value}:{strength}" for hash_value, strength in valid_loras])
    
    return fingerprint