"""Model initialization and management"""
import os
import torch
import threading
from transformers import AutoModelForCausalLM, AutoTokenizer
from llama_index.llms.huggingface import HuggingFaceLLM
from llama_index.embeddings.huggingface import HuggingFaceEmbedding
from logger import logger
import config
import spaces

try:
    from TTS.api import TTS
    TTS_AVAILABLE = True
except ImportError:
    TTS_AVAILABLE = False
    TTS = None

try:
    from transformers import WhisperProcessor, WhisperForConditionalGeneration
    try:
        import torchaudio
    except ImportError:
        torchaudio = None
    WHISPER_AVAILABLE = True
except ImportError:
    WHISPER_AVAILABLE = False
    WhisperProcessor = None
    WhisperForConditionalGeneration = None
    torchaudio = None

# Model loading state tracking
_model_loading_states = {}
_model_loading_lock = threading.Lock()

def set_model_loading_state(model_name: str, state: str):
    """
    Set model loading state: 'loading', 'loaded', 'error'
    Note: No GPU decorator needed - this just sets a dictionary value, no GPU access required.
    """
    with _model_loading_lock:
        _model_loading_states[model_name] = state
        logger.debug(f"Model {model_name} state set to: {state}")

def get_model_loading_state(model_name: str) -> str:
    """
    Get model loading state: 'loading', 'loaded', 'error', or 'unknown'
    Note: No GPU decorator needed - this just reads a dictionary value, no GPU access required.
    """
    with _model_loading_lock:
        return _model_loading_states.get(model_name, "unknown")

def is_model_loaded(model_name: str) -> bool:
    """Check if model is loaded and ready"""
    with _model_loading_lock:
        return (model_name in config.global_medical_models and 
                config.global_medical_models[model_name] is not None and
                _model_loading_states.get(model_name) == "loaded")

def initialize_medical_model(model_name: str):
    """Initialize medical model (MedSwin) - download on demand"""
    if model_name not in config.global_medical_models or config.global_medical_models[model_name] is None:
        set_model_loading_state(model_name, "loading")
        logger.info(f"Initializing medical model: {model_name}...")
        try:
            model_path = config.MEDSWIN_MODELS[model_name]
            tokenizer = AutoTokenizer.from_pretrained(model_path, token=config.HF_TOKEN)
            model = AutoModelForCausalLM.from_pretrained(
                model_path,
                device_map="auto",
                trust_remote_code=True,
                token=config.HF_TOKEN,
                torch_dtype=torch.float16
            )
            config.global_medical_models[model_name] = model
            config.global_medical_tokenizers[model_name] = tokenizer
            set_model_loading_state(model_name, "loaded")
            logger.info(f"Medical model {model_name} initialized successfully")
        except Exception as e:
            set_model_loading_state(model_name, "error")
            logger.error(f"Failed to initialize medical model {model_name}: {e}")
            raise
    else:
        # Model already loaded, ensure state is set
        if get_model_loading_state(model_name) != "loaded":
            set_model_loading_state(model_name, "loaded")
    return config.global_medical_models[model_name], config.global_medical_tokenizers[model_name]

def initialize_tts_model():
    """Initialize TTS model for text-to-speech"""
    if not TTS_AVAILABLE:
        logger.warning("TTS library not installed. TTS features will be disabled.")
        return None
    if config.global_tts_model is None:
        try:
            logger.info("Initializing TTS model for voice generation...")
            config.global_tts_model = TTS(model_name=config.TTS_MODEL, progress_bar=False)
            logger.info("TTS model initialized successfully")
        except Exception as e:
            logger.warning(f"TTS model initialization failed: {e}")
            logger.warning("TTS features will be disabled. If pyworld dependency is missing, try: pip install TTS --no-deps && pip install coqui-tts")
            config.global_tts_model = None
    return config.global_tts_model

def initialize_whisper_model():
    """Initialize Whisper model for speech-to-text (ASR) from Hugging Face"""
    if not WHISPER_AVAILABLE:
        logger.warning("Whisper transformers not installed. ASR features will be disabled.")
        return None
    if config.global_whisper_model is None:
        try:
            logger.info("Initializing Whisper model (openai/whisper-large-v3-turbo) from Hugging Face...")
            model_id = "openai/whisper-large-v3-turbo"
            processor = WhisperProcessor.from_pretrained(model_id, token=config.HF_TOKEN)
            model = WhisperForConditionalGeneration.from_pretrained(
                model_id,
                device_map="auto",
                torch_dtype=torch.float16,
                token=config.HF_TOKEN
            )
            # Store both processor and model
            config.global_whisper_model = {"processor": processor, "model": model}
            logger.info(f"Whisper model ({model_id}) initialized successfully")
        except Exception as e:
            logger.warning(f"Whisper model initialization failed: {e}")
            logger.warning("ASR features will be disabled. Install with: pip install transformers torchaudio")
            config.global_whisper_model = None
    return config.global_whisper_model

def get_or_create_embed_model():
    """Reuse embedding model to avoid reloading weights each request"""
    if config.global_embed_model is None:
        logger.info("Initializing shared embedding model for RAG retrieval...")
        config.global_embed_model = HuggingFaceEmbedding(model_name=config.EMBEDDING_MODEL, token=config.HF_TOKEN)
    return config.global_embed_model

def get_llm_for_rag(temperature=0.7, max_new_tokens=256, top_p=0.95, top_k=50):
    """Get LLM for RAG indexing (uses medical model)"""
    medical_model_obj, medical_tokenizer = initialize_medical_model(config.DEFAULT_MEDICAL_MODEL)
    
    return HuggingFaceLLM(
        context_window=4096,
        max_new_tokens=max_new_tokens,
        tokenizer=medical_tokenizer,
        model=medical_model_obj,
        generate_kwargs={
            "do_sample": True,
            "temperature": temperature,
            "top_k": top_k,
            "top_p": top_p
        }
    )