"""
Loads and wraps the base pretrained model.
Supported architectures:
  - google/flan-t5-xl          (recommended, 3B)
  - google/flan-t5-large       (780M, resource-constrained)
  - facebook/bart-large        (400M, excellent denoiser)
  - meta-llama/Meta-Llama-3.1-8B-Instruct (8B, best quality)
"""

from transformers import (
    AutoTokenizer, AutoModelForSeq2SeqLM,
    AutoModelForCausalLM, BitsAndBytesConfig
)
from peft import get_peft_model, LoraConfig, TaskType
import torch
from loguru import logger


ENCODER_DECODER_MODELS = {
    "flan-t5-xl": "google/flan-t5-xl",
    "flan-t5-large": "google/flan-t5-large",
    "flan-t5-base": "google/flan-t5-base",
    "flan-t5-small": "google/flan-t5-small",
    "bart-large": "facebook/bart-large",
}

DECODER_ONLY_MODELS = {
    "llama-3.1-8b": "meta-llama/Meta-Llama-3.1-8B-Instruct",
}


def load_model_and_tokenizer(model_key: str, quantize: bool = False, use_lora: bool = True,
                              lora_config_dict: dict = None):
    """
    Load a pretrained model with optional LoRA and quantization.

    Args:
        model_key: Key from ENCODER_DECODER_MODELS or DECODER_ONLY_MODELS
        quantize: Whether to use 4-bit quantization
        use_lora: Whether to apply LoRA adapters
        lora_config_dict: Optional dict with LoRA hyperparams (r, lora_alpha, etc.)

    Returns:
        Tuple of (model, tokenizer, is_seq2seq)
    """
    # Determine model type and HuggingFace identifier
    is_seq2seq = model_key in ENCODER_DECODER_MODELS
    is_causal = model_key in DECODER_ONLY_MODELS

    if not is_seq2seq and not is_causal:
        raise ValueError(
            f"Unknown model key: '{model_key}'. "
            f"Available: {list(ENCODER_DECODER_MODELS.keys()) + list(DECODER_ONLY_MODELS.keys())}"
        )

    model_name = ENCODER_DECODER_MODELS.get(model_key) or DECODER_ONLY_MODELS.get(model_key)
    logger.info(f"Loading model: {model_name} (seq2seq={is_seq2seq}, quantize={quantize}, lora={use_lora})")

    # Load tokenizer
    tokenizer = AutoTokenizer.from_pretrained(model_name)
    if tokenizer.pad_token is None:
        tokenizer.pad_token = tokenizer.eos_token

    # Configure quantization if requested
    model_kwargs = {
        "torch_dtype": torch.float32,  # CPU-optimised: use float32 for stability
    }

    # Detect device
    device = "cpu"
    if torch.cuda.is_available():
        device = "cuda"
        # Use bfloat16 if Ampere+, else float16
        if torch.cuda.get_device_capability()[0] >= 8:
            model_kwargs["torch_dtype"] = torch.bfloat16
        else:
            model_kwargs["torch_dtype"] = torch.float16

    if quantize and device == "cuda":
        bnb_config = BitsAndBytesConfig(
            load_in_4bit=True,
            bnb_4bit_quant_type="nf4",
            bnb_4bit_compute_dtype=model_kwargs["torch_dtype"],
            bnb_4bit_use_double_quant=True,
        )
        model_kwargs["quantization_config"] = bnb_config
        logger.info("Using 4-bit NF4 quantization")
    elif quantize and device == "cpu":
        logger.warning("Quantization requested but no GPU available, skipping")

    # Load model
    if is_seq2seq:
        model = AutoModelForSeq2SeqLM.from_pretrained(model_name, **model_kwargs)
    else:
        model = AutoModelForCausalLM.from_pretrained(model_name, **model_kwargs)

    # Move to device if not quantized (quantized models are already on device)
    if not quantize or device == "cpu":
        model = model.to(device)

    logger.info(f"Model loaded on {device} with dtype {model_kwargs.get('torch_dtype')}")

    # Apply LoRA if requested
    if use_lora:
        lora_cfg = lora_config_dict or {}
        task_type = TaskType.SEQ_2_SEQ_LM if is_seq2seq else TaskType.CAUSAL_LM

        # Default target modules based on model architecture
        default_targets = {
            "flan-t5-xl": ["q", "v", "k", "o", "wi_0", "wi_1", "wo"],
            "flan-t5-large": ["q", "v", "k", "o", "wi_0", "wi_1", "wo"],
            "flan-t5-base": ["q", "v", "k", "o", "wi_0", "wi_1", "wo"],
            "flan-t5-small": ["q", "v", "k", "o", "wi_0", "wi_1", "wo"],
            "bart-large": ["q_proj", "v_proj", "k_proj", "out_proj"],
            "llama-3.1-8b": ["q_proj", "v_proj", "k_proj", "o_proj", "gate_proj", "up_proj", "down_proj"],
        }

        lora_config = LoraConfig(
            task_type=task_type,
            r=lora_cfg.get("r", 16),
            lora_alpha=lora_cfg.get("lora_alpha", 32),
            lora_dropout=lora_cfg.get("lora_dropout", 0.05),
            target_modules=lora_cfg.get("target_modules", default_targets.get(model_key, ["q", "v"])),
            bias="none",
        )

        model = get_peft_model(model, lora_config)
        trainable_params = sum(p.numel() for p in model.parameters() if p.requires_grad)
        total_params = sum(p.numel() for p in model.parameters())
        logger.info(
            f"LoRA applied: {trainable_params:,} trainable params / {total_params:,} total "
            f"({100 * trainable_params / total_params:.2f}%)"
        )

    return model, tokenizer, is_seq2seq