"""
Custom handler for HuggingFace Inference Endpoints.
Loads Qwen 2.5 7B base model (4-bit quantized) + K-GPT v1 LoRA adapter.
Uses 4-bit NF4 quantization so the model fits on a T4 GPU (16GB VRAM).
"""
from typing import Dict, List, Any
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from peft import PeftModel


class EndpointHandler:
    def __init__(self, path: str = ""):
        """
        Load the base model (4-bit quantized) and apply the LoRA adapter.
        'path' is the local directory where the HF repo was cloned.
        """
        base_model_id = "Qwen/Qwen2.5-7B"

        # Load tokenizer from the adapter repo
        self.tokenizer = AutoTokenizer.from_pretrained(path)
        if self.tokenizer.pad_token is None:
            self.tokenizer.pad_token = self.tokenizer.eos_token

        # 4-bit NF4 quantization config (matches training QLoRA config)
        bnb_config = BitsAndBytesConfig(
            load_in_4bit=True,
            bnb_4bit_quant_type="nf4",
            bnb_4bit_compute_dtype=torch.float16,
            bnb_4bit_use_double_quant=True,
        )

        # Load base model in 4-bit (~4GB VRAM instead of 14GB)
        base_model = AutoModelForCausalLM.from_pretrained(
            base_model_id,
            quantization_config=bnb_config,
            device_map="auto",
            trust_remote_code=True,
        )

        # Load LoRA adapter on top of quantized base
        self.model = PeftModel.from_pretrained(base_model, path)
        self.model.eval()

    def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
        """
        Process inference requests.
        Supports both simple text input and chat-style messages.
        """
        inputs = data.get("inputs", "")
        parameters = data.get("parameters", {})

        # Handle chat-style messages
        if isinstance(inputs, list):
            prompt = ""
            for msg in inputs:
                role = msg.get("role", "user").upper()
                content = msg.get("content", "")
                prompt += f"{role}: {content}\n"
            prompt += "ASSISTANT: "
            inputs = prompt

        # Default parameters
        max_new_tokens = parameters.get("max_new_tokens", 256)
        temperature = parameters.get("temperature", 0.7)
        top_p = parameters.get("top_p", 0.9)
        do_sample = parameters.get("do_sample", temperature > 0)

        # Tokenize
        input_ids = self.tokenizer(inputs, return_tensors="pt").to(self.model.device)

        # Generate
        with torch.no_grad():
            outputs = self.model.generate(
                **input_ids,
                max_new_tokens=max_new_tokens,
                temperature=temperature if do_sample else None,
                top_p=top_p if do_sample else None,
                do_sample=do_sample,
                pad_token_id=self.tokenizer.eos_token_id,
            )

        # Decode only the new tokens
        generated = outputs[0][input_ids["input_ids"].shape[1]:]
        text = self.tokenizer.decode(generated, skip_special_tokens=True)

        return [{"generated_text": text}]