# pip install accelerate

from transformers import AutoProcessor, Gemma3ForConditionalGeneration, TextIteratorStreamer
from PIL import Image
import requests
import torch
from threading import Thread
import logging
import time
import pynvml

class Gemma:
    def __init__(self, model_id):
        self.model_id = model_id
        self.model = Gemma3ForConditionalGeneration.from_pretrained(
            model_id, device_map="auto", torch_dtype=torch.bfloat16
        ).eval()
        self.processor = AutoProcessor.from_pretrained(model_id)

        self.handle = None
        if torch.cuda.is_available():
            try:
                pynvml.nvmlInit()
                device_id = next(self.model.parameters()).device.index
                self.handle = pynvml.nvmlDeviceGetHandleByIndex(device_id)
            except Exception as e:
                logging.error(f"Failed to initialize NVML: {e}")

    def __del__(self):
        if self.handle:
            try:
                pynvml.nvmlShutdown()
            except:
                pass

    def generate(self, video, prompt):
        start_time = time.time()
        
        messages = [
            {
                "role": "system",
                "content": [{"type": "text", "text": "You are a helpful assistant."}]
            },

            {
                "role": "user",
                "content": [
                    {"type": "text", "text": prompt}]
            }
        ]

        for image in video: 
            messages[1]["content"].append({"type": "image", "image": image})

        print(messages)
        inputs = self.processor.apply_chat_template(
            messages, add_generation_prompt=True, tokenize=True,
            return_dict=True, return_tensors="pt"
        ).to(self.model.device)

        logging.info(f"Prompt token length: {len(inputs.input_ids[0])}")

        streamer = TextIteratorStreamer(self.processor, skip_prompt=True, skip_special_tokens=True)

        generation_kwargs = dict(
            **inputs,
            streamer=streamer,
            max_new_tokens=512
        )

        thread = Thread(target=self.model.generate, kwargs=generation_kwargs)
        thread.start()
        
        full_response = ""
        print("Response: ", end="")
        first_token_time = None
        for new_text in streamer:
            if first_token_time is None:
                first_token_time = time.time()
            full_response += new_text   
            print(new_text, end="", flush=True)
        print()
        thread.join()

        end_time = time.time()
        
        if first_token_time is not None:
            generation_time = end_time - first_token_time
        else:
            generation_time = 0
        
        num_generated_tokens = len(self.processor.tokenizer(full_response).input_ids)
        tokens_per_second = num_generated_tokens / generation_time if generation_time > 0 else 0

        peak_memory_mb = 0
        if self.handle:
            mem_info = pynvml.nvmlDeviceGetMemoryInfo(self.handle)
            peak_memory_mb = mem_info.used / (1024 * 1024)

        return {
            "response": full_response,
            "tokens_per_second": tokens_per_second,
            "peak_gpu_memory_mb": peak_memory_mb,
            "num_generated_tokens": num_generated_tokens,
        }