from PIL import Image
import torch
from transformers import AutoModel, AutoTokenizer, TextIteratorStreamer
from transformers.image_utils import load_image
from threading import Thread
import logging
import time
import pynvml

class MiniCPM:
    def __init__(self, model_id):
        self.model_id = model_id
        self.model = AutoModel.from_pretrained(
            model_id, 
            trust_remote_code=True,
            attn_implementation='sdpa', 
            torch_dtype=torch.bfloat16
        )
        self.model = self.model.eval().cuda()
        self.tokenizer = AutoTokenizer.from_pretrained(
            model_id, trust_remote_code=True
        )

        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()

        images = [Image.open(frame).convert('RGB') for frame in video]
        content = images + [prompt]
        msgs = [{'role': 'user', 'content': content}]

        # MiniCPM's chat method handles streaming internally
        res = self.model.chat(
            image=None,  
            msgs=msgs,
            tokenizer=self.tokenizer,
            stream=True
        )
        
        full_response = ""
        print("Response: ", end="")
        first_token_time = None
        for new_text in res:
            if first_token_time is None:
                first_token_time = time.time()
            full_response += new_text
            print(new_text, end="", flush=True)
        print()
        
        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.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,
        }