import torch
from transformers import AutoModelForCausalLM, BitsAndBytesConfig
from deepseek_vl.models import VLChatProcessor
from deepseek_vl.utils.io import load_pil_images

# 模型路徑
model_path = "deepseek-ai/deepseek-vl-7b-chat"

# 讀取 processor
vl_chat_processor: VLChatProcessor = VLChatProcessor.from_pretrained(model_path)
tokenizer = vl_chat_processor.tokenizer

# ==== 量化模型設定 ====
bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,                 # 4-bit 量化
    bnb_4bit_compute_dtype=torch.float16,
    bnb_4bit_use_double_quant=True
)

vl_gpt: AutoModelForCausalLM = AutoModelForCausalLM.from_pretrained(
    model_path,
    trust_remote_code=True,
    device_map="auto",
    quantization_config=bnb_config
)
vl_gpt.eval()

# 範例對話
conversation = [
    {
        "role": "User",
        "content": "<image_placeholder>Describe each stage of this image.",
        "images": ["./images/training_pipelines.png"]
    },
    {
        "role": "Assistant",
        "content": ""
    }
]

# ==== 逐張圖片處理，降低 VRAM 使用 ====
answers = []
for conv in conversation:
    pil_images = load_pil_images([conv])
    prepare_inputs = vl_chat_processor(
        conversations=[conv],
        images=pil_images,
        force_batchify=True
    ).to(vl_gpt.device)

    inputs_embeds = vl_gpt.prepare_inputs_embeds(**prepare_inputs)

    # 減少生成長度 max_new_tokens
    outputs = vl_gpt.language_model.generate(
        inputs_embeds=inputs_embeds,
        attention_mask=prepare_inputs.attention_mask,
        pad_token_id=tokenizer.eos_token_id,
        bos_token_id=tokenizer.bos_token_id,
        eos_token_id=tokenizer.eos_token_id,
        max_new_tokens=128,   # 原本 512 → 128
        do_sample=False,
        use_cache=True
    )

    answer = tokenizer.decode(outputs[0].cpu().tolist(), skip_special_tokens=True)
    answers.append(f"{prepare_inputs['sft_format'][0]} {answer}")

# 輸出結果
for ans in answers:
    print(ans)