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import torch
from transformers import AutoModelForCausalLM

from deepseek_vl.models import MultiModalityCausalLM, VLChatProcessor
from deepseek_vl.utils.io import load_pil_images

# specify the path to the model
model_path = "deepseek-ai/deepseek-vl-7b-chat"
vl_chat_processor: VLChatProcessor = VLChatProcessor.from_pretrained(model_path)
tokenizer = vl_chat_processor.tokenizer

vl_gpt: MultiModalityCausalLM = AutoModelForCausalLM.from_pretrained(
    model_path, trust_remote_code=True
)
vl_gpt = vl_gpt.to(torch.bfloat16).cuda().eval()

# single image conversation example
conversation = [
    {
        "role": "User",
        "content": "<image_placeholder>Describe each stage of this image.",
        "images": ["./images/training_pipelines.jpg"],
    },
    {"role": "Assistant", "content": ""},
]

# multiple images (or in-context learning) conversation example
# conversation = [
#     {
#         "role": "User",
#         "content": "<image_placeholder>A dog wearing nothing in the foreground, "
#                    "<image_placeholder>a dog wearing a santa hat, "
#                    "<image_placeholder>a dog wearing a wizard outfit, and "
#                    "<image_placeholder>what's the dog wearing?",
#         "images": [
#             "images/dog_a.png",
#             "images/dog_b.png",
#             "images/dog_c.png",
#             "images/dog_d.png",
#         ],
#     },
#     {"role": "Assistant", "content": ""}
# ]

# load images and prepare for inputs
pil_images = load_pil_images(conversation)
prepare_inputs = vl_chat_processor(
    conversations=conversation, images=pil_images, force_batchify=True
).to(vl_gpt.device)

# run image encoder to get the image embeddings
inputs_embeds = vl_gpt.prepare_inputs_embeds(**prepare_inputs)

# run the model to get the response
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=512,
    do_sample=False,
    use_cache=True,
)

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