import re
import requests
import torch
from PIL import Image
from transformers import AutoProcessor
from datasets import load_dataset
from llmcompressor import oneshot
from llmcompressor.modifiers.quantization import GPTQModifier
from llmcompressor.transformers.tracing import TraceableLlavaForConditionalGeneration

# Load model.
model_id = "llama-joycaption-beta-one-hf-llava"
model = TraceableLlavaForConditionalGeneration.from_pretrained(
    model_id, device_map="auto", torch_dtype="bfloat16"
)
processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)

# Oneshot arguments
DATASET_ID = "lmms-lab/flickr30k"
DATASET_SPLIT = "test"
NUM_CALIBRATION_SAMPLES = 512
MAX_SEQUENCE_LENGTH = 2048

PROMPT = "Write a long descriptive caption for this image in a formal tone."

ds = load_dataset(DATASET_ID, split=f"{DATASET_SPLIT}[:{NUM_CALIBRATION_SAMPLES}]")
ds = ds.shuffle(seed=42)


def preprocess_function(example):
    # Build the conversation
    convo = [
        {
            "role": "system",
            "content": "You are a helpful image captioner.",
        },
        {
            "role": "user",
            "content": PROMPT,
        },
        {"role": "assistant", "content": " ".join(example["caption"])},
    ]

    # Format the conversation
    # WARNING: HF's handling of chat's on Llava models is very fragile.  This specific combination of processor.apply_chat_template(), and processor() works
    # but if using other combinations always inspect the final input_ids to ensure they are correct.  Often times you will end up with multiple <bos> tokens
    # if not careful, which can make the model perform poorly.
    convo_string = processor.apply_chat_template(
        convo, tokenize=False, add_generation_prompt=True
    )
    assert isinstance(convo_string, str)

    # Process the inputs
    inputs = processor(
        text=[convo_string], images=[example["image"]], return_tensors="pt"
    ).to("cuda")
    inputs["pixel_values"] = inputs["pixel_values"].to(torch.bfloat16)
    return inputs


ds = ds.map(preprocess_function)


# Define a oneshot data collator for multimodal inputs.
def data_collator(batch):
    assert len(batch) == 1
    # return {key: torch.tensor(value) for key, value in batch[0].items()}
    return {
        "input_ids": torch.LongTensor(batch[0]["input_ids"]),
        "attention_mask": torch.tensor(batch[0]["attention_mask"]),
        "pixel_values": torch.tensor(batch[0]["pixel_values"]),
    }


# Recipe
recipe = [
    GPTQModifier(
        targets="Linear",
        scheme="W8A8",
        sequential_targets=["LlamaDecoderLayer"],
        ignore=["re:.*lm_head", "re:vision_tower.*", "re:multi_modal_projector.*"],
    ),
]
SAVE_DIR = model_id + "-W8A8"

# Perform oneshot
oneshot(
    model=model,
    tokenizer=model_id,
    dataset=ds,
    splits={"calibration": f"{DATASET_SPLIT}[:{NUM_CALIBRATION_SAMPLES}]"},
    recipe=recipe,
    max_seq_length=MAX_SEQUENCE_LENGTH,
    num_calibration_samples=NUM_CALIBRATION_SAMPLES,
    trust_remote_code_model=True,
    data_collator=data_collator,
    output_dir=SAVE_DIR,
)

# Confirm generations of the quantized model look sane.
print("========== SAMPLE GENERATION ==============")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "text", "text": "Please describe the animal in this image\n"},
            {"type": "image"},
        ],
    },
]
prompt = processor.apply_chat_template(messages, add_generation_prompt=True)
image_url = "http://images.cocodataset.org/train2017/000000231895.jpg"
raw_image = Image.open(requests.get(image_url, stream=True).raw)

inputs = processor(images=[raw_image], text=prompt, return_tensors="pt").to("cuda")
output = model.generate(**inputs, max_new_tokens=100)
print(processor.decode(output[0], skip_special_tokens=True))
print("==========================================")