#!/usr/bin/env python3

import argparse
import json
import os
import torch

from datasets import Dataset, Image  # Use PIL?
from functools import partial
from glob import glob
from peft import LoraConfig
from transformers import (
    AutoProcessor,
    AutoModelForCausalLM,
    AutoModelForImageTextToText,
    TrainingArguments,
    BitsAndBytesConfig,
)
from trl import SFTTrainer


def make_dataset(base="./baseline"):  # TODO: Make actual hf dataset
    prompt = "You are a professional English-German translator and also a renowned photography critic.\n\nWrite a detailed caption for this image in a single sentence. Translate the caption into German. The output needs to be JSON, the keys being 'English' and 'German' for the respective captions. Only output the JSON, nothing else." + "<start_of_image>"
    user_prompts = []
    images = []
    assistant_replies = []
    for filename in glob(f"{base}/*.jsonl"):
        with open(filename, "r") as f:
            data = json.loads(f.read())
        image_path = f"../d/Images/{os.path.basename(filename).removesuffix(".jsonl")}.jpg"
        user_prompts.append(prompt)
        assistant_replies.append(json.dumps({
            "English": data["English"],
            "German": data["Translation"],
        }, ensure_ascii=False, indent=0))
        images.append(image_path)

    return Dataset.from_dict({"image": images, "user": user_prompts, "assistant": assistant_replies}).cast_column("image", Image())


def add_chat_text(example, processor):
    messages = [
        {"role": "user", "content": example["user"]},
        {"role": "assistant", "content": example["assistant"]},
    ]
    example["text"] = processor.tokenizer.apply_chat_template(
        messages,
        tokenize=False,
        add_generation_prompt=False,
    )

    return example


def collate(batch, processor):  # FIXME: Support batch_size > 1
    images  = [i["image"] for i in batch]
    texts = [i["text"]  for i in batch]
    out = processor(
        text=texts,
        images=images,
        padding=True,
        truncation=True,
        max_length=512,
        return_tensors="pt",
    )

    image_token_id = [
        processor.tokenizer.convert_tokens_to_ids(
            processor.tokenizer.special_tokens_map["boi_token"]
        )
    ]

    out["labels"] = out["input_ids"].clone()

    out["labels"][labels == processor.tokenizer.pad_token_id] = -100
    out["labels"][labels == image_token_id] = -100
    out["labels"][labels == 262144] = -100

    return out

def main():
    parser = argparse.ArgumentParser()
    parser.add_argument("--model", default="google/gemma-3-4b-it")
    parser.add_argument("--batch-size", default=1)
    parser.add_argument("--gradient-accumulation", default=4)
    parser.add_argument("--learning-rate", default=1e-4)
    parser.add_argument("--epochs", default=1)
    parser.add_argument("--warmup-ratio", default=0.03)
    parser.add_argument("--scheduler-type", default="constant")
    parser.add_argument("--logging-steps", default=10)
    parser.add_argument("--lora-alpha", default=32)
    parser.add_argument("--lora-dropout", default=0.05)
    parser.add_argument("--lora-r", default=16)
    args = parser.parse_args()

    bnb_config = BitsAndBytesConfig(
        load_in_4bit=True,
        bnb_4bit_use_double_quant=True,
        bnb_4bit_quant_type="nf4",
        bnb_4bit_compute_dtype=torch.bfloat16,
    )

    processor = AutoProcessor.from_pretrained(args.model, use_fast=True)

    #model = AutoModelForCausalLM.from_pretrained(
    model = AutoModelForImageTextToText.from_pretrained(
        args.model,
        quantization_config=bnb_config,
        device_map="auto",
        low_cpu_mem_usage=True,
    )

    peft_config = LoraConfig(
        lora_alpha=args.lora_alpha,
        lora_dropout=args.lora_dropout,
        r=args.lora_r,
        task_type="CAUSAL_LM",
        bias="none",
        target_modules="all-linear",
        modules_to_save=["lm_head", "embed_tokens"],
    )

    dev_ds = make_dataset("./baseline/files_dev").map(partial(add_chat_text, processor=processor))
    train_ds = make_dataset("./baseline/files_train").map(partial(add_chat_text, processor=processor))

    args = TrainingArguments(
        output_dir="gemma3-mm-sft-lora",
        per_device_train_batch_size=args.batch_size,
        gradient_accumulation_steps=args.gradient_accumulation,
        num_train_epochs=args.epochs,
        learning_rate=args.learning_rate,
        warmup_ratio=args.warmup_ratio,
        lr_scheduler_type=args.scheduler_type,
        fp16=False,
        bf16=True,
        gradient_checkpointing=True,
        gradient_checkpointing_kwargs={"use_reentrant": False},
        optim="adamw_torch_8bit",  # Alternative from BnB: paged_adamw_8bit
        remove_unused_columns=False,
        logging_steps=args.logging_steps,
        save_strategy="epoch",
    )

    trainer = SFTTrainer(
        model=model,
        train_dataset=train_ds,
        eval_dataset=dev_ds,
        data_collator=partial(collate, processor=processor),
        args=args,
        peft_config=peft_config,
    )

    trainer.train()
    trainer.save_model()


if __name__ == "__main__":
    main()