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# /// script
# dependencies = [
#     "trl>=0.24.0",
#     "peft>=0.17.0",
#     "transformers>=5.14.0",
#     "datasets>=4.0",
#     "trackio",
#     "Pillow>=10.0",
#     "accelerate>=1.0",
#     "torch>=2.5",
#     "torchvision>=0.20",
#     "num2words",
# ]
# ///
"""LoRA SFT of Gemma 4 for GUI grounding + web-agent action prediction.
Trains on the flat dataset built by prep_data.py (image, system, user,
assistant, source). Each row becomes one multimodal chat example:
    system : the GUI-agent system prompt
    user   : [screenshot] + instruction + previous actions
    assistant (label): <think>…</think> <code>action(...)</code>
Only the assistant turn contributes to the loss (the prompt + image tokens are
masked to -100 in the collator). Adapters are pushed to the Hub.
Launch via HF Jobs (see gemma4/launch_train.sh) — never run untethered; the
Jobs box is ephemeral so push_to_hub must be on.
"""

import argparse

import torch
import torch.nn as nn
from datasets import load_dataset
from peft import LoraConfig
from transformers import AutoModelForImageTextToText, AutoProcessor
from trl import SFTConfig, SFTTrainer

PROJ_SUFFIXES = ("q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj")


def find_lora_targets(model):
    """FULL module names of the language-tower projection Linears.
    Gemma 4's vision tower wraps its projections in `Gemma4ClippableLinear`
    (not `nn.Linear`), which PEFT can't adapt, and matching by bare suffix (e.g.
    "q_proj") would also grab those and crash. So we walk the graph and return
    the *fully-qualified* names of the real `nn.Linear` projections under
    `language_model`. PEFT matches those exactly, leaving the vision encoder
    frozen and skipping MoE expert Parameters (which aren't Modules).
    """
    targets = []
    for name, module in model.named_modules():
        if not isinstance(module, nn.Linear):
            continue
        if "language_model" not in name:
            continue
        if name.split(".")[-1] in PROJ_SUFFIXES:
            targets.append(name)
    return targets


def build_collator(processor, model_config):
    """Render our (system/user/assistant + image) rows into a batch.
    Gemma 4 uses an image-text-to-text processor: we pass the chat template
    with an image placeholder in the user turn, tokenize, and mask padding plus
    every image-structural token (the soft image token + <start_of_image> /
    <end_of_image>) so loss is computed only over real text tokens. We read the
    exact ids from the model config so this can't drift with the tokenizer.
    """
    image_token_ids = {
        tid for tid in [
            getattr(model_config, "image_token_id", None),
            getattr(model_config, "boi_token_id", None),
            getattr(model_config, "eoi_token_id", None),
            getattr(processor, "image_token_id", None),
        ] if isinstance(tid, int)
    }
    print(f"[train] masking image/pad token ids: {sorted(image_token_ids)}")
    processor.tokenizer.padding_side = "right"  # so the prompt is a clean prefix

    def to_messages(example):
        # IMPORTANT: match inference (gemma4/server.py via mlx_vlm) EXACTLY —
        # one user turn holding [image] + "SYSTEM\n\nuser", then the assistant.
        # Gemma's chat template has no separate system role, and mlx_vlm wraps a
        # raw prompt as a single user turn, so we mirror that here to keep the
        # train and inference token streams identical.
        return [
            {"role": "user", "content": [
                {"type": "image"},
                {"type": "text", "text": f"{example['system']}\n\n{example['user']}"},
            ]},
            {"role": "assistant", "content": [{"type": "text", "text": example["assistant"]}]},
        ]

    def collate(examples):
        full_texts, prompt_texts, images = [], [], []
        for ex in examples:
            msgs = to_messages(ex)
            full_texts.append(
                processor.apply_chat_template(msgs, tokenize=False, add_generation_prompt=False)
            )
            # Prompt = everything up to (and including) the assistant header, so
            # its token length marks where the completion begins.
            prompt_texts.append(
                processor.apply_chat_template(msgs[:-1], tokenize=False, add_generation_prompt=True)
            )
            images.append([ex["image"].convert("RGB")])

        batch = processor(text=full_texts, images=images, return_tensors="pt", padding=True)
        # Second pass with the SAME images so the measured prompt length includes
        # the expanded soft-image tokens — the completion offset is then exact.
        prompt_batch = processor(text=prompt_texts, images=images, return_tensors="pt", padding=True)
        prompt_lens = prompt_batch["attention_mask"].sum(dim=1)

        labels = batch["input_ids"].clone()
        labels[batch["attention_mask"] == 0] = -100     # padding
        for tid in image_token_ids:
            labels[labels == tid] = -100                 # image structural tokens
        for i, plen in enumerate(prompt_lens):
            labels[i, : int(plen)] = -100                # prompt (assistant-only loss)
        batch["labels"] = labels
        return batch

    return collate


def main():
    ap = argparse.ArgumentParser()
    ap.add_argument("--model", default="google/gemma-4-E4B-it")
    ap.add_argument("--dataset", required=True)
    ap.add_argument("--hub-model-id", required=True)
    ap.add_argument("--epochs", type=float, default=1.0)
    ap.add_argument("--batch-size", type=int, default=2)
    ap.add_argument("--grad-accum", type=int, default=8)
    ap.add_argument("--lr", type=float, default=2e-4)
    ap.add_argument("--max-steps", type=int, default=-1)
    ap.add_argument("--eval-frac", type=float, default=0.03)
    ap.add_argument("--eval-steps", type=int, default=50)
    ap.add_argument("--save-steps", type=int, default=100)
    ap.add_argument("--project", default="gemma4-gui-agent")
    ap.add_argument("--run-name", default="gemma4-e4b-lora")
    ap.add_argument("--private", action="store_true", default=True)
    args = ap.parse_args()

    print(f"[train] loading dataset {args.dataset}")
    ds = load_dataset(args.dataset, split="train")
    split = ds.train_test_split(test_size=args.eval_frac, seed=42)
    train_ds, eval_ds = split["train"], split["test"]
    print(f"[train] {len(train_ds)} train / {len(eval_ds)} eval")

    print(f"[train] loading {args.model}")
    processor = AutoProcessor.from_pretrained(args.model)
    model = AutoModelForImageTextToText.from_pretrained(
        args.model, dtype=torch.bfloat16, attn_implementation="eager",
    )

    # Adapt attention + MLP projections across the language tower; leave the
    # vision encoder frozen (grounding signal comes from the LM reading tokens).
    targets = find_lora_targets(model)
    print(f"[train] LoRA targets: {len(targets)} language-model Linear projections "
          f"(e.g. {targets[:2]})")
    if not targets:
        raise RuntimeError("no language_model projection Linears found for LoRA")
    peft_config = LoraConfig(
        r=16, lora_alpha=32, lora_dropout=0.05, bias="none",
        task_type="CAUSAL_LM",
        target_modules=targets,
        modules_to_save=None,
    )

    sft_config = SFTConfig(
        output_dir=args.hub_model_id.split("/")[-1],
        per_device_train_batch_size=args.batch_size,
        per_device_eval_batch_size=args.batch_size,
        gradient_accumulation_steps=args.grad_accum,
        num_train_epochs=args.epochs,
        max_steps=args.max_steps,
        learning_rate=args.lr,
        warmup_ratio=0.03,
        lr_scheduler_type="cosine",
        logging_steps=5,
        eval_strategy="steps",
        eval_steps=args.eval_steps,
        save_strategy="steps",
        save_steps=args.save_steps,
        save_total_limit=2,
        bf16=True,
        gradient_checkpointing=True,
        gradient_checkpointing_kwargs={"use_reentrant": False},
        dataset_kwargs={"skip_prepare_dataset": True},  # we collate raw rows
        remove_unused_columns=False,
        max_length=None,  # never truncate image tokens
        push_to_hub=True,
        hub_model_id=args.hub_model_id,
        hub_private_repo=args.private,
        hub_strategy="every_save",
        report_to="trackio",
        run_name=args.run_name,
        project=args.project,
        # A private model repo forces Trackio's per-push "static" space private
        # too, which it rejects (browser-only snapshots must be public) and that
        # crashes the Hub push. Disable the static snapshot; the live Trackio
        # dashboard during training still works.
        trackio_static_space_id=False,
    )

    trainer = SFTTrainer(
        model=model,
        args=sft_config,
        train_dataset=train_ds,
        eval_dataset=eval_ds,
        data_collator=build_collator(processor, model.config),
        peft_config=peft_config,
        processing_class=processor,
    )

    trainer.train()
    trainer.save_model(sft_config.output_dir)
    trainer.push_to_hub()
    print(f"[train] pushed adapters to {args.hub_model_id}")


if __name__ == "__main__":
    main()

	# /// script
	# dependencies = [
	# "trl>=0.24.0",
	# "peft>=0.17.0",
	# "transformers>=5.14.0",
	# "datasets>=4.0",
	# "trackio",
	# "Pillow>=10.0",
	# "accelerate>=1.0",
	# "torch>=2.5",
	# "torchvision>=0.20",
	# "num2words",
	# ]
	# ///
	"""LoRA SFT of Gemma 4 for GUI grounding + web-agent action prediction.

	Trains on the flat dataset built by prep_data.py (image, system, user,
	assistant, source). Each row becomes one multimodal chat example:

	system : the GUI-agent system prompt
	user : [screenshot] + instruction + previous actions
	assistant (label): <think>…</think> <code>action(...)</code>

	Only the assistant turn contributes to the loss (the prompt + image tokens are
	masked to -100 in the collator). Adapters are pushed to the Hub.

	Launch via HF Jobs (see gemma4/launch_train.sh) — never run untethered; the
	Jobs box is ephemeral so push_to_hub must be on.
	"""

	import argparse

	import torch
	import torch.nn as nn
	from datasets import load_dataset
	from peft import LoraConfig
	from transformers import AutoModelForImageTextToText, AutoProcessor
	from trl import SFTConfig, SFTTrainer

	PROJ_SUFFIXES = ("q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj")


	def find_lora_targets(model):
	"""FULL module names of the language-tower projection Linears.

	Gemma 4's vision tower wraps its projections in `Gemma4ClippableLinear`
	(not `nn.Linear`), which PEFT can't adapt, and matching by bare suffix (e.g.
	"q_proj") would also grab those and crash. So we walk the graph and return
	the fully-qualified names of the real `nn.Linear` projections under
	`language_model`. PEFT matches those exactly, leaving the vision encoder
	frozen and skipping MoE expert Parameters (which aren't Modules).
	"""
	targets = []
	for name, module in model.named_modules():
	if not isinstance(module, nn.Linear):
	continue
	if "language_model" not in name:
	continue
	if name.split(".")[-1] in PROJ_SUFFIXES:
	targets.append(name)
	return targets


	def build_collator(processor, model_config):
	"""Render our (system/user/assistant + image) rows into a batch.

	Gemma 4 uses an image-text-to-text processor: we pass the chat template
	with an image placeholder in the user turn, tokenize, and mask padding plus
	every image-structural token (the soft image token + <start_of_image> /
	<end_of_image>) so loss is computed only over real text tokens. We read the
	exact ids from the model config so this can't drift with the tokenizer.
	"""
	image_token_ids = {
	tid for tid in [
	getattr(model_config, "image_token_id", None),
	getattr(model_config, "boi_token_id", None),
	getattr(model_config, "eoi_token_id", None),
	getattr(processor, "image_token_id", None),
	] if isinstance(tid, int)
	}
	print(f"[train] masking image/pad token ids: {sorted(image_token_ids)}")
	processor.tokenizer.padding_side = "right" # so the prompt is a clean prefix

	def to_messages(example):
	# IMPORTANT: match inference (gemma4/server.py via mlx_vlm) EXACTLY —
	# one user turn holding [image] + "SYSTEM\n\nuser", then the assistant.
	# Gemma's chat template has no separate system role, and mlx_vlm wraps a
	# raw prompt as a single user turn, so we mirror that here to keep the
	# train and inference token streams identical.
	return [
	{"role": "user", "content": [
	{"type": "image"},
	{"type": "text", "text": f"{example['system']}\n\n{example['user']}"},
	]},
	{"role": "assistant", "content": [{"type": "text", "text": example["assistant"]}]},
	]

	def collate(examples):
	full_texts, prompt_texts, images = [], [], []
	for ex in examples:
	msgs = to_messages(ex)
	full_texts.append(
	processor.apply_chat_template(msgs, tokenize=False, add_generation_prompt=False)
	)
	# Prompt = everything up to (and including) the assistant header, so
	# its token length marks where the completion begins.
	prompt_texts.append(
	processor.apply_chat_template(msgs[:-1], tokenize=False, add_generation_prompt=True)
	)
	images.append([ex["image"].convert("RGB")])

	batch = processor(text=full_texts, images=images, return_tensors="pt", padding=True)
	# Second pass with the SAME images so the measured prompt length includes
	# the expanded soft-image tokens — the completion offset is then exact.
	prompt_batch = processor(text=prompt_texts, images=images, return_tensors="pt", padding=True)
	prompt_lens = prompt_batch["attention_mask"].sum(dim=1)

	labels = batch["input_ids"].clone()
	labels[batch["attention_mask"] == 0] = -100 # padding
	for tid in image_token_ids:
	labels[labels == tid] = -100 # image structural tokens
	for i, plen in enumerate(prompt_lens):
	labels[i, : int(plen)] = -100 # prompt (assistant-only loss)
	batch["labels"] = labels
	return batch

	return collate


	def main():
	ap = argparse.ArgumentParser()
	ap.add_argument("--model", default="google/gemma-4-E4B-it")
	ap.add_argument("--dataset", required=True)
	ap.add_argument("--hub-model-id", required=True)
	ap.add_argument("--epochs", type=float, default=1.0)
	ap.add_argument("--batch-size", type=int, default=2)
	ap.add_argument("--grad-accum", type=int, default=8)
	ap.add_argument("--lr", type=float, default=2e-4)
	ap.add_argument("--max-steps", type=int, default=-1)
	ap.add_argument("--eval-frac", type=float, default=0.03)
	ap.add_argument("--eval-steps", type=int, default=50)
	ap.add_argument("--save-steps", type=int, default=100)
	ap.add_argument("--project", default="gemma4-gui-agent")
	ap.add_argument("--run-name", default="gemma4-e4b-lora")
	ap.add_argument("--private", action="store_true", default=True)
	args = ap.parse_args()

	print(f"[train] loading dataset {args.dataset}")
	ds = load_dataset(args.dataset, split="train")
	split = ds.train_test_split(test_size=args.eval_frac, seed=42)
	train_ds, eval_ds = split["train"], split["test"]
	print(f"[train] {len(train_ds)} train / {len(eval_ds)} eval")

	print(f"[train] loading {args.model}")
	processor = AutoProcessor.from_pretrained(args.model)
	model = AutoModelForImageTextToText.from_pretrained(
	args.model, dtype=torch.bfloat16, attn_implementation="eager",
	)

	# Adapt attention + MLP projections across the language tower; leave the
	# vision encoder frozen (grounding signal comes from the LM reading tokens).
	targets = find_lora_targets(model)
	print(f"[train] LoRA targets: {len(targets)} language-model Linear projections "
	f"(e.g. {targets[:2]})")
	if not targets:
	raise RuntimeError("no language_model projection Linears found for LoRA")
	peft_config = LoraConfig(
	r=16, lora_alpha=32, lora_dropout=0.05, bias="none",
	task_type="CAUSAL_LM",
	target_modules=targets,
	modules_to_save=None,
	)

	sft_config = SFTConfig(
	output_dir=args.hub_model_id.split("/")[-1],
	per_device_train_batch_size=args.batch_size,
	per_device_eval_batch_size=args.batch_size,
	gradient_accumulation_steps=args.grad_accum,
	num_train_epochs=args.epochs,
	max_steps=args.max_steps,
	learning_rate=args.lr,
	warmup_ratio=0.03,
	lr_scheduler_type="cosine",
	logging_steps=5,
	eval_strategy="steps",
	eval_steps=args.eval_steps,
	save_strategy="steps",
	save_steps=args.save_steps,
	save_total_limit=2,
	bf16=True,
	gradient_checkpointing=True,
	gradient_checkpointing_kwargs={"use_reentrant": False},
	dataset_kwargs={"skip_prepare_dataset": True}, # we collate raw rows
	remove_unused_columns=False,
	max_length=None, # never truncate image tokens
	push_to_hub=True,
	hub_model_id=args.hub_model_id,
	hub_private_repo=args.private,
	hub_strategy="every_save",
	report_to="trackio",
	run_name=args.run_name,
	project=args.project,
	# A private model repo forces Trackio's per-push "static" space private
	# too, which it rejects (browser-only snapshots must be public) and that
	# crashes the Hub push. Disable the static snapshot; the live Trackio
	# dashboard during training still works.
	trackio_static_space_id=False,
	)

	trainer = SFTTrainer(
	model=model,
	args=sft_config,
	train_dataset=train_ds,
	eval_dataset=eval_ds,
	data_collator=build_collator(processor, model.config),
	peft_config=peft_config,
	processing_class=processor,
	)

	trainer.train()
	trainer.save_model(sft_config.output_dir)
	trainer.push_to_hub()
	print(f"[train] pushed adapters to {args.hub_model_id}")


	if __name__ == "__main__":
	main()