"""LoRA DPO on live-fault chosen/rejected pairs. Not GRPO — that reward still pays for submits."""

from __future__ import annotations

import json
from pathlib import Path

from .constants import (
    DEFAULT_DPO_ADAPTER_DIR,
    DEFAULT_DPO_PACK,
    LORA_ALPHA,
    LORA_RANK,
)
from .sft import _filter_kwargs, _load_pack, lora_target_modules


def train_dpo(
    *,
    pack_path: Path = DEFAULT_DPO_PACK,
    output_dir: Path = DEFAULT_DPO_ADAPTER_DIR,
    model_dir: Path,
    max_seq_len: int = 4096,
    max_steps: int = 30,
    lr: float = 5e-6,
    beta: float = 0.1,
    per_device_batch_size: int = 1,
    grad_accum: int = 8,
    lora_rank: int = LORA_RANK,
    smoke: bool = False,
) -> Path:
    from local_eval.cuda_env import apply as apply_cuda

    apply_cuda()
    pack_path = Path(pack_path)
    output_dir = Path(output_dir)
    output_dir.mkdir(parents=True, exist_ok=True)
    rows = _load_pack(pack_path)
    if not rows:
        raise ValueError(f"empty dpo pack: {pack_path}")
    if smoke:
        rows = rows[:16]
        max_steps = min(max_steps, 8)
        max_seq_len = min(max_seq_len, 2048)

    import torch
    from datasets import Dataset
    from peft import LoraConfig, get_peft_model
    from transformers import AutoModelForCausalLM, AutoTokenizer
    from trl import DPOConfig, DPOTrainer

    tokenizer = AutoTokenizer.from_pretrained(str(model_dir), trust_remote_code=False)
    if tokenizer.pad_token is None:
        tokenizer.pad_token = tokenizer.eos_token
    tokenizer.padding_side = "left"

    dataset = Dataset.from_list(
        [
            {
                "prompt": row["prompt"],
                "chosen": row["chosen"],
                "rejected": row["rejected"],
            }
            for row in rows
        ]
    )

    model = AutoModelForCausalLM.from_pretrained(
        str(model_dir),
        torch_dtype=torch.bfloat16,
        trust_remote_code=False,
        attn_implementation="sdpa",
    )
    model.config.use_cache = False
    if hasattr(model, "enable_input_require_grads"):
        model.enable_input_require_grads()
    model = get_peft_model(
        model,
        LoraConfig(
            r=lora_rank,
            lora_alpha=LORA_ALPHA,
            lora_dropout=0.05,
            bias="none",
            task_type="CAUSAL_LM",
            target_modules=lora_target_modules(model),
        ),
    )
    model.print_trainable_parameters()

    args_kwargs = dict(
        output_dir=str(output_dir),
        bf16=True,
        learning_rate=lr,
        per_device_train_batch_size=per_device_batch_size,
        gradient_accumulation_steps=grad_accum,
        gradient_checkpointing=True,
        logging_steps=1,
        save_steps=max(max_steps, 50),
        warmup_ratio=0.03,
        lr_scheduler_type="cosine",
        report_to=[],
        max_length=max_seq_len,
        max_steps=max_steps,
        beta=beta,
        remove_unused_columns=False,
    )
    config = DPOConfig(**_filter_kwargs(DPOConfig, args_kwargs))
    trainer = DPOTrainer(
        model=model,
        ref_model=None,
        args=config,
        train_dataset=dataset,
        processing_class=tokenizer,
    )
    trainer.train()
    trainer.save_model(str(output_dir))
    tokenizer.save_pretrained(str(output_dir))
    (output_dir / "dpo-report.json").write_text(
        json.dumps(
            {
                "pack": str(pack_path),
                "model": str(model_dir),
                "n": len(rows),
                "max_steps": max_steps,
                "max_seq_len": max_seq_len,
                "lr": lr,
                "beta": beta,
                "lora_rank": lora_rank,
                "smoke": smoke,
            },
            indent=2,
        )
        + "\n"
    )
    print(f"dpo adapter: {output_dir}", flush=True)
    return output_dir