"""Prepare and train a model on a dataset. Can also infer from a model or merge lora"""

import os
import signal
import sys
from dataclasses import dataclass
from pathlib import Path
from typing import Optional

import torch
import transformers.modelcard
from accelerate.logging import get_logger
from datasets import Dataset
from optimum.bettertransformer import BetterTransformer
from pkg_resources import get_distribution  # type: ignore
from transformers.deepspeed import is_deepspeed_zero3_enabled

from axolotl.common.cli import TrainerCliArgs
from axolotl.logging_config import configure_logging
from axolotl.utils.dict import DictDefault
from axolotl.utils.freeze import freeze_parameters_except
from axolotl.utils.models import load_model, load_tokenizer
from axolotl.utils.trainer import setup_trainer

project_root = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
src_dir = os.path.join(project_root, "src")
sys.path.insert(0, src_dir)

configure_logging()
LOG = get_logger("axolotl.train")


@dataclass
class TrainDatasetMeta:
    """
    dataclass to capture the dataset specific options for training
    """

    train_dataset: Dataset
    eval_dataset: Optional[Dataset] = None
    total_num_steps: Optional[int] = None


def train(
    *, cfg: DictDefault, cli_args: TrainerCliArgs, dataset_meta: TrainDatasetMeta
):
    # load the tokenizer first
    LOG.debug(
        f"loading tokenizer... {cfg.tokenizer_config or cfg.base_model_config}",
        main_process_only=True,
    )
    tokenizer = load_tokenizer(cfg)

    train_dataset = dataset_meta.train_dataset
    eval_dataset = dataset_meta.eval_dataset
    total_num_steps = dataset_meta.total_num_steps

    # Load the model and tokenizer
    msg = "loading model"
    if cfg.adapter:
        msg += " and peft_config..."
    LOG.debug(msg)
    model, peft_config = load_model(cfg, tokenizer, inference=cli_args.inference)
    model_ref = None
    if cfg.rl:
        # load the model again for model_ref/baseline
        model_ref, _ = load_model(
            cfg, tokenizer, inference=cli_args.inference, reference_model=True
        )

    safe_serialization = cfg.save_safetensors is True

    if cfg.resume_from_checkpoint is None and cfg.auto_resume_from_checkpoints:
        possible_checkpoints = [
            str(cp) for cp in Path(cfg.output_dir).glob("checkpoint-*")
        ]
        if len(possible_checkpoints) > 0:
            sorted_paths = sorted(
                possible_checkpoints,
                key=lambda path: int(path.split("-")[-1]),
            )
            cfg.resume_from_checkpoint = sorted_paths[-1]
            LOG.info(
                f"Using Auto-resume functionality to start with checkpoint at {cfg.resume_from_checkpoint}"
            )
    resume_from_checkpoint = cfg.resume_from_checkpoint

    if cfg.unfrozen_parameters:
        freeze_parameters_except(model, cfg.unfrozen_parameters)

    trainer = setup_trainer(
        cfg, train_dataset, eval_dataset, (model, model_ref), tokenizer, total_num_steps
    )

    if hasattr(model, "config"):
        model.config.use_cache = False

    # go ahead and presave, so we have the adapter config available to inspect
    if peft_config:
        LOG.info(f"Pre-saving adapter config to {cfg.output_dir}")
        peft_config.save_pretrained(cfg.output_dir)
    # additionally presave the tokenizer and model configs
    if not Path(cfg.output_dir).is_dir():
        os.makedirs(cfg.output_dir, exist_ok=True)
    tokenizer.save_pretrained(str(Path(cfg.output_dir)))
    if hasattr(model, "config"):
        model.config.save_pretrained(str(Path(cfg.output_dir)))

    # In case we want to stop early with ctrl+c, this is a nice to have to save the pretrained model
    if cfg.local_rank == 0:

        def terminate_handler(_, __, model):
            if cfg.flash_optimum:
                model = BetterTransformer.reverse(model)
            model.save_pretrained(cfg.output_dir, safe_serialization=safe_serialization)
            sys.exit(0)

        signal.signal(
            signal.SIGINT, lambda signum, frame: terminate_handler(signum, frame, model)
        )

    badge_markdown = """[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)"""
    transformers.modelcard.AUTOGENERATED_TRAINER_COMMENT += f"\n{badge_markdown}"

    if getattr(cfg, "axolotl_config_path"):
        raw_axolotl_cfg = Path(cfg.axolotl_config_path)
        version = get_distribution("axolotl").version
        if raw_axolotl_cfg.is_file():
            transformers.modelcard.AUTOGENERATED_TRAINER_COMMENT += f"\n<details><summary>See axolotl config</summary>\n\naxolotl version: `{version}`\n```yaml\n{raw_axolotl_cfg.read_text(encoding='utf-8')}\n```\n\n</details><br>\n"

    LOG.info("Starting trainer...")
    if cfg.group_by_length:
        LOG.info("hang tight... sorting dataset for group_by_length")

    pretrain_hooks(cfg, trainer)
    if cfg.flash_optimum:
        with torch.backends.cuda.sdp_kernel(
            enable_flash=True, enable_math=True, enable_mem_efficient=True
        ):
            trainer.train(resume_from_checkpoint=resume_from_checkpoint)
    else:
        trainer.train(resume_from_checkpoint=resume_from_checkpoint)
    post_train_hooks(cfg, trainer)

    LOG.info(f"Training Completed!!! Saving pre-trained model to {cfg.output_dir}")

    # post training
    for name, module in model.named_modules():
        if hasattr(module, "_post_training"):
            module._post_training(model, name)  # pylint: disable=protected-access

    if trainer.is_fsdp_enabled:
        trainer.accelerator.state.fsdp_plugin.set_state_dict_type("FULL_STATE_DICT")
        LOG.info("Set FSDP state dict type to FULL_STATE_DICT for saving.")

    if cfg.relora_steps:
        if cfg.adapter == "lora" and not (cfg.load_in_4bit or cfg.load_in_8bit):
            model = model.merge_and_unload()
        else:
            # final model weights have already been saved by `ReLoRACallback.on_train_end`
            return model, tokenizer

    # TODO do we need this fix? https://huggingface.co/docs/accelerate/usage_guides/fsdp#saving-and-loading
    # only save on rank 0, otherwise it corrupts output on multi-GPU when multiple processes attempt to write the same file
    if cfg.fsdp:
        trainer.save_model(cfg.output_dir)
    elif cfg.deepspeed and is_deepspeed_zero3_enabled():
        # Copied over from: https://github.com/huggingface/accelerate/blob/5ae611118057232f441055f7ef9ba0b0f2b8d533/docs/source/usage_guides/deepspeed.md#saving-and-loading
        trainer.accelerator.wait_for_everyone()
        unwrapped_model = trainer.accelerator.unwrap_model(trainer.model_wrapped)

        # Saves the whole/unpartitioned fp16 model when in ZeRO Stage-3 to the output directory if
        # `stage3_gather_16bit_weights_on_model_save` is True in DeepSpeed Config file or
        # `zero3_save_16bit_model` is True in DeepSpeed Plugin.
        # For Zero Stages 1 and 2, models are saved as usual in the output directory.
        # The model name saved is `pytorch_model.bin`
        unwrapped_model.save_pretrained(
            cfg.output_dir,
            is_main_process=trainer.accelerator.is_main_process,
            save_function=trainer.accelerator.save,
            state_dict=trainer.accelerator.get_state_dict(trainer.model_wrapped),
        )
    elif cfg.local_rank == 0:
        if cfg.flash_optimum:
            model = BetterTransformer.reverse(model)

        model.save_pretrained(cfg.output_dir, safe_serialization=safe_serialization)

    if not cfg.hub_model_id:
        trainer.create_model_card(model_name=cfg.output_dir.lstrip("./"))
    elif cfg.hub_model_id:
        # defensively push to the hub to ensure the model card is updated
        trainer.push_to_hub()

    return model, tokenizer


def pretrain_hooks(_cfg, _trainer):
    """
    Run hooks right before kicking off the training
    :param cfg:
    :param trainer:
    :return:
    """


def post_train_hooks(_cfg, _trainer):
    """
    Run hooks right after training completes
    :param cfg:
    :param trainer:
    :return:
    """