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

import importlib
import logging
import os
import random
import signal
import sys
from dataclasses import dataclass, field
from pathlib import Path
from typing import Any, Dict, List, Optional, Union

import fire
import torch
import transformers
import yaml

# add src to the pythonpath so we don't need to pip install this
from optimum.bettertransformer import BetterTransformer
from transformers import GenerationConfig, TextStreamer

from axolotl.logging_config import configure_logging
from axolotl.utils.config import normalize_config, validate_config
from axolotl.utils.data import prepare_dataset
from axolotl.utils.dict import DictDefault
from axolotl.utils.distributed import is_main_process
from axolotl.utils.models import load_model, load_model_config, load_tokenizer
from axolotl.utils.tokenization import check_dataset_labels
from axolotl.utils.trainer import setup_trainer
from axolotl.utils.wandb import setup_wandb_env_vars

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 = logging.getLogger("axolotl.scripts")

os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1"


@dataclass
class TrainerCliArgs:
    """
    dataclass representing the various non-training arguments
    """

    debug: bool = field(default=False)
    inference: bool = field(default=False)
    merge_lora: bool = field(default=False)
    prepare_ds_only: bool = field(default=False)
    prompter: Optional[str] = field(default=None)
    shard: bool = field(default=False)


def print_axolotl_text_art():
    ascii_art = """
                           dP            dP   dP
                           88            88   88
.d8888b. dP.  .dP .d8888b. 88 .d8888b. d8888P 88
88'  `88  `8bd8'  88'  `88 88 88'  `88   88   88
88.  .88  .d88b.  88.  .88 88 88.  .88   88   88
`88888P8 dP'  `dP `88888P' dP `88888P'   dP   dP
"""

    if is_main_process():
        print(ascii_art)


def get_multi_line_input() -> Optional[str]:
    print("Give me an instruction (Ctrl + D to finish): ")
    instruction = ""
    for line in sys.stdin:
        instruction += line  # pylint: disable=consider-using-join
    # instruction = pathlib.Path("/proc/self/fd/0").read_text()
    return instruction


def do_inference(cfg, model, tokenizer, prompter: Optional[str]):
    if prompter == "None":
        prompter = None
    default_tokens = {"unk_token": "<unk>", "bos_token": "<s>", "eos_token": "</s>"}

    for token, symbol in default_tokens.items():
        # If the token isn't already specified in the config, add it
        if not (cfg.special_tokens and token in cfg.special_tokens):
            tokenizer.add_special_tokens({token: symbol})

    prompter_module = None
    if prompter:
        prompter_module = getattr(
            importlib.import_module("axolotl.prompters"), prompter
        )

    if cfg.landmark_attention:
        from axolotl.monkeypatch.llama_landmark_attn import set_model_mem_id

        set_model_mem_id(model, tokenizer)
        model.set_mem_cache_args(
            max_seq_len=255, mem_freq=50, top_k=5, max_cache_size=None
        )

    model = model.to(cfg.device)

    while True:
        print("=" * 80)
        # support for multiline inputs
        instruction = get_multi_line_input()
        if not instruction:
            return
        if prompter_module:
            prompt: str = next(
                prompter_module().build_prompt(instruction=instruction.strip("\n"))
            )
        else:
            prompt = instruction.strip()
        batch = tokenizer(prompt, return_tensors="pt", add_special_tokens=True)

        print("=" * 40)
        model.eval()
        with torch.no_grad():
            generation_config = GenerationConfig(
                repetition_penalty=1.1,
                max_new_tokens=1024,
                temperature=0.9,
                top_p=0.95,
                top_k=40,
                bos_token_id=tokenizer.bos_token_id,
                eos_token_id=tokenizer.eos_token_id,
                pad_token_id=tokenizer.pad_token_id,
                do_sample=True,
                use_cache=True,
                return_dict_in_generate=True,
                output_attentions=False,
                output_hidden_states=False,
                output_scores=False,
            )
            streamer = TextStreamer(tokenizer)
            generated = model.generate(
                inputs=batch["input_ids"].to(cfg.device),
                generation_config=generation_config,
                streamer=streamer,
            )
        print("=" * 40)
        print(tokenizer.decode(generated["sequences"].cpu().tolist()[0]))


def choose_config(path: Path):
    yaml_files = list(path.glob("*.yml"))

    if not yaml_files:
        raise ValueError(
            "No YAML config files found in the specified directory. Are you using a .yml extension?"
        )

    if len(yaml_files) == 1:
        print(f"Using default YAML file '{yaml_files[0]}'")
        return yaml_files[0]

    print("Choose a YAML file:")
    for idx, file in enumerate(yaml_files):
        print(f"{idx + 1}. {file}")

    chosen_file = None
    while chosen_file is None:
        try:
            choice = int(input("Enter the number of your choice: "))
            if 1 <= choice <= len(yaml_files):
                chosen_file = yaml_files[choice - 1]
            else:
                print("Invalid choice. Please choose a number from the list.")
        except ValueError:
            print("Invalid input. Please enter a number.")

    return chosen_file


def check_not_in(list1: List[str], list2: Union[Dict[str, Any], List[str]]) -> bool:
    return not any(el in list2 for el in list1)


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

    if not (
        cli_args.shard or cli_args.merge_lora or cli_args.inference
    ):  # don't need to load dataset for these
        train_dataset, eval_dataset, total_num_steps = prepare_dataset(cfg, tokenizer)

    if cli_args.debug or cfg.debug:
        LOG.info("check_dataset_labels...")
        check_dataset_labels(
            train_dataset.select(
                [random.randrange(0, len(train_dataset) - 1) for _ in range(5)]  # nosec
            ),
            tokenizer,
        )

    if cli_args.prepare_ds_only:
        LOG.info("Finished preparing dataset. Exiting...")
        return

    # Load the model and tokenizer
    LOG.info("loading model and (optionally) peft_config...")
    model, peft_config = load_model(cfg, tokenizer, inference=cli_args.inference)

    safe_serialization = cfg.save_safetensors is True

    if cli_args.merge_lora and cfg.adapter is not None:
        LOG.info("running merge of LoRA with base model")
        model = model.merge_and_unload()
        model.to(dtype=torch.float16)

        if cfg.local_rank == 0:
            LOG.info("saving merged model")
            model.save_pretrained(
                str(Path(cfg.output_dir) / "merged"),
                safe_serialization=safe_serialization,
            )
            tokenizer.save_pretrained(str(Path(cfg.output_dir) / "merged"))
        return

    if cli_args.inference:
        LOG.debug("Running inference on model")
        do_inference(cfg, model, tokenizer, prompter=cli_args.prompter)
        return

    if cli_args.shard:
        LOG.debug("Re-saving model w/ sharding")
        model.save_pretrained(cfg.output_dir, safe_serialization=safe_serialization)
        return

    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

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

    model.config.use_cache = False

    if torch.__version__ >= "2" and sys.platform != "win32":
        LOG.info("Compiling torch model")
        model = torch.compile(model)

    # 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)

    # 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)
        )

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

    if not Path(cfg.output_dir).is_dir():
        os.makedirs(cfg.output_dir, exist_ok=True)
    tokenizer.save_pretrained(cfg.output_dir)
    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)

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

    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

    # 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.local_rank == 0:
        if cfg.flash_optimum:
            model = BetterTransformer.reverse(model)

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


def load_cfg(config: Path = Path("examples/"), **kwargs):
    if Path(config).is_dir():
        config = choose_config(config)

    # load the config from the yaml file
    with open(config, encoding="utf-8") as file:
        cfg: DictDefault = DictDefault(yaml.safe_load(file))
    # if there are any options passed in the cli, if it is something that seems valid from the yaml,
    # then overwrite the value
    cfg_keys = cfg.keys()
    for k, _ in kwargs.items():
        # if not strict, allow writing to cfg even if it's not in the yml already
        if k in cfg_keys or not cfg.strict:
            # handle booleans
            if isinstance(cfg[k], bool):
                cfg[k] = bool(kwargs[k])
            else:
                cfg[k] = kwargs[k]

    model_config = load_model_config(cfg)

    # figure out if the model is llama
    cfg.is_llama_derived_model = (
        (hasattr(model_config, "model_type") and model_config.model_type == "llama")
        or cfg.is_llama_derived_model
        or "llama" in cfg.base_model
        or (cfg.model_type and "llama" in cfg.model_type.lower())
    )
    validate_config(cfg)

    normalize_config(cfg)

    setup_wandb_env_vars(cfg)
    return cfg


def do_train(config: Path = Path("examples/"), **kwargs):
    print_axolotl_text_art()
    parsed_cfg = load_cfg(config, **kwargs)
    parser = transformers.HfArgumentParser((TrainerCliArgs))
    parsed_cli_args, _ = parser.parse_args_into_dataclasses(
        return_remaining_strings=True
    )
    train(cfg=parsed_cfg, cli_args=parsed_cli_args)


if __name__ == "__main__":
    fire.Fire(do_train)