import importlib
import logging
import os
import random
import signal
import sys
from pathlib import Path
from typing import Optional

import fire
import torch
import yaml
from attrdict import AttrDefault

# add src to the pythonpath so we don't need to pip install this
from axolotl.utils.tokenization import check_dataset_labels
from axolotl.utils.validation import validate_config

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)

from axolotl.utils.data import load_prepare_datasets
from axolotl.utils.models import load_model
from axolotl.utils.trainer import setup_trainer
from axolotl.utils.wandb import setup_wandb_env_vars

logging.basicConfig(level=os.getenv("LOG_LEVEL", "INFO"))
DEFAULT_DATASET_PREPARED_PATH = "last_run_prepared"


def choose_device(cfg):
    def get_device():
        if torch.cuda.is_available():
            return f"cuda:{cfg.local_rank}"
        else:
            try:
                if torch.backends.mps.is_available():
                    return "mps"
            except:
                return "cpu"

    cfg.device = get_device()
    if cfg.device == "cuda":
        cfg.device_map = {"": cfg.local_rank}
    else:
        cfg.device_map = {"": cfg.device}


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


def do_inference(cfg, model, tokenizer, prompter="AlpacaPrompter"):
    tokenizer.add_special_tokens({"unk_token": "<unk>"})
    tokenizer.add_special_tokens({"bos_token": "<s>"})
    tokenizer.add_special_tokens({"eos_token": "</s>"})

    prompter_module = getattr(importlib.import_module("axolotl.prompters"), prompter)

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

        model.eval()
        with torch.no_grad():
            # gc = GenerationConfig()  # TODO swap out and use this
            generated = model.generate(
                inputs=batch["input_ids"].to(cfg.device),
                do_sample=True,
                use_cache=True,
                repetition_penalty=1.1,
                max_new_tokens=100,
                temperature=0.9,
                top_p=0.95,
                top_k=40,
                return_dict_in_generate=True,
                output_attentions=False,
                output_hidden_states=False,
                output_scores=False,
            )
        print(tokenizer.decode(generated["sequences"].cpu().tolist()[0]))


def choose_config(path: Path):
    yaml_files = [file for file in path.glob("*.yml")]

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

    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 train(
    config: Path = Path("configs/"),
    prepare_ds_only: bool = False,
    **kwargs,
):
    if Path(config).is_dir():
        config = choose_config(config)

    # load the config from the yaml file
    with open(config, "r") as f:
        cfg: AttrDefault = AttrDefault(lambda: None, yaml.load(f, Loader=yaml.Loader))
    # 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 = dict(cfg).keys()
    for k in kwargs:
        # if not strict, allow writing to cfg even if it's not in the yml already
        if k in cfg_keys or cfg.strict is False:
            # handle booleans
            if isinstance(cfg[k], bool):
                cfg[k] = bool(kwargs[k])
            else:
                cfg[k] = kwargs[k]

    # setup some derived config / hyperparams
    cfg.gradient_accumulation_steps = cfg.batch_size // cfg.micro_batch_size
    cfg.world_size = int(os.environ.get("WORLD_SIZE", 1))
    cfg.local_rank = int(os.environ.get("LOCAL_RANK", 0))
    choose_device(cfg)
    cfg.ddp = cfg.ddp if cfg.ddp is not None else cfg.world_size != 1
    if cfg.ddp:
        cfg.device_map = {"": int(os.environ.get("LOCAL_RANK", 0))}
        cfg.gradient_accumulation_steps = (
            cfg.gradient_accumulation_steps // cfg.world_size
        )
    setup_wandb_env_vars(cfg)
    if cfg.device == "mps":
        cfg.load_in_8bit = False
        cfg.tf32 = False
        if cfg.bf16:
            cfg.fp16 = True
        cfg.bf16 = False

    validate_config(cfg)

    # Load the model and tokenizer
    logging.info("loading model, tokenizer, and peft_config...")
    model, tokenizer, peft_config = load_model(
        cfg.base_model,
        cfg.base_model_config,
        cfg.model_type,
        cfg.tokenizer_type,
        cfg,
        adapter=cfg.adapter,
        inference=("inference" in kwargs),
    )

    if "merge_lora" in kwargs and cfg.adapter is not None:
        logging.info("running merge of LoRA with base model")
        model = model.merge_and_unload()

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

    if "inference" in kwargs:
        logging.info("calling do_inference function")
        do_inference(cfg, model, tokenizer)
        return

    if "shard" in kwargs:
        model.save_pretrained(cfg.output_dir)
        return

    train_dataset, eval_dataset = load_prepare_datasets(
        tokenizer, cfg, DEFAULT_DATASET_PREPARED_PATH
    )

    if cfg.debug:
        logging.info("check_dataset_labels...")
        check_dataset_labels(
            train_dataset.select(
                [random.randrange(0, len(train_dataset) - 1) for i in range(5)]
            ),
            tokenizer,
        )

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

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

    model.config.use_cache = False

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

    # go ahead and presave, so we have the adapter config available to inspect
    if peft_config:
        logging.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:
        signal.signal(
            signal.SIGINT,
            lambda signal, frame: (model.save_pretrained(cfg.output_dir), exit(0)),
        )

    logging.info("Starting trainer...")
    if cfg.group_by_length:
        logging.info("hang tight... sorting dataset for group_by_length")
    resume_from_checkpoint = cfg.resume_from_checkpoint
    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])
            )
            resume_from_checkpoint = sorted_paths[-1]
            logging.info(
                f"Using Auto-resume functionality to start with checkpoint at {resume_from_checkpoint}"
            )
    trainer.train(resume_from_checkpoint=resume_from_checkpoint)

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

    # 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.local_rank == 0:
        model.save_pretrained(cfg.output_dir)
    # trainer.save_model(cfg.output_dir)  # TODO this may be needed for deepspeed to work? need to review another time


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