import torch.nn as nn
from torch.utils.data import Dataset
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LRScheduler

from dataclasses import dataclass, field
from typing import Callable, List, Optional


@dataclass
class TrainConfig:
    # basic setting
    model: nn.Module = field(default=None, metadata={"help": "Model for training."})
    strategy: str = field(default=None, metadata={"help": "Training strategy."})
    dataset: Dataset = field(default=None, metadata={"help": "Dataset for training."})
    optimizer_fn: Callable[[nn.Module], Optimizer] = field(
        default=None, metadata={"help": "Optimizer factory for training."}
    )
    scheduler_fn: Callable[[Optimizer], LRScheduler] = field(
        default=None, metadata={"help": "Scheduler factory for training."}
    )
    n_epoch: int = field(default=1, metadata={"help": "Number of epochs for training."})
    batch_size: int = field(default=4, metadata={"help": "Batch size for training."})
    accumulation_steps: int = field(
        default=1, metadata={"help": "Number of iterations between steps."}
    )
    max_grad_norm: float = field(
        default=1.0, metadata={"help": "Maximum gradient norm."}
    )

    # checkpoint setting
    start_epoch: int = field(default=0, metadata={"help": "Start epoch for training."})
    start_batch: int = field(
        default=0, metadata={"help": "Start batch iteration for training."}
    )
    ckpt_dir: str = field(
        default="./checkpoint", metadata={"help": "Checkpoint directory."}
    )
    ckpt_interval: int = field(
        default=5000, metadata={"help": "Number of iterations between checkpoints."}
    )

    # dataloader setting
    random_seed: int = field(default=3407, metadata={"help": "Random seed."})
    num_workers: int = field(
        default=0, metadata={"help": "Number of workers for dataloader."}
    )
    prefetch_factor: Optional[int] = field(
        default=None, metadata={"help": "Prefetch factor for dataloader."}
    )
    pin_memory: bool = field(
        default=False, metadata={"help": "Pin memory for dataloader."}
    )

    # distributed training
    nprocs: int = field(
        default=1, metadata={"help": "Number of processes for distributed training."}
    )
    backend: str = field(
        default="nccl", metadata={"help": "Distributed training backend."}
    )
    master_addr: str = field(
        default="localhost",
        metadata={"help": "Master address for distributed training."},
    )
    master_port: str = field(
        default="29500", metadata={"help": "Master port for distributed training."}
    )
    parallel_wrapper: Optional[Callable] = field(
        default=None, metadata={"help": "Parallel function for training."}
    )
    state_dict_fn: Optional[Callable] = field(
        default=None, metadata={"help": "Parallel function for state  dict saving."}
    )

    # others
    device_ids: Optional[List[int]] = field(
        default=None, metadata={"help": "Device ids for distributed training."}
    )
    device_type: str = field(
        default="cuda", metadata={"help": "Device type for distributed training."}
    )
    extra_kwargs: dict = field(
        default_factory=dict, metadata={"help": "Other arguments."}
    )

    def __post_init__(self):
        self.validate()

    def validate(self):
        required_fields = [
            "model",
            "strategy",
            "dataset",
            "optimizer_fn",
            "scheduler_fn",
        ]

        for field_name in required_fields:
            if getattr(self, field_name) is None:
                raise ValueError(f"{field_name} is required.")