import os
from dataclasses import dataclass, field
from typing import Optional


@dataclass
class DataTrainingArguments:
    train_data: Optional[str] = field(
        default=None, metadata={"help": "Path to pretrain data"}
    )
    tokenizer_name: Optional[str] = field(
        default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
    )
    max_seq_length: Optional[int] = field(
        default=512,
        metadata={
            "help": "The maximum total input sequence length after tokenization. Sequences longer "
                    "than this will be truncated. Default to the max input length of the model."
        },
    )
    encoder_mlm_probability: float = field(default=0.3, metadata={"help": "mask ratio for encoder"})
    decoder_mlm_probability: float = field(default=0.5, metadata={"help": "mask ratio for decoder"})

    def __post_init__(self):
        if not os.path.exists(self.train_data):
            raise FileNotFoundError(f"cannot find file: {self.train_data}, please set a true path")


@dataclass
class ModelArguments:
    """
    Arguments pertaining to which model/config/tokenizer we are going to fine-tune, or train from scratch.
    """
    model_name_or_path: Optional[str] = field(
        default='bert-base-uncased',
        metadata={
            "help": "The model checkpoint for weights initialization."
                    "Don't set if you want to train a model from scratch."
        },
    )
    config_name: Optional[str] = field(
        default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
    )