from dataclasses import dataclass, field
from typing import Optional

from transformers import TrainingArguments


@dataclass
class ModelArgs:
    # * base
    model_name_or_path: str = field(
        default="meta-llama/Llama-2-7b-chat-hf",
        metadata={
            "help": "Path to pretrained model or model identifier from huggingface.co/models"
        },
    )
    super_tokenizer_name_or_path: str = field(
        default="/share/ninglu_shao/code/PluginTransformer/data/outputs/90k_0104+8-longalpaca_0106/super_tokenizer",
        metadata={
            "help": "Path to pretrained model or model identifier from huggingface.co/models"
        },
    )
    # * other
    super_tokenizer_num_hidden_layers: int = field(
        default=8,
        metadata={"help": "Encoder model's layer num."},
    )
    is_model_frozen: bool = field(
        default=True,
        metadata={"help": "Freeze or not decoder model."},
    )
    use_flash_attention_2: bool = field(
        default=True,
        metadata={"help": "Use flash attention 2 or not."},
    )
    dtype: str = field(
        default="bf16",
    )
    device_map: Optional[str] = field(
        default=None,
    )


@dataclass
class DataArgs:
    # * base
    dataset_list: str = field(
        default="wikipedia",
        metadata={"help": "Path of dataset"},
    )
    dataset_save_dir: str = field(
        default="/share/ninglu_shao/data/PluginTransformer",
        metadata={"help": "The path to save dataset."},
    )

    def __post_init__(self):
        self.dataset_list = [dataset.strip() for dataset in self.dataset_list.split(",")]


@dataclass
class TrainingArgs(TrainingArguments):
    # * base
    output_dir: str = field(
        default="outputs/test_4",
        metadata={
            "help": "The output directory where the model predictions and checkpoints will be written."
        },
    )
    overwrite_output_dir: bool = field(
        default=False,
        metadata={"help": "If True, overwrite the content of the output directory."},
    )
    # * basic train parameter
    learning_rate: float = field(
        default=1e-4,
        metadata={"help": "The initial learning rate for optimizer."},
    )
    warmup_ratio: float = field(
        default=0.1,
        metadata={"help": "The ratio of warmup steps for optimizer."},
    )
    num_train_epochs: float = field(
        default=1,
        metadata={"help": "Total number of training epochs to perform."},
    )
    per_device_train_batch_size: int = field(
        default=8,
        metadata={"help": "The batch size per GPU/TPU core/CPU for training."},
    )
    # * data parameter
    dataloader_num_workers: int = field(
        default=32,
        metadata={"help": "Number of subprocesses to use for data loading."},
    )
    remove_unused_columns: bool = field(
        default=False,
        metadata={
            "help": "Whether or not to automatically remove the columns unused by the model forward method."
        },
    )
    # * save & log parameter
    save_strategy: str = field(
        default="steps",
        metadata={"help": "The checkpoint save strategy to adopt during training."},
    )
    save_steps: int = field(
        default=500,
        metadata={"help": "Saving frequency according to saving strategy"},
    )
    save_total_limit: int = field(
        default=None,
        metadata={"help": "How many checkpoints to keep in the output_dir."},
    )
    logging_steps: int = field(
        default=10,
        metadata={"help": "Logging frequency according to logging strategy."},
    )
    # * half precesion & ddp parameter & gradient_checkpointing
    fp16: bool = field(
        default=False,
        metadata={
            "help": "Whether to use fp16 16-bit (mixed) precision training instead of 32-bit training."
        },
    )
    bf16: bool = field(
        default=True,
        metadata={
            "help": "Whether to use bf16 16-bit (mixed) precision training instead of 32-bit training."
        },
    )
    # ddp_find_unused_parameters: bool = field(
    #     default=False,
    #     metadata={
    #         "help": "When using distributed training, the value of the flag find_unused_parameters passed to DistributedDataParallel."
    #     },
    # )


@dataclass
class GenerationArgs:
    do_sample: bool = field(
        default=False,
        metadata={"help": "Sample when decoding?"},
    )
    num_return_sequences: int = field(
        default=1,
        metadata={"help": "How many sequences to generate?"},
    )
    max_length: int = field(
        default=1024,
        metadata={"help": "Maximum length."},
    )                                                                                                                           