from dataclasses import dataclass, field

from typing import Optional

import transformers

@dataclass
class ModelArguments:
    model_name_or_path: Optional[str] = field(default='lmsys/vicuna-13b-v1.5')
    version: Optional[str] = field(default="v0")
    freeze_backbone: bool = field(default=False)
    tune_mm_mlp_adapter: bool = field(default=False)
    vision_tower: Optional[str] = field(default=None)
    mm_vision_select_layer: Optional[int] = field(default=-1)   # default to the last layer
    pretrain_mm_mlp_align: Optional[str] = field(default=None)
    mm_projector_type: Optional[str] = field(default='linear')
    mm_use_im_start_end: bool = field(default=False)
    mm_use_im_patch_token: bool = field(default=True)
    mm_vision_select_feature: Optional[str] = field(default="patch")


@dataclass
class DataArguments:
    data_path: str = field(default=None,
                           metadata={"help": "Path to the training data."})
    lazy_preprocess: bool = False
    is_multimodal: bool = False
    image_folder: Optional[str] = field(default=None)
    image_aspect_ratio: str = 'square'


@dataclass
class TrainingArguments(transformers.TrainingArguments):
    cache_dir: Optional[str] = field(default=None)
    optim: str = field(default="adamw_torch")
    remove_unused_columns: bool = field(default=False)
    freeze_mm_mlp_adapter: bool = field(default=False)
    mpt_attn_impl: Optional[str] = field(default="triton")
    model_max_length: int = field(
        default=512,
        metadata={
            "help":
            "Maximum sequence length. Sequences will be right padded (and possibly truncated)."
        },
    )
    double_quant: bool = field(
        default=True,
        metadata={"help": "Compress the quantization statistics through double quantization."}
    )
    quant_type: str = field(
        default="nf4",
        metadata={"help": "Quantization data type to use. Should be one of `fp4` or `nf4`."}
    )
    bits: int = field(
        default=16,
        metadata={"help": "How many bits to use."}
    )
    lora_enable: bool = False
    
    mm_projector_lr: Optional[float] = None
    group_by_modality_length: bool = field(default=False)
    
    stage : str = field(
        default='',
        metadata={"help": "Which stage of training"}
    )
