import torch
from transformers import PretrainedConfig


class STDiT2Config(PretrainedConfig):
    
    model_type = "stdit2"

    def __init__(
        self,
        input_size=(None, None, None),
        input_sq_size=32,
        in_channels=4,
        patch_size=(1, 2, 2),
        hidden_size=1152,
        depth=28,
        num_heads=16,
        mlp_ratio=4.0,
        class_dropout_prob=0.1,
        pred_sigma=True,
        drop_path=0.0,
        no_temporal_pos_emb=False,
        caption_channels=4096,
        model_max_length=120,
        freeze=None,
        qk_norm=False,
        enable_flash_attn=False,
        enable_layernorm_kernel=False,
        enable_sequence_parallelism=False,
        **kwargs,
    ):
        self.input_size = input_size
        self.input_sq_size = input_sq_size
        self.in_channels = in_channels
        self.patch_size = patch_size
        self.hidden_size = hidden_size
        self.depth = depth
        self.num_heads = num_heads
        self.mlp_ratio = mlp_ratio
        self.class_dropout_prob = class_dropout_prob
        self.pred_sigma = pred_sigma
        self.drop_path = drop_path
        self.no_temporal_pos_emb = no_temporal_pos_emb
        self.caption_channels = caption_channels
        self.model_max_length = model_max_length
        self.freeze = freeze
        self.qk_norm = qk_norm
        self.enable_flash_attn = enable_flash_attn
        self.enable_layernorm_kernel = enable_layernorm_kernel
        self.enable_sequence_parallelism = enable_sequence_parallelism
        super().__init__(**kwargs)