import os
from .clip_encoder import CLIPVisionTower
from .imagebind import ImageBindWrapper
from .open_clip_encoder import OpenCLIPVisionTower
from .siglip_encoder import SigLipVisionTower
from .clip_encoder import CLIPVisionTower, CLIPVisionTowerS2

from .eva_clip.eva_clip_encoder import EvaClipVisionTower
from .dev_eva_clip.eva_vit import EvaViTWrapper

from blip3o.model.nextdit_crossattn import NextDiTCrossAttnConfig, NextDiTCrossAttn
from blip3o.model.sana_crossattn import SanaCrossAttnConfig, SanaCrossAttn

from diffusers.models import AutoencoderKL
from diffusers.schedulers import FlowMatchEulerDiscreteScheduler, DPMSolverMultistepScheduler
from diffusers import SanaTransformer2DModel
import torch

def build_vision_tower(vision_tower_cfg, **kwargs):
    vision_tower = getattr(vision_tower_cfg, 'mm_vision_tower', getattr(vision_tower_cfg, 'vision_tower', None))
    is_absolute_path_exists = os.path.exists(vision_tower)
    use_s2 = getattr(vision_tower_cfg, 's2', False)
    if "siglip" in vision_tower:
        return SigLipVisionTower(vision_tower, vision_tower_cfg=vision_tower_cfg, **kwargs)
    if "eva" in vision_tower:
        return EvaClipVisionTower(vision_tower, args=vision_tower_cfg, **kwargs)
    if is_absolute_path_exists or vision_tower.startswith("openai") or vision_tower.startswith("laion") or "ShareGPT4V" in vision_tower:
        if use_s2:
            return CLIPVisionTowerS2(vision_tower, args=vision_tower_cfg, **kwargs)
        else:
            return CLIPVisionTower(vision_tower, args=vision_tower_cfg, **kwargs)

    raise ValueError(f'Unknown vision tower: {vision_tower}')




def build_gen_vision_tower(vision_tower_cfg, **kwargs):
    vision_tower = getattr(vision_tower_cfg, 'gen_vision_tower')
    is_absolute_path_exists = os.path.exists(vision_tower)
    use_s2 = getattr(vision_tower_cfg, 's2', False)
    if "siglip" in vision_tower:
        return SigLipVisionTower(vision_tower, vision_tower_cfg=vision_tower_cfg, **kwargs)
    if "eva" in vision_tower:
        return EvaClipVisionTower(vision_tower, args=vision_tower_cfg, **kwargs)
    if is_absolute_path_exists or vision_tower.startswith("openai") or vision_tower.startswith("laion") or "ShareGPT4V" in vision_tower:
        if use_s2:
            return CLIPVisionTowerS2(vision_tower, args=vision_tower_cfg, **kwargs)
        else:
            return CLIPVisionTower(vision_tower, args=vision_tower_cfg, **kwargs)

    raise ValueError(f'Unknown vision tower: {vision_tower}')



def build_dit(vision_tower_cfg, **kwargs):
    vision_tower_cfg.hidden_size = 896
    print("="*20, "vision tower config", vision_tower_cfg, "="*20)
    # if not hasattr(vision_tower_cfg, "hidden_size"):
    #     if "3B" in vision_tower_cfg.model_name_or_path:
    #         vision_tower_cfg.hidden_size = 2048
    #     elif "7B" in vision_tower_cfg.model_name_or_path:
    #         vision_tower_cfg.hidden_size = 3584
    #     else:
    #         vision_tower_cfg.hidden_size = 3072
    print("="*20, "Building SANA with hidden size", vision_tower_cfg.hidden_size, "="*20)
    

    #dit = NextDiTCrossAttn(NextDiTCrossAttnConfig(latent_embedding_size=vision_tower_cfg.hidden_size))
    #noise_scheduler = FlowMatchEulerDiscreteScheduler.from_pretrained("Alpha-VLLM/Lumina-Next-SFT-diffusers", subfolder="scheduler")
    #dit = SanaTransformer2DModel.from_pretrained("Efficient-Large-Model/Sana_600M_512px_diffusers", subfolder="transformer", torch_dtype=torch.float16,)

    # dit = SanaCrossAttn(SanaCrossAttnConfig()) #cross_attention_dim=vision_tower_cfg.hidden_size))
    #dit = SanaTransformer2DModel.from_pretrained("Efficient-Large-Model/Sana_600M_512px_diffusers", subfolder="transformer")
    dit = SanaTransformer2DModel.from_pretrained("Efficient-Large-Model/Sana_600M_1024px_diffusers",ignore_missing_keys=True, ignore_mismatched_sizes=True,device_map="cpu", subfolder="transformer")
    noise_scheduler = DPMSolverMultistepScheduler.from_pretrained("Efficient-Large-Model/Sana_600M_1024px_diffusers",subfolder="scheduler")

    return dit, noise_scheduler