"""Strict, component-only loaders. No language model allocation or remote execution."""
import json,pathlib,torch
from torch import nn
from safetensors.torch import load_file
try:
 from transformers.initialization import no_init_weights
except ImportError:
 from transformers.modeling_utils import no_init_weights

@no_init_weights()
def build_model(root,dtype=None,attention='sdpa'):
 root=pathlib.Path(root);kind=root.name
 if (root/'pixel_native.py').exists():
  from pixel_io import PixelUMMPixelIO
  model=PixelUMMPixelIO();kind='pixelumm_pixel_io'
 elif (root/'sensenova').exists():
  from pixel_io import SenseNovaPixelIO
  model=SenseNovaPixelIO(json.loads((root/'source_config.json').read_text()));kind='sensenova_pixel_io'
 elif (root/'component_config.json').exists():
  from mingtok.modeling_mingtok import MingTok,MingTokConfig
  cfg=json.loads((root/'component_config.json').read_text());model=MingTok(MingTokConfig(**cfg));kind='mingtok'
 elif (root/'vision_encoder').exists():
  from vision_encoder import UniARVisionConfig,UniARVisionModel
  cfg=UniARVisionConfig.from_dict(json.loads((root/'source_config.json').read_text()));cfg._attn_implementation=attention
  model=UniARVisionModel(cfg);kind='uniar_bsq'
 else:
  import llada_tokenizer as native
  cfg=json.loads((root/'source_config.json').read_text());vc=native.make_vision_config(cfg);vc._attn_implementation=attention
  model=nn.Module();model.model=nn.Module();model.model.visual=native.VisionEncoder(vc);model.model.vqmodel=native.VQVAE(native.make_vq_config(cfg));kind='llada_image_tokenizer'
 if dtype is None:dtype=torch.bfloat16 if kind=='uniar_bsq' else torch.float32
 return model.to(dtype=dtype).eval()

def tokenizer_view(model,root):
 """Expose upstream PIL->token API around strictly loaded LLaDA modules."""
 import llada_tokenizer as native
 t=native.ImageTokenizer.__new__(native.ImageTokenizer)
 t.device=next(model.parameters()).device;t.dtype=next(model.parameters()).dtype
 t.visual=model.model.visual;t.vqmodel=model.model.vqmodel
 t.image_processor=native.ImagePreprocessor(pathlib.Path(root)/'preprocessor_config.json')
 t.spatial_merge_size=t.visual.spatial_merge_size
 return t

def load_model(root,dtype=None,attention='sdpa',device='cpu'):
 root=pathlib.Path(root);model=build_model(root,dtype,attention)
 state=load_file(str(root/'model.safetensors'),device='cpu')
 model.load_state_dict(state,strict=True);del state
 return model.to(device).eval()

@no_init_weights()
def load_sigvq(root,dtype=torch.float32,device='cpu'):
 """Load LLaDA's separate token-to-decoder conditioning bridge with strict state checks."""
 from sigvq import SigVQ
 root=pathlib.Path(root);model=SigVQ(vocab_size=16384,inner_dim=4096).to(dtype=dtype)
 state=torch.load(root/'sigvq_embedding.pt',map_location='cpu',weights_only=True)
 model.load_state_dict(state,strict=True);del state
 return model.to(device).eval()