"""Reviewed native Slayer loaders. Only pinned config/tokenizer/safetensors are downloaded."""
import hashlib,json
from pathlib import Path
from types import SimpleNamespace
FILES=('sub1b_native.py','sub1b_slayer139.py','sub1b_gollem.py','sub1b_balanced.py')
def fingerprints():
 root=Path(__file__).resolve().parent
 return {n:hashlib.sha256((root/n).read_bytes()).hexdigest() for n in FILES}
def load(info):
 import torch
 from huggingface_hub import hf_hub_download
 from safetensors.torch import load_model
 from tokenizers import Tokenizer
 files={n:hf_hub_download(info['id'],n,revision=info['revision'],token=False) for n in ('config.json','model.safetensors','tokenizer.json')}
 config=json.loads(Path(files['config.json']).read_text());adapter=info['adapter']
 if adapter=='balanced':
  from sub1b_balanced import BalancedLM
  native=BalancedLM(config)
 elif adapter in ('gollem','slayer139'):
  if adapter=='gollem':from sub1b_gollem import GPT
  else:from sub1b_slayer139 import GPT
  cfg=SimpleNamespace(**config);native=GPT(cfg.vocab,cfg.n_layer,cfg.n_embd,cfg.n_head,cfg.block,cfg)
 else:raise ValueError('Unreviewed native adapter')
 load_model(native,files['model.safetensors'],strict=True);native=native.float().cuda().eval()
 raw=Tokenizer.from_file(files['tokenizer.json']);raw.no_truncation();raw.no_padding()
 class Tokens:
  bos_token_id=config.get('bos_token_id',0);eos_token_id=config.get('eos_token_id',0)
  def encode(self,text,add_special_tokens=False):return raw.encode(text,add_special_tokens=add_special_tokens).ids
 class Wrapper(torch.nn.Module):
  def __init__(self):super().__init__();self.native=native
  def forward(self,ids,attention_mask,position_ids,use_cache,logits_to_keep):
   # Native architectures have no padding-mask interface. Strip padding for each forward;
   # preserve exact positions and predictive context, never let pad tokens change a score.
   values=[]
   for row,mask in zip(ids,attention_mask):
    logits=self.native(row[mask.bool()][None]);logits=logits[0] if isinstance(logits,tuple) else logits
    logits=logits[:,-logits_to_keep:]
    if logits.shape[1]<logits_to_keep:logits=torch.nn.functional.pad(logits,(0,0,logits_to_keep-logits.shape[1],0))
    values.append(logits)
   return SimpleNamespace(logits=torch.cat(values,dim=0))
 return Tokens(),Wrapper().eval()