"""Debug harness: run static_graph against a list of models and report depth/leaves."""
import sys, os, time, traceback
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))

# Avoid torch import cascade — static_graph doesn't need torch.
os.environ.setdefault("TRANSFORMERS_NO_ADVISORY_WARNINGS", "1")

from backend.model_loader import _load_config_robust
from backend.static_graph import build_static_graph, find_arch_class

MODELS = [
    "bert-base-uncased",
    "prajjwal1/bert-tiny",
    "openai/whisper-tiny",
    "openai/whisper-base",
    "Qwen/Qwen2.5-0.5B",
    "google/vit-base-patch16-224",
    "google-t5/t5-small",
    "openai-community/gpt2",
    "distilbert/distilbert-base-uncased",
    "FacebookAI/roberta-base",
    "microsoft/deberta-v3-base",
    "google/flan-t5-base",
    "facebook/bart-base",
    "mistralai/Mistral-7B-v0.1",
    "meta-llama/Llama-3.2-1B",
    "Qwen/Qwen3.6-35B-A3B",
    "stabilityai/stablelm-2-1_6b",
    "microsoft/phi-2",
    "google/gemma-2-2b",
    "openai/clip-vit-base-patch32",
]

def report(model_id):
    print(f"\n=== {model_id} ===")
    try:
        cfg = _load_config_robust(model_id)
    except Exception as e:
        print(f"  CONFIG FAIL: {e}")
        return None
    cls, mod = find_arch_class(cfg)
    print(f"  arch={cfg.architectures} model_type={getattr(cfg,'model_type',None)} → cls={cls.__name__ if cls else None}")
    if cls is None:
        return None
    t0 = time.time()
    try:
        g = build_static_graph(cfg)
    except Exception as e:
        print(f"  GRAPH FAIL: {e}")
        traceback.print_exc()
        return None
    elapsed = time.time() - t0
    depths = [n["depth"] for n in g["nodes"]]
    leaf = sum(1 for n in g["nodes"] if n["is_leaf"])
    kinds = {}
    for n in g["nodes"]:
        kinds[n["kind"]] = kinds.get(n["kind"], 0) + 1
    print(f"  nodes={len(g['nodes'])} edges={len(g['edges'])} max_depth={max(depths)} leaves={leaf} t={elapsed*1000:.0f}ms")
    print(f"  kinds={kinds}")
    # Top-level + 1 sample of each depth
    by_depth = {}
    for n in g["nodes"]:
        by_depth.setdefault(n["depth"], []).append(n)
    for d in sorted(by_depth)[:6]:
        sample = by_depth[d][0]
        print(f"   d={d} count={len(by_depth[d])} ex: {sample['module_class']} args={sample['config']}")
    return g

for m in MODELS:
    report(m)