from safetensors.torch import save_file
import torch
from torchvision.models import resnet34

model_path = "RESNET_34_cancer_350px_lr_1e-2_decay_5_jitter_val6slides_harder_tcga_none_0403_0204_0.9826153355179645_16.t7"

orig_model = torch.load(model_path, map_location="cpu")
state_dict = orig_model["model"].module.state_dict()
keys_missing = [
    "bn1.num_batches_tracked",
    "layer1.0.bn1.num_batches_tracked",
    "layer1.0.bn2.num_batches_tracked",
    "layer1.1.bn1.num_batches_tracked",
    "layer1.1.bn2.num_batches_tracked",
    "layer1.2.bn1.num_batches_tracked",
    "layer1.2.bn2.num_batches_tracked",
    "layer2.0.bn1.num_batches_tracked",
    "layer2.0.bn2.num_batches_tracked",
    "layer2.0.downsample.1.num_batches_tracked",
    "layer2.1.bn1.num_batches_tracked",
    "layer2.1.bn2.num_batches_tracked",
    "layer2.2.bn1.num_batches_tracked",
    "layer2.2.bn2.num_batches_tracked",
    "layer2.3.bn1.num_batches_tracked",
    "layer2.3.bn2.num_batches_tracked",
    "layer3.0.bn1.num_batches_tracked",
    "layer3.0.bn2.num_batches_tracked",
    "layer3.0.downsample.1.num_batches_tracked",
    "layer3.1.bn1.num_batches_tracked",
    "layer3.1.bn2.num_batches_tracked",
    "layer3.2.bn1.num_batches_tracked",
    "layer3.2.bn2.num_batches_tracked",
    "layer3.3.bn1.num_batches_tracked",
    "layer3.3.bn2.num_batches_tracked",
    "layer3.4.bn1.num_batches_tracked",
    "layer3.4.bn2.num_batches_tracked",
    "layer3.5.bn1.num_batches_tracked",
    "layer3.5.bn2.num_batches_tracked",
    "layer4.0.bn1.num_batches_tracked",
    "layer4.0.bn2.num_batches_tracked",
    "layer4.0.downsample.1.num_batches_tracked",
    "layer4.1.bn1.num_batches_tracked",
    "layer4.1.bn2.num_batches_tracked",
    "layer4.2.bn1.num_batches_tracked",
    "layer4.2.bn2.num_batches_tracked",
]
assert not any(
    key in state_dict.keys() for key in keys_missing
), "key present that should be missing"
for key in keys_missing:
    state_dict[key] = torch.as_tensor(0)
torch.save(state_dict, "pytorch_model.pt")
save_file(state_dict, "model.safetensors")

model = resnet34(weights=None)
model.fc = torch.nn.Linear(model.fc.in_features, out_features=5, bias=True)
model.load_state_dict(state_dict)
model_jit = torch.jit.script(model, example_inputs=[(torch.ones(1, 3, 224, 224),)])
torch.jit.save(model_jit, "torchscript_model.bin")