old_contents: import numpy as np
  import pytest
  import torch
  from PIL import Image
  
  import clip
  
  
  @pytest.mark.parametrize('model_name', clip.available_models())
  def test_consistency(model_name):
      device = "cpu"
      jit_model, transform = clip.load(model_name, device=device)
      py_model, _ = clip.load(model_name, device=device, jit=False)
  
      image = transform(Image.open("CLIP.png")).unsqueeze(0).to(device)
      text = clip.tokenize(["a diagram", "a dog", "a cat"]).to(device)
  
      with torch.no_grad():
          logits_per_image, _ = jit_model(image, text)
          jit_probs = logits_per_image.softmax(dim=-1).cpu().numpy()
  
          logits_per_image, _ = py_model(image, text)
          py_probs = logits_per_image.softmax(dim=-1).cpu().numpy()
  
      assert np.allclose(jit_probs, py_probs, atol=0.01, rtol=0.1)
  
new_contents: import numpy as np
  import pytest
  import torch
  from PIL import Image
  
  import clip
  
  
  @pytest.mark.parametrize('model_name', clip.available_models())
  def test_consistency(model_name):
      device = "cpu"
      jit_model, transform = clip.load(model_name, device=device, jit=True)
      py_model, _ = clip.load(model_name, device=device, jit=False)
  
      image = transform(Image.open("CLIP.png")).unsqueeze(0).to(device)
      text = clip.tokenize(["a diagram", "a dog", "a cat"]).to(device)
  
      with torch.no_grad():
          logits_per_image, _ = jit_model(image, text)
          jit_probs = logits_per_image.softmax(dim=-1).cpu().numpy()
  
          logits_per_image, _ = py_model(image, text)
          py_probs = logits_per_image.softmax(dim=-1).cpu().numpy()
  
      assert np.allclose(jit_probs, py_probs, atol=0.01, rtol=0.1)
  
subject: test fix