model tests for biogpt, etc

Former-commit-id: aed02ffaac

swarms/models/idefics.py

@@ -39,7 +39,7 @@ class Idefics:
     # Usage
     ```
     from swarms.models import idefics
-    
+
     model = idefics()
 
     user_input = "User: What is in this image? https://upload.wikimedia.org/wikipedia/commons/8/86/Id%C3%A9fix.JPG"

tests/chunkers/basechunker.py

@@ -1,5 +1,11 @@
 import pytest
-from swarms.chunkers.base import BaseChunker, TextArtifact, ChunkSeparator, OpenAiTokenizer  # adjust the import paths accordingly
+from swarms.chunkers.base import (
+    BaseChunker,
+    TextArtifact,
+    ChunkSeparator,
+    OpenAiTokenizer,
+)  # adjust the import paths accordingly
+
 
 # 1. Test Initialization
 def test_chunker_initialization():
@@ -7,14 +13,17 @@ def test_chunker_initialization():
     assert isinstance(chunker, BaseChunker)
     assert chunker.max_tokens == chunker.tokenizer.max_tokens
 
+
 def test_default_separators():
     chunker = BaseChunker()
     assert chunker.separators == BaseChunker.DEFAULT_SEPARATORS
 
+
 def test_default_tokenizer():
     chunker = BaseChunker()
     assert isinstance(chunker.tokenizer, OpenAiTokenizer)
 
+
 # 2. Test Basic Chunking
 @pytest.mark.parametrize(
     "input_text, expected_output",
@@ -29,6 +38,7 @@ def test_basic_chunk(input_text, expected_output):
     result = chunker.chunk(input_text)
     assert result == expected_output
 
+
 # 3. Test Chunking with Different Separators
 def test_custom_separators():
     custom_separator = ChunkSeparator(";")
@@ -38,6 +48,7 @@ def test_custom_separators():
     result = chunker.chunk(input_text)
     assert result == expected_output
 
+
 # 4. Test Recursive Chunking
 def test_recursive_chunking():
     chunker = BaseChunker(max_tokens=5)
@@ -47,22 +58,25 @@ def test_recursive_chunking():
         TextArtifact("is a"),
         TextArtifact("more"),
         TextArtifact("complex"),
-        TextArtifact("text.")
+        TextArtifact("text."),
     ]
     result = chunker.chunk(input_text)
     assert result == expected_output
 
+
 # 5. Test Edge Cases and Special Scenarios
 def test_empty_text():
     chunker = BaseChunker()
     result = chunker.chunk("")
     assert result == []
 
+
 def test_whitespace_text():
     chunker = BaseChunker()
     result = chunker.chunk("     ")
     assert result == [TextArtifact("     ")]
 
+
 def test_single_word():
     chunker = BaseChunker()
     result = chunker.chunk("Hello")

tests/models/biogpt.py

@@ -0,0 +1,207 @@
+from unittest.mock import patch
+
+# Import necessary modules
+import pytest
+import torch
+from transformers import BioGptForCausalLM, BioGptTokenizer
+
+
+
+# Fixture for BioGPT instance
+@pytest.fixture
+def biogpt_instance():
+    from swarms.models import (
+        BioGPT,  
+    )
+
+    return BioGPT()
+
+
+# 36. Test if BioGPT provides a response for a simple biomedical question
+def test_biomedical_response_1(biogpt_instance):
+    question = "What are the functions of the mitochondria?"
+    response = biogpt_instance(question)
+    assert response and isinstance(response, str)
+
+
+# 37. Test for a genetics-based question
+def test_genetics_response(biogpt_instance):
+    question = "Can you explain the Mendelian inheritance?"
+    response = biogpt_instance(question)
+    assert response and isinstance(response, str)
+
+
+# 38. Test for a question about viruses
+def test_virus_response(biogpt_instance):
+    question = "How do RNA viruses replicate?"
+    response = biogpt_instance(question)
+    assert response and isinstance(response, str)
+
+
+# 39. Test for a cell biology related question
+def test_cell_biology_response(biogpt_instance):
+    question = "Describe the cell cycle and its phases."
+    response = biogpt_instance(question)
+    assert response and isinstance(response, str)
+
+
+# 40. Test for a question about protein structure
+def test_protein_structure_response(biogpt_instance):
+    question = "What's the difference between alpha helix and beta sheet structures in proteins?"
+    response = biogpt_instance(question)
+    assert response and isinstance(response, str)
+
+
+# 41. Test for a pharmacology question
+def test_pharmacology_response(biogpt_instance):
+    question = "How do beta blockers work?"
+    response = biogpt_instance(question)
+    assert response and isinstance(response, str)
+
+
+# 42. Test for an anatomy-based question
+def test_anatomy_response(biogpt_instance):
+    question = "Describe the structure of the human heart."
+    response = biogpt_instance(question)
+    assert response and isinstance(response, str)
+
+
+# 43. Test for a question about bioinformatics
+def test_bioinformatics_response(biogpt_instance):
+    question = "What is a BLAST search?"
+    response = biogpt_instance(question)
+    assert response and isinstance(response, str)
+
+
+# 44. Test for a neuroscience question
+def test_neuroscience_response(biogpt_instance):
+    question = "Explain the function of synapses in the nervous system."
+    response = biogpt_instance(question)
+    assert response and isinstance(response, str)
+
+
+# 45. Test for an immunology question
+def test_immunology_response(biogpt_instance):
+    question = "What is the role of T cells in the immune response?"
+    response = biogpt_instance(question)
+    assert response and isinstance(response, str)
+
+
+def test_init(bio_gpt):
+    assert bio_gpt.model_name == "microsoft/biogpt"
+    assert bio_gpt.max_length == 500
+    assert bio_gpt.num_return_sequences == 5
+    assert bio_gpt.do_sample is True
+    assert bio_gpt.min_length == 100
+
+
+def test_call(bio_gpt, monkeypatch):
+    def mock_pipeline(*args, **kwargs):
+        class MockGenerator:
+            def __call__(self, text, **kwargs):
+                return ["Generated text"]
+
+        return MockGenerator()
+
+    monkeypatch.setattr("transformers.pipeline", mock_pipeline)
+    result = bio_gpt("Input text")
+    assert result == ["Generated text"]
+
+
+def test_get_features(bio_gpt):
+    features = bio_gpt.get_features("Input text")
+    assert "last_hidden_state" in features
+
+
+def test_beam_search_decoding(bio_gpt):
+    generated_text = bio_gpt.beam_search_decoding("Input text")
+    assert isinstance(generated_text, str)
+
+
+def test_set_pretrained_model(bio_gpt):
+    bio_gpt.set_pretrained_model("new_model")
+    assert bio_gpt.model_name == "new_model"
+
+
+def test_get_config(bio_gpt):
+    config = bio_gpt.get_config()
+    assert "vocab_size" in config
+
+
+def test_save_load_model(tmp_path, bio_gpt):
+    bio_gpt.save_model(tmp_path)
+    bio_gpt.load_from_path(tmp_path)
+    assert bio_gpt.model_name == "microsoft/biogpt"
+
+
+def test_print_model(capsys, bio_gpt):
+    bio_gpt.print_model()
+    captured = capsys.readouterr()
+    assert "BioGptForCausalLM" in captured.out
+
+
+# 26. Test if set_pretrained_model changes the model_name
+def test_set_pretrained_model_name_change(biogpt_instance):
+    biogpt_instance.set_pretrained_model("new_model_name")
+    assert biogpt_instance.model_name == "new_model_name"
+
+
+# 27. Test get_config return type
+def test_get_config_return_type(biogpt_instance):
+    config = biogpt_instance.get_config()
+    assert isinstance(config, type(biogpt_instance.model.config))
+
+
+# 28. Test saving model functionality by checking if files are created
+@patch.object(BioGptForCausalLM, "save_pretrained")
+@patch.object(BioGptTokenizer, "save_pretrained")
+def test_save_model(mock_save_model, mock_save_tokenizer, biogpt_instance):
+    path = "test_path"
+    biogpt_instance.save_model(path)
+    mock_save_model.assert_called_once_with(path)
+    mock_save_tokenizer.assert_called_once_with(path)
+
+
+# 29. Test loading model from path
+@patch.object(BioGptForCausalLM, "from_pretrained")
+@patch.object(BioGptTokenizer, "from_pretrained")
+def test_load_from_path(mock_load_model, mock_load_tokenizer, biogpt_instance):
+    path = "test_path"
+    biogpt_instance.load_from_path(path)
+    mock_load_model.assert_called_once_with(path)
+    mock_load_tokenizer.assert_called_once_with(path)
+
+
+# 30. Test print_model doesn't raise any error
+def test_print_model_metadata(biogpt_instance):
+    try:
+        biogpt_instance.print_model()
+    except Exception as e:
+        pytest.fail(f"print_model() raised an exception: {e}")
+
+
+# 31. Test that beam_search_decoding uses the correct number of beams
+@patch.object(BioGptForCausalLM, "generate")
+def test_beam_search_decoding_num_beams(mock_generate, biogpt_instance):
+    biogpt_instance.beam_search_decoding("test_sentence", num_beams=7)
+    _, kwargs = mock_generate.call_args
+    assert kwargs["num_beams"] == 7
+
+
+# 32. Test if beam_search_decoding handles early_stopping
+@patch.object(BioGptForCausalLM, "generate")
+def test_beam_search_decoding_early_stopping(mock_generate, biogpt_instance):
+    biogpt_instance.beam_search_decoding("test_sentence", early_stopping=False)
+    _, kwargs = mock_generate.call_args
+    assert kwargs["early_stopping"] is False
+
+
+# 33. Test get_features return type
+def test_get_features_return_type(biogpt_instance):
+    result = biogpt_instance.get_features("This is a sample text.")
+    assert isinstance(result, torch.nn.modules.module.Module)
+
+
+# 34. Test if default model is set correctly during initialization
+def test_default_model_name(biogpt_instance):
+    assert biogpt_instance.model_name == "microsoft/biogpt"

tests/models/fuyu.py

@@ -5,11 +5,13 @@ from swarms.models import Fuyu
 from transformers import FuyuProcessor, FuyuImageProcessor
 from PIL import Image
 
+
 # Basic test to ensure instantiation of class.
 def test_fuyu_initialization():
     fuyu_instance = Fuyu()
     assert isinstance(fuyu_instance, Fuyu)
 
+
 # Using parameterized testing for different init parameters.
 @pytest.mark.parametrize(
     "pretrained_path, device_map, max_new_tokens",
@@ -24,73 +26,92 @@ def test_fuyu_parameters(pretrained_path, device_map, max_new_tokens):
     assert fuyu_instance.device_map == device_map
     assert fuyu_instance.max_new_tokens == max_new_tokens
 
+
 # Fixture for creating a Fuyu instance.
 @pytest.fixture
 def fuyu_instance():
     return Fuyu()
 
+
 # Test using the fixture.
 def test_fuyu_processor_initialization(fuyu_instance):
     assert isinstance(fuyu_instance.processor, FuyuProcessor)
     assert isinstance(fuyu_instance.image_processor, FuyuImageProcessor)
 
+
 # Test exception when providing an invalid image path.
 def test_invalid_image_path(fuyu_instance):
     with pytest.raises(FileNotFoundError):
         fuyu_instance("Hello", "invalid/path/to/image.png")
 
+
 # Using monkeypatch to replace the Image.open method to simulate a failure.
 def test_image_open_failure(fuyu_instance, monkeypatch):
-
     def mock_open(*args, **kwargs):
         raise Exception("Mocked failure")
 
     monkeypatch.setattr(Image, "open", mock_open)
 
     with pytest.raises(Exception, match="Mocked failure"):
-        fuyu_instance("Hello", "https://plus.unsplash.com/premium_photo-1687149699194-0207c04bc6e8?auto=format&fit=crop&q=80&w=1378&ixlib=rb-4.0.3&ixid=M3wxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8fA%3D%3D")
+        fuyu_instance(
+            "Hello",
+            "https://plus.unsplash.com/premium_photo-1687149699194-0207c04bc6e8?auto=format&fit=crop&q=80&w=1378&ixlib=rb-4.0.3&ixid=M3wxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8fA%3D%3D",
+        )
+
 
 # Marking a slow test.
 @pytest.mark.slow
 def test_fuyu_model_output(fuyu_instance):
     # This is a dummy test and may not be functional without real data.
-    output = fuyu_instance("Hello, my name is", "https://plus.unsplash.com/premium_photo-1687149699194-0207c04bc6e8?auto=format&fit=crop&q=80&w=1378&ixlib=rb-4.0.3&ixid=M3wxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8fA%3D%3D")
+    output = fuyu_instance(
+        "Hello, my name is",
+        "https://plus.unsplash.com/premium_photo-1687149699194-0207c04bc6e8?auto=format&fit=crop&q=80&w=1378&ixlib=rb-4.0.3&ixid=M3wxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8fA%3D%3D",
+    )
     assert isinstance(output, str)
 
+
 def test_tokenizer_type(fuyu_instance):
     assert "tokenizer" in dir(fuyu_instance)
 
+
 def test_processor_has_image_processor_and_tokenizer(fuyu_instance):
     assert fuyu_instance.processor.image_processor == fuyu_instance.image_processor
     assert fuyu_instance.processor.tokenizer == fuyu_instance.tokenizer
 
+
 def test_model_device_map(fuyu_instance):
     assert fuyu_instance.model.device_map == fuyu_instance.device_map
 
+
 # Testing maximum tokens setting
 def test_max_new_tokens_setting(fuyu_instance):
     assert fuyu_instance.max_new_tokens == 7
 
+
 # Test if an exception is raised when invalid text is provided.
 def test_invalid_text_input(fuyu_instance):
     with pytest.raises(Exception):
         fuyu_instance(None, "path/to/image.png")
 
+
 # Test if an exception is raised when empty text is provided.
 def test_empty_text_input(fuyu_instance):
     with pytest.raises(Exception):
         fuyu_instance("", "path/to/image.png")
 
+
 # Test if an exception is raised when a very long text is provided.
 def test_very_long_text_input(fuyu_instance):
     with pytest.raises(Exception):
         fuyu_instance("A" * 10000, "path/to/image.png")
 
+
 # Check model's default device map
 def test_default_device_map():
     fuyu_instance = Fuyu()
     assert fuyu_instance.device_map == "cuda:0"
 
+
 # Testing if processor is correctly initialized
 def test_processor_initialization(fuyu_instance):
     assert isinstance(fuyu_instance.processor, FuyuProcessor)

tests/models/idefics.py

@@ -3,97 +3,117 @@ from unittest.mock import patch
 import torch
 from swarms.models.idefics import Idefics, IdeficsForVisionText2Text, AutoProcessor
 
+
 @pytest.fixture
 def idefics_instance():
-    with patch("torch.cuda.is_available", return_value=False):  # Assuming tests are run on CPU for simplicity
+    with patch(
+        "torch.cuda.is_available", return_value=False
+    ):  # Assuming tests are run on CPU for simplicity
         instance = Idefics()
     return instance
 
+
 # Basic Tests
 def test_init_default(idefics_instance):
     assert idefics_instance.device == "cpu"
     assert idefics_instance.max_length == 100
     assert not idefics_instance.chat_history
 
+
 @pytest.mark.parametrize(
     "device,expected",
     [
-        (None, "cpu"), 
+        (None, "cpu"),
         ("cuda", "cuda"),
         ("cpu", "cpu"),
-    ]
+    ],
 )
 def test_init_device(device, expected):
-    with patch("torch.cuda.is_available", return_value=True if expected == "cuda" else False):
+    with patch(
+        "torch.cuda.is_available", return_value=True if expected == "cuda" else False
+    ):
         instance = Idefics(device=device)
     assert instance.device == expected
 
+
 # Test `run` method
 def test_run(idefics_instance):
     prompts = [["User: Test"]]
-    with patch.object(idefics_instance, "processor") as mock_processor, \
-         patch.object(idefics_instance, "model") as mock_model:
+    with patch.object(idefics_instance, "processor") as mock_processor, patch.object(
+        idefics_instance, "model"
+    ) as mock_model:
         mock_processor.return_value = {"input_ids": torch.tensor([1, 2, 3])}
         mock_model.generate.return_value = torch.tensor([1, 2, 3])
         mock_processor.batch_decode.return_value = ["Test"]
-        
+
         result = idefics_instance.run(prompts)
-        
+
     assert result == ["Test"]
 
+
 # Test `__call__` method (using the same logic as run for simplicity)
 def test_call(idefics_instance):
     prompts = [["User: Test"]]
-    with patch.object(idefics_instance, "processor") as mock_processor, \
-         patch.object(idefics_instance, "model") as mock_model:
+    with patch.object(idefics_instance, "processor") as mock_processor, patch.object(
+        idefics_instance, "model"
+    ) as mock_model:
         mock_processor.return_value = {"input_ids": torch.tensor([1, 2, 3])}
         mock_model.generate.return_value = torch.tensor([1, 2, 3])
         mock_processor.batch_decode.return_value = ["Test"]
-        
+
         result = idefics_instance(prompts)
-        
+
     assert result == ["Test"]
 
+
 # Test `chat` method
 def test_chat(idefics_instance):
     user_input = "User: Hello"
     response = "Model: Hi there!"
     with patch.object(idefics_instance, "run", return_value=[response]):
         result = idefics_instance.chat(user_input)
-    
+
     assert result == response
     assert idefics_instance.chat_history == [user_input, response]
 
+
 # Test `set_checkpoint` method
 def test_set_checkpoint(idefics_instance):
     new_checkpoint = "new_checkpoint"
-    with patch.object(IdeficsForVisionText2Text, "from_pretrained") as mock_from_pretrained, \
-         patch.object(AutoProcessor, "from_pretrained"):
+    with patch.object(
+        IdeficsForVisionText2Text, "from_pretrained"
+    ) as mock_from_pretrained, patch.object(AutoProcessor, "from_pretrained"):
         idefics_instance.set_checkpoint(new_checkpoint)
-    
+
     mock_from_pretrained.assert_called_with(new_checkpoint, torch_dtype=torch.bfloat16)
 
+
 # Test `set_device` method
 def test_set_device(idefics_instance):
     new_device = "cuda"
     with patch.object(idefics_instance.model, "to"):
         idefics_instance.set_device(new_device)
-    
+
     assert idefics_instance.device == new_device
 
+
 # Test `set_max_length` method
 def test_set_max_length(idefics_instance):
     new_length = 150
     idefics_instance.set_max_length(new_length)
     assert idefics_instance.max_length == new_length
 
+
 # Test `clear_chat_history` method
 def test_clear_chat_history(idefics_instance):
     idefics_instance.chat_history = ["User: Test", "Model: Response"]
     idefics_instance.clear_chat_history()
     assert not idefics_instance.chat_history
 
+
 # Exception Tests
 def test_run_with_empty_prompts(idefics_instance):
-    with pytest.raises(Exception):  # Replace Exception with the actual exception that may arise for an empty prompt.
+    with pytest.raises(
+        Exception
+    ):  # Replace Exception with the actual exception that may arise for an empty prompt.
         idefics_instance.run([])

tests/models/vilt.py

@@ -2,20 +2,23 @@ import pytest
 from unittest.mock import patch, Mock
 from swarms.models.vilt import Vilt, Image, requests
 
+
 # Fixture for Vilt instance
 @pytest.fixture
 def vilt_instance():
     return Vilt()
 
+
 # 1. Test Initialization
 def test_vilt_initialization(vilt_instance):
     assert isinstance(vilt_instance, Vilt)
     assert vilt_instance.processor is not None
     assert vilt_instance.model is not None
 
+
 # 2. Test Model Predictions
-@patch.object(requests, 'get')
-@patch.object(Image, 'open')
+@patch.object(requests, "get")
+@patch.object(Image, "open")
 def test_vilt_prediction(mock_image_open, mock_requests_get, vilt_instance):
     mock_image = Mock()
     mock_image_open.return_value = mock_image
@@ -23,13 +26,21 @@ def test_vilt_prediction(mock_image_open, mock_requests_get, vilt_instance):
 
     # It's a mock response, so no real answer expected
     with pytest.raises(Exception):  # Ensure exception is more specific
-        vilt_instance("What is this image", "https://images.unsplash.com/photo-1582538885592-e70a5d7ab3d3?ixlib=rb-4.0.3&ixid=M3wxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8fA%3D%3D&auto=format&fit=crop&w=1770&q=80")
+        vilt_instance(
+            "What is this image",
+            "https://images.unsplash.com/photo-1582538885592-e70a5d7ab3d3?ixlib=rb-4.0.3&ixid=M3wxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8fA%3D%3D&auto=format&fit=crop&w=1770&q=80",
+        )
+
 
 # 3. Test Exception Handling for network
-@patch.object(requests, 'get', side_effect=requests.RequestException("Network error"))
+@patch.object(requests, "get", side_effect=requests.RequestException("Network error"))
 def test_vilt_network_exception(vilt_instance):
     with pytest.raises(requests.RequestException):
-        vilt_instance("What is this image", "https://images.unsplash.com/photo-1582538885592-e70a5d7ab3d3?ixlib=rb-4.0.3&ixid=M3wxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8fA%3D%3D&auto=format&fit=crop&w=1770&q=80")
+        vilt_instance(
+            "What is this image",
+            "https://images.unsplash.com/photo-1582538885592-e70a5d7ab3d3?ixlib=rb-4.0.3&ixid=M3wxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8fA%3D%3D&auto=format&fit=crop&w=1770&q=80",
+        )
+
 
 # Parameterized test cases for different inputs
 @pytest.mark.parametrize(
@@ -39,26 +50,37 @@ def test_vilt_network_exception(vilt_instance):
         ("Who is in the image?", "http://example.com/image2.jpg"),
         ("Where was this picture taken?", "http://example.com/image3.jpg"),
         # ... Add more scenarios
-    ]
+    ],
 )
 def test_vilt_various_inputs(text, image_url, vilt_instance):
     with pytest.raises(Exception):  # Again, ensure exception is more specific
         vilt_instance(text, image_url)
 
+
 # Test with invalid or empty text
 @pytest.mark.parametrize(
     "text,image_url",
     [
-        ("", "https://images.unsplash.com/photo-1582538885592-e70a5d7ab3d3?ixlib=rb-4.0.3&ixid=M3wxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8fA%3D%3D&auto=format&fit=crop&w=1770&q=80"),
-        (None, "https://images.unsplash.com/photo-1582538885592-e70a5d7ab3d3?ixlib=rb-4.0.3&ixid=M3wxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8fA%3D%3D&auto=format&fit=crop&w=1770&q=80"),
-        (" ", "https://images.unsplash.com/photo-1582538885592-e70a5d7ab3d3?ixlib=rb-4.0.3&ixid=M3wxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8fA%3D%3D&auto=format&fit=crop&w=1770&q=80"),
+        (
+            "",
+            "https://images.unsplash.com/photo-1582538885592-e70a5d7ab3d3?ixlib=rb-4.0.3&ixid=M3wxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8fA%3D%3D&auto=format&fit=crop&w=1770&q=80",
+        ),
+        (
+            None,
+            "https://images.unsplash.com/photo-1582538885592-e70a5d7ab3d3?ixlib=rb-4.0.3&ixid=M3wxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8fA%3D%3D&auto=format&fit=crop&w=1770&q=80",
+        ),
+        (
+            " ",
+            "https://images.unsplash.com/photo-1582538885592-e70a5d7ab3d3?ixlib=rb-4.0.3&ixid=M3wxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8fA%3D%3D&auto=format&fit=crop&w=1770&q=80",
+        ),
         # ... Add more scenarios
-    ]
+    ],
 )
 def test_vilt_invalid_text(text, image_url, vilt_instance):
     with pytest.raises(ValueError):
         vilt_instance(text, image_url)
 
+
 # Test with invalid or empty image_url
 @pytest.mark.parametrize(
     "text,image_url",
@@ -66,9 +88,8 @@ def test_vilt_invalid_text(text, image_url, vilt_instance):
         ("What is this?", ""),
         ("Who is in the image?", None),
         ("Where was this picture taken?", " "),
-    ]
+    ],
 )
 def test_vilt_invalid_image_url(text, image_url, vilt_instance):
     with pytest.raises(ValueError):
         vilt_instance(text, image_url)
-