swarms/tests/models/test_bioclip.py

# Import necessary modules and define fixtures if needed
import pytest
import torch
from PIL import Image
from swarms.models.bioclip import BioClip


# Define fixtures if needed
@pytest.fixture
def sample_image_path():
    return "path_to_sample_image.jpg"


@pytest.fixture
def clip_instance():
    return BioClip(
        "microsoft/BiomedCLIP-PubMedBERT_256-vit_base_patch16_224"
    )


# Basic tests for the BioClip class
def test_clip_initialization(clip_instance):
    assert isinstance(clip_instance.model, torch.nn.Module)
    assert hasattr(clip_instance, "model_path")
    assert hasattr(clip_instance, "preprocess_train")
    assert hasattr(clip_instance, "preprocess_val")
    assert hasattr(clip_instance, "tokenizer")
    assert hasattr(clip_instance, "device")


def test_clip_call_method(clip_instance, sample_image_path):
    labels = [
        "adenocarcinoma histopathology",
        "brain MRI",
        "covid line chart",
        "squamous cell carcinoma histopathology",
        "immunohistochemistry histopathology",
        "bone X-ray",
        "chest X-ray",
        "pie chart",
        "hematoxylin and eosin histopathology",
    ]
    result = clip_instance(sample_image_path, labels)
    assert isinstance(result, dict)
    assert len(result) == len(labels)


def test_clip_plot_image_with_metadata(
    clip_instance, sample_image_path
):
    metadata = {
        "filename": "sample_image.jpg",
        "top_probs": {"label1": 0.75, "label2": 0.65},
    }
    clip_instance.plot_image_with_metadata(
        sample_image_path, metadata
    )


# More test cases can be added to cover additional functionality and edge cases


# Parameterized tests for different image and label combinations
@pytest.mark.parametrize(
    "image_path, labels",
    [
        ("image1.jpg", ["label1", "label2"]),
        ("image2.jpg", ["label3", "label4"]),
        # Add more image and label combinations
    ],
)
def test_clip_parameterized_calls(clip_instance, image_path, labels):
    result = clip_instance(image_path, labels)
    assert isinstance(result, dict)
    assert len(result) == len(labels)


# Test image preprocessing
def test_clip_image_preprocessing(clip_instance, sample_image_path):
    image = Image.open(sample_image_path)
    processed_image = clip_instance.preprocess_val(image)
    assert isinstance(processed_image, torch.Tensor)


# Test label tokenization
def test_clip_label_tokenization(clip_instance):
    labels = ["label1", "label2"]
    tokenized_labels = clip_instance.tokenizer(labels)
    assert isinstance(tokenized_labels, torch.Tensor)
    assert tokenized_labels.shape[0] == len(labels)


# More tests can be added to cover other methods and edge cases


# End-to-end tests with actual images and labels
def test_clip_end_to_end(clip_instance, sample_image_path):
    labels = [
        "adenocarcinoma histopathology",
        "brain MRI",
        "covid line chart",
        "squamous cell carcinoma histopathology",
        "immunohistochemistry histopathology",
        "bone X-ray",
        "chest X-ray",
        "pie chart",
        "hematoxylin and eosin histopathology",
    ]
    result = clip_instance(sample_image_path, labels)
    assert isinstance(result, dict)
    assert len(result) == len(labels)


# Test label tokenization with long labels
def test_clip_long_labels(clip_instance):
    labels = ["label" + str(i) for i in range(100)]
    tokenized_labels = clip_instance.tokenizer(labels)
    assert isinstance(tokenized_labels, torch.Tensor)
    assert tokenized_labels.shape[0] == len(labels)


# Test handling of multiple image files
def test_clip_multiple_images(clip_instance, sample_image_path):
    labels = ["label1", "label2"]
    image_paths = [sample_image_path, "image2.jpg"]
    results = clip_instance(image_paths, labels)
    assert isinstance(results, list)
    assert len(results) == len(image_paths)
    for result in results:
        assert isinstance(result, dict)
        assert len(result) == len(labels)


# Test model inference performance
def test_clip_inference_performance(
    clip_instance, sample_image_path, benchmark
):
    labels = [
        "adenocarcinoma histopathology",
        "brain MRI",
        "covid line chart",
        "squamous cell carcinoma histopathology",
        "immunohistochemistry histopathology",
        "bone X-ray",
        "chest X-ray",
        "pie chart",
        "hematoxylin and eosin histopathology",
    ]
    result = benchmark(clip_instance, sample_image_path, labels)
    assert isinstance(result, dict)
    assert len(result) == len(labels)


# Test different preprocessing pipelines
def test_clip_preprocessing_pipelines(
    clip_instance, sample_image_path
):
    image = Image.open(sample_image_path)

    # Test preprocessing for training
    processed_image_train = clip_instance.preprocess_train(image)
    assert isinstance(processed_image_train, torch.Tensor)

    # Test preprocessing for validation
    processed_image_val = clip_instance.preprocess_val(image)
    assert isinstance(processed_image_val, torch.Tensor)


# ...