import lightly

# Create the Lightly client to connect to the API.
client = lightly.api.ApiWorkflowClient(token="YOUR_TOKEN")

# Let's fetch the dataset we created above, by name
client.set_dataset_id_by_name("pedestrian-videos-datapool")

# Schedule the compute run using our custom config.
# We show here the full default config so you can easily edit the
# values according to your needs.
client.schedule_compute_worker_run(
    worker_config={
        "enable_corruptness_check": True,
        "remove_exact_duplicates": True,
        "enable_training": False,
        "pretagging": False,
        "pretagging_debug": False,
    },
    selection_config={
        "n_samples": 100,
        "strategies": [
            {
                "input": {"type": "EMBEDDINGS"},
                "strategy": {
                    "type": "DIVERSITY",
                    "stopping_condition_minimum_distance": 0.2,
                },
            }
        ],
    },
    lightly_config={
        "loader": {
            "batch_size": 128,
            "shuffle": True,
            "num_workers": -1,
            "drop_last": True,
        },
        "model": {"name": "resnet-18", "out_dim": 128, "num_ftrs": 32, "width": 1},
        "trainer": {"gpus": 1, "max_epochs": 1, "precision": 16},
        "criterion": {"temperature": 0.5},
        "optimizer": {"lr": 1, "weight_decay": 0.00001},
        "collate": {
            "input_size": 64,
            "cj_prob": 0.8,
            "cj_bright": 0.7,
            "cj_contrast": 0.7,
            "cj_sat": 0.7,
            "cj_hue": 0.2,
            "min_scale": 0.15,
            "random_gray_scale": 0.2,
            "gaussian_blur": 0.0,
            "kernel_size": 0.1,
            "vf_prob": 0,
            "hf_prob": 0.5,
            "rr_prob": 0,
        },
    },
)
