Organize CLI arguments into groups

[djghosh13]

Use parser.add_argument_group() to organize the CLI arguments logically; currently, the help text is just a wall of text, and it's likely to only get longer over time. (Not a high priority though)

options:
  -h, --help            show this help message and exit
  --dataset DATASET     Dataset to use for the benchmark
  --split SPLIT         Dataset split to use
  --model MODEL         Model architecture to use from OpenCLIP
  --pretrained PRETRAINED
                        Model checkpoint name to use from OpenCLIP
  --task {zeroshot_classification,zeroshot_retrieval,linear_probe}
  --amp                 whether to use mixed precision
  --num_workers NUM_WORKERS
  --recall_k RECALL_K [RECALL_K ...]
                        for retrieval, select the k for Recall@K metric.
  --fewshot_k FEWSHOT_K
                        for linear probe, how many shots. -1 = whole dataset.
  --fewshot_epochs FEWSHOT_EPOCHS
                        for linear probe, how many epochs.
  --fewshot_lr FEWSHOT_LR
                        for linear probe, what is the learning rate.
  --skip_load           for linear probes, when everything is cached, no need
                        to load model.
  --seed SEED           random seed.
  --batch_size BATCH_SIZE
  --model_cache_dir MODEL_CACHE_DIR
                        directory to where downloaded models are cached
  --dataset_root DATASET_ROOT
                        dataset root folder where the datasets are downloaded.
  --feature_root FEATURE_ROOT
                        feature root folder where the features are stored.
  --annotation_file ANNOTATION_FILE
                        text annotation file for retrieval datasets. Only
                        needed for when `--task` is `zeroshot_retrieval`.
  --language LANGUAGE   language of classname and prompts to use for zeroshot
                        classification.
  --output OUTPUT       output file where to dump the metrics
  --verbose             verbose mode
  --cupl                Use natural language prompt from CuPL paper
  --save_clf SAVE_CLF   optionally save the classification layer output by the
                        text tower
  --load_clfs LOAD_CLFS [LOAD_CLFS ...]
                        optionally load and average mutliple layers output by
                        text towers.