>>> import arckit
>>> train_set, eval_set = arckit.load_data() # Load ARC-AGI-2 train/eval.
>>> train_set, eval_set = arckit.load_data("kaggle") # Load ARC Prize 2025
>>> train_set, eval_set = arckit.load_data("arcagi") # Load latest ARC-AGI-1

# TaskSets are iterable and indexable
>>> train_set 
<TaskSet: 400 tasks>
>>> train_set[0]
<Task-train 007bbfb7 | 5 train | 1 test>

# Indexing can be done by task ID
>>> train_set[0] == train_set['007bbfb7']
True

# You can load specific tasks by ID
>>> task = arckit.load_single('007bbfb7')

>>> task.dataset
'train'
>>> task.id
'007bbfb7'

## Extracting task Grids
>>> task.train # o task.test
=>  List[Tuple[ndarray, ndarray]] # of input/output pairs

>>> task.train[0][0] # input of 1st train example
array([[0, 7, 7],
       [7, 7, 7],
       [0, 7, 7]])

# Tasks can be previewed (with colour!) in Python.
>>> train_set[15].show()
                                <Task-train 0d3d703e | 4 train | 1 test>
┏━━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━━┳━━┳━━━━━━━━┓
┃ A-in 3x3 ┃ A-out 3x3 ┃ B-in 3x3 ┃ B-out 3x3 ┃ C-in 3x3 ┃ C-out 3x3 ┃ D-in 3x3 ┃ D-out 3x3 ┃  ┃ TA-in  ┃
┡━━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━━╇━━╇━━━━━━━━┩
│  3 1 2   │   4 5 6   │  2 3 8   │   6 4 9   │  5 8 6   │   1 9 2   │  9 4 2   │   8 3 6   │  │ 8 1 3  │
│  3 1 2   │   4 5 6   │  2 3 8   │   6 4 9   │  5 8 6   │   1 9 2   │  9 4 2   │   8 3 6   │  │ 8 1 3  │
│  3 1 2   │   4 5 6   │  2 3 8   │   6 4 9   │  5 8 6   │   1 9 2   │  9 4 2   │   8 3 6   │  │ 8 1 3  │
└──────────┴───────────┴──────────┴───────────┴──────────┴───────────┴──────────┴───────────┴──┴────────┘

# Get task in original ARC format following fchollet's repo.
>>> task.to_dict()
=>  {
    "id": str,
    "train": List[{"input": List[List[int]], "output": List[List[int]]}],
    "test":  List[{"input": List[List[int]], "output": List[List[int]]}]
    }

>>> eval_set.score_submission(
    'submission.csv', # Submission with two columns output_id,output in Kaggle fomrat
    topn=2,           # How many predictions to consider (default: 2)
    return_correct=False # Whether to return a list of which tasks were solved
    )

>>> import arckit.vis as vis
>>> grid = vis.draw_grid(task.train['2013d3e2'][0], xmax=3, ymax=3, padding=.5, label='Example')
>>> vis.output_drawing(grid, "images/grid_example.png") # svg/pdf/png

>>> task = vis.draw_task(train_set[0], width=10, height=6, label='Example')
>>> vis.output_drawing(task, "images/arcshow_example.png") # svg/pdf/png

>>> task = train_set[0] # Get the first task
>>> for i, (input_array, output_array) in enumerate(task.train): # Loop through the training examples
>>>     print(f"Training Example {i+1}")
>>>     print("Input:")
>>>     vis.print_grid(input_array)
>>>     print("Output:")
>>>     vis.print_grid(output_array)
>>>     print()