Upload mxbi-arckit.txt

mxbi-arckit.txt

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+Directory structure:
+└── mxbi-arckit/
+    ├── README.md
+    ├── LICENSE
+    ├── pyproject.toml
+    ├── arckit/
+    │   ├── __init__.py
+    │   ├── cli.py
+    │   ├── data.py
+    │   ├── importtool.py
+    │   └── vis.py
+    └── .github/
+        └── workflows/
+            └── pypi.yml
+
+================================================
+FILE: README.md
+================================================
+# arckit
+
+[![PyPI version](https://badge.fury.io/py/arckit.svg)](https://badge.fury.io/py/arckit)
+
+![Example visualisation of ARC grids](./images/allgrids10.svg)
+
+Python and command-line tools for easily working with the ARC, ARC-AGI & ARC-AGI-2 datasets. 
+
+```bash
+pip install -U arckit
+```
+
+Arckit provides tools for loading the data in a friendly format (without a separate download!), visualizing the data with high-quality vector graphics, and evaluating models on the dataset.
+
+✨ **NEW in v1.0:** Added ARC-AGI-2 & ARC Prize 2025 Data, updated evaluation ruleset.
+
+## 🐍 Python API
+
+### Loading the dataset
+
+```python
+>>> 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')
+```
+
+### Interacting with tasks
+
+```python
+>>> 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]]}]
+    }
+```
+
+### Scoring a submission file:
+
+To evaluate a submission in [Kaggle ARC format](https://www.kaggle.com/competitions/abstraction-and-reasoning-challenge/overview/evaluation):
+
+```python
+>>> 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
+    )
+```
+
+> **Note:** the default `topn` was changed from 3 to 2 in v1.0 to match changes in the official evaluation.
+
+### Loading a specific dataset version
+
+The ARC-AGI datasets have had [several bugfixes](https://github.com/arcprize/ARC-AGI-2/blob/main/changelog.md) since original release. Additionally, a new ARC-AGI-2 dataset has been released for competitions starting in 2025. By default, the `latest` version of ARC-AGI-2 is loaded, but you can specify a `version` parameter to both `load_data` and `load_single` to load other datasets. 
+
+**The version options are:**
+
+- `latest`, `arcagi2`: The latest version of the ARC-AGI-2 dataset (currently: `f3283f7`)
+- `arcagi`: The latest version of the ARC-AGI dataset (currently: `aa922be`)
+- `kaggle`, `kaggle2025`: The data for the 2025 Kaggle competition based on ARC-AGI-2 (currently: `kaggle250808`)
+- `kaggle2024`: The data for the 2024 Kaggle competition based on ARC-AGI (pinned)
+- `arc`, `kaggle2019`: The original ARC data, as in the 2019 Kaggle competition (pinned)
+
+> **Note:** You may wish to pin your data to a specific version number to avoid underlying data changes during research. To do this, use the most specific name available when loading data, or pin the installed version of `arckit` in your environment.
+
+## 🖼️ Creating visualisations
+
+The `arckit.vis` submodule provides useful functions for creating vector graphics visualisations of tasks, using the `drawsvg` module. The docstrings for these functions provide more detailed information as well as additional options.
+
+```python
+>>> 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
+```
+
+![Example of arckit visualisation](./images/grid_example.png)
+
+When drawing tasks, arckit will intelligently resize all of the grids such that the total size of the illustration does not exceed the chosen width/height.
+
+```python
+>>> task = vis.draw_task(train_set[0], width=10, height=6, label='Example')
+>>> vis.output_drawing(task, "images/arcshow_example.png") # svg/pdf/png
+```
+
+![Example of arckit output](./images/arcsave_example.png)
+
+Alternatively, the `print_grid` function outputs a grid directly to the terminal without creating any files.
+
+```python
+>>> 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()
+```
+
+<img src="./images/arc_terminal_print_grid_example.png" width="200">
+
+## 💻 Command-line tools
+
+`arcshow` draws a visualisation of a specific task straight to the console:
+
+![Example of arcshow command output (with colours)](./images/arcshow_example.png)
+
+`arcsave` saves a visualisation of a specific task to a file (pdf/svg/png), and is useful for inspecting tasks or producing high quality graphics showing specific tasks (e.g. for a paper). Tasks can be specified by their hex ID or by dataset, e.g. `train0`.
+
+```bash
+usage: arcsave [-h] [--output OUTPUT] task_id width height
+
+Save a task to a image file.
+
+positional arguments:
+  task_id          The task id to save. Can either be a task ID or a string e.g. `train0`
+  width            The width of the output image
+  height           The height of the output image
+
+optional arguments:
+  -h, --help       show this help message and exit
+  --output OUTPUT  The output file to save to. Must end in .svg/.pdf/.png. By default, pdf is used.
+  ```
+
+![Example of arcsave command output](./images/arcsave_example.png)
+
+## 💡 Contributions
+
+Any relevant contributions are very welcome! Please feel free to open an issue or pull request, or drop me an email if you want to discuss any possible changes.
+
+## 📜 Acknowledgements
+
+The ARC and ARC-AGI-2 datasets was graciously released by Francois Chollet under [Apache 2.0](https://github.com/fchollet/ARC/blob/master/LICENSE) and can be found in original format in [these](https://github.com/fchollet/ARC) [repositories](https://github.com/arcprize/ARC-AGI-2).  The dataset is reproduced within the `arckit` package under the same license.
+
+
+
+================================================
+FILE: LICENSE
+================================================
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+
+================================================
+FILE: pyproject.toml
+================================================
+[build-system]
+requires = ["setuptools>=61.0", "wheel"]
+build-backend = "setuptools.build_meta"
+
+[project]
+name = "arckit"
+version = "1.0.1" # Remember to change in __init__.py
+description = "Tools for working with the Abstraction & Reasoning Corpus (ARC-AGI)"
+readme = "README.md"
+authors = [
+    {name = "Mikel Bober-Irizar", email = "mikel@mxbi.net"}
+]
+license = "Apache-2.0"
+requires-python = ">=3.8"
+keywords = ["arc", "agi", "reasoning", "abstraction"]
+classifiers = [
+    "Development Status :: 5 - Production/Stable",
+    "Programming Language :: Python :: 3",
+    "Programming Language :: Python :: 3.8",
+    "Programming Language :: Python :: 3.9",
+    "Programming Language :: Python :: 3.10",
+    "Programming Language :: Python :: 3.11",
+    "Programming Language :: Python :: 3.12",
+    "Programming Language :: Python :: 3.13",
+    "Topic :: Scientific/Engineering :: Artificial Intelligence",
+]
+dependencies = [
+    "numpy",
+    "rich",
+    "drawsvg",
+]
+
+[project.urls]
+Homepage = "https://github.com/mxbi/arckit"
+Repository = "https://github.com/mxbi/arckit"
+Issues = "https://github.com/mxbi/arckit/issues"
+
+[project.scripts]
+arctask = "arckit.cli:taskprint"
+arcsave = "arckit.cli:tasksave"
+
+[tool.setuptools.packages.find]
+include = ["arckit*"]
+exclude = ["arckit.__pycache__*"]
+
+[tool.setuptools.package-data]
+arckit = ["data/*.json"]
+
+[tool.setuptools.exclude-package-data]
+arckit = ["__pycache__/*", "*.py[co]"]
+
+
+================================================
+FILE: arckit/__init__.py
+================================================
+from .data import Task, load_data, fmt_grid, load_single
+from .vis import draw_grid, draw_task, output_drawing
+
+__version__ = "1.0.1"
+__version_info__ = (1, 0, 1)
+
+
+================================================
+FILE: arckit/cli.py
+================================================
+import sys
+import argparse
+from .data import load_single
+from .vis import draw_task, output_drawing
+
+def taskprint():
+    if len(sys.argv) < 2:
+        print("Usage: arctask <task_id>")
+        return
+
+    task = load_single(sys.argv[1])
+
+    task.show()
+
+def tasksave():
+    parser = argparse.ArgumentParser(description='Save a task to a image file.')
+    parser.add_argument('task_id', type=str, help='The task id to save. Can either be a task ID or a string e.g. `train0`')
+    parser.add_argument('width', type=int, help='The width of the output image', default=20)
+    parser.add_argument('height', type=int, help='The height of the output image', default=10)
+    parser.add_argument('--output', type=str, help='The output file to save to. Must end in .svg/.pdf/.png. By default, pdf is used.', default=False, required=False)
+    args = parser.parse_args()
+
+    task = load_single(args.task_id)
+
+    drawing = draw_task(task, width=args.width, height=args.height)
+    out_fn = args.output or f'{task.id}_{drawing.width}x{drawing.height}.pdf'
+    print(f'Drawn task {task.id} ({drawing.width}x{drawing.height}), saving to {out_fn}')
+
+    output_drawing(drawing, out_fn)
+
+
+================================================
+FILE: arckit/data.py
+================================================
+import json
+import numpy as np
+import os
+import csv
+
+from rich import print
+from rich.panel import Panel
+from rich.table import Table
+from rich.text import Text
+
+def idx2chr(idx):
+    return chr(idx + 65)
+
+def fmt_grid(grid, colour=True, spaces=True):
+    grid_str = []
+    if not colour:
+        for row in grid:
+            if spaces == 'gpt':
+                grid_str.append(''.join([' ' + str(x) for x in row]))
+            elif spaces:
+                grid_str.append(' '.join([str(x) for x in row]))
+            else:
+                grid_str.append(''.join([str(x) for x in row]))
+
+        return "\n".join(grid_str)
+    else:
+        if spaces:
+            cmap = dict({i: (str(i) + ' ', f"color({i})") for i in range(10)}, **{str(i): (str(i) + ' ', f"color({i})") for i in range(10)})
+        else:
+            cmap = dict({i: (str(i), f"color({i})") for i in range(10)}, **{str(i): (str(i), f"color({i})") for i in range(10)})
+
+        for row in grid:
+            grid_str += [cmap[digit] for digit in row]
+            grid_str += ["\n"]
+
+        return Text.assemble(*grid_str[:-1])
+
+class Task:
+    def __init__(self, id, train, test, dataset=None, version=None):
+        self.dataset = dataset
+        self.version = version
+        self.id = id
+        self.train = [(np.array(example['input']), np.array(example['output'])) for example in train]
+        self.test = [(np.array(example['input']), np.array(example['output'])) for example in test]
+
+        self.color_count = max
+
+    def __lt__(self, other):
+        return self.id < other.id
+
+    def __hash__(self):
+        return hash(self.id)
+
+    @classmethod
+    def from_json(cls, json_file):
+        with open(json_file) as f:
+            data = json.load(f)
+
+            # train_examples = [(np.array(example['input']), np.array(example['output'])) for example in data['train']]
+            # test_examples = [(np.array(example['input']), np.array(example['output'])) for example in data['test']]
+
+            return cls(os.path.basename(json_file)[:-5], data['train'], data['test'])
+
+    def __repr__(self):
+        return f"<Task-{self.dataset} {self.id} | {len(self.train)} train | {len(self.test)} test>"
+
+    def show(self, answer=False):
+        table = Table(title=repr(self), show_lines=True)
+
+        data = []
+        for i, (input, output) in enumerate(self.train):
+            data += [fmt_grid(input), fmt_grid(output)]
+            ix, iy = input.shape
+            ox, oy = output.shape
+            table.add_column(f"{idx2chr(i)}-in {ix}x{iy}", justify="center", no_wrap=True)
+            table.add_column(f"{idx2chr(i)}-out {ox}x{oy}", justify="center", no_wrap=True)
+
+        data.append('')
+        table.add_column("")
+        for i, (input, output) in enumerate(self.test):
+            table.add_column(f"T{idx2chr(i)}-in", justify="center", header_style="bold", no_wrap=True)
+            if answer:
+                table.add_column(f"T{idx2chr(i)}-out", justify="center", header_style="bold", no_wrap=True)
+                data += [fmt_grid(input), fmt_grid(output)]
+            else:
+                data += [fmt_grid(input)]
+
+        table.add_row(*data)
+        print(table)
+        return table
+
+    def to_dict(self):
+        return {
+            "id": self.id,
+            "train": [{"input": input.tolist(), "output": output.tolist()} for input, output in self.train],
+            "test": [{"input": input.tolist(), "output": output.tolist()} for input, output in self.test]
+        }
+    
+    def dreamcoder_format(self):
+        # TODO: Separate out the train/test data
+        # Currently, dreamcoder gets to search on the train examples too
+        # This is included to keep compatibility with Simon's results, but should be corrected and written about
+        grids = []
+        for inp, out in self.train:
+            grids += [inp, out]
+        for inp, out in self.test:
+            grids += [inp, out]
+        return {"name": self.id, "grids": grids}
+
+    def scoreA(self, output):
+        output = np.array(output).astype(int)
+        if output.shape != self.test[0][1].shape:
+            return False
+        return (output == self.test[0][1]).all()
+
+    def gpt_prompt(self, i_test, mode="chatgpt", include_completion=False, rot90=False, transpose=False, spaces=True):
+        if mode == "chatgpt":
+            prompt = "We are playing a game which involves transforming an input grid of digits into an output grid of digits. In general, digits form objects in 2D and the task is to perform some spatial transformation of these objects to go from the input grid to the output grid. All the information about the transformation is contained within the input pairs themselves, and your answer will only be correct if the output grid is exactly correct, so this is what I expect from you. I will begin by giving you several examples of input-output pairs. You will then be given a new input grid, and you must provide the corresponding output grid.\n"
+        elif mode == "gpt3":
+            # prompt = "We are playing a game which involves transforming an input grid of digits into an output grid of digits. Every below pair of grids contains the same transformation. In general, digits form objects in 2D and the task is to perform some spatial transformation of these objects to go from the input tile to the output tile. One such example of tiles is below.\n"
+            prompt = "We are playing a game which involves transformaing a 2D input grid of digits into an output grid of digits. Every below pair of grids contains the same transformation (e.g. rotation, symmetry, manipulation of objects). Each Input grid is followed by an Output grid which applies the same transformation as previous Input/Output pairs. One such example is below.\n"
+        else:
+            raise ValueError(f"Unknown mode: {mode}")
+
+        i = 1
+        for input, output in self.train:
+            if rot90:
+                input = np.rot90(input)
+                output = np.rot90(output)
+            if transpose:
+                input = np.transpose(input)
+                output = np.transpose(output)
+
+            prompt += f"""Input {i}: 
+{fmt_grid(input, colour=False, spaces=spaces)}
+Output {i}: 
+{fmt_grid(output, colour=False, spaces=spaces)}\n
+"""
+            i += 1
+
+        test_grid = self.test[i_test][0]
+        if rot90:
+            test_grid = np.rot90(test_grid)
+        if transpose:
+            test_grid = np.transpose(test_grid)
+
+        prompt += f"Input {i}:\n"
+        prompt += f"{fmt_grid(test_grid, colour=False, spaces=spaces)}"
+        if mode == "gpt3":
+            prompt += f"\nOutput {i}:"
+        else:
+            prompt += f"\nOutput {i}: (please provide the output grid only)\n"
+
+        if include_completion:
+            if rot90:
+                output = np.rot90(self.test[i_test][1])
+            if transpose:
+                output = np.transpose(self.test[i_test][1])
+            prompt += f"\n{fmt_grid(self.test[i_test][1], colour=False, spaces=spaces)}"
+        return prompt
+
+class TaskSet:
+    def __init__(self, tasks):
+        tasks = sorted(tasks)
+        self.tasks = tasks
+        self.task_dict = {task.id: task for task in tasks}
+
+    def __getitem__(self, item):
+        if isinstance(item, slice):
+            return TaskSet(self.tasks[item])
+        get = self.task_dict.get(item)
+        if get is None:
+            try:
+                return self.tasks[item]
+            except (TypeError, IndexError):
+                raise KeyError(f"Task {item} not found")
+        return get
+
+    def __len__(self):
+        return len(self.tasks)
+
+    def __iter__(self):
+        return iter(self.tasks)
+
+    def __repr__(self):
+        return f"<TaskSet: {len(self.tasks)} tasks>"
+
+    def score_submission(self,fn: str, topn=2, return_correct=False) -> int:
+        """
+        Score a submission file, in Kaggle csv format
+        Two columns: output_id,output
+        """
+        from collections import defaultdict
+        preds = defaultdict(list)
+
+        with open(fn) as f:
+            reader = csv.DictReader(f)
+            for row in reader:
+                task_id, test_num = row['output_id'].split('_')
+                test_num = int(test_num)
+                assert test_num == len(preds[task_id]), f'Predictions must be in order'
+                 # Predictions must be in order
+
+                row_preds = row['output'].strip().split(' ')
+                row_preds = row_preds[:topn] # max 3 preds
+
+                try:
+                    array_preds = []
+                    for p in row_preds:
+                        p = p.strip('|').split('|')
+                        p = [[int(x) for x in row] for row in p]
+                        array_preds.append(np.array(p))
+                except:
+                    raise ValueError(f'Could not parse prediction: {row_preds}')
+
+                preds[task_id].append(array_preds)
+
+        total_score = 0
+        correct = set()
+        for task in self.tasks:
+            test_examples = task.test
+            assert len(test_examples) == len(preds[task.id])
+
+            score = 0
+            for ex, ex_preds in zip(test_examples, preds[task.id]):
+                if any([np.array_equal(ex[1], pred) for pred in ex_preds]):
+                    score += 1
+
+            if score == len(test_examples):
+                total_score += 1
+                correct.add(task.id)
+
+        if return_correct:
+            return total_score, correct
+        else:
+            return total_score
+
+def get_data_json(version):
+    version = version.lower()
+    if version in ['latest', 'arcagi2', 'f3283f7']:
+        return json.load(open(f"{os.path.dirname(__file__)}/data/arcagi2_f3283f7.json"))
+
+    elif version in ['arcagi', 'aa922be', 'arcagi1', 'arc-agi-1', 'arc-agi']:
+        return json.load(open(f"{os.path.dirname(__file__)}/data/arcagi_aa922be.json"))
+    
+    elif version in ['kaggle', 'kaggle2025', 'kaggle250808']:
+        return json.load(open(f"{os.path.dirname(__file__)}/data/kaggle2025_250808.json"))
+
+    elif version in ['kaggle2024']:
+        return json.load(open(f"{os.path.dirname(__file__)}/data/kaggle2024.json"))
+    
+    elif version in ['arc', 'kaggle2019']:
+        return json.load(open(f"{os.path.dirname(__file__)}/data/arc1.json"))
+    
+    else:
+        raise ValueError(f"Unknown ARC dataset version: {version}")
+
+def load_data(version='latest') -> tuple[TaskSet, TaskSet]:
+    """
+    Load the ARC dataset from disk. Optionally, specify a specific version of the dataset to load.
+    """
+
+    data = get_data_json(version)
+        
+    train_tasks = []
+    eval_tasks = []
+    for id, task in data['train'].items():
+        train_tasks.append(Task(id, task['train'], task['test'], 'train', version=version))
+
+    for id, task in data['eval'].items():
+        eval_tasks.append(Task(id, task['train'], task['test'], 'eval', version=version))
+
+    return TaskSet(train_tasks), TaskSet(eval_tasks)
+
+
+def load_single(id: str, version='latest') -> Task:
+    """
+    Load a single task from disk. IDs are of the form 'train0', 'eval14', '007bbfb7', etc.
+    Note that if iterating through tasks, it is more efficient to use load_data() and index into it.
+
+    Optionally, specify a specific version of the dataset to load.
+    """
+
+    data = get_data_json(version)
+        
+    # task = data['train'][id]
+    # return Task(id, task['train'], task['test'], 'train'
+    if id.startswith('train'):
+        dataset_tasks = sorted(data['train'].items())
+        taskid, task = dataset_tasks[int(id[5:])]
+        return Task(taskid, task['train'], task['test'], 'train', version=version)
+    elif id.startswith('eval'):
+        dataset_tasks = sorted(data['eval'].items())
+        taskid, task = dataset_tasks[int(id[4:])]
+        return Task(taskid, task['train'], task['test'], 'eval', version=version)
+    elif id in data['train']:
+        task = data['train'][id]
+        return Task(id, task['train'], task['test'], 'train', version=version)
+    elif id in data['eval']:
+        task = data['eval'][id]
+        return Task(id, task['train'], task['test'], 'eval', version=version)
+    else:
+        raise ValueError(f"Unknown task id: {id}")
+
+if __name__ == "__main__":
+    train_tasks, eval_tasks = load_data()
+    print(train_tasks)
+    print(eval_tasks)
+    print(train_tasks["007bbfb7"])
+    for i in range(10):
+        train_tasks[i].show()
+
+    print(train_tasks["08ed6ac7"].gpt_prompt("gpt3"))
+
+
+
+================================================
+FILE: arckit/importtool.py
+================================================
+import glob
+import ujson # dumps minified by default
+import os
+import argparse
+
+
+def import_arc_agi_2(repo_path, commit_hash):
+    train_files = sorted(glob.glob(f'{repo_path}/data/training/*.json'))
+    eval_files = sorted(glob.glob(f'{repo_path}/data/evaluation/*.json'))
+
+    print(f"Found {len(train_files)} train tasks, {len(eval_files)} eval tasks")
+
+    data = {"train": {}, "eval": {}}
+    for json_file in train_files:
+        taskname = os.path.basename(json_file).split('.')[0]
+        taskdata = ujson.load(open(json_file))
+        data['train'][taskname] = taskdata
+
+    for json_file in eval_files:
+        taskname = os.path.basename(json_file).split('.')[0]
+        taskdata = ujson.load(open(json_file))
+        data['eval'][taskname] = taskdata
+
+    output_path = f"{os.path.dirname(__file__)}/data/arcagi2_{commit_hash}.json"
+
+    ujson.dump(data, open(output_path, "w"))
+
+def import_kaggle_2025(repo_path, id):
+    train_challenges = ujson.load(open(f'{repo_path}/arc-agi_training_challenges.json', 'r'))
+    eval_challenges = ujson.load(open(f'{repo_path}/arc-agi_evaluation_challenges.json', 'r'))
+    train_solutions = ujson.load(open(f'{repo_path}/arc-agi_training_solutions.json', 'r'))
+    eval_solutions = ujson.load(open(f'{repo_path}/arc-agi_evaluation_solutions.json', 'r'))
+
+    # Populate the solutions into the challenges
+    train_data = {}
+    for challenge, challenge_data in train_challenges.items():
+        challenge_test = train_solutions[challenge]
+        for i, test in enumerate(challenge_test):
+            challenge_data['test'][i]['output'] = test
+
+        train_data[challenge] = challenge_data
+
+    eval_data = {}
+    for challenge, challenge_data in eval_challenges.items():
+        challenge_test = eval_solutions[challenge]
+        for i, test in enumerate(challenge_test):
+            challenge_data['test'][i]['output'] = test
+
+        eval_data[challenge] = challenge_data
+
+    data = {"train": train_data, "eval": eval_data}
+
+    output_path = f"{os.path.dirname(__file__)}/data/kaggle2025_{id}.json"
+    ujson.dump(data, open(output_path, "w"))
+        
+
+def compare_json(json1="arckit/data/kaggle2025_250808.json", json2="arckit/data/arcagi2_f3283f7.json"):
+    json1 = ujson.load(open(json1, "r"))
+    json2 = ujson.load(open(json2, "r"))
+    assert len(json1['train']) == len(json2['train'])
+    assert len(json1['eval']) == len(json2['eval'])
+    for k, v in json1['train'].items():
+        if k not in json2['train']:
+            print(k, "missing")
+        if str(json2['train'][k]) != str(v):
+            print(k, "not matching")
+    for k, v in json2['eval'].items():
+        if k not in json1['eval']:
+            print(k, "missing")
+        if str(json1['eval'][k]) != str(v):
+            print(k, "not matching")
+    exit()
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--arcagi2', action="store_true")
+    parser.add_argument('--kaggle2025', action="store_true")
+    parser.add_argument('--repo-path')
+    parser.add_argument('--id')
+    args = parser.parse_args()
+
+    if args.arcagi2:
+        import_arc_agi_2(args.repo_path, args.id)
+    elif args.kaggle2025:
+        import_kaggle_2025(args.repo_path, args.id)
+    else:
+        print("Unknown importer.")
+
+
+================================================
+FILE: arckit/vis.py
+================================================
+import drawsvg
+import numpy as np
+import io
+import rich
+
+cmap = [
+    '#252525', # black
+    '#0074D9', # blue
+    '#FF4136', # red
+    '#37D449', #2ECC40', # green
+    '#FFDC00', # yellow
+    '#E6E6E6', # grey
+    '#F012BE', # pink
+    '#FF871E', # orange
+    '#54D2EB', #7FDBFF', # light blue
+    '#8D1D2C',#870C25', # brown
+    '#FFFFFF'
+        ]
+
+bg_color = '#EEEFF6' # White
+
+def draw_grid(grid, xmax=10, ymax=10, padding=.5, extra_bottom_padding=0.5, group=False, add_size=True, label='', bordercol='#111111ff'):
+    """
+    Draws a grid, 
+
+    Parameters
+    ----------
+    grid : np.ndarray
+        The grid to draw
+    xmax : float, optional
+        The maximum horizontal size of the drawing, by default 10
+    ymax : float, optional
+        The maximum vertical size of the drawing, by default 10
+    padding : float, optional
+        The padding around the grid, half on each side, by default .5
+    extra_bottom_padding : float, optional
+        Extra padding at the bottom of the drawing, by default 0.5
+        This is used to draw the label
+    group : bool, optional
+        If enabled, return a drawsvg.Group to include within a bigger drawing
+        Otherwise, return a drawsvg.Drawing (default)
+    label : str, optional
+        A label to draw at the bottom left of the drawing, by default ''
+        This does not affect drawing of the size.
+    bordercol : str, optional
+        The colour of the border, by default '#111111ff'
+    """
+    # Size is the total size of the LARGER axis.
+    # With 0.5 cell padding, we consider the grid to be 1 unit larger than the number of cells
+    # padding *= size # padding is proportional
+    
+    gridy, gridx = grid.shape
+    # Calculate cell size based on the two restrictions
+    # The actual cell size is the minimum of the two
+    cellsize_x = xmax / gridx
+    cellsize_y = ymax / gridy
+    cellsize = min(cellsize_x, cellsize_y)
+
+    xsize = gridx * cellsize
+    ysize = gridy * cellsize
+
+    line_thickness = 0.01
+    circle_radius = 0
+    border_width = 0.08
+    lt = line_thickness / 2
+
+    if group:
+        drawing = drawsvg.Group()
+    else:
+        drawing = drawsvg.Drawing(xsize+padding, ysize+padding+extra_bottom_padding, origin=(-0.5*padding, -0.5*padding))
+        # Add background rectangle first
+        drawing.append(drawsvg.Rectangle(
+            -0.5*padding, -0.5*padding,                          # x, y position with extra padding
+            xsize+padding, ysize+padding+extra_bottom_padding,   # width, height with padding
+            fill=bg_color                                        # background color `bg_color`
+        ))
+        drawing.set_pixel_scale(40)
+    # drawing = drawsvg.Group()
+    for j, row in enumerate(grid):
+        for i, cell in enumerate(row):
+            drawing.append(drawsvg.Rectangle(i*cellsize+lt, j*cellsize+lt, cellsize-lt, cellsize-lt, fill=cmap[cell]))
+    
+    # white dot at each vertex
+    if circle_radius > 0:
+        for i in range(1, gridx):
+            for j in range(1, gridy):
+                drawing.append(drawsvg.Circle(i*cellsize, j*cellsize, circle_radius, fill='white'))
+        
+    # Add a border
+    bw = border_width / 3 # slightly more than 2 to avoid white border
+    drawing.append(drawsvg.Rectangle(-bw, -bw, xsize+bw*2, ysize+bw*2, fill='none', stroke=bordercol, stroke_width=border_width))
+
+    if not group:
+        drawing.embed_google_font('Anuphan:wght@400;600;700', text=set(f'Input Output 0123456789x Test Task ABCDEFGHIJ? abcdefghjklmnopqrstuvwxyz ABCDEFGHIJKLMNOPQRSTUVWXYZ'))
+
+    # Write size on the bottom right
+    # drawing.append(drawsvg.Text(text=f'{gridx}x{gridy}', x=-0.05, y=-0.25, font_size=padding/4, fill='black', text_anchor='start'))
+    fontsize = (padding/2 + extra_bottom_padding)/2
+    if add_size:
+        drawing.append(drawsvg.Text(text=f'{gridx}x{gridy}', x=xsize, y=ysize+fontsize*1.25+0, font_size=fontsize, fill='black', text_anchor='end', font_family='Anuphan'))
+    if label:
+        drawing.append(drawsvg.Text(text=label, x=-0.1*fontsize, y=ysize+fontsize*1.25+0, font_size=fontsize, fill='black', text_anchor='start', font_family='Anuphan', font_weight='600'))
+    
+    if group:
+        # return group, origin, (xsize, ysize)
+        return drawing, (-0.5*padding, -0.5*padding), (xsize+padding, ysize+padding+extra_bottom_padding)
+    return drawing
+
+def draw_task(task, width=30, height=12, include_test=False, label=True, bordercols=['#111111ff', '#111111ff'], shortdesc=False):
+    """
+    Plot an entire task vertically, fitting the desired dimensions.
+    The output is displayed below the input, with an arrow in between.
+
+    Note that dimensions are a best effort, you should check .width and .height on the output
+    
+    Parameters
+    ----------
+    task : Task
+        The task to plot
+    width : float, optional
+        The desired width of the drawing, by default 30
+    height : float, optional
+        The desired height of the drawing, by default 12
+    include_test : bool, optional
+        If enabled, include the test examples in the plot, by default False
+        If set to 'all', ALSO include the output of the test examples
+    label: bool, default True
+    bordercols: list of str, default None
+    """
+
+    padding = 0.5
+    bonus_padding = 0.25
+    io_gap = 0.4
+    ymax = (height - padding - bonus_padding - io_gap)/2
+
+    if include_test:
+        examples = task.train + task.test
+    else:
+        examples = task.train
+    n_train = len(task.train)
+    paddingless_width = width - padding * len(examples)
+
+    max_widths = np.zeros(len(examples))
+    # If any examples would exceed the height restriction, scale their width down and redistribute the space
+    for i, (input_grid, output_grid) in enumerate(examples):
+        input_grid_ratio = input_grid.shape[1] / input_grid.shape[0]
+        output_grid_ratio = output_grid.shape[1] / output_grid.shape[0]
+        max_ratio = max(input_grid_ratio, output_grid_ratio) # could be min
+        xmax = ymax * max_ratio
+        max_widths[i] = xmax
+
+    # Allocate paddingless width to each example
+    allocation = np.zeros_like(max_widths)
+    increment = 0.01
+    for i in range(int(paddingless_width//increment)):
+        incr = (allocation + increment) <= max_widths
+        allocation[incr] += increment / incr.sum()
+
+    drawlist = []
+    x_ptr = 0
+    y_ptr = 0
+    for i, (input_grid, output_grid) in enumerate(examples):
+        if shortdesc:
+            if i >= n_train:
+                input_label = ''#f'T{i-n_train+1}'
+                output_label = ''#f'T{i-n_train+1}'
+            else:
+                input_label = ''#f'I{i+1}'
+                output_label = ''#f'O{i+1}'
+        else:
+            if i >= n_train:
+                input_label = f'Test {i-n_train+1}'
+                output_label = f'Test {i-n_train+1}'
+            else:
+                input_label = f'Input {i+1}'
+                output_label = f'Output {i+1}'
+
+        # input_label, output_label = '', ''
+
+        input_grid, offset, (input_x, input_y) = draw_grid(input_grid, padding=padding, xmax=allocation[i], ymax=ymax, group=True, label=input_label, extra_bottom_padding=0.5, bordercol=bordercols[0])
+        output_grid, offset, (output_x, output_y) = draw_grid(output_grid, padding=padding, xmax=allocation[i], ymax=ymax, group=True, label=output_label, extra_bottom_padding=0.5, bordercol=bordercols[1])
+
+        drawlist.append(drawsvg.Use(input_grid, x=x_ptr + (allocation[i]+padding-input_x)/2 - offset[0], y=-offset[1]))
+        # drawlist.append(drawsvg.Use(output_grid, x=x_ptr + (allocation[i]+padding-output_x)/2 - offset[0], y=ymax-offset[1]+2))
+        x_ptr += max(input_x, output_x)
+        y_ptr = max(y_ptr, input_y)
+
+    x_ptr = 0
+    y_ptr2 = 0
+    for i, (input_grid, output_grid) in enumerate(examples):
+        if shortdesc:
+            if i >= n_train:
+                input_label = ''#f'T{i-n_train+1}'
+                output_label = ''#f'T{i-n_train+1}'
+            else:
+                input_label = ''#f'I{i+1}'
+                output_label = ''#f'O{i+1}'
+        else:
+            if i >= n_train:
+                input_label = f'Test {i-n_train+1}'
+                output_label = f'Test {i-n_train+1}'
+            else:
+                input_label = f'Input {i+1}'
+                output_label = f'Output {i+1}'
+        # input_label, output_label = '', ''
+
+        input_grid, offset, (input_x, input_y) = draw_grid(input_grid, padding=padding, xmax=allocation[i], ymax=ymax, group=True, label=input_label, extra_bottom_padding=0.5, bordercol=bordercols[0])
+        output_grid, offset, (output_x, output_y) = draw_grid(output_grid, padding=padding, xmax=allocation[i], ymax=ymax, group=True, label=output_label, extra_bottom_padding=0.5, bordercol=bordercols[1])
+                # Down arrow
+        drawlist.append(drawsvg.Line(
+            x_ptr + input_x/2, 
+            y_ptr + padding - 0.6, 
+            x_ptr + input_x/2, 
+            y_ptr + padding + io_gap - 0.6, 
+            stroke_width=0.05, stroke='#888888'))
+        drawlist.append(drawsvg.Line(
+            x_ptr + input_x/2 - 0.15,
+            y_ptr + padding + io_gap - 0.8,
+            x_ptr + input_x/2,
+            y_ptr + padding + io_gap - 0.6,
+            stroke_width=0.05, stroke='#888888'))
+        drawlist.append(drawsvg.Line(
+            x_ptr + input_x/2 + 0.15,
+            y_ptr + padding + io_gap - 0.8,
+            x_ptr + input_x/2,
+            y_ptr + padding + io_gap - 0.6,
+            stroke_width=0.05, stroke='#888888'))
+        
+
+        if i < n_train or include_test == 'all':
+            drawlist.append(drawsvg.Use(output_grid, x=x_ptr + (allocation[i]+padding-output_x)/2 - offset[0], y=y_ptr-offset[1]+io_gap))
+        else:
+            # Add a question mark
+            drawlist.append(drawsvg.Text(
+                '?',
+                x=x_ptr + (allocation[i]+padding)/2,
+                y=y_ptr + output_y/2+bonus_padding,
+                font_size=1,
+                font_family='Anuphan',
+                font_weight='700',
+                fill='#333333',
+                text_anchor='middle',
+                alignment_baseline='middle',
+            ))
+        x_ptr += max(input_x, output_x)
+        y_ptr2 = max(y_ptr2, y_ptr+output_y+io_gap)
+
+    x_ptr = round(x_ptr, 1)
+    y_ptr2 = round(y_ptr2, 1)
+    d = drawsvg.Drawing(x_ptr, y_ptr2+0.2, origin=(0, 0))
+    d.append(drawsvg.Rectangle(0, 0, '100%', '100%', fill='#eeeff6'))
+    d.embed_google_font('Anuphan:wght@400;600;700', text=set(f'Input Output 0123456789x Test Task ABCDEFGHIJ? abcdefghjklmnopqrstuvwxyz ABCDEFGHIJKLMNOPQRSTUVWXYZ'))
+    for item in drawlist:
+        d.append(item)
+
+    fontsize=0.3
+    d.append(drawsvg.Text(f"Task {task.id}", x=x_ptr-0.1, y=y_ptr2+0.1, font_size=fontsize, font_family='Anuphan', font_weight='600', fill='#666666', text_anchor='end', alignment_baseline='bottom'))
+
+    d.set_pixel_scale(40)
+    return d
+
+def output_drawing(d: drawsvg.Drawing, filename: str, context=None):
+    if filename.endswith('.svg'):
+        d.save_svg(filename)
+    elif filename.endswith('.png'):
+        d.save_png(filename)
+    elif filename.endswith('.pdf'):
+        buffer = io.StringIO()
+        d.as_svg(output_file=buffer, context=context)
+
+        import cairosvg
+        cairosvg.svg2pdf(bytestring=buffer.getvalue(), write_to=filename)
+    else:
+        raise ValueError(f'Unknown file extension for {filename}')
+
+def print_grid(grid: np.ndarray):
+    """
+    Print a grid to the terminal using rich library
+
+    Parameters
+    ----------
+    grid : np.ndarray
+        the standard grid format for Task 
+    """
+    CELL_WIDTH = 2
+
+    def get_color(color_str):
+        color_str = color_str.strip('#')
+        return rich.color.Color.from_rgb(*bytes.fromhex(color_str))
+
+    # Translate 'cmap' to rich style with respective color as background
+    rich_scmap = {
+        i: rich.style.Style(bgcolor=get_color(color))
+        for i, color in enumerate(cmap)
+    }
+
+    # Create and populate rich table
+    table = rich.table.Table.grid(expand=False)
+    
+    height, width = grid.shape
+    
+    # Add columns
+    for x in range(width):
+        table.add_column()
+        table.columns[x]._cells = [''] * height
+    
+    table.rows = [rich.table.Row()] * height
+
+    # Populate cells
+    for y in range(height):
+        for x in range(width):
+            color_idx = grid[y, x]
+            table.columns[x]._cells[y] = rich.text.Text(' ' * CELL_WIDTH, style=rich_scmap[color_idx])
+
+    rich.print(table)
+
+
+
+================================================
+FILE: .github/workflows/pypi.yml
+================================================
+name: Build & Release
+
+on:
+  workflow_dispatch:
+    inputs:
+      environment:
+        description: 'Choose PyPI environment'
+        required: true
+        default: 'test'
+        type: choice
+        options:
+        - test
+        - production
+  push:
+
+jobs:
+  build:
+    name: Build wheel
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v4
+
+      - name: Set up Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: ${{ inputs.python_version || '3.11' }}
+
+      - name: Install build backend
+        run: |
+          python -m pip install --upgrade pip
+          python -m pip install build
+
+      - name: Build dists
+        run: |
+          python -m build
+
+      - name: Upload dists artifact
+        uses: actions/upload-artifact@v4
+        with:
+          name: dist
+          path: dist/
+
+  publish:
+    name: Publish to PyPI
+    needs: build
+    runs-on: ubuntu-latest
+    permissions:
+      id-token: write
+    if: github.event_name == 'workflow_dispatch'
+    steps:
+      - name: Download dists artifact
+        uses: actions/download-artifact@v4
+        with:
+          name: dist
+          path: dist/
+
+      - name: Publish to TestPyPI
+        if: inputs.environment == 'test'
+        uses: pypa/gh-action-pypi-publish@release/v1
+        with:
+          repository-url: https://test.pypi.org/legacy/
+
+      - name: Publish to Production PyPI
+        if: inputs.environment == 'production'
+        uses: pypa/gh-action-pypi-publish@release/v1
+