Metadata-Version: 2.4
Name: ym-pure-ml
Version: 1.2.8
Summary: Transparent, NumPy-only deep learning framework for teaching, small-scale projects, prototyping, and reproducible experiments.
Author: Yehor Mishchyriak
License:                                  Apache License
                                   Version 2.0, January 2004
                                http://www.apache.org/licenses/
        
           TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
        
           1. Definitions.
        
              "License" shall mean the terms and conditions for use, reproduction,
              and distribution as defined by Sections 1 through 9 of this document.
        
              "Licensor" shall mean the copyright owner or entity authorized by
              the copyright owner that is granting the License.
        
              "Legal Entity" shall mean the union of the acting entity and all
              other entities that control, are controlled by, or are under common
              control with that entity. For the purposes of this definition,
              "control" means (i) the power, direct or indirect, to cause the
              direction or management of such entity, whether by contract or
              otherwise, or (ii) ownership of fifty percent (50%) or more of the
              outstanding shares, or (iii) beneficial ownership of such entity.
        
              "You" (or "Your") shall mean an individual or Legal Entity
              exercising permissions granted by this License.
        
              "Source" form shall mean the preferred form for making modifications,
              including but not limited to software source code, documentation
              source, and configuration files.
        
              "Object" form shall mean any form resulting from mechanical
              transformation or translation of a Source form, including but
              not limited to compiled object code, generated documentation,
              and conversions to other media types.
        
              "Work" shall mean the work of authorship, whether in Source or
              Object form, made available under the License, as indicated by a
              copyright notice that is included in or attached to the work
              (an example is provided in the Appendix below).
        
              "Derivative Works" shall mean any work, whether in Source or Object
              form, that is based on (or derived from) the Work and for which the
              editorial revisions, annotations, elaborations, or other modifications
              represent, as a whole, an original work of authorship. For the purposes
              of this License, Derivative Works shall not include works that remain
              separable from, or merely link (or bind by name) to the interfaces of,
              the Work and Derivative Works thereof.
        
              "Contribution" shall mean any work of authorship, including
              the original version of the Work and any modifications or additions
              to that Work or Derivative Works thereof, that is intentionally
              submitted to Licensor for inclusion in the Work by the copyright owner
              or by an individual or Legal Entity authorized to submit on behalf of
              the copyright owner. For the purposes of this definition, "submitted"
              means any form of electronic, verbal, or written communication sent
              to the Licensor or its representatives, including but not limited to
              communication on electronic mailing lists, source code control systems,
              and issue tracking systems that are managed by, or on behalf of, the
              Licensor for the purpose of discussing and improving the Work, but
              excluding communication that is conspicuously marked or otherwise
              designated in writing by the copyright owner as "Not a Contribution."
        
              "Contributor" shall mean Licensor and any individual or Legal Entity
              on behalf of whom a Contribution has been received by Licensor and
              subsequently incorporated within the Work.
        
           2. Grant of Copyright License. Subject to the terms and conditions of
              this License, each Contributor hereby grants to You a perpetual,
              worldwide, non-exclusive, no-charge, royalty-free, irrevocable
              copyright license to reproduce, prepare Derivative Works of,
              publicly display, publicly perform, sublicense, and distribute the
              Work and such Derivative Works in Source or Object form.
        
           3. Grant of Patent License. Subject to the terms and conditions of
              this License, each Contributor hereby grants to You a perpetual,
              worldwide, non-exclusive, no-charge, royalty-free, irrevocable
              (except as stated in this section) patent license to make, have made,
              use, offer to sell, sell, import, and otherwise transfer the Work,
              where such license applies only to those patent claims licensable
              by such Contributor that are necessarily infringed by their
              Contribution(s) alone or by combination of their Contribution(s)
              with the Work to which such Contribution(s) was submitted. If You
              institute patent litigation against any entity (including a
              cross-claim or counterclaim in a lawsuit) alleging that the Work
              or a Contribution incorporated within the Work constitutes direct
              or contributory patent infringement, then any patent licenses
              granted to You under this License for that Work shall terminate
              as of the date such litigation is filed.
        
           4. Redistribution. You may reproduce and distribute copies of the
              Work or Derivative Works thereof in any medium, with or without
              modifications, and in Source or Object form, provided that You
              meet the following conditions:
        
              (a) You must give any other recipients of the Work or
                  Derivative Works a copy of this License; and
        
              (b) You must cause any modified files to carry prominent notices
                  stating that You changed the files; and
        
              (c) You must retain, in the Source form of any Derivative Works
                  that You distribute, all copyright, patent, trademark, and
                  attribution notices from the Source form of the Work,
                  excluding those notices that do not pertain to any part of
                  the Derivative Works; and
        
              (d) If the Work includes a "NOTICE" text file as part of its
                  distribution, then any Derivative Works that You distribute must
                  include a readable copy of the attribution notices contained
                  within such NOTICE file, excluding those notices that do not
                  pertain to any part of the Derivative Works, in at least one
                  of the following places: within a NOTICE text file distributed
                  as part of the Derivative Works; within the Source form or
                  documentation, if provided along with the Derivative Works; or,
                  within a display generated by the Derivative Works, if and
                  wherever such third-party notices normally appear. The contents
                  of the NOTICE file are for informational purposes only and
                  do not modify the License. You may add Your own attribution
                  notices within Derivative Works that You distribute, alongside
                  or as an addendum to the NOTICE text from the Work, provided
                  that such additional attribution notices cannot be construed
                  as modifying the License.
        
              You may add Your own copyright statement to Your modifications and
              may provide additional or different license terms and conditions
              for use, reproduction, or distribution of Your modifications, or
              for any such Derivative Works as a whole, provided Your use,
              reproduction, and distribution of the Work otherwise complies with
              the conditions stated in this License.
        
           5. Submission of Contributions. Unless You explicitly state otherwise,
              any Contribution intentionally submitted for inclusion in the Work
              by You to the Licensor shall be under the terms and conditions of
              this License, without any additional terms or conditions.
              Notwithstanding the above, nothing herein shall supersede or modify
              the terms of any separate license agreement you may have executed
              with Licensor regarding such Contributions.
        
           6. Trademarks. This License does not grant permission to use the trade
              names, trademarks, service marks, or product names of the Licensor,
              except as required for reasonable and customary use in describing the
              origin of the Work and reproducing the content of the NOTICE file.
        
           7. Disclaimer of Warranty. Unless required by applicable law or
              agreed to in writing, Licensor provides the Work (and each
              Contributor provides its Contributions) on an "AS IS" BASIS,
              WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
              implied, including, without limitation, any warranties or conditions
              of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
              PARTICULAR PURPOSE. You are solely responsible for determining the
              appropriateness of using or redistributing the Work and assume any
              risks associated with Your exercise of permissions under this License.
        
           8. Limitation of Liability. In no event and under no legal theory,
              whether in tort (including negligence), contract, or otherwise,
              unless required by applicable law (such as deliberate and grossly
              negligent acts) or agreed to in writing, shall any Contributor be
              liable to You for damages, including any direct, indirect, special,
              incidental, or consequential damages of any character arising as a
              result of this License or out of the use or inability to use the
              Work (including but not limited to damages for loss of goodwill,
              work stoppage, computer failure or malfunction, or any and all
              other commercial damages or losses), even if such Contributor
              has been advised of the possibility of such damages.
        
           9. Accepting Warranty or Additional Liability. While redistributing
              the Work or Derivative Works thereof, You may choose to offer,
              and charge a fee for, acceptance of support, warranty, indemnity,
              or other liability obligations and/or rights consistent with this
              License. However, in accepting such obligations, You may act only
              on Your own behalf and on Your sole responsibility, not on behalf
              of any other Contributor, and only if You agree to indemnify,
              defend, and hold each Contributor harmless for any liability
              incurred by, or claims asserted against, such Contributor by reason
              of your accepting any such warranty or additional liability.
        
           END OF TERMS AND CONDITIONS
        
           APPENDIX: How to apply the Apache License to your work.
        
              To apply the Apache License to your work, attach the following
              boilerplate notice, with the fields enclosed by brackets "[]"
              replaced with your own identifying information. (Don't include
              the brackets!)  The text should be enclosed in the appropriate
              comment syntax for the file format. We also recommend that a
              file or class name and description of purpose be included on the
              same "printed page" as the copyright notice for easier
              identification within third-party archives.
        
           Copyright 2025 Yehor Mishchyriak
        
           Licensed under the Apache License, Version 2.0 (the "License");
           you may not use this file except in compliance with the License.
           You may obtain a copy of the License at
        
               http://www.apache.org/licenses/LICENSE-2.0
        
           Unless required by applicable law or agreed to in writing, software
           distributed under the License is distributed on an "AS IS" BASIS,
           WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
           See the License for the specific language governing permissions and
           limitations under the License.
        
Project-URL: Homepage, https://github.com/Yehor-Mishchyriak/PureML
Project-URL: Source, https://github.com/Yehor-Mishchyriak/PureML
Project-URL: Tracker, https://github.com/Yehor-Mishchyriak/PureML/issues
Project-URL: Documentation, https://ymishchyriak.com/docs/PUREML-DOCS
Keywords: numpy,deep-learning,autodiff,education
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3.11
Description-Content-Type: text/markdown
License-File: LICENSE
License-File: NOTICE
Requires-Dist: numpy>=2.0
Requires-Dist: zarr>=3.1
Provides-Extra: tests
Dynamic: license-file

# PureML — a tiny, transparent deep-learning framework in NumPy

[![PyPI](https://img.shields.io/pypi/v/ym-pure-ml)](https://pypi.org/project/ym-pure-ml/)
[![License](https://img.shields.io/badge/license-Apache--2.0-blue)](https://github.com/Yehor-Mishchyriak/PureML/blob/main/LICENSE)
![Python](https://img.shields.io/badge/python-3.11%2B-blue)
[![Docs](https://img.shields.io/badge/docs-available-blue)](https://ymishchyriak.com/docs/PUREML-DOCS)
[![status](https://joss.theoj.org/papers/3aa26bc026244dcf3a477bd74ce4c0ff/status.svg)](https://joss.theoj.org/papers/3aa26bc026244dcf3a477bd74ce4c0ff)

![LOGO](https://raw.githubusercontent.com/Yehor-Mishchyriak/PureML/main/assets/PureML_Logo_cropped.png)

PureML is a learning-friendly deep-learning framework built entirely on top of **NumPy**. It aims to be **small, readable, and hackable** while still being practical for real experiments and teaching.

- **No hidden magic** — a Tensor class + autodiff engine with dynamic computation graph and efficient VJPs for backward passes
- **Batteries included** — core layers (Affine, Dropout, BatchNorm1d), common losses, common optimizers, and a `DataLoader`
- **Self-contained dataset demo** — a ready-to-use MNIST reader and an end-to-end “MNIST Beater” model
- **Portable persistence** — zarr-backed `ArrayStorage` with zip compression for saving/loading model state

> If you like **scikit-learn’s** simplicity and wish **deep learning** felt the same way for small/medium projects, PureML is for you.

---

## Install

PureML targets Python **3.11+** and NumPy **2.x**.

```bash
pip install ym-pure-ml
```

The only runtime deps are: `numpy`, `zarr`

---

# UPDATES: [(skip update messages)](#quickstart-fit-mnist-in-a-few-lines)

## v1.2.8 — What’s new
- **Modernization of packaging and CI/CD, documentation and attribution fixes, and the addition of community guidelines.**

## v1.2.7 — What’s new
- **BUG FIX: DataLoader's seed**
  - DataLoader now respects the provided `seed` (instead of always generating a new one) so shuffling is reproducible across runs when a seed is set.

## v1.2.6 — What’s new
- Small update: added logo to the docs and the PyPi page, as well as the README

## v1.2.5 — What’s new
- **`Affine` and `Embedding` layers now ensure grads are ALWAYS tracked for supplied W and b tensors**
- Dedicated docs site is live: https://ymishchyriak.com/docs/PUREML-DOCS (mirrors this repo and stays current).

## v1.2.4 — What’s new
- **Affine: optional bias**
  - Affine(fan_in, fan_out, bias=False, ...) is now supported.
  - When bias=False, the layer keeps a zero bias tensor with requires_grad=False and excludes it from .parameters.
  - **Checkpointing**: use_bias is persisted in named_buffers() and honored by apply_state().
    - Turning bias off during apply_state() zeroes the stored bias and freezes it.
    - Turning bias on reuses the same tensor and re-enables requires_grad.
  - Loader accepts weight arrays shaped either (fan_in, fan_out) or (fan_out, fan_in) and auto-transposes to internal (n, m).

## v1.2.3 — What’s new

  - Added utility functions `rng_from_seed(seed: int | None = None)` and `get_random_seed()` for reproducible RNG creation.  
    - `rng_from_seed` returns both the RNG and the resolved seed; if no seed is provided, `get_random_seed()` generates one using cryptographically secure OS randomness.  
  - All core layers (`Affine`, `Embedding`, `Dropout`, etc.) now support explicit seeds for deterministic initialization.  
  - Seeds and initialization methods are now persisted in checkpoints and automatically restored via each layer’s `apply_state()` method.  
  - Fixed the "Fit MNIST in a few lines" README example — it now correctly calls the `.numpy()` method (parentheses were missing).

## v1.2.2 — What’s new

- **Autograd: correct `detach` semantics (+ in-place variant)**
  - `Tensor.detach()` now returns a **new leaf tensor** that **shares storage**, has **no creator**, and **`requires_grad=False`**.
  - New **in-place** `Tensor.detach_()` for stopping tracking on the current object.
  - New `Tensor.requires_grad_(bool)` toggler (in-place), PyTorch-style.
  - Migration note: if you relied on the old in-place behavior of `detach()`, switch to `detach_()` or reassign: `x = x.detach()`.

- **Safe array export API**
  - New `Tensor.numpy(copy=True, readonly=False)` helper:
    - `copy=True` returns a defensive copy (default).
    - `readonly=True` marks the returned array non-writable (works with views or copies).
  - Rationale: keep `.data` as the **mutable** param buffer for optimizers, while providing a safe way for read-only exports.
  - Bottom-line: DO NOT ACCESS `.data` attribute directly, unless you REALLY need it! Instead, call .numpy() API. In future updates, .data may be hidden completely to avoid users accidentally mutating tensors.

- **Graph utilities: iterative and memory-safe**
  - `_collect_graph()` rewritten as an **iterative** ancestor walk (no recursion limits).
  - `zero_grad_graph()` and `detach_graph()` now use a **single traversal** and
    - free each node’s cached forward context via `fn._free_fwd_ctx()` before unlinking,
    - set `t._creator=None`, `t.grad=None`,
    - and (for `detach_graph`) **`t.requires_grad=False`** to prevent future history building.
  - Net effect: **lower peak memory** and safer teardown of large graphs.

- **Docs/logging polish**
  - Clearer docstrings for graph collection (it collects **upstream/ancestor** nodes).
  - More informative debug logs for backward/graph utilities.

## v1.2.1 — What’s new

- **BUG FIX: NN base-class API**
  - Now, self(x, y, ...) does not error within a class inheriting from NN. Previously, `__call__` function expected a single tensor, but now the signature is (*args, **kwargs), so you can define the .predict method with any signature and still use self(...) interface.
- **training_utils: TensorDataset now ALWAYS returns Tensor instances**
  - Previously, if you initialized a TensorDataset from numpy arrays, it would return numpy array instances via `__getitem__`; Now, we enforce Tensor output, which protects us from downstream errors. In case you want to access the numpy data, just call .numpy() method on your Tensor.

## v1.2.0 — What’s new

- **Autodiff-aware slicing (NumPy semantics) for `Tensor`**
  - Supports ints, slices, ellipsis (`...`), `None` (newaxis), boolean masks, and advanced integer arrays.
  - Backward pass **scatter-adds** into a zeros-like array of the input’s shape (handles repeated/overlapping indices correctly).

- **`Embedding` layer**
  - A learned lookup table for integer indices: input `(...,)` of ints → output `(..., D)` embeddings.
  - API: `Embedding(V, D, pad_idx=None, W=None)`.
  - If `pad_idx` is set, that row is **initialized to zeros** and **receives no gradient** (useful for `<PAD>` tokens).
  - Correctly accumulates gradients for repeated indices.

- **BUG FIX: `TensorValuedFunction` context merging**
  - User-supplied forward contexts are now **merged into** the node’s internal context and **persist through backward**.
  - Previously, the node could overwrite the provided context in some advanced cases, leading to missing cached values (e.g., `padding_idx`, flattened indices) during gradient computation.

---

## Quickstart: Fit MNIST in a few lines

```python
from pureml.models.neural_networks import MNIST_BEATER
from pureml.datasets import MnistDataset

# 1) Load data (train uses one-hot labels; test gives class indices)
with MnistDataset("train") as train, MnistDataset("test") as test:
    # 2) Build the tiny network: Affine(784→256) → ReLU → Affine(256→10)
    model = MNIST_BEATER().train()

    # 3) Fit on the training set
    model.fit(train, batch_size=128, num_epochs=5)

    # 4) Switch to eval: model.predict returns class indices
    model.eval()
    # Example: run on one batch from the test set
    X_test, y_test = test[:128]
    preds = model(X_test)
    print(preds.numpy()[:10])  # class ids
```

What you get out of the box:

- A tiny network that learns MNIST
- Clean logging of epoch loss
- An inference mode (`.eval()`) that returns class indices directly

---

## Core concepts

### 1) Tensors & Autodiff
PureML wraps NumPy arrays in `Tensor` objects that record operations and expose `.backward()` for gradient calculation. The Tensor supports:
- Elementwise + matmul ops (`+ - * / **`, `@`, `.T`)
- Reshaping helpers like `.reshape(...)` and `.flatten(...)`
- Non-grad ops like `.argmax(...)`
- A `no_grad` context manager for inference/metrics

> The goal is **clarity**: gradients are implemented as explicit vector-Jacobian products (VJPs) you can read in one file.

### 2) Layers
- **Affine (Linear)** — `Y = X @ W + b` (with sensible init)
- **Dropout**
- **BatchNorm1d** — with running mean/variance buffers and momentum

Layers expose:
- `.parameters` (trainables)
- `.named_buffers()` (non-trainable state)
- `.train()` / `.eval()` modes

### 3) Losses
- `MSE`
- `BCE` (probabilities) and `Sigmoid+BCE` (logits)
- `CCE` (categorical cross-entropy; supports `from_logits=True`)

### 4) Optimizers & Schedulers

PureML ships with four optimizers and three lightweight LR schedulers. All optimizers share the same interface:

- Construct with a flat list of model params (`model.parameters`) and a base learning rate.
- Call `optim.zero_grad()` → backprop → `optim.step()` each iteration.
- Optional **weight decay** is supported in both classic (coupled L2) and AdamW-style **decoupled** forms via `decoupled_wd` (defaults to `True`).
- All have robust checkpointing: `save_state("path")` writes a single `.pureml.zip`; `load_state("path")` restores hyperparameters, per-parameter slots (e.g., momentums), and even current parameter values for deterministic resume.

**Available optimizers**

- **SGD** — stochastic gradient descent with optional momentum.  
  - Args: `lr`, `beta=0.0` (momentum), `weight_decay=0.0`, `decoupled_wd=True`  
  - Update (with momentum):  
    `v ← β·v + (1−β)·g`, then (AdamW-style if decoupled) `w ← w − lr·(wd·w) − lr·v`

- **AdaGrad** — per-parameter adaptive rates via accumulated squared grads.  
  - Args: `lr`, `weight_decay=0.0`, `delta=1e-7`, `decoupled_wd=True`  
  - Accumulator: `r ← r + g⊙g`; update: `w ← w − lr·g / (sqrt(r)+δ)`

- **RMSProp** — EMA of squared grads.  
  - Args: `lr`, `weight_decay=0.0`, `beta=0.9`, `delta=1e-6`, `decoupled_wd=True`  
  - Accumulator: `r ← EMA_β(g⊙g)`; update: `w ← w − lr·g / (sqrt(r)+δ)`

- **Adam / AdamW** — first & second moments with bias correction.  
  - Args: `lr`, `weight_decay=0.0`, `beta1=0.9`, `beta2=0.999`, `delta=1e-8`, `decoupled_wd=True`  
  - Moments: `v ← EMA_{β1}(g)`, `r ← EMA_{β2}(g⊙g)`  
    Bias-correct: `v̂ = v/(1−β1^t)`, `r̂ = r/(1−β2^t)`  
    Update (AdamW if decoupled): `w ← w − lr·(wd·w) − lr· v̂/(sqrt(r̂)+δ)`

> **Coupled vs decoupled weight decay:**  
> Set `decoupled_wd=False` to apply classic L2 regularization **through the gradient** (`g ← g + wd·w`).  
> Leave it as `True` (default) for AdamW-style **parameter decay** (`w ← w − lr·wd·w`) applied separately from the gradient step.

**LR schedulers**

Schedulers wrap an optimizer and update `optim.lr` when you call `sched.step()`:

- `StepLR(optim, step_size, gamma=0.1)` → piecewise constant: multiply by `gamma` every `step_size` steps.
- `ExponentialLR(optim, gamma)` → smooth exponential decay each step.
- `CosineAnnealingLR(optim, T_max, eta_min=0.0)` → half-cosine from `base_lr` to `eta_min` over `T_max` steps.

All schedulers expose `save_state(...)` / `load_state(...)` and `step(n=1) -> new_lr`.

**Usage**

```python
from pureml.optimizers import Adam, StepLR   # also: SGD, AdaGrad, RMSProp; ExponentialLR, CosineAnnealingLR
from pureml.losses import CCE
from pureml.training_utils import DataLoader
from pureml.models.neural_networks import MNIST_BEATER
from pureml.datasets.MNIST import MnistDataset

model = MNIST_BEATER().train()

optim = Adam(model.parameters, lr=1e-3, weight_decay=1e-2)   # AdamW by default (decoupled_wd=True)
sched = StepLR(optim, step_size=1000, gamma=0.5)             # optional

for epoch in range(5):
    for X, Y in DataLoader(MnistDataset('train'), batch_size=128, shuffle=True):
        optim.zero_grad()
        logits = model(X)
        loss = CCE(Y, logits, from_logits=True)
        loss.backward()
        optim.step()
        sched.step()  # call per-batch or per-epoch as you prefer
```

### 5) Data utilities
- Minimal `Dataset` protocol (`__len__`, `__getitem__`)
- `DataLoader` with batching, shuffling, slice fast-paths, and an optional seeded RNG
- Helpers like `one_hot(...)` and `multi_hot(...)`

---

## Saving & Loading

PureML provides two levels of persistence:

- **Parameters only** — compact save/load of learnable weights
- **Full state** — parameters **+** buffers **+** top-level literals (versioned), using zarr with Blosc(zstd) compression inside a `.zip`

```python
# Save only trainable parameters
model.save("mnist_params")

# Save full state (params + buffers + literals) to .pureml.zip
model.save_state("mnist_full_state")

# Load later
model = MNIST_BEATER().eval().load_state("mnist_full_state.pureml.zip")
```

---

## MNIST dataset included

The repo ships a compressed zarr archive of MNIST (uint8, 28×28). The `MnistDataset`:
- Normalizes images to `[0,1]` float64
- Uses one-hot labels for training mode
- Supports slicing and context-manager cleanup

---

## Why PureML?

- **Read the source, learn the math.** Every gradient is explicit and local.
- **Great for teaching & research notes.** Small enough to copy into slides or notebooks.
- **Fast enough for classic datasets.** Vectorized NumPy code + light I/O.

If you need GPUs, distributed training, or huge model zoos, you should use PyTorch/JAX. PureML is intentionally light.

---

## Continuous Development (the following will be added soon)

- Convolutional layers and pooling
- Recurrent Layers
- Extra evaluation metrics (Precision, Recall, F1-Score)
- Training visualisation utilities

---

## Contributing

Issues, enhancement suggestions, and discussions are welcome!
If you want to contribute new code, please see the [guidelines](https://github.com/Yehor-Mishchyriak/PureML/blob/main/CONTRIBUTING.md).

---

## Code of Conduct
Please be respectful and constructive in all interactions. By participating in this project you agree to uphold a positive, harassment-free environment for everyone. If you experience or witness unacceptable behavior, please open a confidential issue or contact the [maintainer](https://github.com/Yehor-Mishchyriak) directly.

---

## License

Apache-2.0 — see `LICENSE` in this repo.
