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+Metadata-Version: 2.1
+Name: scipy
+Version: 1.18.0
+Summary: Fundamental algorithms for scientific computing in Python
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+         Name: libquadmath
+         Files: scipy.libs/libquadmath*.so
+         Description: dynamically linked to files compiled with gcc
+         Availability: https://gcc.gnu.org/git/?p=gcc.git;a=tree;f=libquadmath
+         License: LGPL-2.1-or-later
+             GCC Quad-Precision Math Library
+             Copyright (C) 2010-2019 Free Software Foundation, Inc.
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+             This file is part of the libquadmath library.
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+             https://www.gnu.org/licenses/old-licenses/lgpl-2.1.html
+Classifier: Development Status :: 5 - Production/Stable
+Classifier: Intended Audience :: Science/Research
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+Classifier: Programming Language :: Python :: 3.14
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+Classifier: Operating System :: POSIX
+Classifier: Operating System :: Unix
+Classifier: Operating System :: MacOS
+Project-URL: homepage, https://scipy.org/
+Project-URL: documentation, https://docs.scipy.org/doc/scipy/
+Project-URL: source, https://github.com/scipy/scipy
+Project-URL: download, https://github.com/scipy/scipy/releases
+Project-URL: tracker, https://github.com/scipy/scipy/issues
+Requires-Python: >=3.12
+Requires-Dist: numpy<2.8,>=2.0.0
+Provides-Extra: test
+Requires-Dist: pytest>=8.0.0; extra == "test"
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+Requires-Dist: scipy-doctest>=2.0.0; extra == "test"
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+Requires-Dist: pydata-sphinx-theme>=0.15.2; extra == "doc"
+Requires-Dist: sphinx-copybutton; extra == "doc"
+Requires-Dist: sphinx-design>=0.4.0; extra == "doc"
+Requires-Dist: matplotlib>=3.5; extra == "doc"
+Requires-Dist: numpydoc; extra == "doc"
+Requires-Dist: jupytext; extra == "doc"
+Requires-Dist: myst-nb>=1.2.0; extra == "doc"
+Requires-Dist: pooch; extra == "doc"
+Requires-Dist: jupyterlite-sphinx>=0.19.1; extra == "doc"
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+Requires-Dist: ruff>=0.12.0; extra == "dev"
+Requires-Dist: cython-lint>=0.12.2; extra == "dev"
+Description-Content-Type: text/x-rst
+.. image:: https://raw.githubusercontent.com/scipy/scipy/main/doc/source/_static/logo.svg
+  :target: https://scipy.org
+  :width: 110
+  :height: 110
+  :align: left
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+  :target: https://numfocus.org
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+  :target: https://pypi.org/project/scipy/
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+  :target: https://www.nature.com/articles/s41592-019-0686-2
+.. image:: https://insights.linuxfoundation.org/api/badge/health-score?project=scipy
+  :target: https://insights.linuxfoundation.org/project/scipy
+SciPy (pronounced "Sigh Pie") is an open-source software for mathematics,
+science, and engineering. It includes modules for statistics, optimization,
+integration, linear algebra, Fourier transforms, signal and image processing,
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+- **Website:** https://scipy.org
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+- **SciPy development forum:** https://discuss.scientific-python.org/c/contributor/scipy
+- **Stack Overflow:** https://stackoverflow.com/questions/tagged/scipy
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+- **Contributing:** https://scipy.github.io/devdocs/dev/index.html
+- **Bug reports:** https://github.com/scipy/scipy/issues
+- **Code of Conduct:** https://docs.scipy.org/doc/scipy/dev/conduct/code_of_conduct.html
+- **Report a security vulnerability:** via Tidelift, as explained in
+  `our docs on Security <https://scipy.github.io/devdocs/tutorial/security.html>`__
+- **Citing in your work:** https://www.scipy.org/citing-scipy/
+SciPy is built to work with
+NumPy arrays, and provides many user-friendly and efficient numerical routines,
+such as routines for numerical integration and optimization. Together, they
+run on all popular operating systems, are quick to install, and are free of
+charge. NumPy and SciPy are easy to use, but powerful enough to be depended
+upon by some of the world's leading scientists and engineers. If you need to
+manipulate numbers on a computer and display or publish the results, give
+SciPy a try!
+For the installation instructions, see `our install
+guide <https://scipy.org/install/>`__.
+Call for Contributions
+----------------------
+We appreciate and welcome contributions. Small improvements or fixes are always appreciated; issues labeled as "good
+first issue" may be a good starting point. Have a look at `our contributing
+guide <https://scipy.github.io/devdocs/dev/index.html>`__.
+Writing code isn’t the only way to contribute to SciPy. You can also:
+- review pull requests
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+- develop graphic design for our brand assets and promotional materials
+- help with outreach and onboard new contributors
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+If you’re unsure where to start or how your skills fit in, reach out! You can
+ask on the `forum <https://discuss.scientific-python.org/c/contributor/scipy>`__
+or here, on GitHub, by leaving a comment on a relevant issue that is already
+open.
+If you are new to contributing to open source, `this
+guide <https://opensource.guide/how-to-contribute/>`__ helps explain why, what,
+and how to get involved.

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+Name: optuna
+Version: 4.9.0
+Summary: A hyperparameter optimization framework
+Author: Takuya Akiba
+Project-URL: homepage, https://optuna.org/
+Project-URL: repository, https://github.com/optuna/optuna
+Project-URL: documentation, https://optuna.readthedocs.io
+Project-URL: bugtracker, https://github.com/optuna/optuna/issues
+Classifier: Development Status :: 5 - Production/Stable
+Classifier: Intended Audience :: Science/Research
+Classifier: Intended Audience :: Developers
+Classifier: License :: OSI Approved :: MIT License
+Classifier: Programming Language :: Python :: 3
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+Requires-Dist: lightgbm; extra == "document"
+Requires-Dist: matplotlib!=3.6.0; extra == "document"
+Requires-Dist: pandas; extra == "document"
+Requires-Dist: pillow; extra == "document"
+Requires-Dist: plotly>=4.9.0; extra == "document"
+Requires-Dist: scikit-learn; extra == "document"
+Requires-Dist: sphinx; extra == "document"
+Requires-Dist: sphinx-copybutton; extra == "document"
+Requires-Dist: sphinx-gallery; extra == "document"
+Requires-Dist: sphinx-notfound-page; extra == "document"
+Requires-Dist: sphinx_rtd_theme>=1.2.0; extra == "document"
+Requires-Dist: torch; extra == "document"
+Requires-Dist: torchvision; extra == "document"
+Provides-Extra: optional
+Requires-Dist: boto3; extra == "optional"
+Requires-Dist: cmaes>=0.12.0; extra == "optional"
+Requires-Dist: google-cloud-storage; extra == "optional"
+Requires-Dist: matplotlib!=3.6.0; extra == "optional"
+Requires-Dist: pandas; extra == "optional"
+Requires-Dist: plotly>=4.9.0; extra == "optional"
+Requires-Dist: redis; extra == "optional"
+Requires-Dist: scikit-learn>=0.24.2; extra == "optional"
+Requires-Dist: scipy; extra == "optional"
+Requires-Dist: torch; extra == "optional"
+Requires-Dist: greenlet; extra == "optional"
+Requires-Dist: grpcio; extra == "optional"
+Requires-Dist: protobuf>=5.28.1; extra == "optional"
+Dynamic: license-file
+<div align="center"><img src="https://raw.githubusercontent.com/optuna/optuna/master/docs/image/optuna-logo.png" width="800"/></div>
+# Optuna: A hyperparameter optimization framework
+[![Python](https://img.shields.io/badge/python-3.9%20%7C%203.10%20%7C%203.11%20%7C%203.12%20%7C%203.13%20%7C%203.14-blue)](https://www.python.org)
+[![pypi](https://img.shields.io/pypi/v/optuna.svg)](https://pypi.python.org/pypi/optuna)
+[![conda](https://img.shields.io/conda/vn/conda-forge/optuna.svg)](https://anaconda.org/conda-forge/optuna)
+[![GitHub license](https://img.shields.io/badge/license-MIT-blue.svg)](https://github.com/optuna/optuna)
+[![Read the Docs](https://readthedocs.org/projects/optuna/badge/?version=stable)](https://optuna.readthedocs.io/en/stable/)
+:link: [**Website**](https://optuna.org/)
+| :page_with_curl: [**Docs**](https://optuna.readthedocs.io/en/stable/)
+| :gear: [**Install Guide**](https://optuna.readthedocs.io/en/stable/installation.html)
+| :pencil: [**Tutorial**](https://optuna.readthedocs.io/en/stable/tutorial/index.html)
+| :bulb: [**Examples**](https://github.com/optuna/optuna-examples)
+| [**Twitter**](https://twitter.com/OptunaAutoML)
+| [**LinkedIn**](https://www.linkedin.com/showcase/optuna/)
+| [**Medium**](https://medium.com/optuna)
+*Optuna* is an automatic hyperparameter optimization software framework, particularly designed
+for machine learning. It features an imperative, *define-by-run* style user API. Thanks to our
+*define-by-run* API, the code written with Optuna enjoys high modularity, and the user of
+Optuna can dynamically construct the search spaces for the hyperparameters.
+## :loudspeaker: News
+<!-- TODO: when you add a new line, please delete the oldest line -->
+* **Mar 16, 2026**: Optuna 4.8.0 is out! Check out [the release note](https://github.com/optuna/optuna/releases/tag/v4.8.0) for details.
+* **Jan 19, 2026**: Optuna 4.7.0 is out! Check out [the release note](https://github.com/optuna/optuna/releases/tag/v4.7.0) for details.
+* **Nov 10, 2025**: A new article [Announcing Optuna 4.6](https://medium.com/optuna/announcing-optuna-4-6-a9e82183ab07) has been published.
+* **Oct 28, 2025**: A new article [AutoSampler: Full Support for Multi-Objective & Constrained Optimization](https://medium.com/optuna/autosampler-full-support-for-multi-objective-constrained-optimization-c1c4fc957ba2) has been published.
+* **Sep 22, 2025**: A new article [[Optuna v4.5] Gaussian Process-Based Sampler (GPSampler) Can Now Perform Constrained Multi-Objective Optimization](https://medium.com/optuna/optuna-v4-5-81e78d8e077a) has been published.
+* **Jun 16, 2025**: Optuna 4.4.0 has been released! Check out [the release blog](https://medium.com/optuna/announcing-optuna-4-4-ece661493126).
+## :fire: Key Features
+Optuna has modern functionalities as follows:
+- [Lightweight, versatile, and platform agnostic architecture](https://optuna.readthedocs.io/en/stable/tutorial/10_key_features/001_first.html)
+  - Handle a wide variety of tasks with a simple installation that has few requirements.
+- [Pythonic search spaces](https://optuna.readthedocs.io/en/stable/tutorial/10_key_features/002_configurations.html)
+  - Define search spaces using familiar Python syntax including conditionals and loops.
+- [Efficient optimization algorithms](https://optuna.readthedocs.io/en/stable/tutorial/10_key_features/003_efficient_optimization_algorithms.html)
+  - Adopt state-of-the-art algorithms for sampling hyperparameters and efficiently pruning unpromising trials.
+- [Easy parallelization](https://optuna.readthedocs.io/en/stable/tutorial/10_key_features/004_distributed.html)
+  - Scale studies to tens or hundreds of workers with little or no changes to the code.
+- [Quick visualization](https://optuna.readthedocs.io/en/stable/tutorial/10_key_features/005_visualization.html)
+  - Inspect optimization histories from a variety of plotting functions.
+## Basic Concepts
+We use the terms *study* and *trial* as follows:
+- Study: optimization based on an objective function
+- Trial: a single execution of the objective function
+Please refer to the sample code below. The goal of a *study* is to find out the optimal set of
+hyperparameter values (e.g., `regressor` and `svr_c`) through multiple *trials* (e.g.,
+`n_trials=100`). Optuna is a framework designed for automation and acceleration of
+optimization *studies*.
+<details open>
+<summary>Sample code with scikit-learn</summary>
+[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](http://colab.research.google.com/github/optuna/optuna-examples/blob/main/quickstart.ipynb)
+```python
+import optuna
+import sklearn
+# Define an objective function to be minimized.
+def objective(trial):
+    # Invoke suggest methods of a Trial object to generate hyperparameters.
+    regressor_name = trial.suggest_categorical("regressor", ["SVR", "RandomForest"])
+    if regressor_name == "SVR":
+        svr_c = trial.suggest_float("svr_c", 1e-10, 1e10, log=True)
+        regressor_obj = sklearn.svm.SVR(C=svr_c)
+    else:
+        rf_max_depth = trial.suggest_int("rf_max_depth", 2, 32)
+        regressor_obj = sklearn.ensemble.RandomForestRegressor(max_depth=rf_max_depth)
+    X, y = sklearn.datasets.fetch_california_housing(return_X_y=True)
+    X_train, X_val, y_train, y_val = sklearn.model_selection.train_test_split(X, y, random_state=0)
+    regressor_obj.fit(X_train, y_train)
+    y_pred = regressor_obj.predict(X_val)
+    error = sklearn.metrics.mean_squared_error(y_val, y_pred)
+    return error  # An objective value linked with the Trial object.
+study = optuna.create_study()  # Create a new study.
+study.optimize(objective, n_trials=100)  # Invoke optimization of the objective function.
+```
+</details>
+> [!NOTE]
+> More examples can be found in [optuna/optuna-examples](https://github.com/optuna/optuna-examples).
+>
+> The examples cover diverse problem setups such as multi-objective optimization, constrained optimization, pruning, and distributed optimization.
+## Installation
+Optuna is available at [the Python Package Index](https://pypi.org/project/optuna/) and on [Anaconda Cloud](https://anaconda.org/conda-forge/optuna).
+```bash
+# PyPI
+$ pip install optuna
+```
+```bash
+# Anaconda Cloud
+$ conda install -c conda-forge optuna
+```
+> [!IMPORTANT]
+> Optuna supports Python 3.9 or newer.
+## Integrations
+Optuna has integration features with various third-party libraries. Integrations can be found in [optuna/optuna-integration](https://github.com/optuna/optuna-integration) and the document is available [here](https://optuna-integration.readthedocs.io/en/stable/index.html).
+<details>
+<summary>Supported integration libraries</summary>
+* [Catboost](https://github.com/optuna/optuna-examples/tree/main/catboost/catboost_pruning.py)
+* [Dask](https://github.com/optuna/optuna-examples/tree/main/dask/dask_simple.py)
+* [fastai](https://github.com/optuna/optuna-examples/tree/main/fastai/fastai_simple.py)
+* [Keras](https://github.com/optuna/optuna-examples/tree/main/keras/keras_integration.py)
+* [LightGBM](https://github.com/optuna/optuna-examples/tree/main/lightgbm/lightgbm_integration.py)
+* [PyTorch](https://github.com/optuna/optuna-examples/tree/main/pytorch/pytorch_simple.py)
+* [PyTorch Ignite](https://github.com/optuna/optuna-examples/tree/main/pytorch/pytorch_ignite_simple.py)
+* [PyTorch Lightning](https://github.com/optuna/optuna-examples/tree/main/pytorch/pytorch_lightning_simple.py)
+* [TensorFlow](https://github.com/optuna/optuna-examples/tree/main/tensorflow/tensorflow_estimator_integration.py)
+* [tf.keras](https://github.com/optuna/optuna-examples/tree/main/tfkeras/tfkeras_integration.py)
+* [XGBoost](https://github.com/optuna/optuna-examples/tree/main/xgboost/xgboost_integration.py)
+</details>
+## Web Dashboard
+[Optuna Dashboard](https://github.com/optuna/optuna-dashboard) is a real-time web dashboard for Optuna.
+You can check the optimization history, hyperparameter importance, etc. in graphs and tables.
+You don't need to create a Python script to call [Optuna's visualization](https://optuna.readthedocs.io/en/stable/reference/visualization/index.html) functions.
+Feature requests and bug reports are welcome!
+![optuna-dashboard](https://user-images.githubusercontent.com/5564044/204975098-95c2cb8c-0fb5-4388-abc4-da32f56cb4e5.gif)
+`optuna-dashboard` can be installed via pip:
+```shell
+$ pip install optuna-dashboard
+```
+> [!TIP]
+> Please check out the convenience of Optuna Dashboard using the sample code below.
+<details>
+<summary>Sample code to launch Optuna Dashboard</summary>
+Save the following code as `optimize_toy.py`.
+```python
+import optuna
+def objective(trial):
+    x1 = trial.suggest_float("x1", -100, 100)
+    x2 = trial.suggest_float("x2", -100, 100)
+    return x1**2 + 0.01 * x2**2
+study = optuna.create_study(storage="sqlite:///db.sqlite3")  # Create a new study with database.
+study.optimize(objective, n_trials=100)
+```
+Then try the commands below:
+```shell
+# Run the study specified above
+$ python optimize_toy.py
+# Launch the dashboard based on the storage `sqlite:///db.sqlite3`
+$ optuna-dashboard sqlite:///db.sqlite3
+...
+Listening on http://localhost:8080/
+Hit Ctrl-C to quit.
+```
+</details>
+## OptunaHub
+[OptunaHub](https://hub.optuna.org/) is a feature-sharing platform for Optuna.
+You can use the registered features and publish your packages.
+### Use registered features
+`optunahub` can be installed via pip:
+```shell
+$ pip install optunahub
+# Install AutoSampler dependencies (CPU only is sufficient for PyTorch)
+$ pip install cmaes scipy torch --extra-index-url https://download.pytorch.org/whl/cpu
+```
+You can load registered module with `optunahub.load_module`.
+```python
+import optuna
+import optunahub
+def objective(trial: optuna.Trial) -> float:
+    x = trial.suggest_float("x", -5, 5)
+    y = trial.suggest_float("y", -5, 5)
+    return x**2 + y**2
+module = optunahub.load_module(package="samplers/auto_sampler")
+study = optuna.create_study(sampler=module.AutoSampler())
+study.optimize(objective, n_trials=10)
+print(study.best_trial.value, study.best_trial.params)
+```
+For more details, please refer to [the optunahub documentation](https://optuna.github.io/optunahub/).
+### Publish your packages
+You can publish your package via [optunahub-registry](https://github.com/optuna/optunahub-registry).
+See the [Tutorials for Contributors](https://optuna.github.io/optunahub/tutorials_for_contributors.html) in OptunaHub.
+## Communication
+- [GitHub Discussions] for questions.
+- [GitHub Issues] for bug reports and feature requests.
+[GitHub Discussions]: https://github.com/optuna/optuna/discussions
+[GitHub issues]: https://github.com/optuna/optuna/issues
+## Contribution
+Any contributions to Optuna are more than welcome!
+If you are new to Optuna, please check the [good first issues](https://github.com/optuna/optuna/labels/good%20first%20issue). They are relatively simple, well-defined, and often good starting points for you to get familiar with the contribution workflow and other developers.
+If you already have contributed to Optuna, we recommend the other [contribution-welcome issues](https://github.com/optuna/optuna/labels/contribution-welcome).
+For general guidelines on how to contribute to the project, take a look at [CONTRIBUTING.md](./CONTRIBUTING.md).
+## Reference
+If you use Optuna in one of your research projects, please cite [our KDD paper](https://doi.org/10.1145/3292500.3330701) "Optuna: A Next-generation Hyperparameter Optimization Framework":
+<details open>
+<summary>BibTeX</summary>
+```bibtex
+@inproceedings{akiba2019optuna,
+  title={{O}ptuna: A Next-Generation Hyperparameter Optimization Framework},
+  author={Akiba, Takuya and Sano, Shotaro and Yanase, Toshihiko and Ohta, Takeru and Koyama, Masanori},
+  booktitle={The 25th ACM SIGKDD International Conference on Knowledge Discovery \& Data Mining},
+  pages={2623--2631},
+  year={2019}
+}
+```
+</details>
+## License
+MIT License (see [LICENSE](./LICENSE)).
+Optuna uses the codes from SciPy and fdlibm projects (see [LICENSE_THIRD_PARTY](./LICENSE_THIRD_PARTY)).

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+Name: kiwisolver
+Version: 1.5.0
+Summary: A fast implementation of the Cassowary constraint solver
+Author-email: The Nucleic Development Team <sccolbert@gmail.com>
+Maintainer-email: "Matthieu C. Dartiailh" <m.dartiailh@gmail.com>
+License: =========================
+         The Kiwi licensing terms
+        =========================
+        Kiwi is licensed under the terms of the Modified BSD License (also known as
+        New or Revised BSD), as follows:
+        Copyright (c) 2013-2026, Nucleic Development Team
+        All rights reserved.
+        Redistribution and use in source and binary forms, with or without
+        modification, are permitted provided that the following conditions are met:
+        Redistributions of source code must retain the above copyright notice, this
+        list of conditions and the following disclaimer.
+        Redistributions in binary form must reproduce the above copyright notice, this
+        list of conditions and the following disclaimer in the documentation and/or
+        other materials provided with the distribution.
+        Neither the name of the Nucleic Development Team nor the names of its
+        contributors may be used to endorse or promote products derived from this
+        software without specific prior written permission.
+        THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+        ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+        WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+        DISCLAIMED.  IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE
+        FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+        DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+        SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+        CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+        OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+        OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+        About Kiwi
+        ----------
+        Chris Colbert began the Kiwi project in December 2013 in an effort to
+        create a blisteringly fast UI constraint solver. Chris is still the
+        project lead.
+        The Nucleic Development Team is the set of all contributors to the Nucleic
+        project and its subprojects.
+        The core team that coordinates development on GitHub can be found here:
+        http://github.com/nucleic. The current team consists of:
+        * Chris Colbert
+        Our Copyright Policy
+        --------------------
+        Nucleic uses a shared copyright model. Each contributor maintains copyright
+        over their contributions to Nucleic. But, it is important to note that these
+        contributions are typically only changes to the repositories. Thus, the Nucleic
+        source code, in its entirety is not the copyright of any single person or
+        institution. Instead, it is the collective copyright of the entire Nucleic
+        Development Team. If individual contributors want to maintain a record of what
+        changes/contributions they have specific copyright on, they should indicate
+        their copyright in the commit message of the change, when they commit the
+        change to one of the Nucleic repositories.
+        With this in mind, the following banner should be used in any source code file
+        to indicate the copyright and license terms:
+        #------------------------------------------------------------------------------
+        # Copyright (c) 2013-2026, Nucleic Development Team.
+        #
+        # Distributed under the terms of the Modified BSD License.
+        #
+        # The full license is in the file LICENSE, distributed with this software.
+        #------------------------------------------------------------------------------
+Project-URL: homepage, https://github.com/nucleic/kiwi
+Project-URL: documentation, https://kiwisolver.readthedocs.io/en/latest/
+Project-URL: repository, https://github.com/nucleic/kiwi
+Project-URL: changelog, https://github.com/nucleic/kiwi/blob/main/releasenotes.rst
+Classifier: License :: OSI Approved :: BSD License
+Classifier: Programming Language :: Python
+Classifier: Programming Language :: Python :: 3
+Classifier: Programming Language :: Python :: 3.10
+Classifier: Programming Language :: Python :: 3.11
+Classifier: Programming Language :: Python :: 3.12
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+Classifier: Programming Language :: Python :: 3.14
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+Classifier: Programming Language :: Python :: Implementation :: PyPy
+Classifier: Programming Language :: Python :: Implementation :: GraalPy
+Requires-Python: >=3.10
+Description-Content-Type: text/x-rst
+License-File: LICENSE
+Dynamic: license-file

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+Name: openpyxl
+Version: 3.1.5
+Summary: A Python library to read/write Excel 2010 xlsx/xlsm files
+Home-page: https://openpyxl.readthedocs.io
+Author: See AUTHORS
+Author-email: charlie.clark@clark-consulting.eu
+License: MIT
+Project-URL: Documentation, https://openpyxl.readthedocs.io/en/stable/
+Project-URL: Source, https://foss.heptapod.net/openpyxl/openpyxl
+Project-URL: Tracker, https://foss.heptapod.net/openpyxl/openpyxl/-/issues
+Classifier: Development Status :: 5 - Production/Stable
+Classifier: Operating System :: MacOS :: MacOS X
+Classifier: Operating System :: Microsoft :: Windows
+Classifier: Operating System :: POSIX
+Classifier: License :: OSI Approved :: MIT License
+Classifier: Programming Language :: Python
+Classifier: Programming Language :: Python :: 3.6
+Classifier: Programming Language :: Python :: 3.7
+Classifier: Programming Language :: Python :: 3.8
+Classifier: Programming Language :: Python :: 3.9
+Classifier: Programming Language :: Python :: 3.10
+Classifier: Programming Language :: Python :: 3.11
+Requires-Python: >=3.8
+License-File: LICENCE.rst
+Requires-Dist: et-xmlfile
+.. image:: https://coveralls.io/repos/bitbucket/openpyxl/openpyxl/badge.svg?branch=default
+    :target: https://coveralls.io/bitbucket/openpyxl/openpyxl?branch=default
+    :alt: coverage status
+Introduction
+------------
+openpyxl is a Python library to read/write Excel 2010 xlsx/xlsm/xltx/xltm files.
+It was born from lack of existing library to read/write natively from Python
+the Office Open XML format.
+All kudos to the PHPExcel team as openpyxl was initially based on PHPExcel.
+Security
+--------
+By default openpyxl does not guard against quadratic blowup or billion laughs
+xml attacks. To guard against these attacks install defusedxml.
+Mailing List
+------------
+The user list can be found on http://groups.google.com/group/openpyxl-users
+Sample code::
+    from openpyxl import Workbook
+    wb = Workbook()
+    # grab the active worksheet
+    ws = wb.active
+    # Data can be assigned directly to cells
+    ws['A1'] = 42
+    # Rows can also be appended
+    ws.append([1, 2, 3])
+    # Python types will automatically be converted
+    import datetime
+    ws['A2'] = datetime.datetime.now()
+    # Save the file
+    wb.save("sample.xlsx")
+Documentation
+-------------
+The documentation is at: https://openpyxl.readthedocs.io
+* installation methods
+* code examples
+* instructions for contributing
+Release notes: https://openpyxl.readthedocs.io/en/stable/changes.html

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+Name: cycler
+Version: 0.12.1
+Summary: Composable style cycles
+Author-email: Thomas A Caswell <matplotlib-users@python.org>
+License: Copyright (c) 2015, matplotlib project
+        All rights reserved.
+        Redistribution and use in source and binary forms, with or without
+        modification, are permitted provided that the following conditions are met:
+        * Redistributions of source code must retain the above copyright notice, this
+          list of conditions and the following disclaimer.
+        * Redistributions in binary form must reproduce the above copyright notice,
+          this list of conditions and the following disclaimer in the documentation
+          and/or other materials provided with the distribution.
+        * Neither the name of the matplotlib project nor the names of its
+          contributors may be used to endorse or promote products derived from
+          this software without specific prior written permission.
+        THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+        AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+        IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+        DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+        FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+        DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+        SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+        CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+        OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+        OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+Project-URL: homepage, https://matplotlib.org/cycler/
+Project-URL: repository, https://github.com/matplotlib/cycler
+Keywords: cycle kwargs
+Classifier: License :: OSI Approved :: BSD License
+Classifier: Development Status :: 4 - Beta
+Classifier: Programming Language :: Python :: 3
+Classifier: Programming Language :: Python :: 3.8
+Classifier: Programming Language :: Python :: 3.9
+Classifier: Programming Language :: Python :: 3.10
+Classifier: Programming Language :: Python :: 3.11
+Classifier: Programming Language :: Python :: 3.12
+Classifier: Programming Language :: Python :: 3 :: Only
+Requires-Python: >=3.8
+Description-Content-Type: text/x-rst
+License-File: LICENSE
+Provides-Extra: docs
+Requires-Dist: ipython ; extra == 'docs'
+Requires-Dist: matplotlib ; extra == 'docs'
+Requires-Dist: numpydoc ; extra == 'docs'
+Requires-Dist: sphinx ; extra == 'docs'
+Provides-Extra: tests
+Requires-Dist: pytest ; extra == 'tests'
+Requires-Dist: pytest-cov ; extra == 'tests'
+Requires-Dist: pytest-xdist ; extra == 'tests'
+|PyPi|_ |Conda|_ |Supported Python versions|_ |GitHub Actions|_ |Codecov|_
+.. |PyPi| image:: https://img.shields.io/pypi/v/cycler.svg?style=flat
+.. _PyPi: https://pypi.python.org/pypi/cycler
+.. |Conda| image:: https://img.shields.io/conda/v/conda-forge/cycler
+.. _Conda:  https://anaconda.org/conda-forge/cycler
+.. |Supported Python versions| image:: https://img.shields.io/pypi/pyversions/cycler.svg
+.. _Supported Python versions: https://pypi.python.org/pypi/cycler
+.. |GitHub Actions| image:: https://github.com/matplotlib/cycler/actions/workflows/tests.yml/badge.svg
+.. _GitHub Actions: https://github.com/matplotlib/cycler/actions
+.. |Codecov| image:: https://codecov.io/github/matplotlib/cycler/badge.svg?branch=main&service=github
+.. _Codecov: https://codecov.io/github/matplotlib/cycler?branch=main
+cycler: composable cycles
+=========================
+Docs: https://matplotlib.org/cycler/

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+Metadata-Version: 2.4
+Name: pip
+Version: 26.2
+Summary: The PyPA recommended tool for installing Python packages.
+Author-email: The pip developers <distutils-sig@python.org>
+Requires-Python: >=3.10
+Description-Content-Type: text/x-rst
+License-Expression: MIT
+Classifier: Development Status :: 5 - Production/Stable
+Classifier: Intended Audience :: Developers
+Classifier: Topic :: Software Development :: Build Tools
+Classifier: Programming Language :: Python
+Classifier: Programming Language :: Python :: 3
+Classifier: Programming Language :: Python :: 3 :: Only
+Classifier: Programming Language :: Python :: 3.10
+Classifier: Programming Language :: Python :: 3.11
+Classifier: Programming Language :: Python :: 3.12
+Classifier: Programming Language :: Python :: 3.13
+Classifier: Programming Language :: Python :: 3.14
+Classifier: Programming Language :: Python :: 3.15
+Classifier: Programming Language :: Python :: Implementation :: CPython
+Classifier: Programming Language :: Python :: Implementation :: PyPy
+License-File: AUTHORS.txt
+License-File: LICENSE.txt
+License-File: src/pip/_vendor/cachecontrol/LICENSE.txt
+License-File: src/pip/_vendor/certifi/LICENSE
+License-File: src/pip/_vendor/distlib/LICENSE.txt
+License-File: src/pip/_vendor/distro/LICENSE
+License-File: src/pip/_vendor/idna/LICENSE.md
+License-File: src/pip/_vendor/msgpack/COPYING
+License-File: src/pip/_vendor/packaging/LICENSE
+License-File: src/pip/_vendor/packaging/LICENSE.APACHE
+License-File: src/pip/_vendor/packaging/LICENSE.BSD
+License-File: src/pip/_vendor/pkg_resources/LICENSE
+License-File: src/pip/_vendor/platformdirs/LICENSE
+License-File: src/pip/_vendor/pygments/LICENSE
+License-File: src/pip/_vendor/pyproject_hooks/LICENSE
+License-File: src/pip/_vendor/requests/LICENSE
+License-File: src/pip/_vendor/resolvelib/LICENSE
+License-File: src/pip/_vendor/rich/LICENSE
+License-File: src/pip/_vendor/tomli/LICENSE
+License-File: src/pip/_vendor/tomli_w/LICENSE
+License-File: src/pip/_vendor/truststore/LICENSE
+License-File: src/pip/_vendor/urllib3/LICENSE.txt
+Project-URL: Changelog, https://pip.pypa.io/en/stable/news/
+Project-URL: Documentation, https://pip.pypa.io
+Project-URL: Homepage, https://pip.pypa.io/
+Project-URL: Source, https://github.com/pypa/pip
+pip - The Python Package Installer
+==================================
+.. |pypi-version| image:: https://img.shields.io/pypi/v/pip.svg
+   :target: https://pypi.org/project/pip/
+   :alt: PyPI
+.. |python-versions| image:: https://img.shields.io/pypi/pyversions/pip
+   :target: https://pypi.org/project/pip
+   :alt: PyPI - Python Version
+.. |docs-badge| image:: https://readthedocs.org/projects/pip/badge/?version=latest
+   :target: https://pip.pypa.io/en/latest
+   :alt: Documentation
+|pypi-version| |python-versions| |docs-badge|
+Pip is the `package installer`_ for Python. You can use pip to install packages from the `Python Package Index`_ and other indexes.
+Please take a look at our documentation for how to install and use pip:
+* `Installation`_
+* `Usage`_
+We release updates regularly, with a new version every 3 months. Find more details in our documentation:
+* `Release notes`_
+* `Release process`_
+If you find bugs, need help, or want to talk to the developers, please use our mailing lists or chat rooms:
+* `Issue tracking`_
+* `Discourse channel`_
+* `User IRC`_
+If you want to get involved, head over to GitHub to get the source code, look at our development documentation and feel free to jump on the developer mailing lists and chat rooms:
+* `GitHub page`_
+* `Development documentation`_
+* `Development IRC`_
+Code of Conduct
+---------------
+Everyone interacting in the pip project's codebases, issue trackers, chat
+rooms, and mailing lists is expected to follow the `PSF Code of Conduct`_.
+.. _package installer: https://packaging.python.org/guides/tool-recommendations/
+.. _Python Package Index: https://pypi.org
+.. _Installation: https://pip.pypa.io/en/stable/installation/
+.. _Usage: https://pip.pypa.io/en/stable/
+.. _Release notes: https://pip.pypa.io/en/stable/news.html
+.. _Release process: https://pip.pypa.io/en/latest/development/release-process/
+.. _GitHub page: https://github.com/pypa/pip
+.. _Development documentation: https://pip.pypa.io/en/latest/development
+.. _Issue tracking: https://github.com/pypa/pip/issues
+.. _Discourse channel: https://discuss.python.org/c/packaging
+.. _User IRC: https://kiwiirc.com/nextclient/#ircs://irc.libera.chat:+6697/pypa
+.. _Development IRC: https://kiwiirc.com/nextclient/#ircs://irc.libera.chat:+6697/pypa-dev
+.. _PSF Code of Conduct: https://github.com/pypa/.github/blob/main/CODE_OF_CONDUCT.md

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+Metadata-Version: 2.4
+Name: hf-xet
+Version: 1.5.2
+Classifier: Development Status :: 5 - Production/Stable
+Classifier: License :: OSI Approved :: Apache Software License
+Classifier: Programming Language :: Rust
+Classifier: Programming Language :: Python :: Implementation :: CPython
+Classifier: Programming Language :: Python :: Implementation :: PyPy
+Classifier: Programming Language :: Python :: 3
+Classifier: Programming Language :: Python :: 3 :: Only
+Classifier: Programming Language :: Python :: 3.8
+Classifier: Programming Language :: Python :: 3.9
+Classifier: Programming Language :: Python :: 3.10
+Classifier: Programming Language :: Python :: 3.11
+Classifier: Programming Language :: Python :: 3.12
+Classifier: Programming Language :: Python :: 3.13
+Classifier: Programming Language :: Python :: 3.14
+Classifier: Programming Language :: Python :: Free Threading
+Classifier: Programming Language :: Python :: Free Threading :: 2 - Beta
+Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
+Requires-Dist: pytest ; extra == 'tests'
+Provides-Extra: tests
+License-File: LICENSE
+Summary: Fast transfer of large files with the Hugging Face Hub.
+Maintainer-email: Rajat Arya <rajat@rajatarya.com>, Jared Sulzdorf <j.sulzdorf@gmail.com>, Di Xiao <di@huggingface.co>, Assaf Vayner <assaf@huggingface.co>, Hoyt Koepke <hoytak@gmail.com>
+License-Expression: Apache-2.0
+Requires-Python: >=3.8
+Description-Content-Type: text/markdown; charset=UTF-8; variant=GFM
+Project-URL: Documentation, https://huggingface.co/docs/hub/xet/index
+Project-URL: Homepage, https://github.com/huggingface/xet-core
+Project-URL: Issues, https://github.com/huggingface/xet-core/issues
+Project-URL: Repository, https://github.com/huggingface/xet-core.git
+<!---
+Copyright 2024 The HuggingFace Team. All rights reserved.
+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.
+-->
+<p align="center">
+    <a href="https://github.com/huggingface/xet-core/blob/main/LICENSE"><img alt="License" src="https://img.shields.io/github/license/huggingface/xet-core.svg?color=blue"></a>
+    <a href="https://github.com/huggingface/xet-core/releases"><img alt="GitHub release" src="https://img.shields.io/github/release/huggingface/xet-core.svg"></a>
+    <a href="https://github.com/huggingface/xet-core/blob/main/CODE_OF_CONDUCT.md"><img alt="Contributor Covenant" src="https://img.shields.io/badge/Contributor%20Covenant-v2.0%20adopted-ff69b4.svg"></a>
+</p>
+<h3 align="center">
+  <p>🤗 hf-xet - xet client tech, used in <a target="_blank" href="https://github.com/huggingface/huggingface_hub/">huggingface_hub</a></p>
+</h3>
+## Welcome
+`hf-xet` enables `huggingface_hub` to utilize xet storage for uploading and downloading to HF Hub. Xet storage provides chunk-based deduplication, efficient storage/retrieval with local disk caching, and backwards compatibility with Git LFS. This library is not meant to be used directly, and is instead intended to be used from [huggingface_hub](https://pypi.org/project/huggingface-hub).
+## Key features
+♻ **chunk-based deduplication implementation**: avoid transferring and storing chunks that are shared across binary files (models, datasets, etc).
+🤗 **Python bindings**: bindings for [huggingface_hub](https://github.com/huggingface/huggingface_hub/) package.
+↔ **network communications**: concurrent communication to HF Hub Xet backend services (CAS).
+🔖 **local disk caching**: chunk-based cache that sits alongside the existing [huggingface_hub disk cache](https://huggingface.co/docs/huggingface_hub/guides/manage-cache).
+## Installation
+Install the `hf_xet` package with [pip](https://pypi.org/project/hf-xet/):
+```bash
+pip install hf_xet
+```
+## Quick Start
+`hf_xet` is not intended to be run independently as it is expected to be used from `huggingface_hub`, so to get started with `huggingface_hub` check out the documentation [here]("https://hf.co/docs/huggingface_hub").
+## Contributions (feature requests, bugs, etc.) are encouraged & appreciated 💙💚💛💜🧡❤️
+Please join us in making hf-xet better. We value everyone's contributions. Code is not the only way to help. Answering questions, helping each other, improving documentation, filing issues all help immensely. If you are interested in contributing (please do!), check out the [contribution guide](https://github.com/huggingface/xet-core/blob/main/CONTRIBUTING.md) for this repository.

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