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+Metadata-Version: 2.4
+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
+Classifier: Programming Language :: Python :: 3.13
+Classifier: Programming Language :: Python :: 3.14
+Classifier: Programming Language :: Python :: Implementation :: CPython
+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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+Metadata-Version: 2.4
+Name: colorlog
+Version: 6.10.1
+Summary: Add colours to the output of Python's logging module.
+Home-page: https://github.com/borntyping/python-colorlog
+Author: Sam Clements
+Author-email: sam@borntyping.co.uk
+License: MIT License
+Classifier: Development Status :: 5 - Production/Stable
+Classifier: Environment :: Console
+Classifier: Intended Audience :: Developers
+Classifier: License :: OSI Approved :: MIT License
+Classifier: Operating System :: OS Independent
+Classifier: Programming Language :: Python
+Classifier: Programming Language :: Python :: 3
+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
+Classifier: Programming Language :: Python :: 3.12
+Classifier: Programming Language :: Python :: 3.13
+Classifier: Topic :: Terminals
+Classifier: Topic :: Utilities
+Requires-Python: >=3.6
+Description-Content-Type: text/markdown
+License-File: LICENSE
+Requires-Dist: colorama; sys_platform == "win32"
+Provides-Extra: development
+Requires-Dist: black; extra == "development"
+Requires-Dist: flake8; extra == "development"
+Requires-Dist: mypy; extra == "development"
+Requires-Dist: pytest; extra == "development"
+Requires-Dist: types-colorama; extra == "development"
+Dynamic: author
+Dynamic: author-email
+Dynamic: classifier
+Dynamic: description
+Dynamic: description-content-type
+Dynamic: home-page
+Dynamic: license
+Dynamic: license-file
+Dynamic: provides-extra
+Dynamic: requires-python
+Dynamic: summary
+
+# Log formatting with colors!
+
+[![](https://img.shields.io/pypi/v/colorlog.svg)](https://pypi.org/project/colorlog/)
+[![](https://img.shields.io/pypi/l/colorlog.svg)](https://pypi.org/project/colorlog/)
+
+Add colours to the output of Python's `logging` module.
+
+* [Source on GitHub](https://github.com/borntyping/python-colorlog)
+* [Packages on PyPI](https://pypi.org/pypi/colorlog/)
+
+## Status
+
+colorlog currently requires Python 3.6 or higher. Older versions (below 5.x.x) 
+support Python 2.6 and above.
+
+* colorlog 6.x requires Python 3.6 or higher.
+* colorlog 5.x is an interim version that will warn Python 2 users to downgrade.
+* colorlog 4.x is the final version supporting Python 2.
+
+[colorama] is included as a required dependency and initialised when using 
+colorlog on Windows.
+
+This library is over a decade old and supported a wide set of Python versions
+for most of its life, which has made it a difficult library to add new features
+to. colorlog 6 may break backwards compatibility so that newer features
+can be added more easily, but may still not accept all changes or feature
+requests. colorlog 4 might accept essential bugfixes but should not be
+considered actively maintained and will not accept any major changes or new
+features.
+
+## Installation
+
+Install from PyPI with:
+
+```bash
+pip install colorlog
+```
+
+Several Linux distributions provide official packages ([Debian], [Arch], [Fedora], 
+[Gentoo], [OpenSuse] and [Ubuntu]), and others have user provided packages
+([BSD ports], [Conda]).
+
+## Usage
+
+```python
+import colorlog
+
+handler = colorlog.StreamHandler()
+handler.setFormatter(colorlog.ColoredFormatter(
+	'%(log_color)s%(levelname)s:%(name)s:%(message)s'))
+
+logger = colorlog.getLogger('example')
+logger.addHandler(handler)
+```
+
+The `ColoredFormatter` class takes several arguments:
+
+- `format`: The format string used to output the message (required).
+- `datefmt`: An optional date format passed to the base class. See [`logging.Formatter`][Formatter].
+- `reset`: Implicitly adds a color reset code to the message output, unless the output already ends with one. Defaults to `True`.
+- `log_colors`: A mapping of record level names to color names. The defaults can be found in `colorlog.default_log_colors`, or the below example.
+- `secondary_log_colors`: A mapping of names to `log_colors` style mappings, defining additional colors that can be used in format strings. See below for an example.
+- `style`: Available on Python 3.2 and above. See [`logging.Formatter`][Formatter].
+
+Color escape codes can be selected based on the log records level, by adding
+parameters to the format string:
+
+- `log_color`: Return the color associated with the records level.
+- `<name>_log_color`: Return another color based on the records level if the formatter has secondary colors configured (see `secondary_log_colors` below).
+
+Multiple escape codes can be used at once by joining them with commas when
+configuring the color for a log level (but can't be used directly in the format
+string). For example, `black,bg_white` would use the escape codes for black
+text on a white background.
+
+The following escape codes are made available for use in the format string:
+
+- `{color}`, `fg_{color}`, `bg_{color}`: Foreground and background colors.
+- `bold`, `bold_{color}`, `fg_bold_{color}`, `bg_bold_{color}`: Bold/bright colors.
+- `thin`, `thin_{color}`, `fg_thin_{color}`: Thin colors (terminal dependent).
+- `reset`: Clear all formatting (both foreground and background colors).
+
+The available color names are:
+
+- `black`
+- `red`
+- `green`
+- `yellow`
+- `blue`,
+- `purple`
+- `cyan`
+- `white`
+
+You can also use "bright" colors. These aren't standard ANSI codes, and
+support for these varies wildly across different terminals.
+
+- `light_black`
+- `light_red`
+- `light_green`
+- `light_yellow`
+- `light_blue`
+- `light_purple`
+- `light_cyan`
+- `light_white`
+
+## Examples
+
+![Example output](docs/example.png)
+
+The following code creates a `ColoredFormatter` for use in a logging setup,
+using the default values for each argument.
+
+```python
+from colorlog import ColoredFormatter
+
+formatter = ColoredFormatter(
+	"%(log_color)s%(levelname)-8s%(reset)s %(blue)s%(message)s",
+	datefmt=None,
+	reset=True,
+	log_colors={
+		'DEBUG':    'cyan',
+		'INFO':     'green',
+		'WARNING':  'yellow',
+		'ERROR':    'red',
+		'CRITICAL': 'red,bg_white',
+	},
+	secondary_log_colors={},
+	style='%'
+)
+```
+
+### Using `secondary_log_colors`
+
+Secondary log colors are a way to have more than one color that is selected
+based on the log level. Each key in `secondary_log_colors` adds an attribute
+that can be used in format strings (`message` becomes `message_log_color`), and
+has a corresponding value that is identical in format to the `log_colors`
+argument.
+
+The following example highlights the level name using the default log colors,
+and highlights the message in red for `error` and `critical` level log messages.
+
+```python
+from colorlog import ColoredFormatter
+
+formatter = ColoredFormatter(
+	"%(log_color)s%(levelname)-8s%(reset)s %(message_log_color)s%(message)s",
+	secondary_log_colors={
+		'message': {
+			'ERROR':    'red',
+			'CRITICAL': 'red'
+		}
+	}
+)
+```
+
+### With [`dictConfig`][dictConfig]
+
+```python
+logging.config.dictConfig({
+	'formatters': {
+		'colored': {
+			'()': 'colorlog.ColoredFormatter',
+			'format': "%(log_color)s%(levelname)-8s%(reset)s %(blue)s%(message)s"
+		}
+	}
+})
+```
+
+A full example dictionary can be found in `tests/test_colorlog.py`.
+
+### With [`fileConfig`][fileConfig]
+
+```ini
+...
+
+[formatters]
+keys=color
+
+[formatter_color]
+class=colorlog.ColoredFormatter
+format=%(log_color)s%(levelname)-8s%(reset)s %(bg_blue)s[%(name)s]%(reset)s %(message)s from fileConfig
+datefmt=%m-%d %H:%M:%S
+```
+
+An instance of ColoredFormatter created with those arguments will then be used
+by any handlers that are configured to use the `color` formatter.
+
+A full example configuration can be found in `tests/test_config.ini`.
+
+### With custom log levels
+
+ColoredFormatter will work with custom log levels added with
+[`logging.addLevelName`][addLevelName]:
+
+```python
+import logging, colorlog
+TRACE = 5
+logging.addLevelName(TRACE, 'TRACE')
+formatter = colorlog.ColoredFormatter(log_colors={'TRACE': 'yellow'})
+handler = logging.StreamHandler()
+handler.setFormatter(formatter)
+logger = logging.getLogger('example')
+logger.addHandler(handler)
+logger.setLevel('TRACE')
+logger.log(TRACE, 'a message using a custom level')
+```
+
+## Tests
+
+Tests similar to the above examples are found in `tests/test_colorlog.py`.
+
+## Status
+
+colorlog is in maintenance mode. I try and ensure bugfixes are published,
+but compatibility a wide set of Python versions makes this a difficult
+codebase to add features to. Any changes that might break backwards
+compatibility for existing users will not be considered.
+
+## Alternatives
+
+There are some more modern libraries for improving Python logging you may
+find useful.
+
+- [structlog]
+- [jsonlog]
+
+## Projects using colorlog
+
+GitHub provides [a list of projects that depend on colorlog][dependents].
+
+Some early adopters included [Errbot], [Pythran], and [zenlog].
+
+## Licence
+
+Copyright (c) 2012-2025 Sam Clements <sam@borntyping.co.uk>
+
+Permission is hereby granted, free of charge, to any person obtaining a copy of
+this software and associated documentation files (the "Software"), to deal in
+the Software without restriction, including without limitation the rights to
+use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of
+the Software, and to permit persons to whom the Software is furnished to do so,
+subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS
+FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
+COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
+IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
+CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+
+[dictConfig]: http://docs.python.org/3/library/logging.config.html#logging.config.dictConfig
+[fileConfig]: http://docs.python.org/3/library/logging.config.html#logging.config.fileConfig
+[addLevelName]: https://docs.python.org/3/library/logging.html#logging.addLevelName
+[Formatter]: http://docs.python.org/3/library/logging.html#logging.Formatter
+[tox]: http://tox.readthedocs.org/
+[Arch]: https://archlinux.org/packages/extra/any/python-colorlog/
+[BSD ports]: https://www.freshports.org/devel/py-colorlog/
+[colorama]: https://pypi.python.org/pypi/colorama
+[Conda]: https://anaconda.org/conda-forge/colorlog
+[Debian]: [https://packages.debian.org/buster/python3-colorlog](https://packages.debian.org/buster/python3-colorlog)
+[Errbot]: http://errbot.io/
+[Fedora]: https://src.fedoraproject.org/rpms/python-colorlog
+[Gentoo]: https://packages.gentoo.org/packages/dev-python/colorlog
+[OpenSuse]: http://rpm.pbone.net/index.php3?stat=3&search=python-colorlog&srodzaj=3
+[Pythran]: https://github.com/serge-sans-paille/pythran
+[Ubuntu]: https://launchpad.net/python-colorlog
+[zenlog]: https://github.com/ManufacturaInd/python-zenlog
+[structlog]: https://www.structlog.org/en/stable/
+[jsonlog]: https://github.com/borntyping/jsonlog
+[dependents]: https://github.com/borntyping/python-colorlog/network/dependents?package_id=UGFja2FnZS01MDk3NDcyMQ%3D%3D

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+Metadata-Version: 2.1
+Name: timm
+Version: 1.0.26
+Summary: PyTorch Image Models
+Keywords: pytorch,image-classification
+Author-Email: Ross Wightman <ross@huggingface.co>
+License: Apache-2.0
+Classifier: Development Status :: 5 - Production/Stable
+Classifier: Intended Audience :: Education
+Classifier: Intended Audience :: Science/Research
+Classifier: License :: OSI Approved :: Apache Software License
+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: Topic :: Scientific/Engineering
+Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
+Classifier: Topic :: Software Development
+Classifier: Topic :: Software Development :: Libraries
+Classifier: Topic :: Software Development :: Libraries :: Python Modules
+Project-URL: homepage, https://github.com/huggingface/pytorch-image-models
+Project-URL: documentation, https://huggingface.co/docs/timm/en/index
+Project-URL: repository, https://github.com/huggingface/pytorch-image-models
+Requires-Python: >=3.8
+Requires-Dist: torch
+Requires-Dist: torchvision
+Requires-Dist: pyyaml
+Requires-Dist: huggingface_hub
+Requires-Dist: safetensors
+Description-Content-Type: text/markdown
+
+# PyTorch Image Models
+- [What's New](#whats-new)
+- [Introduction](#introduction)
+- [Models](#models)
+- [Features](#features)
+- [Results](#results)
+- [Getting Started (Documentation)](#getting-started-documentation)
+- [Train, Validation, Inference Scripts](#train-validation-inference-scripts)
+- [Awesome PyTorch Resources](#awesome-pytorch-resources)
+- [Licenses](#licenses)
+- [Citing](#citing)
+
+## What's New
+
+## March 23, 2026
+* Improve pickle checkpoint handling security. Default all loading to `weights_only=True`, add safe_global for ArgParse.
+* Improve attention mask handling for core ViT/EVA models & layers. Resolve bool masks, pass `is_causal` through for SSL tasks.
+* Fix class & register token uses with ViT and no pos embed enabled.
+* Add Patch Representation Refinement (PRR) as a pooling option in ViT. Thanks Sina (https://github.com/sinahmr).
+* Improve consistency of output projection / MLP dimensions for attention pooling layers.
+* Hiera model F.SDPA optimization to allow Flash Attention kernel use.
+* Caution added to SGDP optimizer.
+* Release 1.0.26. First maintenance release since my departure from Hugging Face.
+
+## Feb 23, 2026
+* Add token distillation training support to distillation task wrappers
+* Remove some torch.jit usage in prep for official deprecation
+* Caution added to AdamP optimizer
+* Call reset_parameters() even if meta-device init so that buffers get init w/ hacks like init_empty_weights
+* Tweak Muon optimizer to work with DTensor/FSDP2 (clamp_ instead of clamp_min_, alternate NS branch for DTensor)
+* Release 1.0.25
+
+## Jan 21, 2026
+* **Compat Break**: Fix oversight w/ QKV vs MLP bias in `ParallelScalingBlock` (& `DiffParallelScalingBlock`)
+  * Does not impact any trained `timm` models but could impact downstream use.
+
+## Jan 5 & 6, 2026
+* Release 1.0.24
+* Add new benchmark result csv files for inference timing on all models w/ RTX Pro 6000, 5090, and 4090 cards w/ PyTorch 2.9.1
+* Fix moved module error in deprecated timm.models.layers import path that impacts legacy imports
+* Release 1.0.23
+
+## Dec 30, 2025
+* Add better NAdaMuon trained `dpwee`, `dwee`, `dlittle` (differential) ViTs with a small boost over previous runs
+  * https://huggingface.co/timm/vit_dlittle_patch16_reg1_gap_256.sbb_nadamuon_in1k (83.24% top-1)
+  * https://huggingface.co/timm/vit_dwee_patch16_reg1_gap_256.sbb_nadamuon_in1k  (81.80% top-1)
+  * https://huggingface.co/timm/vit_dpwee_patch16_reg1_gap_256.sbb_nadamuon_in1k (81.67% top-1)
+* Add a ~21M param `timm` variant of the CSATv2 model at 512x512 & 640x640
+  * https://huggingface.co/timm/csatv2_21m.sw_r640_in1k (83.13% top-1)
+  * https://huggingface.co/timm/csatv2_21m.sw_r512_in1k (82.58% top-1)
+* Factor non-persistent param init out of `__init__` into a common method that can be externally called via `init_non_persistent_buffers()` after meta-device init. 
+  
+## Dec 12, 2025
+* Add CSATV2 model (thanks https://github.com/gusdlf93) -- a lightweight but high res model with DCT stem & spatial attention. https://huggingface.co/Hyunil/CSATv2
+* Add AdaMuon and NAdaMuon optimizer support to existing `timm` Muon impl. Appears more competitive vs AdamW with familiar hparams for image tasks.
+* End of year PR cleanup, merge aspects of several long open PR
+  * Merge differential attention (`DiffAttention`), add corresponding `DiffParallelScalingBlock` (for ViT), train some wee vits
+    * https://huggingface.co/timm/vit_dwee_patch16_reg1_gap_256.sbb_in1k
+    * https://huggingface.co/timm/vit_dpwee_patch16_reg1_gap_256.sbb_in1k
+  * Add a few pooling modules, `LsePlus` and `SimPool`
+  * Cleanup, optimize `DropBlock2d` (also add support to ByobNet based models)
+* Bump unit tests to PyTorch 2.9.1 + Python 3.13 on upper end, lower still PyTorch 1.13 + Python 3.10
+  
+## Dec 1, 2025
+* Add lightweight task abstraction, add logits and feature distillation support to train script via new tasks.
+* Remove old APEX AMP support
+
+## Nov 4, 2025
+* Fix LayerScale / LayerScale2d init bug (init values ignored), introduced in 1.0.21. Thanks https://github.com/Ilya-Fradlin
+* Release 1.0.22
+
+## Oct 31, 2025 🎃
+* Update imagenet & OOD variant result csv files to include a few new models and verify correctness over several torch & timm versions
+* EfficientNet-X and EfficientNet-H B5 model weights added as part of a hparam search for AdamW vs Muon (still iterating on Muon runs)
+
+## Oct 16-20, 2025
+* Add an impl of the Muon optimizer (based on https://github.com/KellerJordan/Muon) with customizations
+  * extra flexibility and improved handling for conv weights and fallbacks for weight shapes not suited for orthogonalization
+  * small speedup for NS iterations by reducing allocs and using fused (b)add(b)mm ops
+  * by default uses AdamW (or NAdamW if `nesterov=True`) updates if muon not suitable for parameter shape (or excluded via param group flag)
+  * like torch impl, select from several LR scale adjustment fns via `adjust_lr_fn`
+  * select from several NS coefficient presets or specify your own via `ns_coefficients`
+* First 2 steps of 'meta' device model initialization supported
+  * Fix several ops that were breaking creation under 'meta' device context
+  * Add device & dtype factory kwarg support to all models and modules (anything inherting from nn.Module) in `timm`
+* License fields added to pretrained cfgs in code
+* Release 1.0.21
+
+## Sept 21, 2025
+* Remap DINOv3 ViT weight tags from `lvd_1689m` -> `lvd1689m` to match (same for `sat_493m` -> `sat493m`)
+* Release 1.0.20
+
+## Sept 17, 2025
+* DINOv3 (https://arxiv.org/abs/2508.10104) ConvNeXt and ViT models added. ConvNeXt models were mapped to existing `timm` model. ViT support done via the EVA base model w/ a new `RotaryEmbeddingDinoV3` to match the DINOv3 specific RoPE impl
+  * HuggingFace Hub: https://huggingface.co/collections/timm/timm-dinov3-68cb08bb0bee365973d52a4d
+* MobileCLIP-2 (https://arxiv.org/abs/2508.20691) vision encoders. New MCI3/MCI4 FastViT variants added and weights mapped to existing FastViT and B, L/14 ViTs.
+* MetaCLIP-2 Worldwide (https://arxiv.org/abs/2507.22062) ViT encoder weights added.
+* SigLIP-2 (https://arxiv.org/abs/2502.14786) NaFlex ViT encoder weights added via timm NaFlexViT model.
+* Misc fixes and contributions
+
+## July 23, 2025
+* Add `set_input_size()` method to EVA models, used by OpenCLIP 3.0.0 to allow resizing for timm based encoder models.
+* Release 1.0.18, needed for PE-Core S & T models in OpenCLIP 3.0.0
+* Fix small typing issue that broke Python 3.9 compat. 1.0.19 patch release.
+
+## July 21, 2025
+* ROPE support added to NaFlexViT. All models covered by the EVA base (`eva.py`) including EVA, EVA02, Meta PE ViT, `timm` SBB ViT w/ ROPE, and Naver ROPE-ViT can be now loaded in NaFlexViT when `use_naflex=True` passed at model creation time
+* More Meta PE ViT encoders added, including small/tiny variants, lang variants w/ tiling, and more spatial variants.
+* PatchDropout fixed with NaFlexViT and also w/ EVA models (regression after adding Naver ROPE-ViT)
+* Fix XY order with grid_indexing='xy', impacted non-square image use in 'xy' mode (only ROPE-ViT and PE impacted).
+
+## July 7, 2025
+* MobileNet-v5 backbone tweaks for improved Google Gemma 3n behaviour (to pair with updated official weights)
+  * Add stem bias (zero'd in updated weights, compat break with old weights)
+  * GELU -> GELU (tanh approx). A minor change to be closer to JAX
+* Add two arguments to layer-decay support, a min scale clamp and 'no optimization' scale threshold
+* Add 'Fp32' LayerNorm, RMSNorm, SimpleNorm variants that can be enabled to force computation of norm in float32
+* Some typing, argument cleanup for norm, norm+act layers done with above
+* Support Naver ROPE-ViT (https://github.com/naver-ai/rope-vit) in `eva.py`, add RotaryEmbeddingMixed module for mixed mode, weights on HuggingFace Hub
+
+|model                                             |img_size|top1  |top5  |param_count|
+|--------------------------------------------------|--------|------|------|-----------|
+|vit_large_patch16_rope_mixed_ape_224.naver_in1k  |224     |84.84 |97.122|304.4      |
+|vit_large_patch16_rope_mixed_224.naver_in1k      |224     |84.828|97.116|304.2      |
+|vit_large_patch16_rope_ape_224.naver_in1k        |224     |84.65 |97.154|304.37     |
+|vit_large_patch16_rope_224.naver_in1k            |224     |84.648|97.122|304.17     |
+|vit_base_patch16_rope_mixed_ape_224.naver_in1k   |224     |83.894|96.754|86.59      |
+|vit_base_patch16_rope_mixed_224.naver_in1k       |224     |83.804|96.712|86.44      |
+|vit_base_patch16_rope_ape_224.naver_in1k         |224     |83.782|96.61 |86.59      |
+|vit_base_patch16_rope_224.naver_in1k             |224     |83.718|96.672|86.43      |
+|vit_small_patch16_rope_224.naver_in1k            |224     |81.23 |95.022|21.98      |
+|vit_small_patch16_rope_mixed_224.naver_in1k      |224     |81.216|95.022|21.99      |
+|vit_small_patch16_rope_ape_224.naver_in1k        |224     |81.004|95.016|22.06      |
+|vit_small_patch16_rope_mixed_ape_224.naver_in1k  |224     |80.986|94.976|22.06      |
+* Some cleanup of ROPE modules, helpers, and FX tracing leaf registration
+* Preparing version 1.0.17 release
+
+## June 26, 2025
+* MobileNetV5 backbone (w/ encoder only variant) for [Gemma 3n](https://ai.google.dev/gemma/docs/gemma-3n#parameters) image encoder
+* Version 1.0.16 released
+
+## June 23, 2025
+* Add F.grid_sample based 2D and factorized pos embed resize to NaFlexViT. Faster when lots of different sizes (based on example by https://github.com/stas-sl).
+* Further speed up patch embed resample by replacing vmap with matmul (based on snippet by https://github.com/stas-sl).
+* Add 3 initial native aspect NaFlexViT checkpoints created while testing, ImageNet-1k and 3 different pos embed configs w/ same hparams.
+
+ | Model | Top-1 Acc | Top-5 Acc | Params (M) | Eval Seq Len |
+ |:---|:---:|:---:|:---:|:---:|
+ | [naflexvit_base_patch16_par_gap.e300_s576_in1k](https://hf.co/timm/naflexvit_base_patch16_par_gap.e300_s576_in1k) | 83.67 | 96.45 | 86.63 | 576 |
+ | [naflexvit_base_patch16_parfac_gap.e300_s576_in1k](https://hf.co/timm/naflexvit_base_patch16_parfac_gap.e300_s576_in1k) | 83.63 | 96.41 | 86.46 | 576 |
+ | [naflexvit_base_patch16_gap.e300_s576_in1k](https://hf.co/timm/naflexvit_base_patch16_gap.e300_s576_in1k) | 83.50 | 96.46 | 86.63 | 576 |
+* Support gradient checkpointing for `forward_intermediates` and fix some checkpointing bugs. Thanks https://github.com/brianhou0208
+* Add 'corrected weight decay' (https://arxiv.org/abs/2506.02285) as option to AdamW (legacy), Adopt, Kron, Adafactor (BV), Lamb, LaProp, Lion, NadamW, RmsPropTF, SGDW optimizers
+* Switch PE (perception encoder) ViT models to use native timm weights instead of remapping on the fly
+* Fix cuda stream bug in prefetch loader
+  
+## June 5, 2025
+* Initial NaFlexVit model code. NaFlexVit is a Vision Transformer with:
+  1. Encapsulated embedding and position encoding in a single module
+  2. Support for nn.Linear patch embedding on pre-patchified (dictionary) inputs
+  3. Support for NaFlex variable aspect, variable resolution (SigLip-2: https://arxiv.org/abs/2502.14786)
+  4. Support for FlexiViT variable patch size (https://arxiv.org/abs/2212.08013)
+  5. Support for NaViT fractional/factorized position embedding (https://arxiv.org/abs/2307.06304)
+* Existing vit models in `vision_transformer.py` can be loaded into the NaFlexVit model by adding the `use_naflex=True` flag to `create_model`
+  * Some native weights coming soon
+* A full NaFlex data pipeline is available that allows training / fine-tuning / evaluating with variable aspect / size images
+  * To enable in `train.py` and `validate.py` add the `--naflex-loader` arg, must be used with a NaFlexVit
+* To evaluate an existing (classic) ViT loaded in NaFlexVit model w/ NaFlex data pipe:
+  * `python validate.py /imagenet --amp -j 8 --model vit_base_patch16_224 --model-kwargs use_naflex=True --naflex-loader --naflex-max-seq-len 256` 
+* The training has some extra args features worth noting
+  * The `--naflex-train-seq-lens'` argument specifies which sequence lengths to randomly pick from per batch during training
+  * The `--naflex-max-seq-len` argument sets the target sequence length for validation
+  * Adding `--model-kwargs enable_patch_interpolator=True --naflex-patch-sizes 12 16 24` will enable random patch size selection per-batch w/ interpolation
+  * The `--naflex-loss-scale` arg changes loss scaling mode per batch relative to the batch size, `timm` NaFlex loading changes the batch size for each seq len
+
+## May 28, 2025
+* Add a number of small/fast models thanks to https://github.com/brianhou0208
+  * SwiftFormer - [(ICCV2023) SwiftFormer: Efficient Additive Attention for Transformer-based Real-time Mobile Vision Applications](https://github.com/Amshaker/SwiftFormer) 
+  * FasterNet - [(CVPR2023) Run, Don’t Walk: Chasing Higher FLOPS for Faster Neural Networks](https://github.com/JierunChen/FasterNet)
+  * SHViT - [(CVPR2024) SHViT: Single-Head Vision Transformer with Memory Efficient](https://github.com/ysj9909/SHViT)
+  * StarNet - [(CVPR2024) Rewrite the Stars](https://github.com/ma-xu/Rewrite-the-Stars)
+  * GhostNet-V3 [GhostNetV3: Exploring the Training Strategies for Compact Models](https://github.com/huawei-noah/Efficient-AI-Backbones/tree/master/ghostnetv3_pytorch)
+* Update EVA ViT (closest match) to support Perception Encoder models (https://arxiv.org/abs/2504.13181) from Meta, loading Hub weights but I still need to push dedicated `timm` weights
+  * Add some flexibility to ROPE impl
+* Big increase in number of models supporting `forward_intermediates()` and some additional fixes thanks to https://github.com/brianhou0208
+  * DaViT, EdgeNeXt, EfficientFormerV2, EfficientViT(MIT), EfficientViT(MSRA), FocalNet, GCViT, HGNet /V2, InceptionNeXt, Inception-V4, MambaOut, MetaFormer, NesT, Next-ViT, PiT, PVT V2, RepGhostNet, RepViT, ResNetV2, ReXNet, TinyViT, TResNet, VoV
+* TNT model updated w/ new weights `forward_intermediates()` thanks to https://github.com/brianhou0208
+* Add `local-dir:` pretrained schema, can use `local-dir:/path/to/model/folder` for model name to source model / pretrained cfg & weights Hugging Face Hub models (config.json + weights file) from a local folder.
+* Fixes, improvements for onnx export
+    
+## Feb 21, 2025
+* SigLIP 2 ViT image encoders added (https://huggingface.co/collections/timm/siglip-2-67b8e72ba08b09dd97aecaf9)
+  * Variable resolution / aspect NaFlex versions are a WIP
+* Add 'SO150M2' ViT weights trained with SBB recipes, great results, better for ImageNet than previous attempt w/ less training.
+  * `vit_so150m2_patch16_reg1_gap_448.sbb_e200_in12k_ft_in1k` - 88.1% top-1
+  * `vit_so150m2_patch16_reg1_gap_384.sbb_e200_in12k_ft_in1k` - 87.9% top-1
+  * `vit_so150m2_patch16_reg1_gap_256.sbb_e200_in12k_ft_in1k` - 87.3% top-1
+  * `vit_so150m2_patch16_reg4_gap_256.sbb_e200_in12k`
+* Updated InternViT-300M '2.5' weights
+* Release 1.0.15
+
+## Feb 1, 2025
+* FYI PyTorch 2.6 & Python 3.13 are tested and working w/ current main and released version of `timm`
+
+## Jan 27, 2025
+* Add Kron Optimizer (PSGD w/ Kronecker-factored preconditioner) 
+  * Code from https://github.com/evanatyourservice/kron_torch
+  * See also https://sites.google.com/site/lixilinx/home/psgd
+
+## Jan 19, 2025
+* Fix loading of LeViT safetensor weights, remove conversion code which should have been deactivated
+* Add 'SO150M' ViT weights trained with SBB recipes, decent results, but not optimal shape for ImageNet-12k/1k pretrain/ft
+  * `vit_so150m_patch16_reg4_gap_256.sbb_e250_in12k_ft_in1k` - 86.7% top-1
+  * `vit_so150m_patch16_reg4_gap_384.sbb_e250_in12k_ft_in1k` - 87.4% top-1
+  * `vit_so150m_patch16_reg4_gap_256.sbb_e250_in12k`
+* Misc typing, typo, etc. cleanup
+* 1.0.14 release to get above LeViT fix out
+
+## Jan 9, 2025
+* Add support to train and validate in pure `bfloat16` or `float16`
+* `wandb` project name arg added by https://github.com/caojiaolong, use arg.experiment for name
+* Fix old issue w/ checkpoint saving not working on filesystem w/o hard-link support (e.g. FUSE fs mounts)
+* 1.0.13 release
+
+## Jan 6, 2025
+* Add `torch.utils.checkpoint.checkpoint()` wrapper in `timm.models` that defaults `use_reentrant=False`, unless `TIMM_REENTRANT_CKPT=1` is set in env.
+
+## Dec 31, 2024
+* `convnext_nano` 384x384 ImageNet-12k pretrain & fine-tune. https://huggingface.co/models?search=convnext_nano%20r384
+* Add AIM-v2 encoders from https://github.com/apple/ml-aim, see on Hub: https://huggingface.co/models?search=timm%20aimv2
+* Add PaliGemma2 encoders from https://github.com/google-research/big_vision to existing PaliGemma, see on Hub: https://huggingface.co/models?search=timm%20pali2
+* Add missing L/14 DFN2B 39B CLIP ViT, `vit_large_patch14_clip_224.dfn2b_s39b`
+* Fix existing `RmsNorm` layer & fn to match standard formulation, use PT 2.5 impl when possible. Move old impl to `SimpleNorm` layer, it's LN w/o centering or bias. There were only two `timm` models using it, and they have been updated.
+* Allow override of `cache_dir` arg for model creation
+* Pass through `trust_remote_code` for HF datasets wrapper
+* `inception_next_atto` model added by creator
+* Adan optimizer caution, and Lamb decoupled weight decay options
+* Some feature_info metadata fixed by https://github.com/brianhou0208
+* All OpenCLIP and JAX (CLIP, SigLIP, Pali, etc) model weights that used load time remapping were given their own HF Hub instances so that they work with `hf-hub:` based loading, and thus will work with new Transformers `TimmWrapperModel`
+
+## Introduction
+
+Py**T**orch **Im**age **M**odels (`timm`) is a collection of image models, layers, utilities, optimizers, schedulers, data-loaders / augmentations, and reference training / validation scripts that aim to pull together a wide variety of SOTA models with ability to reproduce ImageNet training results.
+
+The work of many others is present here. I've tried to make sure all source material is acknowledged via links to github, arxiv papers, etc in the README, documentation, and code docstrings. Please let me know if I missed anything.
+
+## Features
+
+### Models
+
+All model architecture families include variants with pretrained weights. There are specific model variants without any weights, it is NOT a bug. Help training new or better weights is always appreciated.
+
+* Aggregating Nested Transformers - https://arxiv.org/abs/2105.12723
+* BEiT - https://arxiv.org/abs/2106.08254
+* BEiT-V2 - https://arxiv.org/abs/2208.06366
+* BEiT3 - https://arxiv.org/abs/2208.10442
+* Big Transfer ResNetV2 (BiT) - https://arxiv.org/abs/1912.11370
+* Bottleneck Transformers - https://arxiv.org/abs/2101.11605
+* CaiT (Class-Attention in Image Transformers) - https://arxiv.org/abs/2103.17239
+* CoaT (Co-Scale Conv-Attentional Image Transformers) - https://arxiv.org/abs/2104.06399
+* CoAtNet (Convolution and Attention) - https://arxiv.org/abs/2106.04803
+* ConvNeXt - https://arxiv.org/abs/2201.03545
+* ConvNeXt-V2 - http://arxiv.org/abs/2301.00808
+* ConViT (Soft Convolutional Inductive Biases Vision Transformers)- https://arxiv.org/abs/2103.10697
+* CspNet (Cross-Stage Partial Networks) - https://arxiv.org/abs/1911.11929
+* DeiT - https://arxiv.org/abs/2012.12877
+* DeiT-III - https://arxiv.org/pdf/2204.07118.pdf
+* DenseNet - https://arxiv.org/abs/1608.06993
+* DLA - https://arxiv.org/abs/1707.06484
+* DPN (Dual-Path Network) - https://arxiv.org/abs/1707.01629
+* EdgeNeXt - https://arxiv.org/abs/2206.10589
+* EfficientFormer - https://arxiv.org/abs/2206.01191
+* EfficientFormer-V2 - https://arxiv.org/abs/2212.08059
+* EfficientNet (MBConvNet Family)
+    * EfficientNet NoisyStudent (B0-B7, L2) - https://arxiv.org/abs/1911.04252
+    * EfficientNet AdvProp (B0-B8) - https://arxiv.org/abs/1911.09665
+    * EfficientNet (B0-B7) - https://arxiv.org/abs/1905.11946
+    * EfficientNet-EdgeTPU (S, M, L) - https://ai.googleblog.com/2019/08/efficientnet-edgetpu-creating.html
+    * EfficientNet V2 - https://arxiv.org/abs/2104.00298
+    * FBNet-C - https://arxiv.org/abs/1812.03443
+    * MixNet - https://arxiv.org/abs/1907.09595
+    * MNASNet B1, A1 (Squeeze-Excite), and Small - https://arxiv.org/abs/1807.11626
+    * MobileNet-V2 - https://arxiv.org/abs/1801.04381
+    * Single-Path NAS - https://arxiv.org/abs/1904.02877
+    * TinyNet - https://arxiv.org/abs/2010.14819
+* EfficientViT (MIT) - https://arxiv.org/abs/2205.14756
+* EfficientViT (MSRA) - https://arxiv.org/abs/2305.07027
+* EVA - https://arxiv.org/abs/2211.07636
+* EVA-02 - https://arxiv.org/abs/2303.11331
+* FasterNet - https://arxiv.org/abs/2303.03667
+* FastViT - https://arxiv.org/abs/2303.14189
+* FlexiViT - https://arxiv.org/abs/2212.08013
+* FocalNet (Focal Modulation Networks) - https://arxiv.org/abs/2203.11926
+* GCViT (Global Context Vision Transformer) - https://arxiv.org/abs/2206.09959
+* GhostNet - https://arxiv.org/abs/1911.11907
+* GhostNet-V2 - https://arxiv.org/abs/2211.12905
+* GhostNet-V3 - https://arxiv.org/abs/2404.11202
+* gMLP - https://arxiv.org/abs/2105.08050
+* GPU-Efficient Networks - https://arxiv.org/abs/2006.14090
+* Halo Nets - https://arxiv.org/abs/2103.12731
+* HGNet / HGNet-V2 - TBD
+* HRNet - https://arxiv.org/abs/1908.07919
+* InceptionNeXt - https://arxiv.org/abs/2303.16900
+* Inception-V3 - https://arxiv.org/abs/1512.00567
+* Inception-ResNet-V2 and Inception-V4 - https://arxiv.org/abs/1602.07261
+* Lambda Networks - https://arxiv.org/abs/2102.08602
+* LeViT (Vision Transformer in ConvNet's Clothing) - https://arxiv.org/abs/2104.01136
+* MambaOut - https://arxiv.org/abs/2405.07992
+* MaxViT (Multi-Axis Vision Transformer) - https://arxiv.org/abs/2204.01697
+* MetaFormer (PoolFormer-v2, ConvFormer, CAFormer) - https://arxiv.org/abs/2210.13452
+* MLP-Mixer - https://arxiv.org/abs/2105.01601
+* MobileCLIP - https://arxiv.org/abs/2311.17049
+* MobileNet-V3 (MBConvNet w/ Efficient Head) - https://arxiv.org/abs/1905.02244
+  * FBNet-V3 - https://arxiv.org/abs/2006.02049
+  * HardCoRe-NAS - https://arxiv.org/abs/2102.11646
+  * LCNet - https://arxiv.org/abs/2109.15099
+* MobileNetV4 - https://arxiv.org/abs/2404.10518
+* MobileOne - https://arxiv.org/abs/2206.04040
+* MobileViT - https://arxiv.org/abs/2110.02178
+* MobileViT-V2 - https://arxiv.org/abs/2206.02680
+* MViT-V2 (Improved Multiscale Vision Transformer) - https://arxiv.org/abs/2112.01526
+* NASNet-A - https://arxiv.org/abs/1707.07012
+* NesT - https://arxiv.org/abs/2105.12723
+* Next-ViT - https://arxiv.org/abs/2207.05501
+* NFNet-F - https://arxiv.org/abs/2102.06171
+* NF-RegNet / NF-ResNet - https://arxiv.org/abs/2101.08692
+* PE (Perception Encoder) - https://arxiv.org/abs/2504.13181
+* PNasNet - https://arxiv.org/abs/1712.00559
+* PoolFormer (MetaFormer) - https://arxiv.org/abs/2111.11418
+* Pooling-based Vision Transformer (PiT) - https://arxiv.org/abs/2103.16302
+* PVT-V2 (Improved Pyramid Vision Transformer) - https://arxiv.org/abs/2106.13797
+* RDNet (DenseNets Reloaded) - https://arxiv.org/abs/2403.19588
+* RegNet - https://arxiv.org/abs/2003.13678
+* RegNetZ - https://arxiv.org/abs/2103.06877
+* RepVGG - https://arxiv.org/abs/2101.03697
+* RepGhostNet - https://arxiv.org/abs/2211.06088
+* RepViT - https://arxiv.org/abs/2307.09283
+* ResMLP - https://arxiv.org/abs/2105.03404
+* ResNet/ResNeXt
+    * ResNet (v1b/v1.5) - https://arxiv.org/abs/1512.03385
+    * ResNeXt - https://arxiv.org/abs/1611.05431
+    * 'Bag of Tricks' / Gluon C, D, E, S variations - https://arxiv.org/abs/1812.01187
+    * Weakly-supervised (WSL) Instagram pretrained / ImageNet tuned ResNeXt101 - https://arxiv.org/abs/1805.00932
+    * Semi-supervised (SSL) / Semi-weakly Supervised (SWSL) ResNet/ResNeXts - https://arxiv.org/abs/1905.00546
+    * ECA-Net (ECAResNet) - https://arxiv.org/abs/1910.03151v4
+    * Squeeze-and-Excitation Networks (SEResNet) - https://arxiv.org/abs/1709.01507
+    * ResNet-RS - https://arxiv.org/abs/2103.07579
+* Res2Net - https://arxiv.org/abs/1904.01169
+* ResNeSt - https://arxiv.org/abs/2004.08955
+* ReXNet - https://arxiv.org/abs/2007.00992
+* ROPE-ViT - https://arxiv.org/abs/2403.13298
+* SelecSLS - https://arxiv.org/abs/1907.00837
+* Selective Kernel Networks - https://arxiv.org/abs/1903.06586
+* Sequencer2D - https://arxiv.org/abs/2205.01972
+* SHViT - https://arxiv.org/abs/2401.16456
+* SigLIP (image encoder) - https://arxiv.org/abs/2303.15343
+* SigLIP 2 (image encoder) - https://arxiv.org/abs/2502.14786
+* StarNet - https://arxiv.org/abs/2403.19967
+* SwiftFormer - https://arxiv.org/pdf/2303.15446
+* Swin S3 (AutoFormerV2) - https://arxiv.org/abs/2111.14725
+* Swin Transformer - https://arxiv.org/abs/2103.14030
+* Swin Transformer V2 - https://arxiv.org/abs/2111.09883
+* TinyViT - https://arxiv.org/abs/2207.10666
+* Transformer-iN-Transformer (TNT) - https://arxiv.org/abs/2103.00112
+* TResNet - https://arxiv.org/abs/2003.13630
+* Twins (Spatial Attention in Vision Transformers) - https://arxiv.org/pdf/2104.13840.pdf
+* VGG - https://arxiv.org/abs/1409.1556
+* Visformer - https://arxiv.org/abs/2104.12533
+* Vision Transformer - https://arxiv.org/abs/2010.11929
+* ViTamin - https://arxiv.org/abs/2404.02132
+* VOLO (Vision Outlooker) - https://arxiv.org/abs/2106.13112
+* VovNet V2 and V1 - https://arxiv.org/abs/1911.06667
+* Xception - https://arxiv.org/abs/1610.02357
+* Xception (Modified Aligned, Gluon) - https://arxiv.org/abs/1802.02611
+* Xception (Modified Aligned, TF) - https://arxiv.org/abs/1802.02611
+* XCiT (Cross-Covariance Image Transformers) - https://arxiv.org/abs/2106.09681
+
+### Optimizers
+To see full list of optimizers w/ descriptions: `timm.optim.list_optimizers(with_description=True)`
+
+Included optimizers available via `timm.optim.create_optimizer_v2` factory method:
+* `adabelief` an implementation of AdaBelief adapted from https://github.com/juntang-zhuang/Adabelief-Optimizer - https://arxiv.org/abs/2010.07468
+* `adafactor` adapted from [FAIRSeq impl](https://github.com/pytorch/fairseq/blob/master/fairseq/optim/adafactor.py) - https://arxiv.org/abs/1804.04235
+* `adafactorbv` adapted from [Big Vision](https://github.com/google-research/big_vision/blob/main/big_vision/optax.py) - https://arxiv.org/abs/2106.04560
+* `adahessian` by [David Samuel](https://github.com/davda54/ada-hessian) - https://arxiv.org/abs/2006.00719
+* `adamp` and `sgdp` by [Naver ClovAI](https://github.com/clovaai) - https://arxiv.org/abs/2006.08217
+* `adamuon` and `nadamuon` as per https://github.com/Chongjie-Si/AdaMuon - https://arxiv.org/abs/2507.11005
+* `adan` an implementation of Adan adapted from https://github.com/sail-sg/Adan - https://arxiv.org/abs/2208.06677
+* `adopt` ADOPT adapted from https://github.com/iShohei220/adopt - https://arxiv.org/abs/2411.02853
+* `kron` PSGD w/ Kronecker-factored preconditioner from https://github.com/evanatyourservice/kron_torch - https://sites.google.com/site/lixilinx/home/psgd
+* `lamb` an implementation of Lamb and LambC (w/ trust-clipping) cleaned up and modified to support use with XLA - https://arxiv.org/abs/1904.00962
+* `laprop` optimizer from https://github.com/Z-T-WANG/LaProp-Optimizer - https://arxiv.org/abs/2002.04839
+* `lars` an implementation of LARS and LARC (w/ trust-clipping) - https://arxiv.org/abs/1708.03888
+* `lion` and implementation of Lion adapted from https://github.com/google/automl/tree/master/lion - https://arxiv.org/abs/2302.06675
+* `lookahead` adapted from impl by [Liam](https://github.com/alphadl/lookahead.pytorch) - https://arxiv.org/abs/1907.08610
+* `madgrad` an implementation of MADGRAD adapted from https://github.com/facebookresearch/madgrad - https://arxiv.org/abs/2101.11075
+* `mars` MARS optimizer from https://github.com/AGI-Arena/MARS - https://arxiv.org/abs/2411.10438
+* `muon` MUON optimizer from https://github.com/KellerJordan/Muon with numerous additions and improved non-transformer behaviour
+* `nadam` an implementation of Adam w/ Nesterov momentum
+* `nadamw` an implementation of AdamW (Adam w/ decoupled weight-decay) w/ Nesterov momentum. A simplified impl based on https://github.com/mlcommons/algorithmic-efficiency
+* `novograd` by [Masashi Kimura](https://github.com/convergence-lab/novograd) - https://arxiv.org/abs/1905.11286
+* `radam` by [Liyuan Liu](https://github.com/LiyuanLucasLiu/RAdam) - https://arxiv.org/abs/1908.03265
+* `rmsprop_tf` adapted from PyTorch RMSProp by myself. Reproduces much improved Tensorflow RMSProp behaviour
+* `sgdw` and implementation of SGD w/ decoupled weight-decay
+* `fused<name>` optimizers by name with [NVIDIA Apex](https://github.com/NVIDIA/apex/tree/master/apex/optimizers) installed
+* `bnb<name>` optimizers by name with [BitsAndBytes](https://github.com/TimDettmers/bitsandbytes) installed
+* `cadamw`, `clion`, and more 'Cautious' optimizers from https://github.com/kyleliang919/C-Optim - https://arxiv.org/abs/2411.16085
+* `adam`, `adamw`, `rmsprop`, `adadelta`, `adagrad`, and `sgd` pass through to `torch.optim` implementations
+* `c` suffix (eg `adamc`, `nadamc` to implement 'corrected weight decay' in https://arxiv.org/abs/2506.02285)
+  
+### Augmentations
+* Random Erasing from [Zhun Zhong](https://github.com/zhunzhong07/Random-Erasing/blob/master/transforms.py) - https://arxiv.org/abs/1708.04896)
+* Mixup - https://arxiv.org/abs/1710.09412
+* CutMix - https://arxiv.org/abs/1905.04899
+* AutoAugment (https://arxiv.org/abs/1805.09501) and RandAugment (https://arxiv.org/abs/1909.13719) ImageNet configurations modeled after impl for EfficientNet training (https://github.com/tensorflow/tpu/blob/master/models/official/efficientnet/autoaugment.py)
+* AugMix w/ JSD loss, JSD w/ clean + augmented mixing support works with AutoAugment and RandAugment as well - https://arxiv.org/abs/1912.02781
+* SplitBachNorm - allows splitting batch norm layers between clean and augmented (auxiliary batch norm) data
+
+### Regularization
+* DropPath aka "Stochastic Depth" - https://arxiv.org/abs/1603.09382
+* DropBlock - https://arxiv.org/abs/1810.12890
+* Blur Pooling - https://arxiv.org/abs/1904.11486
+
+### Other
+
+Several (less common) features that I often utilize in my projects are included. Many of their additions are the reason why I maintain my own set of models, instead of using others' via PIP:
+
+* All models have a common default configuration interface and API for
+    * accessing/changing the classifier - `get_classifier` and `reset_classifier`
+    * doing a forward pass on just the features - `forward_features` (see [documentation](https://huggingface.co/docs/timm/feature_extraction))
+    * these makes it easy to write consistent network wrappers that work with any of the models
+* All models support multi-scale feature map extraction (feature pyramids) via create_model (see [documentation](https://huggingface.co/docs/timm/feature_extraction))
+    * `create_model(name, features_only=True, out_indices=..., output_stride=...)`
+    * `out_indices` creation arg specifies which feature maps to return, these indices are 0 based and generally correspond to the `C(i + 1)` feature level.
+    * `output_stride` creation arg controls output stride of the network by using dilated convolutions. Most networks are stride 32 by default. Not all networks support this.
+    * feature map channel counts, reduction level (stride) can be queried AFTER model creation via the `.feature_info` member
+* All models have a consistent pretrained weight loader that adapts last linear if necessary, and from 3 to 1 channel input if desired
+* High performance [reference training, validation, and inference scripts](https://huggingface.co/docs/timm/training_script) that work in several process/GPU modes:
+    * NVIDIA DDP w/ a single GPU per process, multiple processes with APEX present (AMP mixed-precision optional)
+    * PyTorch DistributedDataParallel w/ multi-gpu, single process (AMP disabled as it crashes when enabled)
+    * PyTorch w/ single GPU single process (AMP optional)
+* A dynamic global pool implementation that allows selecting from average pooling, max pooling, average + max, or concat([average, max]) at model creation. All global pooling is adaptive average by default and compatible with pretrained weights.
+* A 'Test Time Pool' wrapper that can wrap any of the included models and usually provides improved performance doing inference with input images larger than the training size. Idea adapted from original DPN implementation when I ported (https://github.com/cypw/DPNs)
+* Learning rate schedulers
+  * Ideas adopted from
+     * [AllenNLP schedulers](https://github.com/allenai/allennlp/tree/master/allennlp/training/learning_rate_schedulers)
+     * [FAIRseq lr_scheduler](https://github.com/pytorch/fairseq/tree/master/fairseq/optim/lr_scheduler)
+     * SGDR: Stochastic Gradient Descent with Warm Restarts (https://arxiv.org/abs/1608.03983)
+  * Schedulers include `step`, `cosine` w/ restarts, `tanh` w/ restarts, `plateau`
+* Space-to-Depth by [mrT23](https://github.com/mrT23/TResNet/blob/master/src/models/tresnet/layers/space_to_depth.py) (https://arxiv.org/abs/1801.04590)
+* Adaptive Gradient Clipping (https://arxiv.org/abs/2102.06171, https://github.com/deepmind/deepmind-research/tree/master/nfnets)
+* An extensive selection of channel and/or spatial attention modules:
+    * Bottleneck Transformer - https://arxiv.org/abs/2101.11605
+    * CBAM - https://arxiv.org/abs/1807.06521
+    * Effective Squeeze-Excitation (ESE) - https://arxiv.org/abs/1911.06667
+    * Efficient Channel Attention (ECA) - https://arxiv.org/abs/1910.03151
+    * Gather-Excite (GE) - https://arxiv.org/abs/1810.12348
+    * Global Context (GC) - https://arxiv.org/abs/1904.11492
+    * Halo - https://arxiv.org/abs/2103.12731
+    * Involution - https://arxiv.org/abs/2103.06255
+    * Lambda Layer - https://arxiv.org/abs/2102.08602
+    * Non-Local (NL) -  https://arxiv.org/abs/1711.07971
+    * Squeeze-and-Excitation (SE) - https://arxiv.org/abs/1709.01507
+    * Selective Kernel (SK) - (https://arxiv.org/abs/1903.06586
+    * Split (SPLAT) - https://arxiv.org/abs/2004.08955
+    * Shifted Window (SWIN) - https://arxiv.org/abs/2103.14030
+
+## Results
+
+Model validation results can be found in the [results tables](results/README.md)
+
+## Getting Started (Documentation)
+
+The official documentation can be found at https://huggingface.co/docs/hub/timm. Documentation contributions are welcome.
+
+[Getting Started with PyTorch Image Models (timm): A Practitioner’s Guide](https://towardsdatascience.com/getting-started-with-pytorch-image-models-timm-a-practitioners-guide-4e77b4bf9055-2/) by [Chris Hughes](https://github.com/Chris-hughes10) is an extensive blog post covering many aspects of `timm` in detail.
+
+[timmdocs](http://timm.fast.ai/) is an alternate set of documentation for `timm`. A big thanks to [Aman Arora](https://github.com/amaarora) for his efforts creating timmdocs.
+
+[paperswithcode](https://paperswithcode.com/lib/timm) is a good resource for browsing the models within `timm`.
+
+## Train, Validation, Inference Scripts
+
+The root folder of the repository contains reference train, validation, and inference scripts that work with the included models and other features of this repository. They are adaptable for other datasets and use cases with a little hacking. See [documentation](https://huggingface.co/docs/timm/training_script).
+
+## Awesome PyTorch Resources
+
+One of the greatest assets of PyTorch is the community and their contributions. A few of my favourite resources that pair well with the models and components here are listed below.
+
+### Object Detection, Instance and Semantic Segmentation
+* Detectron2 - https://github.com/facebookresearch/detectron2
+* Segmentation Models (Semantic) - https://github.com/qubvel/segmentation_models.pytorch
+* EfficientDet (Obj Det, Semantic soon) - https://github.com/rwightman/efficientdet-pytorch
+
+### Computer Vision / Image Augmentation
+* Albumentations - https://github.com/albumentations-team/albumentations
+* Kornia - https://github.com/kornia/kornia
+
+### Knowledge Distillation
+* RepDistiller - https://github.com/HobbitLong/RepDistiller
+* torchdistill - https://github.com/yoshitomo-matsubara/torchdistill
+
+### Metric Learning
+* PyTorch Metric Learning - https://github.com/KevinMusgrave/pytorch-metric-learning
+
+### Training / Frameworks
+* fastai - https://github.com/fastai/fastai
+* lightly_train - https://github.com/lightly-ai/lightly-train
+
+### Deployment
+* timmx (Export timm models to ONNX, CoreML, LiteRT, TensorRT, and more) - https://github.com/Boulaouaney/timmx
+
+## Licenses
+
+### Code
+The code here is licensed Apache 2.0. I've taken care to make sure any third party code included or adapted has compatible (permissive) licenses such as MIT, BSD, etc. I've made an effort to avoid any GPL / LGPL conflicts. That said, it is your responsibility to ensure you comply with licenses here and conditions of any dependent licenses. Where applicable, I've linked the sources/references for various components in docstrings. If you think I've missed anything please create an issue.
+
+### Pretrained Weights
+So far all of the pretrained weights available here are pretrained on ImageNet with a select few that have some additional pretraining (see extra note below). ImageNet was released for non-commercial research purposes only (https://image-net.org/download). It's not clear what the implications of that are for the use of pretrained weights from that dataset. Any models I have trained with ImageNet are done for research purposes and one should assume that the original dataset license applies to the weights. It's best to seek legal advice if you intend to use the pretrained weights in a commercial product.
+
+#### Pretrained on more than ImageNet
+Several weights included or references here were pretrained with proprietary datasets that I do not have access to. These include the Facebook WSL, SSL, SWSL ResNe(Xt) and the Google Noisy Student EfficientNet models. The Facebook models have an explicit non-commercial license (CC-BY-NC 4.0, https://github.com/facebookresearch/semi-supervised-ImageNet1K-models, https://github.com/facebookresearch/WSL-Images). The Google models do not appear to have any restriction beyond the Apache 2.0 license (and ImageNet concerns). In either case, you should contact Facebook or Google with any questions.
+
+## Citing
+
+### BibTeX
+
+```bibtex
+@misc{rw2019timm,
+  author = {Ross Wightman},
+  title = {PyTorch Image Models},
+  year = {2019},
+  publisher = {GitHub},
+  journal = {GitHub repository},
+  doi = {10.5281/zenodo.4414861},
+  howpublished = {\url{https://github.com/rwightman/pytorch-image-models}}
+}
+```
+
+### Latest DOI
+
+[![DOI](https://zenodo.org/badge/168799526.svg)](https://zenodo.org/badge/latestdoi/168799526)

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+Metadata-Version: 2.1
+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: optuna
+Version: 4.8.0
+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
+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 :: 3 :: Only
+Classifier: Topic :: Scientific/Engineering
+Classifier: Topic :: Scientific/Engineering :: Mathematics
+Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
+Classifier: Topic :: Software Development
+Classifier: Topic :: Software Development :: Libraries
+Classifier: Topic :: Software Development :: Libraries :: Python Modules
+Requires-Python: >=3.9
+Description-Content-Type: text/markdown
+License-File: LICENSE
+License-File: LICENSE_THIRD_PARTY
+Requires-Dist: alembic>=1.5.0
+Requires-Dist: colorlog
+Requires-Dist: numpy
+Requires-Dist: packaging>=20.0
+Requires-Dist: sqlalchemy>=1.4.2
+Requires-Dist: tqdm
+Requires-Dist: PyYAML
+Provides-Extra: checking
+Requires-Dist: mypy; extra == "checking"
+Requires-Dist: mypy_boto3_s3; extra == "checking"
+Requires-Dist: ruff; extra == "checking"
+Requires-Dist: scipy-stubs; python_version >= "3.10" and extra == "checking"
+Requires-Dist: types-PyYAML; extra == "checking"
+Requires-Dist: types-redis; extra == "checking"
+Requires-Dist: types-setuptools; extra == "checking"
+Requires-Dist: types-tqdm; extra == "checking"
+Requires-Dist: typing_extensions>=3.10.0.0; extra == "checking"
+Provides-Extra: document
+Requires-Dist: ase; extra == "document"
+Requires-Dist: cmaes>=0.12.0; extra == "document"
+Requires-Dist: fvcore; extra == "document"
+Requires-Dist: kaleido<0.4; extra == "document"
+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"
+Provides-Extra: test
+Requires-Dist: fakeredis[lua]; extra == "test"
+Requires-Dist: kaleido<0.4; extra == "test"
+Requires-Dist: moto; extra == "test"
+Requires-Dist: pytest; extra == "test"
+Requires-Dist: pytest-xdist; extra == "test"
+Requires-Dist: scipy>=1.9.2; extra == "test"
+Requires-Dist: torch; extra == "test"
+Requires-Dist: greenlet; extra == "test"
+Requires-Dist: grpcio; extra == "test"
+Requires-Dist: protobuf>=5.28.1; extra == "test"
+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
+Help us create the next version of Optuna!
+
+Optuna 5.0 Roadmap published for review. Please take a look at [the planned improvements to Optuna](https://medium.com/optuna/optuna-v5-roadmap-ac7d6935a878), and share your feedback in [the github issues](https://github.com/optuna/optuna/labels/v5). PR contributions also welcome!
+
+Please take a few minutes to fill in [this survey](https://forms.gle/wVwLCQ9g6st6AXuq9), and let us know how you use Optuna now and what improvements you'd like.🤔
+All questions are optional. 🙇‍♂️
+
+<!-- TODO: when you add a new line, please delete the oldest line -->
+* **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).
+* **May 26, 2025**: Optuna 5.0 roadmap has been published! See [the blog](https://medium.com/optuna/optuna-v5-roadmap-ac7d6935a878) for more details.
+
+## :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.
+>
+> Also, we provide Optuna docker images on [DockerHub](https://hub.docker.com/r/optuna/optuna).
+
+## 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)
+* [MLflow](https://github.com/optuna/optuna-examples/tree/main/mlflow/keras_mlflow.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)
+* [TensorBoard](https://github.com/optuna/optuna-examples/tree/main/tensorboard/tensorboard_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)
+* [Weights & Biases](https://github.com/optuna/optuna-examples/tree/main/wandb/wandb_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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+Metadata-Version: 2.4
+Name: alembic
+Version: 1.18.4
+Summary: A database migration tool for SQLAlchemy.
+Author-email: Mike Bayer <mike_mp@zzzcomputing.com>
+License-Expression: MIT
+Project-URL: Homepage, https://alembic.sqlalchemy.org
+Project-URL: Documentation, https://alembic.sqlalchemy.org/en/latest/
+Project-URL: Changelog, https://alembic.sqlalchemy.org/en/latest/changelog.html
+Project-URL: Source, https://github.com/sqlalchemy/alembic/
+Project-URL: Issue Tracker, https://github.com/sqlalchemy/alembic/issues/
+Classifier: Development Status :: 5 - Production/Stable
+Classifier: Intended Audience :: Developers
+Classifier: Environment :: Console
+Classifier: Operating System :: OS Independent
+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
+Classifier: Programming Language :: Python :: 3.13
+Classifier: Programming Language :: Python :: Implementation :: CPython
+Classifier: Programming Language :: Python :: Implementation :: PyPy
+Classifier: Topic :: Database :: Front-Ends
+Requires-Python: >=3.10
+Description-Content-Type: text/x-rst
+License-File: LICENSE
+Requires-Dist: SQLAlchemy>=1.4.23
+Requires-Dist: Mako
+Requires-Dist: typing-extensions>=4.12
+Requires-Dist: tomli; python_version < "3.11"
+Provides-Extra: tz
+Requires-Dist: tzdata; extra == "tz"
+Dynamic: license-file
+
+Alembic is a database migrations tool written by the author
+of `SQLAlchemy <http://www.sqlalchemy.org>`_.  A migrations tool
+offers the following functionality:
+
+* Can emit ALTER statements to a database in order to change
+  the structure of tables and other constructs
+* Provides a system whereby "migration scripts" may be constructed;
+  each script indicates a particular series of steps that can "upgrade" a
+  target database to a new version, and optionally a series of steps that can
+  "downgrade" similarly, doing the same steps in reverse.
+* Allows the scripts to execute in some sequential manner.
+
+The goals of Alembic are:
+
+* Very open ended and transparent configuration and operation.   A new
+  Alembic environment is generated from a set of templates which is selected
+  among a set of options when setup first occurs. The templates then deposit a
+  series of scripts that define fully how database connectivity is established
+  and how migration scripts are invoked; the migration scripts themselves are
+  generated from a template within that series of scripts. The scripts can
+  then be further customized to define exactly how databases will be
+  interacted with and what structure new migration files should take.
+* Full support for transactional DDL.   The default scripts ensure that all
+  migrations occur within a transaction - for those databases which support
+  this (Postgresql, Microsoft SQL Server), migrations can be tested with no
+  need to manually undo changes upon failure.
+* Minimalist script construction.  Basic operations like renaming
+  tables/columns, adding/removing columns, changing column attributes can be
+  performed through one line commands like alter_column(), rename_table(),
+  add_constraint(). There is no need to recreate full SQLAlchemy Table
+  structures for simple operations like these - the functions themselves
+  generate minimalist schema structures behind the scenes to achieve the given
+  DDL sequence.
+* "auto generation" of migrations. While real world migrations are far more
+  complex than what can be automatically determined, Alembic can still
+  eliminate the initial grunt work in generating new migration directives
+  from an altered schema.  The ``--autogenerate`` feature will inspect the
+  current status of a database using SQLAlchemy's schema inspection
+  capabilities, compare it to the current state of the database model as
+  specified in Python, and generate a series of "candidate" migrations,
+  rendering them into a new migration script as Python directives. The
+  developer then edits the new file, adding additional directives and data
+  migrations as needed, to produce a finished migration. Table and column
+  level changes can be detected, with constraints and indexes to follow as
+  well.
+* Full support for migrations generated as SQL scripts.   Those of us who
+  work in corporate environments know that direct access to DDL commands on a
+  production database is a rare privilege, and DBAs want textual SQL scripts.
+  Alembic's usage model and commands are oriented towards being able to run a
+  series of migrations into a textual output file as easily as it runs them
+  directly to a database. Care must be taken in this mode to not invoke other
+  operations that rely upon in-memory SELECTs of rows - Alembic tries to
+  provide helper constructs like bulk_insert() to help with data-oriented
+  operations that are compatible with script-based DDL.
+* Non-linear, dependency-graph versioning.   Scripts are given UUID
+  identifiers similarly to a DVCS, and the linkage of one script to the next
+  is achieved via human-editable markers within the scripts themselves.
+  The structure of a set of migration files is considered as a
+  directed-acyclic graph, meaning any migration file can be dependent
+  on any other arbitrary set of migration files, or none at
+  all.  Through this open-ended system, migration files can be organized
+  into branches, multiple roots, and mergepoints, without restriction.
+  Commands are provided to produce new branches, roots, and merges of
+  branches automatically.
+* Provide a library of ALTER constructs that can be used by any SQLAlchemy
+  application. The DDL constructs build upon SQLAlchemy's own DDLElement base
+  and can be used standalone by any application or script.
+* At long last, bring SQLite and its inability to ALTER things into the fold,
+  but in such a way that SQLite's very special workflow needs are accommodated
+  in an explicit way that makes the most of a bad situation, through the
+  concept of a "batch" migration, where multiple changes to a table can
+  be batched together to form a series of instructions for a single, subsequent
+  "move-and-copy" workflow.   You can even use "move-and-copy" workflow for
+  other databases, if you want to recreate a table in the background
+  on a busy system.
+
+Documentation and status of Alembic is at https://alembic.sqlalchemy.org/
+
+The SQLAlchemy Project
+======================
+
+Alembic is part of the `SQLAlchemy Project <https://www.sqlalchemy.org>`_ and
+adheres to the same standards and conventions as the core project.
+
+Development / Bug reporting / Pull requests
+___________________________________________
+
+Please refer to the
+`SQLAlchemy Community Guide <https://www.sqlalchemy.org/develop.html>`_ for
+guidelines on coding and participating in this project.
+
+Code of Conduct
+_______________
+
+Above all, SQLAlchemy places great emphasis on polite, thoughtful, and
+constructive communication between users and developers.
+Please see our current Code of Conduct at
+`Code of Conduct <https://www.sqlalchemy.org/codeofconduct.html>`_.
+
+License
+=======
+
+Alembic is distributed under the `MIT license
+<https://opensource.org/licenses/MIT>`_.

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@@ -0,0 +1,87 @@
+Metadata-Version: 2.4
+Name: hf-xet
+Version: 1.4.2
+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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@@ -0,0 +1,51 @@
+Metadata-Version: 2.1
+Name: et_xmlfile
+Version: 2.0.0
+Summary: An implementation of lxml.xmlfile for the standard library
+Home-page: https://foss.heptapod.net/openpyxl/et_xmlfile
+Author: See AUTHORS.txt
+Author-email: charlie.clark@clark-consulting.eu
+License: MIT
+Project-URL: Documentation, https://openpyxl.pages.heptapod.net/et_xmlfile/
+Project-URL: Source, https://foss.heptapod.net/openpyxl/et_xmlfile
+Project-URL: Tracker, https://foss.heptapod.net/openpyxl/et_xmfile/-/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 :: 3.8
+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
+Requires-Python: >=3.8
+License-File: LICENCE.python
+License-File: LICENCE.rst
+License-File: AUTHORS.txt
+
+.. image:: https://foss.heptapod.net/openpyxl/et_xmlfile/badges/branch/default/coverage.svg
+    :target: https://coveralls.io/bitbucket/openpyxl/et_xmlfile?branch=default
+    :alt: coverage status
+
+et_xmfile
+=========
+
+XML can use lots of memory, and et_xmlfile is a low memory library for creating large XML files
+And, although the standard library already includes an incremental parser, `iterparse` it has no equivalent when writing XML. Once an element has been added to the tree, it is written to
+the file or stream and the memory is then cleared.
+
+This module is based upon the `xmlfile module from lxml <http://lxml.de/api.html#incremental-xml-generation>`_ with the aim of allowing code to be developed that will work with both libraries.
+It was developed initially for the openpyxl project, but is now a standalone module.
+
+The code was written by Elias Rabel as part of the `Python Düsseldorf <http://pyddf.de>`_ openpyxl sprint in September 2014.
+
+Proper support for incremental writing was provided by Daniel Hillier in 2024
+
+Note on performance
+-------------------
+
+The code was not developed with performance in mind, but turned out to be faster than the existing SAX-based implementation but is generally slower than lxml's xmlfile.
+There is one area where an optimisation for lxml may negatively affect the performance of et_xmfile and that is when using the `.element()` method on the xmlfile context manager. It is, therefore, recommended simply to create Elements write these directly, as in the sample code.