Add files using upload-large-folder tool

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+Metadata-Version: 2.4
+Name: Mako
+Version: 1.3.12
+Summary: A super-fast templating language that borrows the best ideas from the existing templating languages.
+Home-page: https://www.makotemplates.org/
+Author: Mike Bayer
+Author-email: mike@zzzcomputing.com
+License: MIT
+Project-URL: Documentation, https://docs.makotemplates.org
+Project-URL: Issue Tracker, https://github.com/sqlalchemy/mako
+Classifier: Development Status :: 5 - Production/Stable
+Classifier: License :: OSI Approved :: MIT License
+Classifier: Environment :: Web Environment
+Classifier: Intended Audience :: Developers
+Classifier: Programming Language :: Python
+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 :: Implementation :: CPython
+Classifier: Programming Language :: Python :: Implementation :: PyPy
+Classifier: Topic :: Internet :: WWW/HTTP :: Dynamic Content
+Requires-Python: >=3.8
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+License-File: LICENSE
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+Provides-Extra: babel
+Requires-Dist: Babel; extra == "babel"
+Provides-Extra: lingua
+Requires-Dist: lingua; extra == "lingua"
+Dynamic: license-file
+
+=========================
+Mako Templates for Python
+=========================
+
+Mako is a template library written in Python. It provides a familiar, non-XML 
+syntax which compiles into Python modules for maximum performance. Mako's 
+syntax and API borrows from the best ideas of many others, including Django
+templates, Cheetah, Myghty, and Genshi. Conceptually, Mako is an embedded 
+Python (i.e. Python Server Page) language, which refines the familiar ideas
+of componentized layout and inheritance to produce one of the most 
+straightforward and flexible models available, while also maintaining close 
+ties to Python calling and scoping semantics.
+
+Nutshell
+========
+
+::
+
+    <%inherit file="base.html"/>
+    <%
+        rows = [[v for v in range(0,10)] for row in range(0,10)]
+    %>
+    <table>
+        % for row in rows:
+            ${makerow(row)}
+        % endfor
+    </table>
+
+    <%def name="makerow(row)">
+        <tr>
+        % for name in row:
+            <td>${name}</td>\
+        % endfor
+        </tr>
+    </%def>
+
+Philosophy
+===========
+
+Python is a great scripting language. Don't reinvent the wheel...your templates can handle it !
+
+Documentation
+==============
+
+See documentation for Mako at https://docs.makotemplates.org/en/latest/
+
+License
+========
+
+Mako is licensed under an MIT-style license (see LICENSE).
+Other incorporated projects may be licensed under different licenses.
+All licenses allow for non-commercial and commercial use.

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+Name: huggingface_hub
+Version: 1.26.0
+Summary: Client library to download and publish models, datasets and other repos on the huggingface.co hub
+Home-page: https://github.com/huggingface/huggingface_hub
+Author: Hugging Face, Inc.
+Author-email: julien@huggingface.co
+License: Apache-2.0
+Keywords: model-hub machine-learning models natural-language-processing deep-learning pytorch pretrained-models
+Classifier: Intended Audience :: Developers
+Classifier: Intended Audience :: Education
+Classifier: Intended Audience :: Science/Research
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+Requires-Dist: types-toml; extra == "dev"
+Requires-Dist: types-tqdm; extra == "dev"
+Requires-Dist: types-urllib3; extra == "dev"
+Dynamic: author
+Dynamic: author-email
+Dynamic: classifier
+Dynamic: description
+Dynamic: description-content-type
+Dynamic: home-page
+Dynamic: keywords
+Dynamic: license
+Dynamic: license-file
+Dynamic: provides-extra
+Dynamic: requires-dist
+Dynamic: requires-python
+Dynamic: summary
+
+<p align="center">
+  <picture>
+    <source media="(prefers-color-scheme: dark)" srcset="https://huggingface.co/datasets/huggingface/documentation-images/raw/main/huggingface_hub-dark.svg">
+    <source media="(prefers-color-scheme: light)" srcset="https://huggingface.co/datasets/huggingface/documentation-images/raw/main/huggingface_hub.svg">
+    <img alt="huggingface_hub library logo" src="https://huggingface.co/datasets/huggingface/documentation-images/raw/main/huggingface_hub.svg" width="352" height="59" style="max-width: 100%">
+  </picture>
+  <br/>
+  <br/>
+</p> 
+
+<p align="center">
+    <i>The official CLI and Python client for the Hugging Face Hub.</i>
+    <br/>
+    <a href="#what-is-huggingface_hub">About</a>
+    ·
+    <a href="https://huggingface.co/docs/huggingface_hub">Documentation</a>
+    ·
+    <a href="https://huggingface.co/docs/huggingface_hub/en/installation">Install</a>
+    ·
+    <a href="https://huggingface.co/docs/huggingface_hub/en/guides/cli">CLI Guide</a>
+    ·
+    <a href="https://github.com/huggingface/huggingface_hub/blob/main/CONTRIBUTING.md">Contributing</a>
+</p>
+
+<p align="center">
+    <a href="https://huggingface.co/docs/huggingface_hub/en/index"><img alt="Documentation" src="https://img.shields.io/website/http/huggingface.co/docs/huggingface_hub/index.svg?down_color=red&down_message=offline&up_message=online&label=doc"></a>
+    <a href="https://github.com/huggingface/huggingface_hub/releases"><img alt="GitHub release" src="https://img.shields.io/github/release/huggingface/huggingface_hub.svg"></a>
+    <a href="https://github.com/huggingface/huggingface_hub"><img alt="PyPi version" src="https://img.shields.io/pypi/pyversions/huggingface_hub.svg"></a>
+    <a href="https://pypi.org/project/huggingface-hub"><img alt="PyPI - Downloads" src="https://img.shields.io/pypi/dm/huggingface_hub"></a>
+    <a href="https://codecov.io/gh/huggingface/huggingface_hub"><img alt="Code coverage" src="https://codecov.io/gh/huggingface/huggingface_hub/branch/main/graph/badge.svg?token=RXP95LE2XL"></a>
+</p>
+
+<h4 align="center">
+    <p>
+        <b>English</b> |
+        <a href="https://github.com/huggingface/huggingface_hub/blob/main/i18n/README_de.md">Deutsch</a> |
+        <a href="https://github.com/huggingface/huggingface_hub/blob/main/i18n/README_fr.md">Français</a> |
+        <a href="https://github.com/huggingface/huggingface_hub/blob/main/i18n/README_hi.md">हिंदी</a> |
+        <a href="https://github.com/huggingface/huggingface_hub/blob/main/i18n/README_ko.md">한국어</a> |
+        <a href="https://github.com/huggingface/huggingface_hub/blob/main/i18n/README_cn.md">中文 (简体)</a> |
+        <a href="https://github.com/huggingface/huggingface_hub/blob/main/i18n/README_kn.md">ಕನ್ನಡ</a>
+    </p>
+</h4>
+
+## Quick start
+
+Install the [`hf` CLI](https://huggingface.co/docs/huggingface_hub/en/guides/cli) with the standalone installer:
+
+```bash
+# On macOS and Linux.
+curl -LsSf https://hf.co/cli/install.sh | bash
+```
+
+```powershell
+# On Windows.
+powershell -ExecutionPolicy ByPass -c "irm https://hf.co/cli/install.ps1 | iex"
+```
+
+Log in, then start working with the Hub:
+
+```bash
+# Log in (use --token $HF_TOKEN in non-interactive environments)
+hf auth login
+
+# Find models served by Inference Providers
+hf models ls --warm
+
+# Download a model
+hf download Qwen/Qwen3-0.6B
+
+# Upload files to your own repo
+hf upload username/my-cool-model ./model.safetensors
+
+# Sync a local folder to a storage bucket
+hf buckets sync ./checkpoints hf://buckets/username/my-bucket
+
+# Run a job on Hugging Face infrastructure
+hf jobs run python:3.12 python -c "print('Hello from the cloud!')"
+
+# Discover everything else
+hf --help
+```
+
+The Hub uses tokens to authenticate applications (see [docs](https://huggingface.co/docs/hub/security-tokens)). Check out the [CLI guide](https://huggingface.co/docs/huggingface_hub/en/guides/cli) for a tour of the main features.
+
+## What is `huggingface_hub`?
+
+The `huggingface_hub` library allows you to interact with the [Hugging Face Hub](https://huggingface.co/), a platform democratizing open-source Machine Learning for creators and collaborators. Discover pre-trained models and datasets for your projects, play with the thousands of machine learning apps hosted on the Hub, or create and share your own models, datasets and demos with the community. Everything ships in one package with two interfaces: the [`hf` CLI](https://huggingface.co/docs/huggingface_hub/en/guides/cli) for your terminal and the `huggingface_hub` library for Python — both designed to work well for humans and AI agents. Use them to:
+
+- [Download files](https://huggingface.co/docs/huggingface_hub/en/guides/download) from the Hub.
+- [Upload files](https://huggingface.co/docs/huggingface_hub/en/guides/upload) to the Hub.
+- [Manage your repositories](https://huggingface.co/docs/huggingface_hub/en/guides/repository).
+- [Run Inference](https://huggingface.co/docs/huggingface_hub/en/guides/inference) on deployed models.
+- [Run Jobs](https://huggingface.co/docs/huggingface_hub/en/guides/jobs) on Hugging Face infrastructure.
+- [Search](https://huggingface.co/docs/huggingface_hub/en/guides/search) for models, datasets and Spaces.
+- [Share Model Cards](https://huggingface.co/docs/huggingface_hub/en/guides/model-cards) to document your models.
+- [Engage with the community](https://huggingface.co/docs/huggingface_hub/en/guides/community) through PRs and comments.
+- Do all of the above from the terminal with the [`hf` CLI](https://huggingface.co/docs/huggingface_hub/en/guides/cli).
+
+## Built for humans and AI agents
+
+The `hf` CLI is designed for people and coding agents alike: the same commands adapt their output when run by an agent. If you use Claude Code, Codex, Cursor, or another coding agent, install the `hf` CLI Skill — a command reference generated from your installed CLI:
+
+```bash
+# for Codex, Cursor, OpenCode, Pi and other agents that load skills from `.agents/skills`
+hf skills add
+# includes the above + Claude Code
+hf skills add --claude
+```
+
+Learn more in the [Hugging Face CLI for AI agents guide](https://huggingface.co/docs/hub/agents-cli) and the [announcement blog post](https://huggingface.co/blog/hf-cli-for-agents).
+
+## Use the Python library
+
+Install the `huggingface_hub` package with [pip](https://pypi.org/project/huggingface-hub/) (this also installs the `hf` CLI):
+
+```bash
+pip install huggingface_hub
+```
+
+We recommend using [`uv`](https://docs.astral.sh/uv/) for a fast and reliable install:
+
+```bash
+uv pip install huggingface_hub
+```
+
+In order to keep the package minimal by default, `huggingface_hub` comes with optional dependencies useful for some use cases. For example, if you want to use the MCP module, run:
+
+```bash
+pip install "huggingface_hub[mcp]"
+```
+
+To learn more about installation and optional dependencies, check out the [installation guide](https://huggingface.co/docs/huggingface_hub/en/installation).
+
+### Download files
+
+Download a single file
+
+```py
+from huggingface_hub import hf_hub_download
+
+hf_hub_download(repo_id="zai-org/GLM-5.2", filename="config.json")
+```
+
+Or an entire repository
+
+```py
+from huggingface_hub import snapshot_download
+
+snapshot_download("sentence-transformers/all-MiniLM-L6-v2")
+```
+
+Files will be downloaded in a local cache folder. More details in [this guide](https://huggingface.co/docs/huggingface_hub/en/guides/manage-cache).
+
+### Create a repository
+
+```py
+from huggingface_hub import create_repo
+
+create_repo(repo_id="super-cool-model")
+```
+
+### Upload files
+
+Upload a single file
+
+```py
+from huggingface_hub import upload_file
+
+upload_file(
+    path_or_fileobj="/home/lysandre/dummy-test/README.md",
+    path_in_repo="README.md",
+    repo_id="lysandre/test-model",
+)
+```
+
+Or an entire folder
+
+```py
+from huggingface_hub import upload_folder
+
+upload_folder(
+    folder_path="/path/to/local/space",
+    repo_id="username/my-cool-space",
+    repo_type="space",
+)
+```
+
+More details in the [upload guide](https://huggingface.co/docs/huggingface_hub/en/guides/upload).
+
+## Integrating with the Hub.
+
+We're partnering with cool open source ML libraries to provide free model hosting and versioning. You can find the existing integrations [here](https://huggingface.co/docs/hub/libraries).
+
+The advantages are:
+
+- Free model or dataset hosting for libraries and their users.
+- Built-in file versioning, even with very large files, made possible by [Xet](https://huggingface.co/docs/hub/xet/index), the Hub's chunk-deduplicated storage backend.
+- In-browser widgets to play with the uploaded models.
+- Anyone can upload a new model for your library, they just need to add the corresponding tag for the model to be discoverable.
+- Fast downloads! We use Cloudfront (a CDN) to geo-replicate downloads so they're blazing fast from anywhere on the globe.
+- Usage stats and more features to come.
+
+If you would like to integrate your library, feel free to open an issue to begin the discussion. We wrote a [step-by-step guide](https://huggingface.co/docs/hub/adding-a-library) with ❤️ showing how to do this integration.
+
+## Contributions (feature requests, bugs, etc.) are super welcome 💙💚💛💜🧡❤️
+
+Everyone is welcome to contribute, and we value everybody's contribution. Code is not the only way to help the community.
+Answering questions, helping others, reaching out and improving the documentations are immensely valuable to the community.
+We wrote a [contribution guide](https://github.com/huggingface/huggingface_hub/blob/main/CONTRIBUTING.md) to summarize
+how to get started to contribute to this repository.

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+Name: timm
+Version: 1.0.28
+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
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+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
+
+## July 10, 2026
+* Improve optimizer `torch.compile` and tensor learning-rate support.
+* Extend NaFlexViT patch-layout (for NaFlex-CLAP), and `forward_intermediates` (NaFlex dict input) support.
+* Harden pickle loading and improve custom-label inference.
+* Release 1.0.28
+
+## May 27, 2026
+* Add model defs and pretrained weights for EUPE ViT (DINOv3-style) and ConvNeXt models. See the [Efficient Universal Perception Encoder paper](https://arxiv.org/abs/2603.22387).
+* Add TIPSv2 model defs and pretrained weights for (DINOv2-style) ViTs. See the [TIPSv2 paper](https://arxiv.org/abs/2604.12012).
+
+## May 8, 2026
+* Release 1.0.27
+
+## April 23, 2026
+* Add Gemma4 ViT encoders w/ NaFlex pipeline support (variable aspect/size per image). Thanks [Yonghye Kwon](https://github.com/developer0hye)
+* Support DINOv3 weights in NaFlexVit. Thanks [Yonghye Kwon](https://github.com/developer0hye)
+* Some improvements to Muon fallback (AdamW/NadamW) lr behavior
+
+## 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: 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.

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+Name: alembic
+Version: 1.18.5
+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>`_.

.cache/pip/http-v2/e/d/e/d/1/eded1062df3a835b1d654db11016327d0de7276866caf329f6f27b1c.body

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+Metadata-Version: 2.1
+Name: contourpy
+Version: 1.3.3
+Summary: Python library for calculating contours of 2D quadrilateral grids
+Author-Email: Ian Thomas <ianthomas23@gmail.com>
+License: BSD 3-Clause License
+         
+         Copyright (c) 2021-2025, ContourPy Developers.
+         All rights reserved.
+         
+         Redistribution and use in source and binary forms, with or without
+         modification, are permitted provided that the following conditions are met:
+         
+         1. Redistributions of source code must retain the above copyright notice, this
+            list of conditions and the following disclaimer.
+         
+         2. 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.
+         
+         3. Neither the name of the copyright holder 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.
+         
+Classifier: Development Status :: 5 - Production/Stable
+Classifier: Intended Audience :: Developers
+Classifier: Intended Audience :: Science/Research
+Classifier: License :: OSI Approved :: BSD License
+Classifier: Programming Language :: C++
+Classifier: Programming Language :: Python :: 3
+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: Topic :: Scientific/Engineering :: Information Analysis
+Classifier: Topic :: Scientific/Engineering :: Mathematics
+Classifier: Topic :: Scientific/Engineering :: Visualization
+Project-URL: Homepage, https://github.com/contourpy/contourpy
+Project-URL: Changelog, https://contourpy.readthedocs.io/en/latest/changelog.html
+Project-URL: Documentation, https://contourpy.readthedocs.io
+Project-URL: Repository, https://github.com/contourpy/contourpy
+Requires-Python: >=3.11
+Requires-Dist: numpy>=1.25
+Provides-Extra: docs
+Requires-Dist: furo; extra == "docs"
+Requires-Dist: sphinx>=7.2; extra == "docs"
+Requires-Dist: sphinx-copybutton; extra == "docs"
+Provides-Extra: bokeh
+Requires-Dist: bokeh; extra == "bokeh"
+Requires-Dist: selenium; extra == "bokeh"
+Provides-Extra: mypy
+Requires-Dist: contourpy[bokeh,docs]; extra == "mypy"
+Requires-Dist: bokeh; extra == "mypy"
+Requires-Dist: docutils-stubs; extra == "mypy"
+Requires-Dist: mypy==1.17.0; extra == "mypy"
+Requires-Dist: types-Pillow; extra == "mypy"
+Provides-Extra: test
+Requires-Dist: contourpy[test-no-images]; extra == "test"
+Requires-Dist: matplotlib; extra == "test"
+Requires-Dist: Pillow; extra == "test"
+Provides-Extra: test-no-images
+Requires-Dist: pytest; extra == "test-no-images"
+Requires-Dist: pytest-cov; extra == "test-no-images"
+Requires-Dist: pytest-rerunfailures; extra == "test-no-images"
+Requires-Dist: pytest-xdist; extra == "test-no-images"
+Requires-Dist: wurlitzer; extra == "test-no-images"
+Description-Content-Type: text/markdown
+
+<img alt="ContourPy" src="https://raw.githubusercontent.com/contourpy/contourpy/main/docs/_static/contourpy_logo_horiz.svg" height="90">
+
+ContourPy is a Python library for calculating contours of 2D quadrilateral grids.  It is written in C++11 and wrapped using pybind11.
+
+It contains the 2005 and 2014 algorithms used in Matplotlib as well as a newer algorithm that includes more features and is available in both serial and multithreaded versions.  It provides an easy way for Python libraries to use contouring algorithms without having to include Matplotlib as a dependency.
+
+  * **Documentation**: https://contourpy.readthedocs.io
+  * **Source code**: https://github.com/contourpy/contourpy
+
+| | |
+| --- | --- |
+| Latest release | [![PyPI version](https://img.shields.io/pypi/v/contourpy.svg?label=pypi&color=fdae61)](https://pypi.python.org/pypi/contourpy) [![conda-forge version](https://img.shields.io/conda/v/conda-forge/contourpy.svg?label=conda-forge&color=a6d96a)](https://anaconda.org/conda-forge/contourpy) |
+| Downloads | [![PyPi downloads](https://img.shields.io/pypi/dm/contourpy?label=pypi&style=flat&color=fdae61)](https://pepy.tech/project/contourpy) |
+| Python version | [![Platforms](https://img.shields.io/pypi/pyversions/contourpy?color=fdae61)](https://pypi.org/project/contourpy/) |
+| Coverage | [![Codecov](https://img.shields.io/codecov/c/gh/contourpy/contourpy?color=fdae61&label=codecov)](https://app.codecov.io/gh/contourpy/contourpy) |

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.cache/uv/CACHEDIR.TAG

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+Metadata-Version: 2.4
+Name: huggingface_hub
+Version: 1.26.0
+Summary: Client library to download and publish models, datasets and other repos on the huggingface.co hub
+Home-page: https://github.com/huggingface/huggingface_hub
+Author: Hugging Face, Inc.
+Author-email: julien@huggingface.co
+License: Apache-2.0
+Keywords: model-hub machine-learning models natural-language-processing deep-learning pytorch pretrained-models
+Classifier: Intended Audience :: Developers
+Classifier: Intended Audience :: Education
+Classifier: Intended Audience :: Science/Research
+Classifier: License :: OSI Approved :: Apache Software License
+Classifier: Operating System :: OS Independent
+Classifier: Programming Language :: Python :: 3
+Classifier: Programming Language :: Python :: 3 :: Only
+Classifier: Programming Language :: Python :: 3.10
+Classifier: Programming Language :: Python :: 3.11
+Classifier: Programming Language :: Python :: 3.12
+Classifier: Programming Language :: Python :: 3.13
+Classifier: Programming Language :: Python :: 3.14
+Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
+Requires-Python: >=3.10.0
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+License-File: LICENSE
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+Requires-Dist: types-toml; extra == "all"
+Requires-Dist: types-tqdm; extra == "all"
+Requires-Dist: types-urllib3; extra == "all"
+Provides-Extra: dev
+Requires-Dist: authlib>=1.3.2; extra == "dev"
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+Requires-Dist: types-toml; extra == "dev"
+Requires-Dist: types-tqdm; extra == "dev"
+Requires-Dist: types-urllib3; extra == "dev"
+Dynamic: author
+Dynamic: author-email
+Dynamic: classifier
+Dynamic: description
+Dynamic: description-content-type
+Dynamic: home-page
+Dynamic: keywords
+Dynamic: license
+Dynamic: license-file
+Dynamic: provides-extra
+Dynamic: requires-dist
+Dynamic: requires-python
+Dynamic: summary
+
+<p align="center">
+  <picture>
+    <source media="(prefers-color-scheme: dark)" srcset="https://huggingface.co/datasets/huggingface/documentation-images/raw/main/huggingface_hub-dark.svg">
+    <source media="(prefers-color-scheme: light)" srcset="https://huggingface.co/datasets/huggingface/documentation-images/raw/main/huggingface_hub.svg">
+    <img alt="huggingface_hub library logo" src="https://huggingface.co/datasets/huggingface/documentation-images/raw/main/huggingface_hub.svg" width="352" height="59" style="max-width: 100%">
+  </picture>
+  <br/>
+  <br/>
+</p> 
+
+<p align="center">
+    <i>The official CLI and Python client for the Hugging Face Hub.</i>
+    <br/>
+    <a href="#what-is-huggingface_hub">About</a>
+    ·
+    <a href="https://huggingface.co/docs/huggingface_hub">Documentation</a>
+    ·
+    <a href="https://huggingface.co/docs/huggingface_hub/en/installation">Install</a>
+    ·
+    <a href="https://huggingface.co/docs/huggingface_hub/en/guides/cli">CLI Guide</a>
+    ·
+    <a href="https://github.com/huggingface/huggingface_hub/blob/main/CONTRIBUTING.md">Contributing</a>
+</p>
+
+<p align="center">
+    <a href="https://huggingface.co/docs/huggingface_hub/en/index"><img alt="Documentation" src="https://img.shields.io/website/http/huggingface.co/docs/huggingface_hub/index.svg?down_color=red&down_message=offline&up_message=online&label=doc"></a>
+    <a href="https://github.com/huggingface/huggingface_hub/releases"><img alt="GitHub release" src="https://img.shields.io/github/release/huggingface/huggingface_hub.svg"></a>
+    <a href="https://github.com/huggingface/huggingface_hub"><img alt="PyPi version" src="https://img.shields.io/pypi/pyversions/huggingface_hub.svg"></a>
+    <a href="https://pypi.org/project/huggingface-hub"><img alt="PyPI - Downloads" src="https://img.shields.io/pypi/dm/huggingface_hub"></a>
+    <a href="https://codecov.io/gh/huggingface/huggingface_hub"><img alt="Code coverage" src="https://codecov.io/gh/huggingface/huggingface_hub/branch/main/graph/badge.svg?token=RXP95LE2XL"></a>
+</p>
+
+<h4 align="center">
+    <p>
+        <b>English</b> |
+        <a href="https://github.com/huggingface/huggingface_hub/blob/main/i18n/README_de.md">Deutsch</a> |
+        <a href="https://github.com/huggingface/huggingface_hub/blob/main/i18n/README_fr.md">Français</a> |
+        <a href="https://github.com/huggingface/huggingface_hub/blob/main/i18n/README_hi.md">हिंदी</a> |
+        <a href="https://github.com/huggingface/huggingface_hub/blob/main/i18n/README_ko.md">한국어</a> |
+        <a href="https://github.com/huggingface/huggingface_hub/blob/main/i18n/README_cn.md">中文 (简体)</a> |
+        <a href="https://github.com/huggingface/huggingface_hub/blob/main/i18n/README_kn.md">ಕನ್ನಡ</a>
+    </p>
+</h4>
+
+## Quick start
+
+Install the [`hf` CLI](https://huggingface.co/docs/huggingface_hub/en/guides/cli) with the standalone installer:
+
+```bash
+# On macOS and Linux.
+curl -LsSf https://hf.co/cli/install.sh | bash
+```
+
+```powershell
+# On Windows.
+powershell -ExecutionPolicy ByPass -c "irm https://hf.co/cli/install.ps1 | iex"
+```
+
+Log in, then start working with the Hub:
+
+```bash
+# Log in (use --token $HF_TOKEN in non-interactive environments)
+hf auth login
+
+# Find models served by Inference Providers
+hf models ls --warm
+
+# Download a model
+hf download Qwen/Qwen3-0.6B
+
+# Upload files to your own repo
+hf upload username/my-cool-model ./model.safetensors
+
+# Sync a local folder to a storage bucket
+hf buckets sync ./checkpoints hf://buckets/username/my-bucket
+
+# Run a job on Hugging Face infrastructure
+hf jobs run python:3.12 python -c "print('Hello from the cloud!')"
+
+# Discover everything else
+hf --help
+```
+
+The Hub uses tokens to authenticate applications (see [docs](https://huggingface.co/docs/hub/security-tokens)). Check out the [CLI guide](https://huggingface.co/docs/huggingface_hub/en/guides/cli) for a tour of the main features.
+
+## What is `huggingface_hub`?
+
+The `huggingface_hub` library allows you to interact with the [Hugging Face Hub](https://huggingface.co/), a platform democratizing open-source Machine Learning for creators and collaborators. Discover pre-trained models and datasets for your projects, play with the thousands of machine learning apps hosted on the Hub, or create and share your own models, datasets and demos with the community. Everything ships in one package with two interfaces: the [`hf` CLI](https://huggingface.co/docs/huggingface_hub/en/guides/cli) for your terminal and the `huggingface_hub` library for Python — both designed to work well for humans and AI agents. Use them to:
+
+- [Download files](https://huggingface.co/docs/huggingface_hub/en/guides/download) from the Hub.
+- [Upload files](https://huggingface.co/docs/huggingface_hub/en/guides/upload) to the Hub.
+- [Manage your repositories](https://huggingface.co/docs/huggingface_hub/en/guides/repository).
+- [Run Inference](https://huggingface.co/docs/huggingface_hub/en/guides/inference) on deployed models.
+- [Run Jobs](https://huggingface.co/docs/huggingface_hub/en/guides/jobs) on Hugging Face infrastructure.
+- [Search](https://huggingface.co/docs/huggingface_hub/en/guides/search) for models, datasets and Spaces.
+- [Share Model Cards](https://huggingface.co/docs/huggingface_hub/en/guides/model-cards) to document your models.
+- [Engage with the community](https://huggingface.co/docs/huggingface_hub/en/guides/community) through PRs and comments.
+- Do all of the above from the terminal with the [`hf` CLI](https://huggingface.co/docs/huggingface_hub/en/guides/cli).
+
+## Built for humans and AI agents
+
+The `hf` CLI is designed for people and coding agents alike: the same commands adapt their output when run by an agent. If you use Claude Code, Codex, Cursor, or another coding agent, install the `hf` CLI Skill — a command reference generated from your installed CLI:
+
+```bash
+# for Codex, Cursor, OpenCode, Pi and other agents that load skills from `.agents/skills`
+hf skills add
+# includes the above + Claude Code
+hf skills add --claude
+```
+
+Learn more in the [Hugging Face CLI for AI agents guide](https://huggingface.co/docs/hub/agents-cli) and the [announcement blog post](https://huggingface.co/blog/hf-cli-for-agents).
+
+## Use the Python library
+
+Install the `huggingface_hub` package with [pip](https://pypi.org/project/huggingface-hub/) (this also installs the `hf` CLI):
+
+```bash
+pip install huggingface_hub
+```
+
+We recommend using [`uv`](https://docs.astral.sh/uv/) for a fast and reliable install:
+
+```bash
+uv pip install huggingface_hub
+```
+
+In order to keep the package minimal by default, `huggingface_hub` comes with optional dependencies useful for some use cases. For example, if you want to use the MCP module, run:
+
+```bash
+pip install "huggingface_hub[mcp]"
+```
+
+To learn more about installation and optional dependencies, check out the [installation guide](https://huggingface.co/docs/huggingface_hub/en/installation).
+
+### Download files
+
+Download a single file
+
+```py
+from huggingface_hub import hf_hub_download
+
+hf_hub_download(repo_id="zai-org/GLM-5.2", filename="config.json")
+```
+
+Or an entire repository
+
+```py
+from huggingface_hub import snapshot_download
+
+snapshot_download("sentence-transformers/all-MiniLM-L6-v2")
+```
+
+Files will be downloaded in a local cache folder. More details in [this guide](https://huggingface.co/docs/huggingface_hub/en/guides/manage-cache).
+
+### Create a repository
+
+```py
+from huggingface_hub import create_repo
+
+create_repo(repo_id="super-cool-model")
+```
+
+### Upload files
+
+Upload a single file
+
+```py
+from huggingface_hub import upload_file
+
+upload_file(
+    path_or_fileobj="/home/lysandre/dummy-test/README.md",
+    path_in_repo="README.md",
+    repo_id="lysandre/test-model",
+)
+```
+
+Or an entire folder
+
+```py
+from huggingface_hub import upload_folder
+
+upload_folder(
+    folder_path="/path/to/local/space",
+    repo_id="username/my-cool-space",
+    repo_type="space",
+)
+```
+
+More details in the [upload guide](https://huggingface.co/docs/huggingface_hub/en/guides/upload).
+
+## Integrating with the Hub.
+
+We're partnering with cool open source ML libraries to provide free model hosting and versioning. You can find the existing integrations [here](https://huggingface.co/docs/hub/libraries).
+
+The advantages are:
+
+- Free model or dataset hosting for libraries and their users.
+- Built-in file versioning, even with very large files, made possible by [Xet](https://huggingface.co/docs/hub/xet/index), the Hub's chunk-deduplicated storage backend.
+- In-browser widgets to play with the uploaded models.
+- Anyone can upload a new model for your library, they just need to add the corresponding tag for the model to be discoverable.
+- Fast downloads! We use Cloudfront (a CDN) to geo-replicate downloads so they're blazing fast from anywhere on the globe.
+- Usage stats and more features to come.
+
+If you would like to integrate your library, feel free to open an issue to begin the discussion. We wrote a [step-by-step guide](https://huggingface.co/docs/hub/adding-a-library) with ❤️ showing how to do this integration.
+
+## Contributions (feature requests, bugs, etc.) are super welcome 💙💚💛💜🧡❤️
+
+Everyone is welcome to contribute, and we value everybody's contribution. Code is not the only way to help the community.
+Answering questions, helping others, reaching out and improving the documentations are immensely valuable to the community.
+We wrote a [contribution guide](https://github.com/huggingface/huggingface_hub/blob/main/CONTRIBUTING.md) to summarize
+how to get started to contribute to this repository.

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.cache/uv/archive-v0/YbGpm6nADP0vFoZFy8BvB/huggingface_hub-1.26.0.dist-info/WHEEL

@@ -0,0 +1,5 @@
+Wheel-Version: 1.0
+Generator: setuptools (83.0.0)
+Root-Is-Purelib: true
+Tag: py3-none-any
+

.cache/uv/archive-v0/YbGpm6nADP0vFoZFy8BvB/huggingface_hub-1.26.0.dist-info/entry_points.txt

@@ -0,0 +1,7 @@
+[console_scripts]
+hf = huggingface_hub.cli.hf:main
+huggingface-cli = huggingface_hub.cli.deprecated_cli:main
+tiny-agents = huggingface_hub.inference._mcp.cli:app
+
+[fsspec.specs]
+hf = huggingface_hub.HfFileSystem

.cache/uv/archive-v0/YbGpm6nADP0vFoZFy8BvB/huggingface_hub-1.26.0.dist-info/top_level.txt

@@ -0,0 +1 @@
+huggingface_hub

.cache/uv/archive-v0/YbGpm6nADP0vFoZFy8BvB/huggingface_hub/errors.py

@@ -0,0 +1,617 @@
+"""Contains all custom errors."""
+
+from enum import Enum
+from pathlib import Path
+
+from httpx import HTTPError, Response
+
+
+# CACHE ERRORS
+
+
+class CacheNotFound(Exception):
+    """Exception thrown when the Huggingface cache is not found."""
+
+    cache_dir: str | Path
+
+    def __init__(self, msg: str, cache_dir: str | Path, *args, **kwargs):
+        super().__init__(msg, *args, **kwargs)
+        self.cache_dir = cache_dir
+
+
+class CorruptedCacheException(Exception):
+    """Exception for any unexpected structure in the Huggingface cache-system."""
+
+
+class CachedRepoTreeNotFoundError(Exception):
+    """Raised by [`get_cached_repo_tree`] when no tree listing is cached for the requested revision.
+
+    The tree listing is populated as a side effect of [`snapshot_download`].
+    """
+
+
+# HEADERS ERRORS
+
+
+class LocalTokenNotFoundError(EnvironmentError):
+    """Raised if local token is required but not found."""
+
+
+# OIDC ERRORS
+
+
+class OIDCError(Exception):
+    """Raised when keyless CI/CD auth via OIDC token exchange ("Trusted Publishers") cannot proceed.
+
+    Typically because `HF_OIDC_RESOURCE` is set but no id token is available: not running in a
+    supported CI provider and `HF_OIDC_ID_TOKEN` is unset.
+
+    See https://huggingface.co/docs/hub/trusted-publishers.
+    """
+
+
+# DEVICE CODE OAUTH ERRORS
+
+
+class OAuthErrorCode(str, Enum):
+    """Known OAuth `error` codes returned by the Hub's token endpoint (RFC 6749 / RFC 8628)."""
+
+    AUTHORIZATION_PENDING = "authorization_pending"
+    SLOW_DOWN = "slow_down"
+    EXPIRED_TOKEN = "expired_token"
+    ACCESS_DENIED = "access_denied"
+    INVALID_GRANT = "invalid_grant"
+
+
+class DeviceCodeError(Exception):
+    """Raised when the Device Code OAuth login flow (RFC 8628) or an OAuth token refresh fails.
+
+    Covers failures at any step: requesting the device code, polling for the token,
+    authorization denied/expired, or unexpected server responses.
+
+    Attributes:
+        error_code (`str`, *optional*):
+            The OAuth `error` code returned by the server, if any. Known values are listed in
+            [`OAuthErrorCode`] but the server may return other codes.
+    """
+
+    def __init__(self, message: str, error_code: str | None = None):
+        super().__init__(message)
+        self.error_code = error_code
+
+
+# HTTP ERRORS
+
+
+class OfflineModeIsEnabled(ConnectionError):
+    """Raised when a request is made but `HF_HUB_OFFLINE=1` is set as environment variable."""
+
+
+class HfHubHTTPError(HTTPError, OSError):
+    """
+    HTTPError to inherit from for any custom HTTP Error raised in HF Hub.
+
+    Any HTTPError is converted at least into a `HfHubHTTPError`. If some information is
+    sent back by the server, it will be added to the error message.
+
+    Added details:
+    - Request ID sourced from headers in order of precedence: "X-Request-Id", "X-Amzn-Trace-Id", "X-Amz-Cf-Id".
+    - Server error message from the header "X-Error-Message".
+    - Server error message if we can found one in the response body.
+
+    Example:
+    ```py
+        import httpx
+        from huggingface_hub.utils import get_session, hf_raise_for_status, HfHubHTTPError
+
+        response = get_session().post(...)
+        try:
+            hf_raise_for_status(response)
+        except HfHubHTTPError as e:
+            print(str(e)) # formatted message
+            e.request_id, e.server_message # details returned by server
+
+            # Complete the error message with additional information once it's raised
+            e.append_to_message("\n`create_commit` expects the repository to exist.")
+            raise
+    ```
+    """
+
+    def __init__(
+        self,
+        message: str,
+        *,
+        response: Response,
+        server_message: str | None = None,
+    ):
+        self.request_id = (
+            response.headers.get("x-request-id")
+            or response.headers.get("X-Amzn-Trace-Id")
+            or response.headers.get("x-amz-cf-id")
+        )
+        self.server_message = server_message
+        self.response = response
+        self.request = response.request
+        super().__init__(message)
+
+    def append_to_message(self, additional_message: str) -> None:
+        """Append additional information to the `HfHubHTTPError` initial message."""
+        self.args = (self.args[0] + additional_message,) + self.args[1:]
+
+    @classmethod
+    def _reconstruct_hf_hub_http_error(
+        cls, message: str, response: Response, server_message: str | None
+    ) -> "HfHubHTTPError":
+        return cls(message, response=response, server_message=server_message)
+
+    def __reduce_ex__(self, protocol):
+        """Fix pickling of Exception subclass with kwargs. We need to override __reduce_ex__ of the parent class"""
+        return (self.__class__._reconstruct_hf_hub_http_error, (str(self), self.response, self.server_message))
+
+
+# INFERENCE CLIENT ERRORS
+
+
+class InferenceTimeoutError(HTTPError, TimeoutError):
+    """Error raised when a model is unavailable or the request times out."""
+
+
+# INFERENCE ENDPOINT ERRORS
+
+
+class InferenceEndpointError(Exception):
+    """Generic exception when dealing with Inference Endpoints."""
+
+
+class InferenceEndpointTimeoutError(InferenceEndpointError, TimeoutError):
+    """Exception for timeouts while waiting for Inference Endpoint."""
+
+
+# SAFETENSORS ERRORS
+
+
+class SafetensorsParsingError(Exception):
+    """Raised when failing to parse a safetensors file metadata.
+
+    This can be the case if the file is not a safetensors file or does not respect the specification.
+    """
+
+
+class NotASafetensorsRepoError(Exception):
+    """Raised when a repo is not a Safetensors repo i.e. doesn't have either a `model.safetensors` or a
+    `model.safetensors.index.json` file.
+    """
+
+
+# TEXT GENERATION ERRORS
+
+
+class TextGenerationError(HTTPError):
+    """Generic error raised if text-generation went wrong."""
+
+
+# Text Generation Inference Errors
+class ValidationError(TextGenerationError):
+    """Server-side validation error."""
+
+
+class GenerationError(TextGenerationError):
+    pass
+
+
+class OverloadedError(TextGenerationError):
+    pass
+
+
+class IncompleteGenerationError(TextGenerationError):
+    pass
+
+
+class UnknownError(TextGenerationError):
+    pass
+
+
+# VALIDATION ERRORS
+
+
+class HFValidationError(ValueError):
+    """Generic exception thrown by `huggingface_hub` validators.
+
+    Inherits from [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError).
+    """
+
+
+class HfUriError(ValueError):
+    """Raised when an `hf://...` URI is malformed.
+
+    See [`parse_hf_uri`] and the
+    [HF URIs reference](https://huggingface.co/docs/huggingface_hub/main/en/package_reference/hf_uris)
+    for the canonical syntax.
+
+    Inherits from [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError).
+    """
+
+    def __init__(self, uri: str, msg: str):
+        self.uri = uri
+        self.msg = msg
+        full_msg = f"Invalid HF URI '{uri}'. {msg}" if uri else f"Invalid HF URI. {msg}"
+        super().__init__(full_msg)
+
+
+# FILE METADATA ERRORS
+
+
+class DryRunError(OSError):
+    """Error triggered when a dry run is requested but cannot be performed (e.g. invalid repo)."""
+
+
+class FileMetadataError(OSError):
+    """Error triggered when the metadata of a file on the Hub cannot be retrieved (missing ETag or commit_hash).
+
+    Inherits from `OSError` for backward compatibility.
+    """
+
+
+# BUCKET ERRORS
+
+
+class BucketNotFoundError(HfHubHTTPError):
+    """
+    Raised when trying to access a bucket that does not exist.
+
+    Attributes:
+        bucket_id (`str` or `None`):
+            The bucket id (namespace/name) that was not found, if it could be determined from the request URL.
+
+    Example:
+
+    ```py
+    >>> from huggingface_hub import bucket_info
+    >>> bucket_info("<non_existent_bucket>")
+    (...)
+    huggingface_hub.errors.BucketNotFoundError: 404 Client Error. (Request ID: XXX)
+
+    Bucket Not Found for url: https://huggingface.co/api/buckets/namespace/name.
+    Please make sure you specified the correct bucket id (namespace/name).
+    If the bucket is private, make sure you are authenticated and your token has the required permissions.
+    ```
+    """
+
+    bucket_id: str | None = None
+
+
+# JOB ERRORS
+
+
+class JobNotFoundError(HfHubHTTPError):
+    """
+    Raised when trying to access a Job that does not exist.
+
+    Attributes:
+        job_id (`str`):
+            The job id that was not found.
+    """
+
+    job_id: str
+
+
+# REPOSITORY ERRORS
+
+
+class RepositoryNotFoundError(HfHubHTTPError):
+    """
+    Raised when trying to access a hf.co URL with an invalid repository name, or
+    with a private repo name the user does not have access to.
+
+    Attributes:
+        repo_id (`str` or `None`):
+            The repo id that was not found, if it could be determined from the request URL.
+        repo_type (`str` or `None`):
+            The repo type ("model", "dataset", or "space"), if it could be determined from the request URL.
+
+    Example:
+
+    ```py
+    >>> from huggingface_hub import model_info
+    >>> model_info("<non_existent_repository>")
+    (...)
+    huggingface_hub.errors.RepositoryNotFoundError: 401 Client Error. (Request ID: PvMw_VjBMjVdMz53WKIzP)
+
+    Repository Not Found for url: https://huggingface.co/api/models/%3Cnon_existent_repository%3E.
+    Please make sure you specified the correct `repo_id` and `repo_type`.
+    If the repo is private, make sure you are authenticated and your token has the required permissions.
+    Invalid username or password.
+    ```
+    """
+
+    repo_id: str | None = None
+    repo_type: str | None = None
+
+
+class GatedRepoError(RepositoryNotFoundError):
+    """
+    Raised when trying to access a gated repository for which the user is not on the
+    authorized list.
+
+    Note: derives from `RepositoryNotFoundError` to ensure backward compatibility.
+
+    Example:
+
+    ```py
+    >>> from huggingface_hub import model_info
+    >>> model_info("<gated_repository>")
+    (...)
+    huggingface_hub.errors.GatedRepoError: 403 Client Error. (Request ID: ViT1Bf7O_026LGSQuVqfa)
+
+    Cannot access gated repo for url https://huggingface.co/api/models/ardent-figment/gated-model.
+    Access to model ardent-figment/gated-model is restricted and you are not in the authorized list.
+    Visit https://huggingface.co/ardent-figment/gated-model to ask for access.
+    ```
+    """
+
+
+class DisabledRepoError(HfHubHTTPError):
+    """
+    Raised when trying to access a repository that has been disabled by its author.
+
+    Example:
+
+    ```py
+    >>> from huggingface_hub import dataset_info
+    >>> dataset_info("laion/laion-art")
+    (...)
+    huggingface_hub.errors.DisabledRepoError: 403 Client Error. (Request ID: Root=1-659fc3fa-3031673e0f92c71a2260dbe2;bc6f4dfb-b30a-4862-af0a-5cfe827610d8)
+
+    Cannot access repository for url https://huggingface.co/api/datasets/laion/laion-art.
+    Access to this resource is disabled.
+    ```
+    """
+
+
+# REVISION ERROR
+
+
+class RevisionNotFoundError(HfHubHTTPError):
+    """
+    Raised when trying to access a hf.co URL with a valid repository but an invalid
+    revision.
+
+    Attributes:
+        repo_id (`str` or `None`):
+            The repo id, if it could be determined from the request URL.
+        repo_type (`str` or `None`):
+            The repo type ("model", "dataset", or "space"), if it could be determined from the request URL.
+
+    Example:
+
+    ```py
+    >>> from huggingface_hub import hf_hub_download
+    >>> hf_hub_download('bert-base-cased', 'config.json', revision='<non-existent-revision>')
+    (...)
+    huggingface_hub.errors.RevisionNotFoundError: 404 Client Error. (Request ID: Mwhe_c3Kt650GcdKEFomX)
+
+    Revision Not Found for url: https://huggingface.co/bert-base-cased/resolve/%3Cnon-existent-revision%3E/config.json.
+    ```
+    """
+
+    repo_id: str | None = None
+    repo_type: str | None = None
+
+
+class RevisionResolutionError(Exception):
+    """
+    Raised by [`HfApi.resolve_revision`] when a revision cannot be resolved to a commit hash: the Hub could not be
+    reached (offline mode, connection error, timeout, Hub downtime, ...) and no matching entry was found in the
+    local cache.
+    """
+
+
+# ENTRY ERRORS
+class EntryNotFoundError(Exception):
+    """
+    Raised when entry not found, either locally or remotely.
+
+    Example:
+
+    ```py
+    >>> from huggingface_hub import hf_hub_download
+    >>> hf_hub_download('bert-base-cased', '<non-existent-file>')
+    (...)
+    huggingface_hub.errors.RemoteEntryNotFoundError (...)
+    >>> hf_hub_download('bert-base-cased', '<non-existent-file>', local_files_only=True)
+    (...)
+    huggingface_hub.utils.errors.LocalEntryNotFoundError (...)
+    ```
+    """
+
+
+class RemoteEntryNotFoundError(HfHubHTTPError, EntryNotFoundError):
+    """
+    Raised when trying to access a hf.co URL with a valid repository and revision
+    but an invalid filename.
+
+    Attributes:
+        repo_id (`str` or `None`):
+            The repo id, if it could be determined from the request URL.
+        repo_type (`str` or `None`):
+            The repo type ("model", "dataset", or "space"), if it could be determined from the request URL.
+
+    Example:
+
+    ```py
+    >>> from huggingface_hub import hf_hub_download
+    >>> hf_hub_download('bert-base-cased', '<non-existent-file>')
+    (...)
+    huggingface_hub.errors.EntryNotFoundError: 404 Client Error. (Request ID: 53pNl6M0MxsnG5Sw8JA6x)
+
+    Entry Not Found for url: https://huggingface.co/bert-base-cased/resolve/main/%3Cnon-existent-file%3E.
+    ```
+    """
+
+    repo_id: str | None = None
+    repo_type: str | None = None
+
+
+class LocalEntryNotFoundError(FileNotFoundError, EntryNotFoundError):
+    """
+    Raised when trying to access a file or snapshot that is not on the disk when network is
+    disabled or unavailable (connection issue). The entry may exist on the Hub.
+
+    Example:
+
+    ```py
+    >>> from huggingface_hub import hf_hub_download
+    >>> hf_hub_download('bert-base-cased', '<non-cached-file>',  local_files_only=True)
+    (...)
+    huggingface_hub.errors.LocalEntryNotFoundError: Cannot find the requested files in the disk cache and outgoing traffic has been disabled. To enable hf.co look-ups and downloads online, set 'local_files_only' to False.
+    ```
+    """
+
+    def __init__(self, message: str):
+        super().__init__(message)
+
+
+class IncompleteSnapshotError(LocalEntryNotFoundError):
+    """
+    Raised by [`snapshot_download`] when the Hub cannot be reached (offline, connection issue, or
+    `local_files_only=True`) and the cached snapshot is known to be incomplete: some files listed in
+    the repository's cached tree listing are missing from the local snapshot.
+
+    This is a subclass of [`LocalEntryNotFoundError`] for backward compatibility.
+
+    The `snapshot_path` attribute holds the path to the incomplete local snapshot, so a downstream library can locate
+    the latest cached files even though they are known to be incomplete.
+    """
+
+    def __init__(self, message: str, snapshot_path: str):
+        super().__init__(message)
+        self.snapshot_path = snapshot_path
+
+
+# REQUEST ERROR
+class BadRequestError(HfHubHTTPError, ValueError):
+    """
+    Raised by `hf_raise_for_status` when the server returns a HTTP 400 error.
+
+    Example:
+
+    ```py
+    >>> resp = httpx.post("hf.co/api/check", ...)
+    >>> hf_raise_for_status(resp, endpoint_name="check")
+    huggingface_hub.errors.BadRequestError: Bad request for check endpoint: {details} (Request ID: XXX)
+    ```
+    """
+
+
+# DDUF file format ERROR
+
+
+class DDUFError(Exception):
+    """Base exception for errors related to the DDUF format."""
+
+
+class DDUFCorruptedFileError(DDUFError):
+    """Exception thrown when the DDUF file is corrupted."""
+
+
+class DDUFExportError(DDUFError):
+    """Base exception for errors during DDUF export."""
+
+
+class DDUFInvalidEntryNameError(DDUFExportError):
+    """Exception thrown when the entry name is invalid."""
+
+
+# STRICT DATACLASSES ERRORS
+
+
+class StrictDataclassError(Exception):
+    """Base exception for strict dataclasses."""
+
+
+class StrictDataclassDefinitionError(StrictDataclassError):
+    """Exception thrown when a strict dataclass is defined incorrectly."""
+
+
+class StrictDataclassFieldValidationError(StrictDataclassError):
+    """Exception thrown when a strict dataclass fails validation for a given field."""
+
+    def __init__(self, field: str, cause: Exception):
+        error_message = f"Validation error for field '{field}':"
+        error_message += f"\n    {cause.__class__.__name__}: {cause}"
+        super().__init__(error_message)
+
+
+class StrictDataclassClassValidationError(StrictDataclassError):
+    """Exception thrown when a strict dataclass fails validation on a class validator."""
+
+    def __init__(self, validator: str, cause: Exception):
+        error_message = f"Class validation error for validator '{validator}':"
+        error_message += f"\n    {cause.__class__.__name__}: {cause}"
+        super().__init__(error_message)
+
+
+# XET ERRORS
+
+
+class XetDownloadError(Exception):
+    """Exception thrown when the download from Xet Storage fails."""
+
+
+# LFS ERRORS
+
+
+class FileDuplicationError(Exception):
+    """Raised when duplicating files across repos fails."""
+
+
+# CLI ERRORS
+
+
+class CLIError(Exception):
+    """CLI error with clean message (no traceback by default)."""
+
+
+class ConfirmationError(CLIError):
+    """Raised when a confirmation prompt is declined (non-interactive mode)."""
+
+
+class CLIExtensionInstallError(CLIError):
+    """Error during CLI extension installation."""
+
+
+# SANDBOX ERRORS
+
+
+class SandboxError(Exception):
+    """Base exception for sandbox operations (see `huggingface_hub.Sandbox`).
+
+    Attributes:
+        status_code: The HTTP status returned by the in-sandbox server, if the error
+            originated from an API response (e.g. `404` for a missing file). `None` otherwise.
+    """
+
+    def __init__(self, message: str, *, status_code: int | None = None) -> None:
+        super().__init__(message)
+        self.status_code = status_code
+
+
+class SandboxCommandError(SandboxError):
+    """Raised when a command run in a sandbox exits with a non-zero code.
+
+    Attributes:
+        cmd: The command that failed.
+        result: The full `SandboxCommandResult` (exit_code, stdout, stderr, ...).
+    """
+
+    def __init__(self, cmd, result) -> None:
+        self.cmd = cmd
+        self.result = result
+        stderr_tail = result.stderr[-1000:] if result.stderr else "<empty>"
+        if result.timed_out:
+            reason = "timed out"
+        elif result.signal is not None:
+            reason = f"was killed by signal {result.signal}"
+        else:
+            reason = f"exited with code {result.exit_code}"
+        super().__init__(f"Command {cmd!r} {reason}. stderr:\n{stderr_tail}")

.cache/uv/archive-v0/YbGpm6nADP0vFoZFy8BvB/huggingface_hub/fastai_utils.py

@@ -0,0 +1,414 @@
+import json
+import os
+from pathlib import Path
+from pickle import DEFAULT_PROTOCOL, PicklingError
+from typing import Any
+
+from packaging import version
+
+from huggingface_hub import constants, snapshot_download
+from huggingface_hub.hf_api import HfApi
+from huggingface_hub.utils import (
+    SoftTemporaryDirectory,
+    get_fastai_version,
+    get_fastcore_version,
+    get_python_version,
+)
+
+from .utils import logging, validate_hf_hub_args
+
+
+logger = logging.get_logger(__name__)
+
+
+def _check_fastai_fastcore_versions(
+    fastai_min_version: str = "2.4",
+    fastcore_min_version: str = "1.3.27",
+):
+    """
+    Checks that the installed fastai and fastcore versions are compatible for pickle serialization.
+
+    Args:
+        fastai_min_version (`str`, *optional*):
+            The minimum fastai version supported.
+        fastcore_min_version (`str`, *optional*):
+            The minimum fastcore version supported.
+
+    > [!TIP]
+    > Raises the following error:
+    >
+    >     - [`ImportError`](https://docs.python.org/3/library/exceptions.html#ImportError)
+    >       if the fastai or fastcore libraries are not available or are of an invalid version.
+    """
+
+    if (get_fastcore_version() or get_fastai_version()) == "N/A":
+        raise ImportError(
+            f"fastai>={fastai_min_version} and fastcore>={fastcore_min_version} are"
+            f" required. Currently using fastai=={get_fastai_version()} and"
+            f" fastcore=={get_fastcore_version()}."
+        )
+
+    current_fastai_version = version.Version(get_fastai_version())
+    current_fastcore_version = version.Version(get_fastcore_version())
+
+    if current_fastai_version < version.Version(fastai_min_version):
+        raise ImportError(
+            "`push_to_hub_fastai` and `from_pretrained_fastai` require a"
+            f" fastai>={fastai_min_version} version, but you are using fastai version"
+            f" {get_fastai_version()} which is incompatible. Upgrade with `pip install"
+            " fastai==2.5.6`."
+        )
+
+    if current_fastcore_version < version.Version(fastcore_min_version):
+        raise ImportError(
+            "`push_to_hub_fastai` and `from_pretrained_fastai` require a"
+            f" fastcore>={fastcore_min_version} version, but you are using fastcore"
+            f" version {get_fastcore_version()} which is incompatible. Upgrade with"
+            " `pip install fastcore==1.3.27`."
+        )
+
+
+def _check_fastai_fastcore_pyproject_versions(
+    storage_folder: str,
+    fastai_min_version: str = "2.4",
+    fastcore_min_version: str = "1.3.27",
+):
+    """
+    Checks that the `pyproject.toml` file in the directory `storage_folder` has fastai and fastcore versions
+    that are compatible with `from_pretrained_fastai` and `push_to_hub_fastai`. If `pyproject.toml` does not exist
+    or does not contain versions for fastai and fastcore, then it logs a warning.
+
+    Args:
+        storage_folder (`str`):
+            Folder to look for the `pyproject.toml` file.
+        fastai_min_version (`str`, *optional*):
+            The minimum fastai version supported.
+        fastcore_min_version (`str`, *optional*):
+            The minimum fastcore version supported.
+
+    > [!TIP]
+    > Raises the following errors:
+    >
+    >     - [`ImportError`](https://docs.python.org/3/library/exceptions.html#ImportError)
+    >       if the `toml` module is not installed.
+    >     - [`ImportError`](https://docs.python.org/3/library/exceptions.html#ImportError)
+    >       if the `pyproject.toml` indicates a lower than minimum supported version of fastai or fastcore.
+    """
+
+    try:
+        import toml
+    except ModuleNotFoundError:
+        raise ImportError(
+            "`push_to_hub_fastai` and `from_pretrained_fastai` require the toml module."
+            " Install it with `pip install toml`."
+        )
+
+    # Checks that a `pyproject.toml`, with `build-system` and `requires` sections, exists in the repository. If so, get a list of required packages.
+    if not os.path.isfile(f"{storage_folder}/pyproject.toml"):
+        logger.warning(
+            "There is no `pyproject.toml` in the repository that contains the fastai"
+            " `Learner`. The `pyproject.toml` would allow us to verify that your fastai"
+            " and fastcore versions are compatible with those of the model you want to"
+            " load."
+        )
+        return
+    pyproject_toml = toml.load(f"{storage_folder}/pyproject.toml")
+
+    if "build-system" not in pyproject_toml.keys():
+        logger.warning(
+            "There is no `build-system` section in the pyproject.toml of the repository"
+            " that contains the fastai `Learner`. The `build-system` would allow us to"
+            " verify that your fastai and fastcore versions are compatible with those"
+            " of the model you want to load."
+        )
+        return
+    build_system_toml = pyproject_toml["build-system"]
+
+    if "requires" not in build_system_toml.keys():
+        logger.warning(
+            "There is no `requires` section in the pyproject.toml of the repository"
+            " that contains the fastai `Learner`. The `requires` would allow us to"
+            " verify that your fastai and fastcore versions are compatible with those"
+            " of the model you want to load."
+        )
+        return
+    package_versions = build_system_toml["requires"]
+
+    # Extracts contains fastai and fastcore versions from `pyproject.toml` if available.
+    # If the package is specified but not the version (e.g. "fastai" instead of "fastai=2.4"), the default versions are the highest.
+    fastai_packages = [pck for pck in package_versions if pck.startswith("fastai")]
+    if len(fastai_packages) == 0:
+        logger.warning("The repository does not have a fastai version specified in the `pyproject.toml`.")
+    # fastai_version is an empty string if not specified
+    else:
+        fastai_version = str(fastai_packages[0]).partition("=")[2]
+        if fastai_version != "" and version.Version(fastai_version) < version.Version(fastai_min_version):
+            raise ImportError(
+                "`from_pretrained_fastai` requires"
+                f" fastai>={fastai_min_version} version but the model to load uses"
+                f" {fastai_version} which is incompatible."
+            )
+
+    fastcore_packages = [pck for pck in package_versions if pck.startswith("fastcore")]
+    if len(fastcore_packages) == 0:
+        logger.warning("The repository does not have a fastcore version specified in the `pyproject.toml`.")
+    # fastcore_version is an empty string if not specified
+    else:
+        fastcore_version = str(fastcore_packages[0]).partition("=")[2]
+        if fastcore_version != "" and version.Version(fastcore_version) < version.Version(fastcore_min_version):
+            raise ImportError(
+                "`from_pretrained_fastai` requires"
+                f" fastcore>={fastcore_min_version} version, but you are using fastcore"
+                f" version {fastcore_version} which is incompatible."
+            )
+
+
+README_TEMPLATE = """---
+tags:
+- fastai
+---
+
+# Amazing!
+
+🥳 Congratulations on hosting your fastai model on the Hugging Face Hub!
+
+# Some next steps
+1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))!
+
+2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)).
+
+3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)!
+
+Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card.
+
+
+---
+
+
+# Model card
+
+## Model description
+More information needed
+
+## Intended uses & limitations
+More information needed
+
+## Training and evaluation data
+More information needed
+"""
+
+PYPROJECT_TEMPLATE = f"""[build-system]
+requires = ["setuptools>=40.8.0", "wheel", "python={get_python_version()}", "fastai={get_fastai_version()}", "fastcore={get_fastcore_version()}"]
+build-backend = "setuptools.build_meta:__legacy__"
+"""
+
+
+def _create_model_card(repo_dir: Path):
+    """
+    Creates a model card for the repository.
+
+    Args:
+        repo_dir (`Path`):
+            Directory where model card is created.
+    """
+    readme_path = repo_dir / "README.md"
+
+    if not readme_path.exists():
+        with readme_path.open("w", encoding="utf-8") as f:
+            f.write(README_TEMPLATE)
+
+
+def _create_model_pyproject(repo_dir: Path):
+    """
+    Creates a `pyproject.toml` for the repository.
+
+    Args:
+        repo_dir (`Path`):
+            Directory where `pyproject.toml` is created.
+    """
+    pyproject_path = repo_dir / "pyproject.toml"
+
+    if not pyproject_path.exists():
+        with pyproject_path.open("w", encoding="utf-8") as f:
+            f.write(PYPROJECT_TEMPLATE)
+
+
+def _save_pretrained_fastai(
+    learner,
+    save_directory: str | Path,
+    config: dict[str, Any] | None = None,
+):
+    """
+    Saves a fastai learner to `save_directory` in pickle format using the default pickle protocol for the version of python used.
+
+    Args:
+        learner (`Learner`):
+            The `fastai.Learner` you'd like to save.
+        save_directory (`str` or `Path`):
+            Specific directory in which you want to save the fastai learner.
+        config (`dict`, *optional*):
+            Configuration object. Will be uploaded as a .json file. Example: 'https://huggingface.co/espejelomar/fastai-pet-breeds-classification/blob/main/config.json'.
+
+    > [!TIP]
+    > Raises the following error:
+    >
+    >     - [`RuntimeError`](https://docs.python.org/3/library/exceptions.html#RuntimeError)
+    >       if the config file provided is not a dictionary.
+    """
+    _check_fastai_fastcore_versions()
+
+    os.makedirs(save_directory, exist_ok=True)
+
+    # if the user provides config then we update it with the fastai and fastcore versions in CONFIG_TEMPLATE.
+    if config is not None:
+        if not isinstance(config, dict):
+            raise RuntimeError(f"Provided config should be a dict. Got: '{type(config)}'")
+        path = os.path.join(save_directory, constants.CONFIG_NAME)
+        with open(path, "w") as f:
+            json.dump(config, f)
+
+    _create_model_card(Path(save_directory))
+    _create_model_pyproject(Path(save_directory))
+
+    # learner.export saves the model in `self.path`.
+    learner.path = Path(save_directory)
+    os.makedirs(save_directory, exist_ok=True)
+    try:
+        learner.export(
+            fname="model.pkl",
+            pickle_protocol=DEFAULT_PROTOCOL,
+        )
+    except PicklingError:
+        raise PicklingError(
+            "You are using a lambda function, i.e., an anonymous function. `pickle`"
+            " cannot pickle function objects and requires that all functions have"
+            " names. One possible solution is to name the function."
+        )
+
+
+@validate_hf_hub_args
+def from_pretrained_fastai(
+    repo_id: str,
+    revision: str | None = None,
+):
+    """
+    Load pretrained fastai model from the Hub or from a local directory.
+
+    Args:
+        repo_id (`str`):
+            The location where the pickled fastai.Learner is. It can be either of the two:
+                - Hosted on the Hugging Face Hub. E.g.: 'espejelomar/fatai-pet-breeds-classification' or 'distilgpt2'.
+                  You can add a `revision` by appending `@` at the end of `repo_id`. E.g.: `dbmdz/bert-base-german-cased@main`.
+                  Revision is the specific model version to use. Since we use a git-based system for storing models and other
+                  artifacts on the Hugging Face Hub, it can be a branch name, a tag name, or a commit id.
+                - Hosted locally. `repo_id` would be a directory containing the pickle and a pyproject.toml
+                  indicating the fastai and fastcore versions used to build the `fastai.Learner`. E.g.: `./my_model_directory/`.
+        revision (`str`, *optional*):
+            Revision at which the repo's files are downloaded. See documentation of `snapshot_download`.
+
+    Returns:
+        The `fastai.Learner` model in the `repo_id` repo.
+    """
+    _check_fastai_fastcore_versions()
+
+    # Load the `repo_id` repo.
+    # `snapshot_download` returns the folder where the model was stored.
+    # `cache_dir` will be the default '/root/.cache/huggingface/hub'
+    if not os.path.isdir(repo_id):
+        storage_folder = snapshot_download(
+            repo_id=repo_id,
+            revision=revision,
+            library_name="fastai",
+            library_version=get_fastai_version(),
+        )
+    else:
+        storage_folder = repo_id
+
+    _check_fastai_fastcore_pyproject_versions(storage_folder)
+
+    from fastai.learner import load_learner  # type: ignore
+
+    return load_learner(os.path.join(storage_folder, "model.pkl"))
+
+
+@validate_hf_hub_args
+def push_to_hub_fastai(
+    learner,
+    *,
+    repo_id: str,
+    commit_message: str = "Push FastAI model using huggingface_hub.",
+    private: bool | None = None,
+    token: str | None = None,
+    config: dict | None = None,
+    branch: str | None = None,
+    create_pr: bool | None = None,
+    allow_patterns: list[str] | str | None = None,
+    ignore_patterns: list[str] | str | None = None,
+    delete_patterns: list[str] | str | None = None,
+    api_endpoint: str | None = None,
+):
+    """
+    Upload learner checkpoint files to the Hub.
+
+    Use `allow_patterns` and `ignore_patterns` to precisely filter which files should be pushed to the hub. Use
+    `delete_patterns` to delete existing remote files in the same commit. See [`upload_folder`] reference for more
+    details.
+
+    Args:
+        learner (`Learner`):
+            The `fastai.Learner' you'd like to push to the Hub.
+        repo_id (`str`):
+            The repository id for your model in Hub in the format of "namespace/repo_name". The namespace can be your individual account or an organization to which you have write access (for example, 'stanfordnlp/stanza-de').
+        commit_message (`str`, *optional*):
+            Message to commit while pushing. Will default to :obj:`"add model"`.
+        private (`bool`, *optional*):
+            Whether or not the repository created should be private.
+            If `None` (default), will default to been public except if the organization's default is private.
+        token (`str`, *optional*):
+            The Hugging Face account token to use as HTTP bearer authorization for remote files. If :obj:`None`, the token will be asked by a prompt.
+        config (`dict`, *optional*):
+            Configuration object to be saved alongside the model weights.
+        branch (`str`, *optional*):
+            The git branch on which to push the model. This defaults to
+            the default branch as specified in your repository, which
+            defaults to `"main"`.
+        create_pr (`boolean`, *optional*):
+            Whether or not to create a Pull Request from `branch` with that commit.
+            Defaults to `False`.
+        api_endpoint (`str`, *optional*):
+            The API endpoint to use when pushing the model to the hub.
+        allow_patterns (`list[str]` or `str`, *optional*):
+            If provided, only files matching at least one pattern are pushed.
+        ignore_patterns (`list[str]` or `str`, *optional*):
+            If provided, files matching any of the patterns are not pushed.
+        delete_patterns (`list[str]` or `str`, *optional*):
+            If provided, remote files matching any of the patterns will be deleted from the repo.
+
+    Returns:
+        The url of the commit of your model in the given repository.
+
+    > [!TIP]
+    > Raises the following error:
+    >
+    >     - [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError)
+    >       if the user is not log on to the Hugging Face Hub.
+    """
+    _check_fastai_fastcore_versions()
+    api = HfApi(endpoint=api_endpoint)
+    repo_id = api.create_repo(repo_id=repo_id, token=token, private=private, exist_ok=True).repo_id
+
+    # Push the files to the repo in a single commit
+    with SoftTemporaryDirectory() as tmp:
+        saved_path = Path(tmp) / repo_id
+        _save_pretrained_fastai(learner, saved_path, config=config)
+        return api.upload_folder(
+            repo_id=repo_id,
+            token=token,
+            folder_path=saved_path,
+            commit_message=commit_message,
+            revision=branch,
+            create_pr=create_pr,
+            allow_patterns=allow_patterns,
+            ignore_patterns=ignore_patterns,
+            delete_patterns=delete_patterns,
+        )

.cache/uv/archive-v0/YbGpm6nADP0vFoZFy8BvB/huggingface_hub/hub_mixin.py

@@ -0,0 +1,834 @@
+import inspect
+import json
+import os
+from collections.abc import Callable
+from dataclasses import Field, asdict, dataclass, is_dataclass
+from pathlib import Path
+from typing import Any, ClassVar, Protocol, TypeVar
+
+import packaging.version
+
+from . import constants
+from .errors import EntryNotFoundError, HfHubHTTPError
+from .file_download import hf_hub_download
+from .hf_api import HfApi
+from .repocard import ModelCard, ModelCardData
+from .utils import (
+    SoftTemporaryDirectory,
+    is_jsonable,
+    is_safetensors_available,
+    is_simple_optional_type,
+    is_torch_available,
+    logging,
+    unwrap_simple_optional_type,
+    validate_hf_hub_args,
+)
+
+
+if is_torch_available():
+    import torch  # type: ignore
+
+if is_safetensors_available():
+    import safetensors
+    from safetensors.torch import load_model as load_model_as_safetensor
+    from safetensors.torch import save_model as save_model_as_safetensor
+
+
+logger = logging.get_logger(__name__)
+
+
+# Type alias for dataclass instances, copied from https://github.com/python/typeshed/blob/9f28171658b9ca6c32a7cb93fbb99fc92b17858b/stdlib/_typeshed/__init__.pyi#L349
+class DataclassInstance(Protocol):
+    __dataclass_fields__: ClassVar[dict[str, Field]]
+
+
+# Generic variable that is either ModelHubMixin or a subclass thereof
+T = TypeVar("T", bound="ModelHubMixin")
+# Generic variable to represent an args type
+ARGS_T = TypeVar("ARGS_T")
+ENCODER_T = Callable[[ARGS_T], Any]
+DECODER_T = Callable[[Any], ARGS_T]
+CODER_T = tuple[ENCODER_T, DECODER_T]
+
+
+DEFAULT_MODEL_CARD = """
+---
+# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
+# Doc / guide: https://huggingface.co/docs/hub/model-cards
+{{ card_data }}
+---
+
+This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration:
+- Code: {{ repo_url | default("[More Information Needed]", true) }}
+- Paper: {{ paper_url | default("[More Information Needed]", true) }}
+- Docs: {{ docs_url | default("[More Information Needed]", true) }}
+"""
+
+
+@dataclass
+class MixinInfo:
+    model_card_template: str
+    model_card_data: ModelCardData
+    docs_url: str | None = None
+    paper_url: str | None = None
+    repo_url: str | None = None
+
+
+class ModelHubMixin:
+    """
+    A generic mixin to integrate ANY machine learning framework with the Hub.
+
+    To integrate your framework, your model class must inherit from this class. Custom logic for saving/loading models
+    have to be overwritten in  [`_from_pretrained`] and [`_save_pretrained`]. [`PyTorchModelHubMixin`] is a good example
+    of mixin integration with the Hub. Check out our [integration guide](../guides/integrations) for more instructions.
+
+    When inheriting from [`ModelHubMixin`], you can define class-level attributes. These attributes are not passed to
+    `__init__` but to the class definition itself. This is useful to define metadata about the library integrating
+    [`ModelHubMixin`].
+
+    For more details on how to integrate the mixin with your library, checkout the [integration guide](../guides/integrations).
+
+    Args:
+        repo_url (`str`, *optional*):
+            URL of the library repository. Used to generate model card.
+        paper_url (`str`, *optional*):
+            URL of the library paper. Used to generate model card.
+        docs_url (`str`, *optional*):
+            URL of the library documentation. Used to generate model card.
+        model_card_template (`str`, *optional*):
+            Template of the model card. Used to generate model card. Defaults to a generic template.
+        language (`str` or `list[str]`, *optional*):
+            Language supported by the library. Used to generate model card.
+        library_name (`str`, *optional*):
+            Name of the library integrating ModelHubMixin. Used to generate model card.
+        license (`str`, *optional*):
+            License of the library integrating ModelHubMixin. Used to generate model card.
+            E.g: "apache-2.0"
+        license_name (`str`, *optional*):
+            Name of the library integrating ModelHubMixin. Used to generate model card.
+            Only used if `license` is set to `other`.
+            E.g: "coqui-public-model-license".
+        license_link (`str`, *optional*):
+            URL to the license of the library integrating ModelHubMixin. Used to generate model card.
+            Only used if `license` is set to `other` and `license_name` is set.
+            E.g: "https://coqui.ai/cpml".
+        pipeline_tag (`str`, *optional*):
+            Tag of the pipeline. Used to generate model card. E.g. "text-classification".
+        tags (`list[str]`, *optional*):
+            Tags to be added to the model card. Used to generate model card. E.g. ["computer-vision"]
+        coders (`dict[Type, tuple[Callable, Callable]]`, *optional*):
+            Dictionary of custom types and their encoders/decoders. Used to encode/decode arguments that are not
+            jsonable by default. E.g. dataclasses, argparse.Namespace, OmegaConf, etc.
+
+    Example:
+
+    ```python
+    >>> from huggingface_hub import ModelHubMixin
+
+    # Inherit from ModelHubMixin
+    >>> class MyCustomModel(
+    ...         ModelHubMixin,
+    ...         library_name="my-library",
+    ...         tags=["computer-vision"],
+    ...         repo_url="https://github.com/huggingface/my-cool-library",
+    ...         paper_url="https://arxiv.org/abs/2304.12244",
+    ...         docs_url="https://huggingface.co/docs/my-cool-library",
+    ...         # ^ optional metadata to generate model card
+    ...     ):
+    ...     def __init__(self, size: int = 512, device: str = "cpu"):
+    ...         # define how to initialize your model
+    ...         super().__init__()
+    ...         ...
+    ...
+    ...     def _save_pretrained(self, save_directory: Path) -> None:
+    ...         # define how to serialize your model
+    ...         ...
+    ...
+    ...     @classmethod
+    ...     def from_pretrained(
+    ...         cls: type[T],
+    ...         pretrained_model_name_or_path: Union[str, Path],
+    ...         *,
+    ...         force_download: bool = False,
+    ...         token: Optional[Union[str, bool]] = None,
+    ...         cache_dir: Optional[Union[str, Path]] = None,
+    ...         local_files_only: bool = False,
+    ...         revision: Optional[str] = None,
+    ...         **model_kwargs,
+    ...     ) -> T:
+    ...         # define how to deserialize your model
+    ...         ...
+
+    >>> model = MyCustomModel(size=256, device="gpu")
+
+    # Save model weights to local directory
+    >>> model.save_pretrained("my-awesome-model")
+
+    # Push model weights to the Hub
+    >>> model.push_to_hub("my-awesome-model")
+
+    # Download and initialize weights from the Hub
+    >>> reloaded_model = MyCustomModel.from_pretrained("username/my-awesome-model")
+    >>> reloaded_model.size
+    256
+
+    # Model card has been correctly populated
+    >>> from huggingface_hub import ModelCard
+    >>> card = ModelCard.load("username/my-awesome-model")
+    >>> card.data.tags
+    ["x-custom-tag", "pytorch_model_hub_mixin", "model_hub_mixin"]
+    >>> card.data.library_name
+    "my-library"
+    ```
+    """
+
+    _hub_mixin_config: dict | DataclassInstance | None = None
+    # ^ optional config attribute automatically set in `from_pretrained`
+    _hub_mixin_info: MixinInfo
+    # ^ information about the library integrating ModelHubMixin (used to generate model card)
+    _hub_mixin_inject_config: bool  # whether `_from_pretrained` expects `config` or not
+    _hub_mixin_init_parameters: dict[str, inspect.Parameter]  # __init__ parameters
+    _hub_mixin_jsonable_default_values: dict[str, Any]  # default values for __init__ parameters
+    _hub_mixin_jsonable_custom_types: tuple[type, ...]  # custom types that can be encoded/decoded
+    _hub_mixin_coders: dict[type, CODER_T]  # encoders/decoders for custom types
+    # ^ internal values to handle config
+
+    def __init_subclass__(
+        cls,
+        *,
+        # Generic info for model card
+        repo_url: str | None = None,
+        paper_url: str | None = None,
+        docs_url: str | None = None,
+        # Model card template
+        model_card_template: str = DEFAULT_MODEL_CARD,
+        # Model card metadata
+        language: list[str] | None = None,
+        library_name: str | None = None,
+        license: str | None = None,
+        license_name: str | None = None,
+        license_link: str | None = None,
+        pipeline_tag: str | None = None,
+        tags: list[str] | None = None,
+        # How to encode/decode arguments with custom type into a JSON config?
+        coders: None
+        | (
+            dict[type, CODER_T]
+            # Key is a type.
+            # Value is a tuple (encoder, decoder).
+            # Example: {MyCustomType: (lambda x: x.value, lambda data: MyCustomType(data))}
+        ) = None,
+    ) -> None:
+        """Inspect __init__ signature only once when subclassing + handle modelcard."""
+        super().__init_subclass__()
+
+        # Will be reused when creating modelcard
+        tags = tags or []
+        tags.append("model_hub_mixin")
+
+        # Initialize MixinInfo if not existent
+        info = MixinInfo(model_card_template=model_card_template, model_card_data=ModelCardData())
+
+        # If parent class has a MixinInfo, inherit from it as a copy
+        if hasattr(cls, "_hub_mixin_info"):
+            # Inherit model card template from parent class if not explicitly set
+            if model_card_template == DEFAULT_MODEL_CARD:
+                info.model_card_template = cls._hub_mixin_info.model_card_template
+
+            # Inherit from parent model card data
+            info.model_card_data = ModelCardData(**cls._hub_mixin_info.model_card_data.to_dict())
+
+            # Inherit other info
+            info.docs_url = cls._hub_mixin_info.docs_url
+            info.paper_url = cls._hub_mixin_info.paper_url
+            info.repo_url = cls._hub_mixin_info.repo_url
+        cls._hub_mixin_info = info
+
+        # Update MixinInfo with metadata
+        if model_card_template is not None and model_card_template != DEFAULT_MODEL_CARD:
+            info.model_card_template = model_card_template
+        if repo_url is not None:
+            info.repo_url = repo_url
+        if paper_url is not None:
+            info.paper_url = paper_url
+        if docs_url is not None:
+            info.docs_url = docs_url
+        if language is not None:
+            info.model_card_data.language = language
+        if library_name is not None:
+            info.model_card_data.library_name = library_name
+        if license is not None:
+            info.model_card_data.license = license
+        if license_name is not None:
+            info.model_card_data.license_name = license_name
+        if license_link is not None:
+            info.model_card_data.license_link = license_link
+        if pipeline_tag is not None:
+            info.model_card_data.pipeline_tag = pipeline_tag
+        if tags is not None:
+            normalized_tags = list(tags)
+            if info.model_card_data.tags is not None:
+                info.model_card_data.tags.extend(normalized_tags)
+            else:
+                info.model_card_data.tags = normalized_tags
+
+        if info.model_card_data.tags is not None:
+            info.model_card_data.tags = sorted(set(info.model_card_data.tags))
+
+        # Handle encoders/decoders for args
+        cls._hub_mixin_coders = coders or {}
+        cls._hub_mixin_jsonable_custom_types = tuple(cls._hub_mixin_coders.keys())
+
+        # Inspect __init__ signature to handle config
+        cls._hub_mixin_init_parameters = dict(inspect.signature(cls.__init__).parameters)
+        cls._hub_mixin_jsonable_default_values = {
+            param.name: cls._encode_arg(param.default)
+            for param in cls._hub_mixin_init_parameters.values()
+            if param.default is not inspect.Parameter.empty and cls._is_jsonable(param.default)
+        }
+        cls._hub_mixin_inject_config = "config" in inspect.signature(cls._from_pretrained).parameters
+
+    def __new__(cls: type[T], *args, **kwargs) -> T:
+        """Create a new instance of the class and handle config.
+
+        3 cases:
+        - If `self._hub_mixin_config` is already set, do nothing.
+        - If `config` is passed as a dataclass, set it as `self._hub_mixin_config`.
+        - Otherwise, build `self._hub_mixin_config` from default values and passed values.
+        """
+        instance = super().__new__(cls)
+
+        # If `config` is already set, return early
+        if instance._hub_mixin_config is not None:
+            return instance
+
+        # Infer passed values
+        passed_values = {
+            **{
+                key: value
+                for key, value in zip(
+                    # [1:] to skip `self` parameter
+                    list(cls._hub_mixin_init_parameters)[1:],
+                    args,
+                )
+            },
+            **kwargs,
+        }
+
+        # If config passed as dataclass => set it and return early
+        if is_dataclass(passed_values.get("config")):
+            instance._hub_mixin_config = passed_values["config"]
+            return instance
+
+        # Otherwise, build config from default + passed values
+        init_config = {
+            # default values
+            **cls._hub_mixin_jsonable_default_values,
+            # passed values
+            **{
+                key: cls._encode_arg(value)  # Encode custom types as jsonable value
+                for key, value in passed_values.items()
+                if instance._is_jsonable(value)  # Only if jsonable or we have a custom encoder
+            },
+        }
+        passed_config = init_config.pop("config", {})
+
+        # Populate `init_config` with provided config
+        if isinstance(passed_config, dict):
+            init_config.update(passed_config)
+
+        # Set `config` attribute and return
+        if init_config != {}:
+            instance._hub_mixin_config = init_config
+        return instance
+
+    @classmethod
+    def _is_jsonable(cls, value: Any) -> bool:
+        """Check if a value is JSON serializable."""
+        if is_dataclass(value):
+            return True
+        if isinstance(value, cls._hub_mixin_jsonable_custom_types):
+            return True
+        return is_jsonable(value)
+
+    @classmethod
+    def _encode_arg(cls, arg: Any) -> Any:
+        """Encode an argument into a JSON serializable format."""
+        if is_dataclass(arg):
+            return asdict(arg)  # type: ignore[arg-type]
+        for type_, (encoder, _) in cls._hub_mixin_coders.items():
+            if isinstance(arg, type_):
+                if arg is None:
+                    return None
+                return encoder(arg)
+        return arg
+
+    @classmethod
+    def _decode_arg(cls, expected_type: type[ARGS_T], value: Any) -> ARGS_T | None:
+        """Decode a JSON serializable value into an argument."""
+        if is_simple_optional_type(expected_type):
+            if value is None:
+                return None
+            expected_type = unwrap_simple_optional_type(expected_type)  # type: ignore
+        # Dataclass => handle it
+        if is_dataclass(expected_type):
+            return _load_dataclass(expected_type, value)  # type: ignore
+        # Otherwise => check custom decoders
+        for type_, (_, decoder) in cls._hub_mixin_coders.items():
+            if inspect.isclass(expected_type) and issubclass(expected_type, type_):
+                return decoder(value)
+        # Otherwise => don't decode
+        return value
+
+    def save_pretrained(
+        self,
+        save_directory: str | Path,
+        *,
+        config: dict | DataclassInstance | None = None,
+        repo_id: str | None = None,
+        push_to_hub: bool = False,
+        model_card_kwargs: dict[str, Any] | None = None,
+        **push_to_hub_kwargs,
+    ) -> str | None:
+        """
+        Save weights in local directory.
+
+        Args:
+            save_directory (`str` or `Path`):
+                Path to directory in which the model weights and configuration will be saved.
+            config (`dict` or `DataclassInstance`, *optional*):
+                Model configuration specified as a key/value dictionary or a dataclass instance.
+            push_to_hub (`bool`, *optional*, defaults to `False`):
+                Whether or not to push your model to the Huggingface Hub after saving it.
+            repo_id (`str`, *optional*):
+                ID of your repository on the Hub. Used only if `push_to_hub=True`. Will default to the folder name if
+                not provided.
+            model_card_kwargs (`dict[str, Any]`, *optional*):
+                Additional arguments passed to the model card template to customize the model card.
+            push_to_hub_kwargs:
+                Additional key word arguments passed along to the [`~ModelHubMixin.push_to_hub`] method.
+        Returns:
+            `str` or `None`: url of the commit on the Hub if `push_to_hub=True`, `None` otherwise.
+        """
+        save_directory = Path(save_directory)
+        save_directory.mkdir(parents=True, exist_ok=True)
+
+        # Remove config.json if already exists. After `_save_pretrained` we don't want to overwrite config.json
+        # as it might have been saved by the custom `_save_pretrained` already. However we do want to overwrite
+        # an existing config.json if it was not saved by `_save_pretrained`.
+        config_path = save_directory / constants.CONFIG_NAME
+        config_path.unlink(missing_ok=True)
+
+        # save model weights/files (framework-specific)
+        self._save_pretrained(save_directory)
+
+        # save config (if provided and if not serialized yet in `_save_pretrained`)
+        if config is None:
+            config = self._hub_mixin_config
+        if config is not None:
+            if is_dataclass(config):
+                config = asdict(config)  # type: ignore[arg-type]
+            if not config_path.exists():
+                config_str = json.dumps(config, sort_keys=True, indent=2)
+                config_path.write_text(config_str)
+
+        # save model card
+        model_card_path = save_directory / "README.md"
+        model_card_kwargs = model_card_kwargs if model_card_kwargs is not None else {}
+        if not model_card_path.exists():  # do not overwrite if already exists
+            self.generate_model_card(**model_card_kwargs).save(save_directory / "README.md")
+
+        # push to the Hub if required
+        if push_to_hub:
+            kwargs = push_to_hub_kwargs.copy()  # soft-copy to avoid mutating input
+            if config is not None:  # kwarg for `push_to_hub`
+                kwargs["config"] = config
+            if repo_id is None:
+                repo_id = save_directory.name  # Defaults to `save_directory` name
+            return self.push_to_hub(repo_id=repo_id, model_card_kwargs=model_card_kwargs, **kwargs)
+        return None
+
+    def _save_pretrained(self, save_directory: Path) -> None:
+        """
+        Overwrite this method in subclass to define how to save your model.
+        Check out our [integration guide](../guides/integrations) for instructions.
+
+        Args:
+            save_directory (`str` or `Path`):
+                Path to directory in which the model weights and configuration will be saved.
+        """
+        raise NotImplementedError
+
+    @classmethod
+    @validate_hf_hub_args
+    def from_pretrained(
+        cls: type[T],
+        pretrained_model_name_or_path: str | Path,
+        *,
+        force_download: bool = False,
+        token: str | bool | None = None,
+        cache_dir: str | Path | None = None,
+        local_files_only: bool = False,
+        revision: str | None = None,
+        **model_kwargs,
+    ) -> T:
+        """
+        Download a model from the Huggingface Hub and instantiate it.
+
+        Args:
+            pretrained_model_name_or_path (`str`, `Path`):
+                - Either the `model_id` (string) of a model hosted on the Hub, e.g. `bigscience/bloom`.
+                - Or a path to a `directory` containing model weights saved using
+                    [`~transformers.PreTrainedModel.save_pretrained`], e.g., `../path/to/my_model_directory/`.
+            revision (`str`, *optional*):
+                Revision of the model on the Hub. Can be a branch name, a git tag or any commit id.
+                Defaults to the latest commit on `main` branch.
+            force_download (`bool`, *optional*, defaults to `False`):
+                Whether to force (re-)downloading the model weights and configuration files from the Hub, overriding
+                the existing cache.
+            token (`str` or `bool`, *optional*):
+                The token to use as HTTP bearer authorization for remote files. By default, it will use the token
+                cached when running `hf auth login`.
+            cache_dir (`str`, `Path`, *optional*):
+                Path to the folder where cached files are stored.
+            local_files_only (`bool`, *optional*, defaults to `False`):
+                If `True`, avoid downloading the file and return the path to the local cached file if it exists.
+            model_kwargs (`dict`, *optional*):
+                Additional kwargs to pass to the model during initialization.
+        """
+        model_id = str(pretrained_model_name_or_path)
+        config_file: str | None = None
+        if os.path.isdir(model_id):
+            if constants.CONFIG_NAME in os.listdir(model_id):
+                config_file = os.path.join(model_id, constants.CONFIG_NAME)
+            else:
+                logger.warning(f"{constants.CONFIG_NAME} not found in {Path(model_id).resolve()}")
+        else:
+            try:
+                config_file = hf_hub_download(
+                    repo_id=model_id,
+                    filename=constants.CONFIG_NAME,
+                    revision=revision,
+                    cache_dir=cache_dir,
+                    force_download=force_download,
+                    token=token,
+                    local_files_only=local_files_only,
+                )
+            except HfHubHTTPError as e:
+                logger.info(f"{constants.CONFIG_NAME} not found on the HuggingFace Hub: {str(e)}")
+
+        # Read config
+        config = None
+        if config_file is not None:
+            with open(config_file, encoding="utf-8") as f:
+                config = json.load(f)
+
+            # Decode custom types in config
+            for key, value in config.items():
+                if key in cls._hub_mixin_init_parameters:
+                    expected_type = cls._hub_mixin_init_parameters[key].annotation
+                    if expected_type is not inspect.Parameter.empty:
+                        config[key] = cls._decode_arg(expected_type, value)
+
+            # Populate model_kwargs from config
+            for param in cls._hub_mixin_init_parameters.values():
+                if param.name not in model_kwargs and param.name in config:
+                    model_kwargs[param.name] = config[param.name]
+
+            # Check if `config` argument was passed at init
+            if "config" in cls._hub_mixin_init_parameters and "config" not in model_kwargs:
+                # Decode `config` argument if it was passed
+                config_annotation = cls._hub_mixin_init_parameters["config"].annotation
+                config = cls._decode_arg(config_annotation, config)
+
+                # Forward config to model initialization
+                model_kwargs["config"] = config
+
+            # Inject config if `**kwargs` are expected
+            if is_dataclass(cls):
+                for key in cls.__dataclass_fields__:
+                    if key not in model_kwargs and key in config:
+                        model_kwargs[key] = config[key]
+            elif any(param.kind == inspect.Parameter.VAR_KEYWORD for param in cls._hub_mixin_init_parameters.values()):
+                for key, value in config.items():  # type: ignore[union-attr]
+                    if key not in model_kwargs:
+                        model_kwargs[key] = value
+
+            # Finally, also inject if `_from_pretrained` expects it
+            if cls._hub_mixin_inject_config and "config" not in model_kwargs:
+                model_kwargs["config"] = config
+
+        instance = cls._from_pretrained(
+            model_id=str(model_id),
+            revision=revision,
+            cache_dir=cache_dir,
+            force_download=force_download,
+            local_files_only=local_files_only,
+            token=token,
+            **model_kwargs,
+        )
+
+        # Implicitly set the config as instance attribute if not already set by the class
+        # This way `config` will be available when calling `save_pretrained` or `push_to_hub`.
+        if config is not None and (getattr(instance, "_hub_mixin_config", None) in (None, {})):
+            instance._hub_mixin_config = config
+
+        return instance
+
+    @classmethod
+    def _from_pretrained(
+        cls: type[T],
+        *,
+        model_id: str,
+        revision: str | None,
+        cache_dir: str | Path | None,
+        force_download: bool,
+        local_files_only: bool,
+        token: str | bool | None,
+        **model_kwargs,
+    ) -> T:
+        """Overwrite this method in subclass to define how to load your model from pretrained.
+
+        Use [`hf_hub_download`] or [`snapshot_download`] to download files from the Hub before loading them. Most
+        args taken as input can be directly passed to those 2 methods. If needed, you can add more arguments to this
+        method using "model_kwargs". For example [`PyTorchModelHubMixin._from_pretrained`] takes as input a `map_location`
+        parameter to set on which device the model should be loaded.
+
+        Check out our [integration guide](../guides/integrations) for more instructions.
+
+        Args:
+            model_id (`str`):
+                ID of the model to load from the Huggingface Hub (e.g. `bigscience/bloom`).
+            revision (`str`, *optional*):
+                Revision of the model on the Hub. Can be a branch name, a git tag or any commit id. Defaults to the
+                latest commit on `main` branch.
+            force_download (`bool`, *optional*, defaults to `False`):
+                Whether to force (re-)downloading the model weights and configuration files from the Hub, overriding
+                the existing cache.
+            token (`str` or `bool`, *optional*):
+                The token to use as HTTP bearer authorization for remote files. By default, it will use the token
+                cached when running `hf auth login`.
+            cache_dir (`str`, `Path`, *optional*):
+                Path to the folder where cached files are stored.
+            local_files_only (`bool`, *optional*, defaults to `False`):
+                If `True`, avoid downloading the file and return the path to the local cached file if it exists.
+            model_kwargs:
+                Additional keyword arguments passed along to the [`~ModelHubMixin._from_pretrained`] method.
+        """
+        raise NotImplementedError
+
+    @validate_hf_hub_args
+    def push_to_hub(
+        self,
+        repo_id: str,
+        *,
+        config: dict | DataclassInstance | None = None,
+        commit_message: str = "Push model using huggingface_hub.",
+        private: bool | None = None,
+        token: str | None = None,
+        branch: str | None = None,
+        create_pr: bool | None = None,
+        allow_patterns: list[str] | str | None = None,
+        ignore_patterns: list[str] | str | None = None,
+        delete_patterns: list[str] | str | None = None,
+        model_card_kwargs: dict[str, Any] | None = None,
+    ) -> str:
+        """
+        Upload model checkpoint to the Hub.
+
+        Use `allow_patterns` and `ignore_patterns` to precisely filter which files should be pushed to the hub. Use
+        `delete_patterns` to delete existing remote files in the same commit. See [`upload_folder`] reference for more
+        details.
+
+        Args:
+            repo_id (`str`):
+                ID of the repository to push to (example: `"username/my-model"`).
+            config (`dict` or `DataclassInstance`, *optional*):
+                Model configuration specified as a key/value dictionary or a dataclass instance.
+            commit_message (`str`, *optional*):
+                Message to commit while pushing.
+            private (`bool`, *optional*):
+                Whether the repository created should be private.
+                If `None` (default), the repo will be public unless the organization's default is private.
+            token (`str`, *optional*):
+                The token to use as HTTP bearer authorization for remote files. By default, it will use the token
+                cached when running `hf auth login`.
+            branch (`str`, *optional*):
+                The git branch on which to push the model. This defaults to `"main"`.
+            create_pr (`boolean`, *optional*):
+                Whether or not to create a Pull Request from `branch` with that commit. Defaults to `False`.
+            allow_patterns (`list[str]` or `str`, *optional*):
+                If provided, only files matching at least one pattern are pushed.
+            ignore_patterns (`list[str]` or `str`, *optional*):
+                If provided, files matching any of the patterns are not pushed.
+            delete_patterns (`list[str]` or `str`, *optional*):
+                If provided, remote files matching any of the patterns will be deleted from the repo.
+            model_card_kwargs (`dict[str, Any]`, *optional*):
+                Additional arguments passed to the model card template to customize the model card.
+
+        Returns:
+            The url of the commit of your model in the given repository.
+        """
+        api = HfApi(token=token)
+        repo_id = api.create_repo(repo_id=repo_id, private=private, exist_ok=True).repo_id
+
+        # Push the files to the repo in a single commit
+        with SoftTemporaryDirectory() as tmp:
+            saved_path = Path(tmp) / repo_id
+            self.save_pretrained(saved_path, config=config, model_card_kwargs=model_card_kwargs)
+            return api.upload_folder(
+                repo_id=repo_id,
+                repo_type="model",
+                folder_path=saved_path,
+                commit_message=commit_message,
+                revision=branch,
+                create_pr=create_pr,
+                allow_patterns=allow_patterns,
+                ignore_patterns=ignore_patterns,
+                delete_patterns=delete_patterns,
+            )
+
+    def generate_model_card(self, *args, **kwargs) -> ModelCard:
+        card = ModelCard.from_template(
+            card_data=self._hub_mixin_info.model_card_data,
+            template_str=self._hub_mixin_info.model_card_template,
+            repo_url=self._hub_mixin_info.repo_url,
+            paper_url=self._hub_mixin_info.paper_url,
+            docs_url=self._hub_mixin_info.docs_url,
+            **kwargs,
+        )
+        return card
+
+
+class PyTorchModelHubMixin(ModelHubMixin):
+    """
+    Implementation of [`ModelHubMixin`] to provide model Hub upload/download capabilities to PyTorch models. The model
+    is set in evaluation mode by default using `model.eval()` (dropout modules are deactivated). To train the model,
+    you should first set it back in training mode with `model.train()`.
+
+    See [`ModelHubMixin`] for more details on how to use the mixin.
+
+    Example:
+
+    ```python
+    >>> import torch
+    >>> import torch.nn as nn
+    >>> from huggingface_hub import PyTorchModelHubMixin
+
+    >>> class MyModel(
+    ...         nn.Module,
+    ...         PyTorchModelHubMixin,
+    ...         library_name="keras-nlp",
+    ...         repo_url="https://github.com/keras-team/keras-nlp",
+    ...         paper_url="https://arxiv.org/abs/2304.12244",
+    ...         docs_url="https://keras.io/keras_nlp/",
+    ...         # ^ optional metadata to generate model card
+    ...     ):
+    ...     def __init__(self, hidden_size: int = 512, vocab_size: int = 30000, output_size: int = 4):
+    ...         super().__init__()
+    ...         self.param = nn.Parameter(torch.rand(hidden_size, vocab_size))
+    ...         self.linear = nn.Linear(output_size, vocab_size)
+
+    ...     def forward(self, x):
+    ...         return self.linear(x + self.param)
+    >>> model = MyModel(hidden_size=256)
+
+    # Save model weights to local directory
+    >>> model.save_pretrained("my-awesome-model")
+
+    # Push model weights to the Hub
+    >>> model.push_to_hub("my-awesome-model")
+
+    # Download and initialize weights from the Hub
+    >>> model = MyModel.from_pretrained("username/my-awesome-model")
+    >>> model.hidden_size
+    256
+    ```
+    """
+
+    def __init_subclass__(cls, *args, tags: list[str] | None = None, **kwargs) -> None:
+        tags = tags or []
+        tags.append("pytorch_model_hub_mixin")
+        kwargs["tags"] = tags
+        return super().__init_subclass__(*args, **kwargs)
+
+    def _save_pretrained(self, save_directory: Path) -> None:
+        """Save weights from a Pytorch model to a local directory."""
+        model_to_save = self.module if hasattr(self, "module") else self  # type: ignore
+        save_model_as_safetensor(model_to_save, str(save_directory / constants.SAFETENSORS_SINGLE_FILE))  # type: ignore [arg-type]
+
+    @classmethod
+    def _from_pretrained(
+        cls,
+        *,
+        model_id: str,
+        revision: str | None,
+        cache_dir: str | Path | None,
+        force_download: bool,
+        local_files_only: bool,
+        token: str | bool | None,
+        map_location: str = "cpu",
+        strict: bool = False,
+        **model_kwargs,
+    ):
+        """Load Pytorch pretrained weights and return the loaded model."""
+        model = cls(**model_kwargs)
+        if os.path.isdir(model_id):
+            print("Loading weights from local directory")
+            model_file = os.path.join(model_id, constants.SAFETENSORS_SINGLE_FILE)
+            return cls._load_as_safetensor(model, model_file, map_location, strict)
+        else:
+            try:
+                model_file = hf_hub_download(
+                    repo_id=model_id,
+                    filename=constants.SAFETENSORS_SINGLE_FILE,
+                    revision=revision,
+                    cache_dir=cache_dir,
+                    force_download=force_download,
+                    token=token,
+                    local_files_only=local_files_only,
+                )
+                return cls._load_as_safetensor(model, model_file, map_location, strict)
+            except EntryNotFoundError:
+                model_file = hf_hub_download(
+                    repo_id=model_id,
+                    filename=constants.PYTORCH_WEIGHTS_NAME,
+                    revision=revision,
+                    cache_dir=cache_dir,
+                    force_download=force_download,
+                    token=token,
+                    local_files_only=local_files_only,
+                )
+                return cls._load_as_pickle(model, model_file, map_location, strict)
+
+    @classmethod
+    def _load_as_pickle(cls, model: T, model_file: str, map_location: str, strict: bool) -> T:
+        state_dict = torch.load(model_file, map_location=torch.device(map_location), weights_only=True)
+        model.load_state_dict(state_dict, strict=strict)  # type: ignore
+        model.eval()  # type: ignore
+        return model
+
+    @classmethod
+    def _load_as_safetensor(cls, model: T, model_file: str, map_location: str, strict: bool) -> T:
+        if packaging.version.parse(safetensors.__version__) < packaging.version.parse("0.4.3"):  # type: ignore [attr-defined]
+            load_model_as_safetensor(model, model_file, strict=strict)  # type: ignore [arg-type]
+            if map_location != "cpu":
+                logger.warning(
+                    "Loading model weights on other devices than 'cpu' is not supported natively in your version of safetensors."
+                    " This means that the model is loaded on 'cpu' first and then copied to the device."
+                    " This leads to a slower loading time."
+                    " Please update safetensors to version 0.4.3 or above for improved performance."
+                )
+                model.to(map_location)  # type: ignore [attr-defined]
+        else:
+            safetensors.torch.load_model(model, model_file, strict=strict, device=map_location)  # type: ignore [arg-type]
+        model.eval()  # type: ignore
+        return model
+
+
+def _load_dataclass(datacls: type[DataclassInstance], data: dict) -> DataclassInstance:
+    """Load a dataclass instance from a dictionary.
+
+    Fields not expected by the dataclass are ignored.
+    """
+    return datacls(**{k: v for k, v in data.items() if k in datacls.__dataclass_fields__})