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Bump the pip group across 2 directories with 4 updates - #134

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Bump the pip group across 2 directories with 4 updates#134
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Bumps the pip group with 1 update in the /src/dependencies/requirements/base_requirements directory: setuptools.
Bumps the pip group with 3 updates in the /src/maxtext/inference/mlperf directory: sentencepiece, transformers and nltk.

Updates setuptools from 78.1.0 to 83.0.0

Changelog

Sourced from setuptools's changelog.

v83.0.0

Features

  • Require Python 3.10 or later.

Bugfixes

  • MANIFEST.in matching (via FileList) is now insensitive to Unicode normalization form. A pattern authored in one form (e.g. NFC, as typically saved by editors) now matches a file whose name is stored on disk in another (e.g. NFD, as produced by macOS APFS/HFS+). Previously an exclude, global-exclude, recursive-exclude, or prune rule could silently fail to drop a non-ASCII-named file from the source distribution, publishing it despite the exclusion -- see GHSA-h35f-9h28-mq5c.

Deprecations and Removals

  • pypa/distutils#334

v82.0.1

Bugfixes

  • Fix the loading of launcher manifest.xml file. (#5047)
  • Replaced deprecated json.__version__ with fixture in tests. (#5186)

Improved Documentation

  • Add advice about how to improve predictability when installing sdists. (#5168)

Misc

v82.0.0

... (truncated)

Commits
  • 6519f72 Bump version: 82.0.1 → 83.0.0
  • d1151b1 Merge pull request #5250 from pypa/feature/distutils-d7633fbed
  • a2df31e Capture removal of dry_run parameter in changelog.
  • 00144dc Moved newsfragment to the release where it occurred.
  • a4a5a2b Add news fragment.
  • 77470c2 Merge https://github.com/pypa/distutils into feature/distutils-d7633fbed
  • 3c43897 Merge pull request #5247 from pypa/copilot/fix-pypy-version-issue
  • bb6ea66 Bump PyPy from 3.10 to 3.11 in CI workflow
  • a2bc3ac Fix broken intersphinx reference to build's installation docs
  • 2d6a739 Use stacked parametrize decorators instead of itertools.product
  • Additional commits viewable in compare view

Updates sentencepiece from 0.1.99 to 0.2.1

Release notes

Sourced from sentencepiece's releases.

v0.2.1

Major changes

New features

  • [ALL]: Added new build mode to prevent the precompiled normalization rules being embedded in *.so and *.a. (-DSPM_DISABLE_EMBEDDED_DATA=ON). This reduces the runtime size by approximately 1-2 MB. This mode is enabled to build python wheels. The rules are loaded as the data package.

Bug fixes & minor changes

  • [ALL]: Security fix to address a heap overflow issue that could occur when using a model containing an invalid precompiled normalization model.
  • [Python]: Deprecates the wheel package for Linux i686.
  • [Python]: Supported wheel for Windows Arm64. #1114
  • [Python]: Fixed the crash issue on batch decoding #1051
  • [ALL]: Updated the Unicode normalization rule with the latest ICU/Unicode rules.
  • [ALL]: Unused code and build mode cleanup.

v0.2.0

Major changes

N/A

New features

  • [ALL] Added SentencePieceNormalizer class in C++/Python. It supports almost the equivalent feature of spm_normalize. Python Sample C++ Sample
  • [ALL] Added SentencePieceProcessor::Normalize method in C++/Python Python Sample C++ Sample
  • [ALL] Added functionality to override the normalization spec before the processing. Python Sample

Bug fixes & minor changes

  • Introduce better support of using external abseil and protobuf google/sentencepiece#869
  • Build universal binary in OSX release package google/sentencepiece#892
  • Add the set_min_log_level function to python to change the loglevel from the python wrapper. google/sentencepiece#893
  • Uses the logsumexp techniques in marginal probabilities of n-best tokenization to avoid underflow.
  • Support Python 3.12 google/sentencepiece#932
  • Improves the thread utilization in batch encoding/decoding.
  • Fix nasty bug in BPE position encoding.
  • Fix bugs in the handling of duplicated bigrams
Commits
  • 31646a4 Merge pull request #1136 from crusaderky/pytest-run-parallel
  • bcd44b9 free-threading tests
  • 135747f install twine before checking wheel
  • 69fe0b2 install setuptools before making sdist
  • ee1422b install setuptools before making sdist
  • 5ac2fd2 use windows-11-arm runner to test ARM64 wheel on native env.
  • 36b9745 use windows-11-arm runner to test ARM64 wheel on native env.
  • 4f043ae use auto-mode to make wheel with the native binary.
  • 623196e uses arm docker image to build and test wheel
  • 559fd65 re-enable QEMU to enable arm execution
  • Additional commits viewable in compare view

Updates transformers from 4.31.0 to 5.5.0

Release notes

Sourced from transformers's releases.

Release v5.5.0

New Model additions

Gemma4

Gemma 4 is a multimodal model with pretrained and instruction-tuned variants, available in 1B, 13B, and 27B parameters. The architecture is mostly the same as the previous Gemma versions. The key differences are a vision processor that can output images of fixed token budget and a spatial 2D RoPE to encode vision-specific information across height and width axis.

You can find all the original Gemma 4 checkpoints under the Gemma 4 release.

The key difference from previous Gemma releases is the new design to process images of different sizes using a fixed-budget number of tokens. Unlike many models that squash every image into a fixed square (like 224×224), Gemma 4 keeps the image's natural aspect ratio while making it the right size. There a a couple constraints to follow:

  • The total number of pixels must fit within a patch budget
  • Both height and width must be divisible by 48 (= patch size 16 × pooling kernel 3)

[!IMPORTANT] Gemma 4 does not apply the standard ImageNet mean/std normalization that many other vision models use. The model's own patch embedding layer handles the final scaling internally (shifting values to the [-1, 1] range).

The number of "soft tokens" (aka vision tokens) an image processor can produce is configurable. The supported options are outlined below and the default is 280 soft tokens per image.

Soft Tokens Patches (before pooling) Approx. Image Area
70 630 ~161K pixels
140 1,260 ~323K pixels
280 2,520 ~645K pixels
560 5,040 ~1.3M pixels
1,120 10,080 ~2.6M pixels

To encode positional information for each patch in the image, Gemma 4 uses a learned 2D position embedding table. The position table stores up to 10,240 positions per axis, which allows the model to handle very large images. Each position is a learned vector of the same dimensions as the patch embedding. The 2D RoPE which Gemma 4 uses independently rotate half the attention head dimensions for the x-axis and the other half for the y-axis. This allows the model to understand spatial relationships like "above," "below," "left of," and "right of."

NomicBERT

NomicBERT is a BERT-inspired encoder model that applies Rotary Position Embeddings (RoPE) to create reproducible long context text embeddings. It is the first fully reproducible, open-source text embedding model with 8192 context length that outperforms both OpenAI Ada-002 and OpenAI text-embedding-3-small on short-context MTEB and long context LoCo benchmarks. The model generates dense vector embeddings for various tasks including search, clustering, and classification using specific instruction prefixes.

Links: Documentation | Paper

MusicFlamingo

Music Flamingo is a fully open large audio–language model designed for robust understanding and reasoning over music. It builds upon the Audio Flamingo 3 architecture by including Rotary Time Embeddings (RoTE), which injects temporal position information to enable the model to handle audio sequences up to 20 minutes. The model features a unified audio encoder across speech, sound, and music with special sound boundary tokens for improved audio sequence modeling.

Links: Documentation | Paper

... (truncated)

Commits
  • c1c3424 update
  • 20bff68 update release workflow
  • 8956441 v5.5.0
  • 5135e5e casually dropping the most capable open weights on the planet (#45192)
  • a594e09 Internalise the NomicBERT model (#43067)
  • 4932e97 Fix resized LM head weights being overwritten by post_init (#45079)
  • 57e8413 [Qwen3.5 MoE] Add _tp_plan to ForConditionalGeneration (#45124)
  • b10552e Fix TypeError: 'NoneType' object is not iterable in GenerationMixin.generate ...
  • 423f2a3 fix(models): Fix dtype mismatch in SwitchTransformers and TimmWrapperModel (#...
  • ade7a05 Generalize gemma vision mask to videos (#45185)
  • Additional commits viewable in compare view

Updates nltk from 3.8.1 to 3.10.0

Release notes

Sourced from nltk's releases.

v3.10.0

What's Changed

... (truncated)

Changelog

Sourced from nltk's changelog.

Version 3.10.2 2026-08-05

  • Remove inisec.py and document PYTHONSAFEPATH instead
  • Skip draft step in release workflow
  • Fix symlink escape in FramenetCorpusReader (CWE-59)
  • Guard tempfile.gettempdir() when building pathsec allowed roots
  • add tests for transitive_closure

Thanks to the following contributors to 3.10.2: Litesh Ghute, Eric Kafe, Evan Kiefer, tarann26 and Rav Singh Chandan

Version 3.10.1 2026-07-29

  • Expand ~ in env-var paths
  • Validate types after WordNet app pickle deserialization
  • Fix uncontrolled search path in HunposTagger
  • Use exact thirds in masi_distance
  • Avoid retaining bllip import exceptions
  • Fix word_tokenize: pad opening single quote before multi-letter words.
  • Implement Tree.pformat_latex_forest.
  • Prevent module hijacking in inline imports.
  • Fix ReDoS in TweetTokenizer URL and email regexes.

Thanks to the following contributors to 3.10.1: Abhinav, Litesh Ghute, Eric Kafe, Eryk Kaźmierczak, Selim C., Muhtasim Munif Fahim, Triniti K., and Tom Y. Mitich.

Version 3.10.0 2026-06-11

  • Enforce the stricter nltk.pathsec security policy by default
  • Document the new security model and migration guidance
  • Harden resource loading against path traversal and SSRF/DNS-rebinding
  • Harden downloader path handling and block XML entity expansion
  • Close remaining corpus-reader security edge cases
  • Replace unsafe exec() usage in the utility CLI
  • Warn on unpickling user-provided pickles
  • Add HuggingFace datasets integration (nltk.huggingface)
  • Align TnT with Brants (2000) specifications
  • Fix PorterStemmer irregular-form lowercasing in NLTK mode
  • Fix TransitionParser sparse index dtype for scikit-learn 1.9
  • Fix TextCat tie handling
  • Fix WordNet object comparisons for incompatible types
  • Cache WordNet max depth lazily for lch_similarity()
  • Fix CCG variable direction, substitution, and type-raising bugs
  • Fix Jaro similarity for single-character and empty-string cases
  • Improve CI and release-maintenance workflows

Thanks to the following contributors to 3.10.0: 13rac1, alvations, bowiechen, devesh-2002, ekaf, elias-ba, haosenwang1018, HyperPS, ihitamandal, jancallewaert, jhnwnstd,

... (truncated)

Commits
  • bd49f90 allow escaped brackets in Tree.fromstring (#3694)
  • 27b8ad6 don't crash chomsky_normal_form on terminals with siblings (#3693)
  • 52227d2 Use os.name for Windows path handling (#3605)
  • 06c0e2c Avoid RIBES zero division on empty inputs (#3604)
  • a167389 Treat missing unzip output as stale (#3607)
  • c94c967 Fix EOF empty document bug in IEER corpus reader (#3648)
  • 94a259c Enforce restrictive primitive type checking in pathsec wrappers (#3692)
  • 5ac475d fix(security): isolate Stanford Java options and clean temp files (#3683)
  • 986f26e ci(deps): bump the github-actions group with 3 updates (#3691)
  • f26b375 fix(security): prevent pickle RCE in TransitionParser model loading (CWE-502)...
  • Additional commits viewable in compare view

Updates transformers from 4.31.0 to 5.5.0

Release notes

Sourced from transformers's releases.

Release v5.5.0

New Model additions

Gemma4

Gemma 4 is a multimodal model with pretrained and instruction-tuned variants, available in 1B, 13B, and 27B parameters. The architecture is mostly the same as the previous Gemma versions. The key differences are a vision processor that can output images of fixed token budget and a spatial 2D RoPE to encode vision-specific information across height and width axis.

You can find all the original Gemma 4 checkpoints under the Gemma 4 release.

The key difference from previous Gemma releases is the new design to process images of different sizes using a fixed-budget number of tokens. Unlike many models that squash every image into a fixed square (like 224×224), Gemma 4 keeps the image's natural aspect ratio while making it the right size. There a a couple constraints to follow:

  • The total number of pixels must fit within a patch budget
  • Both height and width must be divisible by 48 (= patch size 16 × pooling kernel 3)

[!IMPORTANT] Gemma 4 does not apply the standard ImageNet mean/std normalization that many other vision models use. The model's own patch embedding layer handles the final scaling internally (shifting values to the [-1, 1] range).

The number of "soft tokens" (aka vision tokens) an image processor can produce is configurable. The supported options are outlined below and the default is 280 soft tokens per image.

Soft Tokens Patches (before pooling) Approx. Image Area
70 630 ~161K pixels
140 1,260 ~323K pixels
280 2,520 ~645K pixels
560 5,040 ~1.3M pixels
1,120 10,080 ~2.6M pixels

To encode positional information for each patch in the image, Gemma 4 uses a learned 2D position embedding table. The position table stores up to 10,240 positions per axis, which allows the model to handle very large images. Each position is a learned vector of the same dimensions as the patch embedding. The 2D RoPE which Gemma 4 uses independently rotate half the attention head dimensions for the x-axis and the other half for the y-axis. This allows the model to understand spatial relationships like "above," "below," "left of," and "right of."

NomicBERT

NomicBERT is a BERT-inspired encoder model that applies Rotary Position Embeddings (RoPE) to create reproducible long context text embeddings. It is the first fully reproducible, open-source text embedding model with 8192 context length that outperforms both OpenAI Ada-002 and OpenAI text-embedding-3-small on short-context MTEB and long context LoCo benchmarks. The model generates dense vector embeddings for various tasks including search, clustering, and classification using specific instruction prefixes.

Links: Documentation | Paper

MusicFlamingo

Music Flamingo is a fully open large audio–language model designed for robust understanding and reasoning over music. It builds upon the Audio Flamingo 3 architecture by including Rotary Time Embeddings (RoTE), which injects temporal position information to enable the model to handle audio sequences up to 20 minutes. The model features a unified audio encoder across speech, sound, and music with special sound boundary tokens for improved audio sequence modeling.

Links: Documentation | Paper

... (truncated)

Commits
  • c1c3424 update
  • 20bff68 update release workflow
  • 8956441 v5.5.0
  • 5135e5e casually dropping the most capable open weights on the planet (#45192)
  • a594e09 Internalise the NomicBERT model (#43067)
  • 4932e97 Fix resized LM head weights being overwritten by post_init (#45079)
  • 57e8413 [Qwen3.5 MoE] Add _tp_plan to ForConditionalGeneration (#45124)
  • b10552e Fix TypeError: 'NoneType' object is not iterable in GenerationMixin.generate ...
  • 423f2a3 fix(models): Fix dtype mismatch in SwitchTransformers and TimmWrapperModel (#...
  • ade7a05 Generalize gemma vision mask to videos (#45185)
  • Additional commits viewable in compare view

Updates nltk from 3.8.1 to 3.10.0

Release notes

Sourced from nltk's releases.

v3.10.0

What's Changed

... (truncated)

Changelog

Sourced from nltk's changelog.

Version 3.10.2 2026-08-05

  • Remove inisec.py and document PYTHONSAFEPATH instead
  • Skip draft step in release workflow
  • Fix symlink escape in FramenetCorpusReader (CWE-59)
  • Guard tempfile.gettempdir() when building pathsec allowed roots
  • add tests for transitive_closure

Thanks to the following contributors to 3.10.2: Litesh Ghute, Eric Kafe, Evan Kiefer, tarann26 and Rav Singh Chandan

Version 3.10.1 2026-07-29

  • Expand ~ in env-var paths
  • Validate types after WordNet app pickle deserialization
  • Fix uncontrolled search path in HunposTagger
  • Use exact thirds in masi_distance
  • Avoid retaining bllip import exceptions
  • Fix word_tokenize: pad opening single quote before multi-letter words.
  • Implement Tree.pformat_latex_forest.
  • Prevent module hijacking in inline imports.
  • Fix ReDoS in TweetTokenizer URL and email regexes.

Thanks to the following contributors to 3.10.1: Abhinav, Litesh Ghute, Eric Kafe, Eryk Kaźmierczak, Selim C., Muhtasim Munif Fahim, Triniti K., and Tom Y. Mitich.

Version 3.10.0 2026-06-11

  • Enforce the stricter nltk.pathsec security policy by default
  • Document the new security model and migration guidance
  • Harden resource loading against path traversal and SSRF/DNS-rebinding
  • Harden downloader path handling and block XML entity expansion
  • Close remaining corpus-reader security edge cases
  • Replace unsafe exec() usage in the utility CLI
  • Warn on unpickling user-provided pickles
  • Add HuggingFace datasets integration (nltk.huggingface)
  • Align TnT with Brants (2000) specifications
  • Fix PorterStemmer irregular-form lowercasing in NLTK mode
  • Fix TransitionParser sparse index dtype for scikit-learn 1.9
  • Fix TextCat tie handling
  • Fix WordNet object comparisons for incompatible types
  • Cache WordNet max depth lazily for lch_similarity()
  • Fix CCG variable direction, substitution, and type-raising bugs
  • Fix Jaro similarity for single-character and empty-string cases
  • Improve CI and release-maintenance workflows

Thanks to the following contributors to 3.10.0: 13rac1, alvations, bowiechen, devesh-2002, ekaf, elias-ba, haosenwang1018, HyperPS, ihitamandal, jancallewaert, jhnwnstd,

... (truncated)

Commits
  • bd49f90 allow escaped brackets in Tree.fromstring (#3694)
  • 27b8ad6 don't crash chomsky_normal_form on terminals with siblings (#3693)
  • 52227d2 Use os.name for Windows path handling (#3605)
  • 06c0e2c Avoid RIBES zero division on empty inputs (#3604)
  • a167389 Treat missing unzip output as stale (#3607)
  • c94c967 Fix EOF empty document bug in IEER corpus reader (#3648)
  • 94a259c Enforce restrictive primitive type checking in pathsec wrappers (#3692)
  • 5ac475d fix(security): isolate Stanford Java options and clean temp files (#3683)
  • 986f26e ci(deps): bump the github-actions group with 3 updates (#3691)
  • f26b375 fix(security): prevent pickle RCE in TransitionParser model loading (CWE-502)...
  • Additional commits viewable in compare view

Updates sentencepiece from 0.1.99 to 0.2.1

Release notes

Sourced from sentencepiece's releases.

v0.2.1

Major changes

New features

  • [ALL]: Added new build mode to prevent the precompiled normalization rules being embedded in *.so and *.a. (-DSPM_DISABLE_EMBEDDED_DATA=ON). This reduces the runtime size by approximately 1-2 MB. This mode is enabled to build python wheels. The rules are loaded as the data package.

Bug fixes & minor changes

  • [ALL]: Security fix to address a heap overflow issue that could occur when using a model containing an invalid precompiled normalization model.
  • [Python]: Deprecates the wheel package for Linux i686.
  • [Python]: Supported wheel for Windows Arm64. #1114
  • [Python]: Fixed the crash issue on batch decoding #1051
  • [ALL]: Updated the Unicode normalization rule with the latest ICU/Unicode rules.
  • [ALL]: Unused code and build mode cleanup.

v0.2.0

Major changes

N/A

New features

  • [ALL] Added SentencePieceNormalizer class in C++/Python. It supports almost the equivalent feature of spm_normalize. Python Sample C++ Sample
  • [ALL] Added SentencePieceProcessor::Normalize method in C++/Python Python Sample C++ Sample
  • [ALL] Added functionality to override the normalization spec before the processing. Python Sample

Bug fixes & minor changes

  • Introduce better support of using external abseil and protobuf google/sentencepiece#869
  • Build universal binary in OSX release package google/sentencepiece#892
  • Add the set_min_log_level function to python to change the loglevel from the python wrapper. google/sentencepiece#893
  • Uses the logsumexp techniques in marginal probabilities of n-best tokenization to avoid underflow.
  • Support Python 3.12 google/sentencepiece#932
  • Improves the thread utilization in batch encoding/decoding.
  • Fix nasty bug in BPE position encoding.
  • Fix bugs in the handling of duplicated bigrams
Commits
  • 31646a4 Merge pull request #1136 from crusaderky/pytest-run-parallel
  • bcd44b9 free-threading tests
  • 135747f install twine before checking wheel
  • 69fe0b2 install setuptools before making sdist
  • ee1422b install setuptools before making sdist
  • 5ac2fd2 use windows-11-arm runner to test ARM64 wheel on native env.
  • 36b9745 use windows-11-arm runner to test ARM64 wheel on native env.
    • ...

    Description has been truncated

@dependabot dependabot Bot added dependencies Pull requests that update a dependency file python Pull requests that update python code labels Jul 24, 2026
@dependabot dependabot Bot mentioned this pull request Jul 24, 2026
Closed
@dependabot
dependabot Bot force-pushed the dependabot/pip/src/dependencies/requirements/base_requirements/pip-57ba0104f5 branch from 2261105 to 22b1aed Compare July 30, 2026 20:41
@dependabot
Bump the pip group across 2 directories with 4 updates
c8af92e 
Bumps the pip group with 1 update in the /src/dependencies/requirements/base_requirements directory: [setuptools](https://github.com/pypa/setuptools).
Bumps the pip group with 3 updates in the /src/maxtext/inference/mlperf directory: [sentencepiece](https://github.com/google/sentencepiece), [transformers](https://github.com/huggingface/transformers) and [nltk](https://github.com/nltk/nltk).


Updates `setuptools` from 78.1.0 to 83.0.0
- [Release notes](https://github.com/pypa/setuptools/releases)
- [Changelog](https://github.com/pypa/setuptools/blob/main/NEWS.rst)
- [Commits](pypa/setuptools@v78.1.0...v83.0.0)

Updates `sentencepiece` from 0.1.99 to 0.2.1
- [Release notes](https://github.com/google/sentencepiece/releases)
- [Commits](google/sentencepiece@v0.1.99...v0.2.1)

Updates `transformers` from 4.31.0 to 5.5.0
- [Release notes](https://github.com/huggingface/transformers/releases)
- [Commits](huggingface/transformers@v4.31.0...v5.5.0)

Updates `nltk` from 3.8.1 to 3.10.0
- [Release notes](https://github.com/nltk/nltk/releases)
- [Changelog](https://github.com/nltk/nltk/blob/develop/ChangeLog)
- [Commits](nltk/nltk@3.8.1...v3.10.0)

Updates `transformers` from 4.31.0 to 5.5.0
- [Release notes](https://github.com/huggingface/transformers/releases)
- [Commits](huggingface/transformers@v4.31.0...v5.5.0)

Updates `nltk` from 3.8.1 to 3.10.0
- [Release notes](https://github.com/nltk/nltk/releases)
- [Changelog](https://github.com/nltk/nltk/blob/develop/ChangeLog)
- [Commits](nltk/nltk@3.8.1...v3.10.0)

Updates `sentencepiece` from 0.1.99 to 0.2.1
- [Release notes](https://github.com/google/sentencepiece/releases)
- [Commits](google/sentencepiece@v0.1.99...v0.2.1)

---
updated-dependencies:
- dependency-name: nltk
  dependency-version: 3.10.0
  dependency-type: direct:production
- dependency-name: nltk
  dependency-version: 3.10.0
  dependency-type: direct:production
- dependency-name: sentencepiece
  dependency-version: 0.2.1
  dependency-type: direct:production
- dependency-name: sentencepiece
  dependency-version: 0.2.1
  dependency-type: direct:production
- dependency-name: setuptools
  dependency-version: 83.0.0
  dependency-type: direct:production
- dependency-name: transformers
  dependency-version: 5.5.0
  dependency-type: direct:production
- dependency-name: transformers
  dependency-version: 5.5.0
  dependency-type: direct:production
...

Signed-off-by: dependabot[bot] <support@github.com>
@dependabot
dependabot Bot force-pushed the dependabot/pip/src/dependencies/requirements/base_requirements/pip-57ba0104f5 branch from 22b1aed to c8af92e Compare August 6, 2026 02:41
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