build(deps): bump the production group across 1 directory with 5 updates

[dependabot]

Bumps the production group with 5 updates in the / directory:

Package	From	To
numpy	2.2.6	2.3.1
polars	1.29.0	1.31.0
scikit-learn	1.6.1	1.7.1
statsmodels	0.14.4	0.14.5
transformers	4.52.3	4.53.2

Updates numpy from 2.2.6 to 2.3.1

Release notes

Sourced from numpy's releases.

v2.3.1 (Jun 21, 2025)

NumPy 2.3.1 Release Notes

The NumPy 2.3.1 release is a patch release with several bug fixes, annotation improvements, and better support for OpenBSD. Highlights are:

• Fix bug in matmul for non-contiguous out kwarg parameter
• Fix for Accelerate runtime warnings on M4 hardware
• Fix new in NumPy 2.3.0 np.vectorize casting errors
• Improved support of cpu features for FreeBSD and OpenBSD

This release supports Python versions 3.11-3.13, Python 3.14 will be supported when it is released.

Contributors

A total of 9 people contributed to this release. People with a "+" by their names contributed a patch for the first time.

• Brad Smith +
• Charles Harris
• Developer-Ecosystem-Engineering
• François Rozet
• Joren Hammudoglu
• Matti Picus
• Mugundan Selvanayagam
• Nathan Goldbaum
• Sebastian Berg

Pull requests merged

A total of 12 pull requests were merged for this release.

• #29140: MAINT: Prepare 2.3.x for further development
• #29191: BUG: fix matmul with transposed out arg (#29179)
• #29192: TYP: Backport typing fixes and improvements.
• #29205: BUG: Revert np.vectorize casting to legacy behavior (#29196)
• #29222: TYP: Backport typing fixes
• #29233: BUG: avoid negating unsigned integers in resize implementation...
• #29234: TST: Fix test that uses uninitialized memory (#29232)
• #29235: BUG: Address interaction between SME and FPSR (#29223)
• #29237: BUG: Enforce integer limitation in concatenate (#29231)
• #29238: CI: Add support for building NumPy with LLVM for Win-ARM64
• #29241: ENH: Detect CPU features on OpenBSD ARM and PowerPC64
• #29242: ENH: Detect CPU features on FreeBSD / OpenBSD RISC-V64.

Checksums

MD5

... (truncated)

Commits

• 4d833e5 Merge pull request #29251 from charris/prepare-2.3.1-release
• 13df6d1 REL: Prepare for the NumPy 2.3.1 release [wheel build]
• daeb8bd Merge pull request #29242 from charris/backport-29224
• eff9ae3 Merge pull request #29241 from charris/backport-29221
• c2fa1de ENH: Detect CPU features on FreeBSD / OpenBSD RISC-V64.
• a52c49e ENH: Detect CPU features on OpenBSD ARM and PowerPC64
• b3f56fd Merge pull request #29238 from charris/backport29215
• b20d754 CI: Add conditions to check hash of LLVM package
• 4c4617f CI: Fix reusable LLVM block
• d3fd4a6 CI: fix action.yml naming
• Additional commits viewable in compare view

Updates polars from 1.29.0 to 1.31.0

Release notes

Sourced from polars's releases.

Python Polars 1.31.0

💥 Breaking changes

• Remove old streaming engine (#23103)

⚠️ Deprecations

• Deprecate allow_missing_columns in scan_parquet in favor of missing_columns (#22784)

🚀 Performance improvements

• Improve streaming groupby CSE (#23092)
• Move row index materialization in post-apply to occur after slicing (#22995)
• Add first_(true|false)_idx to BooleanChunked and use in bool arg_(min|max) (#22907)
• Don't go through row encoding for most types on index_of (#22903)
• Optimise low-level null scans and arg_max for bools (when chunked) (#22897)
• Optimize multiscan performance (#22886)

✨ Enhancements

• DataType expressions in Python (#23167)
• Native implementation for Iceberg positional deletes (#23091)
• Remove old streaming engine (#23103)
• Basic implementation of DataTypeExpr in Rust DSL (#23049)
• Add required: bool to ParquetFieldOverwrites (#23013)
• Support serializing name.map_fields (#22997)
• Support serializing Expr::RenameAlias (#22988)
• Remove duplicate verbose logging from FetchedCredentialsCache (#22973)
• Add keys column in finish_callback (#22968)
• Add extra_columns parameter to scan_parquet (#22699)
• Add CORR function to polars SQL (#22690)
• Add per partition sort and finish callback to sinks (#22789)
• Support descendingly-sorted values in search_sorted() (#22825)
• Derive DSL schema (#22866)

🐞 Bug fixes

• Remove axis in show_graph (#23218)
• Remove axis ticks in show_graph (#23210)
• Restrict custom aggregate_function in pivot to pl.element() (#23155)
• Don't leak SourceToken in in-memory sink linearize (#23201)
• Fix panic reading empty parquet with multiple boolean columns (#23159)
• Raise ComputeError instead of panicking in truncate when mixing month/week/day/sub-daily units (#23176)
• Materialize list.eval with unknown type (#23186)
• Only set sorting flag for 1st column with PQ SortingColumns (#23184)
• Typo in AExprBuilder (#23171)
• Null return from var/std on scalar column (#23158)
• Support Datetime broadcast in list.concat (#23137)
• Ensure projection pushdown maintains right table schema (#22603)
• Add Null dtype support to arg_sort_by (#23107)

... (truncated)

Commits

• 6e02c20 Python Polars 1.31.0 (#23226)
• 5c154c8 chore: Change flake to use venv (#23219)
• f0ae4d3 fix(python): Remove axis in show_graph (#23218)
• bc974eb Revert "fix(python): Don't call unnest for objects implementing `__arrow_c_ar...
• 171c847 fix(python): Remove AXIS ticks in show_graph (#23210)
• d4da872 fix: Restrict custom aggregate_function in pivot to pl.element() (#23155)
• 455423c fix: Don't leak SourceToken in in-memory sink linearize (#23201)
• 7c2c513 chore: Add default_alloc feature to py-polars (#23202)
• 383f1b3 Python Polars 1.31.0 pre-release (#23192)
• 6891fec fix: Fix panic reading empty parquet with multiple boolean columns (#23159)
• Additional commits viewable in compare view

Updates scikit-learn from 1.6.1 to 1.7.1

Release notes

Sourced from scikit-learn's releases.

Scikit-learn 1.7.1

We're happy to announce the 1.7.1 release.

This release contains fixes for a few regressions introduced in 1.7.

You can see the changelog here: https://scikit-learn.org/stable/whats_new/v1.7.html#version-1-7-1

You can upgrade with pip as usual:

pip install -U scikit-learn

The conda-forge builds can be installed using:

conda install -c conda-forge scikit-learn

Thanks to everyone who contributed to this release !

Scikit-learn 1.7.0

We're happy to announce the 1.7.0 release.

You can read the release highlights under https://scikit-learn.org/stable/auto_examples/release_highlights/plot_release_highlights_1_7_0.html and the long version of the change log under https://scikit-learn.org/stable/whats_new/v1.7.html

This version supports Python versions 3.10 to 3.13 and features an experimental support of free-threaded CPython.

You can upgrade with pip as usual:

pip install -U scikit-learn

The conda-forge builds can be installed using:

conda install -c conda-forge scikit-learn

Commits

• 3dbf4da [cd build] Trigger wheel builder workflow
• 672a2dd [cd build] Trigger wheel builder workflow
• 35afe8d generate changelog 1.7.1
• 33893bd set version
• f97fe77 CI Avoid miniconda CondaToSNonInteractiveError and stop using the default cha...
• 6cba1d8 FIX: Regression in DecisionBoundaryDisplay.from_estimator with colors and plo...
• 6b2b4b4 🔒 🤖 CI Update lock files for scipy-dev CI build(s) 🔒 🤖 ...
• ba2918e 🔒 🤖 CI Update lock files for free-threaded CI build(s) 🔒 :rob...
• d60cafe 🔒 🤖 CI Update lock files for array-api CI build(s) 🔒 🤖 ...
• a73deee 🔒 🤖 CI Update lock files for main CI build(s) 🔒 🤖 (#31758)
• Additional commits viewable in compare view

Updates statsmodels from 0.14.4 to 0.14.5

Release notes

Sourced from statsmodels's releases.

Release 0.14.5

This patch release fixes an issue with recent SciPy releases (1.16+) that prevented statsmodels from importing. It also addresses some small changes that improve future compatibility.

Commits

• 1107ea5 Merge pull request #9591 from bashtage/rls-0-14-5-notes
• f3b362a MAINT: Update CI
• e2249ab DOC: Final fixes
• 41758ad DOC: Add release note for 0.14.5
• aea8126 Merge pull request #9586 from bashtage/rls-0-14-5
• ccc1948 TST: Change optim options
• 2c5292b MAINT: Reduce noise for future changes
• d9ccc11 MAINT: Reduce noise for future changes
• 699740f TST: xfail test with issues and relax tol
• 132c549 MAINT: Reduce noise for future changes
• Additional commits viewable in compare view

Updates transformers from 4.52.3 to 4.53.2

Release notes

Sourced from transformers's releases.

Patch Release v4.53.2

This patch contains the following bug fixes:

• Fix some bug for finetune and batch infer For GLM-4.1V (#39090)
• [bugfix] fix flash attention 2 unavailable error on Ascend NPU (#39166)
• Fix errors when use verl to train GLM4.1v model (#39199)
• [pagged-attention] fix off-by-1 error in pagged attention generation (#39258)
• [smollm3] add tokenizer mapping for smollm3 (#39271)
• [sliding window] revert and deprecate (#39301)
• fix Glm4v batch videos forward (#39172)
• Add a default value for position_ids in masking_utils (#39310)

Patch Release v4.53.1

This patch contains several bug fixes. The following commits are included:

• Fix: unprotected import of tp plugin (#39083)
• Fix key mapping for VLMs (#39029)
• Several fixes for Gemma3n(#39135)
• [qwen2-vl] fix FA2 inference (#39121)
• [smolvlm] fix video inference (#39147)
• Fix multimodal processor get duplicate arguments when receive kwargs for initialization (#39125)
• when delaying optimizer creation only prepare the model (#39152)
• Add packed tensor format support for flex/sdpa/eager through the mask! (#39194)

Release v4.53.0

Gemma3n

Gemma 3n models are designed for efficient execution on low-resource devices. They are capable of multimodal input, handling text, image, video, and audio input, and generating text outputs, with open weights for pre-trained and instruction-tuned variants. These models were trained with data in over 140 spoken languages.

Gemma 3n models use selective parameter activation technology to reduce resource requirements. This technique allows the models to operate at an effective size of 2B and 4B parameters, which is lower than the total number of parameters they contain. For more information on Gemma 3n's efficient parameter management technology, see the Gemma 3n page.

from transformers import pipeline
import torch
pipe = pipeline(
"image-text-to-text",
torch_dtype=torch.bfloat16,
model="google/gemma-3n-e4b",
device="cuda",
)
output = pipe(
"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/bee.jpg",
text="<image_soft_token> in this image, there is"
)
print(output)
</tr></table>

... (truncated)

Commits

• 37f8b0b style
• d15e157 finally fix glm4v accordingly
• 9873b2f fix attr post conflict
• d6f3155 Fix conflict
• cbac426 Add a default value for position_ids in masking_utils (#39310)
• c122145 fix Glm4v batch videos forward (#39172)
• 9da3f7d [sliding window] revert and deprecate (#39301)
• b2fbc02 [smollm3] add tokenizer mapping for smollm3 (#39271)
• c30a6b7 [pagged-attention] fix off-by-1 error in pagged attention generation (#39258)
• be7d1a9 Fix errors when use verl to train GLM4.1v model (#39199)
• Additional commits viewable in compare view

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