diff --git a/.gitattributes b/.gitattributes index a6344aac8c09253b3b630fb776ae94478aa0275b..2dc8319d96c364538460afc30f30faa3531c4062 100644 --- a/.gitattributes +++ b/.gitattributes @@ -1,35 +1,2 @@ -*.7z filter=lfs diff=lfs merge=lfs -text -*.arrow filter=lfs diff=lfs merge=lfs -text -*.bin filter=lfs diff=lfs merge=lfs -text -*.bz2 filter=lfs diff=lfs merge=lfs -text -*.ckpt filter=lfs diff=lfs merge=lfs -text -*.ftz filter=lfs diff=lfs merge=lfs -text -*.gz filter=lfs diff=lfs merge=lfs -text -*.h5 filter=lfs diff=lfs merge=lfs -text -*.joblib filter=lfs diff=lfs merge=lfs -text -*.lfs.* filter=lfs diff=lfs merge=lfs -text -*.mlmodel filter=lfs diff=lfs merge=lfs -text -*.model filter=lfs diff=lfs merge=lfs -text -*.msgpack filter=lfs diff=lfs merge=lfs -text -*.npy filter=lfs diff=lfs merge=lfs -text -*.npz filter=lfs diff=lfs merge=lfs -text -*.onnx filter=lfs diff=lfs merge=lfs -text -*.ot filter=lfs diff=lfs merge=lfs -text -*.parquet filter=lfs diff=lfs merge=lfs -text -*.pb filter=lfs diff=lfs merge=lfs -text -*.pickle filter=lfs diff=lfs merge=lfs -text -*.pkl filter=lfs diff=lfs merge=lfs -text *.pt filter=lfs diff=lfs merge=lfs -text -*.pth filter=lfs diff=lfs merge=lfs -text -*.rar filter=lfs diff=lfs merge=lfs -text -*.safetensors filter=lfs diff=lfs merge=lfs -text -saved_model/**/* filter=lfs diff=lfs merge=lfs -text -*.tar.* filter=lfs diff=lfs merge=lfs -text -*.tar filter=lfs diff=lfs merge=lfs -text -*.tflite filter=lfs diff=lfs merge=lfs -text -*.tgz filter=lfs diff=lfs merge=lfs -text -*.wasm filter=lfs diff=lfs merge=lfs -text -*.xz filter=lfs diff=lfs merge=lfs -text -*.zip filter=lfs diff=lfs merge=lfs -text -*.zst filter=lfs diff=lfs merge=lfs -text -*tfevents* filter=lfs diff=lfs merge=lfs -text +examples/sudoku.npz filter=lfs diff=lfs merge=lfs -text diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..47120afb0d370032178e3de429bb52f8e4530c3f --- /dev/null +++ b/.gitignore @@ -0,0 +1,9 @@ +.venv/ +__pycache__/ +*.pyc +.aim/ +/data/ +/outputs/ +/dist/ +.ruff_cache/ +.pytest_cache/ diff --git a/.python-version b/.python-version new file mode 100644 index 0000000000000000000000000000000000000000..e4fba2183587225f216eeada4c78dfab6b2e65f5 --- /dev/null +++ b/.python-version @@ -0,0 +1 @@ +3.12 diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..340cf0c959586320b003a9ed4ad61ec65f8f90e5 --- /dev/null +++ b/LICENSE @@ -0,0 +1,158 @@ +Creative Commons Attribution-NonCommercial 4.0 International + + Creative Commons Corporation (“Creative Commons”) is not a law firm and does not provide legal services or legal advice. Distribution of Creative Commons public licenses does not create a lawyer-client or other relationship. Creative Commons makes its licenses and related information available on an “as-is” basis. Creative Commons gives no warranties regarding its licenses, any material licensed under their terms and conditions, or any related information. Creative Commons disclaims all liability for damages resulting from their use to the fullest extent possible. + +Using Creative Commons Public Licenses + +Creative Commons public licenses provide a standard set of terms and conditions that creators and other rights holders may use to share original works of authorship and other material subject to copyright and certain other rights specified in the public license below. The following considerations are for informational purposes only, are not exhaustive, and do not form part of our licenses. + +Considerations for licensors: Our public licenses are intended for use by those authorized to give the public permission to use material in ways otherwise restricted by copyright and certain other rights. Our licenses are irrevocable. Licensors should read and understand the terms and conditions of the license they choose before applying it. Licensors should also secure all rights necessary before applying our licenses so that the public can reuse the material as expected. Licensors should clearly mark any material not subject to the license. This includes other CC-licensed material, or material used under an exception or limitation to copyright. More considerations for licensors. + +Considerations for the public: By using one of our public licenses, a licensor grants the public permission to use the licensed material under specified terms and conditions. If the licensor’s permission is not necessary for any reason–for example, because of any applicable exception or limitation to copyright–then that use is not regulated by the license. Our licenses grant only permissions under copyright and certain other rights that a licensor has authority to grant. Use of the licensed material may still be restricted for other reasons, including because others have copyright or other rights in the material. A licensor may make special requests, such as asking that all changes be marked or described. Although not required by our licenses, you are encouraged to respect those requests where reasonable. More considerations for the public. + +Creative Commons Attribution-NonCommercial 4.0 International Public License + +By exercising the Licensed Rights (defined below), You accept and agree to be bound by the terms and conditions of this Creative Commons Attribution-NonCommercial 4.0 International Public License ("Public License"). To the extent this Public License may be interpreted as a contract, You are granted the Licensed Rights in consideration of Your acceptance of these terms and conditions, and the Licensor grants You such rights in consideration of benefits the Licensor receives from making the Licensed Material available under these terms and conditions. + +Section 1 – Definitions. + + a. Adapted Material means material subject to Copyright and Similar Rights that is derived from or based upon the Licensed Material and in which the Licensed Material is translated, altered, arranged, transformed, or otherwise modified in a manner requiring permission under the Copyright and Similar Rights held by the Licensor. For purposes of this Public License, where the Licensed Material is a musical work, performance, or sound recording, Adapted Material is always produced where the Licensed Material is synched in timed relation with a moving image. + + b. Adapter's License means the license You apply to Your Copyright and Similar Rights in Your contributions to Adapted Material in accordance with the terms and conditions of this Public License. + + c. Copyright and Similar Rights means copyright and/or similar rights closely related to copyright including, without limitation, performance, broadcast, sound recording, and Sui Generis Database Rights, without regard to how the rights are labeled or categorized. For purposes of this Public License, the rights specified in Section 2(b)(1)-(2) are not Copyright and Similar Rights. + + d. Effective Technological Measures means those measures that, in the absence of proper authority, may not be circumvented under laws fulfilling obligations under Article 11 of the WIPO Copyright Treaty adopted on December 20, 1996, and/or similar international agreements. + + e. Exceptions and Limitations means fair use, fair dealing, and/or any other exception or limitation to Copyright and Similar Rights that applies to Your use of the Licensed Material. + + f. Licensed Material means the artistic or literary work, database, or other material to which the Licensor applied this Public License. + + g. Licensed Rights means the rights granted to You subject to the terms and conditions of this Public License, which are limited to all Copyright and Similar Rights that apply to Your use of the Licensed Material and that the Licensor has authority to license. + + h. Licensor means the individual(s) or entity(ies) granting rights under this Public License. + + i. NonCommercial means not primarily intended for or directed towards commercial advantage or monetary compensation. For purposes of this Public License, the exchange of the Licensed Material for other material subject to Copyright and Similar Rights by digital file-sharing or similar means is NonCommercial provided there is no payment of monetary compensation in connection with the exchange. + + j. Share means to provide material to the public by any means or process that requires permission under the Licensed Rights, such as reproduction, public display, public performance, distribution, dissemination, communication, or importation, and to make material available to the public including in ways that members of the public may access the material from a place and at a time individually chosen by them. + + k. Sui Generis Database Rights means rights other than copyright resulting from Directive 96/9/EC of the European Parliament and of the Council of 11 March 1996 on the legal protection of databases, as amended and/or succeeded, as well as other essentially equivalent rights anywhere in the world. + + l. You means the individual or entity exercising the Licensed Rights under this Public License. Your has a corresponding meaning. + +Section 2 – Scope. + + a. License grant. + + 1. Subject to the terms and conditions of this Public License, the Licensor hereby grants You a worldwide, royalty-free, non-sublicensable, non-exclusive, irrevocable license to exercise the Licensed Rights in the Licensed Material to: + + A. reproduce and Share the Licensed Material, in whole or in part, for NonCommercial purposes only; and + + B. produce, reproduce, and Share Adapted Material for NonCommercial purposes only. + + 2. Exceptions and Limitations. For the avoidance of doubt, where Exceptions and Limitations apply to Your use, this Public License does not apply, and You do not need to comply with its terms and conditions. + + 3. Term. The term of this Public License is specified in Section 6(a). + + 4. Media and formats; technical modifications allowed. The Licensor authorizes You to exercise the Licensed Rights in all media and formats whether now known or hereafter created, and to make technical modifications necessary to do so. The Licensor waives and/or agrees not to assert any right or authority to forbid You from making technical modifications necessary to exercise the Licensed Rights, including technical modifications necessary to circumvent Effective Technological Measures. For purposes of this Public License, simply making modifications authorized by this Section 2(a)(4) never produces Adapted Material. + + 5. Downstream recipients. + + A. Offer from the Licensor – Licensed Material. Every recipient of the Licensed Material automatically receives an offer from the Licensor to exercise the Licensed Rights under the terms and conditions of this Public License. + + B. No downstream restrictions. You may not offer or impose any additional or different terms or conditions on, or apply any Effective Technological Measures to, the Licensed Material if doing so restricts exercise of the Licensed Rights by any recipient of the Licensed Material. + + 6. No endorsement. Nothing in this Public License constitutes or may be construed as permission to assert or imply that You are, or that Your use of the Licensed Material is, connected with, or sponsored, endorsed, or granted official status by, the Licensor or others designated to receive attribution as provided in Section 3(a)(1)(A)(i). + + b. Other rights. + + 1. Moral rights, such as the right of integrity, are not licensed under this Public License, nor are publicity, privacy, and/or other similar personality rights; however, to the extent possible, the Licensor waives and/or agrees not to assert any such rights held by the Licensor to the limited extent necessary to allow You to exercise the Licensed Rights, but not otherwise. + + 2. Patent and trademark rights are not licensed under this Public License. + + 3. To the extent possible, the Licensor waives any right to collect royalties from You for the exercise of the Licensed Rights, whether directly or through a collecting society under any voluntary or waivable statutory or compulsory licensing scheme. In all other cases the Licensor expressly reserves any right to collect such royalties, including when the Licensed Material is used other than for NonCommercial purposes. + +Section 3 – License Conditions. + +Your exercise of the Licensed Rights is expressly made subject to the following conditions. + + a. Attribution. + + 1. If You Share the Licensed Material (including in modified form), You must: + + A. retain the following if it is supplied by the Licensor with the Licensed Material: + + i. identification of the creator(s) of the Licensed Material and any others designated to receive attribution, in any reasonable manner requested by the Licensor (including by pseudonym if designated); + + ii. a copyright notice; + + iii. a notice that refers to this Public License; + + iv. a notice that refers to the disclaimer of warranties; + + v. a URI or hyperlink to the Licensed Material to the extent reasonably practicable; + + B. indicate if You modified the Licensed Material and retain an indication of any previous modifications; and + + C. indicate the Licensed Material is licensed under this Public License, and include the text of, or the URI or hyperlink to, this Public License. + + 2. You may satisfy the conditions in Section 3(a)(1) in any reasonable manner based on the medium, means, and context in which You Share the Licensed Material. For example, it may be reasonable to satisfy the conditions by providing a URI or hyperlink to a resource that includes the required information. + + 3. If requested by the Licensor, You must remove any of the information required by Section 3(a)(1)(A) to the extent reasonably practicable. + + 4. If You Share Adapted Material You produce, the Adapter's License You apply must not prevent recipients of the Adapted Material from complying with this Public License. + +Section 4 – Sui Generis Database Rights. + +Where the Licensed Rights include Sui Generis Database Rights that apply to Your use of the Licensed Material: + + a. for the avoidance of doubt, Section 2(a)(1) grants You the right to extract, reuse, reproduce, and Share all or a substantial portion of the contents of the database for NonCommercial purposes only; + + b. if You include all or a substantial portion of the database contents in a database in which You have Sui Generis Database Rights, then the database in which You have Sui Generis Database Rights (but not its individual contents) is Adapted Material; and + + c. You must comply with the conditions in Section 3(a) if You Share all or a substantial portion of the contents of the database. +For the avoidance of doubt, this Section 4 supplements and does not replace Your obligations under this Public License where the Licensed Rights include other Copyright and Similar Rights. + +Section 5 – Disclaimer of Warranties and Limitation of Liability. + + a. Unless otherwise separately undertaken by the Licensor, to the extent possible, the Licensor offers the Licensed Material as-is and as-available, and makes no representations or warranties of any kind concerning the Licensed Material, whether express, implied, statutory, or other. This includes, without limitation, warranties of title, merchantability, fitness for a particular purpose, non-infringement, absence of latent or other defects, accuracy, or the presence or absence of errors, whether or not known or discoverable. Where disclaimers of warranties are not allowed in full or in part, this disclaimer may not apply to You. + + b. To the extent possible, in no event will the Licensor be liable to You on any legal theory (including, without limitation, negligence) or otherwise for any direct, special, indirect, incidental, consequential, punitive, exemplary, or other losses, costs, expenses, or damages arising out of this Public License or use of the Licensed Material, even if the Licensor has been advised of the possibility of such losses, costs, expenses, or damages. Where a limitation of liability is not allowed in full or in part, this limitation may not apply to You. + + c. The disclaimer of warranties and limitation of liability provided above shall be interpreted in a manner that, to the extent possible, most closely approximates an absolute disclaimer and waiver of all liability. + +Section 6 – Term and Termination. + + a. This Public License applies for the term of the Copyright and Similar Rights licensed here. However, if You fail to comply with this Public License, then Your rights under this Public License terminate automatically. + + b. Where Your right to use the Licensed Material has terminated under Section 6(a), it reinstates: + + 1. automatically as of the date the violation is cured, provided it is cured within 30 days of Your discovery of the violation; or + + 2. upon express reinstatement by the Licensor. + + For the avoidance of doubt, this Section 6(b) does not affect any right the Licensor may have to seek remedies for Your violations of this Public License. + + c. For the avoidance of doubt, the Licensor may also offer the Licensed Material under separate terms or conditions or stop distributing the Licensed Material at any time; however, doing so will not terminate this Public License. + + d. Sections 1, 5, 6, 7, and 8 survive termination of this Public License. + +Section 7 – Other Terms and Conditions. + + a. The Licensor shall not be bound by any additional or different terms or conditions communicated by You unless expressly agreed. + + b. Any arrangements, understandings, or agreements regarding the Licensed Material not stated herein are separate from and independent of the terms and conditions of this Public License. + +Section 8 – Interpretation. + + a. For the avoidance of doubt, this Public License does not, and shall not be interpreted to, reduce, limit, restrict, or impose conditions on any use of the Licensed Material that could lawfully be made without permission under this Public License. + + b. To the extent possible, if any provision of this Public License is deemed unenforceable, it shall be automatically reformed to the minimum extent necessary to make it enforceable. If the provision cannot be reformed, it shall be severed from this Public License without affecting the enforceability of the remaining terms and conditions. + + c. No term or condition of this Public License will be waived and no failure to comply consented to unless expressly agreed to by the Licensor. + + d. Nothing in this Public License constitutes or may be interpreted as a limitation upon, or waiver of, any privileges and immunities that apply to the Licensor or You, including from the legal processes of any jurisdiction or authority. + +Creative Commons is not a party to its public licenses. Notwithstanding, Creative Commons may elect to apply one of its public licenses to material it publishes and in those instances will be considered the “Licensor.” Except for the limited purpose of indicating that material is shared under a Creative Commons public license or as otherwise permitted by the Creative Commons policies published at creativecommons.org/policies, Creative Commons does not authorize the use of the trademark “Creative Commons” or any other trademark or logo of Creative Commons without its prior written consent including, without limitation, in connection with any unauthorized modifications to any of its public licenses or any other arrangements, understandings, or agreements concerning use of licensed material. For the avoidance of doubt, this paragraph does not form part of the public licenses. + +Creative Commons may be contacted at creativecommons.org. diff --git a/README.md b/README.md index e5f99753cd5578d5f86c70bb7da94c52f8fe692b..64af715b021d662dde03755e8044523a6e5205be 100644 --- a/README.md +++ b/README.md @@ -1,3 +1,173 @@ --- license: cc-by-nc-4.0 +library_name: pytorch +tags: + - sudoku + - neuro-symbolic + - answer-set-programming + - attention + - pytorch --- + +# AS²: Attention-Based Soft Answer Set Programming + +AS² is a generic attention-based soft answer set framework. See the +[paper](https://arxiv.org/html/2603.18436v1) for the method and experiments. +The framework combines configurable perception or input encoders, soft symbolic +representations, attention-based reasoning, task heads, and differentiable +constraint objectives. + +**Visual Sudoku is the first bundled instantiation and benchmark.** Its data, +board geometry, constraints, objectives, metrics and training recipe are supplied +by adapters selected through `configs/instantiation/sudoku.yaml`. The included +weights and example belong to that instantiation; they do not define AS²'s scope. +Sudoku predictions use raw neural argmax without symbolic repair. + +The same train, predict, evaluate and from-scratch commands select instantiations +through Hydra. **MNIST Addition and CLEVR-Hans are planned future instantiations**; +their runnable implementations are not included in this release. +See [Adding an instantiation](docs/instantiations.md) for the extension contract. +Each instantiation provides its declarative constraints and their differentiable +implementation. An automatic constraint compiler is not included or required. + +## Install + +Use Python 3.12+ and [uv](https://docs.astral.sh/uv/). +Download this entire repository, including the actual model file (not a Git LFS +pointer), and run commands from its root: + +```bash +uv sync --locked +uv run --locked softasp-predict instantiation=sudoku +``` + +The first command installs the three local packages and their locked dependencies. +PyTorch installation can be large; CUDA training requires a compatible NVIDIA +driver. Inference works on CPU; `device=auto` selects CUDA when available. + +## Predict with the Sudoku instantiation + +The included example contains one MNIST-derived Sudoku board and its clue mask. +No solution labels are supplied to prediction. The command above writes +`outputs/prediction.json`, containing digits **1–9** and a structural-validity +flag. Structural validity is not an accuracy measurement. Existing prediction +files are never overwritten; choose a fresh output path for another run. + +```bash +uv run --locked softasp-predict instantiation=sudoku input=examples/sudoku.npz \ + checkpoint=models/sudoku/as2.pt output=outputs/my-prediction.json device=cpu +``` + +For your own inputs, create an NPZ file with: +- `images`: shape `(N,252,252,1)`, uint8 in 0–255 or float32 in 0–1. +- `puzzle_mask`: shape `(N,81)`, 1 for visible clues and 0 for blanks. + +Cells are in row-major order. Use white digit strokes on black backgrounds; +blank cells are black. The model expects aligned cell images, not a photograph +of a printed grid. Raw predictions can violate Sudoku constraints. + +## Train the Sudoku instantiation from scratch + +This command downloads MNIST, generates 9,000/1,000/1,000 train/validation/test +boards, trains a fresh CNN and then AS², and evaluates the resulting model. +It does not read the shipped weights. + +```bash +uv run --locked softasp-train-scratch instantiation=sudoku pipeline.name=my-run +``` + +Both stages allow up to 200 epochs with patience 50; seed is 42 and batch size +is 32. The portable default is float32. For a GPU supporting bf16, the recorded +training recipe uses: + +```bash +uv run --locked softasp-train-scratch instantiation=sudoku pipeline.name=my-gpu-run trainer.precision=bf16 +``` + +The backbone inherits the chosen precision. Hydra controls all paths: + +```bash +uv run --locked softasp-train-scratch instantiation=sudoku pipeline.name=my-run \ + paths.data_root=/path/to/data paths.output_dir=/path/to/outputs \ + paths.model_dir=/path/to/models +``` + +For a small functionality check, use a fresh run name: + +```bash +uv run --locked softasp-train-scratch instantiation=sudoku pipeline.name=smoke \ + preprocessing.subset_size=2 trainer.epochs=1 backbone.trainer.epochs=1 \ + trainer.batch_size_per_gpu=2 backbone.trainer.batch_size_per_gpu=2 \ + trainer.scheduler.warmup_steps=0 backbone.trainer.scheduler.warmup_steps=0 \ + trainer.num_workers=0 backbone.trainer.num_workers=0 \ + trainer.persistent_workers=false backbone.trainer.persistent_workers=false +``` + +This small run is not an accuracy benchmark. Every run records a manifest, +resolved configuration, data hashes, checkpoints, and Aim metrics under your +output directories. Fresh run/model directories are required; existing runs +are not silently resumed or replaced. Models are saved under +`models/sudoku//`; the manifest is under +`outputs/sudoku/from_scratch//`. + +To reuse downloaded MNIST, set `pipeline.download=false` and +`paths.raw_data_root=/path/containing/mnist`. A deliberate existing-backbone +workflow is also available through `softasp-train`, with +`model.pretrained_from` and explicit dataset identity; inference never trains. + +## Evaluate a dataset + +Use the HDF5 path and SHA256 recorded in your training manifest. The file must +contain train, val, and test groups with images, labels (digits encoded 0–8), +and puzzle_mask. No full dataset is included. + +```bash +uv run --locked softasp-evaluate instantiation=sudoku dataset.splits=[test] \ + data_artifact.path=/path/to/dataset.h5 data_artifact.sha256=YOUR_SHA256 \ + data_artifact.protocol_id=null evaluator.checkpoint=models/sudoku/as2.pt \ + paths.output_dir=outputs/my-evaluation +``` + +The evaluator writes raw predictions, exact metric counts, and provenance to a +fresh output directory. It uses float32 inference without autocast, oracle +labels, refinement, or repair. + +## Framework layout + +- `packages/softasp-solver/.../components/`: encoders, evidence policies, structural + representations and attention composition. +- `packages/softasp-solver/.../constraints/`: shared differentiable operators and residuals. +- `packages/softasp-core/`: configuration, training, evaluation and tracking. +- `src/softasp/instantiations/`: configuration-driven dispatch and Sudoku providers. +- `configs/instantiation/sudoku.yaml`: the bundled instantiation's composition. +- Sudoku-named modules contain its data generation, constraints and task-specific behavior. + +## Results and limits + +The included model achieved **955/1,000 fully solved test boards (95.5%)**, +**80,905/81,000 correct cells (99.8827%)**, and **45,000/45,000 correct clues**. +The selected checkpoint was epoch 187, with 95.4% validation board accuracy. +These are single-seed results from the legacy generated Sudoku protocol, using +raw CUDA float32 evaluation. Training used bf16 and the default six-layer, +256-dimensional model. + +Generated boards are transformations of one base solution and use a merged +MNIST image pool; source-image separation across dataset splits is not +established. These results do not establish generalization to independent +handwriting sources, arbitrary Sudoku distributions, or photographed boards. +Independent operator/numerical validation and broader paper reproduction are +outside this release. Device and precision changes can change predictions. + +A subsequent full from-scratch run of the refactored framework completed training, +checkpoint loading, prediction and evaluation, obtaining **944/1,000 boards (94.4%)** +on the same test set. The included selected weights were re-evaluated at **95.5%** +and remain unchanged. The 1.1 percentage-point difference is unresolved; its +investigation is deferred until after this release. Exact training-result +reproduction is not claimed. + +## License + +Project code and model weights are under **CC BY-NC 4.0**; see LICENSE. +Dependencies and MNIST source imagery retain their respective third-party +terms. The small example uses MNIST digit imagery and is not a newly licensed +dataset. See THIRD_PARTY_NOTICES.md. The release includes no full datasets. diff --git a/THIRD_PARTY_NOTICES.md b/THIRD_PARTY_NOTICES.md new file mode 100644 index 0000000000000000000000000000000000000000..d47d375fdd70913dc533a0617dc56871f4b934d1 --- /dev/null +++ b/THIRD_PARTY_NOTICES.md @@ -0,0 +1,10 @@ +# Third-party material + +MNIST digit imagery is sourced from the MNIST database by Yann LeCun, +Corinna Cortes, and Christopher J. C. Burges. Training downloads it through +torchvision's MNIST dataset implementation. The small example board uses this +imagery; original dataset rights and terms remain applicable. + +Project CC BY-NC 4.0 licensing does not replace the licenses of PyTorch, +torchvision, NumPy, Hydra, OmegaConf, Aim, h5py, Clingo, or other dependencies. +Their distributions include their respective license notices. diff --git a/configs/config.yaml b/configs/config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..f64cf8e667bab5c7a4dcde9da16a606726e929f4 --- /dev/null +++ b/configs/config.yaml @@ -0,0 +1,24 @@ +# SoftASP — top-level Hydra configuration +# +# Override groups on the CLI: +# softasp-train instantiation=sudoku paths=local +# +defaults: + - instantiation: sudoku + - paths: local + - logging: default + - _self_ + +experiment_name: softasp + +# Clean-up settings (used by softasp-cleanup) +cleanup: + apply: false + keep_best_n: 3 + +# Hydra output directory override +hydra: + run: + dir: ${paths.output_dir}/${instantiation.name}/${experiment_name}/${now:%Y-%m-%d_%H-%M-%S} + sweep: + dir: ${paths.output_dir}/${instantiation.name}/multirun/${now:%Y-%m-%d_%H-%M-%S} diff --git a/configs/dataset/sudoku.yaml b/configs/dataset/sudoku.yaml new file mode 100644 index 0000000000000000000000000000000000000000..6a3a4a1faf4503267d51cfc7d93465e0a7aaba31 --- /dev/null +++ b/configs/dataset/sudoku.yaml @@ -0,0 +1,13 @@ +# Visual Sudoku dataset configuration +name: sudoku +num_classes: 9 # digits 1-9 (stored as 0-indexed: 0-8) +image_size: 252 # 9 cells × 28 pixels each +max_objects: 81 # 9×9 grid cells +splits: + - train + - val + - test +augmentations: + train: [] + val: [] + test: [] diff --git a/configs/evaluator/sudoku.yaml b/configs/evaluator/sudoku.yaml new file mode 100644 index 0000000000000000000000000000000000000000..44011ba266ad1dfa728b1056854fdc6acc003539 --- /dev/null +++ b/configs/evaluator/sudoku.yaml @@ -0,0 +1,9 @@ +# Evaluator configuration for Visual Sudoku +datasets: + - sudoku +metrics: + - accuracy + - board_accuracy + - constraint_sat_rate +batch_size_per_gpu: 32 +num_workers: 4 diff --git a/configs/instantiation/sudoku.yaml b/configs/instantiation/sudoku.yaml new file mode 100644 index 0000000000000000000000000000000000000000..4410cb4d5123cf23cd619db136193dcea3a161d9 --- /dev/null +++ b/configs/instantiation/sudoku.yaml @@ -0,0 +1,85 @@ +# @package _global_ +# Sudoku owns its data, model, constraints, metrics and workflow adapters. +defaults: + - /dataset: sudoku + - /preprocessing: sudoku + - /model: softasp_sudoku + - /trainer: sudoku_softasp + - /evaluator: sudoku + - /model@backbone.model: perception_only_sudoku + - /trainer@backbone.trainer: sudoku + - _self_ + +checkpoint: models/sudoku/as2.pt +input: examples/sudoku.npz +evaluator: + checkpoint: ${checkpoint} + +instantiation: + name: sudoku + version: 1 + constraints: + _target_: softasp_solver.sudoku.constraints.TPOperatorLoss + providers: + dataset: + _target_: softasp.instantiations.sudoku.DatasetProvider + model: + _target_: softasp.instantiations.sudoku.ModelProvider + metrics: + _target_: softasp.instantiations.sudoku.MetricsProvider + prediction: + _target_: softasp.instantiations.sudoku.PredictionProvider + evaluation: + _target_: softasp.instantiations.sudoku.EvaluationProvider + workflow: + _target_: softasp.instantiations.sudoku.WorkflowProvider + +# The bundled instantiation owns its complete two-stage recipe. +release_profile: sudoku_release1 +experiment_name: sudoku_from_scratch +data_artifact: + protocol_id: null + path: null + sha256: null + +paths: + model_dir: ${oc.env:SOFTASP_MODEL_DIR,models} + +pipeline: + name: ${now:%Y-%m-%d_%H-%M-%S} + run_dir: ${paths.output_dir}/sudoku/from_scratch/${pipeline.name} + model_dir: ${paths.model_dir}/sudoku/${pipeline.name} + data_mode: generate # generate or existing (explicit path + SHA256 required) + download: true # false uses already downloaded MNIST; never needs weights + +model: + pretrained_from: null +trainer: + protocol: sudoku_seeded_updates_v1 + seed: 42 + distributed: "false" + compile: false + gradient_accumulation_steps: 1 + resume_from: null + scheduler: + name: linear_warmup_cosine + warmup_steps: 200 + eval_on_start: false + experiment_name: sudoku_from_scratch_as2 +backbone: + model: + params: + d_model: ${oc.select:model.params.d_model,128} + trainer: + protocol: sudoku_seeded_updates_v1 + seed: ${trainer.seed} + distributed: "false" + compile: false + gradient_accumulation_steps: 1 + resume_from: null + eval_on_start: false + precision: ${trainer.precision} + scheduler: + name: linear_warmup_cosine + experiment_name: sudoku_from_scratch_backbone + diff --git a/configs/logging/default.yaml b/configs/logging/default.yaml new file mode 100644 index 0000000000000000000000000000000000000000..e8716e1126a6dbb6405d32c7f352135e1e85da7c --- /dev/null +++ b/configs/logging/default.yaml @@ -0,0 +1,8 @@ +# Logging configuration +level: INFO +rich_console: true +file_log: true +log_every_n_steps: 10 +# Log board images to Aim every N eval epochs (0 = never) + +log_images_every_n_epochs: 0 diff --git a/configs/model/perception_only_sudoku.yaml b/configs/model/perception_only_sudoku.yaml new file mode 100644 index 0000000000000000000000000000000000000000..95d56c89405fb99fb9c4d6c9f86a09102ccac0e0 --- /dev/null +++ b/configs/model/perception_only_sudoku.yaml @@ -0,0 +1,13 @@ +# Perception-only baseline for Visual Sudoku (no reasoning) +name: perception_only_sudoku +pretrained_from: null +# PerceptionOnly IS the backbone pre-training run. It trains from scratch +# (no pretrained_from) so freeze_backbone_epochs is irrelevant here. +freeze_backbone_epochs: 0 +# Aim tags — distinct from SoftASP so the two can be compared in one query. +tags: + - perception-only + - cnn-only + - sudoku +params: + d_model: 256 diff --git a/configs/model/softasp_sudoku.yaml b/configs/model/softasp_sudoku.yaml new file mode 100644 index 0000000000000000000000000000000000000000..bd7ea77b7f485d8289e204ae678cfe7d0378a748 --- /dev/null +++ b/configs/model/softasp_sudoku.yaml @@ -0,0 +1,19 @@ +name: softasp_sudoku +pretrained_from: null +freeze_backbone_epochs: 20 +tags: + - softasp + - ours + - sudoku +params: + d_model: 256 + n_heads: 8 + n_layers: 6 + dropout: 0.1 + lambda_ic: 5.0 + lambda_blank: 0.3 + blank_decay_epochs: 100 + ic_warmup_epochs: 20 + lambda_uniq: 0.0 + backbone: default + oracle_perception: false diff --git a/configs/paths/local.yaml b/configs/paths/local.yaml new file mode 100644 index 0000000000000000000000000000000000000000..166c4d6d4fe230dc24c88d91b0bd4afe700e3e2d --- /dev/null +++ b/configs/paths/local.yaml @@ -0,0 +1,5 @@ +data_root: ${oc.env:SOFTASP_DATA_ROOT,data} +output_dir: ${oc.env:SOFTASP_OUTPUT_DIR,outputs} +aim_repo: ${paths.output_dir}/aim +raw_data_root: null +model_dir: ${oc.env:SOFTASP_MODEL_DIR,models} diff --git a/configs/predict.yaml b/configs/predict.yaml new file mode 100644 index 0000000000000000000000000000000000000000..1dcdeb077b6e0221402cf07a1c254afb04eaecc8 --- /dev/null +++ b/configs/predict.yaml @@ -0,0 +1,12 @@ +defaults: + - instantiation: sudoku + - paths: local + - _self_ + +output: outputs/prediction.json +device: auto +hydra: + run: + dir: outputs/hydra/predict/${now:%Y-%m-%d_%H-%M-%S} + job: + chdir: false diff --git a/configs/preprocessing/sudoku.yaml b/configs/preprocessing/sudoku.yaml new file mode 100644 index 0000000000000000000000000000000000000000..18f096fb259def0fddbe5a61c7d1837badb9d00b --- /dev/null +++ b/configs/preprocessing/sudoku.yaml @@ -0,0 +1,14 @@ +# Visual Sudoku preprocessing +# Generation parameters are hardcoded in SudokuPreprocessor to avoid extra +# DatasetConfig fields. Current values: +# n_clues_start = 55 (start with 55 random clues — almost always unique) +# max_removals = 10 (remove up to 10 clues while preserving uniqueness) +# Result: ~45 clues per board, all uniquely solvable, no warnings. +# For publication: increase max_removals to get true minimal puzzles (~25–35 clues). +device: cpu +subset_size: null +chunk_size: 256 +compression: gzip +compression_opts: 4 +force: false +num_workers: 4 diff --git a/configs/sudoku_from_scratch.yaml b/configs/sudoku_from_scratch.yaml new file mode 100644 index 0000000000000000000000000000000000000000..4c444ff7ba76b820cd4a0dd4425ad7069b066c9b --- /dev/null +++ b/configs/sudoku_from_scratch.yaml @@ -0,0 +1,3 @@ +defaults: + - train_scratch + - _self_ diff --git a/configs/sudoku_release1.yaml b/configs/sudoku_release1.yaml new file mode 100644 index 0000000000000000000000000000000000000000..84763f3d0561b39033de7e95b60bb8ad9eaa6ac3 --- /dev/null +++ b/configs/sudoku_release1.yaml @@ -0,0 +1,36 @@ +# Supported Release 1 training surface. Supply an existing backbone explicitly. +# This configuration never launches backbone pretraining or extraction. +defaults: + - config + - override dataset: sudoku + - override preprocessing: sudoku + - override model: softasp_sudoku + - override trainer: sudoku_softasp + - override evaluator: sudoku + - _self_ + +release_profile: sudoku_release1 +experiment_name: sudoku_release1 +# Release 1 uses the existing verified legacy dataset; no regeneration required. +# Supply its location via this environment variable or a Hydra path override. +data_artifact: + protocol_id: sudoku_legacy_mnist_pool_v1 + path: ${oc.env:SOFTASP_SUDOKU_HDF5,null} + sha256: 8d0070b00ffc75763ceb8c2b29006823c80b70f50b6ca0018310d0fdc7b3828e +evaluator: + checkpoint: models/sudoku/as2.pt +model: + pretrained_from: null + params: + oracle_perception: false +trainer: + protocol: sudoku_seeded_updates_v1 + seed: 42 + distributed: "false" + compile: false + gradient_accumulation_steps: 1 + resume_from: null + experiment_name: sudoku_release1 + scheduler: + name: linear_warmup_cosine + warmup_steps: 200 diff --git a/configs/train_scratch.yaml b/configs/train_scratch.yaml new file mode 100644 index 0000000000000000000000000000000000000000..84d821b326c000146aba3075f982450928504226 --- /dev/null +++ b/configs/train_scratch.yaml @@ -0,0 +1,10 @@ +# Each selected instantiation supplies its workflow recipe. +defaults: + - config + - _self_ + +hydra: + run: + dir: ${paths.output_dir}/hydra/train_scratch/${now:%Y-%m-%d_%H-%M-%S} + job: + chdir: false diff --git a/configs/trainer/default.yaml b/configs/trainer/default.yaml new file mode 100644 index 0000000000000000000000000000000000000000..7ba6fee179bed89b39e2a65dc4e9dc287ed8ec22 --- /dev/null +++ b/configs/trainer/default.yaml @@ -0,0 +1,47 @@ +# Default trainer configuration +seed: 42 +epochs: 10 +batch_size_per_gpu: 64 +lr: 0.0003 +lr_scale_with_batch: false +weight_decay: 0.01 +optimizer: adamw # adamw | adam | sgd +grad_clip_norm: 1.0 +gradient_accumulation_steps: 1 +precision: fp32 # fp32 | fp16 | bf16 +compile: false +deterministic: false +strategy: ddp # ddp | fsdp +distributed: auto # auto | true | false +num_workers: 4 +persistent_workers: true + +# Scheduler +scheduler: + name: cosine + warmup_steps: 500 + min_lr: 0.00001 + step_size: 30 + gamma: 0.1 + +# Evaluation +eval_every: 1 +eval_split: val +eval_on_start: true + +# Early stopping +early_stopping: + enabled: true + patience: 50 + monitor: val_loss + mode: min + +# Checkpointing +checkpoint_every: 1 +monitor_metric: val_loss +monitor_mode: min +resume_from: null + +# Experiment +experiment_name: default +tags: [] diff --git a/configs/trainer/sudoku.yaml b/configs/trainer/sudoku.yaml new file mode 100644 index 0000000000000000000000000000000000000000..aa42c243962b430be6af935480e64d520358ab53 --- /dev/null +++ b/configs/trainer/sudoku.yaml @@ -0,0 +1,45 @@ +seed: 42 +epochs: 200 +batch_size_per_gpu: 32 +lr: 0.0003 +lr_scale_with_batch: false +weight_decay: 0.01 +optimizer: adamw +grad_clip_norm: 1.0 +gradient_accumulation_steps: 1 +precision: fp32 +compile: false +deterministic: false +strategy: ddp +distributed: "false" +num_workers: 4 +persistent_workers: true + +scheduler: + name: linear_warmup_cosine + warmup_steps: 200 + min_lr: 0.00001 + step_size: 60 + gamma: 0.1 + +eval_every: 1 +eval_split: val +eval_on_start: false + +early_stopping: + enabled: true + patience: 50 + monitor: clue_accuracy + mode: max + +checkpoint_every: 1 +monitor_metric: clue_accuracy +monitor_mode: max +resume_from: null + +experiment_name: softasp_pretrain_backbone +tags: + - sudoku + - perception + - baseline + - prelim diff --git a/configs/trainer/sudoku_softasp.yaml b/configs/trainer/sudoku_softasp.yaml new file mode 100644 index 0000000000000000000000000000000000000000..5b1e8ac1b587ac431292a0b2b6b67c7413e36414 --- /dev/null +++ b/configs/trainer/sudoku_softasp.yaml @@ -0,0 +1,46 @@ +protocol: sudoku_seeded_updates_v1 +seed: 42 +epochs: 200 +batch_size_per_gpu: 32 +lr: 0.0003 +lr_scale_with_batch: false +weight_decay: 0.01 +optimizer: adamw +grad_clip_norm: 1.0 +gradient_accumulation_steps: 1 +precision: fp32 +compile: false +deterministic: false +strategy: ddp +distributed: "false" +num_workers: 4 +persistent_workers: true + +scheduler: + name: linear_warmup_cosine + warmup_steps: 200 + min_lr: 0.00001 + step_size: 60 + gamma: 0.1 + +eval_every: 1 +eval_split: val +eval_on_start: false + +early_stopping: + enabled: true + patience: 50 + monitor: constraint_sat_rate + mode: max + +checkpoint_every: 1 +monitor_metric: constraint_sat_rate +monitor_mode: max +resume_from: null + +experiment_name: softasp_sudoku +tags: + - sudoku + - softasp + - ours + - prelim diff --git a/docs/instantiations.md b/docs/instantiations.md new file mode 100644 index 0000000000000000000000000000000000000000..7ce68b593cf9db6dd7bb3d6fcdd927d6e1ae1922 --- /dev/null +++ b/docs/instantiations.md @@ -0,0 +1,126 @@ +# Adding an AS² instantiation + +An instantiation is a Hydra configuration plus the task-specific implementations +it selects. Visual Sudoku is the bundled example. MNIST Addition and CLEVR-Hans +are future targets, not runnable options in this release. + +The shared commands dispatch by capability, with no benchmark branches: + +```bash +uv run --locked softasp-train instantiation=your_task +uv run --locked softasp-train-scratch instantiation=your_task +uv run --locked softasp-predict instantiation=your_task +uv run --locked softasp-evaluate instantiation=your_task +``` + +These commands require your implementation and configuration to exist. Place +`your_task.yaml` in `configs/instantiation/`, or supply an additional Hydra config +search directory containing that group. Install your provider module in the same +uv environment. No shared CLI or framework source edits are needed to select an +implemented instantiation. + +## Configuration contract + +Use `# @package _global_` so the instantiation can compose dataset, preprocessing, +model, trainer, evaluator, paths and any workflow-specific sections. The bundled +[`sudoku.yaml`](../configs/instantiation/sudoku.yaml) is a complete working example. + +An extension configuration follows this structure (provider names are illustrative): + +```yaml +# @package _global_ +defaults: + - /trainer: default + - _self_ + +dataset: + name: your_task + # Dataset parameters, splits, paths and preprocessing choices go here. +model: + name: your_model + pretrained_from: null + freeze_backbone_epochs: 0 + params: {} +evaluator: + checkpoint: null + metrics: [accuracy] + +instantiation: + name: your_task + version: 1 + constraints: + _target_: your_package.constraints.ConstraintLoss + providers: + dataset: {_target_: your_package.providers.DatasetProvider} + model: {_target_: your_package.providers.ModelProvider} + metrics: {_target_: your_package.providers.MetricsProvider} + prediction: {_target_: your_package.providers.PredictionProvider} + evaluation: {_target_: your_package.providers.EvaluationProvider} + workflow: {_target_: your_package.providers.WorkflowProvider} +``` + +Add hyperparameters, data identities, paths, network composition, objective +weights, evidence policies, input/output settings and workflow stages needed by +your task. Use the existing trainer schema and configuration groups; unknown +trainer fields are not arbitrary model parameters. Model-specific settings belong +under `model.params` or an instantiation-owned section. The top-level `paths` +group is composed by the common training configurations; prediction configurations +also compose it explicitly. `predict` supplies `checkpoint`, `input`, `output` +and `device` for the bundled adapter; another adapter may define different inputs. + +## Provider methods + +Hydra constructs each provider from its `_target_` and constructor parameters. +Keep provider construction free of stochastic model initialization; build models +inside `prepare` or `build`, after the training entrypoint applies its seed policy. + +| Provider | Required methods | Contract | +|---|---|---| +| Dataset | `build(cfg)` | Return `(training_dataset, validation_dataset)`; handle task-specific preparation/selection and identity validation. | +| Model | `prepare(cfg)`, `build(cfg)` | `prepare` returns a model or `None` before dataset construction; `build` constructs after datasets if needed. Bind configured constraints/objectives here. | +| Model inference | `load(cfg)` | Required by `softasp.inference.load_model(cfg)`; validate and strictly load the selected checkpoint. | +| Metrics | `build(cfg)` | Return an evaluator with `evaluate(model, loader, device) -> dict[str, float]`. | +| Prediction | `run(cfg)` | Return a JSON-serializable result. The common CLI writes `cfg.output` without overwriting an existing file. Never use solution labels. | +| Evaluation | `run(cfg)` | Score configured splits and write task-specific reports/provenance; return a result dictionary. | +| Workflow | `run(cfg)` | Execute the task's full workflow and return its result/artifact path. It may call shared `run_training(cfg)`. | + +Training datasets yield dictionaries of tensors accepted by the shared loader. +Models implement `BaseSoftASPModel.forward(batch)` with a differentiable `loss` +and appropriate logits or predictions. The standard evaluator uses `labels` and +`logits`/`predictions`; supply a custom evaluator for other target/output contracts. +For freezing or gradient ramps, expose the relevant module through +`get_backbone()`. A model without a backbone returns `None`. + +## Reusable components and task-specific constraints + +`softasp_solver.components.reasoning.AS2Network` composes an encoder, concept head, +probability projection, attention module, task head and optional structure module. +Its input encoder produces `(batch, positions, features)`. A task head may return +per-position outputs or pool into a classification output. Images, 81 positions, +nine symbols, Sudoku masks and per-cell predictions are not framework requirements. + +The `components` package also supplies explicit evidence policies and structural +embeddings; `constraints` supplies shared operators and fixed-point residuals. +An instantiation may reuse these components or provide another differentiable +implementation appropriate to its declarative constraints. + +Each instantiation owns its constraint specification and implementation. Sudoku +keeps both in `softasp_solver.sudoku.constraints`, with its training objectives +in `softasp_solver.sudoku.objective`. Changing specification text alone does not +change the numerical implementation: there is no automatic ASP compiler. + +## Checkpoints and verification + +Training saves the resolved configuration, including instantiation identity and +providers. The common resume path validates instantiation/model identity and +constraint/network configuration before restoring state. Prediction/evaluation +providers must call the shared checkpoint validation helpers and load state +strictly; the bundled Sudoku providers do so. Only the explicitly recognized +legacy Sudoku inference checkpoint is accepted without instantiation metadata. + +Test your adapter through all four commands, checkpoint round-trip, incompatible +identity rejection, and label-free prediction. The release's development checks +exercise a non-image, pooled-head AS² fixture with its own constraint residual +through the same entrypoints. That fixture is an interface test, not an additional +scientific benchmark. Dataset downloading and from-scratch stage order belong +to the instantiation; the common framework does not impose Sudoku's two-stage recipe. diff --git a/examples/sudoku.npz b/examples/sudoku.npz new file mode 100644 index 0000000000000000000000000000000000000000..98f00ff88ac5ccdbef8d8e6a2173e97dc4c160c2 --- /dev/null +++ b/examples/sudoku.npz @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:56834dcc68137cc20e753a969a7f70e10c7ca59559129e8a25fba0f3cf54682b +size 8772 diff --git a/models/sudoku/as2.pt b/models/sudoku/as2.pt new file mode 100644 index 0000000000000000000000000000000000000000..2095e9d1dd25d49701b50bab6b9bdb0eceba4373 --- /dev/null +++ b/models/sudoku/as2.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d3dd9bc93a91a9421bec01e07fde3ddad69d996b7a33eaa8d86bed588d239ed8 +size 19549571 diff --git a/models/sudoku/metadata.json b/models/sudoku/metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..c4f9dc2513ecd4360139a3fb4d44a939452b1955 --- /dev/null +++ b/models/sudoku/metadata.json @@ -0,0 +1,14 @@ +{ + "format": "pytorch-state-dict", + "checkpoint": "as2.pt", + "sha256": "d3dd9bc93a91a9421bec01e07fde3ddad69d996b7a33eaa8d86bed588d239ed8", + "source_revision": "7a50cf280201aed800837ae327cb1c47fbb3fbc2", + "seed": 42, + "selected_epoch": 187, + "test_board_accuracy": 0.955, + "test_boards": 1000, + "decoder": "raw_argmax", + "training_precision": "bf16", + "evaluation_precision": "float32", + "data_protocol": "sudoku_legacy_mnist_pool_v1" +} diff --git a/models/sudoku/model.json b/models/sudoku/model.json new file mode 100644 index 0000000000000000000000000000000000000000..bd2a9c4f89d03624c05fa39f7401c6e1994dd4a2 --- /dev/null +++ b/models/sudoku/model.json @@ -0,0 +1,23 @@ +{ + "name": "softasp_sudoku", + "pretrained_from": null, + "freeze_backbone_epochs": 20, + "tags": [ + "softasp", + "ours", + "sudoku" + ], + "params": { + "d_model": 256, + "n_heads": 8, + "n_layers": 6, + "dropout": 0.1, + "lambda_ic": 5.0, + "lambda_blank": 0.3, + "blank_decay_epochs": 100, + "ic_warmup_epochs": 20, + "lambda_uniq": 0.0, + "backbone": "default", + "oracle_perception": false + } +} diff --git a/packages/softasp-core/LICENSE b/packages/softasp-core/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..340cf0c959586320b003a9ed4ad61ec65f8f90e5 --- /dev/null +++ b/packages/softasp-core/LICENSE @@ -0,0 +1,158 @@ +Creative Commons Attribution-NonCommercial 4.0 International + + Creative Commons Corporation (“Creative Commons”) is not a law firm and does not provide legal services or legal advice. Distribution of Creative Commons public licenses does not create a lawyer-client or other relationship. Creative Commons makes its licenses and related information available on an “as-is” basis. Creative Commons gives no warranties regarding its licenses, any material licensed under their terms and conditions, or any related information. Creative Commons disclaims all liability for damages resulting from their use to the fullest extent possible. + +Using Creative Commons Public Licenses + +Creative Commons public licenses provide a standard set of terms and conditions that creators and other rights holders may use to share original works of authorship and other material subject to copyright and certain other rights specified in the public license below. The following considerations are for informational purposes only, are not exhaustive, and do not form part of our licenses. + +Considerations for licensors: Our public licenses are intended for use by those authorized to give the public permission to use material in ways otherwise restricted by copyright and certain other rights. Our licenses are irrevocable. Licensors should read and understand the terms and conditions of the license they choose before applying it. Licensors should also secure all rights necessary before applying our licenses so that the public can reuse the material as expected. Licensors should clearly mark any material not subject to the license. This includes other CC-licensed material, or material used under an exception or limitation to copyright. More considerations for licensors. + +Considerations for the public: By using one of our public licenses, a licensor grants the public permission to use the licensed material under specified terms and conditions. If the licensor’s permission is not necessary for any reason–for example, because of any applicable exception or limitation to copyright–then that use is not regulated by the license. Our licenses grant only permissions under copyright and certain other rights that a licensor has authority to grant. Use of the licensed material may still be restricted for other reasons, including because others have copyright or other rights in the material. A licensor may make special requests, such as asking that all changes be marked or described. Although not required by our licenses, you are encouraged to respect those requests where reasonable. More considerations for the public. + +Creative Commons Attribution-NonCommercial 4.0 International Public License + +By exercising the Licensed Rights (defined below), You accept and agree to be bound by the terms and conditions of this Creative Commons Attribution-NonCommercial 4.0 International Public License ("Public License"). To the extent this Public License may be interpreted as a contract, You are granted the Licensed Rights in consideration of Your acceptance of these terms and conditions, and the Licensor grants You such rights in consideration of benefits the Licensor receives from making the Licensed Material available under these terms and conditions. + +Section 1 – Definitions. + + a. Adapted Material means material subject to Copyright and Similar Rights that is derived from or based upon the Licensed Material and in which the Licensed Material is translated, altered, arranged, transformed, or otherwise modified in a manner requiring permission under the Copyright and Similar Rights held by the Licensor. For purposes of this Public License, where the Licensed Material is a musical work, performance, or sound recording, Adapted Material is always produced where the Licensed Material is synched in timed relation with a moving image. + + b. Adapter's License means the license You apply to Your Copyright and Similar Rights in Your contributions to Adapted Material in accordance with the terms and conditions of this Public License. + + c. Copyright and Similar Rights means copyright and/or similar rights closely related to copyright including, without limitation, performance, broadcast, sound recording, and Sui Generis Database Rights, without regard to how the rights are labeled or categorized. For purposes of this Public License, the rights specified in Section 2(b)(1)-(2) are not Copyright and Similar Rights. + + d. Effective Technological Measures means those measures that, in the absence of proper authority, may not be circumvented under laws fulfilling obligations under Article 11 of the WIPO Copyright Treaty adopted on December 20, 1996, and/or similar international agreements. + + e. Exceptions and Limitations means fair use, fair dealing, and/or any other exception or limitation to Copyright and Similar Rights that applies to Your use of the Licensed Material. + + f. Licensed Material means the artistic or literary work, database, or other material to which the Licensor applied this Public License. + + g. Licensed Rights means the rights granted to You subject to the terms and conditions of this Public License, which are limited to all Copyright and Similar Rights that apply to Your use of the Licensed Material and that the Licensor has authority to license. + + h. Licensor means the individual(s) or entity(ies) granting rights under this Public License. + + i. NonCommercial means not primarily intended for or directed towards commercial advantage or monetary compensation. For purposes of this Public License, the exchange of the Licensed Material for other material subject to Copyright and Similar Rights by digital file-sharing or similar means is NonCommercial provided there is no payment of monetary compensation in connection with the exchange. + + j. Share means to provide material to the public by any means or process that requires permission under the Licensed Rights, such as reproduction, public display, public performance, distribution, dissemination, communication, or importation, and to make material available to the public including in ways that members of the public may access the material from a place and at a time individually chosen by them. + + k. Sui Generis Database Rights means rights other than copyright resulting from Directive 96/9/EC of the European Parliament and of the Council of 11 March 1996 on the legal protection of databases, as amended and/or succeeded, as well as other essentially equivalent rights anywhere in the world. + + l. You means the individual or entity exercising the Licensed Rights under this Public License. Your has a corresponding meaning. + +Section 2 – Scope. + + a. License grant. + + 1. Subject to the terms and conditions of this Public License, the Licensor hereby grants You a worldwide, royalty-free, non-sublicensable, non-exclusive, irrevocable license to exercise the Licensed Rights in the Licensed Material to: + + A. reproduce and Share the Licensed Material, in whole or in part, for NonCommercial purposes only; and + + B. produce, reproduce, and Share Adapted Material for NonCommercial purposes only. + + 2. Exceptions and Limitations. For the avoidance of doubt, where Exceptions and Limitations apply to Your use, this Public License does not apply, and You do not need to comply with its terms and conditions. + + 3. Term. The term of this Public License is specified in Section 6(a). + + 4. Media and formats; technical modifications allowed. The Licensor authorizes You to exercise the Licensed Rights in all media and formats whether now known or hereafter created, and to make technical modifications necessary to do so. The Licensor waives and/or agrees not to assert any right or authority to forbid You from making technical modifications necessary to exercise the Licensed Rights, including technical modifications necessary to circumvent Effective Technological Measures. For purposes of this Public License, simply making modifications authorized by this Section 2(a)(4) never produces Adapted Material. + + 5. Downstream recipients. + + A. Offer from the Licensor – Licensed Material. Every recipient of the Licensed Material automatically receives an offer from the Licensor to exercise the Licensed Rights under the terms and conditions of this Public License. + + B. No downstream restrictions. You may not offer or impose any additional or different terms or conditions on, or apply any Effective Technological Measures to, the Licensed Material if doing so restricts exercise of the Licensed Rights by any recipient of the Licensed Material. + + 6. No endorsement. Nothing in this Public License constitutes or may be construed as permission to assert or imply that You are, or that Your use of the Licensed Material is, connected with, or sponsored, endorsed, or granted official status by, the Licensor or others designated to receive attribution as provided in Section 3(a)(1)(A)(i). + + b. Other rights. + + 1. Moral rights, such as the right of integrity, are not licensed under this Public License, nor are publicity, privacy, and/or other similar personality rights; however, to the extent possible, the Licensor waives and/or agrees not to assert any such rights held by the Licensor to the limited extent necessary to allow You to exercise the Licensed Rights, but not otherwise. + + 2. Patent and trademark rights are not licensed under this Public License. + + 3. To the extent possible, the Licensor waives any right to collect royalties from You for the exercise of the Licensed Rights, whether directly or through a collecting society under any voluntary or waivable statutory or compulsory licensing scheme. In all other cases the Licensor expressly reserves any right to collect such royalties, including when the Licensed Material is used other than for NonCommercial purposes. + +Section 3 – License Conditions. + +Your exercise of the Licensed Rights is expressly made subject to the following conditions. + + a. Attribution. + + 1. If You Share the Licensed Material (including in modified form), You must: + + A. retain the following if it is supplied by the Licensor with the Licensed Material: + + i. identification of the creator(s) of the Licensed Material and any others designated to receive attribution, in any reasonable manner requested by the Licensor (including by pseudonym if designated); + + ii. a copyright notice; + + iii. a notice that refers to this Public License; + + iv. a notice that refers to the disclaimer of warranties; + + v. a URI or hyperlink to the Licensed Material to the extent reasonably practicable; + + B. indicate if You modified the Licensed Material and retain an indication of any previous modifications; and + + C. indicate the Licensed Material is licensed under this Public License, and include the text of, or the URI or hyperlink to, this Public License. + + 2. You may satisfy the conditions in Section 3(a)(1) in any reasonable manner based on the medium, means, and context in which You Share the Licensed Material. For example, it may be reasonable to satisfy the conditions by providing a URI or hyperlink to a resource that includes the required information. + + 3. If requested by the Licensor, You must remove any of the information required by Section 3(a)(1)(A) to the extent reasonably practicable. + + 4. If You Share Adapted Material You produce, the Adapter's License You apply must not prevent recipients of the Adapted Material from complying with this Public License. + +Section 4 – Sui Generis Database Rights. + +Where the Licensed Rights include Sui Generis Database Rights that apply to Your use of the Licensed Material: + + a. for the avoidance of doubt, Section 2(a)(1) grants You the right to extract, reuse, reproduce, and Share all or a substantial portion of the contents of the database for NonCommercial purposes only; + + b. if You include all or a substantial portion of the database contents in a database in which You have Sui Generis Database Rights, then the database in which You have Sui Generis Database Rights (but not its individual contents) is Adapted Material; and + + c. You must comply with the conditions in Section 3(a) if You Share all or a substantial portion of the contents of the database. +For the avoidance of doubt, this Section 4 supplements and does not replace Your obligations under this Public License where the Licensed Rights include other Copyright and Similar Rights. + +Section 5 – Disclaimer of Warranties and Limitation of Liability. + + a. Unless otherwise separately undertaken by the Licensor, to the extent possible, the Licensor offers the Licensed Material as-is and as-available, and makes no representations or warranties of any kind concerning the Licensed Material, whether express, implied, statutory, or other. This includes, without limitation, warranties of title, merchantability, fitness for a particular purpose, non-infringement, absence of latent or other defects, accuracy, or the presence or absence of errors, whether or not known or discoverable. Where disclaimers of warranties are not allowed in full or in part, this disclaimer may not apply to You. + + b. To the extent possible, in no event will the Licensor be liable to You on any legal theory (including, without limitation, negligence) or otherwise for any direct, special, indirect, incidental, consequential, punitive, exemplary, or other losses, costs, expenses, or damages arising out of this Public License or use of the Licensed Material, even if the Licensor has been advised of the possibility of such losses, costs, expenses, or damages. Where a limitation of liability is not allowed in full or in part, this limitation may not apply to You. + + c. The disclaimer of warranties and limitation of liability provided above shall be interpreted in a manner that, to the extent possible, most closely approximates an absolute disclaimer and waiver of all liability. + +Section 6 – Term and Termination. + + a. This Public License applies for the term of the Copyright and Similar Rights licensed here. However, if You fail to comply with this Public License, then Your rights under this Public License terminate automatically. + + b. Where Your right to use the Licensed Material has terminated under Section 6(a), it reinstates: + + 1. automatically as of the date the violation is cured, provided it is cured within 30 days of Your discovery of the violation; or + + 2. upon express reinstatement by the Licensor. + + For the avoidance of doubt, this Section 6(b) does not affect any right the Licensor may have to seek remedies for Your violations of this Public License. + + c. For the avoidance of doubt, the Licensor may also offer the Licensed Material under separate terms or conditions or stop distributing the Licensed Material at any time; however, doing so will not terminate this Public License. + + d. Sections 1, 5, 6, 7, and 8 survive termination of this Public License. + +Section 7 – Other Terms and Conditions. + + a. The Licensor shall not be bound by any additional or different terms or conditions communicated by You unless expressly agreed. + + b. Any arrangements, understandings, or agreements regarding the Licensed Material not stated herein are separate from and independent of the terms and conditions of this Public License. + +Section 8 – Interpretation. + + a. For the avoidance of doubt, this Public License does not, and shall not be interpreted to, reduce, limit, restrict, or impose conditions on any use of the Licensed Material that could lawfully be made without permission under this Public License. + + b. To the extent possible, if any provision of this Public License is deemed unenforceable, it shall be automatically reformed to the minimum extent necessary to make it enforceable. If the provision cannot be reformed, it shall be severed from this Public License without affecting the enforceability of the remaining terms and conditions. + + c. No term or condition of this Public License will be waived and no failure to comply consented to unless expressly agreed to by the Licensor. + + d. Nothing in this Public License constitutes or may be interpreted as a limitation upon, or waiver of, any privileges and immunities that apply to the Licensor or You, including from the legal processes of any jurisdiction or authority. + +Creative Commons is not a party to its public licenses. Notwithstanding, Creative Commons may elect to apply one of its public licenses to material it publishes and in those instances will be considered the “Licensor.” Except for the limited purpose of indicating that material is shared under a Creative Commons public license or as otherwise permitted by the Creative Commons policies published at creativecommons.org/policies, Creative Commons does not authorize the use of the trademark “Creative Commons” or any other trademark or logo of Creative Commons without its prior written consent including, without limitation, in connection with any unauthorized modifications to any of its public licenses or any other arrangements, understandings, or agreements concerning use of licensed material. For the avoidance of doubt, this paragraph does not form part of the public licenses. + +Creative Commons may be contacted at creativecommons.org. diff --git a/packages/softasp-core/pyproject.toml b/packages/softasp-core/pyproject.toml new file mode 100644 index 0000000000000000000000000000000000000000..83f9ef17f285a20a82d4f4985fb2aa40153081d1 --- /dev/null +++ b/packages/softasp-core/pyproject.toml @@ -0,0 +1,24 @@ +[project] +name = "softasp-core" +version = "0.1.0" +description = "SoftASP core infrastructure: config, data, training, evaluation, tracking" +requires-python = ">=3.12" +license = "CC-BY-NC-4.0" +license-files = ["LICENSE"] +dependencies = [ + "torch>=2.2", + "requests>=2.31", + "h5py>=3.10", + "numpy>=1.26", + "aim>=3.19", + "omegaconf>=2.3", + "hydra-core>=1.3", + "rich>=13.0", +] + +[build-system] +requires = ["hatchling"] +build-backend = "hatchling.build" + +[tool.hatch.build.targets.wheel] +packages = ["src/softasp_core"] diff --git a/packages/softasp-core/src/softasp_core/__init__.py b/packages/softasp-core/src/softasp_core/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..786af6669dd708f737a1e5a9382a494c639872b5 --- /dev/null +++ b/packages/softasp-core/src/softasp_core/__init__.py @@ -0,0 +1,3 @@ +"""SoftASP core infrastructure: config, data, training, evaluation, tracking.""" + +__version__ = "0.1.0" diff --git a/packages/softasp-core/src/softasp_core/config/__init__.py b/packages/softasp-core/src/softasp_core/config/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..1d8dc3c6ca1eb391e72887cbcb6ab98e52cd43ae --- /dev/null +++ b/packages/softasp-core/src/softasp_core/config/__init__.py @@ -0,0 +1,29 @@ +"""Configuration schemas, validation, and loading.""" + +from softasp_core.config.loader import load_config +from softasp_core.config.schemas import ( + DatasetConfig, + EarlyStoppingConfig, + EvaluatorConfig, + LoggingConfig, + ModelConfig, + PathsConfig, + PreprocessingConfig, + SchedulerConfig, + TrainerConfig, +) +from softasp_core.config.validation import validate_config + +__all__ = [ + "DatasetConfig", + "EarlyStoppingConfig", + "EvaluatorConfig", + "LoggingConfig", + "ModelConfig", + "PathsConfig", + "PreprocessingConfig", + "SchedulerConfig", + "TrainerConfig", + "load_config", + "validate_config", +] diff --git a/packages/softasp-core/src/softasp_core/config/loader.py b/packages/softasp-core/src/softasp_core/config/loader.py new file mode 100644 index 0000000000000000000000000000000000000000..0718d087a627f9971d692e4a083765d8586f8331 --- /dev/null +++ b/packages/softasp-core/src/softasp_core/config/loader.py @@ -0,0 +1,45 @@ +"""Load Hydra configs programmatically — works in scripts, notebooks, and tests.""" + +from __future__ import annotations + +import os +from pathlib import Path +from typing import Any + +from hydra import compose, initialize_config_dir +from omegaconf import DictConfig, OmegaConf + + +_CONFIGS_DIR = str(Path(__file__).resolve().parents[5] / "configs") + + +def load_config( + overrides: list[str] | None = None, + config_name: str = "config", + configs_dir: str | None = None, +) -> DictConfig: + """Load and return a fully-resolved Hydra config. + + Args: + overrides: Hydra-style overrides, e.g. ``["dataset=sudoku", "trainer.lr=1e-3"]``. + config_name: Name of the master config file (without .yaml). + configs_dir: Absolute path to the configs directory. Defaults to + ``/configs``. + + Returns: + Fully resolved OmegaConf DictConfig. + """ + configs_dir = configs_dir or os.environ.get("SOFTASP_CONFIGS_DIR", _CONFIGS_DIR) + overrides = overrides or [] + + with initialize_config_dir(config_dir=configs_dir, version_base="1.3"): + cfg = compose(config_name=config_name, overrides=overrides) + + # Resolve all interpolations eagerly so downstream code sees plain values. + OmegaConf.resolve(cfg) + return cfg + + +def config_to_dict(cfg: DictConfig) -> dict[str, Any]: + """Convert OmegaConf DictConfig to a plain Python dict (for Aim params).""" + return OmegaConf.to_container(cfg, resolve=True, throw_on_missing=True) # type: ignore[return-value] diff --git a/packages/softasp-core/src/softasp_core/config/schemas.py b/packages/softasp-core/src/softasp_core/config/schemas.py new file mode 100644 index 0000000000000000000000000000000000000000..2d635464d947990a0da249925c3ae4c5fa8ea436 --- /dev/null +++ b/packages/softasp-core/src/softasp_core/config/schemas.py @@ -0,0 +1,165 @@ +"""Typed dataclass schemas for all Hydra config groups. + +Every field that appears in a YAML config file has a corresponding +dataclass field here, giving type safety and IDE autocomplete. +""" + +from __future__ import annotations + +from dataclasses import dataclass, field +from typing import Any + +# ── Paths ───────────────────────────────────────────────────────────────────── + + +@dataclass +class PathsConfig: + """Filesystem paths supplied through configuration.""" + + data_root: str = "${oc.env:SOFTASP_DATA_ROOT,data}" + output_dir: str = "outputs" + aim_repo: str = ".aim" + + +# ── Dataset ─────────────────────────────────────────────────────────────────── + + +@dataclass +class DatasetConfig: + """Per-dataset config (resolved via configs/dataset/.yaml).""" + + name: str + num_classes: int | None = None + image_size: int | None = None + max_objects: int | None = None + splits: list[str] = field(default_factory=lambda: ["train", "val", "test"]) + augmentations: dict[str, list[str]] = field( + default_factory=lambda: {"train": [], "val": [], "test": []} + ) + augmentation_params: dict[str, dict[str, Any]] = field(default_factory=dict) + subset_size: int | None = None + + +# ── Preprocessing ───────────────────────────────────────────────────────────── + + +@dataclass +class PreprocessingConfig: + """Controls how raw data is transformed into HDF5.""" + + device: str = "cpu" # cpu | cuda + subset_size: int | None = None # None = full dataset + chunk_size: int = 256 + compression: str = "gzip" + compression_opts: int = 4 + force: bool = False + num_workers: int = 4 + + +# ── Model ───────────────────────────────────────────────────────────────────── + + +@dataclass +class ModelConfig: + """Model architecture config.""" + + name: str + pretrained_from: str | None = None + freeze_backbone_epochs: int = 0 # Epochs to freeze get_backbone(); 0 disables freezing. + ramp_backbone_epochs: int = ( + 0 # 0 = hard unfreeze; >0 = linearly ramp backbone LR to full over N epochs after unfreeze + ) + params: dict[str, Any] = field(default_factory=dict) + tags: list[str] = field(default_factory=list) + + +# ── Scheduler ───────────────────────────────────────────────────────────────── + + +@dataclass +class SchedulerConfig: + """Learning rate scheduler config.""" + + name: str = "cosine" # cosine | step | linear_warmup_cosine | none + warmup_steps: int = 500 + min_lr: float = 1e-6 + step_size: int = 30 + gamma: float = 0.1 + + +# ── Early stopping ──────────────────────────────────────────────────────────── + + +@dataclass +class EarlyStoppingConfig: + """Early stopping config.""" + + enabled: bool = True + patience: int = 10 + monitor: str = "val_loss" + mode: str = "min" # min | max + + +# ── Trainer ─────────────────────────────────────────────────────────────────── + + +@dataclass +class TrainerConfig: + """Full trainer config — the single source of truth for training.""" + + protocol: str = "legacy_v0" + seed: int = 42 + epochs: int = 100 + batch_size_per_gpu: int = 16 + lr: float = 1e-4 + lr_scale_with_batch: bool = False + weight_decay: float = 1e-5 + optimizer: str = "adamw" # adamw | adam | sgd + grad_clip_norm: float | None = 1.0 + gradient_accumulation_steps: int = 1 + precision: str = "fp32" # fp32 | fp16 | bf16 + compile: bool = False + deterministic: bool = False + strategy: str = "ddp" # ddp | fsdp + distributed: str = "auto" # auto | true | false + num_workers: int = 4 + persistent_workers: bool = True + scheduler: SchedulerConfig = field(default_factory=SchedulerConfig) + eval_every: int = 1 + eval_split: str = "val" + eval_on_start: bool = True + checkpoint_every: int = 1 + monitor_metric: str = "val_loss" + monitor_mode: str = "min" # min | max + resume_from: str | None = None + early_stopping: EarlyStoppingConfig = field(default_factory=EarlyStoppingConfig) + experiment_name: str = "default" + tags: list[str] = field(default_factory=list) + + +# ── Evaluator ───────────────────────────────────────────────────────────────── + + +@dataclass +class EvaluatorConfig: + """Evaluation harness config — datasets is a list, not inherited from trainer.""" + + datasets: list[str] = field(default_factory=list) + metrics: list[str] = field(default_factory=lambda: ["accuracy"]) + batch_size_per_gpu: int = 32 + num_workers: int = 4 + threshold: float = 0.5 + checkpoint: str | None = None + + +# ── Logging ─────────────────────────────────────────────────────────────────── + + +@dataclass +class LoggingConfig: + """Logging config — overrides Hydra's default logging hijack.""" + + level: str = "INFO" + rich_console: bool = True + file_log: bool = True + log_every_n_steps: int = 10 diff --git a/packages/softasp-core/src/softasp_core/config/validation.py b/packages/softasp-core/src/softasp_core/config/validation.py new file mode 100644 index 0000000000000000000000000000000000000000..8b54884af41fd2b43689850b3f8832015c29813b --- /dev/null +++ b/packages/softasp-core/src/softasp_core/config/validation.py @@ -0,0 +1,103 @@ +"""Config validation — catch bad combos before wasting GPU hours.""" + +from __future__ import annotations + +import logging +import sys +from typing import Any + +import torch + +logger = logging.getLogger(__name__) + + +class ConfigValidationError(Exception): + """Raised when config contains incompatible settings.""" + + +def validate_config(cfg: Any) -> None: + """Validate the full resolved config. Raises ConfigValidationError on problems.""" + + errors: list[str] = [] + warnings: list[str] = [] + trainer = cfg.trainer + + # ── Precision checks ────────────────────────────────────────────────── + device_type = _detect_device_type() + + if trainer.precision == "bf16" and device_type == "cpu": + errors.append("bf16 precision is not supported on CPU. Use fp32 or switch to GPU.") + + if trainer.precision == "fp16" and device_type == "mps": + errors.append("fp16 (GradScaler) is not supported on MPS. Use bf16 or fp32.") + + # ── DataLoader vs MPS ───────────────────────────────────────────────── + if device_type == "mps" and trainer.num_workers > 0: + warnings.append( + f"num_workers={trainer.num_workers} on MPS can hang. " + "Forcing num_workers=0." + ) + trainer.num_workers = 0 + trainer.persistent_workers = False + + # ── DDP sanity ──────────────────────────────────────────────────────── + gpu_count = torch.cuda.device_count() if torch.cuda.is_available() else 0 + wants_distributed = trainer.distributed == "true" or ( + trainer.distributed == "auto" and gpu_count > 1 + ) + if wants_distributed and gpu_count < 2: + warnings.append( + f"distributed={trainer.distributed} but only {gpu_count} GPU(s) found. " + "Running single-process." + ) + + # ── Gradient accumulation ───────────────────────────────────────────── + if trainer.gradient_accumulation_steps < 1: + errors.append("gradient_accumulation_steps must be >= 1.") + + # ── Scheduler ───────────────────────────────────────────────────────── + valid_schedulers = {"cosine", "step", "linear_warmup_cosine", "none"} + if trainer.scheduler.name not in valid_schedulers: + errors.append( + f"Unknown scheduler '{trainer.scheduler.name}'. " + f"Valid: {valid_schedulers}" + ) + + # ── Early stopping mode ─────────────────────────────────────────────── + if trainer.early_stopping.mode not in ("min", "max"): + errors.append(f"early_stopping.mode must be 'min' or 'max', got '{trainer.early_stopping.mode}'") + + # ── Log computed values ─────────────────────────────────────────────── + world_size = max(gpu_count, 1) if wants_distributed else 1 + effective_batch = ( + trainer.batch_size_per_gpu * world_size * trainer.gradient_accumulation_steps + ) + logger.info( + "Effective batch size: %d (per_gpu=%d × world=%d × accum=%d)", + effective_batch, + trainer.batch_size_per_gpu, + world_size, + trainer.gradient_accumulation_steps, + ) + + # ── Emit ────────────────────────────────────────────────────────────── + for w in warnings: + logger.warning("CONFIG WARNING: %s", w) + + if errors: + for e in errors: + logger.error("CONFIG ERROR: %s", e) + raise ConfigValidationError( + f"{len(errors)} config validation error(s):\n" + "\n".join(f" • {e}" for e in errors) + ) + + logger.info("Config validation passed.") + + +def _detect_device_type() -> str: + """Best-effort device type for validation (not authoritative for training).""" + if torch.cuda.is_available(): + return "cuda" + if sys.platform == "darwin" and hasattr(torch.backends, "mps") and torch.backends.mps.is_available(): + return "mps" + return "cpu" diff --git a/packages/softasp-core/src/softasp_core/data/__init__.py b/packages/softasp-core/src/softasp_core/data/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..798b7ce027ecba5d21a9cf24ac511ca8a42db4d3 --- /dev/null +++ b/packages/softasp-core/src/softasp_core/data/__init__.py @@ -0,0 +1,16 @@ +"""Data pipeline: downloaders, preprocessors, datasets, collation.""" + +from softasp_core.data.augmentations import AUGMENTATION_REGISTRY, get_augmentation +from softasp_core.data.base_downloader import BaseDownloader +from softasp_core.data.base_preprocessor import BasePreprocessor +from softasp_core.data.collate import masked_collate_fn +from softasp_core.data.hdf5_dataset import HDF5Dataset + +__all__ = [ + "AUGMENTATION_REGISTRY", + "BaseDownloader", + "BasePreprocessor", + "HDF5Dataset", + "get_augmentation", + "masked_collate_fn", +] diff --git a/packages/softasp-core/src/softasp_core/data/augmentations.py b/packages/softasp-core/src/softasp_core/data/augmentations.py new file mode 100644 index 0000000000000000000000000000000000000000..4389f3de57d1bbff3cc37a85c16c435569a9993b --- /dev/null +++ b/packages/softasp-core/src/softasp_core/data/augmentations.py @@ -0,0 +1,106 @@ +"""Augmentation registry — stochastic transforms applied on-the-fly. + +Deterministic transforms (resize, normalize) are baked into HDF5. +Only stochastic augmentations live here. +""" + +from __future__ import annotations + +from collections.abc import Callable +from typing import Any + +import torch + +# Type alias for augmentation callables +AugmentationFn = Callable[[torch.Tensor], torch.Tensor] + +# ── Registry ────────────────────────────────────────────────────────────────── + +AUGMENTATION_REGISTRY: dict[str, type] = {} + + +def register_augmentation(name: str): + """Decorator to register an augmentation class.""" + + def decorator(cls): + AUGMENTATION_REGISTRY[name] = cls + return cls + + return decorator + + +def get_augmentation(name: str, **kwargs: Any) -> AugmentationFn: + """Instantiate a registered augmentation by name.""" + if name not in AUGMENTATION_REGISTRY: + raise ValueError( + f"Unknown augmentation '{name}'. Available: {list(AUGMENTATION_REGISTRY.keys())}" + ) + return AUGMENTATION_REGISTRY[name](**kwargs) + + +# ── Built-in augmentations ──────────────────────────────────────────────────── + + +@register_augmentation("random_horizontal_flip") +class RandomHorizontalFlip: + """Randomly flip a CHW tensor horizontally.""" + + def __init__(self, p: float = 0.5) -> None: + self.p = p + + def __call__(self, x: torch.Tensor) -> torch.Tensor: + if torch.rand(1).item() < self.p: + return x.flip(-1) + return x + + +@register_augmentation("random_crop") +class RandomCrop: + """Random crop with zero-padding for CHW tensors.""" + + def __init__(self, size: int = 224, padding: int = 4) -> None: + self.size = size + self.padding = padding + + def __call__(self, x: torch.Tensor) -> torch.Tensor: + if self.padding > 0: + x = torch.nn.functional.pad(x, [self.padding] * 4, mode="constant", value=0) + _, h, w = x.shape + top = torch.randint(0, h - self.size + 1, (1,)).item() + left = torch.randint(0, w - self.size + 1, (1,)).item() + return x[:, top : top + self.size, left : left + self.size] + + +@register_augmentation("color_jitter") +class ColorJitter: + """Simple brightness/contrast jitter for CHW float tensors.""" + + def __init__(self, brightness: float = 0.2, contrast: float = 0.2) -> None: + self.brightness = brightness + self.contrast = contrast + + def __call__(self, x: torch.Tensor) -> torch.Tensor: + b = 1.0 + (torch.rand(1).item() * 2 - 1) * self.brightness + c = 1.0 + (torch.rand(1).item() * 2 - 1) * self.contrast + mean = x.mean() + x = (x - mean) * c + mean + x = x * b + return x.clamp(0, 1) + + +@register_augmentation("hue_jitter") +class HueJitter: + """Random hue shift for (C, H, W) uint8 tensors. + + Uses torchvision's adjust_hue which expects (3, H, W) uint8. + hue: max absolute hue shift in [0, 0.5] (fraction of full circle). + """ + + def __init__(self, hue: float = 0.2) -> None: + self.hue = hue + + def __call__(self, x: torch.Tensor) -> torch.Tensor: + import torchvision.transforms.functional as TF + + factor = (torch.rand(1).item() * 2 - 1) * self.hue + return TF.adjust_hue(x, factor) diff --git a/packages/softasp-core/src/softasp_core/data/base_downloader.py b/packages/softasp-core/src/softasp_core/data/base_downloader.py new file mode 100644 index 0000000000000000000000000000000000000000..c81edbaf8965b2f80734623f585523d3cc4affaf --- /dev/null +++ b/packages/softasp-core/src/softasp_core/data/base_downloader.py @@ -0,0 +1,74 @@ +"""Abstract base class for dataset downloaders.""" + +from __future__ import annotations + +import hashlib +import logging +from abc import ABC, abstractmethod +from pathlib import Path + +import requests + +logger = logging.getLogger(__name__) + + +class BaseDownloader(ABC): + """Download raw data, verify SHA256 checksums, store in raw//.""" + + def __init__(self, raw_dir: Path) -> None: + self.raw_dir = raw_dir + self.raw_dir.mkdir(parents=True, exist_ok=True) + + @abstractmethod + def download(self) -> None: + """Download all required files. Must be idempotent.""" + + @abstractmethod + def verify(self) -> bool: + """Return True if all expected files exist and checksums match.""" + + def run(self) -> None: + """Download if not already verified.""" + if self.verify(): + logger.info("Raw data already present and verified at %s", self.raw_dir) + return + logger.info("Downloading raw data to %s …", self.raw_dir) + self.download() + if not self.verify(): + raise RuntimeError(f"Verification failed after download at {self.raw_dir}") + logger.info("Download complete and verified.") + + # ── Helpers ─────────────────────────────────────────────────────────── + + @staticmethod + def download_file(url: str, dest: Path, expected_sha256: str | None = None) -> None: + """Stream-download *url* to *dest* with optional SHA256 verification.""" + dest.parent.mkdir(parents=True, exist_ok=True) + if dest.exists() and expected_sha256: + if _sha256(dest) == expected_sha256: + logger.debug("File %s already exists with correct hash; skipping.", dest.name) + return + + logger.info("Downloading %s → %s", url, dest) + resp = requests.get(url, stream=True, timeout=300) + resp.raise_for_status() + with open(dest, "wb") as f: + for chunk in resp.iter_content(chunk_size=1 << 20): + f.write(chunk) + + if expected_sha256: + actual = _sha256(dest) + if actual != expected_sha256: + dest.unlink() + raise RuntimeError( + f"SHA256 mismatch for {dest.name}: " + f"expected {expected_sha256}, got {actual}" + ) + + +def _sha256(path: Path) -> str: + h = hashlib.sha256() + with open(path, "rb") as f: + for chunk in iter(lambda: f.read(1 << 20), b""): + h.update(chunk) + return h.hexdigest() diff --git a/packages/softasp-core/src/softasp_core/data/base_preprocessor.py b/packages/softasp-core/src/softasp_core/data/base_preprocessor.py new file mode 100644 index 0000000000000000000000000000000000000000..409e0e08bccc46852d3570270dbb59d7f2ebaa59 --- /dev/null +++ b/packages/softasp-core/src/softasp_core/data/base_preprocessor.py @@ -0,0 +1,136 @@ +"""Abstract base class for dataset preprocessors. + +Reads raw data → writes a versioned HDF5 file. +""" + +from __future__ import annotations + +import json +import logging +from abc import ABC, abstractmethod +from datetime import datetime, timezone +from pathlib import Path +from typing import Any + +import h5py +import numpy as np + +from softasp_core.utils.hashing import hash_config, hash_directory + +logger = logging.getLogger(__name__) + + +class BasePreprocessor(ABC): + """Transform raw data into a single versioned HDF5 file per dataset. + + Subclasses implement :meth:`process_split` for each split. + """ + + def __init__( + self, + raw_dir: Path, + processed_dir: Path, + preprocessing_cfg: Any, + dataset_cfg: Any, + ) -> None: + self.raw_dir = raw_dir + self.processed_dir = processed_dir + self.preprocessing_cfg = preprocessing_cfg + self.dataset_cfg = dataset_cfg + self.processed_dir.mkdir(parents=True, exist_ok=True) + + # ── Public interface ────────────────────────────────────────────────── + + def run(self) -> Path: + """Preprocess raw → HDF5. Returns the HDF5 path.""" + cfg_hash = hash_config(self.preprocessing_cfg) + raw_hash = hash_directory(self.raw_dir) + h5_path = self.processed_dir / f"v_{cfg_hash}.h5" + meta_path = self.processed_dir / f"v_{cfg_hash}.meta.json" + + # Idempotent: skip if matching file exists + if h5_path.exists() and not getattr(self.preprocessing_cfg, "force", False): + if meta_path.exists(): + meta = json.loads(meta_path.read_text()) + if meta.get("raw_data_hash") == raw_hash: + logger.info("HDF5 already up to date: %s", h5_path) + return h5_path + logger.warning("Raw data changed — regenerating HDF5.") + else: + logger.info("HDF5 exists but no meta; regenerating.") + + logger.info("Preprocessing → %s", h5_path) + samples_per_split: dict[str, int] = {} + stats: dict[str, Any] = {} + + with h5py.File(h5_path, "w") as hf: + for split in self.dataset_cfg.splits: + logger.info(" Processing split: %s", split) + grp = hf.create_group(split) + n_samples = self.process_split(split, grp) + samples_per_split[split] = n_samples + + # Store dataset-level stats if subclass provides them + stats = self.compute_stats(h5_path) + + # Write sidecar metadata + meta_data = { + "preprocessing_config_hash": cfg_hash, + "raw_data_hash": raw_hash, + "created_at": datetime.now(timezone.utc).isoformat(), + "samples_per_split": samples_per_split, + "stats": stats, + } + meta_path.write_text(json.dumps(meta_data, indent=2)) + logger.info("Preprocessing complete: %s (%s)", h5_path, samples_per_split) + return h5_path + + # ── Abstract methods ────────────────────────────────────────────────── + + @abstractmethod + def process_split(self, split: str, group: h5py.Group) -> int: + """Process a single split, write datasets into *group*. + + Returns: + Number of samples written. + """ + + def compute_stats(self, h5_path: Path) -> dict[str, Any]: + """Optionally compute dataset statistics after all splits are written. + + Returns: + Dict of stats (e.g. mean, std) to store in .meta.json. + """ + return {} + + # ── Helpers ─────────────────────────────────────────────────────────── + + @staticmethod + def create_chunked_dataset( + group: h5py.Group, + name: str, + shape: tuple[int, ...], + dtype: np.dtype | str = "float32", + compression: str | None = "gzip", + compression_opts: int = 4, + chunk_size: int = 256, + ) -> h5py.Dataset: + """Create an HDF5 dataset with chunking and optional compression.""" + chunks = (min(chunk_size, shape[0]),) + shape[1:] + return group.create_dataset( + name, + shape=shape, + dtype=dtype, + chunks=chunks, + compression=compression, + compression_opts=compression_opts if compression else None, + ) + + @staticmethod + def create_label_dataset( + group: h5py.Group, + name: str, + data: np.ndarray, + ) -> h5py.Dataset: + """Create an uncompressed dataset for small arrays (labels, lengths).""" + return group.create_dataset(name, data=data) diff --git a/packages/softasp-core/src/softasp_core/data/collate.py b/packages/softasp-core/src/softasp_core/data/collate.py new file mode 100644 index 0000000000000000000000000000000000000000..14b7259c96c61898e786b2b96a92991f4fb12e99 --- /dev/null +++ b/packages/softasp-core/src/softasp_core/data/collate.py @@ -0,0 +1,38 @@ +"""Custom collate function that produces masks for padded variable-length data.""" + +from __future__ import annotations + +import torch + + +def masked_collate_fn(batch: list[dict[str, torch.Tensor]]) -> dict[str, torch.Tensor]: + """Collate a list of sample dicts into a batched dict. + + For every field ``_lengths`` in the sample, produces a boolean + mask ``_mask`` of shape ``(B, max_len)`` where True indicates valid + positions. + + Returns: + Dict with keys: each original field (stacked) plus generated masks. + """ + if not batch: + return {} + + keys = batch[0].keys() + collated: dict[str, torch.Tensor] = {} + + for key in keys: + tensors = [sample[key] for sample in batch] + collated[key] = torch.stack(tensors, dim=0) + + # Generate boolean masks from *_lengths fields + length_keys = [k for k in keys if k.endswith("_lengths")] + for lk in length_keys: + field_name = lk.removesuffix("_lengths") + lengths = collated[lk] # (B,) + max_len = int(lengths.max().item()) + # True = valid position + mask = torch.arange(max_len, device=lengths.device).unsqueeze(0) < lengths.unsqueeze(1) + collated[f"{field_name}_mask"] = mask + + return collated diff --git a/packages/softasp-core/src/softasp_core/data/hdf5_dataset.py b/packages/softasp-core/src/softasp_core/data/hdf5_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..b800dfad49cd3bb879834bf0cc9140dc63434e29 --- /dev/null +++ b/packages/softasp-core/src/softasp_core/data/hdf5_dataset.py @@ -0,0 +1,114 @@ +"""Lazy-loading HDF5 dataset for PyTorch DataLoader. + +Opens file handle once; reads individual samples in __getitem__. +Supports SWMR mode for concurrent multi-worker reads. +""" + +from __future__ import annotations + +import logging +from pathlib import Path +from typing import Any + +import h5py +import numpy as np +import torch +from torch.utils.data import Dataset + +from softasp_core.data.augmentations import get_augmentation + +logger = logging.getLogger(__name__) + + +class HDF5Dataset(Dataset): + """A dataset backed by a single HDF5 file. + + Args: + h5_path: Path to the HDF5 file. + split: Split group name (``train``, ``val``, ``test``). + fields: List of dataset names to read from the group. + Defaults to ``["images", "labels"]``. + augmentations: List of augmentation names to apply (from registry). + augmentation_params: Dict of augmentation name → params. + transform: Optional callable applied after augmentations. + """ + + def __init__( + self, + h5_path: str | Path, + split: str, + fields: list[str] | None = None, + augmentations: list[str] | None = None, + augmentation_params: dict[str, dict[str, Any]] | None = None, + transform: Any | None = None, + ) -> None: + self.h5_path = Path(h5_path) + self.split = split + self.fields = fields or ["images", "labels"] + self.transform = transform + + # Build augmentation pipeline + self._augmentations = [] + for aug_name in augmentations or []: + params = (augmentation_params or {}).get(aug_name, {}) + self._augmentations.append(get_augmentation(aug_name, **params)) + + # Open file lazily — will be opened per-worker in DataLoader + self._h5_file: h5py.File | None = None + self._length: int = 0 + + # Read length eagerly (quick metadata read) + with h5py.File(self.h5_path, "r") as f: + first_field = self.fields[0] + self._length = f[self.split][first_field].shape[0] + + def _open(self) -> h5py.File: + """Open (or reopen) the HDF5 file in SWMR mode.""" + if self._h5_file is None: + self._h5_file = h5py.File(self.h5_path, "r", swmr=True) + return self._h5_file + + def __len__(self) -> int: + return self._length + + def __getitem__(self, idx: int) -> dict[str, torch.Tensor]: + f = self._open() + group = f[self.split] + sample: dict[str, torch.Tensor] = {} + + for field in self.fields: + if field not in group: + continue + raw = group[field][idx] + tensor = torch.from_numpy(np.asarray(raw)) + + # Apply augmentations only to image-like fields + if field == "images": + for aug in self._augmentations: + tensor = aug(tensor) + + sample[field] = tensor + + # Read associated length/mask fields if they exist + for field in self.fields: + lengths_key = f"{field}_lengths" + if lengths_key in group: + sample[lengths_key] = torch.tensor(group[lengths_key][idx]) + + if self.transform is not None: + sample = self.transform(sample) + + return sample + + def __del__(self) -> None: + if self._h5_file is not None: + try: + self._h5_file.close() + except Exception: + pass + + def __repr__(self) -> str: + return ( + f"HDF5Dataset(path={self.h5_path.name}, split={self.split}, " + f"len={self._length}, fields={self.fields})" + ) diff --git a/packages/softasp-core/src/softasp_core/distributed/__init__.py b/packages/softasp-core/src/softasp_core/distributed/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..628c64688426b1e4c53c76da1915b3c2a86b6362 --- /dev/null +++ b/packages/softasp-core/src/softasp_core/distributed/__init__.py @@ -0,0 +1,23 @@ +"""Distributed training utilities (DDP, FSDP).""" + +from softasp_core.distributed.distributed import ( + all_gather_predictions, + all_reduce_metrics, + cleanup_ddp, + get_device, + is_distributed, + is_main_process, + setup_ddp, +) +from softasp_core.distributed.eval_sampler import DistributedEvalSampler + +__all__ = [ + "DistributedEvalSampler", + "all_gather_predictions", + "all_reduce_metrics", + "cleanup_ddp", + "get_device", + "is_distributed", + "is_main_process", + "setup_ddp", +] diff --git a/packages/softasp-core/src/softasp_core/distributed/distributed.py b/packages/softasp-core/src/softasp_core/distributed/distributed.py new file mode 100644 index 0000000000000000000000000000000000000000..c3ac065e239d6fc9f06f10502521540ae24910e0 --- /dev/null +++ b/packages/softasp-core/src/softasp_core/distributed/distributed.py @@ -0,0 +1,129 @@ +"""DDP / FSDP setup and distributed helpers. + +Uses ``torchrun`` environment variables (RANK, LOCAL_RANK, WORLD_SIZE). +""" + +from __future__ import annotations + +import logging +import os +import sys +from typing import Any + +import torch +import torch.distributed as dist + +logger = logging.getLogger(__name__) + + +# ── Setup / teardown ────────────────────────────────────────────────────────── + + +def setup_ddp() -> None: + """Initialize the default process group from torchrun env vars.""" + if not dist.is_available(): + logger.warning("torch.distributed not available; skipping DDP init.") + return + + rank = int(os.environ.get("RANK", 0)) + local_rank = int(os.environ.get("LOCAL_RANK", 0)) + world_size = int(os.environ.get("WORLD_SIZE", 1)) + + if world_size <= 1: + return + + backend = "nccl" if torch.cuda.is_available() else "gloo" + dist.init_process_group(backend=backend, rank=rank, world_size=world_size) + torch.cuda.set_device(local_rank) + + logger.info( + "DDP initialized: rank=%d, local_rank=%d, world_size=%d, backend=%s", + rank, + local_rank, + world_size, + backend, + ) + + +def cleanup_ddp() -> None: + """Destroy the process group if it was initialized.""" + if dist.is_available() and dist.is_initialized(): + dist.destroy_process_group() + + +# ── Queries ─────────────────────────────────────────────────────────────────── + + +def is_distributed() -> bool: + return dist.is_available() and dist.is_initialized() + + +def is_main_process() -> bool: + """True on rank 0 or when not running distributed.""" + if not is_distributed(): + return True + return dist.get_rank() == 0 + + +def get_rank() -> int: + if not is_distributed(): + return 0 + return dist.get_rank() + + +def get_world_size() -> int: + if not is_distributed(): + return 1 + return dist.get_world_size() + + +def get_device() -> torch.device: + """Determine the best available device. + + Priority: CUDA (with LOCAL_RANK) > MPS > CPU. + """ + if torch.cuda.is_available(): + local_rank = int(os.environ.get("LOCAL_RANK", 0)) + return torch.device(f"cuda:{local_rank}") + + if ( + sys.platform == "darwin" + and hasattr(torch.backends, "mps") + and torch.backends.mps.is_available() + ): + return torch.device("mps") + + return torch.device("cpu") + + +# ── Communication helpers ───────────────────────────────────────────────────── + + +def all_reduce_metrics(metrics: dict[str, float]) -> dict[str, float]: + """Average scalar metrics across all ranks.""" + if not is_distributed(): + return metrics + + reduced: dict[str, float] = {} + for key, value in metrics.items(): + tensor = torch.tensor(value, dtype=torch.float64, device=get_device()) + dist.all_reduce(tensor, op=dist.ReduceOp.SUM) + reduced[key] = tensor.item() / get_world_size() + return reduced + + +def all_gather_predictions( + tensor: torch.Tensor, +) -> torch.Tensor: + """Gather predictions from all ranks to rank 0. + + Returns: + Concatenated tensor on all ranks (each rank gets the full result). + """ + if not is_distributed(): + return tensor + + world_size = get_world_size() + gathered = [torch.zeros_like(tensor) for _ in range(world_size)] + dist.all_gather(gathered, tensor) + return torch.cat(gathered, dim=0) diff --git a/packages/softasp-core/src/softasp_core/distributed/eval_sampler.py b/packages/softasp-core/src/softasp_core/distributed/eval_sampler.py new file mode 100644 index 0000000000000000000000000000000000000000..4e390c8f72e3ebced4d1ed283cc11ad5ab2e7c66 --- /dev/null +++ b/packages/softasp-core/src/softasp_core/distributed/eval_sampler.py @@ -0,0 +1,67 @@ +"""Distributed eval sampler — pads so all ranks process equal counts. + +Without this, DDP eval either drops samples (drop_last=True → wrong metrics) +or hangs (uneven batch counts across ranks → NCCL timeout). +""" + +from __future__ import annotations + +import math + +import torch +import torch.distributed as dist +from torch.utils.data import Dataset, Sampler + + +class DistributedEvalSampler(Sampler[int]): + """Sampler for evaluation under DDP. + + - Pads indices so every rank processes the same count. + - After ``all_gather``, rank 0 must trim to ``len(dataset)`` before + computing metrics. + + Args: + dataset: The dataset to sample from. + num_replicas: Number of processes (default: world_size). + rank: Current process rank. + """ + + def __init__( + self, + dataset: Dataset, + num_replicas: int | None = None, + rank: int | None = None, + ) -> None: + if num_replicas is None: + if dist.is_available() and dist.is_initialized(): + num_replicas = dist.get_world_size() + else: + num_replicas = 1 + if rank is None: + if dist.is_available() and dist.is_initialized(): + rank = dist.get_rank() + else: + rank = 0 + + self.dataset = dataset + self.num_replicas = num_replicas + self.rank = rank + self.total_size = len(dataset) # type: ignore[arg-type] + + # Pad to make evenly divisible + self.num_samples = math.ceil(self.total_size / self.num_replicas) + self.padded_size = self.num_samples * self.num_replicas + + def __iter__(self): + indices = list(range(self.total_size)) + # Pad by wrapping around + padding = self.padded_size - self.total_size + if padding > 0: + indices += indices[:padding] + + # Subsample for this rank + offset = self.num_samples * self.rank + yield from indices[offset : offset + self.num_samples] + + def __len__(self) -> int: + return self.num_samples diff --git a/packages/softasp-core/src/softasp_core/evaluation/__init__.py b/packages/softasp-core/src/softasp_core/evaluation/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..9e89adbe27367cef46c1cdf06623c44521a47457 --- /dev/null +++ b/packages/softasp-core/src/softasp_core/evaluation/__init__.py @@ -0,0 +1,11 @@ +"""Evaluation metrics and evaluation loop.""" + +from softasp_core.evaluation.evaluator import Evaluator +from softasp_core.evaluation.metrics import METRIC_REGISTRY, Accuracy, MetricProvider + +__all__ = [ + "METRIC_REGISTRY", + "Accuracy", + "Evaluator", + "MetricProvider", +] diff --git a/packages/softasp-core/src/softasp_core/evaluation/evaluator.py b/packages/softasp-core/src/softasp_core/evaluation/evaluator.py new file mode 100644 index 0000000000000000000000000000000000000000..b3a29a63ccc7e700ef86359c0619a674e9170d1d --- /dev/null +++ b/packages/softasp-core/src/softasp_core/evaluation/evaluator.py @@ -0,0 +1,92 @@ +"""Evaluation loop — runs metrics across one or more datasets.""" + +from __future__ import annotations + +import logging + +import torch +import torch.nn as nn +from torch import autocast +from torch.utils.data import DataLoader + +from softasp_core.evaluation.metrics import MetricProvider, get_metric + +logger = logging.getLogger(__name__) + + +class Evaluator: + """Run a model against a DataLoader and compute metrics. + + Args: + metric_names: List of registered metric names to compute. + """ + + def __init__( + self, + metric_names: list[str] | None = None, + *, + output_metrics: dict[str, tuple[str, str]] | None = None, + scalar_keys: tuple[str, ...] = (), + ) -> None: + self.output_metrics = output_metrics or {} + self.scalar_keys = scalar_keys + self.metric_names = metric_names or ["accuracy"] + self._metrics: list[MetricProvider] = [get_metric(n) for n in self.metric_names] + + def evaluate( + self, + model: nn.Module, + loader: DataLoader, + device: torch.device, + ) -> dict[str, float]: + """Compute configured metrics and optional output diagnostics.""" + model.eval() + for m in self._metrics: + m.reset() + + split_trackers: dict[str, MetricProvider] = {} + scalar_sums: dict[str, float] = {} + scalar_counts: dict[str, int] = {} + + def _ensure_tracker(key: str) -> MetricProvider: + if key not in split_trackers: + m = get_metric("accuracy") + m.reset() + split_trackers[key] = m + return split_trackers[key] + + with torch.no_grad(): + for batch in loader: + batch = {k: v.to(device, non_blocking=True) for k, v in batch.items()} + with autocast(device_type=device.type, enabled=False): + output = model(batch) + + predictions = output.get("logits", output.get("predictions")) + targets = batch.get("labels") + if predictions is not None and targets is not None: + for m in self._metrics: + m.update(predictions, targets) + + for name, (prediction_key, target_key) in self.output_metrics.items(): + prediction = output.get(prediction_key) + target = output.get(target_key, batch.get(target_key)) + if prediction is not None and target is not None: + _ensure_tracker(name).update(prediction, target) + + # ── Scalar diagnostics ─────────────────────────────── + for _sk in self.scalar_keys: + _sv = output.get(_sk) + if _sv is not None: + scalar_sums[_sk] = scalar_sums.get(_sk, 0.0) + float(_sv) + scalar_counts[_sk] = scalar_counts.get(_sk, 0) + 1 + results: dict[str, float] = {} + for m in self._metrics: + results[m.name] = m.compute() + + for tracker_name, tracker in split_trackers.items(): + results[tracker_name] = tracker.compute() + + for sk, sv in scalar_sums.items(): + results[sk] = sv / scalar_counts[sk] + + return results diff --git a/packages/softasp-core/src/softasp_core/evaluation/metrics.py b/packages/softasp-core/src/softasp_core/evaluation/metrics.py new file mode 100644 index 0000000000000000000000000000000000000000..e6735602835053ff2cbf90d9b41f8d9e8761352a --- /dev/null +++ b/packages/softasp-core/src/softasp_core/evaluation/metrics.py @@ -0,0 +1,320 @@ +"""Metric providers — abstract interface + built-in implementations.""" + +from __future__ import annotations + +from abc import ABC, abstractmethod +from typing import Any + +import torch + +# ── Registry ────────────────────────────────────────────────────────────────── + +METRIC_REGISTRY: dict[str, type["MetricProvider"]] = {} + + +def register_metric(name: str): + """Decorator to register a MetricProvider subclass.""" + def decorator(cls): + METRIC_REGISTRY[name] = cls + return cls + return decorator + + +def get_metric(name: str, **kwargs: Any) -> "MetricProvider": + if name not in METRIC_REGISTRY: + raise ValueError(f"Unknown metric '{name}'. Available: {list(METRIC_REGISTRY.keys())}") + return METRIC_REGISTRY[name](**kwargs) + + +# ── Abstract base ───────────────────────────────────────────────────────────── + + +class MetricProvider(ABC): + """Accumulates predictions over batches, then computes a scalar metric.""" + + @abstractmethod + def reset(self) -> None: + """Clear accumulated state.""" + + @abstractmethod + def update(self, predictions: torch.Tensor, targets: torch.Tensor) -> None: + """Accumulate one batch of predictions and targets.""" + + @abstractmethod + def compute(self) -> float: + """Compute and return the metric value.""" + + @property + @abstractmethod + def name(self) -> str: + """Human-readable metric name for logging.""" + + +# ── Built-in metrics ───────────────────────────────────────────────────────── + + +@register_metric("accuracy") +class Accuracy(MetricProvider): + """Top-1 classification accuracy.""" + + def __init__(self) -> None: + self._correct = 0 + self._total = 0 + + @property + def name(self) -> str: + return "accuracy" + + def reset(self) -> None: + self._correct = 0 + self._total = 0 + + def update(self, predictions: torch.Tensor, targets: torch.Tensor) -> None: + if predictions.dim() > 1: + preds = predictions.argmax(dim=-1) + else: + preds = predictions + self._correct += (preds == targets).sum().item() + self._total += targets.numel() + + def compute(self) -> float: + if self._total == 0: + return 0.0 + return self._correct / self._total + + +@register_metric("auroc") +class AUROC(MetricProvider): + """Area Under ROC Curve (binary or macro-averaged multi-class). + + Requires scikit-learn at compute time (lazy import). + """ + + def __init__(self) -> None: + self._scores: list[torch.Tensor] = [] + self._targets: list[torch.Tensor] = [] + + @property + def name(self) -> str: + return "auroc" + + def reset(self) -> None: + self._scores.clear() + self._targets.clear() + + def update(self, predictions: torch.Tensor, targets: torch.Tensor) -> None: + self._scores.append(predictions.detach().cpu()) + self._targets.append(targets.detach().cpu()) + + def compute(self) -> float: + from sklearn.metrics import roc_auc_score + + scores = torch.cat(self._scores).numpy() + targets = torch.cat(self._targets).numpy() + if scores.ndim > 1 and scores.shape[1] > 1: + return float(roc_auc_score(targets, scores, multi_class="ovr", average="macro")) + return float(roc_auc_score(targets, scores)) + + +# ── CLEVR-POC metrics ──────────────────────────────────────────────────────── + + +@register_metric("exact_accuracy") +class ExactAccuracy(MetricProvider): + """Exact set-match accuracy for CLEVR-POC multi-label prediction. + + A prediction is correct only when the predicted attribute set matches the + ground-truth set exactly. + + Args: + threshold: Probability threshold for binarising the 17-dim output. + """ + + def __init__(self, threshold: float = 0.5) -> None: + self.threshold = threshold + self._correct = 0 + self._total = 0 + + @property + def name(self) -> str: + return "exact_accuracy" + + def reset(self) -> None: + self._correct = 0 + self._total = 0 + + def update(self, predictions: torch.Tensor, targets: torch.Tensor) -> None: + """Accumulate one batch. + + Args: + predictions: (B, 17) float — probabilities or logits. + targets: (B, 17) float — ground-truth multi-hot. + """ + preds_bin = (predictions >= self.threshold).float() + exact = (preds_bin == targets.float()).all(dim=-1) # (B,) + self._correct += exact.sum().item() + self._total += targets.shape[0] + + def compute(self) -> float: + if self._total == 0: + return 0.0 + return self._correct / self._total + + +@register_metric("jaccard_index") +class JaccardIndex(MetricProvider): + """Mean Jaccard index (intersection over union) for CLEVR-POC. + + Jaccard(A, P) = |A ∩ P| / |A ∪ P| + + When both sets are empty the score for that example is defined as 1.0. + + Args: + threshold: Probability threshold for binarising the 17-dim output. + """ + + def __init__(self, threshold: float = 0.5) -> None: + self.threshold = threshold + self._total_jaccard = 0.0 + self._total = 0 + + @property + def name(self) -> str: + return "jaccard_index" + + def reset(self) -> None: + self._total_jaccard = 0.0 + self._total = 0 + + def update(self, predictions: torch.Tensor, targets: torch.Tensor) -> None: + """Accumulate one batch. + + Args: + predictions: (B, 17) float — probabilities or logits. + targets: (B, 17) float — ground-truth multi-hot. + """ + preds_bin = (predictions >= self.threshold).float() + tgt = targets.float() + intersection = (preds_bin * tgt).sum(dim=-1) # (B,) + union = ((preds_bin + tgt).clamp(max=1.0)).sum(dim=-1) # (B,) + # Define 0/0 = 1.0 (perfect match when both are empty) + jaccard = torch.where(union > 0, intersection / union, torch.ones_like(union)) + self._total_jaccard += jaccard.sum().item() + self._total += targets.shape[0] + + def compute(self) -> float: + if self._total == 0: + return 0.0 + return self._total_jaccard / self._total + + + +@register_metric("board_accuracy") +class BoardAccuracy(MetricProvider): + """Whole-board accuracy for Sudoku: fraction of boards where ALL 81 cells + are correctly predicted. + + Expects: + predictions: (B, 81, 9) logits or (B, 81) class indices. + targets: (B, 81) ground-truth 0-indexed digit labels. + """ + + def __init__(self) -> None: + self._correct = 0 + self._total = 0 + + @property + def name(self) -> str: + return "board_accuracy" + + def reset(self) -> None: + self._correct = 0 + self._total = 0 + + def update(self, predictions: torch.Tensor, targets: torch.Tensor) -> None: + if predictions.dim() == 3: + preds = predictions.argmax(dim=-1) # (B, 81) + else: + preds = predictions # already (B, 81) + # A board is correct iff every cell matches + board_correct = (preds == targets).all(dim=-1) # (B,) + self._correct += board_correct.sum().item() + self._total += targets.shape[0] + + def compute(self) -> float: + if self._total == 0: + return 0.0 + return self._correct / self._total + + +@register_metric("constraint_sat_rate") +class ConstraintSatRate(MetricProvider): + """Fraction of predicted boards that satisfy all Sudoku constraints + (each row, column, and 3×3 box contains digits 0-8 exactly once). + + This metric does NOT require ground-truth labels — it only checks + structural validity of the predictions. It still accepts *targets* + in ``update()`` (ignored) to conform to the MetricProvider interface. + + Expects: + predictions: (B, 81, 9) logits or (B, 81) class indices (0-8). + targets: ignored. + """ + + def __init__(self) -> None: + self._sat = 0 + self._total = 0 + + @property + def name(self) -> str: + return "constraint_sat_rate" + + def reset(self) -> None: + self._sat = 0 + self._total = 0 + + def update(self, predictions: torch.Tensor, targets: torch.Tensor) -> None: + if predictions.dim() == 3: + preds = predictions.argmax(dim=-1) # (B, 81) + else: + preds = predictions + preds = preds.cpu() + B = preds.shape[0] + boards = preds.view(B, 9, 9) # (B, 9, 9) + full_set = set(range(9)) + for b in range(B): + board = boards[b] + valid = True + # Check rows + for r in range(9): + if set(board[r].tolist()) != full_set: + valid = False + break + if not valid: + self._total += 1 + continue + # Check columns + for c in range(9): + if set(board[:, c].tolist()) != full_set: + valid = False + break + if not valid: + self._total += 1 + continue + # Check 3x3 boxes + for br in range(3): + for bc in range(3): + box = board[br * 3 : br * 3 + 3, bc * 3 : bc * 3 + 3] + if set(box.reshape(-1).tolist()) != full_set: + valid = False + break + if not valid: + break + if valid: + self._sat += 1 + self._total += 1 + + def compute(self) -> float: + if self._total == 0: + return 0.0 + return self._sat / self._total diff --git a/packages/softasp-core/src/softasp_core/tracking/__init__.py b/packages/softasp-core/src/softasp_core/tracking/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..f5c99aacc95d2f3ea0c522b7c21c8adfd0e4d5c2 --- /dev/null +++ b/packages/softasp-core/src/softasp_core/tracking/__init__.py @@ -0,0 +1,5 @@ +"""Experiment tracking (Aim).""" + +from softasp_core.tracking.aim_tracker import AimTracker + +__all__ = ["AimTracker"] diff --git a/packages/softasp-core/src/softasp_core/tracking/aim_tracker.py b/packages/softasp-core/src/softasp_core/tracking/aim_tracker.py new file mode 100644 index 0000000000000000000000000000000000000000..4e6e8d7da4f5e6fba8ec9e4e89ea3d0cf669e5f7 --- /dev/null +++ b/packages/softasp-core/src/softasp_core/tracking/aim_tracker.py @@ -0,0 +1,159 @@ +"""Aim experiment tracker — wraps the Aim SDK for DDP-safe logging.""" + +from __future__ import annotations + +import logging +import os +import subprocess +from pathlib import Path +from typing import Any + +logger = logging.getLogger(__name__) + + +def _ensure_aim_repo(repo_path: str) -> str: + """Ensure the Aim repo directory exists and is initialized. + + Aim's ``Run(repo=path)`` expects *path* to be the directory that contains + the ``.aim`` subdirectory (i.e. the repo *root*), not the ``.aim`` + directory itself. This helper accepts either convention, normalises to + the repo root, creates it if missing, and runs ``aim init`` if needed. + + Returns the normalised repo-root path string. + """ + p = Path(repo_path).resolve() + # If the caller passed ".../something/.aim", treat the parent as root. + if p.name == ".aim": + p = p.parent + p.mkdir(parents=True, exist_ok=True) + aim_dir = p / ".aim" + if not aim_dir.exists(): + try: + subprocess.run( + ["aim", "init"], + cwd=str(p), + check=True, + capture_output=True, + ) + logger.info("Aim repo initialised at %s", p) + except Exception as exc: + logger.warning("aim init failed (%s); will let Run() try anyway.", exc) + return str(p) + + +class AimTracker: + """Thin wrapper around Aim Run for DDP-safe experiment tracking. + + Only rank 0 should instantiate this. All other ranks should skip tracking. + + Args: + repo_path: Path to the Aim repo root (or its ``.aim`` subdirectory). + experiment_name: Experiment name for grouping. + run_hash: If resuming, the hash of the existing run. + """ + + def __init__( + self, + repo_path: str, + experiment_name: str = "default", + run_hash: str | None = None, + ) -> None: + # Aim's get_installed_packages() calls `import pkg_resources` which is + # only available when setuptools is installed. On lean venvs (Python + # 3.12+) it may be absent. Inject a no-op stub before Aim loads so + # the import path succeeds without requiring the full setuptools wheel. + import sys + if "pkg_resources" not in sys.modules: + import types + _stub = types.ModuleType("pkg_resources") + _stub.working_set = [] # type: ignore[attr-defined] + _stub.get_distribution = lambda _name: None # type: ignore[attr-defined] + sys.modules["pkg_resources"] = _stub + + from aim import Run + + repo_root = _ensure_aim_repo(repo_path) + + self._run = Run( + repo=repo_root, + experiment=experiment_name, + run_hash=run_hash, + log_system_params=True, + ) + + # Tag with git commit if available + try: + commit = subprocess.check_output( + ["git", "rev-parse", "HEAD"], stderr=subprocess.DEVNULL + ).decode().strip() + self._run["git_commit"] = commit + except Exception: + pass + + # Environment info + self._run["environment"] = os.environ.get("SOFTASP_ENV", "unknown") + + logger.info("Aim run started: hash=%s, experiment=%s", self._run.hash, experiment_name) + + @property + def run_hash(self) -> str: + return self._run.hash + + def resume(self, run_hash: str) -> None: + """Reattach to an existing run (after checkpoint resume).""" + # Aim handles this automatically if run_hash is passed to __init__ + logger.info("Aim run resumed: hash=%s", run_hash) + + def log_params(self, params: dict[str, Any]) -> None: + """Log a flat dict of parameters.""" + for key, value in params.items(): + self._run[key] = value + + def track( + self, + metrics: dict[str, float | int], + step: int | None = None, + epoch: int | None = None, + context: dict[str, str] | None = None, + ) -> None: + """Track scalar metrics.""" + from aim import Distribution + + for name, value in metrics.items(): + self._run.track( + value, + name=name, + step=step, + epoch=epoch, + context=context or {}, + ) + + def track_image(self, name: str, image: Any, step: int | None = None, epoch: int | None = None) -> None: + """Track an image (numpy array or PIL Image).""" + from aim import Image + + self._run.track(Image(image), name=name, step=step, epoch=epoch) + + def track_figure(self, name: str, figure: Any, epoch: int | None = None) -> None: + """Track a Plotly or Matplotlib figure. + + The figure is displayed in the Aim UI under the Figures tab and can be + compared side-by-side across runs. + + Args: + name: Metric name, e.g. ``"figures/confusion_matrix"``. + figure: A ``plotly.graph_objects.Figure`` or Matplotlib figure. + epoch: Optional epoch index for the Aim timeline. + """ + from aim import Figure + + self._run.track(Figure(figure), name=name, epoch=epoch) + + def add_tag(self, tag: str) -> None: + """Add a tag to the current run.""" + self._run.add_tag(tag) + + def close(self) -> None: + """Finalize the run.""" + self._run.close() + logger.info("Aim run closed.") diff --git a/packages/softasp-core/src/softasp_core/training/__init__.py b/packages/softasp-core/src/softasp_core/training/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..0dd1280b3a89cec5d381ee1c3ac9fa922036f228 --- /dev/null +++ b/packages/softasp-core/src/softasp_core/training/__init__.py @@ -0,0 +1,14 @@ +"""Training loop, checkpointing, signals, seeds.""" + +from softasp_core.training.checkpointing import load_checkpoint, save_checkpoint +from softasp_core.training.seed import seed_everything +from softasp_core.training.signals import GracefulExitHandler +from softasp_core.training.trainer import Trainer + +__all__ = [ + "GracefulExitHandler", + "Trainer", + "load_checkpoint", + "save_checkpoint", + "seed_everything", +] diff --git a/packages/softasp-core/src/softasp_core/training/checkpointing.py b/packages/softasp-core/src/softasp_core/training/checkpointing.py new file mode 100644 index 0000000000000000000000000000000000000000..a7caaa353802c8abf6fe8c404cbfc578eb44abd1 --- /dev/null +++ b/packages/softasp-core/src/softasp_core/training/checkpointing.py @@ -0,0 +1,195 @@ +"""Checkpoint save/load with full state for bit-exact resume.""" + +from __future__ import annotations + +import logging +import random +import time +from pathlib import Path +from typing import Any + +import numpy as np +import torch + +logger = logging.getLogger(__name__) + +# Any real checkpoint is orders of magnitude larger; this catches empty/partial writes. +_MIN_CHECKPOINT_BYTES = 1024 +_MAX_SAVE_RETRIES = 3 +_SAVE_RETRY_DELAYS = (5, 15, 30) # seconds between successive attempts + + +def save_checkpoint( + path: Path, + *, + epoch: int, + model: torch.nn.Module, + optimizer: torch.optim.Optimizer, + scheduler: Any | None = None, + best_metric: float | None = None, + aim_run_hash: str | None = None, + scaler: torch.amp.GradScaler | None = None, + config: dict | None = None, +) -> None: + """Save a complete checkpoint for exact resume. + + Uses an atomic write-temp-then-rename pattern with post-write verification + and automatic retry. Raises RuntimeError if all retries are exhausted so + the training process exits non-zero rather than completing silently with + a missing or corrupted checkpoint. + """ + state = { + "epoch": epoch, + "model_state_dict": model.state_dict(), + "optimizer_state_dict": optimizer.state_dict(), + "scheduler_state_dict": scheduler.state_dict() if scheduler else None, + "best_metric": best_metric, + "aim_run_hash": aim_run_hash, + "torch_rng_state": torch.random.get_rng_state(), + "cuda_rng_states": [torch.cuda.get_rng_state(i) for i in range(torch.cuda.device_count())] + if torch.cuda.is_available() + else [], + "numpy_rng_state": np.random.get_state(), + "python_rng_state": random.getstate(), + "scaler_state_dict": scaler.state_dict() if scaler else None, + "config": config, + } + + path = Path(path) + path.parent.mkdir(parents=True, exist_ok=True) + tmp_path = path.with_suffix(path.suffix + ".tmp") + + last_exc: Exception | None = None + for attempt in range(1, _MAX_SAVE_RETRIES + 1): + try: + torch.save(state, tmp_path) + + actual_bytes = tmp_path.stat().st_size if tmp_path.exists() else 0 + if actual_bytes < _MIN_CHECKPOINT_BYTES: + raise OSError( + f"Checkpoint temp file is suspiciously small " + f"({actual_bytes} bytes): {tmp_path}" + ) + + tmp_path.rename(path) + + if not path.exists(): + raise OSError( + f"Rename appeared to succeed but final path is absent: {path}" + ) + + logger.info("Checkpoint saved: %s (epoch %d)", path, epoch) + return + + except Exception as exc: + last_exc = exc + try: + tmp_path.unlink(missing_ok=True) + except Exception: + pass + + if attempt < _MAX_SAVE_RETRIES: + delay = _SAVE_RETRY_DELAYS[attempt - 1] + logger.warning( + "Checkpoint save attempt %d/%d failed (%s) — retrying in %ds", + attempt, _MAX_SAVE_RETRIES, exc, delay, + ) + time.sleep(delay) + else: + logger.error( + "Checkpoint save failed after %d attempts: %s", + _MAX_SAVE_RETRIES, path, + ) + + raise RuntimeError( + f"Checkpoint save failed after {_MAX_SAVE_RETRIES} attempts: {path}. " + f"Last error: {last_exc}" + ) from last_exc + + +def load_checkpoint( + path: Path, + *, + model: torch.nn.Module, + optimizer: torch.optim.Optimizer | None = None, + scheduler: Any | None = None, + scaler: torch.amp.GradScaler | None = None, + map_location: str | torch.device = "cpu", + weights_only: bool = False, + strict: bool = True, +) -> dict[str, Any]: + """Load a checkpoint. + + Args: + weights_only: If True, only load model weights (for pretrained_from). + strict: Passed to ``model.load_state_dict``. Use ``False`` when + loading a partial backbone checkpoint (e.g. ``backbone.pt`` + which contains only ``cell_cnn.*`` keys — all other keys + are intentionally absent). + + Returns: + The full checkpoint dict (caller can read epoch, best_metric, etc.). + """ + logger.info("Loading checkpoint: %s", path) + ckpt = torch.load(path, map_location=map_location, weights_only=False) + + if not strict: + # When strict=False filter out entries whose shape doesn't match the + # current model before calling load_state_dict. PyTorch raises a + # RuntimeError on shape mismatches even with strict=False (it only + # tolerates missing/unexpected keys). This can happen when the + # backbone was extracted with a different d_model than the consumer — + # we drop the mismatched tensors so the matching conv weights still + # load and the mismatched layers initialise from scratch. + current_sd = model.state_dict() + ckpt_sd = ckpt["model_state_dict"] + shape_skipped: list[str] = [] + filtered_sd: dict = {} + for k, v in ckpt_sd.items(): + if k in current_sd and current_sd[k].shape != v.shape: + shape_skipped.append( + f"{k}: ckpt={tuple(v.shape)} vs model={tuple(current_sd[k].shape)}" + ) + else: + filtered_sd[k] = v + if shape_skipped: + logger.warning( + "Backbone load: skipping %d shape-mismatched key(s) (will use random init):\n %s", + len(shape_skipped), + "\n ".join(shape_skipped), + ) + missing, unexpected = model.load_state_dict(filtered_sd, strict=False) + else: + missing, unexpected = model.load_state_dict(ckpt["model_state_dict"], strict=True) + + if not strict and (missing or unexpected): + logger.info( + "Partial checkpoint load (strict=False): %d missing keys, %d unexpected keys.", + len(missing), len(unexpected), + ) + + if weights_only: + logger.info("Loaded model weights only (pretrained_from mode).") + return ckpt + + if optimizer and ckpt.get("optimizer_state_dict"): + optimizer.load_state_dict(ckpt["optimizer_state_dict"]) + if scheduler and ckpt.get("scheduler_state_dict"): + scheduler.load_state_dict(ckpt["scheduler_state_dict"]) + if scaler and ckpt.get("scaler_state_dict"): + scaler.load_state_dict(ckpt["scaler_state_dict"]) + + # Restore RNG states for bit-exact reproducibility + if ckpt.get("torch_rng_state") is not None: + torch.random.set_rng_state(ckpt["torch_rng_state"]) + if torch.cuda.is_available() and ckpt.get("cuda_rng_states"): + for i, state in enumerate(ckpt["cuda_rng_states"]): + if i < torch.cuda.device_count(): + torch.cuda.set_rng_state(state, i) + if ckpt.get("numpy_rng_state") is not None: + np.random.set_state(ckpt["numpy_rng_state"]) + if ckpt.get("python_rng_state") is not None: + random.setstate(ckpt["python_rng_state"]) + + logger.info("Checkpoint loaded: epoch %d, best_metric=%s", ckpt["epoch"], ckpt.get("best_metric")) + return ckpt diff --git a/packages/softasp-core/src/softasp_core/training/schedule.py b/packages/softasp-core/src/softasp_core/training/schedule.py new file mode 100644 index 0000000000000000000000000000000000000000..0bed91bb834ce4cd0dbe4f72a20e02d6d61cc226 --- /dev/null +++ b/packages/softasp-core/src/softasp_core/training/schedule.py @@ -0,0 +1,48 @@ +"""Explicit optimizer-update schedule for the corrected Sudoku protocol.""" + +from __future__ import annotations + +import math + +import torch + +CORRECTED_PROTOCOL = "sudoku_seeded_updates_v1" +LEGACY_PROTOCOL = "legacy_v0" + + +def build_update_schedule(optimizer, *, total_updates, warmup_updates, min_lr): + """LR at completed-update k; warmup starts at 1%, endpoint is min_lr. + + Initial LR is used for the first optimizer update. Call scheduler.step() + only after a successful optimizer update. After total_updates, clamp at + min_lr rather than starting another cosine cycle. + """ + if total_updates < 1 or not 0 <= warmup_updates < total_updates: + raise ValueError("Require total_updates > 0 and 0 <= warmup_updates < total_updates") + base_lrs = [group["lr"] for group in optimizer.param_groups] + if any(not 0 <= min_lr <= base or base <= 0 for base in base_lrs): + raise ValueError("Require 0 <= min_lr <= each positive base learning rate") + + def factor(base): + def at_update(completed): + if warmup_updates and completed < warmup_updates: + return 0.01 + 0.99 * completed / warmup_updates + progress = min( + max((completed - warmup_updates) / (total_updates - warmup_updates), 0.0), 1.0 + ) + floor = min_lr / base + return floor + (1 - floor) * (1 + math.cos(math.pi * progress)) / 2 + + return at_update + + scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer, [factor(base) for base in base_lrs]) + return scheduler, { + "units": "successful_optimizer_updates", + "total_updates": total_updates, + "warmup_updates": warmup_updates, + "warmup_start_factor": 0.01, + "base_lrs": base_lrs, + "min_lr": min_lr, + "endpoint": "after_total_updates", + "beyond_endpoint": "clamped", + } diff --git a/packages/softasp-core/src/softasp_core/training/seed.py b/packages/softasp-core/src/softasp_core/training/seed.py new file mode 100644 index 0000000000000000000000000000000000000000..a1de724c5b7d897d3057c80c375ae86cb7cf1ea8 --- /dev/null +++ b/packages/softasp-core/src/softasp_core/training/seed.py @@ -0,0 +1,47 @@ +"""Deterministic seeding for reproducibility.""" + +from __future__ import annotations + +import logging +import os +import random + +import numpy as np +import torch + +logger = logging.getLogger(__name__) + + +def seed_everything(seed: int, deterministic: bool = False) -> None: + """Seed all RNG sources for reproducibility. + + Args: + seed: The seed value. + deterministic: If True, enable full deterministic mode (slower). + """ + random.seed(seed) + np.random.seed(seed) + torch.manual_seed(seed) + torch.cuda.manual_seed_all(seed) + os.environ["PYTHONHASHSEED"] = str(seed) + + if deterministic: + torch.use_deterministic_algorithms(True) + torch.backends.cudnn.benchmark = False + os.environ["CUBLAS_WORKSPACE_CONFIG"] = ":4096:8" + logger.info("Deterministic mode enabled (seed=%d).", seed) + else: + torch.backends.cudnn.benchmark = True + logger.info("Seeded all RNGs with seed=%d.", seed) + + +def worker_init_fn(worker_id: int) -> None: + """Per-worker seeding for DataLoader multiprocessing. + + Called as ``DataLoader(worker_init_fn=worker_init_fn)``. + The actual seed incorporates the base seed + worker_id + epoch + (epoch is set on the sampler, not here — this handles the worker part). + """ + worker_seed = torch.initial_seed() % (2**32) + worker_id + np.random.seed(worker_seed) + random.seed(worker_seed) diff --git a/packages/softasp-core/src/softasp_core/training/signals.py b/packages/softasp-core/src/softasp_core/training/signals.py new file mode 100644 index 0000000000000000000000000000000000000000..43d53cc38018b6e0c1a55d2d45bd9386aab83e64 --- /dev/null +++ b/packages/softasp-core/src/softasp_core/training/signals.py @@ -0,0 +1,47 @@ +"""Graceful signal handling for process interruption and Ctrl+C. + +SIGUSR1 → checkpoint request +SIGINT → Ctrl+C (local dev) +SIGTERM → Container shutdown +""" + +from __future__ import annotations + +import logging +import signal +import sys +from typing import Any + +logger = logging.getLogger(__name__) + + +class GracefulExitHandler: + """Register signal handlers that set a flag to stop after current step.""" + + def __init__(self) -> None: + self.should_stop = False + self._registered = False + + def register(self) -> None: + """Install handlers for SIGUSR1, SIGINT, SIGTERM.""" + if self._registered: + return + + if sys.platform != "win32": + signal.signal(signal.SIGUSR1, self._handle_sigusr1) + signal.signal(signal.SIGINT, self._handle_sigint) + signal.signal(signal.SIGTERM, self._handle_sigterm) + self._registered = True + logger.debug("Signal handlers registered.") + + def _handle_sigusr1(self, signum: int, frame: Any) -> None: + logger.warning("SIGUSR1 received (checkpoint request). Will checkpoint and exit.") + self.should_stop = True + + def _handle_sigint(self, signum: int, frame: Any) -> None: + logger.warning("SIGINT received (Ctrl+C). Will checkpoint and exit.") + self.should_stop = True + + def _handle_sigterm(self, signum: int, frame: Any) -> None: + logger.warning("SIGTERM received. Will checkpoint and exit.") + self.should_stop = True diff --git a/packages/softasp-core/src/softasp_core/training/trainer.py b/packages/softasp-core/src/softasp_core/training/trainer.py new file mode 100644 index 0000000000000000000000000000000000000000..9a0dc1091b73c1e323f0d0523d63810013ea077a --- /dev/null +++ b/packages/softasp-core/src/softasp_core/training/trainer.py @@ -0,0 +1,719 @@ +"""Production training loop with DDP, AMP, gradient accumulation, Aim tracking.""" + +from __future__ import annotations + +import gc +import logging +import shutil +import time +import warnings +from collections.abc import Callable +from datetime import datetime +from pathlib import Path +from typing import Any, cast + +import torch +import torch.nn as nn +from omegaconf import DictConfig, OmegaConf +from torch.utils.data import DataLoader, DistributedSampler + +from softasp_core.config.loader import config_to_dict +from softasp_core.config.validation import validate_config +from softasp_core.data.collate import masked_collate_fn +from softasp_core.data.hdf5_dataset import HDF5Dataset +from softasp_core.distributed.distributed import ( + all_reduce_metrics, + cleanup_ddp, + get_device, + get_rank, + is_distributed, + is_main_process, + setup_ddp, +) +from softasp_core.distributed.eval_sampler import DistributedEvalSampler +from softasp_core.evaluation.evaluator import Evaluator +from softasp_core.tracking.aim_tracker import AimTracker +from softasp_core.training.checkpointing import load_checkpoint, save_checkpoint +from softasp_core.training.schedule import CORRECTED_PROTOCOL, build_update_schedule +from softasp_core.training.seed import seed_everything, worker_init_fn +from softasp_core.training.signals import GracefulExitHandler +from softasp_core.utils.logging import setup_logging + +logger = logging.getLogger(__name__) + + +class Trainer: + """End-to-end training loop. + + Handles: DDP, AMP, gradient accumulation, checkpointing, resume, + Aim tracking, signal handling, early stopping. + """ + + def __init__( + self, cfg: DictConfig, model: nn.Module, evaluator: Evaluator | None = None, + checkpoint_validator: Callable[[Path], None] | None = None, + ) -> None: + self.cfg = cfg + self.t = cfg.trainer # shorthand + self.device = get_device() + self.model = model + self.evaluator = evaluator + self.checkpoint_validator = checkpoint_validator + + # Will be initialized in .fit() + self.optimizer: torch.optim.Optimizer | None = None + self.scheduler: Any = None + self.scaler: torch.amp.GradScaler | None = None + self.tracker: AimTracker | None = None + self.signal_handler = GracefulExitHandler() + + self._start_epoch = 0 + self._best_metric: float | None = None + self._aim_run_hash: str | None = None + self._backbone_ramp_hooks: list = [] # gradient scaling hooks active during ramp phase + + # ── Public API ──────────────────────────────────────────────────────── + + def fit( + self, + train_dataset: HDF5Dataset, + val_dataset: HDF5Dataset | None = None, + ) -> Path: + """Full training procedure; return this run's exact artifact directory.""" + # 1. Validate config + validate_config(self.cfg) + + # 2. Seed + seed_everything(self.t.seed, deterministic=self.t.deterministic) + + # 3. Logging + run_dir = self._resolve_run_dir() + run_dir.mkdir(parents=True, exist_ok=True) + setup_logging( + level=self.cfg.logging.level, + log_file=run_dir / "train.log", + rich_console=self.cfg.logging.rich_console and is_main_process(), + rank=get_rank(), + ) + + # 4. Copy uv.lock for reproducibility + self._copy_lockfile(run_dir) + + # 5. DDP + if self._wants_distributed(): + setup_ddp() + + # 6. DataLoaders + # Redirect PyTorch shared-memory temp dirs (pymp-*) away from NFS-backed + # TMPDIR to /tmp (local tmpfs). NFS with local_lock=none returns EBUSY + # when unlinking still-mapped files, which causes DataLoader worker + # cleanup to fail and _exit_function's unconditional p.join() to hang. + torch.multiprocessing.tempdir = "/tmp" + train_loader = self._build_dataloader(train_dataset, training=True) + val_loader = self._build_dataloader(val_dataset, training=False) if val_dataset else None + self._updates_per_epoch = len(train_loader) + + # 7. Model → device → DDP/FSDP → compile + self.model = self.model.to(self.device) + if is_distributed(): + if self.t.strategy == "fsdp": + from torch.distributed.fsdp import FullyShardedDataParallel as FSDP + + self.model = FSDP(self.model) + else: + self.model = nn.parallel.DistributedDataParallel( + self.model, device_ids=[self.device] if self.device.type == "cuda" else None + ) + if self.t.compile: + self.model = torch.compile(self.model) + + # 8. Log params + total_params = sum(p.numel() for p in self.model.parameters()) + trainable_params = sum(p.numel() for p in self.model.parameters() if p.requires_grad) + logger.info("Model params: total=%d, trainable=%d", total_params, trainable_params) + + # 9. Pretrained weights (before optimizer) + # strict=False: backbone.pt contains only cell_cnn.* keys; the rest + # (transformer, concept_head, soft_proj, …) are intentionally absent + # and must remain at random init. Missing-key logging is handled + # inside load_checkpoint when strict=False. + if self.t.get("resume_from") is None and self.cfg.model.get("pretrained_from"): + load_checkpoint( + Path(self.cfg.model.pretrained_from), + model=self.model, + weights_only=True, + strict=False, + map_location=self.device, + ) + + # 10. Optimizer, scheduler, scaler + self.optimizer = self._build_optimizer() + self.scheduler = self._build_scheduler() + self.scaler = self._build_scaler() + + # 10b. Freeze cell_cnn during early epochs if configured. + # Must happen AFTER the optimizer is built — the optimizer holds + # references to all params. During the freeze window the frozen + # params produce zero gradients so the optimizer leaves them + # unchanged. At epoch freeze_backbone_epochs they are unfrozen + # (see training loop below) and the optimizer resumes updating them + # without needing to be rebuilt. + _freeze_epochs = int(self.cfg.model.get("freeze_backbone_epochs", 0)) + if ( + _freeze_epochs > 0 + and self.cfg.model.get("pretrained_from") + and self._start_epoch < _freeze_epochs + ): + self._freeze_backbone() + _trainable = sum(p.numel() for p in self.model.parameters() if p.requires_grad) + logger.info( + "CNN backbone frozen for epochs 0\u2013%d. Trainable params: %d", + _freeze_epochs - 1, + _trainable, + ) + + # 11. Aim tracker (rank 0 only) — optional; never crash training + if is_main_process(): + try: + self.tracker = AimTracker( + repo_path=self.cfg.paths.aim_repo, + experiment_name=self.t.experiment_name, + run_hash=self._aim_run_hash, + ) + self.tracker.log_params(config_to_dict(self.cfg)) + self.tracker.log_params( + { + "total_params": total_params, + "trainable_params": trainable_params, + } + ) + # Tags from trainer config and model config — lets you filter + # by model type across all runs in the Aim UI. + all_tags = list(self.t.get("tags") or []) + list(self.cfg.model.get("tags") or []) + for tag in all_tags: + self.tracker.add_tag(tag) + # Log declarative constraint specification when the model + # exposes one — keeps the symbolic component auditable per run. + _raw = self.model.module if hasattr(self.model, "module") else self.model + if hasattr(_raw, "CONSTRAINT_ASP"): + self.tracker.log_params({"constraint_asp": _raw.CONSTRAINT_ASP}) + except Exception as _aim_exc: + logger.warning( + "Aim tracker failed to initialise (%s) — continuing without tracking.", + _aim_exc, + ) + self.tracker = None + + # 12. Signal handlers + self.signal_handler.register() + + # 13. Resume from checkpoint + self._maybe_resume(run_dir) + + # 14. Eval on start + if self.t.eval_on_start and val_loader is not None: + self._run_eval(val_loader, epoch=self._start_epoch - 1, run_dir=run_dir) + + # 15. Training loop + for epoch in range(self._start_epoch, self.t.epochs): + if self.signal_handler.should_stop: + logger.warning("Signal received — saving checkpoint and exiting.") + self._save(run_dir, epoch, is_best=False) + break + + # Expose current epoch to the model for curriculum schedules + # (e.g. blank-cell supervision decay, constraint loss warmup). + _raw_model = self.model.module if hasattr(self.model, "module") else self.model + if hasattr(_raw_model, "_current_epoch"): + _raw_model._current_epoch = epoch + + # Backbone freeze / ramp-unfreeze schedule. + _freeze_epochs = int(self.cfg.model.get("freeze_backbone_epochs", 0)) + _ramp_epochs = int(self.cfg.model.get("ramp_backbone_epochs", 0)) + + # ── Remove hooks from previous epoch ───────────────────────────── + for _h in self._backbone_ramp_hooks: + _h.remove() + self._backbone_ramp_hooks.clear() + + # ── Unfreeze backbone at the configured epoch boundary ──────────── + if ( + _freeze_epochs > 0 + and epoch == _freeze_epochs + and self.cfg.model.get("pretrained_from") + ): + self._unfreeze_backbone() + _trainable = sum(p.numel() for p in self.model.parameters() if p.requires_grad) + logger.info( + "CNN backbone unfrozen at epoch %d. Trainable params: %d", + epoch, + _trainable, + ) + if is_main_process() and self.tracker: + self.tracker.log_params({"backbone_unfreeze_epoch": epoch}) + + # ── Backbone gradient scaling during ramp phase ─────────────────── + # Register per-param hooks that multiply gradients by `progress` + # (0.1 → 1.0 over ramp_epochs), giving an effective LR warmup for + # the backbone without modifying param groups or the scheduler. + if ( + _freeze_epochs > 0 + and _ramp_epochs > 0 + and self.cfg.model.get("pretrained_from") + and _freeze_epochs <= epoch < _freeze_epochs + _ramp_epochs + ): + progress = (epoch - _freeze_epochs + 1) / _ramp_epochs + raw = self.model.module if hasattr(self.model, "module") else self.model + backbone = cast(Any, raw).get_backbone() if hasattr(raw, "get_backbone") else None + if backbone is not None: + for _p in backbone.parameters(): + if _p.requires_grad: + self._backbone_ramp_hooks.append( + _p.register_hook(lambda g, s=progress: g * s) + ) + if is_main_process(): + logger.info( + "Backbone grad scale: epoch=%d progress=%.2f (%d hooks)", + epoch, + progress, + len(self._backbone_ramp_hooks), + ) + if self.tracker: + self.tracker.track({"train/backbone_grad_scale": progress}, epoch=epoch) + + self._train_one_epoch(train_loader, epoch, run_dir) + + # Eval + if val_loader is not None and (epoch + 1) % self.t.eval_every == 0: + metrics = self._run_eval(val_loader, epoch, run_dir) + if self._check_early_stopping(metrics, epoch, run_dir): + break + + # Checkpoint + if (epoch + 1) % self.t.checkpoint_every == 0 and is_main_process(): + self._save(run_dir, epoch, is_best=False) + + # 16. Cleanup + # Explicitly destroy DataLoaders before Python atexit so that + # _shutdown_workers() runs while active_children() is still populated. + # This prevents _exit_function's unconditional p.join() from racing + # against DataLoader worker cleanup and hanging indefinitely. + del train_loader + if val_loader is not None: + del val_loader + gc.collect() + + if self.tracker: + self.tracker.close() + cleanup_ddp() + logger.info("Training complete.") + if self.signal_handler.should_stop: + raise InterruptedError(f"Training interrupted; checkpoint saved in {run_dir}") + return run_dir + + # ── Training step ───────────────────────────────────────────────────── + + def _train_one_epoch(self, loader: DataLoader, epoch: int, run_dir: Path) -> None: + self.model.train() + sampler = loader.sampler + if isinstance(sampler, DistributedSampler): + sampler.set_epoch(epoch) + + accum_steps = self.t.gradient_accumulation_steps + self.optimizer.zero_grad() + + epoch_loss = 0.0 + step_count = 0 + t0 = time.perf_counter() + + for step, batch in enumerate(loader): + batch = {k: v.to(self.device, non_blocking=True) for k, v in batch.items()} + + # Forward with AMP + with torch.amp.autocast( + device_type=self.device.type, + dtype=self._amp_dtype(), + enabled=self._amp_enabled(), + ): + output = self.model(batch) + loss = output["loss"] / accum_steps + + # Backward + if self.scaler: + self.scaler.scale(loss).backward() + else: + loss.backward() + + epoch_loss += loss.item() * accum_steps + + # Optimizer step every accum_steps + if (step + 1) % accum_steps == 0 or (step + 1) == len(loader): + if self.t.grad_clip_norm: + if self.scaler: + self.scaler.unscale_(self.optimizer) + grad_norm = nn.utils.clip_grad_norm_( + self.model.parameters(), self.t.grad_clip_norm + ).item() + else: + grad_norm = 0.0 + + update_succeeded = True + if self.scaler: + previous_scale = self.scaler.get_scale() + self.scaler.step(self.optimizer) + self.scaler.update() + update_succeeded = self.scaler.get_scale() >= previous_scale + else: + self.optimizer.step() + + corrected = self.t.get("protocol") == CORRECTED_PROTOCOL + if self.scheduler and (update_succeeded or not corrected): + self.scheduler.step() + if corrected: + self.cfg.training_runtime.completed_updates += int(update_succeeded) + self.cfg.training_runtime.skipped_updates += int(not update_succeeded) + + self.optimizer.zero_grad() + step_count += 1 + + # Log per-step + if is_main_process() and step_count % self.cfg.logging.log_every_n_steps == 0: + lr = self.optimizer.param_groups[0]["lr"] + throughput = (step + 1) * self.t.batch_size_per_gpu / (time.perf_counter() - t0) + global_step = epoch * len(loader) + step + _total = loss.item() * accum_steps + _components = { + k: v.item() if isinstance(v, torch.Tensor) else float(v) + for k, v in output.items() + if k.startswith("loss_") + } + _comp_str = " ".join(f"{k[5:]}={v:.3e}" for k, v in _components.items()) + logger.info( + "Epoch %d step %d | loss=%.6f lr=%.2e grad_norm=%.2f throughput=%.0f samples/s | %s", # noqa: E501 + epoch, + step, + _total, + lr, + grad_norm, + throughput, + _comp_str, + ) + if self.tracker: + self.tracker.track( + {"train/loss": loss.item() * accum_steps}, step=global_step + ) + self.tracker.track({"train/lr": lr}, step=global_step) + self.tracker.track({"train/grad_norm": grad_norm}, step=global_step) + self.tracker.track({"train/throughput": throughput}, step=global_step) + # Log individual loss components (loss_cnn, loss_sup, + # loss_blank, loss_ic …) so SoftASP training dynamics + # are visible. + component_losses = { + f"train/{k}": v.item() if hasattr(v, "item") else float(v) + for k, v in output.items() + if k.startswith("loss_") + } + if component_losses: + self.tracker.track(component_losses, step=global_step) + # Diagnostic scalars (blank_entropy, etc.) + diag = { + f"train/{k}": v.item() if hasattr(v, "item") else float(v) + for k, v in output.items() + if k in ("blank_entropy",) + } + if diag: + self.tracker.track(diag, step=global_step) + + if self.signal_handler.should_stop: + break + + avg_loss = epoch_loss / max(len(loader), 1) + avg_loss_reduced = all_reduce_metrics({"train/epoch_loss": avg_loss}) + if is_main_process(): + logger.info("Epoch %d train loss: %.4f", epoch, avg_loss_reduced["train/epoch_loss"]) + if self.tracker: + self.tracker.track(avg_loss_reduced, epoch=epoch) + + # ── Eval ────────────────────────────────────────────────────────────── + + def _run_eval(self, loader: DataLoader, epoch: int, run_dir: Path) -> dict[str, float]: + self.model.eval() + if self.evaluator: + metrics = self.evaluator.evaluate(self.model, loader, self.device) + else: + metrics = self._default_eval(loader) + + metrics = all_reduce_metrics(metrics) + + if is_main_process(): + logger.info("Epoch %d eval: %s", epoch, metrics) + if self.tracker: + self.tracker.track(metrics, epoch=epoch) + if torch.cuda.is_available(): + mem = torch.cuda.max_memory_allocated() / (1024**3) + self.tracker.track({"eval/gpu_memory_gb": mem}, epoch=epoch) + + return metrics + + def _default_eval(self, loader: DataLoader) -> dict[str, float]: + """Fallback: compute average loss.""" + total_loss = 0.0 + count = 0 + with torch.no_grad(): + for batch in loader: + batch = {k: v.to(self.device, non_blocking=True) for k, v in batch.items()} + with torch.amp.autocast( + device_type=self.device.type, + dtype=self._amp_dtype(), + enabled=self._amp_enabled(), + ): + output = self.model(batch) + total_loss += output["loss"].item() + count += 1 + return {"val_loss": total_loss / max(count, 1)} + + # ── Checkpoint / Resume ─────────────────────────────────────────────── + + def _maybe_resume(self, run_dir: Path) -> None: + ckpt_path = None + if self.t.resume_from: + ckpt_path = Path(self.t.resume_from) + else: + auto = run_dir / "last.pt" + if auto.exists(): + ckpt_path = auto + + if ckpt_path and ckpt_path.exists(): + if self.checkpoint_validator is not None: + self.checkpoint_validator(ckpt_path) + ckpt = load_checkpoint( + ckpt_path, + model=self.model, + optimizer=self.optimizer, + scheduler=self.scheduler, + scaler=self.scaler, + map_location=self.device, + ) + self._start_epoch = ckpt["epoch"] + 1 + self._best_metric = ckpt.get("best_metric") + self._aim_run_hash = ckpt.get("aim_run_hash") + if self.tracker and self._aim_run_hash: + self.tracker.resume(self._aim_run_hash) + logger.info("Resumed from epoch %d", self._start_epoch) + + def _save(self, run_dir: Path, epoch: int, is_best: bool) -> None: + if not is_main_process(): + return + save_checkpoint( + run_dir / "last.pt", + epoch=epoch, + model=self.model, + optimizer=self.optimizer, + scheduler=self.scheduler, + best_metric=self._best_metric, + aim_run_hash=self.tracker.run_hash if self.tracker else None, + scaler=self.scaler, + config=config_to_dict(self.cfg), + ) + if is_best: + shutil.copy2(run_dir / "last.pt", run_dir / "best.pt") + logger.info("New best model saved (epoch %d).", epoch) + + # ── Early stopping ──────────────────────────────────────────────────── + + def _check_early_stopping(self, metrics: dict[str, float], epoch: int, run_dir: Path) -> bool: + es = self.t.early_stopping + if not es.enabled: + return False + + current = metrics.get(es.monitor) + if current is None: + return False + + is_better = ( + (self._best_metric is None) + or (es.mode == "min" and current < self._best_metric) + or (es.mode == "max" and current > self._best_metric) + ) + + if is_better: + self._best_metric = current + self._patience_counter = 0 + if is_main_process(): + self._save(run_dir, epoch, is_best=True) + else: + self._patience_counter = getattr(self, "_patience_counter", 0) + 1 + + if self._patience_counter >= es.patience: + logger.info("Early stopping triggered at epoch %d (patience=%d).", epoch, es.patience) + return True + return False + + # ── Builders ────────────────────────────────────────────────────────── + + def _build_dataloader(self, dataset: HDF5Dataset | None, training: bool) -> DataLoader | None: + if dataset is None: + return None + + sampler = None + if is_distributed(): + if training: + sampler = DistributedSampler(dataset, shuffle=True, drop_last=True) + else: + sampler = DistributedEvalSampler(dataset) + + return DataLoader( + dataset, + batch_size=self.t.batch_size_per_gpu, + shuffle=(training and sampler is None), + sampler=sampler, + num_workers=self.t.num_workers, + persistent_workers=self.t.persistent_workers and self.t.num_workers > 0, + collate_fn=masked_collate_fn, + worker_init_fn=worker_init_fn, + pin_memory=self.device.type == "cuda", + drop_last=training, + ) + + def _build_optimizer(self) -> torch.optim.Optimizer: + params = self.model.parameters() + name = self.t.optimizer.lower() + if name == "adamw": + return torch.optim.AdamW(params, lr=self.t.lr, weight_decay=self.t.weight_decay) + if name == "adam": + return torch.optim.Adam(params, lr=self.t.lr, weight_decay=self.t.weight_decay) + if name == "sgd": + return torch.optim.SGD( + params, lr=self.t.lr, weight_decay=self.t.weight_decay, momentum=0.9 + ) + raise ValueError(f"Unknown optimizer: {name}") + + def _build_scheduler(self) -> Any: + # LRScheduler.__init__ calls self.step() once internally before any + # optimizer.step() has run. This is harmless (the scheduler just + # advances to its initial state), but PyTorch emits a spurious warning + # about calling scheduler.step() before optimizer.step(). Suppress it + # here so it doesn't pollute training logs. + with warnings.catch_warnings(): + warnings.filterwarnings( + "ignore", + message=r"Detected call of `lr_scheduler\.step\(\)` before `optimizer\.step\(\)`", + ) + return self._build_scheduler_impl() + + def _build_scheduler_impl(self) -> Any: + name = self.t.scheduler.name + if self.t.get("protocol") == CORRECTED_PROTOCOL: + if self.t.gradient_accumulation_steps != 1 or self.t.distributed != "false": + raise ValueError( + "Corrected Sudoku schedule requires single device and accumulation=1" + ) + if name not in ("cosine", "linear_warmup_cosine"): + raise ValueError( + "Corrected Sudoku schedule requires cosine or linear_warmup_cosine" + ) + warmup = int(self.t.scheduler.warmup_steps) + if name == "cosine" and warmup != 0: + raise ValueError("Use linear_warmup_cosine for warmup, or set warmup_steps=0") + scheduler, effective = build_update_schedule( + self.optimizer, + total_updates=self._updates_per_epoch * self.t.epochs, + warmup_updates=warmup, + min_lr=self.t.scheduler.min_lr, + ) + effective.update( + { + "protocol": CORRECTED_PROTOCOL, + "updates_per_epoch": self._updates_per_epoch, + "seed": self.t.seed, + "initialization_seeded_before_model": bool( + self.cfg.get("initialization_seeded_before_model", False) + ), + "precision": self.t.precision, + "deterministic": self.t.deterministic, + "completed_updates": 0, + "skipped_updates": 0, + } + ) + OmegaConf.update(self.cfg, "training_runtime", effective, force_add=True) + return scheduler + if name == "none": + return None + if name == "cosine": + return torch.optim.lr_scheduler.CosineAnnealingLR( + self.optimizer, T_max=self.t.epochs, eta_min=self.t.scheduler.min_lr + ) + if name == "step": + return torch.optim.lr_scheduler.StepLR( + self.optimizer, step_size=self.t.scheduler.step_size, gamma=self.t.scheduler.gamma + ) + if name == "linear_warmup_cosine": + from torch.optim.lr_scheduler import CosineAnnealingLR, LinearLR, SequentialLR + + warmup = LinearLR( + self.optimizer, start_factor=0.01, total_iters=self.t.scheduler.warmup_steps + ) + cosine = CosineAnnealingLR( + self.optimizer, T_max=self.t.epochs, eta_min=self.t.scheduler.min_lr + ) + return SequentialLR( + self.optimizer, + schedulers=[warmup, cosine], + milestones=[self.t.scheduler.warmup_steps], + ) + raise ValueError(f"Unknown scheduler: {name}") + + def _build_scaler(self) -> torch.amp.GradScaler | None: + if self.t.precision == "fp16" and self.device.type == "cuda": + return torch.amp.GradScaler("cuda") + return None + + # ── Helpers ─────────────────────────────────────────────────────────── + + def _wants_distributed(self) -> bool: + if self.t.distributed == "true": + return True + if self.t.distributed == "false": + return False + # auto + return torch.cuda.device_count() > 1 or "WORLD_SIZE" in __import__("os").environ + + def _amp_enabled(self) -> bool: + return self.t.precision in ("fp16", "bf16") + + def _amp_dtype(self) -> torch.dtype: + if self.t.precision == "bf16": + return torch.bfloat16 + if self.t.precision == "fp16": + return torch.float16 + return torch.float32 + + def _resolve_run_dir(self) -> Path: + seed = getattr(self.t, "seed", None) + suffix = ( + f"seed_{seed}" if seed is not None else datetime.now().strftime("%Y-%m-%d_%H-%M-%S") + ) + return Path(self.cfg.paths.output_dir) / ( + f"{self.cfg.dataset.name}/{self.cfg.model.name}/{self.t.experiment_name}/{suffix}" + ) + + def _copy_lockfile(self, run_dir: Path) -> None: + """Copy uv.lock into the run directory for reproducibility.""" + lock = Path(__file__).resolve().parents[5] / "uv.lock" + if lock.exists(): + shutil.copy2(lock, run_dir / "uv.lock") + + # ── Backbone freeze / unfreeze ───────────────────────────────────────────────────── + + def _set_backbone_trainable(self, trainable: bool) -> None: + raw: Any = cast(Any, self.model) + raw = raw.module if hasattr(raw, "module") else raw + backbone = raw.get_backbone() if hasattr(raw, "get_backbone") else None + if backbone is not None: + backbone.requires_grad_(trainable) + + def _freeze_backbone(self) -> None: + self._set_backbone_trainable(False) + + def _unfreeze_backbone(self) -> None: + self._set_backbone_trainable(True) diff --git a/packages/softasp-core/src/softasp_core/utils/__init__.py b/packages/softasp-core/src/softasp_core/utils/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..935a0a8e30e4e124d819d1990fa839d9dcf46266 --- /dev/null +++ b/packages/softasp-core/src/softasp_core/utils/__init__.py @@ -0,0 +1,10 @@ +"""Shared utilities: hashing, logging.""" + +from softasp_core.utils.hashing import hash_config, hash_directory +from softasp_core.utils.logging import setup_logging + +__all__ = [ + "hash_config", + "hash_directory", + "setup_logging", +] diff --git a/packages/softasp-core/src/softasp_core/utils/hashing.py b/packages/softasp-core/src/softasp_core/utils/hashing.py new file mode 100644 index 0000000000000000000000000000000000000000..5e607aa9cf8604b2e93fe9c41566c7aa240a0e9e --- /dev/null +++ b/packages/softasp-core/src/softasp_core/utils/hashing.py @@ -0,0 +1,39 @@ +"""Hashing utilities for config versioning and data integrity.""" + +from __future__ import annotations + +import hashlib +import json +from pathlib import Path +from typing import Any + +from omegaconf import DictConfig, OmegaConf + + +def hash_config(cfg: Any, length: int = 6) -> str: + """SHA256 hash of a frozen config → first *length* hex chars. + + Used to version HDF5 files: ``v_.h5``. + """ + if isinstance(cfg, DictConfig): + data = OmegaConf.to_container(cfg, resolve=True) + else: + data = cfg + + serialized = json.dumps(data, sort_keys=True, default=str) + return hashlib.sha256(serialized.encode()).hexdigest()[:length] + + +def hash_directory(directory: Path, length: int = 12) -> str: + """SHA256 hash of all file contents in a directory (recursive, sorted). + + Used to detect if raw data changed since last preprocessing. + """ + h = hashlib.sha256() + for path in sorted(directory.rglob("*")): + if path.is_file(): + h.update(str(path.relative_to(directory)).encode()) + with open(path, "rb") as f: + for chunk in iter(lambda: f.read(1 << 20), b""): + h.update(chunk) + return h.hexdigest()[:length] diff --git a/packages/softasp-core/src/softasp_core/utils/logging.py b/packages/softasp-core/src/softasp_core/utils/logging.py new file mode 100644 index 0000000000000000000000000000000000000000..715ee983be509f9548ae85de0d0397944d38f2f2 --- /dev/null +++ b/packages/softasp-core/src/softasp_core/utils/logging.py @@ -0,0 +1,72 @@ +"""Structured logging setup — Rich console + file handler. + +Overrides Hydra's default logging hijack. +""" + +from __future__ import annotations + +import logging +import sys +from pathlib import Path + + +def setup_logging( + level: str = "INFO", + log_file: Path | None = None, + rich_console: bool = True, + rank: int = 0, +) -> None: + """Configure root logger with Rich console and optional file handler. + + Args: + level: Logging level (DEBUG, INFO, WARNING, ERROR). + log_file: Path for file handler. If None, no file logging. + rich_console: Use Rich for colored console output (rank 0 only). + rank: DDP rank. Non-zero ranks only log to file with rank prefix. + """ + root = logging.getLogger() + root.setLevel(getattr(logging, level.upper(), logging.INFO)) + root.handlers.clear() + + fmt = f"[rank {rank}] %(asctime)s %(levelname)s %(name)s: %(message)s" + datefmt = "%Y-%m-%d %H:%M:%S" + + # Console handler (rank 0 only) + if rank == 0: + if rich_console: + try: + from rich.logging import RichHandler + + console_handler = RichHandler( + level=level, + show_time=True, + show_path=False, + markup=True, + rich_tracebacks=True, + ) + root.addHandler(console_handler) + except ImportError: + _add_stream_handler(root, fmt, datefmt, level) + else: + _add_stream_handler(root, fmt, datefmt, level) + + # File handler (all ranks) + if log_file is not None: + log_file.parent.mkdir(parents=True, exist_ok=True) + file_handler = logging.FileHandler(str(log_file), mode="a") + file_handler.setLevel(getattr(logging, level.upper(), logging.INFO)) + file_handler.setFormatter(logging.Formatter(fmt, datefmt=datefmt)) + root.addHandler(file_handler) + + # Suppress noisy third-party loggers + for noisy in ("aim", "h5py", "urllib3", "matplotlib"): + logging.getLogger(noisy).setLevel(logging.WARNING) + + +def _add_stream_handler( + logger: logging.Logger, fmt: str, datefmt: str, level: str +) -> None: + handler = logging.StreamHandler(sys.stdout) + handler.setLevel(getattr(logging, level.upper(), logging.INFO)) + handler.setFormatter(logging.Formatter(fmt, datefmt=datefmt)) + logger.addHandler(handler) diff --git a/packages/softasp-domain/LICENSE b/packages/softasp-domain/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..340cf0c959586320b003a9ed4ad61ec65f8f90e5 --- /dev/null +++ b/packages/softasp-domain/LICENSE @@ -0,0 +1,158 @@ +Creative Commons Attribution-NonCommercial 4.0 International + + Creative Commons Corporation (“Creative Commons”) is not a law firm and does not provide legal services or legal advice. Distribution of Creative Commons public licenses does not create a lawyer-client or other relationship. Creative Commons makes its licenses and related information available on an “as-is” basis. Creative Commons gives no warranties regarding its licenses, any material licensed under their terms and conditions, or any related information. Creative Commons disclaims all liability for damages resulting from their use to the fullest extent possible. + +Using Creative Commons Public Licenses + +Creative Commons public licenses provide a standard set of terms and conditions that creators and other rights holders may use to share original works of authorship and other material subject to copyright and certain other rights specified in the public license below. The following considerations are for informational purposes only, are not exhaustive, and do not form part of our licenses. + +Considerations for licensors: Our public licenses are intended for use by those authorized to give the public permission to use material in ways otherwise restricted by copyright and certain other rights. Our licenses are irrevocable. Licensors should read and understand the terms and conditions of the license they choose before applying it. Licensors should also secure all rights necessary before applying our licenses so that the public can reuse the material as expected. Licensors should clearly mark any material not subject to the license. This includes other CC-licensed material, or material used under an exception or limitation to copyright. More considerations for licensors. + +Considerations for the public: By using one of our public licenses, a licensor grants the public permission to use the licensed material under specified terms and conditions. If the licensor’s permission is not necessary for any reason–for example, because of any applicable exception or limitation to copyright–then that use is not regulated by the license. Our licenses grant only permissions under copyright and certain other rights that a licensor has authority to grant. Use of the licensed material may still be restricted for other reasons, including because others have copyright or other rights in the material. A licensor may make special requests, such as asking that all changes be marked or described. Although not required by our licenses, you are encouraged to respect those requests where reasonable. More considerations for the public. + +Creative Commons Attribution-NonCommercial 4.0 International Public License + +By exercising the Licensed Rights (defined below), You accept and agree to be bound by the terms and conditions of this Creative Commons Attribution-NonCommercial 4.0 International Public License ("Public License"). To the extent this Public License may be interpreted as a contract, You are granted the Licensed Rights in consideration of Your acceptance of these terms and conditions, and the Licensor grants You such rights in consideration of benefits the Licensor receives from making the Licensed Material available under these terms and conditions. + +Section 1 – Definitions. + + a. Adapted Material means material subject to Copyright and Similar Rights that is derived from or based upon the Licensed Material and in which the Licensed Material is translated, altered, arranged, transformed, or otherwise modified in a manner requiring permission under the Copyright and Similar Rights held by the Licensor. For purposes of this Public License, where the Licensed Material is a musical work, performance, or sound recording, Adapted Material is always produced where the Licensed Material is synched in timed relation with a moving image. + + b. Adapter's License means the license You apply to Your Copyright and Similar Rights in Your contributions to Adapted Material in accordance with the terms and conditions of this Public License. + + c. Copyright and Similar Rights means copyright and/or similar rights closely related to copyright including, without limitation, performance, broadcast, sound recording, and Sui Generis Database Rights, without regard to how the rights are labeled or categorized. For purposes of this Public License, the rights specified in Section 2(b)(1)-(2) are not Copyright and Similar Rights. + + d. Effective Technological Measures means those measures that, in the absence of proper authority, may not be circumvented under laws fulfilling obligations under Article 11 of the WIPO Copyright Treaty adopted on December 20, 1996, and/or similar international agreements. + + e. Exceptions and Limitations means fair use, fair dealing, and/or any other exception or limitation to Copyright and Similar Rights that applies to Your use of the Licensed Material. + + f. Licensed Material means the artistic or literary work, database, or other material to which the Licensor applied this Public License. + + g. Licensed Rights means the rights granted to You subject to the terms and conditions of this Public License, which are limited to all Copyright and Similar Rights that apply to Your use of the Licensed Material and that the Licensor has authority to license. + + h. Licensor means the individual(s) or entity(ies) granting rights under this Public License. + + i. NonCommercial means not primarily intended for or directed towards commercial advantage or monetary compensation. For purposes of this Public License, the exchange of the Licensed Material for other material subject to Copyright and Similar Rights by digital file-sharing or similar means is NonCommercial provided there is no payment of monetary compensation in connection with the exchange. + + j. Share means to provide material to the public by any means or process that requires permission under the Licensed Rights, such as reproduction, public display, public performance, distribution, dissemination, communication, or importation, and to make material available to the public including in ways that members of the public may access the material from a place and at a time individually chosen by them. + + k. Sui Generis Database Rights means rights other than copyright resulting from Directive 96/9/EC of the European Parliament and of the Council of 11 March 1996 on the legal protection of databases, as amended and/or succeeded, as well as other essentially equivalent rights anywhere in the world. + + l. You means the individual or entity exercising the Licensed Rights under this Public License. Your has a corresponding meaning. + +Section 2 – Scope. + + a. License grant. + + 1. Subject to the terms and conditions of this Public License, the Licensor hereby grants You a worldwide, royalty-free, non-sublicensable, non-exclusive, irrevocable license to exercise the Licensed Rights in the Licensed Material to: + + A. reproduce and Share the Licensed Material, in whole or in part, for NonCommercial purposes only; and + + B. produce, reproduce, and Share Adapted Material for NonCommercial purposes only. + + 2. Exceptions and Limitations. For the avoidance of doubt, where Exceptions and Limitations apply to Your use, this Public License does not apply, and You do not need to comply with its terms and conditions. + + 3. Term. The term of this Public License is specified in Section 6(a). + + 4. Media and formats; technical modifications allowed. The Licensor authorizes You to exercise the Licensed Rights in all media and formats whether now known or hereafter created, and to make technical modifications necessary to do so. The Licensor waives and/or agrees not to assert any right or authority to forbid You from making technical modifications necessary to exercise the Licensed Rights, including technical modifications necessary to circumvent Effective Technological Measures. For purposes of this Public License, simply making modifications authorized by this Section 2(a)(4) never produces Adapted Material. + + 5. Downstream recipients. + + A. Offer from the Licensor – Licensed Material. Every recipient of the Licensed Material automatically receives an offer from the Licensor to exercise the Licensed Rights under the terms and conditions of this Public License. + + B. No downstream restrictions. You may not offer or impose any additional or different terms or conditions on, or apply any Effective Technological Measures to, the Licensed Material if doing so restricts exercise of the Licensed Rights by any recipient of the Licensed Material. + + 6. No endorsement. Nothing in this Public License constitutes or may be construed as permission to assert or imply that You are, or that Your use of the Licensed Material is, connected with, or sponsored, endorsed, or granted official status by, the Licensor or others designated to receive attribution as provided in Section 3(a)(1)(A)(i). + + b. Other rights. + + 1. Moral rights, such as the right of integrity, are not licensed under this Public License, nor are publicity, privacy, and/or other similar personality rights; however, to the extent possible, the Licensor waives and/or agrees not to assert any such rights held by the Licensor to the limited extent necessary to allow You to exercise the Licensed Rights, but not otherwise. + + 2. Patent and trademark rights are not licensed under this Public License. + + 3. To the extent possible, the Licensor waives any right to collect royalties from You for the exercise of the Licensed Rights, whether directly or through a collecting society under any voluntary or waivable statutory or compulsory licensing scheme. In all other cases the Licensor expressly reserves any right to collect such royalties, including when the Licensed Material is used other than for NonCommercial purposes. + +Section 3 – License Conditions. + +Your exercise of the Licensed Rights is expressly made subject to the following conditions. + + a. Attribution. + + 1. If You Share the Licensed Material (including in modified form), You must: + + A. retain the following if it is supplied by the Licensor with the Licensed Material: + + i. identification of the creator(s) of the Licensed Material and any others designated to receive attribution, in any reasonable manner requested by the Licensor (including by pseudonym if designated); + + ii. a copyright notice; + + iii. a notice that refers to this Public License; + + iv. a notice that refers to the disclaimer of warranties; + + v. a URI or hyperlink to the Licensed Material to the extent reasonably practicable; + + B. indicate if You modified the Licensed Material and retain an indication of any previous modifications; and + + C. indicate the Licensed Material is licensed under this Public License, and include the text of, or the URI or hyperlink to, this Public License. + + 2. You may satisfy the conditions in Section 3(a)(1) in any reasonable manner based on the medium, means, and context in which You Share the Licensed Material. For example, it may be reasonable to satisfy the conditions by providing a URI or hyperlink to a resource that includes the required information. + + 3. If requested by the Licensor, You must remove any of the information required by Section 3(a)(1)(A) to the extent reasonably practicable. + + 4. If You Share Adapted Material You produce, the Adapter's License You apply must not prevent recipients of the Adapted Material from complying with this Public License. + +Section 4 – Sui Generis Database Rights. + +Where the Licensed Rights include Sui Generis Database Rights that apply to Your use of the Licensed Material: + + a. for the avoidance of doubt, Section 2(a)(1) grants You the right to extract, reuse, reproduce, and Share all or a substantial portion of the contents of the database for NonCommercial purposes only; + + b. if You include all or a substantial portion of the database contents in a database in which You have Sui Generis Database Rights, then the database in which You have Sui Generis Database Rights (but not its individual contents) is Adapted Material; and + + c. You must comply with the conditions in Section 3(a) if You Share all or a substantial portion of the contents of the database. +For the avoidance of doubt, this Section 4 supplements and does not replace Your obligations under this Public License where the Licensed Rights include other Copyright and Similar Rights. + +Section 5 – Disclaimer of Warranties and Limitation of Liability. + + a. Unless otherwise separately undertaken by the Licensor, to the extent possible, the Licensor offers the Licensed Material as-is and as-available, and makes no representations or warranties of any kind concerning the Licensed Material, whether express, implied, statutory, or other. This includes, without limitation, warranties of title, merchantability, fitness for a particular purpose, non-infringement, absence of latent or other defects, accuracy, or the presence or absence of errors, whether or not known or discoverable. Where disclaimers of warranties are not allowed in full or in part, this disclaimer may not apply to You. + + b. To the extent possible, in no event will the Licensor be liable to You on any legal theory (including, without limitation, negligence) or otherwise for any direct, special, indirect, incidental, consequential, punitive, exemplary, or other losses, costs, expenses, or damages arising out of this Public License or use of the Licensed Material, even if the Licensor has been advised of the possibility of such losses, costs, expenses, or damages. Where a limitation of liability is not allowed in full or in part, this limitation may not apply to You. + + c. The disclaimer of warranties and limitation of liability provided above shall be interpreted in a manner that, to the extent possible, most closely approximates an absolute disclaimer and waiver of all liability. + +Section 6 – Term and Termination. + + a. This Public License applies for the term of the Copyright and Similar Rights licensed here. However, if You fail to comply with this Public License, then Your rights under this Public License terminate automatically. + + b. Where Your right to use the Licensed Material has terminated under Section 6(a), it reinstates: + + 1. automatically as of the date the violation is cured, provided it is cured within 30 days of Your discovery of the violation; or + + 2. upon express reinstatement by the Licensor. + + For the avoidance of doubt, this Section 6(b) does not affect any right the Licensor may have to seek remedies for Your violations of this Public License. + + c. For the avoidance of doubt, the Licensor may also offer the Licensed Material under separate terms or conditions or stop distributing the Licensed Material at any time; however, doing so will not terminate this Public License. + + d. Sections 1, 5, 6, 7, and 8 survive termination of this Public License. + +Section 7 – Other Terms and Conditions. + + a. The Licensor shall not be bound by any additional or different terms or conditions communicated by You unless expressly agreed. + + b. Any arrangements, understandings, or agreements regarding the Licensed Material not stated herein are separate from and independent of the terms and conditions of this Public License. + +Section 8 – Interpretation. + + a. For the avoidance of doubt, this Public License does not, and shall not be interpreted to, reduce, limit, restrict, or impose conditions on any use of the Licensed Material that could lawfully be made without permission under this Public License. + + b. To the extent possible, if any provision of this Public License is deemed unenforceable, it shall be automatically reformed to the minimum extent necessary to make it enforceable. If the provision cannot be reformed, it shall be severed from this Public License without affecting the enforceability of the remaining terms and conditions. + + c. No term or condition of this Public License will be waived and no failure to comply consented to unless expressly agreed to by the Licensor. + + d. Nothing in this Public License constitutes or may be interpreted as a limitation upon, or waiver of, any privileges and immunities that apply to the Licensor or You, including from the legal processes of any jurisdiction or authority. + +Creative Commons is not a party to its public licenses. Notwithstanding, Creative Commons may elect to apply one of its public licenses to material it publishes and in those instances will be considered the “Licensor.” Except for the limited purpose of indicating that material is shared under a Creative Commons public license or as otherwise permitted by the Creative Commons policies published at creativecommons.org/policies, Creative Commons does not authorize the use of the trademark “Creative Commons” or any other trademark or logo of Creative Commons without its prior written consent including, without limitation, in connection with any unauthorized modifications to any of its public licenses or any other arrangements, understandings, or agreements concerning use of licensed material. For the avoidance of doubt, this paragraph does not form part of the public licenses. + +Creative Commons may be contacted at creativecommons.org. diff --git a/packages/softasp-domain/pyproject.toml b/packages/softasp-domain/pyproject.toml new file mode 100644 index 0000000000000000000000000000000000000000..29c7d7cc93bc40c267f71321833c6759241dd424 --- /dev/null +++ b/packages/softasp-domain/pyproject.toml @@ -0,0 +1,26 @@ +[project] +name = "softasp-domain" +version = "0.1.0" +description = "SoftASP dataset implementations (downloaders, preprocessors, datasets)" +requires-python = ">=3.12" +license = "CC-BY-NC-4.0" +license-files = ["LICENSE"] +dependencies = [ + "softasp-core", + "torch>=2.2", + "torchvision>=0.17", + "Pillow>=10.0", + "requests>=2.31", + "certifi>=2024.0", + "clingo>=5.7", +] + +[tool.uv.sources] +softasp-core = { workspace = true } + +[build-system] +requires = ["hatchling"] +build-backend = "hatchling.build" + +[tool.hatch.build.targets.wheel] +packages = ["src/softasp_domain"] diff --git a/packages/softasp-domain/src/softasp_domain/__init__.py b/packages/softasp-domain/src/softasp_domain/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..274557cd05ce1596dac52b4597c41b0dfe163e28 --- /dev/null +++ b/packages/softasp-domain/src/softasp_domain/__init__.py @@ -0,0 +1,7 @@ +"""SoftASP domain packages — dataset downloaders, preprocessors, and HDF5 datasets.""" + +__version__ = "0.1.0" + +from softasp_domain.registry import DOMAIN_REGISTRY, get_domain, register_domain + +__all__ = ["DOMAIN_REGISTRY", "get_domain", "register_domain"] diff --git a/packages/softasp-domain/src/softasp_domain/mnist_download.py b/packages/softasp-domain/src/softasp_domain/mnist_download.py new file mode 100644 index 0000000000000000000000000000000000000000..041c94f1f0da91a8c22ce29a84058c1a1672b756 --- /dev/null +++ b/packages/softasp-domain/src/softasp_domain/mnist_download.py @@ -0,0 +1,54 @@ +"""Shared MNIST download bootstrap helpers.""" + +from __future__ import annotations + +import logging +import os + +logger = logging.getLogger(__name__) + +_MNIST_MIRRORS = ( + "https://storage.googleapis.com/cvdf-datasets/mnist/", + "https://ossci-datasets.s3.amazonaws.com/mnist/", +) + + +def configure_mnist_download(mnist_cls: type) -> None: + """Patch MNIST download prerequisites for HPC environments.""" + _ensure_ssl_certificates() + existing_mirrors = list(getattr(mnist_cls, "mirrors", [])) + merged_mirrors = list(dict.fromkeys([*_MNIST_MIRRORS, *existing_mirrors])) + if merged_mirrors != existing_mirrors: + mnist_cls.mirrors = merged_mirrors + logger.debug("Configured MNIST mirrors: %s", merged_mirrors) + + +def _ensure_ssl_certificates() -> None: + if os.environ.get("SSL_CERT_FILE"): + return + + cert_path = _get_certifi_bundle_path() + if cert_path: + os.environ["SSL_CERT_FILE"] = cert_path + os.environ.setdefault("REQUESTS_CA_BUNDLE", cert_path) + logger.debug("Using certifi CA bundle for MNIST download: %s", cert_path) + return + + # Last-resort fallback for HPC environments where certifi is unavailable and + # the system CA bundle cannot verify MNIST download hosts (e.g., Palmetto). + # Scoped to this process only; does not persist across jobs. + import ssl + + ssl._create_default_https_context = ssl._create_unverified_context + logger.warning( + "certifi unavailable; SSL verification disabled for MNIST downloads. " + "Install certifi (`uv add certifi`) to eliminate this warning." + ) + + +def _get_certifi_bundle_path() -> str | None: + try: + import certifi + except ImportError: + return None + return certifi.where() diff --git a/packages/softasp-domain/src/softasp_domain/registry.py b/packages/softasp-domain/src/softasp_domain/registry.py new file mode 100644 index 0000000000000000000000000000000000000000..cc8a4dbbc0606cf6f506722cb5a664fe33f18227 --- /dev/null +++ b/packages/softasp-domain/src/softasp_domain/registry.py @@ -0,0 +1,43 @@ +"""Domain registry — maps dataset name to (downloader, preprocessor, dataset_cls).""" + +from __future__ import annotations + +from dataclasses import dataclass + + +@dataclass(frozen=True) +class DomainEntry: + """Container for a registered domain.""" + + downloader_cls: type + preprocessor_cls: type + dataset_cls: type + + +DOMAIN_REGISTRY: dict[str, DomainEntry] = {} + + +def register_domain( + name: str, + *, + downloader: type, + preprocessor: type, + dataset: type, +): + """Register a domain triple (downloader, preprocessor, dataset_cls).""" + DOMAIN_REGISTRY[name] = DomainEntry( + downloader_cls=downloader, + preprocessor_cls=preprocessor, + dataset_cls=dataset, + ) + + +def get_domain(name: str) -> DomainEntry: + """Retrieve a domain entry by name.""" + if name not in DOMAIN_REGISTRY: + raise ValueError(f"Unknown domain '{name}'. Available: {list(DOMAIN_REGISTRY.keys())}") + return DOMAIN_REGISTRY[name] + + +# Auto-import submodules to trigger registration +from softasp_domain.sudoku import dataset as _sudoku # noqa: F401, E402 diff --git a/packages/softasp-domain/src/softasp_domain/sudoku/__init__.py b/packages/softasp-domain/src/softasp_domain/sudoku/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..f54feeafd24f14e09c8a81d61ce20a21942f8c26 --- /dev/null +++ b/packages/softasp-domain/src/softasp_domain/sudoku/__init__.py @@ -0,0 +1 @@ +"""Visual Sudoku domain subpackage.""" diff --git a/packages/softasp-domain/src/softasp_domain/sudoku/dataset.py b/packages/softasp-domain/src/softasp_domain/sudoku/dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..6c93e6106dbbc6ebfcd5d8e46ec3032640bb8ab8 --- /dev/null +++ b/packages/softasp-domain/src/softasp_domain/sudoku/dataset.py @@ -0,0 +1,41 @@ +"""Visual Sudoku HDF5-backed dataset, ready for DataLoader. + +Wraps the base ``HDF5Dataset`` with Sudoku-specific field mapping and +registers the domain triple so it can be instantiated by name. +""" + +from __future__ import annotations + +from pathlib import Path + +from softasp_core.data.hdf5_dataset import HDF5Dataset +from softasp_domain.registry import register_domain +from softasp_domain.sudoku.downloader import SudokuDownloader +from softasp_domain.sudoku.preprocessor import SudokuPreprocessor + + +class SudokuDataset(HDF5Dataset): + """Visual Sudoku dataset backed by a preprocessed HDF5 file. + + Returns dicts with keys: + - ``images``: (252, 252, 1) uint8 tensor → converted to float by model + - ``labels``: (81,) int64 tensor (0-indexed digit labels, 0=digit 1) + - ``puzzle_mask``: (81,) int64 tensor (1 for clue cells, 0 for blank) + """ + + def __init__(self, h5_path: str | Path, split: str = "train", **kwargs) -> None: + super().__init__( + h5_path=str(h5_path), + split=split, + fields=["images", "labels", "puzzle_mask"], + **kwargs, + ) + + +# Register the domain so CLI can resolve it by name +register_domain( + "sudoku", + downloader=SudokuDownloader, + preprocessor=SudokuPreprocessor, + dataset=SudokuDataset, +) diff --git a/packages/softasp-domain/src/softasp_domain/sudoku/downloader.py b/packages/softasp-domain/src/softasp_domain/sudoku/downloader.py new file mode 100644 index 0000000000000000000000000000000000000000..882d8c601ccb00f5bda5e61fe8fb32f552139f7a --- /dev/null +++ b/packages/softasp-domain/src/softasp_domain/sudoku/downloader.py @@ -0,0 +1,56 @@ +"""Sudoku dataset downloader — downloads MNIST for rendering Sudoku boards. + +Usage:: + + from softasp_domain.sudoku.downloader import SudokuDownloader + dl = SudokuDownloader(raw_dir=Path("data/raw/sudoku")) + dl.run() +""" + +from __future__ import annotations + +import logging +from pathlib import Path + +from softasp_core.data.base_downloader import BaseDownloader + +logger = logging.getLogger(__name__) + + +class SudokuDownloader(BaseDownloader): + """Download MNIST as the source imagery for Visual Sudoku boards.""" + + def __init__(self, raw_dir: Path) -> None: + super().__init__(raw_dir) + self.mnist_dir = self.raw_dir / "mnist" + + @property + def name(self) -> str: + return "sudoku" + + def download(self) -> None: + import torchvision + + from softasp_domain.mnist_download import configure_mnist_download + + configure_mnist_download(torchvision.datasets.MNIST) + self.mnist_dir.mkdir(parents=True, exist_ok=True) + logger.info("Downloading MNIST to %s …", self.mnist_dir) + torchvision.datasets.MNIST( + root=str(self.mnist_dir), train=True, download=True + ) + torchvision.datasets.MNIST( + root=str(self.mnist_dir), train=False, download=True + ) + logger.info("MNIST download complete.") + + def verify(self) -> bool: + """Check that MNIST processed files exist.""" + processed = self.mnist_dir / "MNIST" / "processed" + raw = self.mnist_dir / "MNIST" / "raw" + # torchvision >= 0.13 stores raw .gz files; older versions use processed/ + if raw.exists() and any(raw.glob("*.gz")): + return True + if processed.exists() and any(processed.glob("*.pt")): + return True + return False diff --git a/packages/softasp-domain/src/softasp_domain/sudoku/generator.py b/packages/softasp-domain/src/softasp_domain/sudoku/generator.py new file mode 100644 index 0000000000000000000000000000000000000000..3c21f8e121da5a48f7aa73551dead029b9ec1778 --- /dev/null +++ b/packages/softasp-domain/src/softasp_domain/sudoku/generator.py @@ -0,0 +1,325 @@ +"""Visual Sudoku board generator. + +Generates valid Sudoku puzzles and renders them as 252×252 grayscale images +using MNIST digit samples. Blank (unknown) cells are rendered as all-zero +28×28 patches. + +The generation approach: + 1. Start from a canonical valid 9×9 Sudoku board. + 2. Apply random validity-preserving transformations + (row/col/band/stack swaps, digit relabelling). + 3. Remove cells to create a puzzle with a configurable number of clues. + 4. Render each cell using a randomly sampled MNIST image of the correct digit, + or an all-zero patch for blank cells. +""" + +from __future__ import annotations + +import logging +from pathlib import Path + +import numpy as np + +logger = logging.getLogger(__name__) + +# Canonical valid Sudoku board (all digits 1-9) +_BASE_BOARD = np.array( + [ + [1, 2, 3, 4, 5, 6, 7, 8, 9], + [4, 5, 6, 7, 8, 9, 1, 2, 3], + [7, 8, 9, 1, 2, 3, 4, 5, 6], + [2, 3, 1, 5, 6, 4, 8, 9, 7], + [5, 6, 4, 8, 9, 7, 2, 3, 1], + [8, 9, 7, 2, 3, 1, 5, 6, 4], + [3, 1, 2, 6, 4, 5, 9, 7, 8], + [6, 4, 5, 9, 7, 8, 3, 1, 2], + [9, 7, 8, 3, 1, 2, 6, 4, 5], + ], + dtype=np.int64, +) + + +def _shuffle_board(rng: np.random.Generator) -> np.ndarray: + """Produce a random valid Sudoku board via validity-preserving transforms.""" + board = _BASE_BOARD.copy() + + # 1. Relabel digits (random permutation of 1-9) + perm = rng.permutation(9) + 1 # values 1-9 in random order + mapping = np.zeros(10, dtype=np.int64) + for orig, new in enumerate(perm, start=1): + mapping[orig] = new + board = mapping[board] + + # 2. Swap rows within each 3-row band + for band_start in (0, 3, 6): + rows = [band_start, band_start + 1, band_start + 2] + rng.shuffle(rows) + board[band_start : band_start + 3] = board[rows] + + # 3. Swap columns within each 3-column stack + for stack_start in (0, 3, 6): + cols = [stack_start, stack_start + 1, stack_start + 2] + rng.shuffle(cols) + board[:, stack_start : stack_start + 3] = board[:, cols] + + # 4. Swap entire 3-row bands + bands = [0, 1, 2] + rng.shuffle(bands) + new_board = np.empty_like(board) + for dst, src in enumerate(bands): + new_board[dst * 3 : (dst + 1) * 3] = board[src * 3 : (src + 1) * 3] + board = new_board + + # 5. Swap entire 3-column stacks + stacks = [0, 1, 2] + rng.shuffle(stacks) + new_board = np.empty_like(board) + for dst, src in enumerate(stacks): + new_board[:, dst * 3 : (dst + 1) * 3] = board[:, src * 3 : (src + 1) * 3] + board = new_board + + return board + + +_UNIQUE_CHECK_ASP = """\ +has_clue(R,C) :- given(R,C,_). +cell(R,C,V) :- given(R,C,V). +1 { cell(R,C,V) : V = 1..9 } 1 :- R = 1..9, C = 1..9, not has_clue(R,C). +:- R = 1..9, V = 1..9, #count { C : cell(R,C,V) } != 1. +:- C = 1..9, V = 1..9, #count { R : cell(R,C,V) } != 1. +:- BR = 0..2, BC = 0..2, V = 1..9, + #count { R,C : cell(R,C,V), + R >= BR*3+1, R <= BR*3+3, + C >= BC*3+1, C <= BC*3+3 } != 1. +:- R = 1..9, C = 1..9, #count { V : cell(R,C,V) } != 1. +""" + + +def _is_unique(solution: np.ndarray, puzzle_mask: np.ndarray) -> bool: + """Return True iff the puzzle has *exactly one* valid completion. + + Uses Clingo with ``--models=2``: if only one answer set is found the + puzzle is uniquely solvable. Falls back to True (no check) when clingo + is not installed so that dataset generation doesn't hard-fail. + + Args: + solution: (81,) int64, values 1-9. + puzzle_mask: (81,) int64, 1 = clue, 0 = blank. + """ + try: + import clingo # noqa: PLC0415 + except ImportError: + logger.debug("clingo not available — skipping uniqueness check") + return True + + facts = [] + for idx in range(81): + if puzzle_mask[idx] == 1: + r = idx // 9 + 1 + c = idx % 9 + 1 + v = int(solution[idx]) # 1-9 + facts.append(f"given({r},{c},{v}).") + + program = "\n".join(facts) + "\n" + _UNIQUE_CHECK_ASP + ctl = clingo.Control(["--models=2"]) + ctl.add("base", [], program) + ctl.ground([("base", [])]) + + count = 0 + + def on_model(_m: object) -> None: + nonlocal count + count += 1 + + ctl.solve(on_model=on_model) + return count == 1 + + +def generate_puzzle( + rng: np.random.Generator, + n_clues_start: int = 55, + max_removals: int = 10, +) -> tuple[np.ndarray, np.ndarray, np.ndarray]: + """Generate one uniquely-solvable Sudoku puzzle via iterative clue removal. + + Algorithm: + 1. Start with ``n_clues_start`` randomly chosen clues (55 is almost + always already unique — no retry loop needed). + 2. Shuffle those clue indices and iterate through them. For each, try + removing it: if the puzzle remains uniquely solvable (verified with + Clingo), keep the removal; otherwise restore it. + 3. Stop after ``max_removals`` successful removals or when no further + removal preserves uniqueness — whichever comes first. + + Result: a guaranteed uniquely-solvable puzzle with roughly + ``n_clues_start - max_removals`` clues. + + For the publication-quality dataset use ``max_removals`` equal to + ``n_clues_start`` minus the desired minimum clue count and remove the cap + so the algorithm runs to completion (true minimal puzzle). + + Returns: + solution: (81,) int64 array with values 1-9 + puzzle_mask: (81,) int64 array — 1 for clue cells, 0 for blank + puzzle: (9, 9) int64 array — solution values for clue cells, 0 for blank + """ + board = _shuffle_board(rng) + solution = board.flatten() # (81,) values 1-9 + + # Start from n_clues_start randomly chosen clues. + # Guard: the initial random selection must itself be uniquely solvable + # before we enter the removal loop. A non-unique starting config causes + # every removal attempt to fail (removing a clue from a non-unique puzzle + # is still non-unique), so the loop exits without any removal and returns + # a non-unique puzzle. With 55 clues this is very rare but does happen. + _MAX_INIT_RETRIES = 20 + for _attempt in range(_MAX_INIT_RETRIES): + clue_set = set(rng.choice(81, size=n_clues_start, replace=False).tolist()) + puzzle_mask = np.zeros(81, dtype=np.int64) + for idx in clue_set: + puzzle_mask[idx] = 1 + if _is_unique(solution, puzzle_mask): + break + logger.debug( + "Initial %d-clue config not unique (attempt %d) — resampling.", + n_clues_start, _attempt + 1, + ) + else: + # Fallback (astronomically unlikely): use the full board as clues. + logger.warning( + "Could not find a unique %d-clue starting config after %d attempts " + "— using all 81 clues as fallback.", + n_clues_start, _MAX_INIT_RETRIES, + ) + clue_set = set(range(81)) + puzzle_mask = np.ones(81, dtype=np.int64) + + # Iteratively attempt to remove clues while preserving uniqueness. + candidates = list(clue_set) + rng.shuffle(candidates) + removed = 0 + for idx in candidates: + if removed >= max_removals: + break + puzzle_mask[idx] = 0 + if _is_unique(solution, puzzle_mask): + clue_set.discard(idx) + removed += 1 + else: + puzzle_mask[idx] = 1 # restore + + puzzle = np.zeros((9, 9), dtype=np.int64) + for idx in clue_set: + r, c = divmod(idx, 9) + puzzle[r, c] = board[r, c] + + return solution, puzzle_mask, puzzle + + +def load_mnist_by_digit(mnist_root: Path) -> dict[int, np.ndarray]: + """Load MNIST images grouped by digit label. + + Downloads MNIST if not already present (idempotent). The SSL certificate + workaround in ``configure_mnist_download`` is applied first so this works + on HPC nodes such as Palmetto where the default CA bundle cannot verify the + MNIST download hosts. + + Returns: + Dict mapping digit (0-9) → (N_digit, 28, 28) uint8 array + """ + import torchvision + + from softasp_domain.mnist_download import configure_mnist_download + + configure_mnist_download(torchvision.datasets.MNIST) + + by_digit: dict[int, list[np.ndarray]] = {d: [] for d in range(10)} + + for train in (True, False): + ds = torchvision.datasets.MNIST( + root=str(mnist_root), train=train, download=True + ) + for img, label in ds: + by_digit[label].append(np.array(img, dtype=np.uint8)) + + result: dict[int, np.ndarray] = {} + for d, images in by_digit.items(): + result[d] = np.stack(images) + + logger.info( + "MNIST loaded: %s", + {d: arr.shape[0] for d, arr in sorted(result.items())}, + ) + return result + + +def render_board( + puzzle: np.ndarray, + solution: np.ndarray, + puzzle_mask: np.ndarray, + mnist_by_digit: dict[int, np.ndarray], + rng: np.random.Generator, +) -> np.ndarray: + """Render a 252×252 single-channel image of a Sudoku board. + + Clue cells show an MNIST digit image; blank cells are all-zero (black). + + Args: + puzzle: (9, 9) int64 — non-zero for clue cells + solution: (81,) int64 — full solution, values 1-9 + puzzle_mask: (81,) int64 — 1 for clue, 0 for blank + mnist_by_digit: dict digit → (N, 28, 28) uint8 + rng: numpy random generator + + Returns: + (252, 252, 1) uint8 image + """ + board_img = np.zeros((252, 252), dtype=np.uint8) + + for idx in range(81): + r, c = divmod(idx, 9) + if puzzle_mask[idx] == 1: + digit = solution[idx] # 1-9 + pool = mnist_by_digit[digit] + sample_idx = rng.integers(0, len(pool)) + cell_img = pool[sample_idx] # (28, 28) + board_img[r * 28 : (r + 1) * 28, c * 28 : (c + 1) * 28] = cell_img + # else: leave as zeros (blank cell) + + return board_img[:, :, np.newaxis] # (252, 252, 1) + + +def generate_dataset( + n_samples: int, + mnist_root: Path, + rng: np.random.Generator, + n_clues_start: int = 55, + max_removals: int = 10, +) -> dict[str, np.ndarray]: + """Generate a full Visual Sudoku dataset split. + + Returns dict with keys: + images: (N, 252, 252, 1) uint8 + labels: (N, 81) int64, values 0-8 (0-indexed for cross-entropy) + puzzle_mask: (N, 81) int64 — 1 for clue, 0 for blank + """ + mnist_by_digit = load_mnist_by_digit(mnist_root) + + images = np.zeros((n_samples, 252, 252, 1), dtype=np.uint8) + labels = np.zeros((n_samples, 81), dtype=np.int64) + masks = np.zeros((n_samples, 81), dtype=np.int64) + + for i in range(n_samples): + solution, puzzle_mask, puzzle = generate_puzzle( + rng, n_clues_start=n_clues_start, max_removals=max_removals + ) + img = render_board(puzzle, solution, puzzle_mask, mnist_by_digit, rng) + + images[i] = img + labels[i] = solution - 1 # convert 1-9 → 0-8 for cross-entropy + masks[i] = puzzle_mask + + if (i + 1) % 1000 == 0: + logger.info(" Generated %d / %d boards", i + 1, n_samples) + + return {"images": images, "labels": labels, "puzzle_mask": masks} diff --git a/packages/softasp-domain/src/softasp_domain/sudoku/preprocessor.py b/packages/softasp-domain/src/softasp_domain/sudoku/preprocessor.py new file mode 100644 index 0000000000000000000000000000000000000000..387a52887facb46a93c3ecd85ca7551eb8b97057 --- /dev/null +++ b/packages/softasp-domain/src/softasp_domain/sudoku/preprocessor.py @@ -0,0 +1,86 @@ +"""Sudoku preprocessor — generates Visual Sudoku boards and writes HDF5. + +Each split group in the HDF5 file contains: +- ``images``: (N, 252, 252, 1) uint8 — full board image +- ``labels``: (N, 81) int64 — 0-indexed solution (0 = digit 1) +- ``puzzle_mask``: (N, 81) int64 — 1 for clue cells, 0 for blank + +The preprocessor is **idempotent**: it checks a version hash and skips +processing if the output file already matches the current config. +""" + +from __future__ import annotations + +import logging +from pathlib import Path +from typing import Any + +import h5py +import numpy as np +from softasp_core.data.base_preprocessor import BasePreprocessor + +from softasp_domain.sudoku.generator import generate_dataset + +logger = logging.getLogger(__name__) + +# Dataset sizes per split (not stored in YAML to avoid DatasetConfig clash) +_SPLIT_SIZES: dict[str, int] = { + "train": 9000, + "val": 1000, + "test": 1000, +} +# Starting clue count before iterative removal. +# 55 random clues are almost always already uniquely solvable — no retry loop. +# For publication use max_removals = n_clues_start - target_clues and remove +# the cap to get true minimal puzzles (see generator.py docstring). +_N_CLUES_START = 55 +_MAX_REMOVALS = 10 # ≈45 clues per board; fast enough for CPU-only node +_SEED_OFFSETS: dict[str, int] = {"train": 0, "val": 100000, "test": 200000} + + +class SudokuPreprocessor(BasePreprocessor): + """Generate Visual Sudoku boards from MNIST and write to HDF5.""" + + def __init__( + self, + raw_dir: Path, + processed_dir: Path, + preprocessing_cfg: Any, + dataset_cfg: Any, + ) -> None: + super().__init__(raw_dir, processed_dir, preprocessing_cfg, dataset_cfg) + self.mnist_root = self.raw_dir / "mnist" + + def process_split(self, split: str, group: h5py.Group) -> int: + """Generate boards for one split and write into the HDF5 group.""" + n_samples = _SPLIT_SIZES.get(split, 1000) + subset = getattr(self.preprocessing_cfg, "subset_size", None) + if subset is not None: + if isinstance(subset, bool) or not isinstance(subset, int) or subset < 1: + raise ValueError("preprocessing.subset_size must be a positive integer or null") + n_samples = min(n_samples, subset) + seed_offset = _SEED_OFFSETS.get(split, 0) + base_seed = 42 # deterministic generation + + logger.info("Generating %d Visual Sudoku boards for split '%s' …", n_samples, split) + + rng = np.random.default_rng(base_seed + seed_offset) + data = generate_dataset( + n_samples=n_samples, + mnist_root=self.mnist_root, + rng=rng, + n_clues_start=_N_CLUES_START, + max_removals=_MAX_REMOVALS, + ) + + # Write to HDF5 + images = self.create_chunked_dataset( + group, "images", shape=data["images"].shape, dtype="uint8" + ) + images[:] = data["images"] + + self.create_label_dataset(group, "labels", data["labels"]) + self.create_label_dataset(group, "puzzle_mask", data["puzzle_mask"]) + + logger.info(" Finished split '%s' (%d samples).", split, n_samples) + return n_samples diff --git a/packages/softasp-solver/LICENSE b/packages/softasp-solver/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..340cf0c959586320b003a9ed4ad61ec65f8f90e5 --- /dev/null +++ b/packages/softasp-solver/LICENSE @@ -0,0 +1,158 @@ +Creative Commons Attribution-NonCommercial 4.0 International + + Creative Commons Corporation (“Creative Commons”) is not a law firm and does not provide legal services or legal advice. Distribution of Creative Commons public licenses does not create a lawyer-client or other relationship. Creative Commons makes its licenses and related information available on an “as-is” basis. Creative Commons gives no warranties regarding its licenses, any material licensed under their terms and conditions, or any related information. Creative Commons disclaims all liability for damages resulting from their use to the fullest extent possible. + +Using Creative Commons Public Licenses + +Creative Commons public licenses provide a standard set of terms and conditions that creators and other rights holders may use to share original works of authorship and other material subject to copyright and certain other rights specified in the public license below. The following considerations are for informational purposes only, are not exhaustive, and do not form part of our licenses. + +Considerations for licensors: Our public licenses are intended for use by those authorized to give the public permission to use material in ways otherwise restricted by copyright and certain other rights. Our licenses are irrevocable. Licensors should read and understand the terms and conditions of the license they choose before applying it. Licensors should also secure all rights necessary before applying our licenses so that the public can reuse the material as expected. Licensors should clearly mark any material not subject to the license. This includes other CC-licensed material, or material used under an exception or limitation to copyright. More considerations for licensors. + +Considerations for the public: By using one of our public licenses, a licensor grants the public permission to use the licensed material under specified terms and conditions. If the licensor’s permission is not necessary for any reason–for example, because of any applicable exception or limitation to copyright–then that use is not regulated by the license. Our licenses grant only permissions under copyright and certain other rights that a licensor has authority to grant. Use of the licensed material may still be restricted for other reasons, including because others have copyright or other rights in the material. A licensor may make special requests, such as asking that all changes be marked or described. Although not required by our licenses, you are encouraged to respect those requests where reasonable. More considerations for the public. + +Creative Commons Attribution-NonCommercial 4.0 International Public License + +By exercising the Licensed Rights (defined below), You accept and agree to be bound by the terms and conditions of this Creative Commons Attribution-NonCommercial 4.0 International Public License ("Public License"). To the extent this Public License may be interpreted as a contract, You are granted the Licensed Rights in consideration of Your acceptance of these terms and conditions, and the Licensor grants You such rights in consideration of benefits the Licensor receives from making the Licensed Material available under these terms and conditions. + +Section 1 – Definitions. + + a. Adapted Material means material subject to Copyright and Similar Rights that is derived from or based upon the Licensed Material and in which the Licensed Material is translated, altered, arranged, transformed, or otherwise modified in a manner requiring permission under the Copyright and Similar Rights held by the Licensor. For purposes of this Public License, where the Licensed Material is a musical work, performance, or sound recording, Adapted Material is always produced where the Licensed Material is synched in timed relation with a moving image. + + b. Adapter's License means the license You apply to Your Copyright and Similar Rights in Your contributions to Adapted Material in accordance with the terms and conditions of this Public License. + + c. Copyright and Similar Rights means copyright and/or similar rights closely related to copyright including, without limitation, performance, broadcast, sound recording, and Sui Generis Database Rights, without regard to how the rights are labeled or categorized. For purposes of this Public License, the rights specified in Section 2(b)(1)-(2) are not Copyright and Similar Rights. + + d. Effective Technological Measures means those measures that, in the absence of proper authority, may not be circumvented under laws fulfilling obligations under Article 11 of the WIPO Copyright Treaty adopted on December 20, 1996, and/or similar international agreements. + + e. Exceptions and Limitations means fair use, fair dealing, and/or any other exception or limitation to Copyright and Similar Rights that applies to Your use of the Licensed Material. + + f. Licensed Material means the artistic or literary work, database, or other material to which the Licensor applied this Public License. + + g. Licensed Rights means the rights granted to You subject to the terms and conditions of this Public License, which are limited to all Copyright and Similar Rights that apply to Your use of the Licensed Material and that the Licensor has authority to license. + + h. Licensor means the individual(s) or entity(ies) granting rights under this Public License. + + i. NonCommercial means not primarily intended for or directed towards commercial advantage or monetary compensation. For purposes of this Public License, the exchange of the Licensed Material for other material subject to Copyright and Similar Rights by digital file-sharing or similar means is NonCommercial provided there is no payment of monetary compensation in connection with the exchange. + + j. Share means to provide material to the public by any means or process that requires permission under the Licensed Rights, such as reproduction, public display, public performance, distribution, dissemination, communication, or importation, and to make material available to the public including in ways that members of the public may access the material from a place and at a time individually chosen by them. + + k. Sui Generis Database Rights means rights other than copyright resulting from Directive 96/9/EC of the European Parliament and of the Council of 11 March 1996 on the legal protection of databases, as amended and/or succeeded, as well as other essentially equivalent rights anywhere in the world. + + l. You means the individual or entity exercising the Licensed Rights under this Public License. Your has a corresponding meaning. + +Section 2 – Scope. + + a. License grant. + + 1. Subject to the terms and conditions of this Public License, the Licensor hereby grants You a worldwide, royalty-free, non-sublicensable, non-exclusive, irrevocable license to exercise the Licensed Rights in the Licensed Material to: + + A. reproduce and Share the Licensed Material, in whole or in part, for NonCommercial purposes only; and + + B. produce, reproduce, and Share Adapted Material for NonCommercial purposes only. + + 2. Exceptions and Limitations. For the avoidance of doubt, where Exceptions and Limitations apply to Your use, this Public License does not apply, and You do not need to comply with its terms and conditions. + + 3. Term. The term of this Public License is specified in Section 6(a). + + 4. Media and formats; technical modifications allowed. The Licensor authorizes You to exercise the Licensed Rights in all media and formats whether now known or hereafter created, and to make technical modifications necessary to do so. The Licensor waives and/or agrees not to assert any right or authority to forbid You from making technical modifications necessary to exercise the Licensed Rights, including technical modifications necessary to circumvent Effective Technological Measures. For purposes of this Public License, simply making modifications authorized by this Section 2(a)(4) never produces Adapted Material. + + 5. Downstream recipients. + + A. Offer from the Licensor – Licensed Material. Every recipient of the Licensed Material automatically receives an offer from the Licensor to exercise the Licensed Rights under the terms and conditions of this Public License. + + B. No downstream restrictions. You may not offer or impose any additional or different terms or conditions on, or apply any Effective Technological Measures to, the Licensed Material if doing so restricts exercise of the Licensed Rights by any recipient of the Licensed Material. + + 6. No endorsement. Nothing in this Public License constitutes or may be construed as permission to assert or imply that You are, or that Your use of the Licensed Material is, connected with, or sponsored, endorsed, or granted official status by, the Licensor or others designated to receive attribution as provided in Section 3(a)(1)(A)(i). + + b. Other rights. + + 1. Moral rights, such as the right of integrity, are not licensed under this Public License, nor are publicity, privacy, and/or other similar personality rights; however, to the extent possible, the Licensor waives and/or agrees not to assert any such rights held by the Licensor to the limited extent necessary to allow You to exercise the Licensed Rights, but not otherwise. + + 2. Patent and trademark rights are not licensed under this Public License. + + 3. To the extent possible, the Licensor waives any right to collect royalties from You for the exercise of the Licensed Rights, whether directly or through a collecting society under any voluntary or waivable statutory or compulsory licensing scheme. In all other cases the Licensor expressly reserves any right to collect such royalties, including when the Licensed Material is used other than for NonCommercial purposes. + +Section 3 – License Conditions. + +Your exercise of the Licensed Rights is expressly made subject to the following conditions. + + a. Attribution. + + 1. If You Share the Licensed Material (including in modified form), You must: + + A. retain the following if it is supplied by the Licensor with the Licensed Material: + + i. identification of the creator(s) of the Licensed Material and any others designated to receive attribution, in any reasonable manner requested by the Licensor (including by pseudonym if designated); + + ii. a copyright notice; + + iii. a notice that refers to this Public License; + + iv. a notice that refers to the disclaimer of warranties; + + v. a URI or hyperlink to the Licensed Material to the extent reasonably practicable; + + B. indicate if You modified the Licensed Material and retain an indication of any previous modifications; and + + C. indicate the Licensed Material is licensed under this Public License, and include the text of, or the URI or hyperlink to, this Public License. + + 2. You may satisfy the conditions in Section 3(a)(1) in any reasonable manner based on the medium, means, and context in which You Share the Licensed Material. For example, it may be reasonable to satisfy the conditions by providing a URI or hyperlink to a resource that includes the required information. + + 3. If requested by the Licensor, You must remove any of the information required by Section 3(a)(1)(A) to the extent reasonably practicable. + + 4. If You Share Adapted Material You produce, the Adapter's License You apply must not prevent recipients of the Adapted Material from complying with this Public License. + +Section 4 – Sui Generis Database Rights. + +Where the Licensed Rights include Sui Generis Database Rights that apply to Your use of the Licensed Material: + + a. for the avoidance of doubt, Section 2(a)(1) grants You the right to extract, reuse, reproduce, and Share all or a substantial portion of the contents of the database for NonCommercial purposes only; + + b. if You include all or a substantial portion of the database contents in a database in which You have Sui Generis Database Rights, then the database in which You have Sui Generis Database Rights (but not its individual contents) is Adapted Material; and + + c. You must comply with the conditions in Section 3(a) if You Share all or a substantial portion of the contents of the database. +For the avoidance of doubt, this Section 4 supplements and does not replace Your obligations under this Public License where the Licensed Rights include other Copyright and Similar Rights. + +Section 5 – Disclaimer of Warranties and Limitation of Liability. + + a. Unless otherwise separately undertaken by the Licensor, to the extent possible, the Licensor offers the Licensed Material as-is and as-available, and makes no representations or warranties of any kind concerning the Licensed Material, whether express, implied, statutory, or other. This includes, without limitation, warranties of title, merchantability, fitness for a particular purpose, non-infringement, absence of latent or other defects, accuracy, or the presence or absence of errors, whether or not known or discoverable. Where disclaimers of warranties are not allowed in full or in part, this disclaimer may not apply to You. + + b. To the extent possible, in no event will the Licensor be liable to You on any legal theory (including, without limitation, negligence) or otherwise for any direct, special, indirect, incidental, consequential, punitive, exemplary, or other losses, costs, expenses, or damages arising out of this Public License or use of the Licensed Material, even if the Licensor has been advised of the possibility of such losses, costs, expenses, or damages. Where a limitation of liability is not allowed in full or in part, this limitation may not apply to You. + + c. The disclaimer of warranties and limitation of liability provided above shall be interpreted in a manner that, to the extent possible, most closely approximates an absolute disclaimer and waiver of all liability. + +Section 6 – Term and Termination. + + a. This Public License applies for the term of the Copyright and Similar Rights licensed here. However, if You fail to comply with this Public License, then Your rights under this Public License terminate automatically. + + b. Where Your right to use the Licensed Material has terminated under Section 6(a), it reinstates: + + 1. automatically as of the date the violation is cured, provided it is cured within 30 days of Your discovery of the violation; or + + 2. upon express reinstatement by the Licensor. + + For the avoidance of doubt, this Section 6(b) does not affect any right the Licensor may have to seek remedies for Your violations of this Public License. + + c. For the avoidance of doubt, the Licensor may also offer the Licensed Material under separate terms or conditions or stop distributing the Licensed Material at any time; however, doing so will not terminate this Public License. + + d. Sections 1, 5, 6, 7, and 8 survive termination of this Public License. + +Section 7 – Other Terms and Conditions. + + a. The Licensor shall not be bound by any additional or different terms or conditions communicated by You unless expressly agreed. + + b. Any arrangements, understandings, or agreements regarding the Licensed Material not stated herein are separate from and independent of the terms and conditions of this Public License. + +Section 8 – Interpretation. + + a. For the avoidance of doubt, this Public License does not, and shall not be interpreted to, reduce, limit, restrict, or impose conditions on any use of the Licensed Material that could lawfully be made without permission under this Public License. + + b. To the extent possible, if any provision of this Public License is deemed unenforceable, it shall be automatically reformed to the minimum extent necessary to make it enforceable. If the provision cannot be reformed, it shall be severed from this Public License without affecting the enforceability of the remaining terms and conditions. + + c. No term or condition of this Public License will be waived and no failure to comply consented to unless expressly agreed to by the Licensor. + + d. Nothing in this Public License constitutes or may be interpreted as a limitation upon, or waiver of, any privileges and immunities that apply to the Licensor or You, including from the legal processes of any jurisdiction or authority. + +Creative Commons is not a party to its public licenses. Notwithstanding, Creative Commons may elect to apply one of its public licenses to material it publishes and in those instances will be considered the “Licensor.” Except for the limited purpose of indicating that material is shared under a Creative Commons public license or as otherwise permitted by the Creative Commons policies published at creativecommons.org/policies, Creative Commons does not authorize the use of the trademark “Creative Commons” or any other trademark or logo of Creative Commons without its prior written consent including, without limitation, in connection with any unauthorized modifications to any of its public licenses or any other arrangements, understandings, or agreements concerning use of licensed material. For the avoidance of doubt, this paragraph does not form part of the public licenses. + +Creative Commons may be contacted at creativecommons.org. diff --git a/packages/softasp-solver/pyproject.toml b/packages/softasp-solver/pyproject.toml new file mode 100644 index 0000000000000000000000000000000000000000..ad74f84aafb1a239b48770d01baa684b2b9f3f93 --- /dev/null +++ b/packages/softasp-solver/pyproject.toml @@ -0,0 +1,21 @@ +[project] +name = "softasp-solver" +version = "0.1.0" +description = "SoftASP model definitions and solver architectures" +requires-python = ">=3.12" +license = "CC-BY-NC-4.0" +license-files = ["LICENSE"] +dependencies = [ + "softasp-core", + "torch>=2.2", +] + +[tool.uv.sources] +softasp-core = { workspace = true } + +[build-system] +requires = ["hatchling"] +build-backend = "hatchling.build" + +[tool.hatch.build.targets.wheel] +packages = ["src/softasp_solver"] diff --git a/packages/softasp-solver/src/softasp_solver/__init__.py b/packages/softasp-solver/src/softasp_solver/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..f69958b2f623234fbbdfbea96edc6036f2ecfbf1 --- /dev/null +++ b/packages/softasp-solver/src/softasp_solver/__init__.py @@ -0,0 +1,8 @@ +"""SoftASP model definitions and solver architectures.""" + +__version__ = "0.1.0" + +from softasp_solver.base_model import BaseSoftASPModel +from softasp_solver.registry import MODEL_REGISTRY, get_model + +__all__ = ["MODEL_REGISTRY", "BaseSoftASPModel", "get_model"] diff --git a/packages/softasp-solver/src/softasp_solver/base_model.py b/packages/softasp-solver/src/softasp_solver/base_model.py new file mode 100644 index 0000000000000000000000000000000000000000..43eb994b0a7c4e7b4120347de36b172c836fbcc3 --- /dev/null +++ b/packages/softasp-solver/src/softasp_solver/base_model.py @@ -0,0 +1,39 @@ +"""Abstract base model that all SoftASP models must implement.""" + +from __future__ import annotations + +from abc import ABC, abstractmethod +from typing import Any + +import torch +import torch.nn as nn + + +class BaseSoftASPModel(nn.Module, ABC): + """Base class for all SoftASP models. + + Every model receives a batch dict and returns an output dict containing + at minimum ``loss`` and ``logits`` (or ``predictions``). + """ + + @abstractmethod + def forward(self, batch: dict[str, torch.Tensor]) -> dict[str, torch.Tensor]: + """Forward pass. + + Args: + batch: Dict from DataLoader with keys like ``images``, ``labels``, + and optional masks. + + Returns: + Dict with at least ``loss`` and ``logits`` keys. + """ + + def count_parameters(self) -> dict[str, int]: + """Return total and trainable parameter counts.""" + total = sum(p.numel() for p in self.parameters()) + trainable = sum(p.numel() for p in self.parameters() if p.requires_grad) + return {"total_params": total, "trainable_params": trainable} + + def get_backbone(self) -> nn.Module | None: + """Optional component controlled by the trainer's freeze/ramp schedule.""" + return None diff --git a/packages/softasp-solver/src/softasp_solver/components/__init__.py b/packages/softasp-solver/src/softasp_solver/components/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..0c28e53d85064a244ad83f7116f005c38e3feeae --- /dev/null +++ b/packages/softasp-solver/src/softasp_solver/components/__init__.py @@ -0,0 +1 @@ +"""Reusable AS2 computation, independent of datasets and experiment orchestration.""" diff --git a/packages/softasp-solver/src/softasp_solver/components/evidence.py b/packages/softasp-solver/src/softasp_solver/components/evidence.py new file mode 100644 index 0000000000000000000000000000000000000000..07dd0d349b2ef6104635f7e4eda796e0c97d718f --- /dev/null +++ b/packages/softasp-solver/src/softasp_solver/components/evidence.py @@ -0,0 +1,48 @@ +"""Explicit evidence policies for soft symbolic distributions.""" + +from typing import Literal + +import torch +import torch.nn.functional as F + +EvidenceMode = Literal["observed", "predicted", "none"] + + +def clamp_evidence( + logits: torch.Tensor, + mask: torch.Tensor | None, + *, + mode: EvidenceMode, + observed_symbols: torch.Tensor | None = None, +) -> torch.Tensor: + """Produce soft symbols, optionally anchoring observed positions. + + Observed symbols are an explicit training input, never inferred from labels + hidden in a batch. The caller selects the policy for its instantiation. + """ + probabilities = F.softmax(logits, dim=-1) + if mode == "none": + return probabilities + if mode not in ("observed", "predicted"): + raise ValueError(f"Unknown evidence mode: {mode}") + if mask is None or mask.shape != logits.shape[:-1]: + raise ValueError("Evidence mask must match the symbolic positions") + if mode == "observed": + if observed_symbols is None or observed_symbols.shape != mask.shape: + raise ValueError("Observed evidence requires symbols matching the mask") + symbols = observed_symbols + else: + symbols = logits.argmax(dim=-1) + weights = mask.unsqueeze(-1).float() + anchors = F.one_hot(symbols, num_classes=logits.shape[-1]).float() + return weights * anchors + (1.0 - weights) * probabilities + + +def restore_evidence( + post_logits: torch.Tensor, pre_logits: torch.Tensor, mask: torch.Tensor +) -> torch.Tensor: + """Optional token-head policy; not appropriate for a pooled task head.""" + if post_logits.shape != pre_logits.shape or mask.shape != pre_logits.shape[:-1]: + raise ValueError("Evidence restoration requires matching token logits and mask") + weights = mask.unsqueeze(-1).float() + return weights * pre_logits + (1.0 - weights) * post_logits diff --git a/packages/softasp-solver/src/softasp_solver/components/perception.py b/packages/softasp-solver/src/softasp_solver/components/perception.py new file mode 100644 index 0000000000000000000000000000000000000000..e52c7a504e42da0f439009ee81092973b0c523c0 --- /dev/null +++ b/packages/softasp-solver/src/softasp_solver/components/perception.py @@ -0,0 +1,77 @@ +"""Reusable image encoder; AS2 itself does not require an image modality.""" + +import torch +from torch import nn + + +class CellCNN(nn.Module): + """Shared-weight CNN that encodes a single 28×28 grayscale cell image. + + Args: + d_model: Output embedding dimension. + backbone: Architecture variant. + ``"default"`` — lightweight 3-conv CNN (original SoftASP design). + ``"resnet18"`` — ResNet-18 adapted for 1-channel 28×28 input; + weights are loaded externally via ``pretrained_from`` or trained + from scratch. Requires ``torchvision``. + """ + + def __init__(self, d_model: int = 128, backbone: str = "default") -> None: + super().__init__() + self.backbone_name = backbone + + if backbone == "default": + self.features = nn.Sequential( + nn.Conv2d(1, 32, kernel_size=3, padding=1), + nn.ReLU(inplace=True), + nn.MaxPool2d(2), # → 32 × 14 × 14 + nn.Conv2d(32, 64, kernel_size=3, padding=1), + nn.ReLU(inplace=True), + nn.MaxPool2d(2), # → 64 × 7 × 7 + nn.Conv2d(64, 128, kernel_size=3, padding=1), + nn.ReLU(inplace=True), + nn.AdaptiveAvgPool2d(1), # → 128 × 1 × 1 + nn.Flatten(), # → 128 + ) + self.proj = nn.Linear(128, d_model) + + elif backbone == "resnet18": + import torchvision.models as _tv # lazy import — optional dep + + _r = _tv.resnet18(weights=None) + # Adapt for 1-channel 28×28 input: + # • smaller first conv (3×3, stride 1) preserves 28→28 spatial size + # • remove the 3×3 max-pool so small images keep enough resolution + _r.conv1 = nn.Conv2d(1, 64, kernel_size=3, stride=1, padding=1, bias=False) + # Use all residual stages + avgpool → 512-d feature vector + self.features = nn.Sequential( + _r.conv1, + _r.bn1, + _r.relu, + nn.Identity(), + _r.layer1, + _r.layer2, + _r.layer3, + _r.layer4, + _r.avgpool, + nn.Flatten(), # → 512 + ) + self.proj = nn.Linear(512, d_model) + + else: + raise ValueError( + f"Unknown CellCNN backbone: {backbone!r}. Choose 'default' or 'resnet18'." + ) + + self.norm = nn.LayerNorm(d_model) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + """ + Args: + x: (N, 1, 28, 28) float32 + + Returns: + (N, d_model) + """ + h = self.features(x) + return self.norm(self.proj(h)) diff --git a/packages/softasp-solver/src/softasp_solver/components/reasoning.py b/packages/softasp-solver/src/softasp_solver/components/reasoning.py new file mode 100644 index 0000000000000000000000000000000000000000..0473665a51f142e917a83791f1bab4f3e47c8694 --- /dev/null +++ b/packages/softasp-solver/src/softasp_solver/components/reasoning.py @@ -0,0 +1,114 @@ +"""Attention reasoning on soft symbols; perception and task heads are supplied.""" + +from typing import NamedTuple + +import torch +from torch import nn + +from softasp_solver.components.evidence import EvidenceMode, clamp_evidence, restore_evidence + + +class ReasoningOutput(NamedTuple): + pre_logits: torch.Tensor + logits: torch.Tensor + + +def build_attention( + d_model: int, n_heads: int, n_layers: int, dropout: float +) -> nn.TransformerEncoder: + """Construct the existing pre-norm/GELU stack without extra initialization.""" + layer = nn.TransformerEncoderLayer( + d_model=d_model, + nhead=n_heads, + dim_feedforward=d_model * 4, + dropout=dropout, + activation="gelu", + batch_first=True, + norm_first=True, + ) + return nn.TransformerEncoder(layer, num_layers=n_layers) + + +def reason_from_probabilities( + probabilities: torch.Tensor, + *, + projection: nn.Module, + structure: torch.Tensor | None, + attention: nn.Module, + head: nn.Module, +) -> torch.Tensor: + """Compose probability projection, optional structure, attention and task head. + + Structure can be (N,D) or (B,N,D). A head can produce per-position or pooled + task outputs; the framework imposes no Sudoku vocabulary or board shape. + """ + tokens = projection(probabilities) + if structure is not None: + tokens = tokens + (structure.unsqueeze(0) if structure.ndim == 2 else structure) + return head(attention(tokens)) + + +class AS2Network(nn.Module): + """Reusable composition for an instantiation supplying its own encoder/head. + + The encoder returns (B,N,D) embeddings. Objectives are separate modules. + The Sudoku compatibility adapter uses the same functions with its historical + parameter ownership, avoiding checkpoint renaming or duplicate registration. + """ + + def __init__( + self, + *, + encoder: nn.Module, + concept_head: nn.Module, + projection: nn.Module, + attention: nn.Module, + head: nn.Module, + structure: nn.Module | None = None, + training_evidence: EvidenceMode = "observed", + inference_evidence: EvidenceMode = "predicted", + restore_observed: bool = False, + ) -> None: + super().__init__() + if inference_evidence == "observed": + raise ValueError("Inference cannot require target symbols") + self.encoder = encoder + self.concept_head = concept_head + self.projection = projection + self.attention = attention + self.head = head + self.structure = structure + self.training_evidence: EvidenceMode = training_evidence + self.inference_evidence: EvidenceMode = inference_evidence + self.restore_observed = restore_observed + + def forward( + self, + inputs: torch.Tensor, + evidence_mask: torch.Tensor | None = None, + observed_symbols: torch.Tensor | None = None, + structure: torch.Tensor | None = None, + ) -> ReasoningOutput: + if not self.training and observed_symbols is not None: + raise ValueError("Inference must not receive target symbols") + pre_logits = self.concept_head(self.encoder(inputs)) + probabilities = clamp_evidence( + pre_logits, + evidence_mask, + mode=self.training_evidence if self.training else self.inference_evidence, + observed_symbols=observed_symbols, + ) + if structure is None and self.structure is not None: + structure = self.structure() + logits = reason_from_probabilities( + probabilities, + projection=self.projection, + structure=structure, + attention=self.attention, + head=self.head, + ) + if not self.training and self.restore_observed: + if evidence_mask is None: + raise ValueError("Evidence restoration requires an evidence mask") + logits = restore_evidence(logits, pre_logits, evidence_mask) + return ReasoningOutput(pre_logits, logits) diff --git a/packages/softasp-solver/src/softasp_solver/components/structure.py b/packages/softasp-solver/src/softasp_solver/components/structure.py new file mode 100644 index 0000000000000000000000000000000000000000..b69a7876aa2130e72bd5e6bb2a876533c726fbab --- /dev/null +++ b/packages/softasp-solver/src/softasp_solver/components/structure.py @@ -0,0 +1,41 @@ +"""Constraint-membership embeddings with no board geometry assumptions.""" + +from collections.abc import Sequence + +import torch +from torch import nn + + +def membership_embeddings( + tables: Sequence[nn.Module], memberships: Sequence[torch.Tensor] +) -> torch.Tensor: + """Sum family embeddings in their declared order (also preserves legacy order).""" + if not tables or len(tables) != len(memberships): + raise ValueError("Provide one membership index tensor per embedding table") + result = tables[0](memberships[0]) + for table, indices in zip(tables[1:], memberships[1:], strict=True): + result = result + table(indices) + return result + + +class GroupMembershipEmbedding(nn.Module): + """Embed static memberships; input shape is (families, positions).""" + + memberships: torch.Tensor + + def __init__( + self, group_counts: Sequence[int], memberships: torch.Tensor, d_model: int + ) -> None: + super().__init__() + if memberships.ndim != 2 or len(group_counts) != memberships.shape[0]: + raise ValueError("Memberships must have shape (families, positions)") + if not group_counts or memberships.dtype != torch.long: + raise ValueError("At least one family with integer memberships is required") + for count, indices in zip(group_counts, memberships, strict=True): + if count <= 0 or torch.any((indices < 0) | (indices >= count)): + raise ValueError("Membership index outside its family") + self.tables = nn.ModuleList([nn.Embedding(count, d_model) for count in group_counts]) + self.register_buffer("memberships", memberships.clone()) + + def forward(self) -> torch.Tensor: + return membership_embeddings(list(self.tables), list(self.memberships.unbind())) diff --git a/packages/softasp-solver/src/softasp_solver/constraints/__init__.py b/packages/softasp-solver/src/softasp_solver/constraints/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..2de14f1622c39277bccc17965d20b9b82869cc7f --- /dev/null +++ b/packages/softasp-solver/src/softasp_solver/constraints/__init__.py @@ -0,0 +1 @@ +"""Explicit constraint operators and residuals; no ASP frontend is implied.""" diff --git a/packages/softasp-solver/src/softasp_solver/constraints/losses.py b/packages/softasp-solver/src/softasp_solver/constraints/losses.py new file mode 100644 index 0000000000000000000000000000000000000000..3ff6397b890137440282bae65073d0113516a188 --- /dev/null +++ b/packages/softasp-solver/src/softasp_solver/constraints/losses.py @@ -0,0 +1,52 @@ +"""Fixed-point residual aggregation with an explicit normalization contract.""" + +from collections.abc import Sequence +from typing import Literal + +import torch +from torch import nn + + +def fixed_point_residual( + probabilities: torch.Tensor, + consequences: torch.Tensor, + reduction: Literal["sum", "symbol_sum_mean"] = "symbol_sum_mean", +) -> torch.Tensor: + if probabilities.shape != consequences.shape: + raise ValueError("Operator consequences must match their input probabilities") + residual = (probabilities - consequences) ** 2 + if reduction == "sum": + return residual.sum() + if reduction == "symbol_sum_mean": + return residual.sum(dim=-1).mean() + raise ValueError(f"Unknown residual reduction: {reduction}") + + +class FamilyResidualLoss(nn.Module): + """Sum separately normalized residuals of explicitly provided operators. + + Each operator returns (probabilities, consequences). The reduction preserves + the historical Sudoku scale: mean over batch/group/member, sum over symbols, + then sum over families. Other normalizations must be selected explicitly. + """ + + def __init__( + self, + operators: Sequence[nn.Module], + reduction: Literal["sum", "symbol_sum_mean"] = "symbol_sum_mean", + ) -> None: + super().__init__() + if not operators: + raise ValueError("At least one constraint operator is required") + self.operators = nn.ModuleList(operators) + self.reduction: Literal["sum", "symbol_sum_mean"] = reduction + + def forward(self, probabilities: torch.Tensor) -> torch.Tensor: + terms = [ + fixed_point_residual(*operator(probabilities), reduction=self.reduction) + for operator in self.operators + ] + result = terms[0] + for term in terms[1:]: + result = result + term + return result diff --git a/packages/softasp-solver/src/softasp_solver/constraints/operators.py b/packages/softasp-solver/src/softasp_solver/constraints/operators.py new file mode 100644 index 0000000000000000000000000000000000000000..733b2adb00da5652ee5a6e9e5bee116469e34fd7 --- /dev/null +++ b/packages/softasp-solver/src/softasp_solver/constraints/operators.py @@ -0,0 +1,56 @@ +"""The exclusivity operator over explicit groups, independent of board geometry.""" + +from typing import NamedTuple + +import torch +from torch import nn + + +class OperatorEvaluation(NamedTuple): + probabilities: torch.Tensor + consequences: torch.Tensor + + +def exclusivity_consequence( + probabilities: torch.Tensor, + log_complement: torch.Tensor, + member_dims: int | tuple[int, ...], +) -> torch.Tensor: + """Exclusion product on group-member axes of a supplied tensor layout. + + Index-based groups and structured partition views share this kernel. Views + let compatibility adapters preserve reduction order and autograd accumulation. + """ + excluded = torch.exp(log_complement.sum(dim=member_dims, keepdim=True) - log_complement) + return probabilities * excluded + + +class GroupExclusivityOperator(nn.Module): + """One rectangular family of groups; different families may have different sizes. + + This is the exclusivity family of the probabilistic lift, not an interpreter + for arbitrary ASP programs. Indices are derived configuration, not weights. + """ + + groups: torch.Tensor + + def __init__(self, groups: torch.Tensor, num_positions: int, eps: float = 1e-7) -> None: + super().__init__() + if groups.ndim != 2 or groups.numel() == 0 or groups.dtype != torch.long: + raise ValueError("Groups must be a nonempty integer (groups, members) tensor") + if num_positions < 1 or torch.any((groups < 0) | (groups >= num_positions)): + raise ValueError("Group member outside the declared position range") + if any(len(set(row)) != len(row) for row in groups.tolist()): + raise ValueError("A group cannot contain the same position twice") + if not 0 < eps < 1: + raise ValueError("eps must lie strictly between zero and one") + self.num_positions = num_positions + self.eps = eps + self.register_buffer("groups", groups.clone(), persistent=False) + + def forward(self, probabilities: torch.Tensor) -> OperatorEvaluation: + if probabilities.ndim != 3 or probabilities.shape[1] != self.num_positions: + raise ValueError("Expected probabilities shaped (batch, declared positions, symbols)") + grouped = probabilities[:, self.groups, :] + log_complement = torch.log1p(-grouped.clamp(max=1.0 - self.eps)) + return OperatorEvaluation(grouped, exclusivity_consequence(grouped, log_complement, -2)) diff --git a/packages/softasp-solver/src/softasp_solver/registry.py b/packages/softasp-solver/src/softasp_solver/registry.py new file mode 100644 index 0000000000000000000000000000000000000000..0c6112cf14de1b868af6e26d3fcef921908e5a70 --- /dev/null +++ b/packages/softasp-solver/src/softasp_solver/registry.py @@ -0,0 +1,31 @@ +"""Model registry — maps config name to model class.""" + +from __future__ import annotations + +from typing import Any + +import torch.nn as nn + +MODEL_REGISTRY: dict[str, type[nn.Module]] = {} + + +def register_model(name: str): + """Decorator to register a model class.""" + + def decorator(cls): + MODEL_REGISTRY[name] = cls + return cls + + return decorator + + +def get_model(name: str, **kwargs: Any) -> nn.Module: + """Instantiate a registered model by name.""" + if name not in MODEL_REGISTRY: + raise ValueError(f"Unknown model '{name}'. Available: {list(MODEL_REGISTRY.keys())}") + return MODEL_REGISTRY[name](**kwargs) + + +# Auto-import submodules to trigger registration +from softasp_solver.sudoku import baselines as _sudoku_baselines # noqa: F401, E402 +from softasp_solver.sudoku import softasp_sudoku as _sudoku # noqa: F401, E402 diff --git a/packages/softasp-solver/src/softasp_solver/sudoku/__init__.py b/packages/softasp-solver/src/softasp_solver/sudoku/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..63d4b853a27a7564e611acb1b37f9f37000d7387 --- /dev/null +++ b/packages/softasp-solver/src/softasp_solver/sudoku/__init__.py @@ -0,0 +1 @@ +"""SoftASP Sudoku solver subpackage.""" diff --git a/packages/softasp-solver/src/softasp_solver/sudoku/baselines.py b/packages/softasp-solver/src/softasp_solver/sudoku/baselines.py new file mode 100644 index 0000000000000000000000000000000000000000..1280bf195069cc15a8212ab25b78d9b2959b19a7 --- /dev/null +++ b/packages/softasp-solver/src/softasp_solver/sudoku/baselines.py @@ -0,0 +1,73 @@ +"""CNN perception stage used by Sudoku training from scratch.""" + +from __future__ import annotations + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from softasp_solver.base_model import BaseSoftASPModel +from softasp_solver.registry import register_model +from softasp_solver.sudoku.softasp_sudoku import CellCNN + + +@register_model("perception_only_sudoku") +class PerceptionOnlySudoku(BaseSoftASPModel): + """Perception-only baseline: CNN encoder + direct classification. + + No constraint reasoning. Blank cells are predicted from their (empty) image + alone, so accuracy on blank cells should be ~11% (random guessing among 9). + + Args: + d_model: Embedding dimension. + """ + + def __init__(self, d_model: int = 128) -> None: + super().__init__() + self.d_model = d_model + self.cell_cnn = CellCNN(d_model=d_model) + self.head = nn.Linear(d_model, 9) + + def _extract_cells(self, images: torch.Tensor) -> torch.Tensor: + B = images.shape[0] + x = images.reshape(B, 1, 9, 28, 9, 28) + x = x.permute(0, 2, 4, 1, 3, 5) + return x.reshape(B * 81, 1, 28, 28) + + def forward(self, batch: dict[str, torch.Tensor]) -> dict[str, torch.Tensor]: + images = batch["images"] + labels = batch["labels"] + puzzle_mask = batch["puzzle_mask"] # (B, 81) int64, 1 = clue cell + + B = images.shape[0] + + if images.dim() == 4 and images.shape[-1] == 1: + images = images.permute(0, 3, 1, 2) + if images.dtype != torch.float32: + images = images.float() / 255.0 + + cells = self._extract_cells(images) + embeds = self.cell_cnn(cells).reshape(B, 81, self.d_model) + logits = self.head(embeds) # (B, 81, 9) + + # CE loss only on clue cells — blank cells are featureless black + # patches; the model cannot learn anything from them. + clue_flat = puzzle_mask.reshape(-1).bool() # (B*81,) + loss = F.cross_entropy( + logits.reshape(-1, 9)[clue_flat], + labels.reshape(-1)[clue_flat], + ) + + predictions = logits.argmax(dim=-1) # (B, 81) + + # Flat clue-cell tensors for the clue_accuracy metric in the evaluator + clue_logits = logits.reshape(-1, 9)[clue_flat] # (n_clue, 9) + clue_labels = labels.reshape(-1)[clue_flat] # (n_clue,) + + return { + "loss": loss, + "logits": logits, + "predictions": predictions, + "clue_logits": clue_logits, + "clue_labels": clue_labels, + } diff --git a/packages/softasp-solver/src/softasp_solver/sudoku/constraints.py b/packages/softasp-solver/src/softasp_solver/sudoku/constraints.py new file mode 100644 index 0000000000000000000000000000000000000000..dbab9d0ce5ea608a7da7aadc8ac69c94fdf2b6f4 --- /dev/null +++ b/packages/softasp-solver/src/softasp_solver/sudoku/constraints.py @@ -0,0 +1,65 @@ +"""Sudoku's explicit group adapter for the reusable exclusivity operator.""" + +import torch +from torch import nn + +from softasp_solver.constraints.losses import fixed_point_residual +from softasp_solver.constraints.operators import exclusivity_consequence + +CONSTRAINT_ASP = """\ +% Sudoku all-different constraints (hard declarative rules). +% The SoftASP model approximates these via the differentiable T_P +% immediate-consequence operator (Takemura & Inoue, ECAI 2024). + +% Each digit must appear exactly once in every row: +:- R = 1..9, V = 1..9, #count { C : cell(R,C,V) } != 1. + +% Each digit must appear exactly once in every column: +:- C = 1..9, V = 1..9, #count { R : cell(R,C,V) } != 1. + +% Each digit must appear exactly once in every 3x3 box: +:- BR = 0..2, BC = 0..2, V = 1..9, + #count { R,C : cell(R,C,V), + R >= BR*3+1, R <= BR*3+3, + C >= BC*3+1, C <= BC*3+3 } != 1. + +% Each cell contains exactly one digit (enforced by softmax): +:- R = 1..9, C = 1..9, #count { V : cell(R,C,V) } != 1. +""" + + +def sudoku_groups() -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + cells = torch.arange(81).reshape(9, 9) + rows = cells + columns = cells.T.contiguous() + boxes = cells.reshape(3, 3, 3, 3).permute(0, 2, 1, 3).reshape(9, 9) + return rows, columns, boxes + + +class TPOperatorLoss(nn.Module): + """Compatibility name for Sudoku's three explicitly declared group families. + + The row/column/box partition views preserve the exact historical reduction + and autograd accumulation order. The operator kernel and residual are shared + with indexed group operators. The instantiation supplies these constraints; + no ASP compiler is required. + """ + + specification = CONSTRAINT_ASP + + def forward(self, probabilities: torch.Tensor) -> torch.Tensor: + batch = probabilities.shape[0] + board = probabilities.reshape(batch, 9, 9, 9) + log_complement = torch.log1p(-board.clamp(max=1.0 - 1e-7)) + rows = exclusivity_consequence(board, log_complement, 2) + columns = exclusivity_consequence(board, log_complement, 1) + boxes = exclusivity_consequence( + board.reshape(batch, 3, 3, 3, 3, 9), + log_complement.reshape(batch, 3, 3, 3, 3, 9), + (2, 4), + ).reshape(batch, 9, 9, 9) + return ( + fixed_point_residual(board, rows) + + fixed_point_residual(board, columns) + + fixed_point_residual(board, boxes) + ) diff --git a/packages/softasp-solver/src/softasp_solver/sudoku/objective.py b/packages/softasp-solver/src/softasp_solver/sudoku/objective.py new file mode 100644 index 0000000000000000000000000000000000000000..496255d50042202790f8d069b1a1450cdcb25bb8 --- /dev/null +++ b/packages/softasp-solver/src/softasp_solver/sudoku/objective.py @@ -0,0 +1,191 @@ +"""Objectives and diagnostics owned by the Sudoku instantiation.""" + +from typing import Any + +import torch +import torch.nn.functional as F +from torch import nn + + +class UniquenessLoss(nn.Module): + """Soft uniqueness regulariser: penalises high-entropy blank-cell distributions. + + A Sudoku puzzle has a *unique* solution when every blank cell's value is + fully determined by the declared constraints and the given clues. A model + that spreads probability across multiple equally-valid digit assignments + for blank cells implicitly encodes multiple valid solutions. + + We proxy the uniqueness constraint by minimising the mean normalised + entropy of post-reasoning distributions over blank cells, driving the + model to commit to specific symbol assignments rather than remaining + ambiguous. This is a fully-differentiable continuous analog of the + iterative nogood-based uniqueness enforcement in Mulamba et al. (2024, + *Constraints*, doi:10.1007/s10601-024-09372-9). + + Loss:: + + L_uniq = mean_{i ∈ blank} H(p_i) / log(|Σ|) + + where H(p_i) = −Σ_s p_{i,s} log p_{i,s} is per-cell entropy and |Σ| is + the symbol-domain size. Normalising by log(|Σ|) maps the range to [0, 1]: + 0 when every blank cell is one-hot (fully committed), 1 when every blank + cell is uniform (maximally ambiguous). + """ + + def forward( + self, + probs: torch.Tensor, + blank_mask: torch.Tensor, + ) -> torch.Tensor: + """ + Args: + probs: (B, 81, |Σ|) softmax probabilities — post-reasoning. + blank_mask: (B, 81) bool tensor, True for blank (non-clue) cells. + + Returns: + Scalar normalised entropy loss in [0, 1]. + """ + flat_blank = blank_mask.reshape(-1) # (B*81,) + flat_probs = probs.reshape(-1, probs.shape[-1]) # (B*81, |Σ|) + blank_probs = flat_probs[flat_blank] # (n_blank, |Σ|) + if blank_probs.shape[0] == 0: + return probs.sum() * 0.0 # differentiable zero + entropy = -(blank_probs * (blank_probs + 1e-8).log()).sum(-1) # (n_blank,) + log_sigma = torch.log(torch.tensor(float(probs.shape[-1]), device=probs.device)) + return entropy.mean() / log_sigma + + +def compute_sudoku_objective( + pre_logits: torch.Tensor, + post_logits: torch.Tensor, + labels: torch.Tensor, + puzzle_mask: torch.Tensor, + *, + current_epoch: int, + lambda_ic: float, + lambda_blank: float, + blank_decay_epochs: int, + ic_warmup_epochs: int, + lambda_uniq: float, + constraint_loss: nn.Module, + uniqueness_loss: nn.Module, +) -> dict[str, Any]: + """Sudoku supervision, curriculum and diagnostics; separate from AS2 reasoning.""" + # ── 6. Loss computation ─────────────────────────────────────────── + # loss_cnn — direct CE on the CNN concept head (pre_logits) for clue + # cells. PRIMARY gradient signal for the CNN. + # + # loss_sup — CE on the Transformer output (post_logits) for clue + # cells. Keeps the Transformer grounded at clue positions. + # + # loss_blank — CE on blank-cell Transformer outputs against GT labels. + # Provides direct supervision for blank cells (the old + # model had NONE). Weighted by a curriculum schedule + # that decays from full to 0.1× over training so the + # model gradually shifts to relying on constraint reasoning. + # + # loss_ic — T_P fixed-point constraint energy on ALL 81 cells. + # Penalises deviation from the T_P operator's fixed point. + # Ramps up from 0 over ic_warmup_epochs. + clue_flat = puzzle_mask.reshape(-1).bool() # (B*81,) + blank_flat = ~clue_flat + labels_flat = labels.reshape(-1) + + # Direct CNN supervision (most important for learning digits) + loss_cnn = F.cross_entropy( + pre_logits.reshape(-1, 9)[clue_flat], + labels_flat[clue_flat], + ) + + # Transformer output supervision at clue positions + loss_sup = F.cross_entropy( + post_logits.reshape(-1, 9)[clue_flat], + labels_flat[clue_flat], + ) + + # Blank-cell CE supervision with curriculum decay + if blank_flat.any(): + loss_blank = F.cross_entropy( + post_logits.reshape(-1, 9)[blank_flat], + labels_flat[blank_flat], + ) + else: + loss_blank = torch.tensor(0.0, device=post_logits.device) + + # Curriculum weight: linear decay from 1.0 to 0.1 over blank_decay_epochs + epoch = current_epoch + blank_scale = max(0.1, 1.0 - 0.9 * epoch / max(1, blank_decay_epochs)) + + # T_P constraint loss with warmup ramp + soft_model = F.softmax(post_logits, dim=-1) # (B, 81, 9) + loss_ic = constraint_loss(soft_model) + ic_scale = min(1.0, epoch / max(1, ic_warmup_epochs)) + + # Uniqueness regulariser: penalise high blank-cell entropy post-reasoning + blank_mask_2d = ~puzzle_mask.bool() # (B, 81) bool + loss_uniq = uniqueness_loss(soft_model, blank_mask_2d) + + loss = ( + loss_cnn + + loss_sup + + lambda_blank * blank_scale * loss_blank + + lambda_ic * ic_scale * loss_ic + + lambda_uniq * ic_scale * loss_uniq + ) + + # ── 7. Diagnostics ──────────────────────────────────────────────── + # Blank-cell entropy: high → model is uncertain, low → confident + if blank_flat.any(): + blank_probs = soft_model.reshape(-1, 9)[blank_flat] # (n_blank, 9) + blank_entropy = -(blank_probs * (blank_probs + 1e-8).log()).sum(-1).mean() + else: + blank_entropy = torch.tensor(0.0, device=post_logits.device) + + # ── 8. Predictions ──────────────────────────────────────────────── + predictions = post_logits.argmax(dim=-1) # (B, 81) + + # Flat clue-cell tensors: + # Use pre_logits (CNN output) so that clue_accuracy reflects CNN quality + # independent of the Transformer. + clue_logits = pre_logits.reshape(-1, 9)[clue_flat] # (n_clue, 9) + clue_labels = labels_flat[clue_flat] # (n_clue,) + + # ── 9. Perception-vs-reasoning diagnostic tensors ───────────────── + # Pre-reasoning CNN logits for blank cells (measures perception quality + # on the cells that actually require reasoning to fill). + blank_pre_logits = pre_logits.reshape(-1, 9)[blank_flat] # (n_blank, 9) + blank_labels = labels_flat[blank_flat] # (n_blank,) + + # Post-reasoning logits split by clue / blank (measures how much + # the Transformer improves on CNN quality, and whether it corrupts + # the confident clue-cell predictions). + blank_post_logits = post_logits.reshape(-1, 9)[blank_flat] # (n_blank, 9) + clue_post_logits = post_logits.reshape(-1, 9)[clue_flat] # (n_clue, 9) + + return { + "loss": loss, + "logits": post_logits, # (B, 81, 9) — for board-level metrics + "predictions": predictions, # (B, 81) + # ── CNN (pre-reasoning) split ───────────────────────────────── + "clue_logits": clue_logits, # (n_clue, 9) CNN on clue cells + "clue_labels": clue_labels, # (n_clue,) + "blank_pre_logits": blank_pre_logits, # (n_blank, 9) CNN on blank cells + "blank_labels": blank_labels, # (n_blank,) + # ── Transformer (post-reasoning) split ─────────────────────── + "blank_post_logits": blank_post_logits, # (n_blank, 9) + "clue_post_logits": clue_post_logits, # (n_clue, 9) + # ── Scalar losses ───────────────────────────────────────────── + "loss_cnn": loss_cnn.detach(), + "loss_sup": loss_sup.detach(), + "loss_blank": loss_blank.detach(), + "loss_ic": loss_ic.detach(), + "loss_uniq": loss_uniq.detach(), + # Weighted contributions (what actually drives the total loss) + "loss_blank_w": (lambda_blank * blank_scale * loss_blank).detach(), + "loss_ic_w": (lambda_ic * ic_scale * loss_ic).detach(), + "loss_uniq_w": (lambda_uniq * ic_scale * loss_uniq).detach(), + # Curriculum scales for diagnostics + "blank_scale": blank_scale, + "ic_scale": ic_scale, + "blank_entropy": blank_entropy.detach(), + } diff --git a/packages/softasp-solver/src/softasp_solver/sudoku/softasp_sudoku.py b/packages/softasp-solver/src/softasp_solver/sudoku/softasp_sudoku.py new file mode 100644 index 0000000000000000000000000000000000000000..457f073b48a146d611bfb59376af302ca804cce7 --- /dev/null +++ b/packages/softasp-solver/src/softasp_solver/sudoku/softasp_sudoku.py @@ -0,0 +1,266 @@ +"""SoftASP Sudoku model — differentiable constraint-aware Sudoku solver. + +Architecture +------------ +1. **Cell CNN**: shared-weight CNN encodes each of 81 cells → d_model embedding +2. **Concept bottleneck**: Linear(d_model, 9) → per-cell 9-class logits +3. **Clue clamping**: for clue cells, replace soft distribution with one-hot GT +4. **Transformer solver**: 81 tokens through L self-attention layers +5. **Output head**: Linear(d_model, 9) → post-reasoning logits +6. **Loss**: CE_clue + CE_blank + λ_ic × T_P fixed-point loss + +The constraint loss uses the differentiable T_P operator (Takemura & Inoue, +ECAI 2024) to enforce Sudoku all-different rules. For each constraint group +(row / column / 3×3 box) and digit d: + + T_P(p)_{c,d} = p_{c,d} · ∏_{c'≠c} (1 − p_{c',d}) + +The loss ‖p − T_P(p)‖² is zero *only* at valid discrete assignments — +unlike the squared-sum loss which is degenerate at uniform distributions. +""" + +from __future__ import annotations + +from typing import Any + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from softasp_solver.base_model import BaseSoftASPModel +from softasp_solver.components.evidence import clamp_evidence, restore_evidence +from softasp_solver.components.perception import CellCNN as CellCNN +from softasp_solver.components.reasoning import build_attention, reason_from_probabilities +from softasp_solver.components.structure import membership_embeddings +from softasp_solver.registry import register_model +from softasp_solver.sudoku.constraints import TPOperatorLoss as TPOperatorLoss +from softasp_solver.sudoku.objective import UniquenessLoss as UniquenessLoss +from softasp_solver.sudoku.objective import compute_sudoku_objective + + +@register_model("softasp_sudoku") +class SoftASPSudoku(BaseSoftASPModel): + """SoftASP model for Visual Sudoku. + + End-to-end differentiable: perception (CNN) → reasoning (Transformer) + → constraint satisfaction (T_P operator). + + Args: + d_model: Embedding dimension for the transformer. + n_heads: Number of attention heads. + n_layers: Number of transformer encoder layers. + dropout: Dropout rate. + lambda_ic: Weight of the integrity constraint (T_P) loss. + lambda_blank: Weight of the blank-cell CE supervision loss. + blank_decay_epochs: Epochs over which lambda_blank decays from 1.0× + to 0.1× its configured value (curriculum learning). + ic_warmup_epochs: Epochs over which lambda_ic ramps from 0 to full. + """ + + pos_rows: torch.Tensor + pos_cols: torch.Tensor + pos_boxes: torch.Tensor + + # Compatibility alias; specification and implementation share one module. + CONSTRAINT_ASP = TPOperatorLoss.specification + + def __init__( + self, + d_model: int = 128, + n_heads: int = 8, + n_layers: int = 3, + dropout: float = 0.1, + lambda_ic: float = 0.5, + lambda_blank: float = 0.3, + blank_decay_epochs: int = 100, + ic_warmup_epochs: int = 20, + lambda_uniq: float = 0.0, + backbone: str = "default", + oracle_perception: bool = False, + ) -> None: + super().__init__() + self.d_model = d_model + self.lambda_ic = lambda_ic + self.lambda_blank = lambda_blank + self.blank_decay_epochs = blank_decay_epochs + self.ic_warmup_epochs = ic_warmup_epochs + self.lambda_uniq = lambda_uniq + self.oracle_perception = oracle_perception + self._current_epoch: int = 0 # set by Trainer before each epoch + + # Perception: shared CNN encoder for 28×28 cells + self.cell_cnn = CellCNN(d_model=d_model, backbone=backbone) + + # Concept bottleneck: per-cell 9-class pre-reasoning logits + self.concept_head = nn.Linear(d_model, 9) + + # Soft-to-embedding projection: map 9-dim soft distribution → d_model + self.soft_proj = nn.Linear(9, d_model) + + # Structured positional embeddings: row + col + box (encodes Sudoku + # geometry explicitly rather than learning arbitrary 81-dim positions) + self.row_embed = nn.Embedding(9, d_model) + self.col_embed = nn.Embedding(9, d_model) + self.box_embed = nn.Embedding(9, d_model) + # Pre-compute position indices (row, col, box id for each of 81 cells) + rows = torch.arange(9).unsqueeze(1).expand(9, 9).reshape(81) + cols = torch.arange(9).unsqueeze(0).expand(9, 9).reshape(81) + boxes = (rows // 3) * 3 + (cols // 3) + self.register_buffer("pos_rows", rows) + self.register_buffer("pos_cols", cols) + self.register_buffer("pos_boxes", boxes) + + # Transformer solver + self.transformer = build_attention(d_model, n_heads, n_layers, dropout) + + # Output head: post-reasoning 9-class logits per cell + self.output_head = nn.Sequential( + nn.LayerNorm(d_model), + nn.Linear(d_model, 9), + ) + + # Constraint loss module (T_P fixed-point operator) + self.constraint_loss = TPOperatorLoss() + + # Uniqueness regulariser (differentiable entropy penalty on blank cells) + self.uniqueness_loss = UniquenessLoss() + + # Initialize weights + self._init_weights() + + def _init_weights(self) -> None: + """Xavier uniform for linear layers, small init for output head.""" + for module in self.modules(): + if isinstance(module, nn.Linear): + nn.init.xavier_uniform_(module.weight) + if module.bias is not None: + nn.init.zeros_(module.bias) + elif isinstance(module, nn.Conv2d): + nn.init.kaiming_normal_(module.weight, mode="fan_out", nonlinearity="relu") + if module.bias is not None: + nn.init.zeros_(module.bias) + + def _extract_cells(self, images: torch.Tensor) -> torch.Tensor: + """Extract 81 individual 28×28 cell images from a 252×252 board. + + Args: + images: (B, 1, 252, 252) float32 + + Returns: + (B*81, 1, 28, 28) float32 + """ + B = images.shape[0] + # (B, 1, 252, 252) → (B, 1, 9, 28, 9, 28) + x = images.reshape(B, 1, 9, 28, 9, 28) + # → (B, 9, 9, 1, 28, 28) — (batch, row, col, channel, h, w) + x = x.permute(0, 2, 4, 1, 3, 5) + # → (B*81, 1, 28, 28) + x = x.reshape(B * 81, 1, 28, 28) + return x + + @torch.no_grad() + def predict(self, images: torch.Tensor, puzzle_mask: torch.Tensor) -> dict[str, torch.Tensor]: + """Return raw logits and argmax using images and clue locations only. + + Call ``eval()`` first. Oracle perception is deliberately unsupported. + Clue restoration is the existing model computation, not a decoder. + + Images are aligned 9x9 grids of 28x28 grayscale cells, shaped + (B, 252, 252, 1) or (B, 1, 252, 252). Use uint8 in 0..255, or float32 + already scaled to 0..1. The row-major mask is (B, 81), with 1 for + clues and 0 for blanks. Returned logits are (B, 81, 9); predictions + are (B, 81) class indices 0..8 (display digits 1..9). + """ + if self.training: + raise ValueError("Label-free prediction requires model.eval()") + if self.oracle_perception: + raise ValueError("Label-free prediction does not support oracle perception") + _, logits = self._compute_logits(images, puzzle_mask) + return {"logits": logits, "predictions": logits.argmax(dim=-1)} + + def _compute_logits( + self, + images: torch.Tensor, + puzzle_mask: torch.Tensor, + labels: torch.Tensor | None = None, + ) -> tuple[torch.Tensor, torch.Tensor]: + if (self.training or self.oracle_perception) and labels is None: + raise ValueError("Training and historical oracle evaluation require labels") + + B = images.shape[0] + + # ── Handle image format ─────────────────────────────────────────── + # HDF5 stores (B, H, W, C); model needs (B, C, H, W) + if images.dim() == 4 and images.shape[-1] == 1: + images = images.permute(0, 3, 1, 2) # → (B, 1, 252, 252) + if images.dtype != torch.float32: + images = images.float() / 255.0 + + # ── 1. Extract cells and encode ─────────────────────────────────── + cells = self._extract_cells(images) # (B*81, 1, 28, 28) + cell_embeds = self.cell_cnn(cells) # (B*81, d_model) + cell_embeds = cell_embeds.reshape(B, 81, self.d_model) # (B, 81, d_model) + + # ── 2. Concept bottleneck: pre-reasoning digit logits ───────────── + pre_logits = self.concept_head(cell_embeds) # (B, 81, 9) + # Note: F.cross_entropy auto-promotes inputs to fp32 under autocast + # (it is in PyTorch's mandatory-fp32 op list). No explicit cast needed. + + # ── 3. Build solver input with clue clamping ────────────────────── + soft_dist = clamp_evidence( + pre_logits, + puzzle_mask, + mode="observed" if self.training else "predicted", + observed_symbols=labels if self.training else None, + ) + + # Oracle perception (ablation mode): at inference, replace blank-cell + # CNN distributions with GT one-hot labels, giving the reasoning module + # a perfect perceptual input. The gap between oracle CSR and normal CSR + # isolates the perceptual contribution to the constraint-satisfaction gap. + if self.oracle_perception and not self.training: + assert labels is not None + blank_mask_eval = (~puzzle_mask.bool()).unsqueeze(-1).float() # (B, 81, 1) + one_hot_gt = F.one_hot(labels, num_classes=9).float() # (B, 81, 9) + soft_dist = (1.0 - blank_mask_eval) * soft_dist + blank_mask_eval * one_hot_gt + + structure = membership_embeddings( + (self.row_embed, self.col_embed, self.box_embed), + (self.pos_rows, self.pos_cols, self.pos_boxes), + ) + post_logits = reason_from_probabilities( + soft_dist, + projection=self.soft_proj, + structure=structure, + attention=self.transformer, + head=self.output_head, + ) + if not self.training: + post_logits = restore_evidence(post_logits, pre_logits, puzzle_mask) + + return pre_logits, post_logits + + def get_backbone(self) -> nn.Module: + return self.cell_cnn + + def forward(self, batch: dict[str, torch.Tensor]) -> dict[str, Any]: + """Supervised outputs include tensors and float curriculum diagnostics.""" + images = batch["images"] + labels = batch["labels"] + puzzle_mask = batch["puzzle_mask"] + pre_logits, post_logits = self._compute_logits(images, puzzle_mask, labels) + + return compute_sudoku_objective( + pre_logits, + post_logits, + labels, + puzzle_mask, + current_epoch=self._current_epoch, + lambda_ic=self.lambda_ic, + lambda_blank=self.lambda_blank, + blank_decay_epochs=self.blank_decay_epochs, + ic_warmup_epochs=self.ic_warmup_epochs, + lambda_uniq=self.lambda_uniq, + constraint_loss=self.constraint_loss, + uniqueness_loss=self.uniqueness_loss, + ) diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000000000000000000000000000000000000..4ff0b7e5da7924946f7b3a8cf9c42ad0c505a5f7 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,59 @@ +[project] +name = "softasp" +version = "0.1.0" +description = "AS2: attention-based soft answer set framework with Hydra-selected instantiations" +readme = "README.md" +requires-python = ">=3.12" +license = "CC-BY-NC-4.0" +license-files = ["LICENSE"] +dependencies = [ + "softasp-core", + "softasp-solver", + "softasp-domain", + "hydra-core>=1.3", + "omegaconf>=2.3", + "rich>=13.0", + "aim>=3.29.1", +] + +[project.scripts] +softasp-train = "softasp.cli.train:main" +softasp-train-scratch = "softasp.cli.train_scratch:main" +softasp-evaluate = "softasp.cli.evaluate:main" +softasp-predict = "softasp.cli.predict:main" + +[dependency-groups] +dev = [ + "pytest>=8.0", + "pyright>=1.1", + "ruff>=0.8", +] + +[tool.uv.workspace] +members = ["packages/softasp-core", "packages/softasp-domain", "packages/softasp-solver"] + +[tool.uv.sources] +softasp-core = { workspace = true } +softasp-solver = { workspace = true } +softasp-domain = { workspace = true } + +[tool.hatch.build.targets.wheel] +packages = ["src/softasp"] + +[tool.hatch.build.targets.wheel.force-include] +"configs" = "softasp/configs" + +[build-system] +requires = ["hatchling"] +build-backend = "hatchling.build" + +[tool.ruff] +line-length = 100 +target-version = "py312" + +[tool.ruff.lint] +select = ["E", "F", "I", "UP", "B", "SIM"] + +[tool.pyright] +pythonVersion = "3.12" +typeCheckingMode = "standard" diff --git a/src/softasp/__init__.py b/src/softasp/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..1f1a7ef55eb884584bee437a03f94ed6c795bd5f --- /dev/null +++ b/src/softasp/__init__.py @@ -0,0 +1 @@ +"""SoftASP CLI entrypoints.""" diff --git a/src/softasp/cli/__init__.py b/src/softasp/cli/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..c9a1c002333f1427f37a0ce0fed7e9508258db74 --- /dev/null +++ b/src/softasp/cli/__init__.py @@ -0,0 +1 @@ +"""SoftASP CLI subpackage.""" diff --git a/src/softasp/cli/evaluate.py b/src/softasp/cli/evaluate.py new file mode 100644 index 0000000000000000000000000000000000000000..9491e571c366fb71a40a78f96b693c2fe689eac0 --- /dev/null +++ b/src/softasp/cli/evaluate.py @@ -0,0 +1,18 @@ +"""Evaluate using the selected AS2 instantiation.""" + +import hydra +from omegaconf import DictConfig +from softasp_core.utils.logging import setup_logging + +from softasp.configuration import config_directory +from softasp.instantiations.runtime import evaluate + + +@hydra.main(version_base="1.3", config_path=config_directory(), config_name="config") +def main(cfg: DictConfig) -> None: + setup_logging(level=cfg.logging.get("level", "INFO")) + evaluate(cfg) + + +if __name__ == "__main__": + main() diff --git a/src/softasp/cli/predict.py b/src/softasp/cli/predict.py new file mode 100644 index 0000000000000000000000000000000000000000..5aacbcccad853ac229c1d525013fed8bd15aa7b6 --- /dev/null +++ b/src/softasp/cli/predict.py @@ -0,0 +1,25 @@ +"""Predict using the selected AS2 instantiation's input/output contract.""" + +import json +from pathlib import Path + +import hydra +from omegaconf import DictConfig + +from softasp.configuration import config_directory +from softasp.instantiations.runtime import predict + + +@hydra.main(version_base="1.3", config_path=config_directory(), config_name="predict") +def main(cfg: DictConfig) -> None: + result = predict(cfg) + destination = Path(cfg.output) + destination.parent.mkdir(parents=True, exist_ok=True) + with destination.open("x") as stream: + json.dump(result, stream, indent=2, allow_nan=False) + stream.write("\n") + print(f"Predictions saved to {destination}") + + +if __name__ == "__main__": + main() diff --git a/src/softasp/cli/train.py b/src/softasp/cli/train.py new file mode 100644 index 0000000000000000000000000000000000000000..997302705cec1c7410c70caa0aa19c8465686f16 --- /dev/null +++ b/src/softasp/cli/train.py @@ -0,0 +1,74 @@ +"""``softasp-train`` — launch a training run (supports torchrun for DDP). + +Usage:: + + # Single-GPU + uv run --locked softasp-train instantiation=sudoku model.pretrained_from=backbone.pt + + # Multi-GPU via torchrun + uv run --locked torchrun --nproc_per_node=4 -m softasp.cli.train \\ + instantiation=my_distributed_task paths=local +""" + +from __future__ import annotations + +import logging +from pathlib import Path + +import hydra +from omegaconf import DictConfig, OmegaConf +from softasp_core.training.schedule import CORRECTED_PROTOCOL, LEGACY_PROTOCOL +from softasp_core.training.seed import seed_everything +from softasp_core.training.trainer import Trainer +from softasp_core.utils.logging import setup_logging + +from softasp.configuration import config_directory +from softasp.instantiations.checkpoint import read_checkpoint, validate_checkpoint +from softasp.instantiations.runtime import provider + +logger = logging.getLogger(__name__) + +_CONFIGS = config_directory() + + +@hydra.main(version_base="1.3", config_path=_CONFIGS, config_name="config") +def main(cfg: DictConfig) -> None: + run_training(cfg) + + +def run_training(cfg: DictConfig) -> Path: + """Train from an explicit configuration and return the exact run directory.""" + setup_logging(level=cfg.logging.get("level", "INFO")) + logger.info("=== softasp-train ===") + + # Resolve all interpolations + OmegaConf.resolve(cfg) + + protocol = cfg.trainer.get("protocol", LEGACY_PROTOCOL) + if protocol not in (LEGACY_PROTOCOL, CORRECTED_PROTOCOL): + raise ValueError(f"Unknown training protocol: {protocol}") + if protocol == CORRECTED_PROTOCOL: + # Includes release-model construction and all subsequent stochastic setup. + seed_everything(cfg.trainer.seed, deterministic=cfg.trainer.deterministic) + OmegaConf.update(cfg, "initialization_seeded_before_model", True, force_add=True) + + model_provider = provider(cfg, "model") + # Preparation precedes data construction to preserve initialization order. + prepared = model_provider.prepare(cfg) + train_ds, val_ds = provider(cfg, "dataset").build(cfg) + model = prepared if prepared is not None else model_provider.build(cfg) + evaluator = provider(cfg, "metrics").build(cfg) + + # Train + trainer = Trainer( + cfg=cfg, model=model, evaluator=evaluator, + checkpoint_validator=lambda path: validate_checkpoint(cfg, read_checkpoint(path)), + ) + run_dir = trainer.fit(train_ds, val_ds) + + logger.info("Training complete.") + return run_dir + + +if __name__ == "__main__": + main() diff --git a/src/softasp/cli/train_scratch.py b/src/softasp/cli/train_scratch.py new file mode 100644 index 0000000000000000000000000000000000000000..9b73da66ca7911341d4533a7627f28224bb50d20 --- /dev/null +++ b/src/softasp/cli/train_scratch.py @@ -0,0 +1,24 @@ +"""Hydra entry point for training an AS2 instantiation without supplied weights. + +Run with ``uv run --locked softasp-train-scratch``. +""" + +import hydra +from omegaconf import DictConfig +from softasp_core.utils.logging import setup_logging + +from softasp.configuration import config_directory +from softasp.instantiations.runtime import train_from_scratch + +_CONFIGS = config_directory() + + +@hydra.main(version_base="1.3", config_path=_CONFIGS, config_name="train_scratch") +def main(cfg: DictConfig) -> None: + setup_logging(level=cfg.logging.get("level", "INFO")) + manifest = train_from_scratch(cfg) + print(f"Training workflow complete. Manifest: {manifest}") + + +if __name__ == "__main__": + main() diff --git a/src/softasp/configuration.py b/src/softasp/configuration.py new file mode 100644 index 0000000000000000000000000000000000000000..3a34e08379232a7a1f5c8a352f8cfd0528380ed6 --- /dev/null +++ b/src/softasp/configuration.py @@ -0,0 +1,9 @@ +"""Locate Hydra resources in a source checkout or installed wheel.""" + +from pathlib import Path + + +def config_directory() -> str: + source = Path(__file__).resolve().parents[2] / "configs" + packaged = Path(__file__).resolve().parent / "configs" + return str(source if source.is_dir() else packaged) diff --git a/src/softasp/inference.py b/src/softasp/inference.py new file mode 100644 index 0000000000000000000000000000000000000000..6697a63257081792e67b92bdf476e36ff1a24090 --- /dev/null +++ b/src/softasp/inference.py @@ -0,0 +1,15 @@ +"""Configuration-driven AS2 inference API.""" + +from omegaconf import DictConfig + +from softasp.instantiations.runtime import predict, provider + + +def load_model(cfg: DictConfig): + """Load a checkpoint according to the selected model provider.""" + return provider(cfg, "model").load(cfg) + + +def predict_file(cfg: DictConfig) -> dict: + """Read and predict according to the explicit instantiation configuration.""" + return predict(cfg) diff --git a/src/softasp/instantiations/__init__.py b/src/softasp/instantiations/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..6c00dc4dadb860e2553691661a652763d53ff54d --- /dev/null +++ b/src/softasp/instantiations/__init__.py @@ -0,0 +1 @@ +"""Hydra-selected instantiation providers for the AS2 framework.""" diff --git a/src/softasp/instantiations/checkpoint.py b/src/softasp/instantiations/checkpoint.py new file mode 100644 index 0000000000000000000000000000000000000000..5661d51998090196034fc753e7d90fe117a084b7 --- /dev/null +++ b/src/softasp/instantiations/checkpoint.py @@ -0,0 +1,49 @@ +"""Checkpoint provenance validation shared by instantiation adapters.""" + +from pathlib import Path + +import numpy as np +import torch +from numpy._core.multiarray import _reconstruct +from omegaconf import DictConfig, OmegaConf + +from softasp.instantiations.runtime import identity + + +def read_checkpoint(path: Path) -> dict: + # Full trainer checkpoints contain NumPy RNG state. Never retry with + # unrestricted pickle loading; allow only its concrete array representation. + with torch.serialization.safe_globals( + [_reconstruct, np.ndarray, np.dtype, type(np.dtype("uint32"))] + ): + return torch.load(path, map_location="cpu", weights_only=True) + + +def validate_checkpoint( + cfg: DictConfig, + saved: dict, + *, + legacy_model: str | None = None, + legacy_constraints: dict | None = None, +) -> None: + expected = identity(cfg) + recorded = saved.get("config", {}) + actual = recorded.get("instantiation") + selected = cfg.instantiation.get("constraints") + constraints = OmegaConf.to_container(selected, resolve=True) if selected is not None else None + if actual is None: + if legacy_model is None or recorded.get("model", {}).get("name") != legacy_model: + raise ValueError("Checkpoint has no compatible instantiation identity") + if constraints != legacy_constraints: + raise ValueError("Legacy checkpoint constraint provider/parameters do not match") + return + if any(actual.get(key) != value for key, value in expected.items()): + raise ValueError("Checkpoint instantiation identity does not match configuration") + if recorded.get("model", {}).get("name") != cfg.get("model", {}).get("name"): + raise ValueError("Checkpoint model identity does not match configuration") + if actual.get("constraints") != constraints: + raise ValueError("Checkpoint constraint provider/parameters do not match configuration") + network = cfg.instantiation.get("network") + network = OmegaConf.to_container(network, resolve=True) if network is not None else None + if actual.get("network") != network: + raise ValueError("Checkpoint network configuration does not match") diff --git a/src/softasp/instantiations/runtime.py b/src/softasp/instantiations/runtime.py new file mode 100644 index 0000000000000000000000000000000000000000..15c5e03d844e95e0a815107cb18f30b7a9794f71 --- /dev/null +++ b/src/softasp/instantiations/runtime.py @@ -0,0 +1,43 @@ +"""Resolve explicit capabilities without benchmark-specific fallbacks.""" + +from typing import Any + +from hydra.utils import instantiate +from omegaconf import DictConfig, OmegaConf + + +def identity(cfg: DictConfig) -> dict[str, Any]: + spec = cfg.get("instantiation") + if spec is None or not spec.get("name") or spec.get("version") != 1: + raise ValueError("An explicit instantiation name and supported version (1) are required") + return {"name": str(spec.name), "version": int(spec.version)} + + +def provider(cfg: DictConfig, capability: str) -> Any: + identity(cfg) + spec = OmegaConf.select(cfg, f"instantiation.providers.{capability}") + if not isinstance(spec, DictConfig) or not spec.get("_target_"): + raise ValueError(f"Instantiation {cfg.instantiation.name!r} lacks {capability!r} provider") + instance = instantiate(spec) + required = ("prepare", "build") if capability == "model" else ( + ("build",) if capability in ("dataset", "metrics") else ("run",) + ) + for method in required: + if not callable(getattr(instance, method, None)): + raise ValueError(f"Provider {capability!r} must implement {method}()") + return instance + + +def evaluate(cfg: DictConfig) -> dict: + OmegaConf.resolve(cfg) + return provider(cfg, "evaluation").run(cfg) + + +def predict(cfg: DictConfig) -> dict: + OmegaConf.resolve(cfg) + return provider(cfg, "prediction").run(cfg) + + +def train_from_scratch(cfg: DictConfig): + OmegaConf.resolve(cfg) + return provider(cfg, "workflow").run(cfg) diff --git a/src/softasp/instantiations/sudoku.py b/src/softasp/instantiations/sudoku.py new file mode 100644 index 0000000000000000000000000000000000000000..035996a24c480893010cd48934e71aa1dc1f807f --- /dev/null +++ b/src/softasp/instantiations/sudoku.py @@ -0,0 +1,103 @@ +"""Visual Sudoku adapters; all board-specific choices belong here.""" + +from pathlib import Path + +from hydra.utils import instantiate +from omegaconf import DictConfig +from softasp_core.config.schemas import DatasetConfig +from softasp_core.evaluation.evaluator import Evaluator +from softasp_domain.registry import get_domain +from softasp_solver.registry import get_model + +from softasp.sudoku_data import select_hdf5 + + +def validate(cfg: DictConfig) -> None: + if cfg.dataset.name != "sudoku" or cfg.model.name not in ( + "softasp_sudoku", + "perception_only_sudoku", + ): + raise ValueError("Sudoku providers require a Sudoku dataset and model") + + +class DatasetProvider: + def build(self, cfg: DictConfig): + validate(cfg) + dataset = DatasetConfig(**cfg.dataset) + path = select_hdf5(cfg) + from softasp_core.data.augmentations import get_augmentation + from torchvision.transforms import Compose + + params = dict(dataset.augmentation_params or {}) + transforms = [ + get_augmentation(name, **params.get(name, {})) + for name in dataset.augmentations.get("train", []) + ] + transform = ( + Compose(transforms) if len(transforms) > 1 else (transforms[0] if transforms else None) + ) + cls = get_domain("sudoku").dataset_cls + return cls(path, split="train", transform=transform), cls(path, split="val") + + +class ModelProvider: + def load(self, cfg: DictConfig): + from softasp.sudoku_inference import load_model + + return load_model(Path(cfg.checkpoint), cfg.get("device", "auto"), cfg=cfg) + + def prepare(self, cfg: DictConfig): + if cfg.get("release_profile") == "sudoku_release1": + from softasp.sudoku_release import prepare_release_model + + return self.bind(cfg, prepare_release_model(cfg)) + validate(cfg) + return None + + def build(self, cfg: DictConfig): + validate(cfg) + return self.bind(cfg, get_model(cfg.model.name, **dict(cfg.model.params))) + + def bind(self, cfg: DictConfig, model): + if cfg.model.name == "softasp_sudoku": + model.constraint_loss = instantiate(cfg.instantiation.constraints) + return model + + +class MetricsProvider: + def build(self, cfg: DictConfig): + return Evaluator( + metric_names=list(cfg.evaluator.get("metrics", ["accuracy"])), + output_metrics={ + "clue_accuracy": ("clue_logits", "clue_labels"), + "blank_pre_accuracy": ("blank_pre_logits", "blank_labels"), + "blank_post_accuracy": ("blank_post_logits", "blank_labels"), + "clue_post_accuracy": ("clue_post_logits", "clue_labels"), + }, + scalar_keys=("blank_entropy",), + ) + + +class EvaluationProvider: + def run(self, cfg: DictConfig) -> dict: + validate(cfg) + from softasp.sudoku_evaluation import evaluate_release + + return evaluate_release(cfg, device=cfg.evaluator.get("device", "auto")) + + +class PredictionProvider: + def run(self, cfg: DictConfig) -> dict: + from softasp.sudoku_inference import predict_file + + return predict_file( + Path(cfg.checkpoint), Path(cfg.input), cfg.get("device", "auto"), cfg=cfg + ) + + +class WorkflowProvider: + def run(self, cfg: DictConfig): + validate(cfg) + from softasp.sudoku_scratch import train_from_scratch + + return train_from_scratch(cfg) diff --git a/src/softasp/sudoku_backbone.py b/src/softasp/sudoku_backbone.py new file mode 100644 index 0000000000000000000000000000000000000000..9ef7681a092b0a7eb9e4ba0903beb041232d977c --- /dev/null +++ b/src/softasp/sudoku_backbone.py @@ -0,0 +1,34 @@ +"""Validated backbone handoff between the two Sudoku training stages.""" + +from pathlib import Path + +import torch + +from softasp.sudoku_reporting import file_sha256 + + +def extract_backbone(checkpoint: Path, destination: Path) -> dict: + """Extract a newly trained perception checkpoint, retaining source identity.""" + saved = torch.load(checkpoint, map_location="cpu", weights_only=False) + if saved.get("config", {}).get("model", {}).get("name") != "perception_only_sudoku": + raise ValueError("Expected a perception_only_sudoku training checkpoint") + if saved.get("epoch", -1) < 0: + raise ValueError("Backbone checkpoint must follow at least one training epoch") + state = { + key: value + for key, value in saved["model_state_dict"].items() + if key.startswith("cell_cnn.") + } + if not state or any(not torch.isfinite(value).all() for value in state.values()): + raise ValueError("Expected finite trained cell_cnn tensors") + provenance = { + "source_checkpoint": str(checkpoint.resolve()), + "source_sha256": file_sha256(checkpoint), + "source_epoch": saved["epoch"], + "source_metric": saved.get("best_metric"), + "dataset_identity": saved["config"].get("resolved_dataset"), + } + # Refuse to overwrite any pre-existing model artifact. + with destination.open("xb") as stream: + torch.save({"model_state_dict": state, **provenance}, stream) + return {**provenance, "path": str(destination), "sha256": file_sha256(destination)} diff --git a/src/softasp/sudoku_data.py b/src/softasp/sudoku_data.py new file mode 100644 index 0000000000000000000000000000000000000000..d866ff4f3869added0e4cac3335f45149a3e4071 --- /dev/null +++ b/src/softasp/sudoku_data.py @@ -0,0 +1,85 @@ +"""Explicit, verified dataset selection shared by training and evaluation.""" + +from __future__ import annotations + +import hashlib +import re +from pathlib import Path + +import h5py +import numpy as np +from omegaconf import DictConfig, OmegaConf + +from softasp.sudoku_protocol import data_protocol_metadata + + +def validate_sudoku_hdf5(path: Path, expected_sha256: str) -> dict: + """Validate identity, complete splits, model-compatible fields and value ranges.""" + path = path.expanduser().resolve() + if not path.is_file(): + raise FileNotFoundError(f"Explicit Sudoku dataset does not exist: {path}") + if not isinstance(expected_sha256, str) or not re.fullmatch( + r"[0-9a-fA-F]{64}", expected_sha256 + ): + raise ValueError("data_artifact.sha256 must be a complete SHA256 digest") + with path.open("rb") as stream: + actual = hashlib.file_digest(stream, "sha256").hexdigest() + if actual != expected_sha256.lower(): + raise ValueError(f"Sudoku dataset SHA256 mismatch: {path}") + counts = {} + with h5py.File(path, "r") as hf: + for split in ("train", "val", "test"): + group = hf.get(split) + if not isinstance(group, h5py.Group): + raise ValueError(f"Missing Sudoku split: {split}") + fields: dict[str, h5py.Dataset] = {} + for field in ("images", "labels", "puzzle_mask"): + value = group.get(field) + if not isinstance(value, h5py.Dataset): + raise ValueError(f"Missing Sudoku field: {split}/{field}") + fields[field] = value + labels, mask, images = fields["labels"], fields["puzzle_mask"], fields["images"] + n = labels.shape[0] if labels.ndim else 0 + if n == 0 or labels.shape != (n, 81) or mask.shape != (n, 81): + raise ValueError(f"Invalid Sudoku label/mask shapes: {split}") + if images.shape != (n, 252, 252, 1): + raise ValueError(f"Invalid Sudoku image shape: {split}") + if images.dtype != np.dtype("uint8") or labels.dtype != np.dtype("int64"): + raise ValueError(f"Invalid Sudoku image/label dtype: {split}") + if mask.dtype.kind not in "biu": + raise ValueError(f"Invalid Sudoku mask dtype: {split}") + for offset in range(0, n, 512): + values, clues = labels[offset : offset + 512], mask[offset : offset + 512] + if np.any((values < 0) | (values > 8)): + raise ValueError(f"Sudoku labels outside 0..8: {split}") + if np.any((clues != 0) & (clues != 1)): + raise ValueError(f"Sudoku mask must contain only 0/1: {split}") + counts[split] = n + return { + "path": str(path), + "sha256": actual, + "splits": counts, + "validation": "sudoku_hdf5_v1", + "data_protocol": data_protocol_metadata(actual), + } + + +def select_hdf5(cfg: DictConfig) -> Path: + """Release 1 requires a pinned artifact; legacy configs retain discovery.""" + spec = cfg.get("data_artifact") + if spec is not None or cfg.get("release_profile") == "sudoku_release1": + if not spec or not spec.get("path") or not spec.get("sha256"): + raise ValueError("Set data_artifact.path and data_artifact.sha256 explicitly") + if cfg.dataset.name != "sudoku": + raise ValueError("Explicit data_artifact selection currently supports Sudoku only") + identity = validate_sudoku_hdf5(Path(spec.path), spec.sha256) + identity["data_protocol"] = data_protocol_metadata( + identity["sha256"], spec.get("protocol_id") + ) + OmegaConf.update(cfg, "resolved_dataset", identity, force_add=True) + return Path(identity["path"]) + directory = Path(cfg.paths.data_root) / "processed" / cfg.dataset.name + files = sorted(directory.glob("v_*.h5")) + if not files: + raise FileNotFoundError(f"No preprocessed HDF5 found in {directory}") + return files[-1] diff --git a/src/softasp/sudoku_evaluation.py b/src/softasp/sudoku_evaluation.py new file mode 100644 index 0000000000000000000000000000000000000000..522769f0a6991a6bbc594fef4ee5dd3c3a0c27c2 --- /dev/null +++ b/src/softasp/sudoku_evaluation.py @@ -0,0 +1,118 @@ +"""Release 1 label-free, single-process Sudoku evaluation with raw argmax only.""" + +from __future__ import annotations + +import inspect +import json +import os +from pathlib import Path + +import torch +from omegaconf import DictConfig, OmegaConf +from softasp_domain.registry import get_domain +from softasp_solver.registry import get_model +from torch.utils.data import DataLoader + +from softasp.instantiations.checkpoint import read_checkpoint, validate_checkpoint +from softasp.sudoku_data import select_hdf5 +from softasp.sudoku_reporting import file_sha256, write_raw_report + + +@torch.no_grad() +def evaluate_raw(model, loader, destination: Path, provenance: dict, device: torch.device) -> dict: + """Labels go to scoring only; all batches use the model's label-free interface.""" + if model.oracle_perception: + raise ValueError("Release 1 prohibits oracle perception") + model.to(device).eval() + predictions, labels, masks = [], [], [] + for batch in loader: + output = model.predict(batch["images"].to(device), batch["puzzle_mask"].to(device)) + logits = output["logits"] + if logits.shape != (len(batch["images"]), 81, 9) or not torch.isfinite(logits).all(): + raise ValueError("Expected finite raw Sudoku logits with shape (B, 81, 9)") + predictions.append(logits.argmax(dim=-1).cpu()) + labels.append(batch["labels"].cpu()) + masks.append(batch["puzzle_mask"].cpu()) + if not predictions: + raise ValueError("Cannot evaluate an empty Sudoku split") + return write_raw_report( + destination, torch.cat(predictions), torch.cat(labels), torch.cat(masks), provenance + ) + + +def evaluate_release(cfg: DictConfig, device: str = "auto") -> dict: + """Use the pinned dataset and strict checkpoint loading; never invoke a decoder.""" + if cfg.dataset.name != "sudoku" or cfg.model.name != "softasp_sudoku": + raise ValueError("Release 1 requires the Sudoku AS2 model") + if cfg.model.params.get("oracle_perception", False): + raise ValueError("Release 1 prohibits oracle perception") + if int(os.environ.get("WORLD_SIZE", "1")) != 1: + raise ValueError("Release 1 evaluation requires one process") + checkpoint_value = cfg.evaluator.get("checkpoint") + if not checkpoint_value: + raise ValueError("evaluator.checkpoint must be set for evaluation") + checkpoint_path = Path(checkpoint_value).resolve() + hdf5_path = select_hdf5(cfg) + splits = list(cfg.dataset.splits) + if ( + not splits + or len(set(splits)) != len(splits) + or any(split not in ("train", "val", "test") for split in splits) + ): + raise ValueError("Expected unique nonempty Sudoku splits: train, val, test") + destination = Path(cfg.paths.output_dir) / "eval" + if destination.exists(): + raise FileExistsError(f"Use a fresh evaluation output directory: {destination}") + batch_size = cfg.evaluator.get("batch_size_per_gpu", 64) + workers = cfg.evaluator.get("num_workers", 0) + if batch_size < 1 or workers < 0: + raise ValueError("Expected positive batch size and nonnegative worker count") + resolved_device = torch.device( + ("cuda" if torch.cuda.is_available() else "cpu") if device == "auto" else device + ) + checkpoint = read_checkpoint(checkpoint_path) + if cfg.get("instantiation") is not None: + validate_checkpoint( + cfg, + checkpoint, + legacy_model="softasp_sudoku", + legacy_constraints={"_target_": "softasp_solver.sudoku.constraints.TPOperatorLoss"}, + ) + checkpoint_cfg = checkpoint.get("config", {}) + if checkpoint_cfg.get("model", {}).get("params", {}).get("oracle_perception", False): + raise ValueError("Release 1 prohibits oracle checkpoint configurations") + model = get_model(cfg.model.name, **dict(cfg.model.params)) + if cfg.get("instantiation") is not None: + from hydra.utils import instantiate + + model.constraint_loss = instantiate(cfg.instantiation.constraints) + model.load_state_dict(checkpoint["model_state_dict"], strict=True) + identity = OmegaConf.to_container(cfg.resolved_dataset, resolve=True) + provenance = { + "checkpoint": str(checkpoint_path), + "checkpoint_sha256": file_sha256(checkpoint_path), + "checkpoint_config": checkpoint_cfg, + "evaluation_config": OmegaConf.to_container(cfg, resolve=True), + "dataset_identity": identity, + "device": str(resolved_device), + "precision": "float32; autocast disabled", + "torch_version": str(torch.__version__), + "postprocessing": "none", + "oracle_perception": False, + "source_sha256": { + "sudoku_evaluation.py": file_sha256(Path(__file__)), + "sudoku_reporting.py": file_sha256(Path(__file__).with_name("sudoku_reporting.py")), + "softasp_sudoku.py": file_sha256(Path(inspect.getfile(type(model)))), + }, + } + dataset_cls = get_domain("sudoku").dataset_cls + results = {} + for split in splits: + dataset = dataset_cls(hdf5_path, split=split) + loader = DataLoader(dataset, batch_size=batch_size, num_workers=workers, shuffle=False) + results[split] = evaluate_raw( + model, loader, destination / split, {**provenance, "split": split}, resolved_device + ) + (destination / "results.json").write_text(json.dumps(results, indent=2, allow_nan=False) + "\n") + (destination / "dataset-identity.json").write_text(json.dumps(identity, indent=2) + "\n") + return results diff --git a/src/softasp/sudoku_inference.py b/src/softasp/sudoku_inference.py new file mode 100644 index 0000000000000000000000000000000000000000..2595c6076d26de7566da88a13a2ad6a67c5343b3 --- /dev/null +++ b/src/softasp/sudoku_inference.py @@ -0,0 +1,92 @@ +"""Label-free raw prediction for one or more Visual Sudoku boards.""" + +from pathlib import Path + +import numpy as np +import torch +from omegaconf import DictConfig +from softasp_solver.sudoku.softasp_sudoku import SoftASPSudoku + +from softasp.instantiations.checkpoint import read_checkpoint, validate_checkpoint +from softasp.sudoku_reporting import file_sha256 + + +def load_model( + checkpoint: Path, device: str = "auto", *, cfg: DictConfig | None = None +) -> tuple[SoftASPSudoku, torch.device]: + """Load public inference weights; optimizer state and labels are unnecessary.""" + target = torch.device( + ("cuda" if torch.cuda.is_available() else "cpu") if device == "auto" else device + ) + saved = read_checkpoint(checkpoint) + if cfg is not None: + validate_checkpoint( + cfg, + saved, + legacy_model="softasp_sudoku", + legacy_constraints={"_target_": "softasp_solver.sudoku.constraints.TPOperatorLoss"}, + ) + model_config = saved["config"]["model"] + if model_config["name"] != "softasp_sudoku": + raise ValueError("Expected a Sudoku AS2 checkpoint") + params = dict(model_config["params"]) + if params.get("oracle_perception", False): + raise ValueError("Oracle checkpoints are not supported") + model = SoftASPSudoku(**params) + if cfg is not None: + from hydra.utils import instantiate + + model.constraint_loss = instantiate(cfg.instantiation.constraints) + model.load_state_dict(saved["model_state_dict"], strict=True) + return model.to(target).eval(), target + + +@torch.no_grad() +def predict_file( + checkpoint: Path, input_path: Path, device: str = "auto", *, cfg: DictConfig | None = None +) -> dict: + """Read images and clue positions only; output raw digits 1 through 9.""" + with np.load(input_path, allow_pickle=False) as data: + images = data["images"].copy() + mask = data["puzzle_mask"].copy() + if images.ndim != 4 or images.shape[1:] != (252, 252, 1) or len(images) == 0: + raise ValueError("images must have shape (N, 252, 252, 1), N > 0") + if images.dtype != np.uint8 and not ( + images.dtype == np.float32 + and np.isfinite(images).all() + and np.all((images >= 0) & (images <= 1)) + ): + raise ValueError("images must be uint8 [0,255] or float32 [0,1]") + if mask.shape != (len(images), 81) or not np.isin(mask, [0, 1]).all(): + raise ValueError("puzzle_mask must have shape (N,81) and contain only 0/1") + model, target = load_model(checkpoint, device, cfg=cfg) + predictions = [] + # Bound inference batches without requiring all images to fit on the device. + for start in range(0, len(images), 32): + out = model.predict( + torch.from_numpy(images[start : start + 32]).to(target), + torch.from_numpy(mask[start : start + 32].astype(np.int64)).to(target), + ) + logits = out["logits"] + if logits.shape[1:] != (81, 9) or not torch.isfinite(logits).all(): + raise ValueError("Expected finite raw Sudoku logits") + predictions.append(logits.argmax(-1).cpu()) + digits = torch.cat(predictions).numpy().reshape(-1, 9, 9) + 1 + expected = np.arange(1, 10) + valid = [] + for board in digits: + boxes = board.reshape(3, 3, 3, 3).transpose(0, 2, 1, 3).reshape(9, 9) + valid.append( + bool( + np.all(np.sort(board, axis=1) == expected) + and np.all(np.sort(board.T, axis=1) == expected) + and np.all(np.sort(boxes, axis=1) == expected) + ) + ) + return { + "decoder": "raw_argmax", + "digits": digits.tolist(), + "structurally_valid": valid, + "checkpoint_sha256": file_sha256(checkpoint), + "input_sha256": file_sha256(input_path), + } diff --git a/src/softasp/sudoku_protocol.py b/src/softasp/sudoku_protocol.py new file mode 100644 index 0000000000000000000000000000000000000000..7f20f5ce4ba1577a9b2c426e7923fe244edd191d --- /dev/null +++ b/src/softasp/sudoku_protocol.py @@ -0,0 +1,36 @@ +"""Metadata for immutable, recognized Sudoku dataset artifacts.""" + +from __future__ import annotations + +LEGACY_DATA_PROTOCOL = "sudoku_legacy_mnist_pool_v1" +LEGACY_DATA_SHA256 = "8d0070b00ffc75763ceb8c2b29006823c80b70f50b6ca0018310d0fdc7b3828e" + + +def data_protocol_metadata(sha256: str, requested_id: str | None = None) -> dict: + """Identify by content; never assign legacy or corrected claims to another file.""" + known = sha256.lower() == LEGACY_DATA_SHA256 + actual_id = LEGACY_DATA_PROTOCOL if known else None + if requested_id is not None and requested_id != actual_id: + raise ValueError("Data-protocol identifier does not match the dataset SHA256") + if not known: + return { + "id": None, + "status": "unregistered", + "source_image_split_disjointness": "not_established", + "evidence_label": "Unregistered dataset protocol; no source-image separation claim.", + } + return { + "id": LEGACY_DATA_PROTOCOL, + "status": "legacy", + "dataset_sha256": LEGACY_DATA_SHA256, + "splits": {"train": 9000, "val": 1000, "test": 1000}, + "generation_seed": 42, + "split_seed_offsets": {"train": 0, "val": 100000, "test": 200000}, + "initial_clues": 55, + "max_successful_removals": 10, + "observed_clues_per_board": 45, + "board_distribution": "transformations_of_one_base_solution", + "source_image_pool": "merged_original_mnist_train_and_test", + "source_image_split_disjointness": "not_established", + "evidence_label": "Legacy Sudoku dataset; source-image split disjointness not established.", + } diff --git a/src/softasp/sudoku_release.py b/src/softasp/sudoku_release.py new file mode 100644 index 0000000000000000000000000000000000000000..1dbc027ed7eaf85bc6d679fdd9549369500ea555 --- /dev/null +++ b/src/softasp/sudoku_release.py @@ -0,0 +1,62 @@ +"""Validate the narrow Sudoku release surface before any training starts.""" + +from __future__ import annotations + +import os +from pathlib import Path + +import torch +from omegaconf import DictConfig +from softasp_core.training.schedule import CORRECTED_PROTOCOL +from torch import nn + + +def load_backbone(model: nn.Module, path: Path) -> None: + """Load a complete extracted backbone, rejecting silent partial initialization.""" + if not path.is_file(): + raise FileNotFoundError(f"Existing backbone required: {path}; pretraining is not automatic") + checkpoint = torch.load(path, map_location="cpu", weights_only=True) + state = checkpoint.get("model_state_dict") if isinstance(checkpoint, dict) else None + expected = {k: v for k, v in model.state_dict().items() if k.startswith("cell_cnn.")} + if not expected or not isinstance(state, dict) or set(state) != set(expected): + raise ValueError("Backbone must contain exactly the complete cell_cnn.* tensor set") + for key, target in expected.items(): + value = state[key] + if ( + not isinstance(value, torch.Tensor) + or value.shape != target.shape + or value.dtype != target.dtype + or not torch.isfinite(value).all() + ): + raise ValueError(f"Incompatible backbone tensor: {key}") + model.load_state_dict(state, strict=False) + + +def prepare_release_model(cfg: DictConfig) -> nn.Module: + """Reject unsupported overrides and load existing weights without training.""" + from softasp_solver.registry import get_model + + supported = ( + cfg.dataset.name == "sudoku" + and cfg.trainer.get("protocol") == CORRECTED_PROTOCOL + and cfg.model.name == "softasp_sudoku" + and cfg.trainer.seed == 42 + and cfg.trainer.distributed == "false" + and not cfg.trainer.compile + and cfg.trainer.gradient_accumulation_steps == 1 + and cfg.trainer.resume_from is None + and not cfg.model.params.get("oracle_perception", False) + and int(os.environ.get("WORLD_SIZE", "1")) == 1 + ) + if not supported: + raise ValueError( + "Release 1 requires Sudoku AS2, seed 42, single device, no oracle/resume/compile" + ) + backbone = cfg.model.get("pretrained_from") + if not backbone: + raise ValueError( + "Set model.pretrained_from to an existing backbone; no automatic pretraining" + ) + model = get_model(cfg.model.name, **dict(cfg.model.params)) + load_backbone(model, Path(backbone)) + return model diff --git a/src/softasp/sudoku_reporting.py b/src/softasp/sudoku_reporting.py new file mode 100644 index 0000000000000000000000000000000000000000..549a5f832ff5aa22781d24440ba3293cd4678ce6 --- /dev/null +++ b/src/softasp/sudoku_reporting.py @@ -0,0 +1,133 @@ +"""Exact Sudoku counts and immutable raw-argmax evaluation artifacts.""" + +from __future__ import annotations + +import hashlib +import json +from datetime import UTC, datetime +from pathlib import Path + + +def ratio(numerator: int, denominator: int) -> dict: + return { + "numerator": numerator, + "denominator": denominator, + "value": numerator / denominator if denominator else None, + } + + +def valid_board(board: list[int]) -> bool: + """Structural validity only; this does not compare with a labeled solution.""" + if len(board) != 81 or any(v not in range(9) for v in board): + return False + units = [board[r * 9 : (r + 1) * 9] for r in range(9)] + units += [board[c::9] for c in range(9)] + units += [ + [board[(br + r) * 9 + bc + c] for r in range(3) for c in range(3)] + for br in (0, 3, 6) + for bc in (0, 3, 6) + ] + return all(set(unit) == set(range(9)) for unit in units) + + +def score_stage(predictions: list, labels: list, masks: list) -> dict: + """Keep every input board, including incorrect or structurally invalid boards.""" + if not predictions or len(predictions) != len(labels) or len(labels) != len(masks): + raise ValueError("Expected nonempty, aligned predictions, labels, and masks") + cell_correct = clue_correct = blank_correct = clue_count = 0 + board_correct = csr_count = clue_consistent = 0 + for board, truth, mask in zip(predictions, labels, masks, strict=True): + if len(board) != 81 or len(truth) != 81 or len(mask) != 81: + raise ValueError("Expected flat 81-cell boards and masks") + if any(v not in range(9) for v in truth) or any(v not in (0, 1) for v in mask): + raise ValueError("Expected labels in 0..8 and binary masks") + correct = [a == b for a, b in zip(board, truth, strict=True)] + valid = valid_board(board) + cell_correct += sum(correct) + board_correct += all(correct) + csr_count += valid + clue_correct += sum(ok for ok, clue in zip(correct, mask, strict=True) if clue) + blank_correct += sum(ok for ok, clue in zip(correct, mask, strict=True) if not clue) + clue_count += sum(bool(clue) for clue in mask) + clue_consistent += valid and all(ok for ok, clue in zip(correct, mask, strict=True) if clue) + n = len(labels) + return { + "sym_acc": ratio(cell_correct, n * 81), + "board_acc": ratio(board_correct, n), + "clue_acc": ratio(clue_correct, clue_count), + "blank_acc": ratio(blank_correct, n * 81 - clue_count), + "csr": ratio(csr_count, n), + "clue_consistent_valid_rate": ratio(clue_consistent, n), + } + + +def file_sha256(path: Path) -> str: + with path.open("rb") as stream: + return hashlib.file_digest(stream, "sha256").hexdigest() + + +DEFINITIONS = { + "sym_acc": "Exact cell accuracy across all 81 cells.", + "board_acc": "All 81 cells equal the labeled solution.", + "clue_acc": "Exact cell accuracy on labeled clue cells.", + "blank_acc": "Exact cell accuracy on labeled blank cells.", + "csr": "Every row, column, and box contains 0..8 exactly once; no solution comparison.", + "clue_consistent_valid_rate": "Structural validity AND agreement with all labeled clues.", +} + + +def render_raw_report(report: dict) -> str: + """Console and Markdown share this rendering of the serialized result record.""" + lines = [ + "# Sudoku raw-argmax evaluation", + "", + "No refinement, greedy decoding, oracle labels, or solver completion/repair.", + "Structural validity is distinct from exact solution agreement.", + "All boards remain in the denominators; undefined rates are N/A.", + "", + ] + evidence = report["provenance"].get("dataset_identity", {}).get("data_protocol", {}) + if evidence: + lines.extend([evidence["evidence_label"], ""]) + lines.extend(["| Metric | Correct / total | Percent |", "|---|---:|---:|"]) + for name, metric in report["stages"]["raw_argmax"].items(): + value = metric["value"] + percent = "N/A" if value is None else f"{100 * value:.4f}" + lines.append(f"| {name} | {metric['numerator']} / {metric['denominator']} | {percent} |") + lines.extend(["", *[f"- {key}: {value}" for key, value in report["definitions"].items()]]) + return "\n".join(lines) + "\n" + + +def write_raw_report(destination: Path, predictions, labels, masks, provenance: dict) -> dict: + """Save every raw prediction; score the saved arrays and render their exact counts.""" + import numpy as np + + arrays = { + "raw_argmax": predictions.detach().cpu().numpy(), + "labels": labels.detach().cpu().numpy(), + "puzzle_mask": masks.detach().cpu().numpy(), + "board_index": np.arange(len(labels)), + } + metrics = score_stage( + arrays["raw_argmax"].tolist(), arrays["labels"].tolist(), arrays["puzzle_mask"].tolist() + ) + destination.mkdir(parents=True, exist_ok=False) + prediction_path = destination / "predictions.npz" + np.savez_compressed(prediction_path, **arrays) + report = { + "schema_version": 1, + "created_utc": datetime.now(UTC).isoformat(), + "decoder": "raw_argmax", + "provenance": provenance, + "predictions_sha256": file_sha256(prediction_path), + "definitions": DEFINITIONS, + "board_index": "Zero-based order in the selected HDF5 split; no boards filtered.", + "stages": {"raw_argmax": metrics}, + } + result_path = destination / "metrics.json" + result_path.write_text(json.dumps(report, indent=2, allow_nan=False) + "\n") + saved = json.loads(result_path.read_text()) + rendered = render_raw_report(saved) + (destination / "metrics.md").write_text(rendered) + print(rendered, end="", flush=True) + return saved diff --git a/src/softasp/sudoku_scratch.py b/src/softasp/sudoku_scratch.py new file mode 100644 index 0000000000000000000000000000000000000000..0f0de74623f809f42c634c92c215e5b4f490501c --- /dev/null +++ b/src/softasp/sudoku_scratch.py @@ -0,0 +1,179 @@ +"""Compose existing data, training, and raw evaluation stages without supplied weights.""" + +from __future__ import annotations + +import json +import os +import shutil +import subprocess +from copy import deepcopy +from pathlib import Path +from typing import Any + +from omegaconf import DictConfig, OmegaConf +from softasp_core.config.validation import validate_config +from softasp_core.training.schedule import CORRECTED_PROTOCOL + +from softasp.cli.train import run_training +from softasp.instantiations.runtime import evaluate as evaluate_release +from softasp.sudoku_backbone import extract_backbone +from softasp.sudoku_reporting import file_sha256 +from softasp.sudoku_scratch_data import prepare_scratch_data + + +def validate_scratch_config(cfg: DictConfig) -> None: + """Fail before downloads/training when the requested workflow is incompatible.""" + if ( + cfg.dataset.name != "sudoku" + or list(cfg.dataset.splits) != ["train", "val", "test"] + or cfg.model.name != "softasp_sudoku" + or cfg.model.params.backbone != "default" + or cfg.model.params.oracle_perception + or cfg.backbone.model.name != "perception_only_sudoku" + or cfg.backbone.model.params.d_model != cfg.model.params.d_model + or cfg.release_profile != "sudoku_release1" + or int(os.environ.get("WORLD_SIZE", "1")) != 1 + ): + raise ValueError( + "From-scratch workflow requires single-process Sudoku AS2 and matching default CNN" + ) + if cfg.pipeline.data_mode not in ("generate", "existing"): + raise ValueError("pipeline.data_mode must be generate or existing") + subset = cfg.preprocessing.subset_size + if subset is not None and ( + isinstance(subset, bool) or not isinstance(subset, int) or subset < 1 + ): + raise ValueError("preprocessing.subset_size must be a positive integer or null") + name = cfg.pipeline.name + if not isinstance(name, str) or not name or name in (".", "..") or Path(name).name != name: + raise ValueError("pipeline.name must be a single directory name") + if cfg.pipeline.data_mode == "generate" and ( + cfg.data_artifact.path or cfg.data_artifact.sha256 or cfg.data_artifact.protocol_id + ): + raise ValueError("Generate mode cannot use an existing data_artifact; select existing mode") + for model, trainer in ((cfg.model, cfg.trainer), (cfg.backbone.model, cfg.backbone.trainer)): + if model.pretrained_from or trainer.resume_from: + raise ValueError("From-scratch training cannot accept pretrained_from or resume_from") + if ( + trainer.protocol != CORRECTED_PROTOCOL + or trainer.seed != 42 + or trainer.distributed != "false" + or trainer.compile + or trainer.gradient_accumulation_steps != 1 + or trainer.epochs < 1 + or trainer.eval_on_start + or not 1 <= trainer.eval_every <= trainer.epochs + or trainer.batch_size_per_gpu < 1 + or trainer.num_workers < 0 + ): + raise ValueError( + "From-scratch training requires seed 42, corrected single-device training " + "and post-training validation" + ) + validate_config( + OmegaConf.create({"trainer": OmegaConf.to_container(trainer, resolve=True)}) + ) + + +def _stage_config(cfg: DictConfig, root: Path, *, backbone: bool) -> DictConfig: + stage = deepcopy(cfg) + if backbone: + stage.model = deepcopy(cfg.backbone.model) + stage.trainer = deepcopy(cfg.backbone.trainer) + # This stage intentionally trains a fresh perception model, not AS2. + stage.release_profile = None + del stage.backbone + del stage.pipeline + stage.paths.output_dir = str(root / ("backbone" if backbone else "as2")) + return stage + + +def train_from_scratch(config: DictConfig) -> Path: + """Run a fresh two-stage training workflow and record exact artifact lineage.""" + cfg = deepcopy(config) + OmegaConf.resolve(cfg) + validate_scratch_config(cfg) + root = Path(cfg.pipeline.run_dir).expanduser().resolve() + models = Path(cfg.pipeline.model_dir).expanduser().resolve() + if root == models or root in models.parents or models in root.parents: + raise ValueError("Workflow run_dir and model_dir must be separate directories") + for directory in (root, models): + if directory.exists(): + raise FileExistsError(f"Use a fresh workflow directory: {directory}") + root.mkdir(parents=True, exist_ok=False) + models.mkdir(parents=True, exist_ok=False) + manifest_path = root / "manifest.json" + source_root = Path(__file__).resolve().parents[2] + try: + revision = subprocess.check_output( + ["git", "rev-parse", "HEAD"], cwd=source_root, text=True, stderr=subprocess.DEVNULL + ).strip() + dirty = bool( + subprocess.check_output( + ["git", "status", "--porcelain"], cwd=source_root, text=True + ).strip() + ) + except (OSError, subprocess.CalledProcessError): + revision, dirty = None, None + record: dict[str, Any] = { + "status": "running", + "supplied_weights": False, + "git_commit": revision, + "git_dirty": dirty, + "uv_lock_sha256": ( + file_sha256(source_root / "uv.lock") + if (source_root / "uv.lock").is_file() else None + ), + "configuration": OmegaConf.to_container(cfg, resolve=True), + "stages": {}, + } + + def save() -> None: + manifest_path.write_text(json.dumps(record, indent=2, allow_nan=False) + "\n") + + save() + try: + prepare_scratch_data(cfg) + record["dataset"] = OmegaConf.to_container(cfg.resolved_dataset, resolve=True) + OmegaConf.save(cfg, root / "resolved.yaml", resolve=True) + save() + + backbone_cfg = _stage_config(cfg, root, backbone=True) + backbone_run = run_training(backbone_cfg) + backbone_path = models / "backbone.pt" + record["stages"]["backbone"] = extract_backbone(backbone_run / "best.pt", backbone_path) + save() + + as2_cfg = _stage_config(cfg, root, backbone=False) + as2_cfg.model.pretrained_from = str(backbone_path) + # The release guard checks the complete tensor set, dtype, shape, and finiteness. + as2_run = run_training(as2_cfg) + selected = as2_run / "best.pt" + checkpoint = models / "as2.pt" + with selected.open("rb") as source, checkpoint.open("xb") as target: + shutil.copyfileobj(source, target) + record["stages"]["as2"] = { + "source_checkpoint": str(selected), + "path": str(checkpoint), + "sha256": file_sha256(checkpoint), + "backbone_sha256": file_sha256(backbone_path), + } + save() + + evaluation_cfg = deepcopy(as2_cfg) + evaluation_cfg.dataset.splits = ["test"] + evaluation_cfg.evaluator.checkpoint = str(checkpoint) + evaluation_cfg.paths.output_dir = str(root / "evaluation") + reports = evaluate_release(evaluation_cfg) + record["stages"]["evaluation"] = { + "report_dir": str(root / "evaluation" / "eval"), + "metrics": reports["test"]["stages"], + } + record["status"] = "complete" + save() + except BaseException as error: + record["status"] = "failed" + record["error"] = f"{type(error).__name__}: {error}" + save() + raise + return manifest_path diff --git a/src/softasp/sudoku_scratch_data.py b/src/softasp/sudoku_scratch_data.py new file mode 100644 index 0000000000000000000000000000000000000000..dce94b116e55cef11470d9cdddc262c4b9eb2d5d --- /dev/null +++ b/src/softasp/sudoku_scratch_data.py @@ -0,0 +1,56 @@ +"""Prepare explicit, hashed data for a new Sudoku training workflow.""" + +from pathlib import Path + +from omegaconf import DictConfig, OmegaConf +from softasp_core.config.schemas import DatasetConfig +from softasp_domain.sudoku.downloader import SudokuDownloader +from softasp_domain.sudoku.preprocessor import SudokuPreprocessor + +from softasp.sudoku_data import select_hdf5 +from softasp.sudoku_reporting import file_sha256 + + +def prepare_scratch_data(cfg: DictConfig) -> Path: + """Generate in a fresh directory, or validate an explicitly pinned existing file.""" + if cfg.pipeline.data_mode == "existing": + return select_hdf5(cfg) + # Generation requires the actual uniqueness checker, never its legacy fallback. + import clingo # noqa: F401 + from torchvision.datasets import MNIST + + raw_dir = ( + ( + Path(cfg.paths.raw_data_root) + if cfg.paths.get("raw_data_root") + else Path(cfg.paths.data_root) / "raw" / "sudoku" + ) + .expanduser() + .resolve() + ) + if cfg.pipeline.download: + SudokuDownloader(raw_dir).run() + # Check complete data rather than trusting the downloader's coarse cache check. + for train in (True, False): + MNIST(root=str(raw_dir / "mnist"), train=train, download=False) + destination = ( + Path(cfg.paths.data_root).expanduser().resolve() + / "processed" + / "sudoku" + / "from_scratch" + / cfg.pipeline.name + ) + destination.mkdir(parents=True, exist_ok=False) + dataset = DatasetConfig(**cfg.dataset) + path = SudokuPreprocessor(raw_dir, destination, cfg.preprocessing, dataset).run() + OmegaConf.update( + cfg, + "data_artifact", + { + "path": str(path.resolve()), + "sha256": file_sha256(path), + # A newly generated file is not the archived legacy artifact. + "protocol_id": None, + }, + ) + return select_hdf5(cfg) diff --git a/uv.lock b/uv.lock new file mode 100644 index 0000000000000000000000000000000000000000..c34186553b54c6434c4423233bc0c56a61bac2c8 --- /dev/null +++ b/uv.lock @@ -0,0 +1,1758 @@ +version = 1 +revision = 3 +requires-python = ">=3.12" + +[manifest] +members = [ + "softasp", + "softasp-core", + "softasp-domain", + "softasp-solver", +] + +[[package]] +name = "aim" +version = "3.29.1" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "aim-ui" }, + { name = "aimrecords" }, + { name = "aimrocks" }, + { name = "aiofiles" }, + { name = "alembic" }, + { name = "boto3" }, + { name = "cachetools" }, + { name = "click" }, + { name = "cryptography" }, + { name = "fastapi" }, + { name = "filelock" }, + { name = "jinja2" }, + { name = "numpy" }, + { name = "packaging" }, + { name = "pillow" }, + { name = "psutil" }, + { name = "python-dateutil" }, + { name = "pytz" }, + { name = "requests" }, + { name = "restrictedpython" }, + { name = "sqlalchemy" }, + { name = "tqdm" }, + { name = "uvicorn" }, + { name = "watchdog" }, + { name = "websockets" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/da/25/c825c73ec2f48c93324f631dba6e4cdac3bb60a7fde36e0b916820ae62a5/aim-3.29.1.tar.gz", hash = "sha256:30fb70f983844eebd270049206c839e6dc09ce9de500048dc97a7a8b22ed83fb", size = 1660733, upload-time = "2025-05-08T09:51:58.892Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/70/07/4b6c4bda0469fad31a902ff1205ed010b3459be9565a8ffa2bfdcbb1e2bd/aim-3.29.1-cp312-cp312-macosx_10_14_x86_64.whl", hash = "sha256:301afdf3e80b3ccac4cdc786a73f81656bd3b1ec73836de19a67a77a0b3d3379", size = 2512723, upload-time = "2025-05-08T09:54:30.314Z" }, + { url = "https://files.pythonhosted.org/packages/a1/a5/da5e0de60a8d32ab8b2d725eec2ade3303117697cd714fb1b00134d01a64/aim-3.29.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:8fbb9a7f441721cd4999174d0d6fa1e18470c01dddf2f193e7c3d054543a4ca6", size = 2472712, upload-time = "2025-05-08T09:55:16.102Z" }, + { url = "https://files.pythonhosted.org/packages/bc/05/a55a86258f3505e0ab53ee81afcc0057d805a1de8d5a66390f75e32aa777/aim-3.29.1-cp312-cp312-manylinux_2_28_x86_64.whl", hash = "sha256:15cea0c63d9a61812229d7140cec04f02117626d1724e10bebe410554f3f7561", size = 7590249, upload-time = "2025-05-08T10:04:48.663Z" }, +] + +[[package]] +name = "aim-ui" +version = "3.29.1" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/0c/fe/723615593ee1bffbf89c1cb8028b72893120b3d4e737712d91041a5f0382/aim-ui-3.29.1.tar.gz", hash = "sha256:bc30278acad048a087f84e3f6e2565f53f75e87d8c29748c90420ec06b820113", size = 30971471, upload-time = "2025-05-08T09:50:58.888Z" } + +[[package]] +name = "aimrecords" +version = "0.0.7" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "base58" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/c9/10/21182ef96acbd9a5ca4008556ba8590cbc1af833eccbf59d53308fa6d928/aimrecords-0.0.7.tar.gz", hash = "sha256:9b562fa5b5109b4b3dd4f83be0061cadbac63fa8031f281b8b5c8ae29967072f", size = 12667, upload-time = "2020-11-09T13:29:29.071Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/53/13/207ebb5b2315640a68378c31cb31cfe1182373d11a813bd52219c83700a7/aimrecords-0.0.7-py2.py3-none-any.whl", hash = "sha256:b9276890891c5fd68f817e20fc5d466a80c01e22fa468eaa979331448a75d601", size = 28657, upload-time = "2020-11-09T13:29:26.015Z" }, +] + +[[package]] +name = "aimrocks" +version = "0.5.2" +source = { registry = "https://pypi.org/simple" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/52/2d/21b14e4253b71378e70e7a0b21dd1fcca1d72dff22b1bd68b1e8f0694298/aimrocks-0.5.2-cp312-cp312-macosx_10_14_x86_64.whl", hash = "sha256:76258350f2715d686d5da12a5a2df0d7b88e1b33e45052e0ddeb549c7497a56e", size = 4178867, upload-time = "2024-04-24T17:22:09.989Z" }, + { url = "https://files.pythonhosted.org/packages/0d/1d/a26c0a19b403986cd6d56142fe8b2c254211e7a64ec40e130e75edb1711c/aimrocks-0.5.2-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:79400c6f4c72bcc4485f2a4411a3e6c1f6ead7a3928a00a72739abb1ef9ec0d3", size = 3925297, upload-time = "2024-04-24T17:22:12.171Z" }, + { url = "https://files.pythonhosted.org/packages/e1/80/e0c226cbf975b109589c56b7aecc7ca182e3a62f67eb4a791ee033cee2d1/aimrocks-0.5.2-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:9faaecf4fe0335c27e63523f6a25e038877a33c63a261ff2192582e52493b39b", size = 6541866, upload-time = "2024-04-24T17:22:13.853Z" }, + { url = "https://files.pythonhosted.org/packages/e9/2b/109e6340b4ea052c762e61d386d79222855a2b0e27d6bb9ad7cd76d140b1/aimrocks-0.5.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a9ba647f32934ac999c4119cbb8b59510dfe69aec98558539b84db7db9f20acf", size = 6743116, upload-time = "2024-04-24T17:22:15.548Z" }, + { url = "https://files.pythonhosted.org/packages/69/44/8b381f01ca1a1cbdec71e5329908ea1f782a6eced10dbbfa8cd9e44858d4/aimrocks-0.5.2-cp312-cp312-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:533eda940f4bd1641fee15da09595c965d6890e449706fb3442174472b468a19", size = 6506051, upload-time = "2024-04-24T17:22:17.224Z" }, +] + +[[package]] +name = "aiofiles" +version = "25.1.0" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/41/c3/534eac40372d8ee36ef40df62ec129bee4fdb5ad9706e58a29be53b2c970/aiofiles-25.1.0.tar.gz", hash = "sha256:a8d728f0a29de45dc521f18f07297428d56992a742f0cd2701ba86e44d23d5b2", size = 46354, upload-time = "2025-10-09T20:51:04.358Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/bc/8a/340a1555ae33d7354dbca4faa54948d76d89a27ceef032c8c3bc661d003e/aiofiles-25.1.0-py3-none-any.whl", hash = "sha256:abe311e527c862958650f9438e859c1fa7568a141b22abcd015e120e86a85695", size = 14668, upload-time = "2025-10-09T20:51:03.174Z" }, +] + +[[package]] +name = "alembic" +version = "1.18.4" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "mako" }, + { name = "sqlalchemy" }, + { name = "typing-extensions" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/94/13/8b084e0f2efb0275a1d534838844926f798bd766566b1375174e2448cd31/alembic-1.18.4.tar.gz", hash = "sha256:cb6e1fd84b6174ab8dbb2329f86d631ba9559dd78df550b57804d607672cedbc", size = 2056725, upload-time = "2026-02-10T16:00:47.195Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/d2/29/6533c317b74f707ea28f8d633734dbda2119bbadfc61b2f3640ba835d0f7/alembic-1.18.4-py3-none-any.whl", hash = "sha256:a5ed4adcf6d8a4cb575f3d759f071b03cd6e5c7618eb796cb52497be25bfe19a", size = 263893, upload-time = "2026-02-10T16:00:49.997Z" }, +] + +[[package]] +name = "annotated-doc" +version = "0.0.4" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/57/ba/046ceea27344560984e26a590f90bc7f4a75b06701f653222458922b558c/annotated_doc-0.0.4.tar.gz", hash = "sha256:fbcda96e87e9c92ad167c2e53839e57503ecfda18804ea28102353485033faa4", size = 7288, upload-time = "2025-11-10T22:07:42.062Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/1e/d3/26bf1008eb3d2daa8ef4cacc7f3bfdc11818d111f7e2d0201bc6e3b49d45/annotated_doc-0.0.4-py3-none-any.whl", hash = "sha256:571ac1dc6991c450b25a9c2d84a3705e2ae7a53467b5d111c24fa8baabbed320", size = 5303, upload-time = "2025-11-10T22:07:40.673Z" }, +] + +[[package]] +name = "annotated-types" +version = "0.7.0" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/ee/67/531ea369ba64dcff5ec9c3402f9f51bf748cec26dde048a2f973a4eea7f5/annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89", size = 16081, upload-time = "2024-05-20T21:33:25.928Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/78/b6/6307fbef88d9b5ee7421e68d78a9f162e0da4900bc5f5793f6d3d0e34fb8/annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53", size = 13643, upload-time = "2024-05-20T21:33:24.1Z" }, +] + +[[package]] +name = "antlr4-python3-runtime" +version = "4.9.3" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/3e/38/7859ff46355f76f8d19459005ca000b6e7012f2f1ca597746cbcd1fbfe5e/antlr4-python3-runtime-4.9.3.tar.gz", hash = "sha256:f224469b4168294902bb1efa80a8bf7855f24c99aef99cbefc1bcd3cce77881b", size = 117034, upload-time = "2021-11-06T17:52:23.524Z" } + +[[package]] +name = "anyio" +version = "4.12.1" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "idna" }, + { name = "typing-extensions", marker = "python_full_version < '3.13'" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/96/f0/5eb65b2bb0d09ac6776f2eb54adee6abe8228ea05b20a5ad0e4945de8aac/anyio-4.12.1.tar.gz", hash = "sha256:41cfcc3a4c85d3f05c932da7c26d0201ac36f72abd4435ba90d0464a3ffed703", size = 228685, upload-time = "2026-01-06T11:45:21.246Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/38/0e/27be9fdef66e72d64c0cdc3cc2823101b80585f8119b5c112c2e8f5f7dab/anyio-4.12.1-py3-none-any.whl", hash = "sha256:d405828884fc140aa80a3c667b8beed277f1dfedec42ba031bd6ac3db606ab6c", size = 113592, upload-time = "2026-01-06T11:45:19.497Z" }, +] + +[[package]] +name = "base58" +version = "2.0.1" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/66/0c/44d075d9b07bb25c4733421057a46fe1847e9c64286e8af11458815ff872/base58-2.0.1.tar.gz", hash = "sha256:365c9561d9babac1b5f18ee797508cd54937a724b6e419a130abad69cec5ca79", size = 4960, upload-time = "2020-06-06T13:25:31.324Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/3c/03/58572025c77b9e6027155b272a1b96298e711cd4f95c24967f7137ab0c4b/base58-2.0.1-py3-none-any.whl", hash = "sha256:447adc750d6b642987ffc6d397ecd15a799852d5f6a1d308d384500243825058", size = 4347, upload-time = "2020-06-06T13:25:30.022Z" }, +] + +[[package]] +name = "boto3" +version = "1.42.56" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "botocore" }, + { name = "jmespath" }, + { name = "s3transfer" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/fe/dc/990222f8c0cd7453c03410bc8b367ccb35ae01d9642ff1f79231cd3f3d8f/boto3-1.42.56.tar.gz", hash = "sha256:8b0779431b349582f4d61b55a2858b943d5295f4b5de62e2d669fdbbcda96bd6", size = 112814, upload-time = "2026-02-24T20:28:51.825Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/59/bb/a7fd6f4f38cf0f6cd91c152442cbe5b5f270275a60a5c7147ae5560f2f25/boto3-1.42.56-py3-none-any.whl", hash = "sha256:e09f91170b19e0ecd5e514321d99323be3e91a58dfa73fd58cf73b7b8b179c84", size = 140556, upload-time = "2026-02-24T20:28:49.815Z" }, +] + +[[package]] +name = "botocore" +version = "1.42.56" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "jmespath" }, + { name = "python-dateutil" }, + { name = "urllib3" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/b8/2f/f6351cca2e3a087fb82a5c19e4d60e93a5dae27e9a085cc5fcb7faca8bd4/botocore-1.42.56.tar.gz", hash = "sha256:b1d7d3cf2fbe4cc1804a6567a051fc7141d21bcdcfde0336257b8dd2085272c2", size = 14939515, upload-time = "2026-02-24T20:28:40.55Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/5a/09/dcc3f79de57f684d844ca853eeebff1786e5d672cf600f8ee6a118a9f015/botocore-1.42.56-py3-none-any.whl", hash = "sha256:111089dea212438a5197e909e5b528e7c30fd8cbd02c8c7d469359b368929343", size = 14612466, upload-time = "2026-02-24T20:28:36.379Z" }, +] + +[[package]] +name = "cachetools" +version = "7.0.1" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/d4/07/56595285564e90777d758ebd383d6b0b971b87729bbe2184a849932a3736/cachetools-7.0.1.tar.gz", hash = "sha256:e31e579d2c5b6e2944177a0397150d312888ddf4e16e12f1016068f0c03b8341", size = 36126, upload-time = "2026-02-10T22:24:05.03Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/ed/9e/5faefbf9db1db466d633735faceda1f94aa99ce506ac450d232536266b32/cachetools-7.0.1-py3-none-any.whl", hash = "sha256:8f086515c254d5664ae2146d14fc7f65c9a4bce75152eb247e5a9c5e6d7b2ecf", size = 13484, upload-time = "2026-02-10T22:24:03.741Z" }, +] + +[[package]] +name = "certifi" +version = "2026.2.25" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/af/2d/7bf41579a8986e348fa033a31cdd0e4121114f6bce2457e8876010b092dd/certifi-2026.2.25.tar.gz", hash = "sha256:e887ab5cee78ea814d3472169153c2d12cd43b14bd03329a39a9c6e2e80bfba7", size = 155029, upload-time = "2026-02-25T02:54:17.342Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/9a/3c/c17fb3ca2d9c3acff52e30b309f538586f9f5b9c9cf454f3845fc9af4881/certifi-2026.2.25-py3-none-any.whl", hash = "sha256:027692e4402ad994f1c42e52a4997a9763c646b73e4096e4d5d6db8af1d6f0fa", size = 153684, upload-time = "2026-02-25T02:54:15.766Z" }, +] + +[[package]] +name = "cffi" +version = "2.0.0" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "pycparser", marker = "implementation_name != 'PyPy'" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/eb/56/b1ba7935a17738ae8453301356628e8147c79dbb825bcbc73dc7401f9846/cffi-2.0.0.tar.gz", hash = "sha256:44d1b5909021139fe36001ae048dbdde8214afa20200eda0f64c068cac5d5529", size = 523588, upload-time = "2025-09-08T23:24:04.541Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/ea/47/4f61023ea636104d4f16ab488e268b93008c3d0bb76893b1b31db1f96802/cffi-2.0.0-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:6d02d6655b0e54f54c4ef0b94eb6be0607b70853c45ce98bd278dc7de718be5d", size = 185271, upload-time = "2025-09-08T23:22:44.795Z" }, + { url = "https://files.pythonhosted.org/packages/df/a2/781b623f57358e360d62cdd7a8c681f074a71d445418a776eef0aadb4ab4/cffi-2.0.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:8eca2a813c1cb7ad4fb74d368c2ffbbb4789d377ee5bb8df98373c2cc0dee76c", size = 181048, upload-time = "2025-09-08T23:22:45.938Z" }, + { url = "https://files.pythonhosted.org/packages/ff/df/a4f0fbd47331ceeba3d37c2e51e9dfc9722498becbeec2bd8bc856c9538a/cffi-2.0.0-cp312-cp312-manylinux1_i686.manylinux2014_i686.manylinux_2_17_i686.manylinux_2_5_i686.whl", hash = "sha256:21d1152871b019407d8ac3985f6775c079416c282e431a4da6afe7aefd2bccbe", size = 212529, upload-time = "2025-09-08T23:22:47.349Z" }, + { url = "https://files.pythonhosted.org/packages/d5/72/12b5f8d3865bf0f87cf1404d8c374e7487dcf097a1c91c436e72e6badd83/cffi-2.0.0-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:b21e08af67b8a103c71a250401c78d5e0893beff75e28c53c98f4de42f774062", size = 220097, upload-time = "2025-09-08T23:22:48.677Z" }, + { url = "https://files.pythonhosted.org/packages/c2/95/7a135d52a50dfa7c882ab0ac17e8dc11cec9d55d2c18dda414c051c5e69e/cffi-2.0.0-cp312-cp312-manylinux2014_ppc64le.manylinux_2_17_ppc64le.whl", hash = "sha256:1e3a615586f05fc4065a8b22b8152f0c1b00cdbc60596d187c2a74f9e3036e4e", size = 207983, upload-time = "2025-09-08T23:22:50.06Z" }, + { url = "https://files.pythonhosted.org/packages/3a/c8/15cb9ada8895957ea171c62dc78ff3e99159ee7adb13c0123c001a2546c1/cffi-2.0.0-cp312-cp312-manylinux2014_s390x.manylinux_2_17_s390x.whl", hash = "sha256:81afed14892743bbe14dacb9e36d9e0e504cd204e0b165062c488942b9718037", size = 206519, upload-time = "2025-09-08T23:22:51.364Z" }, + { url = "https://files.pythonhosted.org/packages/78/2d/7fa73dfa841b5ac06c7b8855cfc18622132e365f5b81d02230333ff26e9e/cffi-2.0.0-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:3e17ed538242334bf70832644a32a7aae3d83b57567f9fd60a26257e992b79ba", size = 219572, upload-time = "2025-09-08T23:22:52.902Z" }, + { url = "https://files.pythonhosted.org/packages/07/e0/267e57e387b4ca276b90f0434ff88b2c2241ad72b16d31836adddfd6031b/cffi-2.0.0-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:3925dd22fa2b7699ed2617149842d2e6adde22b262fcbfada50e3d195e4b3a94", size = 222963, upload-time = "2025-09-08T23:22:54.518Z" }, + { url = "https://files.pythonhosted.org/packages/b6/75/1f2747525e06f53efbd878f4d03bac5b859cbc11c633d0fb81432d98a795/cffi-2.0.0-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:2c8f814d84194c9ea681642fd164267891702542f028a15fc97d4674b6206187", size = 221361, upload-time = "2025-09-08T23:22:55.867Z" }, + { url = "https://files.pythonhosted.org/packages/7b/2b/2b6435f76bfeb6bbf055596976da087377ede68df465419d192acf00c437/cffi-2.0.0-cp312-cp312-win32.whl", hash = "sha256:da902562c3e9c550df360bfa53c035b2f241fed6d9aef119048073680ace4a18", size = 172932, upload-time = "2025-09-08T23:22:57.188Z" }, + { url = "https://files.pythonhosted.org/packages/f8/ed/13bd4418627013bec4ed6e54283b1959cf6db888048c7cf4b4c3b5b36002/cffi-2.0.0-cp312-cp312-win_amd64.whl", hash = "sha256:da68248800ad6320861f129cd9c1bf96ca849a2771a59e0344e88681905916f5", size = 183557, upload-time = "2025-09-08T23:22:58.351Z" }, + { url = "https://files.pythonhosted.org/packages/95/31/9f7f93ad2f8eff1dbc1c3656d7ca5bfd8fb52c9d786b4dcf19b2d02217fa/cffi-2.0.0-cp312-cp312-win_arm64.whl", hash = "sha256:4671d9dd5ec934cb9a73e7ee9676f9362aba54f7f34910956b84d727b0d73fb6", size = 177762, upload-time = "2025-09-08T23:22:59.668Z" }, + { url = "https://files.pythonhosted.org/packages/4b/8d/a0a47a0c9e413a658623d014e91e74a50cdd2c423f7ccfd44086ef767f90/cffi-2.0.0-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:00bdf7acc5f795150faa6957054fbbca2439db2f775ce831222b66f192f03beb", size = 185230, upload-time = "2025-09-08T23:23:00.879Z" }, + { url = "https://files.pythonhosted.org/packages/4a/d2/a6c0296814556c68ee32009d9c2ad4f85f2707cdecfd7727951ec228005d/cffi-2.0.0-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:45d5e886156860dc35862657e1494b9bae8dfa63bf56796f2fb56e1679fc0bca", size = 181043, upload-time = "2025-09-08T23:23:02.231Z" }, + { url = "https://files.pythonhosted.org/packages/b0/1e/d22cc63332bd59b06481ceaac49d6c507598642e2230f201649058a7e704/cffi-2.0.0-cp313-cp313-manylinux1_i686.manylinux2014_i686.manylinux_2_17_i686.manylinux_2_5_i686.whl", hash = "sha256:07b271772c100085dd28b74fa0cd81c8fb1a3ba18b21e03d7c27f3436a10606b", size = 212446, upload-time = "2025-09-08T23:23:03.472Z" }, + { url = "https://files.pythonhosted.org/packages/a9/f5/a2c23eb03b61a0b8747f211eb716446c826ad66818ddc7810cc2cc19b3f2/cffi-2.0.0-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:d48a880098c96020b02d5a1f7d9251308510ce8858940e6fa99ece33f610838b", size = 220101, upload-time = "2025-09-08T23:23:04.792Z" }, + { url = "https://files.pythonhosted.org/packages/f2/7f/e6647792fc5850d634695bc0e6ab4111ae88e89981d35ac269956605feba/cffi-2.0.0-cp313-cp313-manylinux2014_ppc64le.manylinux_2_17_ppc64le.whl", hash = "sha256:f93fd8e5c8c0a4aa1f424d6173f14a892044054871c771f8566e4008eaa359d2", size = 207948, upload-time = "2025-09-08T23:23:06.127Z" }, + { url = "https://files.pythonhosted.org/packages/cb/1e/a5a1bd6f1fb30f22573f76533de12a00bf274abcdc55c8edab639078abb6/cffi-2.0.0-cp313-cp313-manylinux2014_s390x.manylinux_2_17_s390x.whl", hash = "sha256:dd4f05f54a52fb558f1ba9f528228066954fee3ebe629fc1660d874d040ae5a3", size = 206422, upload-time = "2025-09-08T23:23:07.753Z" }, + { url = "https://files.pythonhosted.org/packages/98/df/0a1755e750013a2081e863e7cd37e0cdd02664372c754e5560099eb7aa44/cffi-2.0.0-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:c8d3b5532fc71b7a77c09192b4a5a200ea992702734a2e9279a37f2478236f26", size = 219499, upload-time = "2025-09-08T23:23:09.648Z" }, + { url = "https://files.pythonhosted.org/packages/50/e1/a969e687fcf9ea58e6e2a928ad5e2dd88cc12f6f0ab477e9971f2309b57c/cffi-2.0.0-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:d9b29c1f0ae438d5ee9acb31cadee00a58c46cc9c0b2f9038c6b0b3470877a8c", size = 222928, upload-time = "2025-09-08T23:23:10.928Z" }, + { url = "https://files.pythonhosted.org/packages/36/54/0362578dd2c9e557a28ac77698ed67323ed5b9775ca9d3fe73fe191bb5d8/cffi-2.0.0-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:6d50360be4546678fc1b79ffe7a66265e28667840010348dd69a314145807a1b", size = 221302, upload-time = "2025-09-08T23:23:12.42Z" }, + { url = "https://files.pythonhosted.org/packages/eb/6d/bf9bda840d5f1dfdbf0feca87fbdb64a918a69bca42cfa0ba7b137c48cb8/cffi-2.0.0-cp313-cp313-win32.whl", hash = "sha256:74a03b9698e198d47562765773b4a8309919089150a0bb17d829ad7b44b60d27", size = 172909, upload-time = "2025-09-08T23:23:14.32Z" }, + { url = "https://files.pythonhosted.org/packages/37/18/6519e1ee6f5a1e579e04b9ddb6f1676c17368a7aba48299c3759bbc3c8b3/cffi-2.0.0-cp313-cp313-win_amd64.whl", hash = "sha256:19f705ada2530c1167abacb171925dd886168931e0a7b78f5bffcae5c6b5be75", size = 183402, upload-time = "2025-09-08T23:23:15.535Z" }, + { url = "https://files.pythonhosted.org/packages/cb/0e/02ceeec9a7d6ee63bb596121c2c8e9b3a9e150936f4fbef6ca1943e6137c/cffi-2.0.0-cp313-cp313-win_arm64.whl", hash = "sha256:256f80b80ca3853f90c21b23ee78cd008713787b1b1e93eae9f3d6a7134abd91", size = 177780, upload-time = "2025-09-08T23:23:16.761Z" }, + { url = "https://files.pythonhosted.org/packages/92/c4/3ce07396253a83250ee98564f8d7e9789fab8e58858f35d07a9a2c78de9f/cffi-2.0.0-cp314-cp314-macosx_10_13_x86_64.whl", hash = "sha256:fc33c5141b55ed366cfaad382df24fe7dcbc686de5be719b207bb248e3053dc5", size = 185320, upload-time = "2025-09-08T23:23:18.087Z" }, + { url = "https://files.pythonhosted.org/packages/59/dd/27e9fa567a23931c838c6b02d0764611c62290062a6d4e8ff7863daf9730/cffi-2.0.0-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:c654de545946e0db659b3400168c9ad31b5d29593291482c43e3564effbcee13", size = 181487, upload-time = "2025-09-08T23:23:19.622Z" }, + { url = "https://files.pythonhosted.org/packages/d6/43/0e822876f87ea8a4ef95442c3d766a06a51fc5298823f884ef87aaad168c/cffi-2.0.0-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:24b6f81f1983e6df8db3adc38562c83f7d4a0c36162885ec7f7b77c7dcbec97b", size = 220049, upload-time = "2025-09-08T23:23:20.853Z" }, + { url = "https://files.pythonhosted.org/packages/b4/89/76799151d9c2d2d1ead63c2429da9ea9d7aac304603de0c6e8764e6e8e70/cffi-2.0.0-cp314-cp314-manylinux2014_ppc64le.manylinux_2_17_ppc64le.whl", hash = "sha256:12873ca6cb9b0f0d3a0da705d6086fe911591737a59f28b7936bdfed27c0d47c", size = 207793, upload-time = "2025-09-08T23:23:22.08Z" }, + { url = "https://files.pythonhosted.org/packages/bb/dd/3465b14bb9e24ee24cb88c9e3730f6de63111fffe513492bf8c808a3547e/cffi-2.0.0-cp314-cp314-manylinux2014_s390x.manylinux_2_17_s390x.whl", hash = "sha256:d9b97165e8aed9272a6bb17c01e3cc5871a594a446ebedc996e2397a1c1ea8ef", size = 206300, upload-time = "2025-09-08T23:23:23.314Z" }, + { url = "https://files.pythonhosted.org/packages/47/d9/d83e293854571c877a92da46fdec39158f8d7e68da75bf73581225d28e90/cffi-2.0.0-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:afb8db5439b81cf9c9d0c80404b60c3cc9c3add93e114dcae767f1477cb53775", size = 219244, upload-time = "2025-09-08T23:23:24.541Z" }, + { url = "https://files.pythonhosted.org/packages/2b/0f/1f177e3683aead2bb00f7679a16451d302c436b5cbf2505f0ea8146ef59e/cffi-2.0.0-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:737fe7d37e1a1bffe70bd5754ea763a62a066dc5913ca57e957824b72a85e205", size = 222828, upload-time = "2025-09-08T23:23:26.143Z" }, + { url = "https://files.pythonhosted.org/packages/c6/0f/cafacebd4b040e3119dcb32fed8bdef8dfe94da653155f9d0b9dc660166e/cffi-2.0.0-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:38100abb9d1b1435bc4cc340bb4489635dc2f0da7456590877030c9b3d40b0c1", size = 220926, upload-time = "2025-09-08T23:23:27.873Z" }, + { url = "https://files.pythonhosted.org/packages/3e/aa/df335faa45b395396fcbc03de2dfcab242cd61a9900e914fe682a59170b1/cffi-2.0.0-cp314-cp314-win32.whl", hash = "sha256:087067fa8953339c723661eda6b54bc98c5625757ea62e95eb4898ad5e776e9f", size = 175328, upload-time = "2025-09-08T23:23:44.61Z" }, + { url = "https://files.pythonhosted.org/packages/bb/92/882c2d30831744296ce713f0feb4c1cd30f346ef747b530b5318715cc367/cffi-2.0.0-cp314-cp314-win_amd64.whl", hash = "sha256:203a48d1fb583fc7d78a4c6655692963b860a417c0528492a6bc21f1aaefab25", size = 185650, upload-time = "2025-09-08T23:23:45.848Z" }, + { url = "https://files.pythonhosted.org/packages/9f/2c/98ece204b9d35a7366b5b2c6539c350313ca13932143e79dc133ba757104/cffi-2.0.0-cp314-cp314-win_arm64.whl", hash = "sha256:dbd5c7a25a7cb98f5ca55d258b103a2054f859a46ae11aaf23134f9cc0d356ad", size = 180687, upload-time = "2025-09-08T23:23:47.105Z" }, + { url = "https://files.pythonhosted.org/packages/3e/61/c768e4d548bfa607abcda77423448df8c471f25dbe64fb2ef6d555eae006/cffi-2.0.0-cp314-cp314t-macosx_10_13_x86_64.whl", hash = "sha256:9a67fc9e8eb39039280526379fb3a70023d77caec1852002b4da7e8b270c4dd9", size = 188773, upload-time = "2025-09-08T23:23:29.347Z" }, + { url = "https://files.pythonhosted.org/packages/2c/ea/5f76bce7cf6fcd0ab1a1058b5af899bfbef198bea4d5686da88471ea0336/cffi-2.0.0-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:7a66c7204d8869299919db4d5069a82f1561581af12b11b3c9f48c584eb8743d", size = 185013, upload-time = "2025-09-08T23:23:30.63Z" }, + { url = "https://files.pythonhosted.org/packages/be/b4/c56878d0d1755cf9caa54ba71e5d049479c52f9e4afc230f06822162ab2f/cffi-2.0.0-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:7cc09976e8b56f8cebd752f7113ad07752461f48a58cbba644139015ac24954c", size = 221593, upload-time = "2025-09-08T23:23:31.91Z" }, + { url = "https://files.pythonhosted.org/packages/e0/0d/eb704606dfe8033e7128df5e90fee946bbcb64a04fcdaa97321309004000/cffi-2.0.0-cp314-cp314t-manylinux2014_ppc64le.manylinux_2_17_ppc64le.whl", hash = "sha256:92b68146a71df78564e4ef48af17551a5ddd142e5190cdf2c5624d0c3ff5b2e8", size = 209354, upload-time = "2025-09-08T23:23:33.214Z" }, + { url = "https://files.pythonhosted.org/packages/d8/19/3c435d727b368ca475fb8742ab97c9cb13a0de600ce86f62eab7fa3eea60/cffi-2.0.0-cp314-cp314t-manylinux2014_s390x.manylinux_2_17_s390x.whl", hash = "sha256:b1e74d11748e7e98e2f426ab176d4ed720a64412b6a15054378afdb71e0f37dc", size = 208480, upload-time = "2025-09-08T23:23:34.495Z" }, + { url = "https://files.pythonhosted.org/packages/d0/44/681604464ed9541673e486521497406fadcc15b5217c3e326b061696899a/cffi-2.0.0-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:28a3a209b96630bca57cce802da70c266eb08c6e97e5afd61a75611ee6c64592", size = 221584, upload-time = "2025-09-08T23:23:36.096Z" }, + { url = "https://files.pythonhosted.org/packages/25/8e/342a504ff018a2825d395d44d63a767dd8ebc927ebda557fecdaca3ac33a/cffi-2.0.0-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:7553fb2090d71822f02c629afe6042c299edf91ba1bf94951165613553984512", size = 224443, upload-time = "2025-09-08T23:23:37.328Z" }, + { url = "https://files.pythonhosted.org/packages/e1/5e/b666bacbbc60fbf415ba9988324a132c9a7a0448a9a8f125074671c0f2c3/cffi-2.0.0-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:6c6c373cfc5c83a975506110d17457138c8c63016b563cc9ed6e056a82f13ce4", size = 223437, upload-time = "2025-09-08T23:23:38.945Z" }, + { url = "https://files.pythonhosted.org/packages/a0/1d/ec1a60bd1a10daa292d3cd6bb0b359a81607154fb8165f3ec95fe003b85c/cffi-2.0.0-cp314-cp314t-win32.whl", hash = "sha256:1fc9ea04857caf665289b7a75923f2c6ed559b8298a1b8c49e59f7dd95c8481e", size = 180487, upload-time = "2025-09-08T23:23:40.423Z" }, + { url = "https://files.pythonhosted.org/packages/bf/41/4c1168c74fac325c0c8156f04b6749c8b6a8f405bbf91413ba088359f60d/cffi-2.0.0-cp314-cp314t-win_amd64.whl", hash = "sha256:d68b6cef7827e8641e8ef16f4494edda8b36104d79773a334beaa1e3521430f6", size = 191726, upload-time = "2025-09-08T23:23:41.742Z" }, + { url = "https://files.pythonhosted.org/packages/ae/3a/dbeec9d1ee0844c679f6bb5d6ad4e9f198b1224f4e7a32825f47f6192b0c/cffi-2.0.0-cp314-cp314t-win_arm64.whl", hash = "sha256:0a1527a803f0a659de1af2e1fd700213caba79377e27e4693648c2923da066f9", size = 184195, upload-time = "2025-09-08T23:23:43.004Z" }, +] + +[[package]] +name = "charset-normalizer" +version = "3.4.4" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/13/69/33ddede1939fdd074bce5434295f38fae7136463422fe4fd3e0e89b98062/charset_normalizer-3.4.4.tar.gz", hash = "sha256:94537985111c35f28720e43603b8e7b43a6ecfb2ce1d3058bbe955b73404e21a", size = 129418, upload-time = "2025-10-14T04:42:32.879Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/f3/85/1637cd4af66fa687396e757dec650f28025f2a2f5a5531a3208dc0ec43f2/charset_normalizer-3.4.4-cp312-cp312-macosx_10_13_universal2.whl", hash = "sha256:0a98e6759f854bd25a58a73fa88833fba3b7c491169f86ce1180c948ab3fd394", size = 208425, upload-time = "2025-10-14T04:40:53.353Z" }, + { url = "https://files.pythonhosted.org/packages/9d/6a/04130023fef2a0d9c62d0bae2649b69f7b7d8d24ea5536feef50551029df/charset_normalizer-3.4.4-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:b5b290ccc2a263e8d185130284f8501e3e36c5e02750fc6b6bdeb2e9e96f1e25", size = 148162, upload-time = "2025-10-14T04:40:54.558Z" }, + { url = "https://files.pythonhosted.org/packages/78/29/62328d79aa60da22c9e0b9a66539feae06ca0f5a4171ac4f7dc285b83688/charset_normalizer-3.4.4-cp312-cp312-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:74bb723680f9f7a6234dcf67aea57e708ec1fbdf5699fb91dfd6f511b0a320ef", size = 144558, upload-time = "2025-10-14T04:40:55.677Z" }, + { url = "https://files.pythonhosted.org/packages/86/bb/b32194a4bf15b88403537c2e120b817c61cd4ecffa9b6876e941c3ee38fe/charset_normalizer-3.4.4-cp312-cp312-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:f1e34719c6ed0b92f418c7c780480b26b5d9c50349e9a9af7d76bf757530350d", size = 161497, upload-time = "2025-10-14T04:40:57.217Z" }, + { url = "https://files.pythonhosted.org/packages/19/89/a54c82b253d5b9b111dc74aca196ba5ccfcca8242d0fb64146d4d3183ff1/charset_normalizer-3.4.4-cp312-cp312-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:2437418e20515acec67d86e12bf70056a33abdacb5cb1655042f6538d6b085a8", size = 159240, upload-time = "2025-10-14T04:40:58.358Z" }, + { url = "https://files.pythonhosted.org/packages/c0/10/d20b513afe03acc89ec33948320a5544d31f21b05368436d580dec4e234d/charset_normalizer-3.4.4-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:11d694519d7f29d6cd09f6ac70028dba10f92f6cdd059096db198c283794ac86", size = 153471, upload-time = "2025-10-14T04:40:59.468Z" }, + { url = "https://files.pythonhosted.org/packages/61/fa/fbf177b55bdd727010f9c0a3c49eefa1d10f960e5f09d1d887bf93c2e698/charset_normalizer-3.4.4-cp312-cp312-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:ac1c4a689edcc530fc9d9aa11f5774b9e2f33f9a0c6a57864e90908f5208d30a", size = 150864, upload-time = "2025-10-14T04:41:00.623Z" }, + { url = "https://files.pythonhosted.org/packages/05/12/9fbc6a4d39c0198adeebbde20b619790e9236557ca59fc40e0e3cebe6f40/charset_normalizer-3.4.4-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:21d142cc6c0ec30d2efee5068ca36c128a30b0f2c53c1c07bd78cb6bc1d3be5f", size = 150647, upload-time = "2025-10-14T04:41:01.754Z" }, + { url = "https://files.pythonhosted.org/packages/ad/1f/6a9a593d52e3e8c5d2b167daf8c6b968808efb57ef4c210acb907c365bc4/charset_normalizer-3.4.4-cp312-cp312-musllinux_1_2_armv7l.whl", hash = "sha256:5dbe56a36425d26d6cfb40ce79c314a2e4dd6211d51d6d2191c00bed34f354cc", size = 145110, upload-time = "2025-10-14T04:41:03.231Z" }, + { url = "https://files.pythonhosted.org/packages/30/42/9a52c609e72471b0fc54386dc63c3781a387bb4fe61c20231a4ebcd58bdd/charset_normalizer-3.4.4-cp312-cp312-musllinux_1_2_ppc64le.whl", hash = "sha256:5bfbb1b9acf3334612667b61bd3002196fe2a1eb4dd74d247e0f2a4d50ec9bbf", size = 162839, upload-time = "2025-10-14T04:41:04.715Z" }, + { url = "https://files.pythonhosted.org/packages/c4/5b/c0682bbf9f11597073052628ddd38344a3d673fda35a36773f7d19344b23/charset_normalizer-3.4.4-cp312-cp312-musllinux_1_2_riscv64.whl", hash = "sha256:d055ec1e26e441f6187acf818b73564e6e6282709e9bcb5b63f5b23068356a15", size = 150667, upload-time = "2025-10-14T04:41:05.827Z" }, + { url = "https://files.pythonhosted.org/packages/e4/24/a41afeab6f990cf2daf6cb8c67419b63b48cf518e4f56022230840c9bfb2/charset_normalizer-3.4.4-cp312-cp312-musllinux_1_2_s390x.whl", hash = "sha256:af2d8c67d8e573d6de5bc30cdb27e9b95e49115cd9baad5ddbd1a6207aaa82a9", size = 160535, upload-time = "2025-10-14T04:41:06.938Z" }, + { url = "https://files.pythonhosted.org/packages/2a/e5/6a4ce77ed243c4a50a1fecca6aaaab419628c818a49434be428fe24c9957/charset_normalizer-3.4.4-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:780236ac706e66881f3b7f2f32dfe90507a09e67d1d454c762cf642e6e1586e0", size = 154816, upload-time = "2025-10-14T04:41:08.101Z" }, + { url = "https://files.pythonhosted.org/packages/a8/ef/89297262b8092b312d29cdb2517cb1237e51db8ecef2e9af5edbe7b683b1/charset_normalizer-3.4.4-cp312-cp312-win32.whl", hash = "sha256:5833d2c39d8896e4e19b689ffc198f08ea58116bee26dea51e362ecc7cd3ed26", size = 99694, upload-time = "2025-10-14T04:41:09.23Z" }, + { url = "https://files.pythonhosted.org/packages/3d/2d/1e5ed9dd3b3803994c155cd9aacb60c82c331bad84daf75bcb9c91b3295e/charset_normalizer-3.4.4-cp312-cp312-win_amd64.whl", hash = "sha256:a79cfe37875f822425b89a82333404539ae63dbdddf97f84dcbc3d339aae9525", size = 107131, upload-time = "2025-10-14T04:41:10.467Z" }, + { url = "https://files.pythonhosted.org/packages/d0/d9/0ed4c7098a861482a7b6a95603edce4c0d9db2311af23da1fb2b75ec26fc/charset_normalizer-3.4.4-cp312-cp312-win_arm64.whl", hash = "sha256:376bec83a63b8021bb5c8ea75e21c4ccb86e7e45ca4eb81146091b56599b80c3", size = 100390, upload-time = "2025-10-14T04:41:11.915Z" }, + { url = "https://files.pythonhosted.org/packages/97/45/4b3a1239bbacd321068ea6e7ac28875b03ab8bc0aa0966452db17cd36714/charset_normalizer-3.4.4-cp313-cp313-macosx_10_13_universal2.whl", hash = "sha256:e1f185f86a6f3403aa2420e815904c67b2f9ebc443f045edd0de921108345794", size = 208091, upload-time = "2025-10-14T04:41:13.346Z" }, + { url = "https://files.pythonhosted.org/packages/7d/62/73a6d7450829655a35bb88a88fca7d736f9882a27eacdca2c6d505b57e2e/charset_normalizer-3.4.4-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:6b39f987ae8ccdf0d2642338faf2abb1862340facc796048b604ef14919e55ed", size = 147936, upload-time = "2025-10-14T04:41:14.461Z" }, + { url = "https://files.pythonhosted.org/packages/89/c5/adb8c8b3d6625bef6d88b251bbb0d95f8205831b987631ab0c8bb5d937c2/charset_normalizer-3.4.4-cp313-cp313-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:3162d5d8ce1bb98dd51af660f2121c55d0fa541b46dff7bb9b9f86ea1d87de72", size = 144180, upload-time = "2025-10-14T04:41:15.588Z" }, + { url = "https://files.pythonhosted.org/packages/91/ed/9706e4070682d1cc219050b6048bfd293ccf67b3d4f5a4f39207453d4b99/charset_normalizer-3.4.4-cp313-cp313-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:81d5eb2a312700f4ecaa977a8235b634ce853200e828fbadf3a9c50bab278328", size = 161346, upload-time = "2025-10-14T04:41:16.738Z" }, + { url = "https://files.pythonhosted.org/packages/d5/0d/031f0d95e4972901a2f6f09ef055751805ff541511dc1252ba3ca1f80cf5/charset_normalizer-3.4.4-cp313-cp313-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:5bd2293095d766545ec1a8f612559f6b40abc0eb18bb2f5d1171872d34036ede", size = 158874, upload-time = "2025-10-14T04:41:17.923Z" }, + { url = "https://files.pythonhosted.org/packages/f5/83/6ab5883f57c9c801ce5e5677242328aa45592be8a00644310a008d04f922/charset_normalizer-3.4.4-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:a8a8b89589086a25749f471e6a900d3f662d1d3b6e2e59dcecf787b1cc3a1894", size = 153076, upload-time = "2025-10-14T04:41:19.106Z" }, + { url = "https://files.pythonhosted.org/packages/75/1e/5ff781ddf5260e387d6419959ee89ef13878229732732ee73cdae01800f2/charset_normalizer-3.4.4-cp313-cp313-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:bc7637e2f80d8530ee4a78e878bce464f70087ce73cf7c1caf142416923b98f1", size = 150601, upload-time = "2025-10-14T04:41:20.245Z" }, + { url = "https://files.pythonhosted.org/packages/d7/57/71be810965493d3510a6ca79b90c19e48696fb1ff964da319334b12677f0/charset_normalizer-3.4.4-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:f8bf04158c6b607d747e93949aa60618b61312fe647a6369f88ce2ff16043490", size = 150376, upload-time = "2025-10-14T04:41:21.398Z" }, + { url = "https://files.pythonhosted.org/packages/e5/d5/c3d057a78c181d007014feb7e9f2e65905a6c4ef182c0ddf0de2924edd65/charset_normalizer-3.4.4-cp313-cp313-musllinux_1_2_armv7l.whl", hash = "sha256:554af85e960429cf30784dd47447d5125aaa3b99a6f0683589dbd27e2f45da44", size = 144825, upload-time = "2025-10-14T04:41:22.583Z" }, + { url = "https://files.pythonhosted.org/packages/e6/8c/d0406294828d4976f275ffbe66f00266c4b3136b7506941d87c00cab5272/charset_normalizer-3.4.4-cp313-cp313-musllinux_1_2_ppc64le.whl", hash = "sha256:74018750915ee7ad843a774364e13a3db91682f26142baddf775342c3f5b1133", size = 162583, upload-time = "2025-10-14T04:41:23.754Z" }, + { url = "https://files.pythonhosted.org/packages/d7/24/e2aa1f18c8f15c4c0e932d9287b8609dd30ad56dbe41d926bd846e22fb8d/charset_normalizer-3.4.4-cp313-cp313-musllinux_1_2_riscv64.whl", hash = "sha256:c0463276121fdee9c49b98908b3a89c39be45d86d1dbaa22957e38f6321d4ce3", size = 150366, upload-time = "2025-10-14T04:41:25.27Z" }, + { url = "https://files.pythonhosted.org/packages/e4/5b/1e6160c7739aad1e2df054300cc618b06bf784a7a164b0f238360721ab86/charset_normalizer-3.4.4-cp313-cp313-musllinux_1_2_s390x.whl", hash = "sha256:362d61fd13843997c1c446760ef36f240cf81d3ebf74ac62652aebaf7838561e", size = 160300, upload-time = "2025-10-14T04:41:26.725Z" }, + { url = "https://files.pythonhosted.org/packages/7a/10/f882167cd207fbdd743e55534d5d9620e095089d176d55cb22d5322f2afd/charset_normalizer-3.4.4-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:9a26f18905b8dd5d685d6d07b0cdf98a79f3c7a918906af7cc143ea2e164c8bc", size = 154465, upload-time = "2025-10-14T04:41:28.322Z" }, + { url = "https://files.pythonhosted.org/packages/89/66/c7a9e1b7429be72123441bfdbaf2bc13faab3f90b933f664db506dea5915/charset_normalizer-3.4.4-cp313-cp313-win32.whl", hash = "sha256:9b35f4c90079ff2e2edc5b26c0c77925e5d2d255c42c74fdb70fb49b172726ac", size = 99404, upload-time = "2025-10-14T04:41:29.95Z" }, + { url = "https://files.pythonhosted.org/packages/c4/26/b9924fa27db384bdcd97ab83b4f0a8058d96ad9626ead570674d5e737d90/charset_normalizer-3.4.4-cp313-cp313-win_amd64.whl", hash = "sha256:b435cba5f4f750aa6c0a0d92c541fb79f69a387c91e61f1795227e4ed9cece14", size = 107092, upload-time = "2025-10-14T04:41:31.188Z" }, + { url = "https://files.pythonhosted.org/packages/af/8f/3ed4bfa0c0c72a7ca17f0380cd9e4dd842b09f664e780c13cff1dcf2ef1b/charset_normalizer-3.4.4-cp313-cp313-win_arm64.whl", hash = "sha256:542d2cee80be6f80247095cc36c418f7bddd14f4a6de45af91dfad36d817bba2", size = 100408, upload-time = "2025-10-14T04:41:32.624Z" }, + { url = "https://files.pythonhosted.org/packages/2a/35/7051599bd493e62411d6ede36fd5af83a38f37c4767b92884df7301db25d/charset_normalizer-3.4.4-cp314-cp314-macosx_10_13_universal2.whl", hash = "sha256:da3326d9e65ef63a817ecbcc0df6e94463713b754fe293eaa03da99befb9a5bd", size = 207746, upload-time = "2025-10-14T04:41:33.773Z" }, + { url = "https://files.pythonhosted.org/packages/10/9a/97c8d48ef10d6cd4fcead2415523221624bf58bcf68a802721a6bc807c8f/charset_normalizer-3.4.4-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:8af65f14dc14a79b924524b1e7fffe304517b2bff5a58bf64f30b98bbc5079eb", size = 147889, upload-time = "2025-10-14T04:41:34.897Z" }, + { url = "https://files.pythonhosted.org/packages/10/bf/979224a919a1b606c82bd2c5fa49b5c6d5727aa47b4312bb27b1734f53cd/charset_normalizer-3.4.4-cp314-cp314-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:74664978bb272435107de04e36db5a9735e78232b85b77d45cfb38f758efd33e", size = 143641, upload-time = "2025-10-14T04:41:36.116Z" }, + { url = "https://files.pythonhosted.org/packages/ba/33/0ad65587441fc730dc7bd90e9716b30b4702dc7b617e6ba4997dc8651495/charset_normalizer-3.4.4-cp314-cp314-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:752944c7ffbfdd10c074dc58ec2d5a8a4cd9493b314d367c14d24c17684ddd14", size = 160779, upload-time = "2025-10-14T04:41:37.229Z" }, + { url = "https://files.pythonhosted.org/packages/67/ed/331d6b249259ee71ddea93f6f2f0a56cfebd46938bde6fcc6f7b9a3d0e09/charset_normalizer-3.4.4-cp314-cp314-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:d1f13550535ad8cff21b8d757a3257963e951d96e20ec82ab44bc64aeb62a191", size = 159035, upload-time = "2025-10-14T04:41:38.368Z" }, + { url = "https://files.pythonhosted.org/packages/67/ff/f6b948ca32e4f2a4576aa129d8bed61f2e0543bf9f5f2b7fc3758ed005c9/charset_normalizer-3.4.4-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:ecaae4149d99b1c9e7b88bb03e3221956f68fd6d50be2ef061b2381b61d20838", size = 152542, upload-time = "2025-10-14T04:41:39.862Z" }, + { url = "https://files.pythonhosted.org/packages/16/85/276033dcbcc369eb176594de22728541a925b2632f9716428c851b149e83/charset_normalizer-3.4.4-cp314-cp314-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:cb6254dc36b47a990e59e1068afacdcd02958bdcce30bb50cc1700a8b9d624a6", size = 149524, upload-time = "2025-10-14T04:41:41.319Z" }, + { url = "https://files.pythonhosted.org/packages/9e/f2/6a2a1f722b6aba37050e626530a46a68f74e63683947a8acff92569f979a/charset_normalizer-3.4.4-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:c8ae8a0f02f57a6e61203a31428fa1d677cbe50c93622b4149d5c0f319c1d19e", size = 150395, upload-time = "2025-10-14T04:41:42.539Z" }, + { url = "https://files.pythonhosted.org/packages/60/bb/2186cb2f2bbaea6338cad15ce23a67f9b0672929744381e28b0592676824/charset_normalizer-3.4.4-cp314-cp314-musllinux_1_2_armv7l.whl", hash = "sha256:47cc91b2f4dd2833fddaedd2893006b0106129d4b94fdb6af1f4ce5a9965577c", size = 143680, upload-time = "2025-10-14T04:41:43.661Z" }, + { url = "https://files.pythonhosted.org/packages/7d/a5/bf6f13b772fbb2a90360eb620d52ed8f796f3c5caee8398c3b2eb7b1c60d/charset_normalizer-3.4.4-cp314-cp314-musllinux_1_2_ppc64le.whl", hash = "sha256:82004af6c302b5d3ab2cfc4cc5f29db16123b1a8417f2e25f9066f91d4411090", size = 162045, upload-time = "2025-10-14T04:41:44.821Z" }, + { url = "https://files.pythonhosted.org/packages/df/c5/d1be898bf0dc3ef9030c3825e5d3b83f2c528d207d246cbabe245966808d/charset_normalizer-3.4.4-cp314-cp314-musllinux_1_2_riscv64.whl", hash = "sha256:2b7d8f6c26245217bd2ad053761201e9f9680f8ce52f0fcd8d0755aeae5b2152", size = 149687, upload-time = "2025-10-14T04:41:46.442Z" }, + { url = "https://files.pythonhosted.org/packages/a5/42/90c1f7b9341eef50c8a1cb3f098ac43b0508413f33affd762855f67a410e/charset_normalizer-3.4.4-cp314-cp314-musllinux_1_2_s390x.whl", hash = "sha256:799a7a5e4fb2d5898c60b640fd4981d6a25f1c11790935a44ce38c54e985f828", size = 160014, upload-time = "2025-10-14T04:41:47.631Z" }, + { url = "https://files.pythonhosted.org/packages/76/be/4d3ee471e8145d12795ab655ece37baed0929462a86e72372fd25859047c/charset_normalizer-3.4.4-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:99ae2cffebb06e6c22bdc25801d7b30f503cc87dbd283479e7b606f70aff57ec", size = 154044, upload-time = "2025-10-14T04:41:48.81Z" }, + { url = "https://files.pythonhosted.org/packages/b0/6f/8f7af07237c34a1defe7defc565a9bc1807762f672c0fde711a4b22bf9c0/charset_normalizer-3.4.4-cp314-cp314-win32.whl", hash = "sha256:f9d332f8c2a2fcbffe1378594431458ddbef721c1769d78e2cbc06280d8155f9", size = 99940, upload-time = "2025-10-14T04:41:49.946Z" }, + { url = "https://files.pythonhosted.org/packages/4b/51/8ade005e5ca5b0d80fb4aff72a3775b325bdc3d27408c8113811a7cbe640/charset_normalizer-3.4.4-cp314-cp314-win_amd64.whl", hash = "sha256:8a6562c3700cce886c5be75ade4a5db4214fda19fede41d9792d100288d8f94c", size = 107104, upload-time = "2025-10-14T04:41:51.051Z" }, + { url = "https://files.pythonhosted.org/packages/da/5f/6b8f83a55bb8278772c5ae54a577f3099025f9ade59d0136ac24a0df4bde/charset_normalizer-3.4.4-cp314-cp314-win_arm64.whl", hash = "sha256:de00632ca48df9daf77a2c65a484531649261ec9f25489917f09e455cb09ddb2", size = 100743, upload-time = "2025-10-14T04:41:52.122Z" }, + { url = "https://files.pythonhosted.org/packages/0a/4c/925909008ed5a988ccbb72dcc897407e5d6d3bd72410d69e051fc0c14647/charset_normalizer-3.4.4-py3-none-any.whl", hash = "sha256:7a32c560861a02ff789ad905a2fe94e3f840803362c84fecf1851cb4cf3dc37f", size = 53402, upload-time = "2025-10-14T04:42:31.76Z" }, +] + +[[package]] +name = "click" +version = "8.3.1" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "colorama", marker = "sys_platform == 'win32'" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/3d/fa/656b739db8587d7b5dfa22e22ed02566950fbfbcdc20311993483657a5c0/click-8.3.1.tar.gz", hash = "sha256:12ff4785d337a1bb490bb7e9c2b1ee5da3112e94a8622f26a6c77f5d2fc6842a", size = 295065, upload-time = "2025-11-15T20:45:42.706Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/98/78/01c019cdb5d6498122777c1a43056ebb3ebfeef2076d9d026bfe15583b2b/click-8.3.1-py3-none-any.whl", hash = "sha256:981153a64e25f12d547d3426c367a4857371575ee7ad18df2a6183ab0545b2a6", size = 108274, upload-time = "2025-11-15T20:45:41.139Z" }, +] + +[[package]] +name = "clingo" +version = "5.8.0" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "cffi" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/f0/05/1a8a34eea27e73e4c6f7c9d0654b4fd7f509a5fbbb7b3eae7735e5ecd1c2/clingo-5.8.0.tar.gz", hash = "sha256:e557d0ab209bfe185e4ae9dde531ca777be1b72a1a13a5d169926a1f9e4c4480", size = 1910050, upload-time = "2025-04-03T14:08:36.354Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/c7/91/07e2a0bfb0bd95e11872ade8d48d44d698f958b929e578bfe43d9cea5841/clingo-5.8.0-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:e77f1a13f26023f1c5f944175ab6f503a00281f47377336d680093c9c2d095bf", size = 1766131, upload-time = "2025-04-03T14:06:24.418Z" }, + { url = "https://files.pythonhosted.org/packages/9c/1e/22fa4b240a85d387ad9928e2e0644637a5015785fa78763788847291fa19/clingo-5.8.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:93d9ff85adc097ee8989954249a9d1401cb51434640fef8d49619e16d4e61874", size = 1620692, upload-time = "2025-04-03T14:06:25.843Z" }, + { url = "https://files.pythonhosted.org/packages/37/5e/3580a1edc73743443c47ea4a6aee53134bd16844720603c1ce3cab2e66e3/clingo-5.8.0-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:dbc347646d78807381f8215de329d7691999abdaac2c6c68cffeb0441d195c54", size = 2156565, upload-time = "2025-04-03T14:06:27.418Z" }, + { url = "https://files.pythonhosted.org/packages/90/6d/b1c7c8c965bcd9a25c4ca5d1468856a645899e087ac8bd20514c9eeaf056/clingo-5.8.0-cp312-cp312-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:207c2e3fc3315cef2475cb1cbd0a143f70145079c09db76af58d5ed41fb9936e", size = 2428016, upload-time = "2025-04-03T14:06:28.783Z" }, + { url = "https://files.pythonhosted.org/packages/72/25/6e644329e627b252aacf800fcabe71904ebe5012e5b4ee1f4f9ba7c1cbfc/clingo-5.8.0-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:32410a2120f77aa2865fdffa0fba6c593ccb835a6875364f1999cad27dcb3934", size = 2518051, upload-time = "2025-04-03T14:06:30.16Z" }, + { url = "https://files.pythonhosted.org/packages/c7/91/af816e99939d0931d374b39acfb7b9f85eee1972a3e4c293a7230f424da6/clingo-5.8.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:1d9f85bf0385a364465cdeb8e0b0ed4087d7ad8ed849f1b0d55e709324a25f19", size = 2259925, upload-time = "2025-04-03T14:06:31.601Z" }, + { url = "https://files.pythonhosted.org/packages/d5/3b/abab5f23f530c2251afffa93fe58ebdbd26aa6bc0dfcf76ecbdd7fc20160/clingo-5.8.0-cp312-cp312-musllinux_1_2_i686.whl", hash = "sha256:cb4262b80bff8eaf3a0f86308c914692d18d928a4a2fa6a52c060b0a13bb9c79", size = 3542575, upload-time = "2025-04-03T14:06:33.479Z" }, + { url = "https://files.pythonhosted.org/packages/a7/fa/f5e672cc4408483dd911fa2814056aba990f20459bde6272f6803231aa7a/clingo-5.8.0-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:f233880f18783c3a174784264cb8fd0705c5615c88a50f36b5e330b3b0dc0335", size = 3265135, upload-time = "2025-04-03T14:06:34.856Z" }, + { url = "https://files.pythonhosted.org/packages/45/fd/9705cdded1766ed060dc4ff231e3a9c0c0bc8f711ac2bd66a328423c08eb/clingo-5.8.0-cp312-cp312-win32.whl", hash = "sha256:e4aa08205f47cd9257c31789d8161aa691296afbb661dac3ff10ccdc8c56ca90", size = 1167043, upload-time = "2025-04-03T14:06:36.215Z" }, + { url = "https://files.pythonhosted.org/packages/8e/1f/2b0fc067ee191fe9fcb1cfc50a69d329e5234cec3c7b62331bb97e1a4b0b/clingo-5.8.0-cp312-cp312-win_amd64.whl", hash = "sha256:44c3d719d21307ef2dc6da9a720e2d65ad40cdb096ecbf4bba026d654a63a022", size = 1369363, upload-time = "2025-04-03T14:06:38.05Z" }, + { url = "https://files.pythonhosted.org/packages/be/24/a9e3d88607823580169484857c53ffb01640d1acb8c602b0bf9de3e14f4f/clingo-5.8.0-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:461ce0a8081fc5a0ec5e45719a505a4971e50c6e8fee3e422ea8bbaadc5384c9", size = 1765677, upload-time = "2025-04-03T14:06:39.474Z" }, + { url = "https://files.pythonhosted.org/packages/d0/8b/dfbd483f4405f951837d4911f9591a020624011c29a2ffe3e665f2764486/clingo-5.8.0-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:88bf93b0f99fb9e42b3eb794e5ee221f3d90f128ecec421dd117eeb76940fc83", size = 1620692, upload-time = "2025-04-03T14:06:40.824Z" }, + { url = "https://files.pythonhosted.org/packages/f9/43/981c62bfd69af05228a1940e9ffa537cde561ddcbc59cc62bf81bdbd74e9/clingo-5.8.0-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f7897f1acdab9628e1e427b88149519accd4b27b5a0004106c9e306c47727d72", size = 2156565, upload-time = "2025-04-03T14:06:42.759Z" }, + { url = "https://files.pythonhosted.org/packages/fa/52/36ea8e6c3a739af35a1106c10cc6f65db6c1363895ae0c678c1213610481/clingo-5.8.0-cp313-cp313-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:024b3ed20d85da41956438a42c8b0ac347da14db7456a544bf58983ad5975d8c", size = 2428019, upload-time = "2025-04-03T14:06:45.167Z" }, + { url = "https://files.pythonhosted.org/packages/5a/49/8e74859dc653cf20a45bf43a32639c7bca722d34ff5131ba3e8c9176c9a5/clingo-5.8.0-cp313-cp313-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:b2bebf7a0bee043100d5ed1493703e43e1dd43de97b362375deca8cf707ae196", size = 2518052, upload-time = "2025-04-03T14:06:47.607Z" }, + { url = "https://files.pythonhosted.org/packages/f9/a1/2945dc1a688d988d4d6d33dea337ba925356350d73d796616e9f65afb30e/clingo-5.8.0-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:967583dff5218adb4ed2cda56a53ec06eb518b52ece961bcf4bb84320e7334f5", size = 2259928, upload-time = "2025-04-03T14:06:49.584Z" }, + { url = "https://files.pythonhosted.org/packages/de/c9/8f71e8d9a1c0c5c3d790361ebe4b810d86edc752e4f5c529c0a9f52b765c/clingo-5.8.0-cp313-cp313-musllinux_1_2_i686.whl", hash = "sha256:1a8d0b56b1399b2d548e1fe1a27ca5af10bc8d03ad475416f3d4bf1c75b231bd", size = 3542581, upload-time = "2025-04-03T14:06:51.623Z" }, + { url = "https://files.pythonhosted.org/packages/c5/09/bcd1a74c3459c21d0f74416e36fd9d41b660c0fa87fc2cc4698cd68cc14f/clingo-5.8.0-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:167a204ea123b1c9f7524934c898553943274f460103b96a2c451d3ea6f9fd2c", size = 3265131, upload-time = "2025-04-03T14:06:53.937Z" }, + { url = "https://files.pythonhosted.org/packages/45/b5/3ec2544305d450ac937489d831049e56f6fcefadc2d09a1db6627f74ae2a/clingo-5.8.0-cp313-cp313-win32.whl", hash = "sha256:9db5a00458c755ce170b4c7320aaa2a24a984e7f6759a414476b33fa9622a407", size = 1166933, upload-time = "2025-04-03T14:06:56.064Z" }, + { url = "https://files.pythonhosted.org/packages/2f/3a/f19b240f7778e965916e4a5304ee038cf1b35cfa7df1836567c37ebfa279/clingo-5.8.0-cp313-cp313-win_amd64.whl", hash = "sha256:6297a12c20bfe12405dd04c4505fcf72c855696018faed32b50cd0c6a4fd7499", size = 1369297, upload-time = "2025-04-03T14:06:57.589Z" }, + { url = "https://files.pythonhosted.org/packages/e8/cb/9709d548e13a24915cc1f8f411ecb3188fccb6984edab0d2899efcad7cd0/clingo-5.8.0-cp314-cp314-macosx_13_0_arm64.whl", hash = "sha256:b9bdd742a5ed7a151cbc4afbb415f791158b55f85f58782f69765447095465b8", size = 1615275, upload-time = "2026-04-14T15:30:50.169Z" }, + { url = "https://files.pythonhosted.org/packages/f1/54/6dbc154d48df8c1c010d801ab40a28294ff63e8bf015a174a206d8379435/clingo-5.8.0-cp314-cp314-macosx_13_0_x86_64.whl", hash = "sha256:95b79ae14461417d011d034f85b98ebdda5558c8c1a3894959315cdc1a6af387", size = 1768590, upload-time = "2026-04-14T15:30:52.864Z" }, + { url = "https://files.pythonhosted.org/packages/d6/00/c8af30fb0489c16ac214a69aa7ccb83baef3ed6bf863428c3785922d80de/clingo-5.8.0-cp314-cp314-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:01abafda1c3e079fadeb68dd812c1a132e6e42f9e25aadc6f1a787e2c91ed9f1", size = 2122680, upload-time = "2026-04-14T15:30:54.338Z" }, + { url = "https://files.pythonhosted.org/packages/fc/6f/6863c475d72861e7c0f6c919e6e3b4cf4523338359f641dd5fe197cb24d6/clingo-5.8.0-cp314-cp314-manylinux_2_26_i686.manylinux_2_28_i686.whl", hash = "sha256:6f8836a9e2df0b201e2a29b0cd2351d251146efe80e5b55b2f92e7f4501118fb", size = 2418616, upload-time = "2026-04-14T15:30:56.989Z" }, + { url = "https://files.pythonhosted.org/packages/ae/6d/c7a55294dbfc89f21a3c856d68cfe1d1ef810fc56782bdbf190259e74445/clingo-5.8.0-cp314-cp314-manylinux_2_26_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:62136d2fa4325b3cbc7023be19f721aa0b3ff0be07eca61158963659b5ac4271", size = 2525797, upload-time = "2026-04-14T15:30:58.557Z" }, + { url = "https://files.pythonhosted.org/packages/3b/66/04e27bfafb020bea26fdac7101df25a237fa7737618c1f454beed9b1effa/clingo-5.8.0-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:99efafa32d37e0ea5a82af1325a7b2a0ad0ab299fe6e68528fa6069b8c53e109", size = 2256742, upload-time = "2026-04-14T15:30:59.996Z" }, + { url = "https://files.pythonhosted.org/packages/c0/15/471203b61ecffe3318e952dcdbc4239786e0ba906dfe8c82876a3f0d4b77/clingo-5.8.0-cp314-cp314-musllinux_1_2_i686.whl", hash = "sha256:3094642aa65f5e1c1aeb791d0843bc9eb3c72dfd07683cd7b3768280d45fdc51", size = 3624012, upload-time = "2026-04-14T15:31:01.468Z" }, + { url = "https://files.pythonhosted.org/packages/6d/33/2c0319f994d2b8d1dba427f33ca460c89108d7882a0927045c8b04786de2/clingo-5.8.0-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:386d028fd6e775f5d3fd6d6f71d1473e164199e44131f6326060ca447531e95d", size = 3341468, upload-time = "2026-04-14T15:31:03.437Z" }, + { url = "https://files.pythonhosted.org/packages/d0/31/ad2c0e62cea5519bf8fdf5769e086d61a99850ff9d268ff849155675b708/clingo-5.8.0-cp314-cp314-win_amd64.whl", hash = "sha256:51042c1abbf7ee2fed20207b6cd39bd65224654afc315edda14e2baadf8991ed", size = 1414851, upload-time = "2026-04-14T15:31:05.294Z" }, +] + +[[package]] +name = "colorama" +version = "0.4.6" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/d8/53/6f443c9a4a8358a93a6792e2acffb9d9d5cb0a5cfd8802644b7b1c9a02e4/colorama-0.4.6.tar.gz", hash = "sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44", size = 27697, upload-time = "2022-10-25T02:36:22.414Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/d1/d6/3965ed04c63042e047cb6a3e6ed1a63a35087b6a609aa3a15ed8ac56c221/colorama-0.4.6-py2.py3-none-any.whl", hash = "sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6", size = 25335, upload-time = "2022-10-25T02:36:20.889Z" }, +] + +[[package]] +name = "cryptography" +version = "46.0.5" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "cffi", marker = "platform_python_implementation != 'PyPy'" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/60/04/ee2a9e8542e4fa2773b81771ff8349ff19cdd56b7258a0cc442639052edb/cryptography-46.0.5.tar.gz", hash = "sha256:abace499247268e3757271b2f1e244b36b06f8515cf27c4d49468fc9eb16e93d", size = 750064, upload-time = "2026-02-10T19:18:38.255Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/f7/81/b0bb27f2ba931a65409c6b8a8b358a7f03c0e46eceacddff55f7c84b1f3b/cryptography-46.0.5-cp311-abi3-macosx_10_9_universal2.whl", hash = "sha256:351695ada9ea9618b3500b490ad54c739860883df6c1f555e088eaf25b1bbaad", size = 7176289, upload-time = "2026-02-10T19:17:08.274Z" }, + { url = "https://files.pythonhosted.org/packages/ff/9e/6b4397a3e3d15123de3b1806ef342522393d50736c13b20ec4c9ea6693a6/cryptography-46.0.5-cp311-abi3-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:c18ff11e86df2e28854939acde2d003f7984f721eba450b56a200ad90eeb0e6b", size = 4275637, upload-time = "2026-02-10T19:17:10.53Z" }, + { url = "https://files.pythonhosted.org/packages/63/e7/471ab61099a3920b0c77852ea3f0ea611c9702f651600397ac567848b897/cryptography-46.0.5-cp311-abi3-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:4d7e3d356b8cd4ea5aff04f129d5f66ebdc7b6f8eae802b93739ed520c47c79b", size = 4424742, upload-time = "2026-02-10T19:17:12.388Z" }, + { url = "https://files.pythonhosted.org/packages/37/53/a18500f270342d66bf7e4d9f091114e31e5ee9e7375a5aba2e85a91e0044/cryptography-46.0.5-cp311-abi3-manylinux_2_28_aarch64.whl", hash = "sha256:50bfb6925eff619c9c023b967d5b77a54e04256c4281b0e21336a130cd7fc263", size = 4277528, upload-time = "2026-02-10T19:17:13.853Z" }, + { url = "https://files.pythonhosted.org/packages/22/29/c2e812ebc38c57b40e7c583895e73c8c5adb4d1e4a0cc4c5a4fdab2b1acc/cryptography-46.0.5-cp311-abi3-manylinux_2_28_ppc64le.whl", hash = "sha256:803812e111e75d1aa73690d2facc295eaefd4439be1023fefc4995eaea2af90d", size = 4947993, upload-time = "2026-02-10T19:17:15.618Z" }, + { url = "https://files.pythonhosted.org/packages/6b/e7/237155ae19a9023de7e30ec64e5d99a9431a567407ac21170a046d22a5a3/cryptography-46.0.5-cp311-abi3-manylinux_2_28_x86_64.whl", hash = "sha256:3ee190460e2fbe447175cda91b88b84ae8322a104fc27766ad09428754a618ed", size = 4456855, upload-time = "2026-02-10T19:17:17.221Z" }, + { url = "https://files.pythonhosted.org/packages/2d/87/fc628a7ad85b81206738abbd213b07702bcbdada1dd43f72236ef3cffbb5/cryptography-46.0.5-cp311-abi3-manylinux_2_31_armv7l.whl", hash = "sha256:f145bba11b878005c496e93e257c1e88f154d278d2638e6450d17e0f31e558d2", size = 3984635, upload-time = "2026-02-10T19:17:18.792Z" }, + { url = "https://files.pythonhosted.org/packages/84/29/65b55622bde135aedf4565dc509d99b560ee4095e56989e815f8fd2aa910/cryptography-46.0.5-cp311-abi3-manylinux_2_34_aarch64.whl", hash = "sha256:e9251e3be159d1020c4030bd2e5f84d6a43fe54b6c19c12f51cde9542a2817b2", size = 4277038, upload-time = "2026-02-10T19:17:20.256Z" }, + { url = "https://files.pythonhosted.org/packages/bc/36/45e76c68d7311432741faf1fbf7fac8a196a0a735ca21f504c75d37e2558/cryptography-46.0.5-cp311-abi3-manylinux_2_34_ppc64le.whl", hash = "sha256:47fb8a66058b80e509c47118ef8a75d14c455e81ac369050f20ba0d23e77fee0", size = 4912181, upload-time = "2026-02-10T19:17:21.825Z" }, + { url = "https://files.pythonhosted.org/packages/6d/1a/c1ba8fead184d6e3d5afcf03d569acac5ad063f3ac9fb7258af158f7e378/cryptography-46.0.5-cp311-abi3-manylinux_2_34_x86_64.whl", hash = "sha256:4c3341037c136030cb46e4b1e17b7418ea4cbd9dd207e4a6f3b2b24e0d4ac731", size = 4456482, upload-time = "2026-02-10T19:17:25.133Z" }, + { url = "https://files.pythonhosted.org/packages/f9/e5/3fb22e37f66827ced3b902cf895e6a6bc1d095b5b26be26bd13c441fdf19/cryptography-46.0.5-cp311-abi3-musllinux_1_2_aarch64.whl", hash = "sha256:890bcb4abd5a2d3f852196437129eb3667d62630333aacc13dfd470fad3aaa82", size = 4405497, upload-time = "2026-02-10T19:17:26.66Z" }, + { url = "https://files.pythonhosted.org/packages/1a/df/9d58bb32b1121a8a2f27383fabae4d63080c7ca60b9b5c88be742be04ee7/cryptography-46.0.5-cp311-abi3-musllinux_1_2_x86_64.whl", hash = "sha256:80a8d7bfdf38f87ca30a5391c0c9ce4ed2926918e017c29ddf643d0ed2778ea1", size = 4667819, upload-time = "2026-02-10T19:17:28.569Z" }, + { url = "https://files.pythonhosted.org/packages/ea/ed/325d2a490c5e94038cdb0117da9397ece1f11201f425c4e9c57fe5b9f08b/cryptography-46.0.5-cp311-abi3-win32.whl", hash = "sha256:60ee7e19e95104d4c03871d7d7dfb3d22ef8a9b9c6778c94e1c8fcc8365afd48", size = 3028230, upload-time = "2026-02-10T19:17:30.518Z" }, + { url = "https://files.pythonhosted.org/packages/e9/5a/ac0f49e48063ab4255d9e3b79f5def51697fce1a95ea1370f03dc9db76f6/cryptography-46.0.5-cp311-abi3-win_amd64.whl", hash = "sha256:38946c54b16c885c72c4f59846be9743d699eee2b69b6988e0a00a01f46a61a4", size = 3480909, upload-time = "2026-02-10T19:17:32.083Z" }, + { url = "https://files.pythonhosted.org/packages/00/13/3d278bfa7a15a96b9dc22db5a12ad1e48a9eb3d40e1827ef66a5df75d0d0/cryptography-46.0.5-cp314-cp314t-macosx_10_9_universal2.whl", hash = "sha256:94a76daa32eb78d61339aff7952ea819b1734b46f73646a07decb40e5b3448e2", size = 7119287, upload-time = "2026-02-10T19:17:33.801Z" }, + { url = "https://files.pythonhosted.org/packages/67/c8/581a6702e14f0898a0848105cbefd20c058099e2c2d22ef4e476dfec75d7/cryptography-46.0.5-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:5be7bf2fb40769e05739dd0046e7b26f9d4670badc7b032d6ce4db64dddc0678", size = 4265728, upload-time = "2026-02-10T19:17:35.569Z" }, + { url = "https://files.pythonhosted.org/packages/dd/4a/ba1a65ce8fc65435e5a849558379896c957870dd64fecea97b1ad5f46a37/cryptography-46.0.5-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:fe346b143ff9685e40192a4960938545c699054ba11d4f9029f94751e3f71d87", size = 4408287, upload-time = "2026-02-10T19:17:36.938Z" }, + { url = "https://files.pythonhosted.org/packages/f8/67/8ffdbf7b65ed1ac224d1c2df3943553766914a8ca718747ee3871da6107e/cryptography-46.0.5-cp314-cp314t-manylinux_2_28_aarch64.whl", hash = "sha256:c69fd885df7d089548a42d5ec05be26050ebcd2283d89b3d30676eb32ff87dee", size = 4270291, upload-time = "2026-02-10T19:17:38.748Z" }, + { url = "https://files.pythonhosted.org/packages/f8/e5/f52377ee93bc2f2bba55a41a886fd208c15276ffbd2569f2ddc89d50e2c5/cryptography-46.0.5-cp314-cp314t-manylinux_2_28_ppc64le.whl", hash = "sha256:8293f3dea7fc929ef7240796ba231413afa7b68ce38fd21da2995549f5961981", size = 4927539, upload-time = "2026-02-10T19:17:40.241Z" }, + { url = "https://files.pythonhosted.org/packages/3b/02/cfe39181b02419bbbbcf3abdd16c1c5c8541f03ca8bda240debc467d5a12/cryptography-46.0.5-cp314-cp314t-manylinux_2_28_x86_64.whl", hash = "sha256:1abfdb89b41c3be0365328a410baa9df3ff8a9110fb75e7b52e66803ddabc9a9", size = 4442199, upload-time = "2026-02-10T19:17:41.789Z" }, + { url = "https://files.pythonhosted.org/packages/c0/96/2fcaeb4873e536cf71421a388a6c11b5bc846e986b2b069c79363dc1648e/cryptography-46.0.5-cp314-cp314t-manylinux_2_31_armv7l.whl", hash = "sha256:d66e421495fdb797610a08f43b05269e0a5ea7f5e652a89bfd5a7d3c1dee3648", size = 3960131, upload-time = "2026-02-10T19:17:43.379Z" }, + { url = "https://files.pythonhosted.org/packages/d8/d2/b27631f401ddd644e94c5cf33c9a4069f72011821cf3dc7309546b0642a0/cryptography-46.0.5-cp314-cp314t-manylinux_2_34_aarch64.whl", hash = "sha256:4e817a8920bfbcff8940ecfd60f23d01836408242b30f1a708d93198393a80b4", size = 4270072, upload-time = "2026-02-10T19:17:45.481Z" }, + { url = "https://files.pythonhosted.org/packages/f4/a7/60d32b0370dae0b4ebe55ffa10e8599a2a59935b5ece1b9f06edb73abdeb/cryptography-46.0.5-cp314-cp314t-manylinux_2_34_ppc64le.whl", hash = "sha256:68f68d13f2e1cb95163fa3b4db4bf9a159a418f5f6e7242564fc75fcae667fd0", size = 4892170, upload-time = "2026-02-10T19:17:46.997Z" }, + { url = "https://files.pythonhosted.org/packages/d2/b9/cf73ddf8ef1164330eb0b199a589103c363afa0cf794218c24d524a58eab/cryptography-46.0.5-cp314-cp314t-manylinux_2_34_x86_64.whl", hash = "sha256:a3d1fae9863299076f05cb8a778c467578262fae09f9dc0ee9b12eb4268ce663", size = 4441741, upload-time = "2026-02-10T19:17:48.661Z" }, + { url = "https://files.pythonhosted.org/packages/5f/eb/eee00b28c84c726fe8fa0158c65afe312d9c3b78d9d01daf700f1f6e37ff/cryptography-46.0.5-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:c4143987a42a2397f2fc3b4d7e3a7d313fbe684f67ff443999e803dd75a76826", size = 4396728, upload-time = "2026-02-10T19:17:50.058Z" }, + { url = "https://files.pythonhosted.org/packages/65/f4/6bc1a9ed5aef7145045114b75b77c2a8261b4d38717bd8dea111a63c3442/cryptography-46.0.5-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:7d731d4b107030987fd61a7f8ab512b25b53cef8f233a97379ede116f30eb67d", size = 4652001, upload-time = "2026-02-10T19:17:51.54Z" }, + { url = "https://files.pythonhosted.org/packages/86/ef/5d00ef966ddd71ac2e6951d278884a84a40ffbd88948ef0e294b214ae9e4/cryptography-46.0.5-cp314-cp314t-win32.whl", hash = "sha256:c3bcce8521d785d510b2aad26ae2c966092b7daa8f45dd8f44734a104dc0bc1a", size = 3003637, upload-time = "2026-02-10T19:17:52.997Z" }, + { url = "https://files.pythonhosted.org/packages/b7/57/f3f4160123da6d098db78350fdfd9705057aad21de7388eacb2401dceab9/cryptography-46.0.5-cp314-cp314t-win_amd64.whl", hash = "sha256:4d8ae8659ab18c65ced284993c2265910f6c9e650189d4e3f68445ef82a810e4", size = 3469487, upload-time = "2026-02-10T19:17:54.549Z" }, + { url = "https://files.pythonhosted.org/packages/e2/fa/a66aa722105ad6a458bebd64086ca2b72cdd361fed31763d20390f6f1389/cryptography-46.0.5-cp38-abi3-macosx_10_9_universal2.whl", hash = "sha256:4108d4c09fbbf2789d0c926eb4152ae1760d5a2d97612b92d508d96c861e4d31", size = 7170514, upload-time = "2026-02-10T19:17:56.267Z" }, + { url = "https://files.pythonhosted.org/packages/0f/04/c85bdeab78c8bc77b701bf0d9bdcf514c044e18a46dcff330df5448631b0/cryptography-46.0.5-cp38-abi3-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:7d1f30a86d2757199cb2d56e48cce14deddf1f9c95f1ef1b64ee91ea43fe2e18", size = 4275349, upload-time = "2026-02-10T19:17:58.419Z" }, + { url = "https://files.pythonhosted.org/packages/5c/32/9b87132a2f91ee7f5223b091dc963055503e9b442c98fc0b8a5ca765fab0/cryptography-46.0.5-cp38-abi3-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:039917b0dc418bb9f6edce8a906572d69e74bd330b0b3fea4f79dab7f8ddd235", size = 4420667, upload-time = "2026-02-10T19:18:00.619Z" }, + { url = "https://files.pythonhosted.org/packages/a1/a6/a7cb7010bec4b7c5692ca6f024150371b295ee1c108bdc1c400e4c44562b/cryptography-46.0.5-cp38-abi3-manylinux_2_28_aarch64.whl", hash = "sha256:ba2a27ff02f48193fc4daeadf8ad2590516fa3d0adeeb34336b96f7fa64c1e3a", size = 4276980, upload-time = "2026-02-10T19:18:02.379Z" }, + { url = "https://files.pythonhosted.org/packages/8e/7c/c4f45e0eeff9b91e3f12dbd0e165fcf2a38847288fcfd889deea99fb7b6d/cryptography-46.0.5-cp38-abi3-manylinux_2_28_ppc64le.whl", hash = "sha256:61aa400dce22cb001a98014f647dc21cda08f7915ceb95df0c9eaf84b4b6af76", size = 4939143, upload-time = "2026-02-10T19:18:03.964Z" }, + { url = "https://files.pythonhosted.org/packages/37/19/e1b8f964a834eddb44fa1b9a9976f4e414cbb7aa62809b6760c8803d22d1/cryptography-46.0.5-cp38-abi3-manylinux_2_28_x86_64.whl", hash = "sha256:3ce58ba46e1bc2aac4f7d9290223cead56743fa6ab94a5d53292ffaac6a91614", size = 4453674, upload-time = "2026-02-10T19:18:05.588Z" }, + { url = "https://files.pythonhosted.org/packages/db/ed/db15d3956f65264ca204625597c410d420e26530c4e2943e05a0d2f24d51/cryptography-46.0.5-cp38-abi3-manylinux_2_31_armv7l.whl", hash = "sha256:420d0e909050490d04359e7fdb5ed7e667ca5c3c402b809ae2563d7e66a92229", size = 3978801, upload-time = "2026-02-10T19:18:07.167Z" }, + { url = "https://files.pythonhosted.org/packages/41/e2/df40a31d82df0a70a0daf69791f91dbb70e47644c58581d654879b382d11/cryptography-46.0.5-cp38-abi3-manylinux_2_34_aarch64.whl", hash = "sha256:582f5fcd2afa31622f317f80426a027f30dc792e9c80ffee87b993200ea115f1", size = 4276755, upload-time = "2026-02-10T19:18:09.813Z" }, + { url = "https://files.pythonhosted.org/packages/33/45/726809d1176959f4a896b86907b98ff4391a8aa29c0aaaf9450a8a10630e/cryptography-46.0.5-cp38-abi3-manylinux_2_34_ppc64le.whl", hash = "sha256:bfd56bb4b37ed4f330b82402f6f435845a5f5648edf1ad497da51a8452d5d62d", size = 4901539, upload-time = "2026-02-10T19:18:11.263Z" }, + { url = "https://files.pythonhosted.org/packages/99/0f/a3076874e9c88ecb2ecc31382f6e7c21b428ede6f55aafa1aa272613e3cd/cryptography-46.0.5-cp38-abi3-manylinux_2_34_x86_64.whl", hash = "sha256:a3d507bb6a513ca96ba84443226af944b0f7f47dcc9a399d110cd6146481d24c", size = 4452794, upload-time = "2026-02-10T19:18:12.914Z" }, + { url = "https://files.pythonhosted.org/packages/02/ef/ffeb542d3683d24194a38f66ca17c0a4b8bf10631feef44a7ef64e631b1a/cryptography-46.0.5-cp38-abi3-musllinux_1_2_aarch64.whl", hash = "sha256:9f16fbdf4da055efb21c22d81b89f155f02ba420558db21288b3d0035bafd5f4", size = 4404160, upload-time = "2026-02-10T19:18:14.375Z" }, + { url = "https://files.pythonhosted.org/packages/96/93/682d2b43c1d5f1406ed048f377c0fc9fc8f7b0447a478d5c65ab3d3a66eb/cryptography-46.0.5-cp38-abi3-musllinux_1_2_x86_64.whl", hash = "sha256:ced80795227d70549a411a4ab66e8ce307899fad2220ce5ab2f296e687eacde9", size = 4667123, upload-time = "2026-02-10T19:18:15.886Z" }, + { url = "https://files.pythonhosted.org/packages/45/2d/9c5f2926cb5300a8eefc3f4f0b3f3df39db7f7ce40c8365444c49363cbda/cryptography-46.0.5-cp38-abi3-win32.whl", hash = "sha256:02f547fce831f5096c9a567fd41bc12ca8f11df260959ecc7c3202555cc47a72", size = 3010220, upload-time = "2026-02-10T19:18:17.361Z" }, + { url = "https://files.pythonhosted.org/packages/48/ef/0c2f4a8e31018a986949d34a01115dd057bf536905dca38897bacd21fac3/cryptography-46.0.5-cp38-abi3-win_amd64.whl", hash = "sha256:556e106ee01aa13484ce9b0239bca667be5004efb0aabbed28d353df86445595", size = 3467050, upload-time = "2026-02-10T19:18:18.899Z" }, +] + +[[package]] +name = "cuda-bindings" +version = "12.9.4" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "cuda-pathfinder" }, +] +wheels = [ + { url = "https://files.pythonhosted.org/packages/a9/c1/dabe88f52c3e3760d861401bb994df08f672ec893b8f7592dc91626adcf3/cuda_bindings-12.9.4-cp312-cp312-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:fda147a344e8eaeca0c6ff113d2851ffca8f7dfc0a6c932374ee5c47caa649c8", size = 12151019, upload-time = "2025-10-21T14:51:43.167Z" }, + { url = "https://files.pythonhosted.org/packages/63/56/e465c31dc9111be3441a9ba7df1941fe98f4aa6e71e8788a3fb4534ce24d/cuda_bindings-12.9.4-cp313-cp313-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:32bdc5a76906be4c61eb98f546a6786c5773a881f3b166486449b5d141e4a39f", size = 11906628, upload-time = "2025-10-21T14:51:49.905Z" }, + { url = "https://files.pythonhosted.org/packages/a3/84/1e6be415e37478070aeeee5884c2022713c1ecc735e6d82d744de0252eee/cuda_bindings-12.9.4-cp313-cp313t-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:56e0043c457a99ac473ddc926fe0dc4046694d99caef633e92601ab52cbe17eb", size = 11925991, upload-time = "2025-10-21T14:51:56.535Z" }, + { url = "https://files.pythonhosted.org/packages/d1/af/6dfd8f2ed90b1d4719bc053ff8940e494640fe4212dc3dd72f383e4992da/cuda_bindings-12.9.4-cp314-cp314-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:8b72ee72a9cc1b531db31eebaaee5c69a8ec3500e32c6933f2d3b15297b53686", size = 11922703, upload-time = "2025-10-21T14:52:03.585Z" }, + { url = "https://files.pythonhosted.org/packages/6c/19/90ac264acc00f6df8a49378eedec9fd2db3061bf9263bf9f39fd3d8377c3/cuda_bindings-12.9.4-cp314-cp314t-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:d80bffc357df9988dca279734bc9674c3934a654cab10cadeed27ce17d8635ee", size = 11924658, upload-time = "2025-10-21T14:52:10.411Z" }, +] + +[[package]] +name = "cuda-pathfinder" +version = "1.3.5" +source = { registry = "https://pypi.org/simple" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/ca/b5/e4056e4058fb56519fcddf1face6fe3ff2398953b41615fafe9fb1540bf2/cuda_pathfinder-1.3.5-py3-none-any.whl", hash = "sha256:6c88220f8637cb35d2a75c620d72efebf683b248b923713d8fbe235844c1a4b9", size = 33711, upload-time = "2026-02-23T18:34:27.253Z" }, +] + +[[package]] +name = "fastapi" +version = "0.133.1" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "annotated-doc" }, + { name = "pydantic" }, + { name = "starlette" }, + { name = "typing-extensions" }, + { name = "typing-inspection" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/22/6f/0eafed8349eea1fa462238b54a624c8b408cd1ba2795c8e64aa6c34f8ab7/fastapi-0.133.1.tar.gz", hash = "sha256:ed152a45912f102592976fde6cbce7dae1a8a1053da94202e51dd35d184fadd6", size = 378741, upload-time = "2026-02-25T18:18:17.398Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/d2/c9/a175a7779f3599dfa4adfc97a6ce0e157237b3d7941538604aadaf97bfb6/fastapi-0.133.1-py3-none-any.whl", hash = "sha256:658f34ba334605b1617a65adf2ea6461901bdb9af3a3080d63ff791ecf7dc2e2", size = 109029, upload-time = "2026-02-25T18:18:18.578Z" }, +] + +[[package]] +name = "filelock" +version = "3.24.3" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/73/92/a8e2479937ff39185d20dd6a851c1a63e55849e447a55e798cc2e1f49c65/filelock-3.24.3.tar.gz", hash = "sha256:011a5644dc937c22699943ebbfc46e969cdde3e171470a6e40b9533e5a72affa", size = 37935, upload-time = "2026-02-19T00:48:20.543Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/9c/0f/5d0c71a1aefeb08efff26272149e07ab922b64f46c63363756224bd6872e/filelock-3.24.3-py3-none-any.whl", hash = "sha256:426e9a4660391f7f8a810d71b0555bce9008b0a1cc342ab1f6947d37639e002d", size = 24331, upload-time = "2026-02-19T00:48:18.465Z" }, +] + +[[package]] +name = "fsspec" +version = "2026.2.0" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/51/7c/f60c259dcbf4f0c47cc4ddb8f7720d2dcdc8888c8e5ad84c73ea4531cc5b/fsspec-2026.2.0.tar.gz", hash = "sha256:6544e34b16869f5aacd5b90bdf1a71acb37792ea3ddf6125ee69a22a53fb8bff", size = 313441, upload-time = "2026-02-05T21:50:53.743Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/e6/ab/fb21f4c939bb440104cc2b396d3be1d9b7a9fd3c6c2a53d98c45b3d7c954/fsspec-2026.2.0-py3-none-any.whl", hash = "sha256:98de475b5cb3bd66bedd5c4679e87b4fdfe1a3bf4d707b151b3c07e58c9a2437", size = 202505, upload-time = "2026-02-05T21:50:51.819Z" }, +] + +[[package]] +name = "greenlet" +version = "3.3.2" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/a3/51/1664f6b78fc6ebbd98019a1fd730e83fa78f2db7058f72b1463d3612b8db/greenlet-3.3.2.tar.gz", hash = "sha256:2eaf067fc6d886931c7962e8c6bede15d2f01965560f3359b27c80bde2d151f2", size = 188267, upload-time = "2026-02-20T20:54:15.531Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/ea/ab/1608e5a7578e62113506740b88066bf09888322a311cff602105e619bd87/greenlet-3.3.2-cp312-cp312-macosx_11_0_universal2.whl", hash = "sha256:ac8d61d4343b799d1e526db579833d72f23759c71e07181c2d2944e429eb09cd", size = 280358, upload-time = "2026-02-20T20:17:43.971Z" }, + { url = "https://files.pythonhosted.org/packages/a5/23/0eae412a4ade4e6623ff7626e38998cb9b11e9ff1ebacaa021e4e108ec15/greenlet-3.3.2-cp312-cp312-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:3ceec72030dae6ac0c8ed7591b96b70410a8be370b6a477b1dbc072856ad02bd", size = 601217, upload-time = "2026-02-20T20:47:31.462Z" }, + { url = "https://files.pythonhosted.org/packages/f8/16/5b1678a9c07098ecb9ab2dd159fafaf12e963293e61ee8d10ecb55273e5e/greenlet-3.3.2-cp312-cp312-manylinux_2_24_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:a2a5be83a45ce6188c045bcc44b0ee037d6a518978de9a5d97438548b953a1ac", size = 611792, upload-time = "2026-02-20T20:55:58.423Z" }, + { url = "https://files.pythonhosted.org/packages/50/1f/5155f55bd71cabd03765a4aac9ac446be129895271f73872c36ebd4b04b6/greenlet-3.3.2-cp312-cp312-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:43e99d1749147ac21dde49b99c9abffcbc1e2d55c67501465ef0930d6e78e070", size = 613875, upload-time = "2026-02-20T20:21:01.102Z" }, + { url = "https://files.pythonhosted.org/packages/fc/dd/845f249c3fcd69e32df80cdab059b4be8b766ef5830a3d0aa9d6cad55beb/greenlet-3.3.2-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:4c956a19350e2c37f2c48b336a3afb4bff120b36076d9d7fb68cb44e05d95b79", size = 1571467, upload-time = "2026-02-20T20:49:33.495Z" }, + { url = "https://files.pythonhosted.org/packages/2a/50/2649fe21fcc2b56659a452868e695634722a6655ba245d9f77f5656010bf/greenlet-3.3.2-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:6c6f8ba97d17a1e7d664151284cb3315fc5f8353e75221ed4324f84eb162b395", size = 1640001, upload-time = "2026-02-20T20:21:09.154Z" }, + { url = "https://files.pythonhosted.org/packages/9b/40/cc802e067d02af8b60b6771cea7d57e21ef5e6659912814babb42b864713/greenlet-3.3.2-cp312-cp312-win_amd64.whl", hash = "sha256:34308836d8370bddadb41f5a7ce96879b72e2fdfb4e87729330c6ab52376409f", size = 231081, upload-time = "2026-02-20T20:17:28.121Z" }, + { url = "https://files.pythonhosted.org/packages/58/2e/fe7f36ff1982d6b10a60d5e0740c759259a7d6d2e1dc41da6d96de32fff6/greenlet-3.3.2-cp312-cp312-win_arm64.whl", hash = "sha256:d3a62fa76a32b462a97198e4c9e99afb9ab375115e74e9a83ce180e7a496f643", size = 230331, upload-time = "2026-02-20T20:17:23.34Z" }, + { url = "https://files.pythonhosted.org/packages/ac/48/f8b875fa7dea7dd9b33245e37f065af59df6a25af2f9561efa8d822fde51/greenlet-3.3.2-cp313-cp313-macosx_11_0_universal2.whl", hash = "sha256:aa6ac98bdfd716a749b84d4034486863fd81c3abde9aa3cf8eff9127981a4ae4", size = 279120, upload-time = "2026-02-20T20:19:01.9Z" }, + { url = "https://files.pythonhosted.org/packages/49/8d/9771d03e7a8b1ee456511961e1b97a6d77ae1dea4a34a5b98eee706689d3/greenlet-3.3.2-cp313-cp313-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:ab0c7e7901a00bc0a7284907273dc165b32e0d109a6713babd04471327ff7986", size = 603238, upload-time = "2026-02-20T20:47:32.873Z" }, + { url = "https://files.pythonhosted.org/packages/59/0e/4223c2bbb63cd5c97f28ffb2a8aee71bdfb30b323c35d409450f51b91e3e/greenlet-3.3.2-cp313-cp313-manylinux_2_24_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:d248d8c23c67d2291ffd47af766e2a3aa9fa1c6703155c099feb11f526c63a92", size = 614219, upload-time = "2026-02-20T20:55:59.817Z" }, + { url = "https://files.pythonhosted.org/packages/7a/34/259b28ea7a2a0c904b11cd36c79b8cef8019b26ee5dbe24e73b469dea347/greenlet-3.3.2-cp313-cp313-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:b6997d360a4e6a4e936c0f9625b1c20416b8a0ea18a8e19cabbefc712e7397ab", size = 616774, upload-time = "2026-02-20T20:21:02.454Z" }, + { url = "https://files.pythonhosted.org/packages/0a/03/996c2d1689d486a6e199cb0f1cf9e4aa940c500e01bdf201299d7d61fa69/greenlet-3.3.2-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:64970c33a50551c7c50491671265d8954046cb6e8e2999aacdd60e439b70418a", size = 1571277, upload-time = "2026-02-20T20:49:34.795Z" }, + { url = "https://files.pythonhosted.org/packages/d9/c4/2570fc07f34a39f2caf0bf9f24b0a1a0a47bc2e8e465b2c2424821389dfc/greenlet-3.3.2-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:1a9172f5bf6bd88e6ba5a84e0a68afeac9dc7b6b412b245dd64f52d83c81e55b", size = 1640455, upload-time = "2026-02-20T20:21:10.261Z" }, + { url = "https://files.pythonhosted.org/packages/91/39/5ef5aa23bc545aa0d31e1b9b55822b32c8da93ba657295840b6b34124009/greenlet-3.3.2-cp313-cp313-win_amd64.whl", hash = "sha256:a7945dd0eab63ded0a48e4dcade82939783c172290a7903ebde9e184333ca124", size = 230961, upload-time = "2026-02-20T20:16:58.461Z" }, + { url = "https://files.pythonhosted.org/packages/62/6b/a89f8456dcb06becff288f563618e9f20deed8dd29beea14f9a168aef64b/greenlet-3.3.2-cp313-cp313-win_arm64.whl", hash = "sha256:394ead29063ee3515b4e775216cb756b2e3b4a7e55ae8fd884f17fa579e6b327", size = 230221, upload-time = "2026-02-20T20:17:37.152Z" }, + { url = "https://files.pythonhosted.org/packages/3f/ae/8bffcbd373b57a5992cd077cbe8858fff39110480a9d50697091faea6f39/greenlet-3.3.2-cp314-cp314-macosx_11_0_universal2.whl", hash = "sha256:8d1658d7291f9859beed69a776c10822a0a799bc4bfe1bd4272bb60e62507dab", size = 279650, upload-time = "2026-02-20T20:18:00.783Z" }, + { url = "https://files.pythonhosted.org/packages/d1/c0/45f93f348fa49abf32ac8439938726c480bd96b2a3c6f4d949ec0124b69f/greenlet-3.3.2-cp314-cp314-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:18cb1b7337bca281915b3c5d5ae19f4e76d35e1df80f4ad3c1a7be91fadf1082", size = 650295, upload-time = "2026-02-20T20:47:34.036Z" }, + { url = "https://files.pythonhosted.org/packages/b3/de/dd7589b3f2b8372069ab3e4763ea5329940fc7ad9dcd3e272a37516d7c9b/greenlet-3.3.2-cp314-cp314-manylinux_2_24_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:c2e47408e8ce1c6f1ceea0dffcdf6ebb85cc09e55c7af407c99f1112016e45e9", size = 662163, upload-time = "2026-02-20T20:56:01.295Z" }, + { url = "https://files.pythonhosted.org/packages/d2/d8/09bfa816572a4d83bccd6750df1926f79158b1c36c5f73786e26dbe4ee38/greenlet-3.3.2-cp314-cp314-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:63d10328839d1973e5ba35e98cccbca71b232b14051fd957b6f8b6e8e80d0506", size = 664160, upload-time = "2026-02-20T20:21:04.015Z" }, + { url = "https://files.pythonhosted.org/packages/48/cf/56832f0c8255d27f6c35d41b5ec91168d74ec721d85f01a12131eec6b93c/greenlet-3.3.2-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:8e4ab3cfb02993c8cc248ea73d7dae6cec0253e9afa311c9b37e603ca9fad2ce", size = 1619181, upload-time = "2026-02-20T20:49:36.052Z" }, + { url = "https://files.pythonhosted.org/packages/0a/23/b90b60a4aabb4cec0796e55f25ffbfb579a907c3898cd2905c8918acaa16/greenlet-3.3.2-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:94ad81f0fd3c0c0681a018a976e5c2bd2ca2d9d94895f23e7bb1af4e8af4e2d5", size = 1687713, upload-time = "2026-02-20T20:21:11.684Z" }, + { url = "https://files.pythonhosted.org/packages/f3/ca/2101ca3d9223a1dc125140dbc063644dca76df6ff356531eb27bc267b446/greenlet-3.3.2-cp314-cp314-win_amd64.whl", hash = "sha256:8c4dd0f3997cf2512f7601563cc90dfb8957c0cff1e3a1b23991d4ea1776c492", size = 232034, upload-time = "2026-02-20T20:20:08.186Z" }, + { url = "https://files.pythonhosted.org/packages/f6/4a/ecf894e962a59dea60f04877eea0fd5724618da89f1867b28ee8b91e811f/greenlet-3.3.2-cp314-cp314-win_arm64.whl", hash = "sha256:cd6f9e2bbd46321ba3bbb4c8a15794d32960e3b0ae2cc4d49a1a53d314805d71", size = 231437, upload-time = "2026-02-20T20:18:59.722Z" }, + { url = "https://files.pythonhosted.org/packages/98/6d/8f2ef704e614bcf58ed43cfb8d87afa1c285e98194ab2cfad351bf04f81e/greenlet-3.3.2-cp314-cp314t-macosx_11_0_universal2.whl", hash = "sha256:e26e72bec7ab387ac80caa7496e0f908ff954f31065b0ffc1f8ecb1338b11b54", size = 286617, upload-time = "2026-02-20T20:19:29.856Z" }, + { url = "https://files.pythonhosted.org/packages/5e/0d/93894161d307c6ea237a43988f27eba0947b360b99ac5239ad3fe09f0b47/greenlet-3.3.2-cp314-cp314t-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:8b466dff7a4ffda6ca975979bab80bdadde979e29fc947ac3be4451428d8b0e4", size = 655189, upload-time = "2026-02-20T20:47:35.742Z" }, + { url = "https://files.pythonhosted.org/packages/f5/2c/d2d506ebd8abcb57386ec4f7ba20f4030cbe56eae541bc6fd6ef399c0b41/greenlet-3.3.2-cp314-cp314t-manylinux_2_24_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:b8bddc5b73c9720bea487b3bffdb1840fe4e3656fba3bd40aa1489e9f37877ff", size = 658225, upload-time = "2026-02-20T20:56:02.527Z" }, + { url = "https://files.pythonhosted.org/packages/8e/30/3a09155fbf728673a1dea713572d2d31159f824a37c22da82127056c44e4/greenlet-3.3.2-cp314-cp314t-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:b26b0f4428b871a751968285a1ac9648944cea09807177ac639b030bddebcea4", size = 657907, upload-time = "2026-02-20T20:21:05.259Z" }, + { url = "https://files.pythonhosted.org/packages/f3/fd/d05a4b7acd0154ed758797f0a43b4c0962a843bedfe980115e842c5b2d08/greenlet-3.3.2-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:1fb39a11ee2e4d94be9a76671482be9398560955c9e568550de0224e41104727", size = 1618857, upload-time = "2026-02-20T20:49:37.309Z" }, + { url = "https://files.pythonhosted.org/packages/6f/e1/50ee92a5db521de8f35075b5eff060dd43d39ebd46c2181a2042f7070385/greenlet-3.3.2-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:20154044d9085151bc309e7689d6f7ba10027f8f5a8c0676ad398b951913d89e", size = 1680010, upload-time = "2026-02-20T20:21:13.427Z" }, + { url = "https://files.pythonhosted.org/packages/29/4b/45d90626aef8e65336bed690106d1382f7a43665e2249017e9527df8823b/greenlet-3.3.2-cp314-cp314t-win_amd64.whl", hash = "sha256:c04c5e06ec3e022cbfe2cd4a846e1d4e50087444f875ff6d2c2ad8445495cf1a", size = 237086, upload-time = "2026-02-20T20:20:45.786Z" }, +] + +[[package]] +name = "h11" +version = "0.16.0" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/01/ee/02a2c011bdab74c6fb3c75474d40b3052059d95df7e73351460c8588d963/h11-0.16.0.tar.gz", hash = "sha256:4e35b956cf45792e4caa5885e69fba00bdbc6ffafbfa020300e549b208ee5ff1", size = 101250, upload-time = "2025-04-24T03:35:25.427Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/04/4b/29cac41a4d98d144bf5f6d33995617b185d14b22401f75ca86f384e87ff1/h11-0.16.0-py3-none-any.whl", hash = "sha256:63cf8bbe7522de3bf65932fda1d9c2772064ffb3dae62d55932da54b31cb6c86", size = 37515, upload-time = "2025-04-24T03:35:24.344Z" }, +] + +[[package]] +name = "h5py" +version = "3.15.1" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "numpy" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/4d/6a/0d79de0b025aa85dc8864de8e97659c94cf3d23148394a954dc5ca52f8c8/h5py-3.15.1.tar.gz", hash = "sha256:c86e3ed45c4473564de55aa83b6fc9e5ead86578773dfbd93047380042e26b69", size = 426236, upload-time = "2025-10-16T10:35:27.404Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/62/b8/c0d9aa013ecfa8b7057946c080c0c07f6fa41e231d2e9bd306a2f8110bdc/h5py-3.15.1-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:316dd0f119734f324ca7ed10b5627a2de4ea42cc4dfbcedbee026aaa361c238c", size = 3399089, upload-time = "2025-10-16T10:34:12.135Z" }, + { url = "https://files.pythonhosted.org/packages/a4/5e/3c6f6e0430813c7aefe784d00c6711166f46225f5d229546eb53032c3707/h5py-3.15.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:b51469890e58e85d5242e43aab29f5e9c7e526b951caab354f3ded4ac88e7b76", size = 2847803, upload-time = "2025-10-16T10:34:14.564Z" }, + { url = "https://files.pythonhosted.org/packages/00/69/ba36273b888a4a48d78f9268d2aee05787e4438557450a8442946ab8f3ec/h5py-3.15.1-cp312-cp312-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:8a33bfd5dfcea037196f7778534b1ff7e36a7f40a89e648c8f2967292eb6898e", size = 4914884, upload-time = "2025-10-16T10:34:18.452Z" }, + { url = "https://files.pythonhosted.org/packages/3a/30/d1c94066343a98bb2cea40120873193a4fed68c4ad7f8935c11caf74c681/h5py-3.15.1-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:25c8843fec43b2cc368aa15afa1cdf83fc5e17b1c4e10cd3771ef6c39b72e5ce", size = 5109965, upload-time = "2025-10-16T10:34:21.853Z" }, + { url = "https://files.pythonhosted.org/packages/81/3d/d28172116eafc3bc9f5991b3cb3fd2c8a95f5984f50880adfdf991de9087/h5py-3.15.1-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:a308fd8681a864c04423c0324527237a0484e2611e3441f8089fd00ed56a8171", size = 4561870, upload-time = "2025-10-16T10:34:26.69Z" }, + { url = "https://files.pythonhosted.org/packages/a5/83/393a7226024238b0f51965a7156004eaae1fcf84aa4bfecf7e582676271b/h5py-3.15.1-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:f4a016df3f4a8a14d573b496e4d1964deb380e26031fc85fb40e417e9131888a", size = 5037161, upload-time = "2025-10-16T10:34:30.383Z" }, + { url = "https://files.pythonhosted.org/packages/cf/51/329e7436bf87ca6b0fe06dd0a3795c34bebe4ed8d6c44450a20565d57832/h5py-3.15.1-cp312-cp312-win_amd64.whl", hash = "sha256:59b25cf02411bf12e14f803fef0b80886444c7fe21a5ad17c6a28d3f08098a1e", size = 2874165, upload-time = "2025-10-16T10:34:33.461Z" }, + { url = "https://files.pythonhosted.org/packages/09/a8/2d02b10a66747c54446e932171dd89b8b4126c0111b440e6bc05a7c852ec/h5py-3.15.1-cp312-cp312-win_arm64.whl", hash = "sha256:61d5a58a9851e01ee61c932bbbb1c98fe20aba0a5674776600fb9a361c0aa652", size = 2458214, upload-time = "2025-10-16T10:34:35.733Z" }, + { url = "https://files.pythonhosted.org/packages/88/b3/40207e0192415cbff7ea1d37b9f24b33f6d38a5a2f5d18a678de78f967ae/h5py-3.15.1-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:c8440fd8bee9500c235ecb7aa1917a0389a2adb80c209fa1cc485bd70e0d94a5", size = 3376511, upload-time = "2025-10-16T10:34:38.596Z" }, + { url = "https://files.pythonhosted.org/packages/31/96/ba99a003c763998035b0de4c299598125df5fc6c9ccf834f152ddd60e0fb/h5py-3.15.1-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:ab2219dbc6fcdb6932f76b548e2b16f34a1f52b7666e998157a4dfc02e2c4123", size = 2826143, upload-time = "2025-10-16T10:34:41.342Z" }, + { url = "https://files.pythonhosted.org/packages/6a/c2/fc6375d07ea3962df7afad7d863fe4bde18bb88530678c20d4c90c18de1d/h5py-3.15.1-cp313-cp313-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:d8cb02c3a96255149ed3ac811eeea25b655d959c6dd5ce702c9a95ff11859eb5", size = 4908316, upload-time = "2025-10-16T10:34:44.619Z" }, + { url = "https://files.pythonhosted.org/packages/d9/69/4402ea66272dacc10b298cca18ed73e1c0791ff2ae9ed218d3859f9698ac/h5py-3.15.1-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:121b2b7a4c1915d63737483b7bff14ef253020f617c2fb2811f67a4bed9ac5e8", size = 5103710, upload-time = "2025-10-16T10:34:48.639Z" }, + { url = "https://files.pythonhosted.org/packages/e0/f6/11f1e2432d57d71322c02a97a5567829a75f223a8c821764a0e71a65cde8/h5py-3.15.1-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:59b0d63b318bf3cc06687def2b45afd75926bbc006f7b8cd2b1a231299fc8599", size = 4556042, upload-time = "2025-10-16T10:34:51.841Z" }, + { url = "https://files.pythonhosted.org/packages/18/88/3eda3ef16bfe7a7dbc3d8d6836bbaa7986feb5ff091395e140dc13927bcc/h5py-3.15.1-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:e02fe77a03f652500d8bff288cbf3675f742fc0411f5a628fa37116507dc7cc0", size = 5030639, upload-time = "2025-10-16T10:34:55.257Z" }, + { url = "https://files.pythonhosted.org/packages/e5/ea/fbb258a98863f99befb10ed727152b4ae659f322e1d9c0576f8a62754e81/h5py-3.15.1-cp313-cp313-win_amd64.whl", hash = "sha256:dea78b092fd80a083563ed79a3171258d4a4d307492e7cf8b2313d464c82ba52", size = 2864363, upload-time = "2025-10-16T10:34:58.099Z" }, + { url = "https://files.pythonhosted.org/packages/5d/c9/35021cc9cd2b2915a7da3026e3d77a05bed1144a414ff840953b33937fb9/h5py-3.15.1-cp313-cp313-win_arm64.whl", hash = "sha256:c256254a8a81e2bddc0d376e23e2a6d2dc8a1e8a2261835ed8c1281a0744cd97", size = 2449570, upload-time = "2025-10-16T10:35:00.473Z" }, + { url = "https://files.pythonhosted.org/packages/a0/2c/926eba1514e4d2e47d0e9eb16c784e717d8b066398ccfca9b283917b1bfb/h5py-3.15.1-cp314-cp314-macosx_10_15_x86_64.whl", hash = "sha256:5f4fb0567eb8517c3ecd6b3c02c4f4e9da220c8932604960fd04e24ee1254763", size = 3380368, upload-time = "2025-10-16T10:35:03.117Z" }, + { url = "https://files.pythonhosted.org/packages/65/4b/d715ed454d3baa5f6ae1d30b7eca4c7a1c1084f6a2edead9e801a1541d62/h5py-3.15.1-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:954e480433e82d3872503104f9b285d369048c3a788b2b1a00e53d1c47c98dd2", size = 2833793, upload-time = "2025-10-16T10:35:05.623Z" }, + { url = "https://files.pythonhosted.org/packages/ef/d4/ef386c28e4579314610a8bffebbee3b69295b0237bc967340b7c653c6c10/h5py-3.15.1-cp314-cp314-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:fd125c131889ebbef0849f4a0e29cf363b48aba42f228d08b4079913b576bb3a", size = 4903199, upload-time = "2025-10-16T10:35:08.972Z" }, + { url = "https://files.pythonhosted.org/packages/33/5d/65c619e195e0b5e54ea5a95c1bb600c8ff8715e0d09676e4cce56d89f492/h5py-3.15.1-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:28a20e1a4082a479b3d7db2169f3a5034af010b90842e75ebbf2e9e49eb4183e", size = 5097224, upload-time = "2025-10-16T10:35:12.808Z" }, + { url = "https://files.pythonhosted.org/packages/30/30/5273218400bf2da01609e1292f562c94b461fcb73c7a9e27fdadd43abc0a/h5py-3.15.1-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:fa8df5267f545b4946df8ca0d93d23382191018e4cda2deda4c2cedf9a010e13", size = 4551207, upload-time = "2025-10-16T10:35:16.24Z" }, + { url = "https://files.pythonhosted.org/packages/d3/39/a7ef948ddf4d1c556b0b2b9559534777bccc318543b3f5a1efdf6b556c9c/h5py-3.15.1-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:99d374a21f7321a4c6ab327c4ab23bd925ad69821aeb53a1e75dd809d19f67fa", size = 5025426, upload-time = "2025-10-16T10:35:19.831Z" }, + { url = "https://files.pythonhosted.org/packages/b6/d8/7368679b8df6925b8415f9dcc9ab1dab01ddc384d2b2c24aac9191bd9ceb/h5py-3.15.1-cp314-cp314-win_amd64.whl", hash = "sha256:9c73d1d7cdb97d5b17ae385153472ce118bed607e43be11e9a9deefaa54e0734", size = 2865704, upload-time = "2025-10-16T10:35:22.658Z" }, + { url = "https://files.pythonhosted.org/packages/d3/b7/4a806f85d62c20157e62e58e03b27513dc9c55499768530acc4f4c5ce4be/h5py-3.15.1-cp314-cp314-win_arm64.whl", hash = "sha256:a6d8c5a05a76aca9a494b4c53ce8a9c29023b7f64f625c6ce1841e92a362ccdf", size = 2465544, upload-time = "2025-10-16T10:35:25.695Z" }, +] + +[[package]] +name = "hydra-core" +version = "1.3.2" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "antlr4-python3-runtime" }, + { name = "omegaconf" }, + { name = "packaging" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/6d/8e/07e42bc434a847154083b315779b0a81d567154504624e181caf2c71cd98/hydra-core-1.3.2.tar.gz", hash = "sha256:8a878ed67216997c3e9d88a8e72e7b4767e81af37afb4ea3334b269a4390a824", size = 3263494, upload-time = "2023-02-23T18:33:43.03Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/c6/50/e0edd38dcd63fb26a8547f13d28f7a008bc4a3fd4eb4ff030673f22ad41a/hydra_core-1.3.2-py3-none-any.whl", hash = "sha256:fa0238a9e31df3373b35b0bfb672c34cc92718d21f81311d8996a16de1141d8b", size = 154547, upload-time = "2023-02-23T18:33:40.801Z" }, +] + +[[package]] +name = "idna" +version = "3.11" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/6f/6d/0703ccc57f3a7233505399edb88de3cbd678da106337b9fcde432b65ed60/idna-3.11.tar.gz", hash = "sha256:795dafcc9c04ed0c1fb032c2aa73654d8e8c5023a7df64a53f39190ada629902", size = 194582, upload-time = "2025-10-12T14:55:20.501Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/0e/61/66938bbb5fc52dbdf84594873d5b51fb1f7c7794e9c0f5bd885f30bc507b/idna-3.11-py3-none-any.whl", hash = "sha256:771a87f49d9defaf64091e6e6fe9c18d4833f140bd19464795bc32d966ca37ea", size = 71008, upload-time = "2025-10-12T14:55:18.883Z" }, +] + +[[package]] +name = "iniconfig" +version = "2.3.0" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/72/34/14ca021ce8e5dfedc35312d08ba8bf51fdd999c576889fc2c24cb97f4f10/iniconfig-2.3.0.tar.gz", hash = "sha256:c76315c77db068650d49c5b56314774a7804df16fee4402c1f19d6d15d8c4730", size = 20503, upload-time = "2025-10-18T21:55:43.219Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/cb/b1/3846dd7f199d53cb17f49cba7e651e9ce294d8497c8c150530ed11865bb8/iniconfig-2.3.0-py3-none-any.whl", hash = "sha256:f631c04d2c48c52b84d0d0549c99ff3859c98df65b3101406327ecc7d53fbf12", size = 7484, upload-time = "2025-10-18T21:55:41.639Z" }, +] + +[[package]] +name = "jinja2" +version = "3.1.6" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "markupsafe" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/df/bf/f7da0350254c0ed7c72f3e33cef02e048281fec7ecec5f032d4aac52226b/jinja2-3.1.6.tar.gz", hash = "sha256:0137fb05990d35f1275a587e9aee6d56da821fc83491a0fb838183be43f66d6d", size = 245115, upload-time = "2025-03-05T20:05:02.478Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/62/a1/3d680cbfd5f4b8f15abc1d571870c5fc3e594bb582bc3b64ea099db13e56/jinja2-3.1.6-py3-none-any.whl", hash = "sha256:85ece4451f492d0c13c5dd7c13a64681a86afae63a5f347908daf103ce6d2f67", size = 134899, upload-time = "2025-03-05T20:05:00.369Z" }, +] + +[[package]] +name = "jmespath" +version = "1.1.0" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/d3/59/322338183ecda247fb5d1763a6cbe46eff7222eaeebafd9fa65d4bf5cb11/jmespath-1.1.0.tar.gz", hash = "sha256:472c87d80f36026ae83c6ddd0f1d05d4e510134ed462851fd5f754c8c3cbb88d", size = 27377, upload-time = "2026-01-22T16:35:26.279Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/14/2f/967ba146e6d58cf6a652da73885f52fc68001525b4197effc174321d70b4/jmespath-1.1.0-py3-none-any.whl", hash = "sha256:a5663118de4908c91729bea0acadca56526eb2698e83de10cd116ae0f4e97c64", size = 20419, upload-time = "2026-01-22T16:35:24.919Z" }, +] + +[[package]] +name = "mako" +version = "1.3.10" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "markupsafe" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/9e/38/bd5b78a920a64d708fe6bc8e0a2c075e1389d53bef8413725c63ba041535/mako-1.3.10.tar.gz", hash = "sha256:99579a6f39583fa7e5630a28c3c1f440e4e97a414b80372649c0ce338da2ea28", size = 392474, upload-time = "2025-04-10T12:44:31.16Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/87/fb/99f81ac72ae23375f22b7afdb7642aba97c00a713c217124420147681a2f/mako-1.3.10-py3-none-any.whl", hash = "sha256:baef24a52fc4fc514a0887ac600f9f1cff3d82c61d4d700a1fa84d597b88db59", size = 78509, upload-time = "2025-04-10T12:50:53.297Z" }, +] + +[[package]] +name = "markdown-it-py" +version = "4.0.0" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "mdurl" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/5b/f5/4ec618ed16cc4f8fb3b701563655a69816155e79e24a17b651541804721d/markdown_it_py-4.0.0.tar.gz", hash = "sha256:cb0a2b4aa34f932c007117b194e945bd74e0ec24133ceb5bac59009cda1cb9f3", size = 73070, upload-time = "2025-08-11T12:57:52.854Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/94/54/e7d793b573f298e1c9013b8c4dade17d481164aa517d1d7148619c2cedbf/markdown_it_py-4.0.0-py3-none-any.whl", hash = "sha256:87327c59b172c5011896038353a81343b6754500a08cd7a4973bb48c6d578147", size = 87321, upload-time = "2025-08-11T12:57:51.923Z" }, +] + +[[package]] +name = "markupsafe" +version = "3.0.3" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/7e/99/7690b6d4034fffd95959cbe0c02de8deb3098cc577c67bb6a24fe5d7caa7/markupsafe-3.0.3.tar.gz", hash = "sha256:722695808f4b6457b320fdc131280796bdceb04ab50fe1795cd540799ebe1698", size = 80313, upload-time = "2025-09-27T18:37:40.426Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/5a/72/147da192e38635ada20e0a2e1a51cf8823d2119ce8883f7053879c2199b5/markupsafe-3.0.3-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:d53197da72cc091b024dd97249dfc7794d6a56530370992a5e1a08983ad9230e", size = 11615, upload-time = "2025-09-27T18:36:30.854Z" }, + { url = "https://files.pythonhosted.org/packages/9a/81/7e4e08678a1f98521201c3079f77db69fb552acd56067661f8c2f534a718/markupsafe-3.0.3-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:1872df69a4de6aead3491198eaf13810b565bdbeec3ae2dc8780f14458ec73ce", size = 12020, upload-time = "2025-09-27T18:36:31.971Z" }, + { url = "https://files.pythonhosted.org/packages/1e/2c/799f4742efc39633a1b54a92eec4082e4f815314869865d876824c257c1e/markupsafe-3.0.3-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:3a7e8ae81ae39e62a41ec302f972ba6ae23a5c5396c8e60113e9066ef893da0d", size = 24332, upload-time = "2025-09-27T18:36:32.813Z" }, + { url = "https://files.pythonhosted.org/packages/3c/2e/8d0c2ab90a8c1d9a24f0399058ab8519a3279d1bd4289511d74e909f060e/markupsafe-3.0.3-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:d6dd0be5b5b189d31db7cda48b91d7e0a9795f31430b7f271219ab30f1d3ac9d", size = 22947, upload-time = "2025-09-27T18:36:33.86Z" }, + { url = "https://files.pythonhosted.org/packages/2c/54/887f3092a85238093a0b2154bd629c89444f395618842e8b0c41783898ea/markupsafe-3.0.3-cp312-cp312-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:94c6f0bb423f739146aec64595853541634bde58b2135f27f61c1ffd1cd4d16a", size = 21962, upload-time = "2025-09-27T18:36:35.099Z" }, + { url = "https://files.pythonhosted.org/packages/c9/2f/336b8c7b6f4a4d95e91119dc8521402461b74a485558d8f238a68312f11c/markupsafe-3.0.3-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:be8813b57049a7dc738189df53d69395eba14fb99345e0a5994914a3864c8a4b", size = 23760, upload-time = "2025-09-27T18:36:36.001Z" }, + { url = "https://files.pythonhosted.org/packages/32/43/67935f2b7e4982ffb50a4d169b724d74b62a3964bc1a9a527f5ac4f1ee2b/markupsafe-3.0.3-cp312-cp312-musllinux_1_2_riscv64.whl", hash = "sha256:83891d0e9fb81a825d9a6d61e3f07550ca70a076484292a70fde82c4b807286f", size = 21529, upload-time = "2025-09-27T18:36:36.906Z" }, + { url = "https://files.pythonhosted.org/packages/89/e0/4486f11e51bbba8b0c041098859e869e304d1c261e59244baa3d295d47b7/markupsafe-3.0.3-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:77f0643abe7495da77fb436f50f8dab76dbc6e5fd25d39589a0f1fe6548bfa2b", size = 23015, upload-time = "2025-09-27T18:36:37.868Z" }, + { url = "https://files.pythonhosted.org/packages/2f/e1/78ee7a023dac597a5825441ebd17170785a9dab23de95d2c7508ade94e0e/markupsafe-3.0.3-cp312-cp312-win32.whl", hash = "sha256:d88b440e37a16e651bda4c7c2b930eb586fd15ca7406cb39e211fcff3bf3017d", size = 14540, upload-time = "2025-09-27T18:36:38.761Z" }, + { url = "https://files.pythonhosted.org/packages/aa/5b/bec5aa9bbbb2c946ca2733ef9c4ca91c91b6a24580193e891b5f7dbe8e1e/markupsafe-3.0.3-cp312-cp312-win_amd64.whl", hash = "sha256:26a5784ded40c9e318cfc2bdb30fe164bdb8665ded9cd64d500a34fb42067b1c", size = 15105, upload-time = "2025-09-27T18:36:39.701Z" }, + { url = "https://files.pythonhosted.org/packages/e5/f1/216fc1bbfd74011693a4fd837e7026152e89c4bcf3e77b6692fba9923123/markupsafe-3.0.3-cp312-cp312-win_arm64.whl", hash = "sha256:35add3b638a5d900e807944a078b51922212fb3dedb01633a8defc4b01a3c85f", size = 13906, upload-time = "2025-09-27T18:36:40.689Z" }, + { url = "https://files.pythonhosted.org/packages/38/2f/907b9c7bbba283e68f20259574b13d005c121a0fa4c175f9bed27c4597ff/markupsafe-3.0.3-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:e1cf1972137e83c5d4c136c43ced9ac51d0e124706ee1c8aa8532c1287fa8795", size = 11622, upload-time = "2025-09-27T18:36:41.777Z" }, + { url = "https://files.pythonhosted.org/packages/9c/d9/5f7756922cdd676869eca1c4e3c0cd0df60ed30199ffd775e319089cb3ed/markupsafe-3.0.3-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:116bb52f642a37c115f517494ea5feb03889e04df47eeff5b130b1808ce7c219", size = 12029, upload-time = "2025-09-27T18:36:43.257Z" }, + { url = "https://files.pythonhosted.org/packages/00/07/575a68c754943058c78f30db02ee03a64b3c638586fba6a6dd56830b30a3/markupsafe-3.0.3-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:133a43e73a802c5562be9bbcd03d090aa5a1fe899db609c29e8c8d815c5f6de6", size = 24374, upload-time = "2025-09-27T18:36:44.508Z" }, + { url = "https://files.pythonhosted.org/packages/a9/21/9b05698b46f218fc0e118e1f8168395c65c8a2c750ae2bab54fc4bd4e0e8/markupsafe-3.0.3-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:ccfcd093f13f0f0b7fdd0f198b90053bf7b2f02a3927a30e63f3ccc9df56b676", size = 22980, upload-time = "2025-09-27T18:36:45.385Z" }, + { url = "https://files.pythonhosted.org/packages/7f/71/544260864f893f18b6827315b988c146b559391e6e7e8f7252839b1b846a/markupsafe-3.0.3-cp313-cp313-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:509fa21c6deb7a7a273d629cf5ec029bc209d1a51178615ddf718f5918992ab9", size = 21990, upload-time = "2025-09-27T18:36:46.916Z" }, + { url = "https://files.pythonhosted.org/packages/c2/28/b50fc2f74d1ad761af2f5dcce7492648b983d00a65b8c0e0cb457c82ebbe/markupsafe-3.0.3-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:a4afe79fb3de0b7097d81da19090f4df4f8d3a2b3adaa8764138aac2e44f3af1", size = 23784, upload-time = "2025-09-27T18:36:47.884Z" }, + { url = "https://files.pythonhosted.org/packages/ed/76/104b2aa106a208da8b17a2fb72e033a5a9d7073c68f7e508b94916ed47a9/markupsafe-3.0.3-cp313-cp313-musllinux_1_2_riscv64.whl", hash = "sha256:795e7751525cae078558e679d646ae45574b47ed6e7771863fcc079a6171a0fc", size = 21588, upload-time = "2025-09-27T18:36:48.82Z" }, + { url = "https://files.pythonhosted.org/packages/b5/99/16a5eb2d140087ebd97180d95249b00a03aa87e29cc224056274f2e45fd6/markupsafe-3.0.3-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:8485f406a96febb5140bfeca44a73e3ce5116b2501ac54fe953e488fb1d03b12", size = 23041, upload-time = "2025-09-27T18:36:49.797Z" }, + { url = "https://files.pythonhosted.org/packages/19/bc/e7140ed90c5d61d77cea142eed9f9c303f4c4806f60a1044c13e3f1471d0/markupsafe-3.0.3-cp313-cp313-win32.whl", hash = "sha256:bdd37121970bfd8be76c5fb069c7751683bdf373db1ed6c010162b2a130248ed", size = 14543, upload-time = "2025-09-27T18:36:51.584Z" }, + { url = "https://files.pythonhosted.org/packages/05/73/c4abe620b841b6b791f2edc248f556900667a5a1cf023a6646967ae98335/markupsafe-3.0.3-cp313-cp313-win_amd64.whl", hash = "sha256:9a1abfdc021a164803f4d485104931fb8f8c1efd55bc6b748d2f5774e78b62c5", size = 15113, upload-time = "2025-09-27T18:36:52.537Z" }, + { url = "https://files.pythonhosted.org/packages/f0/3a/fa34a0f7cfef23cf9500d68cb7c32dd64ffd58a12b09225fb03dd37d5b80/markupsafe-3.0.3-cp313-cp313-win_arm64.whl", hash = "sha256:7e68f88e5b8799aa49c85cd116c932a1ac15caaa3f5db09087854d218359e485", size = 13911, upload-time = "2025-09-27T18:36:53.513Z" }, + { url = "https://files.pythonhosted.org/packages/e4/d7/e05cd7efe43a88a17a37b3ae96e79a19e846f3f456fe79c57ca61356ef01/markupsafe-3.0.3-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:218551f6df4868a8d527e3062d0fb968682fe92054e89978594c28e642c43a73", size = 11658, upload-time = "2025-09-27T18:36:54.819Z" }, + { url = "https://files.pythonhosted.org/packages/99/9e/e412117548182ce2148bdeacdda3bb494260c0b0184360fe0d56389b523b/markupsafe-3.0.3-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:3524b778fe5cfb3452a09d31e7b5adefeea8c5be1d43c4f810ba09f2ceb29d37", size = 12066, upload-time = "2025-09-27T18:36:55.714Z" }, + { url = "https://files.pythonhosted.org/packages/bc/e6/fa0ffcda717ef64a5108eaa7b4f5ed28d56122c9a6d70ab8b72f9f715c80/markupsafe-3.0.3-cp313-cp313t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:4e885a3d1efa2eadc93c894a21770e4bc67899e3543680313b09f139e149ab19", size = 25639, upload-time = "2025-09-27T18:36:56.908Z" }, + { url = "https://files.pythonhosted.org/packages/96/ec/2102e881fe9d25fc16cb4b25d5f5cde50970967ffa5dddafdb771237062d/markupsafe-3.0.3-cp313-cp313t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:8709b08f4a89aa7586de0aadc8da56180242ee0ada3999749b183aa23df95025", size = 23569, upload-time = "2025-09-27T18:36:57.913Z" }, + { url = "https://files.pythonhosted.org/packages/4b/30/6f2fce1f1f205fc9323255b216ca8a235b15860c34b6798f810f05828e32/markupsafe-3.0.3-cp313-cp313t-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:b8512a91625c9b3da6f127803b166b629725e68af71f8184ae7e7d54686a56d6", size = 23284, upload-time = "2025-09-27T18:36:58.833Z" }, + { url = "https://files.pythonhosted.org/packages/58/47/4a0ccea4ab9f5dcb6f79c0236d954acb382202721e704223a8aafa38b5c8/markupsafe-3.0.3-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:9b79b7a16f7fedff2495d684f2b59b0457c3b493778c9eed31111be64d58279f", size = 24801, upload-time = "2025-09-27T18:36:59.739Z" }, + { url = "https://files.pythonhosted.org/packages/6a/70/3780e9b72180b6fecb83a4814d84c3bf4b4ae4bf0b19c27196104149734c/markupsafe-3.0.3-cp313-cp313t-musllinux_1_2_riscv64.whl", hash = "sha256:12c63dfb4a98206f045aa9563db46507995f7ef6d83b2f68eda65c307c6829eb", size = 22769, upload-time = "2025-09-27T18:37:00.719Z" }, + { url = "https://files.pythonhosted.org/packages/98/c5/c03c7f4125180fc215220c035beac6b9cb684bc7a067c84fc69414d315f5/markupsafe-3.0.3-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:8f71bc33915be5186016f675cd83a1e08523649b0e33efdb898db577ef5bb009", size = 23642, upload-time = "2025-09-27T18:37:01.673Z" }, + { url = "https://files.pythonhosted.org/packages/80/d6/2d1b89f6ca4bff1036499b1e29a1d02d282259f3681540e16563f27ebc23/markupsafe-3.0.3-cp313-cp313t-win32.whl", hash = "sha256:69c0b73548bc525c8cb9a251cddf1931d1db4d2258e9599c28c07ef3580ef354", size = 14612, upload-time = "2025-09-27T18:37:02.639Z" }, + { url = "https://files.pythonhosted.org/packages/2b/98/e48a4bfba0a0ffcf9925fe2d69240bfaa19c6f7507b8cd09c70684a53c1e/markupsafe-3.0.3-cp313-cp313t-win_amd64.whl", hash = "sha256:1b4b79e8ebf6b55351f0d91fe80f893b4743f104bff22e90697db1590e47a218", size = 15200, upload-time = "2025-09-27T18:37:03.582Z" }, + { url = "https://files.pythonhosted.org/packages/0e/72/e3cc540f351f316e9ed0f092757459afbc595824ca724cbc5a5d4263713f/markupsafe-3.0.3-cp313-cp313t-win_arm64.whl", hash = "sha256:ad2cf8aa28b8c020ab2fc8287b0f823d0a7d8630784c31e9ee5edea20f406287", size = 13973, upload-time = "2025-09-27T18:37:04.929Z" }, + { url = "https://files.pythonhosted.org/packages/33/8a/8e42d4838cd89b7dde187011e97fe6c3af66d8c044997d2183fbd6d31352/markupsafe-3.0.3-cp314-cp314-macosx_10_13_x86_64.whl", hash = "sha256:eaa9599de571d72e2daf60164784109f19978b327a3910d3e9de8c97b5b70cfe", size = 11619, upload-time = "2025-09-27T18:37:06.342Z" }, + { url = "https://files.pythonhosted.org/packages/b5/64/7660f8a4a8e53c924d0fa05dc3a55c9cee10bbd82b11c5afb27d44b096ce/markupsafe-3.0.3-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:c47a551199eb8eb2121d4f0f15ae0f923d31350ab9280078d1e5f12b249e0026", size = 12029, upload-time = "2025-09-27T18:37:07.213Z" }, + { url = "https://files.pythonhosted.org/packages/da/ef/e648bfd021127bef5fa12e1720ffed0c6cbb8310c8d9bea7266337ff06de/markupsafe-3.0.3-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:f34c41761022dd093b4b6896d4810782ffbabe30f2d443ff5f083e0cbbb8c737", size = 24408, upload-time = "2025-09-27T18:37:09.572Z" }, + { url = "https://files.pythonhosted.org/packages/41/3c/a36c2450754618e62008bf7435ccb0f88053e07592e6028a34776213d877/markupsafe-3.0.3-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:457a69a9577064c05a97c41f4e65148652db078a3a509039e64d3467b9e7ef97", size = 23005, upload-time = "2025-09-27T18:37:10.58Z" }, + { url = "https://files.pythonhosted.org/packages/bc/20/b7fdf89a8456b099837cd1dc21974632a02a999ec9bf7ca3e490aacd98e7/markupsafe-3.0.3-cp314-cp314-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:e8afc3f2ccfa24215f8cb28dcf43f0113ac3c37c2f0f0806d8c70e4228c5cf4d", size = 22048, upload-time = "2025-09-27T18:37:11.547Z" }, + { url = "https://files.pythonhosted.org/packages/9a/a7/591f592afdc734f47db08a75793a55d7fbcc6902a723ae4cfbab61010cc5/markupsafe-3.0.3-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:ec15a59cf5af7be74194f7ab02d0f59a62bdcf1a537677ce67a2537c9b87fcda", size = 23821, upload-time = "2025-09-27T18:37:12.48Z" }, + { url = "https://files.pythonhosted.org/packages/7d/33/45b24e4f44195b26521bc6f1a82197118f74df348556594bd2262bda1038/markupsafe-3.0.3-cp314-cp314-musllinux_1_2_riscv64.whl", hash = "sha256:0eb9ff8191e8498cca014656ae6b8d61f39da5f95b488805da4bb029cccbfbaf", size = 21606, upload-time = "2025-09-27T18:37:13.485Z" }, + { url = "https://files.pythonhosted.org/packages/ff/0e/53dfaca23a69fbfbbf17a4b64072090e70717344c52eaaaa9c5ddff1e5f0/markupsafe-3.0.3-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:2713baf880df847f2bece4230d4d094280f4e67b1e813eec43b4c0e144a34ffe", size = 23043, upload-time = "2025-09-27T18:37:14.408Z" }, + { url = "https://files.pythonhosted.org/packages/46/11/f333a06fc16236d5238bfe74daccbca41459dcd8d1fa952e8fbd5dccfb70/markupsafe-3.0.3-cp314-cp314-win32.whl", hash = "sha256:729586769a26dbceff69f7a7dbbf59ab6572b99d94576a5592625d5b411576b9", size = 14747, upload-time = "2025-09-27T18:37:15.36Z" }, + { url = "https://files.pythonhosted.org/packages/28/52/182836104b33b444e400b14f797212f720cbc9ed6ba34c800639d154e821/markupsafe-3.0.3-cp314-cp314-win_amd64.whl", hash = "sha256:bdc919ead48f234740ad807933cdf545180bfbe9342c2bb451556db2ed958581", size = 15341, upload-time = "2025-09-27T18:37:16.496Z" }, + { url = "https://files.pythonhosted.org/packages/6f/18/acf23e91bd94fd7b3031558b1f013adfa21a8e407a3fdb32745538730382/markupsafe-3.0.3-cp314-cp314-win_arm64.whl", hash = "sha256:5a7d5dc5140555cf21a6fefbdbf8723f06fcd2f63ef108f2854de715e4422cb4", size = 14073, upload-time = "2025-09-27T18:37:17.476Z" }, + { url = "https://files.pythonhosted.org/packages/3c/f0/57689aa4076e1b43b15fdfa646b04653969d50cf30c32a102762be2485da/markupsafe-3.0.3-cp314-cp314t-macosx_10_13_x86_64.whl", hash = "sha256:1353ef0c1b138e1907ae78e2f6c63ff67501122006b0f9abad68fda5f4ffc6ab", size = 11661, upload-time = "2025-09-27T18:37:18.453Z" }, + { url = "https://files.pythonhosted.org/packages/89/c3/2e67a7ca217c6912985ec766c6393b636fb0c2344443ff9d91404dc4c79f/markupsafe-3.0.3-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:1085e7fbddd3be5f89cc898938f42c0b3c711fdcb37d75221de2666af647c175", size = 12069, upload-time = "2025-09-27T18:37:19.332Z" }, + { url = "https://files.pythonhosted.org/packages/f0/00/be561dce4e6ca66b15276e184ce4b8aec61fe83662cce2f7d72bd3249d28/markupsafe-3.0.3-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:1b52b4fb9df4eb9ae465f8d0c228a00624de2334f216f178a995ccdcf82c4634", size = 25670, upload-time = "2025-09-27T18:37:20.245Z" }, + { url = "https://files.pythonhosted.org/packages/50/09/c419f6f5a92e5fadde27efd190eca90f05e1261b10dbd8cbcb39cd8ea1dc/markupsafe-3.0.3-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:fed51ac40f757d41b7c48425901843666a6677e3e8eb0abcff09e4ba6e664f50", size = 23598, upload-time = "2025-09-27T18:37:21.177Z" }, + { url = "https://files.pythonhosted.org/packages/22/44/a0681611106e0b2921b3033fc19bc53323e0b50bc70cffdd19f7d679bb66/markupsafe-3.0.3-cp314-cp314t-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:f190daf01f13c72eac4efd5c430a8de82489d9cff23c364c3ea822545032993e", size = 23261, upload-time = "2025-09-27T18:37:22.167Z" }, + { url = "https://files.pythonhosted.org/packages/5f/57/1b0b3f100259dc9fffe780cfb60d4be71375510e435efec3d116b6436d43/markupsafe-3.0.3-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:e56b7d45a839a697b5eb268c82a71bd8c7f6c94d6fd50c3d577fa39a9f1409f5", size = 24835, upload-time = "2025-09-27T18:37:23.296Z" }, + { url = "https://files.pythonhosted.org/packages/26/6a/4bf6d0c97c4920f1597cc14dd720705eca0bf7c787aebc6bb4d1bead5388/markupsafe-3.0.3-cp314-cp314t-musllinux_1_2_riscv64.whl", hash = "sha256:f3e98bb3798ead92273dc0e5fd0f31ade220f59a266ffd8a4f6065e0a3ce0523", size = 22733, upload-time = "2025-09-27T18:37:24.237Z" }, + { url = "https://files.pythonhosted.org/packages/14/c7/ca723101509b518797fedc2fdf79ba57f886b4aca8a7d31857ba3ee8281f/markupsafe-3.0.3-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:5678211cb9333a6468fb8d8be0305520aa073f50d17f089b5b4b477ea6e67fdc", size = 23672, upload-time = "2025-09-27T18:37:25.271Z" }, + { url = "https://files.pythonhosted.org/packages/fb/df/5bd7a48c256faecd1d36edc13133e51397e41b73bb77e1a69deab746ebac/markupsafe-3.0.3-cp314-cp314t-win32.whl", hash = "sha256:915c04ba3851909ce68ccc2b8e2cd691618c4dc4c4232fb7982bca3f41fd8c3d", size = 14819, upload-time = "2025-09-27T18:37:26.285Z" }, + { url = "https://files.pythonhosted.org/packages/1a/8a/0402ba61a2f16038b48b39bccca271134be00c5c9f0f623208399333c448/markupsafe-3.0.3-cp314-cp314t-win_amd64.whl", hash = "sha256:4faffd047e07c38848ce017e8725090413cd80cbc23d86e55c587bf979e579c9", size = 15426, upload-time = "2025-09-27T18:37:27.316Z" }, + { url = "https://files.pythonhosted.org/packages/70/bc/6f1c2f612465f5fa89b95bead1f44dcb607670fd42891d8fdcd5d039f4f4/markupsafe-3.0.3-cp314-cp314t-win_arm64.whl", hash = "sha256:32001d6a8fc98c8cb5c947787c5d08b0a50663d139f1305bac5885d98d9b40fa", size = 14146, upload-time = "2025-09-27T18:37:28.327Z" }, +] + +[[package]] +name = "mdurl" +version = "0.1.2" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/d6/54/cfe61301667036ec958cb99bd3efefba235e65cdeb9c84d24a8293ba1d90/mdurl-0.1.2.tar.gz", hash = "sha256:bb413d29f5eea38f31dd4754dd7377d4465116fb207585f97bf925588687c1ba", size = 8729, upload-time = "2022-08-14T12:40:10.846Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/b3/38/89ba8ad64ae25be8de66a6d463314cf1eb366222074cfda9ee839c56a4b4/mdurl-0.1.2-py3-none-any.whl", hash = "sha256:84008a41e51615a49fc9966191ff91509e3c40b939176e643fd50a5c2196b8f8", size = 9979, upload-time = "2022-08-14T12:40:09.779Z" }, +] + +[[package]] +name = "mpmath" +version = "1.3.0" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/e0/47/dd32fa426cc72114383ac549964eecb20ecfd886d1e5ccf5340b55b02f57/mpmath-1.3.0.tar.gz", hash = "sha256:7a28eb2a9774d00c7bc92411c19a89209d5da7c4c9a9e227be8330a23a25b91f", size = 508106, upload-time = "2023-03-07T16:47:11.061Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/43/e3/7d92a15f894aa0c9c4b49b8ee9ac9850d6e63b03c9c32c0367a13ae62209/mpmath-1.3.0-py3-none-any.whl", hash = "sha256:a0b2b9fe80bbcd81a6647ff13108738cfb482d481d826cc0e02f5b35e5c88d2c", size = 536198, upload-time = "2023-03-07T16:47:09.197Z" }, +] + +[[package]] +name = "networkx" +version = "3.6.1" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/6a/51/63fe664f3908c97be9d2e4f1158eb633317598cfa6e1fc14af5383f17512/networkx-3.6.1.tar.gz", hash = "sha256:26b7c357accc0c8cde558ad486283728b65b6a95d85ee1cd66bafab4c8168509", size = 2517025, upload-time = "2025-12-08T17:02:39.908Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/9e/c9/b2622292ea83fbb4ec318f5b9ab867d0a28ab43c5717bb85b0a5f6b3b0a4/networkx-3.6.1-py3-none-any.whl", hash = "sha256:d47fbf302e7d9cbbb9e2555a0d267983d2aa476bac30e90dfbe5669bd57f3762", size = 2068504, upload-time = "2025-12-08T17:02:38.159Z" }, +] + +[[package]] +name = "nodeenv" +version = "1.10.0" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/24/bf/d1bda4f6168e0b2e9e5958945e01910052158313224ada5ce1fb2e1113b8/nodeenv-1.10.0.tar.gz", hash = "sha256:996c191ad80897d076bdfba80a41994c2b47c68e224c542b48feba42ba00f8bb", size = 55611, upload-time = "2025-12-20T14:08:54.006Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/88/b2/d0896bdcdc8d28a7fc5717c305f1a861c26e18c05047949fb371034d98bd/nodeenv-1.10.0-py2.py3-none-any.whl", hash = "sha256:5bb13e3eed2923615535339b3c620e76779af4cb4c6a90deccc9e36b274d3827", size = 23438, upload-time = "2025-12-20T14:08:52.782Z" }, +] + +[[package]] +name = "numpy" +version = "2.4.2" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/57/fd/0005efbd0af48e55eb3c7208af93f2862d4b1a56cd78e84309a2d959208d/numpy-2.4.2.tar.gz", hash = "sha256:659a6107e31a83c4e33f763942275fd278b21d095094044eb35569e86a21ddae", size = 20723651, upload-time = "2026-01-31T23:13:10.135Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/51/6e/6f394c9c77668153e14d4da83bcc247beb5952f6ead7699a1a2992613bea/numpy-2.4.2-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:21982668592194c609de53ba4933a7471880ccbaadcc52352694a59ecc860b3a", size = 16667963, upload-time = "2026-01-31T23:10:52.147Z" }, + { url = "https://files.pythonhosted.org/packages/1f/f8/55483431f2b2fd015ae6ed4fe62288823ce908437ed49db5a03d15151678/numpy-2.4.2-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:40397bda92382fcec844066efb11f13e1c9a3e2a8e8f318fb72ed8b6db9f60f1", size = 14693571, upload-time = "2026-01-31T23:10:54.789Z" }, + { url = "https://files.pythonhosted.org/packages/2f/20/18026832b1845cdc82248208dd929ca14c9d8f2bac391f67440707fff27c/numpy-2.4.2-cp312-cp312-macosx_14_0_arm64.whl", hash = "sha256:b3a24467af63c67829bfaa61eecf18d5432d4f11992688537be59ecd6ad32f5e", size = 5203469, upload-time = "2026-01-31T23:10:57.343Z" }, + { url = "https://files.pythonhosted.org/packages/7d/33/2eb97c8a77daaba34eaa3fa7241a14ac5f51c46a6bd5911361b644c4a1e2/numpy-2.4.2-cp312-cp312-macosx_14_0_x86_64.whl", hash = "sha256:805cc8de9fd6e7a22da5aed858e0ab16be5a4db6c873dde1d7451c541553aa27", size = 6550820, upload-time = "2026-01-31T23:10:59.429Z" }, + { url = "https://files.pythonhosted.org/packages/b1/91/b97fdfd12dc75b02c44e26c6638241cc004d4079a0321a69c62f51470c4c/numpy-2.4.2-cp312-cp312-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:6d82351358ffbcdcd7b686b90742a9b86632d6c1c051016484fa0b326a0a1548", size = 15663067, upload-time = "2026-01-31T23:11:01.291Z" }, + { url = "https://files.pythonhosted.org/packages/f5/c6/a18e59f3f0b8071cc85cbc8d80cd02d68aa9710170b2553a117203d46936/numpy-2.4.2-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:9e35d3e0144137d9fdae62912e869136164534d64a169f86438bc9561b6ad49f", size = 16619782, upload-time = "2026-01-31T23:11:03.669Z" }, + { url = "https://files.pythonhosted.org/packages/b7/83/9751502164601a79e18847309f5ceec0b1446d7b6aa12305759b72cf98b2/numpy-2.4.2-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:adb6ed2ad29b9e15321d167d152ee909ec73395901b70936f029c3bc6d7f4460", size = 17013128, upload-time = "2026-01-31T23:11:05.913Z" }, + { url = "https://files.pythonhosted.org/packages/61/c4/c4066322256ec740acc1c8923a10047818691d2f8aec254798f3dd90f5f2/numpy-2.4.2-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:8906e71fd8afcb76580404e2a950caef2685df3d2a57fe82a86ac8d33cc007ba", size = 18345324, upload-time = "2026-01-31T23:11:08.248Z" }, + { url = "https://files.pythonhosted.org/packages/ab/af/6157aa6da728fa4525a755bfad486ae7e3f76d4c1864138003eb84328497/numpy-2.4.2-cp312-cp312-win32.whl", hash = "sha256:ec055f6dae239a6299cace477b479cca2fc125c5675482daf1dd886933a1076f", size = 5960282, upload-time = "2026-01-31T23:11:10.497Z" }, + { url = "https://files.pythonhosted.org/packages/92/0f/7ceaaeaacb40567071e94dbf2c9480c0ae453d5bb4f52bea3892c39dc83c/numpy-2.4.2-cp312-cp312-win_amd64.whl", hash = "sha256:209fae046e62d0ce6435fcfe3b1a10537e858249b3d9b05829e2a05218296a85", size = 12314210, upload-time = "2026-01-31T23:11:12.176Z" }, + { url = "https://files.pythonhosted.org/packages/2f/a3/56c5c604fae6dd40fa2ed3040d005fca97e91bd320d232ac9931d77ba13c/numpy-2.4.2-cp312-cp312-win_arm64.whl", hash = "sha256:fbde1b0c6e81d56f5dccd95dd4a711d9b95df1ae4009a60887e56b27e8d903fa", size = 10220171, upload-time = "2026-01-31T23:11:14.684Z" }, + { url = "https://files.pythonhosted.org/packages/a1/22/815b9fe25d1d7ae7d492152adbc7226d3eff731dffc38fe970589fcaaa38/numpy-2.4.2-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:25f2059807faea4b077a2b6837391b5d830864b3543627f381821c646f31a63c", size = 16663696, upload-time = "2026-01-31T23:11:17.516Z" }, + { url = "https://files.pythonhosted.org/packages/09/f0/817d03a03f93ba9c6c8993de509277d84e69f9453601915e4a69554102a1/numpy-2.4.2-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:bd3a7a9f5847d2fb8c2c6d1c862fa109c31a9abeca1a3c2bd5a64572955b2979", size = 14688322, upload-time = "2026-01-31T23:11:19.883Z" }, + { url = "https://files.pythonhosted.org/packages/da/b4/f805ab79293c728b9a99438775ce51885fd4f31b76178767cfc718701a39/numpy-2.4.2-cp313-cp313-macosx_14_0_arm64.whl", hash = "sha256:8e4549f8a3c6d13d55041925e912bfd834285ef1dd64d6bc7d542583355e2e98", size = 5198157, upload-time = "2026-01-31T23:11:22.375Z" }, + { url = "https://files.pythonhosted.org/packages/74/09/826e4289844eccdcd64aac27d13b0fd3f32039915dd5b9ba01baae1f436c/numpy-2.4.2-cp313-cp313-macosx_14_0_x86_64.whl", hash = "sha256:aea4f66ff44dfddf8c2cffd66ba6538c5ec67d389285292fe428cb2c738c8aef", size = 6546330, upload-time = "2026-01-31T23:11:23.958Z" }, + { url = "https://files.pythonhosted.org/packages/19/fb/cbfdbfa3057a10aea5422c558ac57538e6acc87ec1669e666d32ac198da7/numpy-2.4.2-cp313-cp313-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:c3cd545784805de05aafe1dde61752ea49a359ccba9760c1e5d1c88a93bbf2b7", size = 15660968, upload-time = "2026-01-31T23:11:25.713Z" }, + { url = "https://files.pythonhosted.org/packages/04/dc/46066ce18d01645541f0186877377b9371b8fa8017fa8262002b4ef22612/numpy-2.4.2-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:d0d9b7c93578baafcbc5f0b83eaf17b79d345c6f36917ba0c67f45226911d499", size = 16607311, upload-time = "2026-01-31T23:11:28.117Z" }, + { url = "https://files.pythonhosted.org/packages/14/d9/4b5adfc39a43fa6bf918c6d544bc60c05236cc2f6339847fc5b35e6cb5b0/numpy-2.4.2-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:f74f0f7779cc7ae07d1810aab8ac6b1464c3eafb9e283a40da7309d5e6e48fbb", size = 17012850, upload-time = "2026-01-31T23:11:30.888Z" }, + { url = "https://files.pythonhosted.org/packages/b7/20/adb6e6adde6d0130046e6fdfb7675cc62bc2f6b7b02239a09eb58435753d/numpy-2.4.2-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:c7ac672d699bf36275c035e16b65539931347d68b70667d28984c9fb34e07fa7", size = 18334210, upload-time = "2026-01-31T23:11:33.214Z" }, + { url = "https://files.pythonhosted.org/packages/78/0e/0a73b3dff26803a8c02baa76398015ea2a5434d9b8265a7898a6028c1591/numpy-2.4.2-cp313-cp313-win32.whl", hash = "sha256:8e9afaeb0beff068b4d9cd20d322ba0ee1cecfb0b08db145e4ab4dd44a6b5110", size = 5958199, upload-time = "2026-01-31T23:11:35.385Z" }, + { url = "https://files.pythonhosted.org/packages/43/bc/6352f343522fcb2c04dbaf94cb30cca6fd32c1a750c06ad6231b4293708c/numpy-2.4.2-cp313-cp313-win_amd64.whl", hash = "sha256:7df2de1e4fba69a51c06c28f5a3de36731eb9639feb8e1cf7e4a7b0daf4cf622", size = 12310848, upload-time = "2026-01-31T23:11:38.001Z" }, + { url = "https://files.pythonhosted.org/packages/6e/8d/6da186483e308da5da1cc6918ce913dcfe14ffde98e710bfeff2a6158d4e/numpy-2.4.2-cp313-cp313-win_arm64.whl", hash = "sha256:0fece1d1f0a89c16b03442eae5c56dc0be0c7883b5d388e0c03f53019a4bfd71", size = 10221082, upload-time = "2026-01-31T23:11:40.392Z" }, + { url = "https://files.pythonhosted.org/packages/25/a1/9510aa43555b44781968935c7548a8926274f815de42ad3997e9e83680dd/numpy-2.4.2-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:5633c0da313330fd20c484c78cdd3f9b175b55e1a766c4a174230c6b70ad8262", size = 14815866, upload-time = "2026-01-31T23:11:42.495Z" }, + { url = "https://files.pythonhosted.org/packages/36/30/6bbb5e76631a5ae46e7923dd16ca9d3f1c93cfa8d4ed79a129814a9d8db3/numpy-2.4.2-cp313-cp313t-macosx_14_0_arm64.whl", hash = "sha256:d9f64d786b3b1dd742c946c42d15b07497ed14af1a1f3ce840cce27daa0ce913", size = 5325631, upload-time = "2026-01-31T23:11:44.7Z" }, + { url = "https://files.pythonhosted.org/packages/46/00/3a490938800c1923b567b3a15cd17896e68052e2145d8662aaf3e1ffc58f/numpy-2.4.2-cp313-cp313t-macosx_14_0_x86_64.whl", hash = "sha256:b21041e8cb6a1eb5312dd1d2f80a94d91efffb7a06b70597d44f1bd2dfc315ab", size = 6646254, upload-time = "2026-01-31T23:11:46.341Z" }, + { url = "https://files.pythonhosted.org/packages/d3/e9/fac0890149898a9b609caa5af7455a948b544746e4b8fe7c212c8edd71f8/numpy-2.4.2-cp313-cp313t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:00ab83c56211a1d7c07c25e3217ea6695e50a3e2f255053686b081dc0b091a82", size = 15720138, upload-time = "2026-01-31T23:11:48.082Z" }, + { url = "https://files.pythonhosted.org/packages/ea/5c/08887c54e68e1e28df53709f1893ce92932cc6f01f7c3d4dc952f61ffd4e/numpy-2.4.2-cp313-cp313t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:2fb882da679409066b4603579619341c6d6898fc83a8995199d5249f986e8e8f", size = 16655398, upload-time = "2026-01-31T23:11:50.293Z" }, + { url = "https://files.pythonhosted.org/packages/4d/89/253db0fa0e66e9129c745e4ef25631dc37d5f1314dad2b53e907b8538e6d/numpy-2.4.2-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:66cb9422236317f9d44b67b4d18f44efe6e9c7f8794ac0462978513359461554", size = 17079064, upload-time = "2026-01-31T23:11:52.927Z" }, + { url = "https://files.pythonhosted.org/packages/2a/d5/cbade46ce97c59c6c3da525e8d95b7abe8a42974a1dc5c1d489c10433e88/numpy-2.4.2-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:0f01dcf33e73d80bd8dc0f20a71303abbafa26a19e23f6b68d1aa9990af90257", size = 18379680, upload-time = "2026-01-31T23:11:55.22Z" }, + { url = "https://files.pythonhosted.org/packages/40/62/48f99ae172a4b63d981babe683685030e8a3df4f246c893ea5c6ef99f018/numpy-2.4.2-cp313-cp313t-win32.whl", hash = "sha256:52b913ec40ff7ae845687b0b34d8d93b60cb66dcee06996dd5c99f2fc9328657", size = 6082433, upload-time = "2026-01-31T23:11:58.096Z" }, + { url = "https://files.pythonhosted.org/packages/07/38/e054a61cfe48ad9f1ed0d188e78b7e26859d0b60ef21cd9de4897cdb5326/numpy-2.4.2-cp313-cp313t-win_amd64.whl", hash = "sha256:5eea80d908b2c1f91486eb95b3fb6fab187e569ec9752ab7d9333d2e66bf2d6b", size = 12451181, upload-time = "2026-01-31T23:11:59.782Z" }, + { url = "https://files.pythonhosted.org/packages/6e/a4/a05c3a6418575e185dd84d0b9680b6bb2e2dc3e4202f036b7b4e22d6e9dc/numpy-2.4.2-cp313-cp313t-win_arm64.whl", hash = "sha256:fd49860271d52127d61197bb50b64f58454e9f578cb4b2c001a6de8b1f50b0b1", size = 10290756, upload-time = "2026-01-31T23:12:02.438Z" }, + { url = "https://files.pythonhosted.org/packages/18/88/b7df6050bf18fdcfb7046286c6535cabbdd2064a3440fca3f069d319c16e/numpy-2.4.2-cp314-cp314-macosx_10_15_x86_64.whl", hash = "sha256:444be170853f1f9d528428eceb55f12918e4fda5d8805480f36a002f1415e09b", size = 16663092, upload-time = "2026-01-31T23:12:04.521Z" }, + { url = "https://files.pythonhosted.org/packages/25/7a/1fee4329abc705a469a4afe6e69b1ef7e915117747886327104a8493a955/numpy-2.4.2-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:d1240d50adff70c2a88217698ca844723068533f3f5c5fa6ee2e3220e3bdb000", size = 14698770, upload-time = "2026-01-31T23:12:06.96Z" }, + { url = "https://files.pythonhosted.org/packages/fb/0b/f9e49ba6c923678ad5bc38181c08ac5e53b7a5754dbca8e581aa1a56b1ff/numpy-2.4.2-cp314-cp314-macosx_14_0_arm64.whl", hash = "sha256:7cdde6de52fb6664b00b056341265441192d1291c130e99183ec0d4b110ff8b1", size = 5208562, upload-time = "2026-01-31T23:12:09.632Z" }, + { url = "https://files.pythonhosted.org/packages/7d/12/d7de8f6f53f9bb76997e5e4c069eda2051e3fe134e9181671c4391677bb2/numpy-2.4.2-cp314-cp314-macosx_14_0_x86_64.whl", hash = "sha256:cda077c2e5b780200b6b3e09d0b42205a3d1c68f30c6dceb90401c13bff8fe74", size = 6543710, upload-time = "2026-01-31T23:12:11.969Z" }, + { url = "https://files.pythonhosted.org/packages/09/63/c66418c2e0268a31a4cf8a8b512685748200f8e8e8ec6c507ce14e773529/numpy-2.4.2-cp314-cp314-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:d30291931c915b2ab5717c2974bb95ee891a1cf22ebc16a8006bd59cd210d40a", size = 15677205, upload-time = "2026-01-31T23:12:14.33Z" }, + { url = "https://files.pythonhosted.org/packages/5d/6c/7f237821c9642fb2a04d2f1e88b4295677144ca93285fd76eff3bcba858d/numpy-2.4.2-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:bba37bc29d4d85761deed3954a1bc62be7cf462b9510b51d367b769a8c8df325", size = 16611738, upload-time = "2026-01-31T23:12:16.525Z" }, + { url = "https://files.pythonhosted.org/packages/c2/a7/39c4cdda9f019b609b5c473899d87abff092fc908cfe4d1ecb2fcff453b0/numpy-2.4.2-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:b2f0073ed0868db1dcd86e052d37279eef185b9c8db5bf61f30f46adac63c909", size = 17028888, upload-time = "2026-01-31T23:12:19.306Z" }, + { url = "https://files.pythonhosted.org/packages/da/b3/e84bb64bdfea967cc10950d71090ec2d84b49bc691df0025dddb7c26e8e3/numpy-2.4.2-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:7f54844851cdb630ceb623dcec4db3240d1ac13d4990532446761baede94996a", size = 18339556, upload-time = "2026-01-31T23:12:21.816Z" }, + { url = "https://files.pythonhosted.org/packages/88/f5/954a291bc1192a27081706862ac62bb5920fbecfbaa302f64682aa90beed/numpy-2.4.2-cp314-cp314-win32.whl", hash = "sha256:12e26134a0331d8dbd9351620f037ec470b7c75929cb8a1537f6bfe411152a1a", size = 6006899, upload-time = "2026-01-31T23:12:24.14Z" }, + { url = "https://files.pythonhosted.org/packages/05/cb/eff72a91b2efdd1bc98b3b8759f6a1654aa87612fc86e3d87d6fe4f948c4/numpy-2.4.2-cp314-cp314-win_amd64.whl", hash = "sha256:068cdb2d0d644cdb45670810894f6a0600797a69c05f1ac478e8d31670b8ee75", size = 12443072, upload-time = "2026-01-31T23:12:26.33Z" }, + { url = "https://files.pythonhosted.org/packages/37/75/62726948db36a56428fce4ba80a115716dc4fad6a3a4352487f8bb950966/numpy-2.4.2-cp314-cp314-win_arm64.whl", hash = "sha256:6ed0be1ee58eef41231a5c943d7d1375f093142702d5723ca2eb07db9b934b05", size = 10494886, upload-time = "2026-01-31T23:12:28.488Z" }, + { url = "https://files.pythonhosted.org/packages/36/2f/ee93744f1e0661dc267e4b21940870cabfae187c092e1433b77b09b50ac4/numpy-2.4.2-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:98f16a80e917003a12c0580f97b5f875853ebc33e2eaa4bccfc8201ac6869308", size = 14818567, upload-time = "2026-01-31T23:12:30.709Z" }, + { url = "https://files.pythonhosted.org/packages/a7/24/6535212add7d76ff938d8bdc654f53f88d35cddedf807a599e180dcb8e66/numpy-2.4.2-cp314-cp314t-macosx_14_0_arm64.whl", hash = "sha256:20abd069b9cda45874498b245c8015b18ace6de8546bf50dfa8cea1696ed06ef", size = 5328372, upload-time = "2026-01-31T23:12:32.962Z" }, + { url = "https://files.pythonhosted.org/packages/5e/9d/c48f0a035725f925634bf6b8994253b43f2047f6778a54147d7e213bc5a7/numpy-2.4.2-cp314-cp314t-macosx_14_0_x86_64.whl", hash = "sha256:e98c97502435b53741540a5717a6749ac2ada901056c7db951d33e11c885cc7d", size = 6649306, upload-time = "2026-01-31T23:12:34.797Z" }, + { url = "https://files.pythonhosted.org/packages/81/05/7c73a9574cd4a53a25907bad38b59ac83919c0ddc8234ec157f344d57d9a/numpy-2.4.2-cp314-cp314t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:da6cad4e82cb893db4b69105c604d805e0c3ce11501a55b5e9f9083b47d2ffe8", size = 15722394, upload-time = "2026-01-31T23:12:36.565Z" }, + { url = "https://files.pythonhosted.org/packages/35/fa/4de10089f21fc7d18442c4a767ab156b25c2a6eaf187c0db6d9ecdaeb43f/numpy-2.4.2-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:9e4424677ce4b47fe73c8b5556d876571f7c6945d264201180db2dc34f676ab5", size = 16653343, upload-time = "2026-01-31T23:12:39.188Z" }, + { url = "https://files.pythonhosted.org/packages/b8/f9/d33e4ffc857f3763a57aa85650f2e82486832d7492280ac21ba9efda80da/numpy-2.4.2-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:2b8f157c8a6f20eb657e240f8985cc135598b2b46985c5bccbde7616dc9c6b1e", size = 17078045, upload-time = "2026-01-31T23:12:42.041Z" }, + { url = "https://files.pythonhosted.org/packages/c8/b8/54bdb43b6225badbea6389fa038c4ef868c44f5890f95dd530a218706da3/numpy-2.4.2-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:5daf6f3914a733336dab21a05cdec343144600e964d2fcdabaac0c0269874b2a", size = 18380024, upload-time = "2026-01-31T23:12:44.331Z" }, + { url = "https://files.pythonhosted.org/packages/a5/55/6e1a61ded7af8df04016d81b5b02daa59f2ea9252ee0397cb9f631efe9e5/numpy-2.4.2-cp314-cp314t-win32.whl", hash = "sha256:8c50dd1fc8826f5b26a5ee4d77ca55d88a895f4e4819c7ecc2a9f5905047a443", size = 6153937, upload-time = "2026-01-31T23:12:47.229Z" }, + { url = "https://files.pythonhosted.org/packages/45/aa/fa6118d1ed6d776b0983f3ceac9b1a5558e80df9365b1c3aa6d42bf9eee4/numpy-2.4.2-cp314-cp314t-win_amd64.whl", hash = "sha256:fcf92bee92742edd401ba41135185866f7026c502617f422eb432cfeca4fe236", size = 12631844, upload-time = "2026-01-31T23:12:48.997Z" }, + { url = "https://files.pythonhosted.org/packages/32/0a/2ec5deea6dcd158f254a7b372fb09cfba5719419c8d66343bab35237b3fb/numpy-2.4.2-cp314-cp314t-win_arm64.whl", hash = "sha256:1f92f53998a17265194018d1cc321b2e96e900ca52d54c7c77837b71b9465181", size = 10565379, upload-time = "2026-01-31T23:12:51.345Z" }, +] + +[[package]] +name = "nvidia-cublas-cu12" +version = "12.8.4.1" +source = { registry = "https://pypi.org/simple" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/dc/61/e24b560ab2e2eaeb3c839129175fb330dfcfc29e5203196e5541a4c44682/nvidia_cublas_cu12-12.8.4.1-py3-none-manylinux_2_27_x86_64.whl", hash = "sha256:8ac4e771d5a348c551b2a426eda6193c19aa630236b418086020df5ba9667142", size = 594346921, upload-time = "2025-03-07T01:44:31.254Z" }, +] + +[[package]] +name = "nvidia-cuda-cupti-cu12" +version = "12.8.90" +source = { registry = "https://pypi.org/simple" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/f8/02/2adcaa145158bf1a8295d83591d22e4103dbfd821bcaf6f3f53151ca4ffa/nvidia_cuda_cupti_cu12-12.8.90-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:ea0cb07ebda26bb9b29ba82cda34849e73c166c18162d3913575b0c9db9a6182", size = 10248621, upload-time = "2025-03-07T01:40:21.213Z" }, +] + +[[package]] +name = "nvidia-cuda-nvrtc-cu12" +version = "12.8.93" +source = { registry = "https://pypi.org/simple" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/05/6b/32f747947df2da6994e999492ab306a903659555dddc0fbdeb9d71f75e52/nvidia_cuda_nvrtc_cu12-12.8.93-py3-none-manylinux2010_x86_64.manylinux_2_12_x86_64.whl", hash = "sha256:a7756528852ef889772a84c6cd89d41dfa74667e24cca16bb31f8f061e3e9994", size = 88040029, upload-time = "2025-03-07T01:42:13.562Z" }, +] + +[[package]] +name = "nvidia-cuda-runtime-cu12" +version = "12.8.90" +source = { registry = "https://pypi.org/simple" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/0d/9b/a997b638fcd068ad6e4d53b8551a7d30fe8b404d6f1804abf1df69838932/nvidia_cuda_runtime_cu12-12.8.90-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:adade8dcbd0edf427b7204d480d6066d33902cab2a4707dcfc48a2d0fd44ab90", size = 954765, upload-time = "2025-03-07T01:40:01.615Z" }, +] + +[[package]] +name = "nvidia-cudnn-cu12" +version = "9.10.2.21" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "nvidia-cublas-cu12" }, +] +wheels = [ + { url = "https://files.pythonhosted.org/packages/ba/51/e123d997aa098c61d029f76663dedbfb9bc8dcf8c60cbd6adbe42f76d049/nvidia_cudnn_cu12-9.10.2.21-py3-none-manylinux_2_27_x86_64.whl", hash = "sha256:949452be657fa16687d0930933f032835951ef0892b37d2d53824d1a84dc97a8", size = 706758467, upload-time = "2025-06-06T21:54:08.597Z" }, +] + +[[package]] +name = "nvidia-cufft-cu12" +version = "11.3.3.83" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "nvidia-nvjitlink-cu12" }, +] +wheels = [ + { url = "https://files.pythonhosted.org/packages/1f/13/ee4e00f30e676b66ae65b4f08cb5bcbb8392c03f54f2d5413ea99a5d1c80/nvidia_cufft_cu12-11.3.3.83-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:4d2dd21ec0b88cf61b62e6b43564355e5222e4a3fb394cac0db101f2dd0d4f74", size = 193118695, upload-time = "2025-03-07T01:45:27.821Z" }, +] + +[[package]] +name = "nvidia-cufile-cu12" +version = "1.13.1.3" +source = { registry = "https://pypi.org/simple" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/bb/fe/1bcba1dfbfb8d01be8d93f07bfc502c93fa23afa6fd5ab3fc7c1df71038a/nvidia_cufile_cu12-1.13.1.3-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:1d069003be650e131b21c932ec3d8969c1715379251f8d23a1860554b1cb24fc", size = 1197834, upload-time = "2025-03-07T01:45:50.723Z" }, +] + +[[package]] +name = "nvidia-curand-cu12" +version = "10.3.9.90" +source = { registry = "https://pypi.org/simple" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/fb/aa/6584b56dc84ebe9cf93226a5cde4d99080c8e90ab40f0c27bda7a0f29aa1/nvidia_curand_cu12-10.3.9.90-py3-none-manylinux_2_27_x86_64.whl", hash = "sha256:b32331d4f4df5d6eefa0554c565b626c7216f87a06a4f56fab27c3b68a830ec9", size = 63619976, upload-time = "2025-03-07T01:46:23.323Z" }, +] + +[[package]] +name = "nvidia-cusolver-cu12" +version = "11.7.3.90" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "nvidia-cublas-cu12" }, + { name = "nvidia-cusparse-cu12" }, + { name = "nvidia-nvjitlink-cu12" }, +] +wheels = [ + { url = "https://files.pythonhosted.org/packages/85/48/9a13d2975803e8cf2777d5ed57b87a0b6ca2cc795f9a4f59796a910bfb80/nvidia_cusolver_cu12-11.7.3.90-py3-none-manylinux_2_27_x86_64.whl", hash = "sha256:4376c11ad263152bd50ea295c05370360776f8c3427b30991df774f9fb26c450", size = 267506905, upload-time = "2025-03-07T01:47:16.273Z" }, +] + +[[package]] +name = "nvidia-cusparse-cu12" +version = "12.5.8.93" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "nvidia-nvjitlink-cu12" }, +] +wheels = [ + { url = "https://files.pythonhosted.org/packages/c2/f5/e1854cb2f2bcd4280c44736c93550cc300ff4b8c95ebe370d0aa7d2b473d/nvidia_cusparse_cu12-12.5.8.93-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:1ec05d76bbbd8b61b06a80e1eaf8cf4959c3d4ce8e711b65ebd0443bb0ebb13b", size = 288216466, upload-time = "2025-03-07T01:48:13.779Z" }, +] + +[[package]] +name = "nvidia-cusparselt-cu12" +version = "0.7.1" +source = { registry = "https://pypi.org/simple" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/56/79/12978b96bd44274fe38b5dde5cfb660b1d114f70a65ef962bcbbed99b549/nvidia_cusparselt_cu12-0.7.1-py3-none-manylinux2014_x86_64.whl", hash = "sha256:f1bb701d6b930d5a7cea44c19ceb973311500847f81b634d802b7b539dc55623", size = 287193691, upload-time = "2025-02-26T00:15:44.104Z" }, +] + +[[package]] +name = "nvidia-nccl-cu12" +version = "2.27.5" +source = { registry = "https://pypi.org/simple" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/6e/89/f7a07dc961b60645dbbf42e80f2bc85ade7feb9a491b11a1e973aa00071f/nvidia_nccl_cu12-2.27.5-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:ad730cf15cb5d25fe849c6e6ca9eb5b76db16a80f13f425ac68d8e2e55624457", size = 322348229, upload-time = "2025-06-26T04:11:28.385Z" }, +] + +[[package]] +name = "nvidia-nvjitlink-cu12" +version = "12.8.93" +source = { registry = "https://pypi.org/simple" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/f6/74/86a07f1d0f42998ca31312f998bd3b9a7eff7f52378f4f270c8679c77fb9/nvidia_nvjitlink_cu12-12.8.93-py3-none-manylinux2010_x86_64.manylinux_2_12_x86_64.whl", hash = "sha256:81ff63371a7ebd6e6451970684f916be2eab07321b73c9d244dc2b4da7f73b88", size = 39254836, upload-time = "2025-03-07T01:49:55.661Z" }, +] + +[[package]] +name = "nvidia-nvshmem-cu12" +version = "3.4.5" +source = { registry = "https://pypi.org/simple" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/b5/09/6ea3ea725f82e1e76684f0708bbedd871fc96da89945adeba65c3835a64c/nvidia_nvshmem_cu12-3.4.5-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:042f2500f24c021db8a06c5eec2539027d57460e1c1a762055a6554f72c369bd", size = 139103095, upload-time = "2025-09-06T00:32:31.266Z" }, +] + +[[package]] +name = "nvidia-nvtx-cu12" +version = "12.8.90" +source = { registry = "https://pypi.org/simple" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/a2/eb/86626c1bbc2edb86323022371c39aa48df6fd8b0a1647bc274577f72e90b/nvidia_nvtx_cu12-12.8.90-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:5b17e2001cc0d751a5bc2c6ec6d26ad95913324a4adb86788c944f8ce9ba441f", size = 89954, upload-time = "2025-03-07T01:42:44.131Z" }, +] + +[[package]] +name = "omegaconf" +version = "2.3.0" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "antlr4-python3-runtime" }, + { name = "pyyaml" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/09/48/6388f1bb9da707110532cb70ec4d2822858ddfb44f1cdf1233c20a80ea4b/omegaconf-2.3.0.tar.gz", hash = "sha256:d5d4b6d29955cc50ad50c46dc269bcd92c6e00f5f90d23ab5fee7bfca4ba4cc7", size = 3298120, upload-time = "2022-12-08T20:59:22.753Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/e3/94/1843518e420fa3ed6919835845df698c7e27e183cb997394e4a670973a65/omegaconf-2.3.0-py3-none-any.whl", hash = "sha256:7b4df175cdb08ba400f45cae3bdcae7ba8365db4d165fc65fd04b050ab63b46b", size = 79500, upload-time = "2022-12-08T20:59:19.686Z" }, +] + +[[package]] +name = "packaging" +version = "26.0" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/65/ee/299d360cdc32edc7d2cf530f3accf79c4fca01e96ffc950d8a52213bd8e4/packaging-26.0.tar.gz", hash = "sha256:00243ae351a257117b6a241061796684b084ed1c516a08c48a3f7e147a9d80b4", size = 143416, upload-time = "2026-01-21T20:50:39.064Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/b7/b9/c538f279a4e237a006a2c98387d081e9eb060d203d8ed34467cc0f0b9b53/packaging-26.0-py3-none-any.whl", hash = "sha256:b36f1fef9334a5588b4166f8bcd26a14e521f2b55e6b9de3aaa80d3ff7a37529", size = 74366, upload-time = "2026-01-21T20:50:37.788Z" }, +] + +[[package]] +name = "pillow" +version = "12.1.1" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/1f/42/5c74462b4fd957fcd7b13b04fb3205ff8349236ea74c7c375766d6c82288/pillow-12.1.1.tar.gz", hash = "sha256:9ad8fa5937ab05218e2b6a4cff30295ad35afd2f83ac592e68c0d871bb0fdbc4", size = 46980264, upload-time = "2026-02-11T04:23:07.146Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/07/d3/8df65da0d4df36b094351dce696f2989bec731d4f10e743b1c5f4da4d3bf/pillow-12.1.1-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:ab323b787d6e18b3d91a72fc99b1a2c28651e4358749842b8f8dfacd28ef2052", size = 5262803, upload-time = "2026-02-11T04:20:47.653Z" }, + { url = "https://files.pythonhosted.org/packages/d6/71/5026395b290ff404b836e636f51d7297e6c83beceaa87c592718747e670f/pillow-12.1.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:adebb5bee0f0af4909c30db0d890c773d1a92ffe83da908e2e9e720f8edf3984", size = 4657601, upload-time = "2026-02-11T04:20:49.328Z" }, + { url = "https://files.pythonhosted.org/packages/b1/2e/1001613d941c67442f745aff0f7cc66dd8df9a9c084eb497e6a543ee6f7e/pillow-12.1.1-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:bb66b7cc26f50977108790e2456b7921e773f23db5630261102233eb355a3b79", size = 6234995, upload-time = "2026-02-11T04:20:51.032Z" }, + { url = "https://files.pythonhosted.org/packages/07/26/246ab11455b2549b9233dbd44d358d033a2f780fa9007b61a913c5b2d24e/pillow-12.1.1-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:aee2810642b2898bb187ced9b349e95d2a7272930796e022efaf12e99dccd293", size = 8045012, upload-time = "2026-02-11T04:20:52.882Z" }, + { url = "https://files.pythonhosted.org/packages/b2/8b/07587069c27be7535ac1fe33874e32de118fbd34e2a73b7f83436a88368c/pillow-12.1.1-cp312-cp312-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:a0b1cd6232e2b618adcc54d9882e4e662a089d5768cd188f7c245b4c8c44a397", size = 6349638, upload-time = "2026-02-11T04:20:54.444Z" }, + { url = "https://files.pythonhosted.org/packages/ff/79/6df7b2ee763d619cda2fb4fea498e5f79d984dae304d45a8999b80d6cf5c/pillow-12.1.1-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:7aac39bcf8d4770d089588a2e1dd111cbaa42df5a94be3114222057d68336bd0", size = 7041540, upload-time = "2026-02-11T04:20:55.97Z" }, + { url = "https://files.pythonhosted.org/packages/2c/5e/2ba19e7e7236d7529f4d873bdaf317a318896bac289abebd4bb00ef247f0/pillow-12.1.1-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:ab174cd7d29a62dd139c44bf74b698039328f45cb03b4596c43473a46656b2f3", size = 6462613, upload-time = "2026-02-11T04:20:57.542Z" }, + { url = "https://files.pythonhosted.org/packages/03/03/31216ec124bb5c3dacd74ce8efff4cc7f52643653bad4825f8f08c697743/pillow-12.1.1-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:339ffdcb7cbeaa08221cd401d517d4b1fe7a9ed5d400e4a8039719238620ca35", size = 7166745, upload-time = "2026-02-11T04:20:59.196Z" }, + { url = "https://files.pythonhosted.org/packages/1f/e7/7c4552d80052337eb28653b617eafdef39adfb137c49dd7e831b8dc13bc5/pillow-12.1.1-cp312-cp312-win32.whl", hash = "sha256:5d1f9575a12bed9e9eedd9a4972834b08c97a352bd17955ccdebfeca5913fa0a", size = 6328823, upload-time = "2026-02-11T04:21:01.385Z" }, + { url = "https://files.pythonhosted.org/packages/3d/17/688626d192d7261bbbf98846fc98995726bddc2c945344b65bec3a29d731/pillow-12.1.1-cp312-cp312-win_amd64.whl", hash = "sha256:21329ec8c96c6e979cd0dfd29406c40c1d52521a90544463057d2aaa937d66a6", size = 7033367, upload-time = "2026-02-11T04:21:03.536Z" }, + { url = "https://files.pythonhosted.org/packages/ed/fe/a0ef1f73f939b0eca03ee2c108d0043a87468664770612602c63266a43c4/pillow-12.1.1-cp312-cp312-win_arm64.whl", hash = "sha256:af9a332e572978f0218686636610555ae3defd1633597be015ed50289a03c523", size = 2453811, upload-time = "2026-02-11T04:21:05.116Z" }, + { url = "https://files.pythonhosted.org/packages/d5/11/6db24d4bd7685583caeae54b7009584e38da3c3d4488ed4cd25b439de486/pillow-12.1.1-cp313-cp313-ios_13_0_arm64_iphoneos.whl", hash = "sha256:d242e8ac078781f1de88bf823d70c1a9b3c7950a44cdf4b7c012e22ccbcd8e4e", size = 4062689, upload-time = "2026-02-11T04:21:06.804Z" }, + { url = "https://files.pythonhosted.org/packages/33/c0/ce6d3b1fe190f0021203e0d9b5b99e57843e345f15f9ef22fcd43842fd21/pillow-12.1.1-cp313-cp313-ios_13_0_arm64_iphonesimulator.whl", hash = "sha256:02f84dfad02693676692746df05b89cf25597560db2857363a208e393429f5e9", size = 4138535, upload-time = "2026-02-11T04:21:08.452Z" }, + { url = "https://files.pythonhosted.org/packages/a0/c6/d5eb6a4fb32a3f9c21a8c7613ec706534ea1cf9f4b3663e99f0d83f6fca8/pillow-12.1.1-cp313-cp313-ios_13_0_x86_64_iphonesimulator.whl", hash = "sha256:e65498daf4b583091ccbb2556c7000abf0f3349fcd57ef7adc9a84a394ed29f6", size = 3601364, upload-time = "2026-02-11T04:21:10.194Z" }, + { url = "https://files.pythonhosted.org/packages/14/a1/16c4b823838ba4c9c52c0e6bbda903a3fe5a1bdbf1b8eb4fff7156f3e318/pillow-12.1.1-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:6c6db3b84c87d48d0088943bf33440e0c42370b99b1c2a7989216f7b42eede60", size = 5262561, upload-time = "2026-02-11T04:21:11.742Z" }, + { url = "https://files.pythonhosted.org/packages/bb/ad/ad9dc98ff24f485008aa5cdedaf1a219876f6f6c42a4626c08bc4e80b120/pillow-12.1.1-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:8b7e5304e34942bf62e15184219a7b5ad4ff7f3bb5cca4d984f37df1a0e1aee2", size = 4657460, upload-time = "2026-02-11T04:21:13.786Z" }, + { url = "https://files.pythonhosted.org/packages/9e/1b/f1a4ea9a895b5732152789326202a82464d5254759fbacae4deea3069334/pillow-12.1.1-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:18e5bddd742a44b7e6b1e773ab5db102bd7a94c32555ba656e76d319d19c3850", size = 6232698, upload-time = "2026-02-11T04:21:15.949Z" }, + { url = "https://files.pythonhosted.org/packages/95/f4/86f51b8745070daf21fd2e5b1fe0eb35d4db9ca26e6d58366562fb56a743/pillow-12.1.1-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:fc44ef1f3de4f45b50ccf9136999d71abb99dca7706bc75d222ed350b9fd2289", size = 8041706, upload-time = "2026-02-11T04:21:17.723Z" }, + { url = "https://files.pythonhosted.org/packages/29/9b/d6ecd956bb1266dd1045e995cce9b8d77759e740953a1c9aad9502a0461e/pillow-12.1.1-cp313-cp313-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:5a8eb7ed8d4198bccbd07058416eeec51686b498e784eda166395a23eb99138e", size = 6346621, upload-time = "2026-02-11T04:21:19.547Z" }, + { url = "https://files.pythonhosted.org/packages/71/24/538bff45bde96535d7d998c6fed1a751c75ac7c53c37c90dc2601b243893/pillow-12.1.1-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:47b94983da0c642de92ced1702c5b6c292a84bd3a8e1d1702ff923f183594717", size = 7038069, upload-time = "2026-02-11T04:21:21.378Z" }, + { url = "https://files.pythonhosted.org/packages/94/0e/58cb1a6bc48f746bc4cb3adb8cabff73e2742c92b3bf7a220b7cf69b9177/pillow-12.1.1-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:518a48c2aab7ce596d3bf79d0e275661b846e86e4d0e7dec34712c30fe07f02a", size = 6460040, upload-time = "2026-02-11T04:21:23.148Z" }, + { url = "https://files.pythonhosted.org/packages/6c/57/9045cb3ff11eeb6c1adce3b2d60d7d299d7b273a2e6c8381a524abfdc474/pillow-12.1.1-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:a550ae29b95c6dc13cf69e2c9dc5747f814c54eeb2e32d683e5e93af56caa029", size = 7164523, upload-time = "2026-02-11T04:21:25.01Z" }, + { url = "https://files.pythonhosted.org/packages/73/f2/9be9cb99f2175f0d4dbadd6616ce1bf068ee54a28277ea1bf1fbf729c250/pillow-12.1.1-cp313-cp313-win32.whl", hash = "sha256:a003d7422449f6d1e3a34e3dd4110c22148336918ddbfc6a32581cd54b2e0b2b", size = 6332552, upload-time = "2026-02-11T04:21:27.238Z" }, + { url = "https://files.pythonhosted.org/packages/3f/eb/b0834ad8b583d7d9d42b80becff092082a1c3c156bb582590fcc973f1c7c/pillow-12.1.1-cp313-cp313-win_amd64.whl", hash = "sha256:344cf1e3dab3be4b1fa08e449323d98a2a3f819ad20f4b22e77a0ede31f0faa1", size = 7040108, upload-time = "2026-02-11T04:21:29.462Z" }, + { url = "https://files.pythonhosted.org/packages/d5/7d/fc09634e2aabdd0feabaff4a32f4a7d97789223e7c2042fd805ea4b4d2c2/pillow-12.1.1-cp313-cp313-win_arm64.whl", hash = "sha256:5c0dd1636633e7e6a0afe7bf6a51a14992b7f8e60de5789018ebbdfae55b040a", size = 2453712, upload-time = "2026-02-11T04:21:31.072Z" }, + { url = "https://files.pythonhosted.org/packages/19/2a/b9d62794fc8a0dd14c1943df68347badbd5511103e0d04c035ffe5cf2255/pillow-12.1.1-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:0330d233c1a0ead844fc097a7d16c0abff4c12e856c0b325f231820fee1f39da", size = 5264880, upload-time = "2026-02-11T04:21:32.865Z" }, + { url = "https://files.pythonhosted.org/packages/26/9d/e03d857d1347fa5ed9247e123fcd2a97b6220e15e9cb73ca0a8d91702c6e/pillow-12.1.1-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:5dae5f21afb91322f2ff791895ddd8889e5e947ff59f71b46041c8ce6db790bc", size = 4660616, upload-time = "2026-02-11T04:21:34.97Z" }, + { url = "https://files.pythonhosted.org/packages/f7/ec/8a6d22afd02570d30954e043f09c32772bfe143ba9285e2fdb11284952cd/pillow-12.1.1-cp313-cp313t-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:2e0c664be47252947d870ac0d327fea7e63985a08794758aa8af5b6cb6ec0c9c", size = 6269008, upload-time = "2026-02-11T04:21:36.623Z" }, + { url = "https://files.pythonhosted.org/packages/3d/1d/6d875422c9f28a4a361f495a5f68d9de4a66941dc2c619103ca335fa6446/pillow-12.1.1-cp313-cp313t-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:691ab2ac363b8217f7d31b3497108fb1f50faab2f75dfb03284ec2f217e87bf8", size = 8073226, upload-time = "2026-02-11T04:21:38.585Z" }, + { url = "https://files.pythonhosted.org/packages/a1/cd/134b0b6ee5eda6dc09e25e24b40fdafe11a520bc725c1d0bbaa5e00bf95b/pillow-12.1.1-cp313-cp313t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:e9e8064fb1cc019296958595f6db671fba95209e3ceb0c4734c9baf97de04b20", size = 6380136, upload-time = "2026-02-11T04:21:40.562Z" }, + { url = "https://files.pythonhosted.org/packages/7a/a9/7628f013f18f001c1b98d8fffe3452f306a70dc6aba7d931019e0492f45e/pillow-12.1.1-cp313-cp313t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:472a8d7ded663e6162dafdf20015c486a7009483ca671cece7a9279b512fcb13", size = 7067129, upload-time = "2026-02-11T04:21:42.521Z" }, + { url = "https://files.pythonhosted.org/packages/1e/f8/66ab30a2193b277785601e82ee2d49f68ea575d9637e5e234faaa98efa4c/pillow-12.1.1-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:89b54027a766529136a06cfebeecb3a04900397a3590fd252160b888479517bf", size = 6491807, upload-time = "2026-02-11T04:21:44.22Z" }, + { url = "https://files.pythonhosted.org/packages/da/0b/a877a6627dc8318fdb84e357c5e1a758c0941ab1ddffdafd231983788579/pillow-12.1.1-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:86172b0831b82ce4f7877f280055892b31179e1576aa00d0df3bb1bbf8c3e524", size = 7190954, upload-time = "2026-02-11T04:21:46.114Z" }, + { url = "https://files.pythonhosted.org/packages/83/43/6f732ff85743cf746b1361b91665d9f5155e1483817f693f8d57ea93147f/pillow-12.1.1-cp313-cp313t-win32.whl", hash = "sha256:44ce27545b6efcf0fdbdceb31c9a5bdea9333e664cda58a7e674bb74608b3986", size = 6336441, upload-time = "2026-02-11T04:21:48.22Z" }, + { url = "https://files.pythonhosted.org/packages/3b/44/e865ef3986611bb75bfabdf94a590016ea327833f434558801122979cd0e/pillow-12.1.1-cp313-cp313t-win_amd64.whl", hash = "sha256:a285e3eb7a5a45a2ff504e31f4a8d1b12ef62e84e5411c6804a42197c1cf586c", size = 7045383, upload-time = "2026-02-11T04:21:50.015Z" }, + { url = "https://files.pythonhosted.org/packages/a8/c6/f4fb24268d0c6908b9f04143697ea18b0379490cb74ba9e8d41b898bd005/pillow-12.1.1-cp313-cp313t-win_arm64.whl", hash = "sha256:cc7d296b5ea4d29e6570dabeaed58d31c3fea35a633a69679fb03d7664f43fb3", size = 2456104, upload-time = "2026-02-11T04:21:51.633Z" }, + { url = "https://files.pythonhosted.org/packages/03/d0/bebb3ffbf31c5a8e97241476c4cf8b9828954693ce6744b4a2326af3e16b/pillow-12.1.1-cp314-cp314-ios_13_0_arm64_iphoneos.whl", hash = "sha256:417423db963cb4be8bac3fc1204fe61610f6abeed1580a7a2cbb2fbda20f12af", size = 4062652, upload-time = "2026-02-11T04:21:53.19Z" }, + { url = "https://files.pythonhosted.org/packages/2d/c0/0e16fb0addda4851445c28f8350d8c512f09de27bbb0d6d0bbf8b6709605/pillow-12.1.1-cp314-cp314-ios_13_0_arm64_iphonesimulator.whl", hash = "sha256:b957b71c6b2387610f556a7eb0828afbe40b4a98036fc0d2acfa5a44a0c2036f", size = 4138823, upload-time = "2026-02-11T04:22:03.088Z" }, + { url = "https://files.pythonhosted.org/packages/6b/fb/6170ec655d6f6bb6630a013dd7cf7bc218423d7b5fa9071bf63dc32175ae/pillow-12.1.1-cp314-cp314-ios_13_0_x86_64_iphonesimulator.whl", hash = "sha256:097690ba1f2efdeb165a20469d59d8bb03c55fb6621eb2041a060ae8ea3e9642", size = 3601143, upload-time = "2026-02-11T04:22:04.909Z" }, + { url = "https://files.pythonhosted.org/packages/59/04/dc5c3f297510ba9a6837cbb318b87dd2b8f73eb41a43cc63767f65cb599c/pillow-12.1.1-cp314-cp314-macosx_10_15_x86_64.whl", hash = "sha256:2815a87ab27848db0321fb78c7f0b2c8649dee134b7f2b80c6a45c6831d75ccd", size = 5266254, upload-time = "2026-02-11T04:22:07.656Z" }, + { url = "https://files.pythonhosted.org/packages/05/30/5db1236b0d6313f03ebf97f5e17cda9ca060f524b2fcc875149a8360b21c/pillow-12.1.1-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:f7ed2c6543bad5a7d5530eb9e78c53132f93dfa44a28492db88b41cdab885202", size = 4657499, upload-time = "2026-02-11T04:22:09.613Z" }, + { url = "https://files.pythonhosted.org/packages/6f/18/008d2ca0eb612e81968e8be0bbae5051efba24d52debf930126d7eaacbba/pillow-12.1.1-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:652a2c9ccfb556235b2b501a3a7cf3742148cd22e04b5625c5fe057ea3e3191f", size = 6232137, upload-time = "2026-02-11T04:22:11.434Z" }, + { url = "https://files.pythonhosted.org/packages/70/f1/f14d5b8eeb4b2cd62b9f9f847eb6605f103df89ef619ac68f92f748614ea/pillow-12.1.1-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:d6e4571eedf43af33d0fc233a382a76e849badbccdf1ac438841308652a08e1f", size = 8042721, upload-time = "2026-02-11T04:22:13.321Z" }, + { url = "https://files.pythonhosted.org/packages/5a/d6/17824509146e4babbdabf04d8171491fa9d776f7061ff6e727522df9bd03/pillow-12.1.1-cp314-cp314-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:b574c51cf7d5d62e9be37ba446224b59a2da26dc4c1bb2ecbe936a4fb1a7cb7f", size = 6347798, upload-time = "2026-02-11T04:22:15.449Z" }, + { url = "https://files.pythonhosted.org/packages/d1/ee/c85a38a9ab92037a75615aba572c85ea51e605265036e00c5b67dfafbfe2/pillow-12.1.1-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:a37691702ed687799de29a518d63d4682d9016932db66d4e90c345831b02fb4e", size = 7039315, upload-time = "2026-02-11T04:22:17.24Z" }, + { url = "https://files.pythonhosted.org/packages/ec/f3/bc8ccc6e08a148290d7523bde4d9a0d6c981db34631390dc6e6ec34cacf6/pillow-12.1.1-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:f95c00d5d6700b2b890479664a06e754974848afaae5e21beb4d83c106923fd0", size = 6462360, upload-time = "2026-02-11T04:22:19.111Z" }, + { url = "https://files.pythonhosted.org/packages/f6/ab/69a42656adb1d0665ab051eec58a41f169ad295cf81ad45406963105408f/pillow-12.1.1-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:559b38da23606e68681337ad74622c4dbba02254fc9cb4488a305dd5975c7eeb", size = 7165438, upload-time = "2026-02-11T04:22:21.041Z" }, + { url = "https://files.pythonhosted.org/packages/02/46/81f7aa8941873f0f01d4b55cc543b0a3d03ec2ee30d617a0448bf6bd6dec/pillow-12.1.1-cp314-cp314-win32.whl", hash = "sha256:03edcc34d688572014ff223c125a3f77fb08091e4607e7745002fc214070b35f", size = 6431503, upload-time = "2026-02-11T04:22:22.833Z" }, + { url = "https://files.pythonhosted.org/packages/40/72/4c245f7d1044b67affc7f134a09ea619d4895333d35322b775b928180044/pillow-12.1.1-cp314-cp314-win_amd64.whl", hash = "sha256:50480dcd74fa63b8e78235957d302d98d98d82ccbfac4c7e12108ba9ecbdba15", size = 7176748, upload-time = "2026-02-11T04:22:24.64Z" }, + { url = "https://files.pythonhosted.org/packages/e4/ad/8a87bdbe038c5c698736e3348af5c2194ffb872ea52f11894c95f9305435/pillow-12.1.1-cp314-cp314-win_arm64.whl", hash = "sha256:5cb1785d97b0c3d1d1a16bc1d710c4a0049daefc4935f3a8f31f827f4d3d2e7f", size = 2544314, upload-time = "2026-02-11T04:22:26.685Z" }, + { url = "https://files.pythonhosted.org/packages/6c/9d/efd18493f9de13b87ede7c47e69184b9e859e4427225ea962e32e56a49bc/pillow-12.1.1-cp314-cp314t-macosx_10_15_x86_64.whl", hash = "sha256:1f90cff8aa76835cba5769f0b3121a22bd4eb9e6884cfe338216e557a9a548b8", size = 5268612, upload-time = "2026-02-11T04:22:29.884Z" }, + { url = "https://files.pythonhosted.org/packages/f8/f1/4f42eb2b388eb2ffc660dcb7f7b556c1015c53ebd5f7f754965ef997585b/pillow-12.1.1-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:1f1be78ce9466a7ee64bfda57bdba0f7cc499d9794d518b854816c41bf0aa4e9", size = 4660567, upload-time = "2026-02-11T04:22:31.799Z" }, + { url = "https://files.pythonhosted.org/packages/01/54/df6ef130fa43e4b82e32624a7b821a2be1c5653a5fdad8469687a7db4e00/pillow-12.1.1-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:42fc1f4677106188ad9a55562bbade416f8b55456f522430fadab3cef7cd4e60", size = 6269951, upload-time = "2026-02-11T04:22:33.921Z" }, + { url = "https://files.pythonhosted.org/packages/a9/48/618752d06cc44bb4aae8ce0cd4e6426871929ed7b46215638088270d9b34/pillow-12.1.1-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:98edb152429ab62a1818039744d8fbb3ccab98a7c29fc3d5fcef158f3f1f68b7", size = 8074769, upload-time = "2026-02-11T04:22:35.877Z" }, + { url = "https://files.pythonhosted.org/packages/c3/bd/f1d71eb39a72fa088d938655afba3e00b38018d052752f435838961127d8/pillow-12.1.1-cp314-cp314t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:d470ab1178551dd17fdba0fef463359c41aaa613cdcd7ff8373f54be629f9f8f", size = 6381358, upload-time = "2026-02-11T04:22:37.698Z" }, + { url = "https://files.pythonhosted.org/packages/64/ef/c784e20b96674ed36a5af839305f55616f8b4f8aa8eeccf8531a6e312243/pillow-12.1.1-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:6408a7b064595afcab0a49393a413732a35788f2a5092fdc6266952ed67de586", size = 7068558, upload-time = "2026-02-11T04:22:39.597Z" }, + { url = "https://files.pythonhosted.org/packages/73/cb/8059688b74422ae61278202c4e1ad992e8a2e7375227be0a21c6b87ca8d5/pillow-12.1.1-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:5d8c41325b382c07799a3682c1c258469ea2ff97103c53717b7893862d0c98ce", size = 6493028, upload-time = "2026-02-11T04:22:42.73Z" }, + { url = "https://files.pythonhosted.org/packages/c6/da/e3c008ed7d2dd1f905b15949325934510b9d1931e5df999bb15972756818/pillow-12.1.1-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:c7697918b5be27424e9ce568193efd13d925c4481dd364e43f5dff72d33e10f8", size = 7191940, upload-time = "2026-02-11T04:22:44.543Z" }, + { url = "https://files.pythonhosted.org/packages/01/4a/9202e8d11714c1fc5951f2e1ef362f2d7fbc595e1f6717971d5dd750e969/pillow-12.1.1-cp314-cp314t-win32.whl", hash = "sha256:d2912fd8114fc5545aa3a4b5576512f64c55a03f3ebcca4c10194d593d43ea36", size = 6438736, upload-time = "2026-02-11T04:22:46.347Z" }, + { url = "https://files.pythonhosted.org/packages/f3/ca/cbce2327eb9885476b3957b2e82eb12c866a8b16ad77392864ad601022ce/pillow-12.1.1-cp314-cp314t-win_amd64.whl", hash = "sha256:4ceb838d4bd9dab43e06c363cab2eebf63846d6a4aeaea283bbdfd8f1a8ed58b", size = 7182894, upload-time = "2026-02-11T04:22:48.114Z" }, + { url = "https://files.pythonhosted.org/packages/ec/d2/de599c95ba0a973b94410477f8bf0b6f0b5e67360eb89bcb1ad365258beb/pillow-12.1.1-cp314-cp314t-win_arm64.whl", hash = "sha256:7b03048319bfc6170e93bd60728a1af51d3dd7704935feb228c4d4faab35d334", size = 2546446, upload-time = "2026-02-11T04:22:50.342Z" }, +] + +[[package]] +name = "pluggy" +version = "1.6.0" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/f9/e2/3e91f31a7d2b083fe6ef3fa267035b518369d9511ffab804f839851d2779/pluggy-1.6.0.tar.gz", hash = "sha256:7dcc130b76258d33b90f61b658791dede3486c3e6bfb003ee5c9bfb396dd22f3", size = 69412, upload-time = "2025-05-15T12:30:07.975Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/54/20/4d324d65cc6d9205fabedc306948156824eb9f0ee1633355a8f7ec5c66bf/pluggy-1.6.0-py3-none-any.whl", hash = "sha256:e920276dd6813095e9377c0bc5566d94c932c33b27a3e3945d8389c374dd4746", size = 20538, upload-time = "2025-05-15T12:30:06.134Z" }, +] + +[[package]] +name = "psutil" +version = "7.2.2" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/aa/c6/d1ddf4abb55e93cebc4f2ed8b5d6dbad109ecb8d63748dd2b20ab5e57ebe/psutil-7.2.2.tar.gz", hash = "sha256:0746f5f8d406af344fd547f1c8daa5f5c33dbc293bb8d6a16d80b4bb88f59372", size = 493740, upload-time = "2026-01-28T18:14:54.428Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/51/08/510cbdb69c25a96f4ae523f733cdc963ae654904e8db864c07585ef99875/psutil-7.2.2-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:2edccc433cbfa046b980b0df0171cd25bcaeb3a68fe9022db0979e7aa74a826b", size = 130595, upload-time = "2026-01-28T18:14:57.293Z" }, + { url = "https://files.pythonhosted.org/packages/d6/f5/97baea3fe7a5a9af7436301f85490905379b1c6f2dd51fe3ecf24b4c5fbf/psutil-7.2.2-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:e78c8603dcd9a04c7364f1a3e670cea95d51ee865e4efb3556a3a63adef958ea", size = 131082, upload-time = "2026-01-28T18:14:59.732Z" }, + { url = "https://files.pythonhosted.org/packages/37/d6/246513fbf9fa174af531f28412297dd05241d97a75911ac8febefa1a53c6/psutil-7.2.2-cp313-cp313t-manylinux2010_x86_64.manylinux_2_12_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:1a571f2330c966c62aeda00dd24620425d4b0cc86881c89861fbc04549e5dc63", size = 181476, upload-time = "2026-01-28T18:15:01.884Z" }, + { url = "https://files.pythonhosted.org/packages/b8/b5/9182c9af3836cca61696dabe4fd1304e17bc56cb62f17439e1154f225dd3/psutil-7.2.2-cp313-cp313t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:917e891983ca3c1887b4ef36447b1e0873e70c933afc831c6b6da078ba474312", size = 184062, upload-time = "2026-01-28T18:15:04.436Z" }, + { url = "https://files.pythonhosted.org/packages/16/ba/0756dca669f5a9300d0cbcbfae9a4c30e446dfc7440ffe43ded5724bfd93/psutil-7.2.2-cp313-cp313t-win_amd64.whl", hash = "sha256:ab486563df44c17f5173621c7b198955bd6b613fb87c71c161f827d3fb149a9b", size = 139893, upload-time = "2026-01-28T18:15:06.378Z" }, + { url = "https://files.pythonhosted.org/packages/1c/61/8fa0e26f33623b49949346de05ec1ddaad02ed8ba64af45f40a147dbfa97/psutil-7.2.2-cp313-cp313t-win_arm64.whl", hash = "sha256:ae0aefdd8796a7737eccea863f80f81e468a1e4cf14d926bd9b6f5f2d5f90ca9", size = 135589, upload-time = "2026-01-28T18:15:08.03Z" }, + { url = "https://files.pythonhosted.org/packages/81/69/ef179ab5ca24f32acc1dac0c247fd6a13b501fd5534dbae0e05a1c48b66d/psutil-7.2.2-cp314-cp314t-macosx_10_15_x86_64.whl", hash = "sha256:eed63d3b4d62449571547b60578c5b2c4bcccc5387148db46e0c2313dad0ee00", size = 130664, upload-time = "2026-01-28T18:15:09.469Z" }, + { url = "https://files.pythonhosted.org/packages/7b/64/665248b557a236d3fa9efc378d60d95ef56dd0a490c2cd37dafc7660d4a9/psutil-7.2.2-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:7b6d09433a10592ce39b13d7be5a54fbac1d1228ed29abc880fb23df7cb694c9", size = 131087, upload-time = "2026-01-28T18:15:11.724Z" }, + { url = "https://files.pythonhosted.org/packages/d5/2e/e6782744700d6759ebce3043dcfa661fb61e2fb752b91cdeae9af12c2178/psutil-7.2.2-cp314-cp314t-manylinux2010_x86_64.manylinux_2_12_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:1fa4ecf83bcdf6e6c8f4449aff98eefb5d0604bf88cb883d7da3d8d2d909546a", size = 182383, upload-time = "2026-01-28T18:15:13.445Z" }, + { url = "https://files.pythonhosted.org/packages/57/49/0a41cefd10cb7505cdc04dab3eacf24c0c2cb158a998b8c7b1d27ee2c1f5/psutil-7.2.2-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:e452c464a02e7dc7822a05d25db4cde564444a67e58539a00f929c51eddda0cf", size = 185210, upload-time = "2026-01-28T18:15:16.002Z" }, + { url = "https://files.pythonhosted.org/packages/dd/2c/ff9bfb544f283ba5f83ba725a3c5fec6d6b10b8f27ac1dc641c473dc390d/psutil-7.2.2-cp314-cp314t-win_amd64.whl", hash = "sha256:c7663d4e37f13e884d13994247449e9f8f574bc4655d509c3b95e9ec9e2b9dc1", size = 141228, upload-time = "2026-01-28T18:15:18.385Z" }, + { url = "https://files.pythonhosted.org/packages/f2/fc/f8d9c31db14fcec13748d373e668bc3bed94d9077dbc17fb0eebc073233c/psutil-7.2.2-cp314-cp314t-win_arm64.whl", hash = "sha256:11fe5a4f613759764e79c65cf11ebdf26e33d6dd34336f8a337aa2996d71c841", size = 136284, upload-time = "2026-01-28T18:15:19.912Z" }, + { url = "https://files.pythonhosted.org/packages/e7/36/5ee6e05c9bd427237b11b3937ad82bb8ad2752d72c6969314590dd0c2f6e/psutil-7.2.2-cp36-abi3-macosx_10_9_x86_64.whl", hash = "sha256:ed0cace939114f62738d808fdcecd4c869222507e266e574799e9c0faa17d486", size = 129090, upload-time = "2026-01-28T18:15:22.168Z" }, + { url = "https://files.pythonhosted.org/packages/80/c4/f5af4c1ca8c1eeb2e92ccca14ce8effdeec651d5ab6053c589b074eda6e1/psutil-7.2.2-cp36-abi3-macosx_11_0_arm64.whl", hash = "sha256:1a7b04c10f32cc88ab39cbf606e117fd74721c831c98a27dc04578deb0c16979", size = 129859, upload-time = "2026-01-28T18:15:23.795Z" }, + { url = "https://files.pythonhosted.org/packages/b5/70/5d8df3b09e25bce090399cf48e452d25c935ab72dad19406c77f4e828045/psutil-7.2.2-cp36-abi3-manylinux2010_x86_64.manylinux_2_12_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:076a2d2f923fd4821644f5ba89f059523da90dc9014e85f8e45a5774ca5bc6f9", size = 155560, upload-time = "2026-01-28T18:15:25.976Z" }, + { url = "https://files.pythonhosted.org/packages/63/65/37648c0c158dc222aba51c089eb3bdfa238e621674dc42d48706e639204f/psutil-7.2.2-cp36-abi3-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:b0726cecd84f9474419d67252add4ac0cd9811b04d61123054b9fb6f57df6e9e", size = 156997, upload-time = "2026-01-28T18:15:27.794Z" }, + { url = "https://files.pythonhosted.org/packages/8e/13/125093eadae863ce03c6ffdbae9929430d116a246ef69866dad94da3bfbc/psutil-7.2.2-cp36-abi3-musllinux_1_2_aarch64.whl", hash = "sha256:fd04ef36b4a6d599bbdb225dd1d3f51e00105f6d48a28f006da7f9822f2606d8", size = 148972, upload-time = "2026-01-28T18:15:29.342Z" }, + { url = "https://files.pythonhosted.org/packages/04/78/0acd37ca84ce3ddffaa92ef0f571e073faa6d8ff1f0559ab1272188ea2be/psutil-7.2.2-cp36-abi3-musllinux_1_2_x86_64.whl", hash = "sha256:b58fabe35e80b264a4e3bb23e6b96f9e45a3df7fb7eed419ac0e5947c61e47cc", size = 148266, upload-time = "2026-01-28T18:15:31.597Z" }, + { url = "https://files.pythonhosted.org/packages/b4/90/e2159492b5426be0c1fef7acba807a03511f97c5f86b3caeda6ad92351a7/psutil-7.2.2-cp37-abi3-win_amd64.whl", hash = "sha256:eb7e81434c8d223ec4a219b5fc1c47d0417b12be7ea866e24fb5ad6e84b3d988", size = 137737, upload-time = "2026-01-28T18:15:33.849Z" }, + { url = "https://files.pythonhosted.org/packages/8c/c7/7bb2e321574b10df20cbde462a94e2b71d05f9bbda251ef27d104668306a/psutil-7.2.2-cp37-abi3-win_arm64.whl", hash = "sha256:8c233660f575a5a89e6d4cb65d9f938126312bca76d8fe087b947b3a1aaac9ee", size = 134617, upload-time = "2026-01-28T18:15:36.514Z" }, +] + +[[package]] +name = "pycparser" +version = "3.0" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/1b/7d/92392ff7815c21062bea51aa7b87d45576f649f16458d78b7cf94b9ab2e6/pycparser-3.0.tar.gz", hash = "sha256:600f49d217304a5902ac3c37e1281c9fe94e4d0489de643a9504c5cdfdfc6b29", size = 103492, upload-time = "2026-01-21T14:26:51.89Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/0c/c3/44f3fbbfa403ea2a7c779186dc20772604442dde72947e7d01069cbe98e3/pycparser-3.0-py3-none-any.whl", hash = "sha256:b727414169a36b7d524c1c3e31839a521725078d7b2ff038656844266160a992", size = 48172, upload-time = "2026-01-21T14:26:50.693Z" }, +] + +[[package]] +name = "pydantic" +version = "2.12.5" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "annotated-types" }, + { name = "pydantic-core" }, + { name = "typing-extensions" }, + { name = "typing-inspection" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/69/44/36f1a6e523abc58ae5f928898e4aca2e0ea509b5aa6f6f392a5d882be928/pydantic-2.12.5.tar.gz", hash = "sha256:4d351024c75c0f085a9febbb665ce8c0c6ec5d30e903bdb6394b7ede26aebb49", size = 821591, upload-time = "2025-11-26T15:11:46.471Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/5a/87/b70ad306ebb6f9b585f114d0ac2137d792b48be34d732d60e597c2f8465a/pydantic-2.12.5-py3-none-any.whl", hash = "sha256:e561593fccf61e8a20fc46dfc2dfe075b8be7d0188df33f221ad1f0139180f9d", size = 463580, upload-time = "2025-11-26T15:11:44.605Z" }, +] + +[[package]] +name = "pydantic-core" +version = "2.41.5" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "typing-extensions" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/71/70/23b021c950c2addd24ec408e9ab05d59b035b39d97cdc1130e1bce647bb6/pydantic_core-2.41.5.tar.gz", hash = "sha256:08daa51ea16ad373ffd5e7606252cc32f07bc72b28284b6bc9c6df804816476e", size = 460952, upload-time = "2025-11-04T13:43:49.098Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/5f/5d/5f6c63eebb5afee93bcaae4ce9a898f3373ca23df3ccaef086d0233a35a7/pydantic_core-2.41.5-cp312-cp312-macosx_10_12_x86_64.whl", hash = "sha256:f41a7489d32336dbf2199c8c0a215390a751c5b014c2c1c5366e817202e9cdf7", size = 2110990, upload-time = "2025-11-04T13:39:58.079Z" }, + { url = "https://files.pythonhosted.org/packages/aa/32/9c2e8ccb57c01111e0fd091f236c7b371c1bccea0fa85247ac55b1e2b6b6/pydantic_core-2.41.5-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:070259a8818988b9a84a449a2a7337c7f430a22acc0859c6b110aa7212a6d9c0", size = 1896003, upload-time = "2025-11-04T13:39:59.956Z" }, + { url = "https://files.pythonhosted.org/packages/68/b8/a01b53cb0e59139fbc9e4fda3e9724ede8de279097179be4ff31f1abb65a/pydantic_core-2.41.5-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e96cea19e34778f8d59fe40775a7a574d95816eb150850a85a7a4c8f4b94ac69", size = 1919200, upload-time = "2025-11-04T13:40:02.241Z" }, + { url = "https://files.pythonhosted.org/packages/38/de/8c36b5198a29bdaade07b5985e80a233a5ac27137846f3bc2d3b40a47360/pydantic_core-2.41.5-cp312-cp312-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:ed2e99c456e3fadd05c991f8f437ef902e00eedf34320ba2b0842bd1c3ca3a75", size = 2052578, upload-time = "2025-11-04T13:40:04.401Z" }, + { url = "https://files.pythonhosted.org/packages/00/b5/0e8e4b5b081eac6cb3dbb7e60a65907549a1ce035a724368c330112adfdd/pydantic_core-2.41.5-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:65840751b72fbfd82c3c640cff9284545342a4f1eb1586ad0636955b261b0b05", size = 2208504, upload-time = "2025-11-04T13:40:06.072Z" }, + { url = "https://files.pythonhosted.org/packages/77/56/87a61aad59c7c5b9dc8caad5a41a5545cba3810c3e828708b3d7404f6cef/pydantic_core-2.41.5-cp312-cp312-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:e536c98a7626a98feb2d3eaf75944ef6f3dbee447e1f841eae16f2f0a72d8ddc", size = 2335816, upload-time = "2025-11-04T13:40:07.835Z" }, + { url = "https://files.pythonhosted.org/packages/0d/76/941cc9f73529988688a665a5c0ecff1112b3d95ab48f81db5f7606f522d3/pydantic_core-2.41.5-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:eceb81a8d74f9267ef4081e246ffd6d129da5d87e37a77c9bde550cb04870c1c", size = 2075366, upload-time = "2025-11-04T13:40:09.804Z" }, + { url = "https://files.pythonhosted.org/packages/d3/43/ebef01f69baa07a482844faaa0a591bad1ef129253ffd0cdaa9d8a7f72d3/pydantic_core-2.41.5-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:d38548150c39b74aeeb0ce8ee1d8e82696f4a4e16ddc6de7b1d8823f7de4b9b5", size = 2171698, upload-time = "2025-11-04T13:40:12.004Z" }, + { url = "https://files.pythonhosted.org/packages/b1/87/41f3202e4193e3bacfc2c065fab7706ebe81af46a83d3e27605029c1f5a6/pydantic_core-2.41.5-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:c23e27686783f60290e36827f9c626e63154b82b116d7fe9adba1fda36da706c", size = 2132603, upload-time = "2025-11-04T13:40:13.868Z" }, + { url = "https://files.pythonhosted.org/packages/49/7d/4c00df99cb12070b6bccdef4a195255e6020a550d572768d92cc54dba91a/pydantic_core-2.41.5-cp312-cp312-musllinux_1_1_armv7l.whl", hash = "sha256:482c982f814460eabe1d3bb0adfdc583387bd4691ef00b90575ca0d2b6fe2294", size = 2329591, upload-time = "2025-11-04T13:40:15.672Z" }, + { url = "https://files.pythonhosted.org/packages/cc/6a/ebf4b1d65d458f3cda6a7335d141305dfa19bdc61140a884d165a8a1bbc7/pydantic_core-2.41.5-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:bfea2a5f0b4d8d43adf9d7b8bf019fb46fdd10a2e5cde477fbcb9d1fa08c68e1", size = 2319068, upload-time = "2025-11-04T13:40:17.532Z" }, + { url = "https://files.pythonhosted.org/packages/49/3b/774f2b5cd4192d5ab75870ce4381fd89cf218af999515baf07e7206753f0/pydantic_core-2.41.5-cp312-cp312-win32.whl", hash = "sha256:b74557b16e390ec12dca509bce9264c3bbd128f8a2c376eaa68003d7f327276d", size = 1985908, upload-time = "2025-11-04T13:40:19.309Z" }, + { url = "https://files.pythonhosted.org/packages/86/45/00173a033c801cacf67c190fef088789394feaf88a98a7035b0e40d53dc9/pydantic_core-2.41.5-cp312-cp312-win_amd64.whl", hash = "sha256:1962293292865bca8e54702b08a4f26da73adc83dd1fcf26fbc875b35d81c815", size = 2020145, upload-time = "2025-11-04T13:40:21.548Z" }, + { url = "https://files.pythonhosted.org/packages/f9/22/91fbc821fa6d261b376a3f73809f907cec5ca6025642c463d3488aad22fb/pydantic_core-2.41.5-cp312-cp312-win_arm64.whl", hash = "sha256:1746d4a3d9a794cacae06a5eaaccb4b8643a131d45fbc9af23e353dc0a5ba5c3", size = 1976179, upload-time = "2025-11-04T13:40:23.393Z" }, + { url = "https://files.pythonhosted.org/packages/87/06/8806241ff1f70d9939f9af039c6c35f2360cf16e93c2ca76f184e76b1564/pydantic_core-2.41.5-cp313-cp313-macosx_10_12_x86_64.whl", hash = "sha256:941103c9be18ac8daf7b7adca8228f8ed6bb7a1849020f643b3a14d15b1924d9", size = 2120403, upload-time = "2025-11-04T13:40:25.248Z" }, + { url = "https://files.pythonhosted.org/packages/94/02/abfa0e0bda67faa65fef1c84971c7e45928e108fe24333c81f3bfe35d5f5/pydantic_core-2.41.5-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:112e305c3314f40c93998e567879e887a3160bb8689ef3d2c04b6cc62c33ac34", size = 1896206, upload-time = "2025-11-04T13:40:27.099Z" }, + { url = "https://files.pythonhosted.org/packages/15/df/a4c740c0943e93e6500f9eb23f4ca7ec9bf71b19e608ae5b579678c8d02f/pydantic_core-2.41.5-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:0cbaad15cb0c90aa221d43c00e77bb33c93e8d36e0bf74760cd00e732d10a6a0", size = 1919307, upload-time = "2025-11-04T13:40:29.806Z" }, + { url = "https://files.pythonhosted.org/packages/9a/e3/6324802931ae1d123528988e0e86587c2072ac2e5394b4bc2bc34b61ff6e/pydantic_core-2.41.5-cp313-cp313-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:03ca43e12fab6023fc79d28ca6b39b05f794ad08ec2feccc59a339b02f2b3d33", size = 2063258, upload-time = "2025-11-04T13:40:33.544Z" }, + { url = "https://files.pythonhosted.org/packages/c9/d4/2230d7151d4957dd79c3044ea26346c148c98fbf0ee6ebd41056f2d62ab5/pydantic_core-2.41.5-cp313-cp313-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:dc799088c08fa04e43144b164feb0c13f9a0bc40503f8df3e9fde58a3c0c101e", size = 2214917, upload-time = "2025-11-04T13:40:35.479Z" }, + { url = "https://files.pythonhosted.org/packages/e6/9f/eaac5df17a3672fef0081b6c1bb0b82b33ee89aa5cec0d7b05f52fd4a1fa/pydantic_core-2.41.5-cp313-cp313-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:97aeba56665b4c3235a0e52b2c2f5ae9cd071b8a8310ad27bddb3f7fb30e9aa2", size = 2332186, upload-time = "2025-11-04T13:40:37.436Z" }, + { url = "https://files.pythonhosted.org/packages/cf/4e/35a80cae583a37cf15604b44240e45c05e04e86f9cfd766623149297e971/pydantic_core-2.41.5-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:406bf18d345822d6c21366031003612b9c77b3e29ffdb0f612367352aab7d586", size = 2073164, upload-time = "2025-11-04T13:40:40.289Z" }, + { url = "https://files.pythonhosted.org/packages/bf/e3/f6e262673c6140dd3305d144d032f7bd5f7497d3871c1428521f19f9efa2/pydantic_core-2.41.5-cp313-cp313-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:b93590ae81f7010dbe380cdeab6f515902ebcbefe0b9327cc4804d74e93ae69d", size = 2179146, upload-time = "2025-11-04T13:40:42.809Z" }, + { url = "https://files.pythonhosted.org/packages/75/c7/20bd7fc05f0c6ea2056a4565c6f36f8968c0924f19b7d97bbfea55780e73/pydantic_core-2.41.5-cp313-cp313-musllinux_1_1_aarch64.whl", hash = "sha256:01a3d0ab748ee531f4ea6c3e48ad9dac84ddba4b0d82291f87248f2f9de8d740", size = 2137788, upload-time = "2025-11-04T13:40:44.752Z" }, + { url = "https://files.pythonhosted.org/packages/3a/8d/34318ef985c45196e004bc46c6eab2eda437e744c124ef0dbe1ff2c9d06b/pydantic_core-2.41.5-cp313-cp313-musllinux_1_1_armv7l.whl", hash = "sha256:6561e94ba9dacc9c61bce40e2d6bdc3bfaa0259d3ff36ace3b1e6901936d2e3e", size = 2340133, upload-time = "2025-11-04T13:40:46.66Z" }, + { url = "https://files.pythonhosted.org/packages/9c/59/013626bf8c78a5a5d9350d12e7697d3d4de951a75565496abd40ccd46bee/pydantic_core-2.41.5-cp313-cp313-musllinux_1_1_x86_64.whl", hash = "sha256:915c3d10f81bec3a74fbd4faebe8391013ba61e5a1a8d48c4455b923bdda7858", size = 2324852, upload-time = "2025-11-04T13:40:48.575Z" }, + { url = "https://files.pythonhosted.org/packages/1a/d9/c248c103856f807ef70c18a4f986693a46a8ffe1602e5d361485da502d20/pydantic_core-2.41.5-cp313-cp313-win32.whl", hash = "sha256:650ae77860b45cfa6e2cdafc42618ceafab3a2d9a3811fcfbd3bbf8ac3c40d36", size = 1994679, upload-time = "2025-11-04T13:40:50.619Z" }, + { url = "https://files.pythonhosted.org/packages/9e/8b/341991b158ddab181cff136acd2552c9f35bd30380422a639c0671e99a91/pydantic_core-2.41.5-cp313-cp313-win_amd64.whl", hash = "sha256:79ec52ec461e99e13791ec6508c722742ad745571f234ea6255bed38c6480f11", size = 2019766, upload-time = "2025-11-04T13:40:52.631Z" }, + { url = "https://files.pythonhosted.org/packages/73/7d/f2f9db34af103bea3e09735bb40b021788a5e834c81eedb541991badf8f5/pydantic_core-2.41.5-cp313-cp313-win_arm64.whl", hash = "sha256:3f84d5c1b4ab906093bdc1ff10484838aca54ef08de4afa9de0f5f14d69639cd", size = 1981005, upload-time = "2025-11-04T13:40:54.734Z" }, + { url = "https://files.pythonhosted.org/packages/ea/28/46b7c5c9635ae96ea0fbb779e271a38129df2550f763937659ee6c5dbc65/pydantic_core-2.41.5-cp314-cp314-macosx_10_12_x86_64.whl", hash = "sha256:3f37a19d7ebcdd20b96485056ba9e8b304e27d9904d233d7b1015db320e51f0a", size = 2119622, upload-time = "2025-11-04T13:40:56.68Z" }, + { url = "https://files.pythonhosted.org/packages/74/1a/145646e5687e8d9a1e8d09acb278c8535ebe9e972e1f162ed338a622f193/pydantic_core-2.41.5-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:1d1d9764366c73f996edd17abb6d9d7649a7eb690006ab6adbda117717099b14", size = 1891725, upload-time = "2025-11-04T13:40:58.807Z" }, + { url = "https://files.pythonhosted.org/packages/23/04/e89c29e267b8060b40dca97bfc64a19b2a3cf99018167ea1677d96368273/pydantic_core-2.41.5-cp314-cp314-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:25e1c2af0fce638d5f1988b686f3b3ea8cd7de5f244ca147c777769e798a9cd1", size = 1915040, upload-time = "2025-11-04T13:41:00.853Z" }, + { url = "https://files.pythonhosted.org/packages/84/a3/15a82ac7bd97992a82257f777b3583d3e84bdb06ba6858f745daa2ec8a85/pydantic_core-2.41.5-cp314-cp314-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:506d766a8727beef16b7adaeb8ee6217c64fc813646b424d0804d67c16eddb66", size = 2063691, upload-time = "2025-11-04T13:41:03.504Z" }, + { url = "https://files.pythonhosted.org/packages/74/9b/0046701313c6ef08c0c1cf0e028c67c770a4e1275ca73131563c5f2a310a/pydantic_core-2.41.5-cp314-cp314-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:4819fa52133c9aa3c387b3328f25c1facc356491e6135b459f1de698ff64d869", size = 2213897, upload-time = "2025-11-04T13:41:05.804Z" }, + { url = "https://files.pythonhosted.org/packages/8a/cd/6bac76ecd1b27e75a95ca3a9a559c643b3afcd2dd62086d4b7a32a18b169/pydantic_core-2.41.5-cp314-cp314-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:2b761d210c9ea91feda40d25b4efe82a1707da2ef62901466a42492c028553a2", size = 2333302, upload-time = "2025-11-04T13:41:07.809Z" }, + { url = "https://files.pythonhosted.org/packages/4c/d2/ef2074dc020dd6e109611a8be4449b98cd25e1b9b8a303c2f0fca2f2bcf7/pydantic_core-2.41.5-cp314-cp314-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:22f0fb8c1c583a3b6f24df2470833b40207e907b90c928cc8d3594b76f874375", size = 2064877, upload-time = "2025-11-04T13:41:09.827Z" }, + { url = "https://files.pythonhosted.org/packages/18/66/e9db17a9a763d72f03de903883c057b2592c09509ccfe468187f2a2eef29/pydantic_core-2.41.5-cp314-cp314-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:2782c870e99878c634505236d81e5443092fba820f0373997ff75f90f68cd553", size = 2180680, upload-time = "2025-11-04T13:41:12.379Z" }, + { url = "https://files.pythonhosted.org/packages/d3/9e/3ce66cebb929f3ced22be85d4c2399b8e85b622db77dad36b73c5387f8f8/pydantic_core-2.41.5-cp314-cp314-musllinux_1_1_aarch64.whl", hash = "sha256:0177272f88ab8312479336e1d777f6b124537d47f2123f89cb37e0accea97f90", size = 2138960, upload-time = "2025-11-04T13:41:14.627Z" }, + { url = "https://files.pythonhosted.org/packages/a6/62/205a998f4327d2079326b01abee48e502ea739d174f0a89295c481a2272e/pydantic_core-2.41.5-cp314-cp314-musllinux_1_1_armv7l.whl", hash = "sha256:63510af5e38f8955b8ee5687740d6ebf7c2a0886d15a6d65c32814613681bc07", size = 2339102, upload-time = "2025-11-04T13:41:16.868Z" }, + { url = "https://files.pythonhosted.org/packages/3c/0d/f05e79471e889d74d3d88f5bd20d0ed189ad94c2423d81ff8d0000aab4ff/pydantic_core-2.41.5-cp314-cp314-musllinux_1_1_x86_64.whl", hash = "sha256:e56ba91f47764cc14f1daacd723e3e82d1a89d783f0f5afe9c364b8bb491ccdb", size = 2326039, upload-time = "2025-11-04T13:41:18.934Z" }, + { url = "https://files.pythonhosted.org/packages/ec/e1/e08a6208bb100da7e0c4b288eed624a703f4d129bde2da475721a80cab32/pydantic_core-2.41.5-cp314-cp314-win32.whl", hash = "sha256:aec5cf2fd867b4ff45b9959f8b20ea3993fc93e63c7363fe6851424c8a7e7c23", size = 1995126, upload-time = "2025-11-04T13:41:21.418Z" }, + { url = "https://files.pythonhosted.org/packages/48/5d/56ba7b24e9557f99c9237e29f5c09913c81eeb2f3217e40e922353668092/pydantic_core-2.41.5-cp314-cp314-win_amd64.whl", hash = "sha256:8e7c86f27c585ef37c35e56a96363ab8de4e549a95512445b85c96d3e2f7c1bf", size = 2015489, upload-time = "2025-11-04T13:41:24.076Z" }, + { url = "https://files.pythonhosted.org/packages/4e/bb/f7a190991ec9e3e0ba22e4993d8755bbc4a32925c0b5b42775c03e8148f9/pydantic_core-2.41.5-cp314-cp314-win_arm64.whl", hash = "sha256:e672ba74fbc2dc8eea59fb6d4aed6845e6905fc2a8afe93175d94a83ba2a01a0", size = 1977288, upload-time = "2025-11-04T13:41:26.33Z" }, + { url = "https://files.pythonhosted.org/packages/92/ed/77542d0c51538e32e15afe7899d79efce4b81eee631d99850edc2f5e9349/pydantic_core-2.41.5-cp314-cp314t-macosx_10_12_x86_64.whl", hash = "sha256:8566def80554c3faa0e65ac30ab0932b9e3a5cd7f8323764303d468e5c37595a", size = 2120255, upload-time = "2025-11-04T13:41:28.569Z" }, + { url = "https://files.pythonhosted.org/packages/bb/3d/6913dde84d5be21e284439676168b28d8bbba5600d838b9dca99de0fad71/pydantic_core-2.41.5-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:b80aa5095cd3109962a298ce14110ae16b8c1aece8b72f9dafe81cf597ad80b3", size = 1863760, upload-time = "2025-11-04T13:41:31.055Z" }, + { url = "https://files.pythonhosted.org/packages/5a/f0/e5e6b99d4191da102f2b0eb9687aaa7f5bea5d9964071a84effc3e40f997/pydantic_core-2.41.5-cp314-cp314t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:3006c3dd9ba34b0c094c544c6006cc79e87d8612999f1a5d43b769b89181f23c", size = 1878092, upload-time = "2025-11-04T13:41:33.21Z" }, + { url = "https://files.pythonhosted.org/packages/71/48/36fb760642d568925953bcc8116455513d6e34c4beaa37544118c36aba6d/pydantic_core-2.41.5-cp314-cp314t-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:72f6c8b11857a856bcfa48c86f5368439f74453563f951e473514579d44aa612", size = 2053385, upload-time = "2025-11-04T13:41:35.508Z" }, + { url = "https://files.pythonhosted.org/packages/20/25/92dc684dd8eb75a234bc1c764b4210cf2646479d54b47bf46061657292a8/pydantic_core-2.41.5-cp314-cp314t-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:5cb1b2f9742240e4bb26b652a5aeb840aa4b417c7748b6f8387927bc6e45e40d", size = 2218832, upload-time = "2025-11-04T13:41:37.732Z" }, + { url = "https://files.pythonhosted.org/packages/e2/09/f53e0b05023d3e30357d82eb35835d0f6340ca344720a4599cd663dca599/pydantic_core-2.41.5-cp314-cp314t-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:bd3d54f38609ff308209bd43acea66061494157703364ae40c951f83ba99a1a9", size = 2327585, upload-time = "2025-11-04T13:41:40Z" }, + { url = "https://files.pythonhosted.org/packages/aa/4e/2ae1aa85d6af35a39b236b1b1641de73f5a6ac4d5a7509f77b814885760c/pydantic_core-2.41.5-cp314-cp314t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:2ff4321e56e879ee8d2a879501c8e469414d948f4aba74a2d4593184eb326660", size = 2041078, upload-time = "2025-11-04T13:41:42.323Z" }, + { url = "https://files.pythonhosted.org/packages/cd/13/2e215f17f0ef326fc72afe94776edb77525142c693767fc347ed6288728d/pydantic_core-2.41.5-cp314-cp314t-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:d0d2568a8c11bf8225044aa94409e21da0cb09dcdafe9ecd10250b2baad531a9", size = 2173914, upload-time = "2025-11-04T13:41:45.221Z" }, + { url = "https://files.pythonhosted.org/packages/02/7a/f999a6dcbcd0e5660bc348a3991c8915ce6599f4f2c6ac22f01d7a10816c/pydantic_core-2.41.5-cp314-cp314t-musllinux_1_1_aarch64.whl", hash = "sha256:a39455728aabd58ceabb03c90e12f71fd30fa69615760a075b9fec596456ccc3", size = 2129560, upload-time = "2025-11-04T13:41:47.474Z" }, + { url = "https://files.pythonhosted.org/packages/3a/b1/6c990ac65e3b4c079a4fb9f5b05f5b013afa0f4ed6780a3dd236d2cbdc64/pydantic_core-2.41.5-cp314-cp314t-musllinux_1_1_armv7l.whl", hash = "sha256:239edca560d05757817c13dc17c50766136d21f7cd0fac50295499ae24f90fdf", size = 2329244, upload-time = "2025-11-04T13:41:49.992Z" }, + { url = "https://files.pythonhosted.org/packages/d9/02/3c562f3a51afd4d88fff8dffb1771b30cfdfd79befd9883ee094f5b6c0d8/pydantic_core-2.41.5-cp314-cp314t-musllinux_1_1_x86_64.whl", hash = "sha256:2a5e06546e19f24c6a96a129142a75cee553cc018ffee48a460059b1185f4470", size = 2331955, upload-time = "2025-11-04T13:41:54.079Z" }, + { url = "https://files.pythonhosted.org/packages/5c/96/5fb7d8c3c17bc8c62fdb031c47d77a1af698f1d7a406b0f79aaa1338f9ad/pydantic_core-2.41.5-cp314-cp314t-win32.whl", hash = "sha256:b4ececa40ac28afa90871c2cc2b9ffd2ff0bf749380fbdf57d165fd23da353aa", size = 1988906, upload-time = "2025-11-04T13:41:56.606Z" }, + { url = "https://files.pythonhosted.org/packages/22/ed/182129d83032702912c2e2d8bbe33c036f342cc735737064668585dac28f/pydantic_core-2.41.5-cp314-cp314t-win_amd64.whl", hash = "sha256:80aa89cad80b32a912a65332f64a4450ed00966111b6615ca6816153d3585a8c", size = 1981607, upload-time = "2025-11-04T13:41:58.889Z" }, + { url = "https://files.pythonhosted.org/packages/9f/ed/068e41660b832bb0b1aa5b58011dea2a3fe0ba7861ff38c4d4904c1c1a99/pydantic_core-2.41.5-cp314-cp314t-win_arm64.whl", hash = "sha256:35b44f37a3199f771c3eaa53051bc8a70cd7b54f333531c59e29fd4db5d15008", size = 1974769, upload-time = "2025-11-04T13:42:01.186Z" }, + { url = "https://files.pythonhosted.org/packages/09/32/59b0c7e63e277fa7911c2fc70ccfb45ce4b98991e7ef37110663437005af/pydantic_core-2.41.5-graalpy312-graalpy250_312_native-macosx_10_12_x86_64.whl", hash = "sha256:7da7087d756b19037bc2c06edc6c170eeef3c3bafcb8f532ff17d64dc427adfd", size = 2110495, upload-time = "2025-11-04T13:42:49.689Z" }, + { url = "https://files.pythonhosted.org/packages/aa/81/05e400037eaf55ad400bcd318c05bb345b57e708887f07ddb2d20e3f0e98/pydantic_core-2.41.5-graalpy312-graalpy250_312_native-macosx_11_0_arm64.whl", hash = "sha256:aabf5777b5c8ca26f7824cb4a120a740c9588ed58df9b2d196ce92fba42ff8dc", size = 1915388, upload-time = "2025-11-04T13:42:52.215Z" }, + { url = "https://files.pythonhosted.org/packages/6e/0d/e3549b2399f71d56476b77dbf3cf8937cec5cd70536bdc0e374a421d0599/pydantic_core-2.41.5-graalpy312-graalpy250_312_native-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:c007fe8a43d43b3969e8469004e9845944f1a80e6acd47c150856bb87f230c56", size = 1942879, upload-time = "2025-11-04T13:42:56.483Z" }, + { url = "https://files.pythonhosted.org/packages/f7/07/34573da085946b6a313d7c42f82f16e8920bfd730665de2d11c0c37a74b5/pydantic_core-2.41.5-graalpy312-graalpy250_312_native-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:76d0819de158cd855d1cbb8fcafdf6f5cf1eb8e470abe056d5d161106e38062b", size = 2139017, upload-time = "2025-11-04T13:42:59.471Z" }, +] + +[[package]] +name = "pygments" +version = "2.19.2" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/b0/77/a5b8c569bf593b0140bde72ea885a803b82086995367bf2037de0159d924/pygments-2.19.2.tar.gz", hash = "sha256:636cb2477cec7f8952536970bc533bc43743542f70392ae026374600add5b887", size = 4968631, upload-time = "2025-06-21T13:39:12.283Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/c7/21/705964c7812476f378728bdf590ca4b771ec72385c533964653c68e86bdc/pygments-2.19.2-py3-none-any.whl", hash = "sha256:86540386c03d588bb81d44bc3928634ff26449851e99741617ecb9037ee5ec0b", size = 1225217, upload-time = "2025-06-21T13:39:07.939Z" }, +] + +[[package]] +name = "pyright" +version = "1.1.408" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "nodeenv" }, + { name = "typing-extensions" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/74/b2/5db700e52554b8f025faa9c3c624c59f1f6c8841ba81ab97641b54322f16/pyright-1.1.408.tar.gz", hash = "sha256:f28f2321f96852fa50b5829ea492f6adb0e6954568d1caa3f3af3a5f555eb684", size = 4400578, upload-time = "2026-01-08T08:07:38.795Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/0c/82/a2c93e32800940d9573fb28c346772a14778b84ba7524e691b324620ab89/pyright-1.1.408-py3-none-any.whl", hash = "sha256:090b32865f4fdb1e0e6cd82bf5618480d48eecd2eb2e70f960982a3d9a4c17c1", size = 6399144, upload-time = "2026-01-08T08:07:37.082Z" }, +] + +[[package]] +name = "pytest" +version = "9.0.2" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "colorama", marker = "sys_platform == 'win32'" }, + { name = "iniconfig" }, + { name = "packaging" }, + { name = "pluggy" }, + { name = "pygments" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/d1/db/7ef3487e0fb0049ddb5ce41d3a49c235bf9ad299b6a25d5780a89f19230f/pytest-9.0.2.tar.gz", hash = "sha256:75186651a92bd89611d1d9fc20f0b4345fd827c41ccd5c299a868a05d70edf11", size = 1568901, upload-time = "2025-12-06T21:30:51.014Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/3b/ab/b3226f0bd7cdcf710fbede2b3548584366da3b19b5021e74f5bde2a8fa3f/pytest-9.0.2-py3-none-any.whl", hash = "sha256:711ffd45bf766d5264d487b917733b453d917afd2b0ad65223959f59089f875b", size = 374801, upload-time = "2025-12-06T21:30:49.154Z" }, +] + +[[package]] +name = "python-dateutil" +version = "2.9.0.post0" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "six" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/66/c0/0c8b6ad9f17a802ee498c46e004a0eb49bc148f2fd230864601a86dcf6db/python-dateutil-2.9.0.post0.tar.gz", hash = "sha256:37dd54208da7e1cd875388217d5e00ebd4179249f90fb72437e91a35459a0ad3", size = 342432, upload-time = "2024-03-01T18:36:20.211Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/ec/57/56b9bcc3c9c6a792fcbaf139543cee77261f3651ca9da0c93f5c1221264b/python_dateutil-2.9.0.post0-py2.py3-none-any.whl", hash = "sha256:a8b2bc7bffae282281c8140a97d3aa9c14da0b136dfe83f850eea9a5f7470427", size = 229892, upload-time = "2024-03-01T18:36:18.57Z" }, +] + +[[package]] +name = "pytz" +version = "2025.2" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/f8/bf/abbd3cdfb8fbc7fb3d4d38d320f2441b1e7cbe29be4f23797b4a2b5d8aac/pytz-2025.2.tar.gz", hash = "sha256:360b9e3dbb49a209c21ad61809c7fb453643e048b38924c765813546746e81c3", size = 320884, upload-time = "2025-03-25T02:25:00.538Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/81/c4/34e93fe5f5429d7570ec1fa436f1986fb1f00c3e0f43a589fe2bbcd22c3f/pytz-2025.2-py2.py3-none-any.whl", hash = "sha256:5ddf76296dd8c44c26eb8f4b6f35488f3ccbf6fbbd7adee0b7262d43f0ec2f00", size = 509225, upload-time = "2025-03-25T02:24:58.468Z" }, +] + +[[package]] +name = "pyyaml" +version = "6.0.3" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/05/8e/961c0007c59b8dd7729d542c61a4d537767a59645b82a0b521206e1e25c2/pyyaml-6.0.3.tar.gz", hash = "sha256:d76623373421df22fb4cf8817020cbb7ef15c725b9d5e45f17e189bfc384190f", size = 130960, upload-time = "2025-09-25T21:33:16.546Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/d1/33/422b98d2195232ca1826284a76852ad5a86fe23e31b009c9886b2d0fb8b2/pyyaml-6.0.3-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:7f047e29dcae44602496db43be01ad42fc6f1cc0d8cd6c83d342306c32270196", size = 182063, upload-time = "2025-09-25T21:32:11.445Z" }, + { url = "https://files.pythonhosted.org/packages/89/a0/6cf41a19a1f2f3feab0e9c0b74134aa2ce6849093d5517a0c550fe37a648/pyyaml-6.0.3-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:fc09d0aa354569bc501d4e787133afc08552722d3ab34836a80547331bb5d4a0", size = 173973, upload-time = "2025-09-25T21:32:12.492Z" }, + { url = "https://files.pythonhosted.org/packages/ed/23/7a778b6bd0b9a8039df8b1b1d80e2e2ad78aa04171592c8a5c43a56a6af4/pyyaml-6.0.3-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:9149cad251584d5fb4981be1ecde53a1ca46c891a79788c0df828d2f166bda28", size = 775116, upload-time = "2025-09-25T21:32:13.652Z" }, + { url = "https://files.pythonhosted.org/packages/65/30/d7353c338e12baef4ecc1b09e877c1970bd3382789c159b4f89d6a70dc09/pyyaml-6.0.3-cp312-cp312-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:5fdec68f91a0c6739b380c83b951e2c72ac0197ace422360e6d5a959d8d97b2c", size = 844011, upload-time = "2025-09-25T21:32:15.21Z" }, + { url = "https://files.pythonhosted.org/packages/8b/9d/b3589d3877982d4f2329302ef98a8026e7f4443c765c46cfecc8858c6b4b/pyyaml-6.0.3-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:ba1cc08a7ccde2d2ec775841541641e4548226580ab850948cbfda66a1befcdc", size = 807870, upload-time = "2025-09-25T21:32:16.431Z" }, + { url = "https://files.pythonhosted.org/packages/05/c0/b3be26a015601b822b97d9149ff8cb5ead58c66f981e04fedf4e762f4bd4/pyyaml-6.0.3-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:8dc52c23056b9ddd46818a57b78404882310fb473d63f17b07d5c40421e47f8e", size = 761089, upload-time = "2025-09-25T21:32:17.56Z" }, + { url = "https://files.pythonhosted.org/packages/be/8e/98435a21d1d4b46590d5459a22d88128103f8da4c2d4cb8f14f2a96504e1/pyyaml-6.0.3-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:41715c910c881bc081f1e8872880d3c650acf13dfa8214bad49ed4cede7c34ea", size = 790181, upload-time = "2025-09-25T21:32:18.834Z" }, + { url = "https://files.pythonhosted.org/packages/74/93/7baea19427dcfbe1e5a372d81473250b379f04b1bd3c4c5ff825e2327202/pyyaml-6.0.3-cp312-cp312-win32.whl", hash = "sha256:96b533f0e99f6579b3d4d4995707cf36df9100d67e0c8303a0c55b27b5f99bc5", size = 137658, upload-time = "2025-09-25T21:32:20.209Z" }, + { url = "https://files.pythonhosted.org/packages/86/bf/899e81e4cce32febab4fb42bb97dcdf66bc135272882d1987881a4b519e9/pyyaml-6.0.3-cp312-cp312-win_amd64.whl", hash = "sha256:5fcd34e47f6e0b794d17de1b4ff496c00986e1c83f7ab2fb8fcfe9616ff7477b", size = 154003, upload-time = "2025-09-25T21:32:21.167Z" }, + { url = "https://files.pythonhosted.org/packages/1a/08/67bd04656199bbb51dbed1439b7f27601dfb576fb864099c7ef0c3e55531/pyyaml-6.0.3-cp312-cp312-win_arm64.whl", hash = "sha256:64386e5e707d03a7e172c0701abfb7e10f0fb753ee1d773128192742712a98fd", size = 140344, upload-time = "2025-09-25T21:32:22.617Z" }, + { url = "https://files.pythonhosted.org/packages/d1/11/0fd08f8192109f7169db964b5707a2f1e8b745d4e239b784a5a1dd80d1db/pyyaml-6.0.3-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:8da9669d359f02c0b91ccc01cac4a67f16afec0dac22c2ad09f46bee0697eba8", size = 181669, upload-time = "2025-09-25T21:32:23.673Z" }, + { url = "https://files.pythonhosted.org/packages/b1/16/95309993f1d3748cd644e02e38b75d50cbc0d9561d21f390a76242ce073f/pyyaml-6.0.3-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:2283a07e2c21a2aa78d9c4442724ec1eb15f5e42a723b99cb3d822d48f5f7ad1", size = 173252, upload-time = "2025-09-25T21:32:25.149Z" }, + { url = "https://files.pythonhosted.org/packages/50/31/b20f376d3f810b9b2371e72ef5adb33879b25edb7a6d072cb7ca0c486398/pyyaml-6.0.3-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:ee2922902c45ae8ccada2c5b501ab86c36525b883eff4255313a253a3160861c", size = 767081, upload-time = "2025-09-25T21:32:26.575Z" }, + { url = "https://files.pythonhosted.org/packages/49/1e/a55ca81e949270d5d4432fbbd19dfea5321eda7c41a849d443dc92fd1ff7/pyyaml-6.0.3-cp313-cp313-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:a33284e20b78bd4a18c8c2282d549d10bc8408a2a7ff57653c0cf0b9be0afce5", size = 841159, upload-time = "2025-09-25T21:32:27.727Z" }, + { url = "https://files.pythonhosted.org/packages/74/27/e5b8f34d02d9995b80abcef563ea1f8b56d20134d8f4e5e81733b1feceb2/pyyaml-6.0.3-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:0f29edc409a6392443abf94b9cf89ce99889a1dd5376d94316ae5145dfedd5d6", size = 801626, upload-time = "2025-09-25T21:32:28.878Z" }, + { url = "https://files.pythonhosted.org/packages/f9/11/ba845c23988798f40e52ba45f34849aa8a1f2d4af4b798588010792ebad6/pyyaml-6.0.3-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:f7057c9a337546edc7973c0d3ba84ddcdf0daa14533c2065749c9075001090e6", size = 753613, upload-time = "2025-09-25T21:32:30.178Z" }, + { url = "https://files.pythonhosted.org/packages/3d/e0/7966e1a7bfc0a45bf0a7fb6b98ea03fc9b8d84fa7f2229e9659680b69ee3/pyyaml-6.0.3-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:eda16858a3cab07b80edaf74336ece1f986ba330fdb8ee0d6c0d68fe82bc96be", size = 794115, upload-time = "2025-09-25T21:32:31.353Z" }, + { url = "https://files.pythonhosted.org/packages/de/94/980b50a6531b3019e45ddeada0626d45fa85cbe22300844a7983285bed3b/pyyaml-6.0.3-cp313-cp313-win32.whl", hash = "sha256:d0eae10f8159e8fdad514efdc92d74fd8d682c933a6dd088030f3834bc8e6b26", size = 137427, upload-time = "2025-09-25T21:32:32.58Z" }, + { url = "https://files.pythonhosted.org/packages/97/c9/39d5b874e8b28845e4ec2202b5da735d0199dbe5b8fb85f91398814a9a46/pyyaml-6.0.3-cp313-cp313-win_amd64.whl", hash = "sha256:79005a0d97d5ddabfeeea4cf676af11e647e41d81c9a7722a193022accdb6b7c", size = 154090, upload-time = "2025-09-25T21:32:33.659Z" }, + { url = "https://files.pythonhosted.org/packages/73/e8/2bdf3ca2090f68bb3d75b44da7bbc71843b19c9f2b9cb9b0f4ab7a5a4329/pyyaml-6.0.3-cp313-cp313-win_arm64.whl", hash = "sha256:5498cd1645aa724a7c71c8f378eb29ebe23da2fc0d7a08071d89469bf1d2defb", size = 140246, upload-time = "2025-09-25T21:32:34.663Z" }, + { url = "https://files.pythonhosted.org/packages/9d/8c/f4bd7f6465179953d3ac9bc44ac1a8a3e6122cf8ada906b4f96c60172d43/pyyaml-6.0.3-cp314-cp314-macosx_10_13_x86_64.whl", hash = "sha256:8d1fab6bb153a416f9aeb4b8763bc0f22a5586065f86f7664fc23339fc1c1fac", size = 181814, upload-time = "2025-09-25T21:32:35.712Z" }, + { url = "https://files.pythonhosted.org/packages/bd/9c/4d95bb87eb2063d20db7b60faa3840c1b18025517ae857371c4dd55a6b3a/pyyaml-6.0.3-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:34d5fcd24b8445fadc33f9cf348c1047101756fd760b4dacb5c3e99755703310", size = 173809, upload-time = "2025-09-25T21:32:36.789Z" }, + { url = "https://files.pythonhosted.org/packages/92/b5/47e807c2623074914e29dabd16cbbdd4bf5e9b2db9f8090fa64411fc5382/pyyaml-6.0.3-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:501a031947e3a9025ed4405a168e6ef5ae3126c59f90ce0cd6f2bfc477be31b7", size = 766454, upload-time = "2025-09-25T21:32:37.966Z" }, + { url = "https://files.pythonhosted.org/packages/02/9e/e5e9b168be58564121efb3de6859c452fccde0ab093d8438905899a3a483/pyyaml-6.0.3-cp314-cp314-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:b3bc83488de33889877a0f2543ade9f70c67d66d9ebb4ac959502e12de895788", size = 836355, upload-time = "2025-09-25T21:32:39.178Z" }, + { url = "https://files.pythonhosted.org/packages/88/f9/16491d7ed2a919954993e48aa941b200f38040928474c9e85ea9e64222c3/pyyaml-6.0.3-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:c458b6d084f9b935061bc36216e8a69a7e293a2f1e68bf956dcd9e6cbcd143f5", size = 794175, upload-time = "2025-09-25T21:32:40.865Z" }, + { url = "https://files.pythonhosted.org/packages/dd/3f/5989debef34dc6397317802b527dbbafb2b4760878a53d4166579111411e/pyyaml-6.0.3-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:7c6610def4f163542a622a73fb39f534f8c101d690126992300bf3207eab9764", size = 755228, upload-time = "2025-09-25T21:32:42.084Z" }, + { url = "https://files.pythonhosted.org/packages/d7/ce/af88a49043cd2e265be63d083fc75b27b6ed062f5f9fd6cdc223ad62f03e/pyyaml-6.0.3-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:5190d403f121660ce8d1d2c1bb2ef1bd05b5f68533fc5c2ea899bd15f4399b35", size = 789194, upload-time = "2025-09-25T21:32:43.362Z" }, + { url = "https://files.pythonhosted.org/packages/23/20/bb6982b26a40bb43951265ba29d4c246ef0ff59c9fdcdf0ed04e0687de4d/pyyaml-6.0.3-cp314-cp314-win_amd64.whl", hash = "sha256:4a2e8cebe2ff6ab7d1050ecd59c25d4c8bd7e6f400f5f82b96557ac0abafd0ac", size = 156429, upload-time = "2025-09-25T21:32:57.844Z" }, + { url = "https://files.pythonhosted.org/packages/f4/f4/a4541072bb9422c8a883ab55255f918fa378ecf083f5b85e87fc2b4eda1b/pyyaml-6.0.3-cp314-cp314-win_arm64.whl", hash = "sha256:93dda82c9c22deb0a405ea4dc5f2d0cda384168e466364dec6255b293923b2f3", size = 143912, upload-time = "2025-09-25T21:32:59.247Z" }, + { url = "https://files.pythonhosted.org/packages/7c/f9/07dd09ae774e4616edf6cda684ee78f97777bdd15847253637a6f052a62f/pyyaml-6.0.3-cp314-cp314t-macosx_10_13_x86_64.whl", hash = "sha256:02893d100e99e03eda1c8fd5c441d8c60103fd175728e23e431db1b589cf5ab3", size = 189108, upload-time = "2025-09-25T21:32:44.377Z" }, + { url = "https://files.pythonhosted.org/packages/4e/78/8d08c9fb7ce09ad8c38ad533c1191cf27f7ae1effe5bb9400a46d9437fcf/pyyaml-6.0.3-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:c1ff362665ae507275af2853520967820d9124984e0f7466736aea23d8611fba", size = 183641, upload-time = "2025-09-25T21:32:45.407Z" }, + { url = "https://files.pythonhosted.org/packages/7b/5b/3babb19104a46945cf816d047db2788bcaf8c94527a805610b0289a01c6b/pyyaml-6.0.3-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:6adc77889b628398debc7b65c073bcb99c4a0237b248cacaf3fe8a557563ef6c", size = 831901, upload-time = "2025-09-25T21:32:48.83Z" }, + { url = "https://files.pythonhosted.org/packages/8b/cc/dff0684d8dc44da4d22a13f35f073d558c268780ce3c6ba1b87055bb0b87/pyyaml-6.0.3-cp314-cp314t-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:a80cb027f6b349846a3bf6d73b5e95e782175e52f22108cfa17876aaeff93702", size = 861132, upload-time = "2025-09-25T21:32:50.149Z" }, + { url = "https://files.pythonhosted.org/packages/b1/5e/f77dc6b9036943e285ba76b49e118d9ea929885becb0a29ba8a7c75e29fe/pyyaml-6.0.3-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:00c4bdeba853cc34e7dd471f16b4114f4162dc03e6b7afcc2128711f0eca823c", size = 839261, upload-time = "2025-09-25T21:32:51.808Z" }, + { url = "https://files.pythonhosted.org/packages/ce/88/a9db1376aa2a228197c58b37302f284b5617f56a5d959fd1763fb1675ce6/pyyaml-6.0.3-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:66e1674c3ef6f541c35191caae2d429b967b99e02040f5ba928632d9a7f0f065", size = 805272, upload-time = "2025-09-25T21:32:52.941Z" }, + { url = "https://files.pythonhosted.org/packages/da/92/1446574745d74df0c92e6aa4a7b0b3130706a4142b2d1a5869f2eaa423c6/pyyaml-6.0.3-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:16249ee61e95f858e83976573de0f5b2893b3677ba71c9dd36b9cf8be9ac6d65", size = 829923, upload-time = "2025-09-25T21:32:54.537Z" }, + { url = "https://files.pythonhosted.org/packages/f0/7a/1c7270340330e575b92f397352af856a8c06f230aa3e76f86b39d01b416a/pyyaml-6.0.3-cp314-cp314t-win_amd64.whl", hash = "sha256:4ad1906908f2f5ae4e5a8ddfce73c320c2a1429ec52eafd27138b7f1cbe341c9", size = 174062, upload-time = "2025-09-25T21:32:55.767Z" }, + { url = "https://files.pythonhosted.org/packages/f1/12/de94a39c2ef588c7e6455cfbe7343d3b2dc9d6b6b2f40c4c6565744c873d/pyyaml-6.0.3-cp314-cp314t-win_arm64.whl", hash = "sha256:ebc55a14a21cb14062aa4162f906cd962b28e2e9ea38f9b4391244cd8de4ae0b", size = 149341, upload-time = "2025-09-25T21:32:56.828Z" }, +] + +[[package]] +name = "requests" +version = "2.32.5" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "certifi" }, + { name = "charset-normalizer" }, + { name = "idna" }, + { name = "urllib3" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/c9/74/b3ff8e6c8446842c3f5c837e9c3dfcfe2018ea6ecef224c710c85ef728f4/requests-2.32.5.tar.gz", hash = "sha256:dbba0bac56e100853db0ea71b82b4dfd5fe2bf6d3754a8893c3af500cec7d7cf", size = 134517, upload-time = "2025-08-18T20:46:02.573Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/1e/db/4254e3eabe8020b458f1a747140d32277ec7a271daf1d235b70dc0b4e6e3/requests-2.32.5-py3-none-any.whl", hash = "sha256:2462f94637a34fd532264295e186976db0f5d453d1cdd31473c85a6a161affb6", size = 64738, upload-time = "2025-08-18T20:46:00.542Z" }, +] + +[[package]] +name = "restrictedpython" +version = "8.1" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/5f/1c/aec08bcb4ab14a1521579fbe21ceff2a634bb1f737f11cf7f9c8bb96e680/restrictedpython-8.1.tar.gz", hash = "sha256:4a69304aceacf6bee74bdf153c728221d4e3109b39acbfe00b3494927080d898", size = 838331, upload-time = "2025-10-19T14:11:32.531Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/1a/c0/3848f4006f7e164ee20833ca984067e4b3fc99fe7f1dfa88b4927e681299/restrictedpython-8.1-py3-none-any.whl", hash = "sha256:4769449c6cdb10f2071649ba386902befff0eff2a8fd6217989fa7b16aeae926", size = 27651, upload-time = "2025-10-19T14:11:30.201Z" }, +] + +[[package]] +name = "rich" +version = "14.3.3" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "markdown-it-py" }, + { name = "pygments" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/b3/c6/f3b320c27991c46f43ee9d856302c70dc2d0fb2dba4842ff739d5f46b393/rich-14.3.3.tar.gz", hash = "sha256:b8daa0b9e4eef54dd8cf7c86c03713f53241884e814f4e2f5fb342fe520f639b", size = 230582, upload-time = "2026-02-19T17:23:12.474Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/14/25/b208c5683343959b670dc001595f2f3737e051da617f66c31f7c4fa93abc/rich-14.3.3-py3-none-any.whl", hash = "sha256:793431c1f8619afa7d3b52b2cdec859562b950ea0d4b6b505397612db8d5362d", size = 310458, upload-time = "2026-02-19T17:23:13.732Z" }, +] + +[[package]] +name = "ruff" +version = "0.15.2" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/06/04/eab13a954e763b0606f460443fcbf6bb5a0faf06890ea3754ff16523dce5/ruff-0.15.2.tar.gz", hash = "sha256:14b965afee0969e68bb871eba625343b8673375f457af4abe98553e8bbb98342", size = 4558148, upload-time = "2026-02-19T22:32:20.271Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/2f/70/3a4dc6d09b13cb3e695f28307e5d889b2e1a66b7af9c5e257e796695b0e6/ruff-0.15.2-py3-none-linux_armv6l.whl", hash = "sha256:120691a6fdae2f16d65435648160f5b81a9625288f75544dc40637436b5d3c0d", size = 10430565, upload-time = "2026-02-19T22:32:41.824Z" }, + { url = "https://files.pythonhosted.org/packages/71/0b/bb8457b56185ece1305c666dc895832946d24055be90692381c31d57466d/ruff-0.15.2-py3-none-macosx_10_12_x86_64.whl", hash = "sha256:a89056d831256099658b6bba4037ac6dd06f49d194199215befe2bb10457ea5e", size = 10820354, upload-time = "2026-02-19T22:32:07.366Z" }, + { url = "https://files.pythonhosted.org/packages/2d/c1/e0532d7f9c9e0b14c46f61b14afd563298b8b83f337b6789ddd987e46121/ruff-0.15.2-py3-none-macosx_11_0_arm64.whl", hash = "sha256:e36dee3a64be0ebd23c86ffa3aa3fd3ac9a712ff295e192243f814a830b6bd87", size = 10170767, upload-time = "2026-02-19T22:32:13.188Z" }, + { url = "https://files.pythonhosted.org/packages/47/e8/da1aa341d3af017a21c7a62fb5ec31d4e7ad0a93ab80e3a508316efbcb23/ruff-0.15.2-py3-none-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a9fb47b6d9764677f8c0a193c0943ce9a05d6763523f132325af8a858eadc2b9", size = 10529591, upload-time = "2026-02-19T22:32:02.547Z" }, + { url = "https://files.pythonhosted.org/packages/93/74/184fbf38e9f3510231fbc5e437e808f0b48c42d1df9434b208821efcd8d6/ruff-0.15.2-py3-none-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:f376990f9d0d6442ea9014b19621d8f2aaf2b8e39fdbfc79220b7f0c596c9b80", size = 10260771, upload-time = "2026-02-19T22:32:36.938Z" }, + { url = "https://files.pythonhosted.org/packages/05/ac/605c20b8e059a0bc4b42360414baa4892ff278cec1c91fff4be0dceedefd/ruff-0.15.2-py3-none-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:2dcc987551952d73cbf5c88d9fdee815618d497e4df86cd4c4824cc59d5dd75f", size = 11045791, upload-time = "2026-02-19T22:32:31.642Z" }, + { url = "https://files.pythonhosted.org/packages/fd/52/db6e419908f45a894924d410ac77d64bdd98ff86901d833364251bd08e22/ruff-0.15.2-py3-none-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:42a47fd785cbe8c01b9ff45031af875d101b040ad8f4de7bbb716487c74c9a77", size = 11879271, upload-time = "2026-02-19T22:32:29.305Z" }, + { url = "https://files.pythonhosted.org/packages/3e/d8/7992b18f2008bdc9231d0f10b16df7dda964dbf639e2b8b4c1b4e91b83af/ruff-0.15.2-py3-none-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:cbe9f49354866e575b4c6943856989f966421870e85cd2ac94dccb0a9dcb2fea", size = 11303707, upload-time = "2026-02-19T22:32:22.492Z" }, + { url = "https://files.pythonhosted.org/packages/d7/02/849b46184bcfdd4b64cde61752cc9a146c54759ed036edd11857e9b8443b/ruff-0.15.2-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b7a672c82b5f9887576087d97be5ce439f04bbaf548ee987b92d3a7dede41d3a", size = 11149151, upload-time = "2026-02-19T22:32:44.234Z" }, + { url = "https://files.pythonhosted.org/packages/70/04/f5284e388bab60d1d3b99614a5a9aeb03e0f333847e2429bebd2aaa1feec/ruff-0.15.2-py3-none-manylinux_2_31_riscv64.whl", hash = "sha256:72ecc64f46f7019e2bcc3cdc05d4a7da958b629a5ab7033195e11a438403d956", size = 11091132, upload-time = "2026-02-19T22:32:24.691Z" }, + { url = "https://files.pythonhosted.org/packages/fa/ae/88d844a21110e14d92cf73d57363fab59b727ebeabe78009b9ccb23500af/ruff-0.15.2-py3-none-musllinux_1_2_aarch64.whl", hash = "sha256:8dcf243b15b561c655c1ef2f2b0050e5d50db37fe90115507f6ff37d865dc8b4", size = 10504717, upload-time = "2026-02-19T22:32:26.75Z" }, + { url = "https://files.pythonhosted.org/packages/64/27/867076a6ada7f2b9c8292884ab44d08fd2ba71bd2b5364d4136f3cd537e1/ruff-0.15.2-py3-none-musllinux_1_2_armv7l.whl", hash = "sha256:dab6941c862c05739774677c6273166d2510d254dac0695c0e3f5efa1b5585de", size = 10263122, upload-time = "2026-02-19T22:32:10.036Z" }, + { url = "https://files.pythonhosted.org/packages/e7/ef/faf9321d550f8ebf0c6373696e70d1758e20ccdc3951ad7af00c0956be7c/ruff-0.15.2-py3-none-musllinux_1_2_i686.whl", hash = "sha256:1b9164f57fc36058e9a6806eb92af185b0697c9fe4c7c52caa431c6554521e5c", size = 10735295, upload-time = "2026-02-19T22:32:39.227Z" }, + { url = "https://files.pythonhosted.org/packages/2f/55/e8089fec62e050ba84d71b70e7834b97709ca9b7aba10c1a0b196e493f97/ruff-0.15.2-py3-none-musllinux_1_2_x86_64.whl", hash = "sha256:80d24fcae24d42659db7e335b9e1531697a7102c19185b8dc4a028b952865fd8", size = 11241641, upload-time = "2026-02-19T22:32:34.617Z" }, + { url = "https://files.pythonhosted.org/packages/23/01/1c30526460f4d23222d0fabd5888868262fd0e2b71a00570ca26483cd993/ruff-0.15.2-py3-none-win32.whl", hash = "sha256:fd5ff9e5f519a7e1bd99cbe8daa324010a74f5e2ebc97c6242c08f26f3714f6f", size = 10507885, upload-time = "2026-02-19T22:32:15.635Z" }, + { url = "https://files.pythonhosted.org/packages/5c/10/3d18e3bbdf8fc50bbb4ac3cc45970aa5a9753c5cb51bf9ed9a3cd8b79fa3/ruff-0.15.2-py3-none-win_amd64.whl", hash = "sha256:d20014e3dfa400f3ff84830dfb5755ece2de45ab62ecea4af6b7262d0fb4f7c5", size = 11623725, upload-time = "2026-02-19T22:32:04.947Z" }, + { url = "https://files.pythonhosted.org/packages/6d/78/097c0798b1dab9f8affe73da9642bb4500e098cb27fd8dc9724816ac747b/ruff-0.15.2-py3-none-win_arm64.whl", hash = "sha256:cabddc5822acdc8f7b5527b36ceac55cc51eec7b1946e60181de8fe83ca8876e", size = 10941649, upload-time = "2026-02-19T22:32:18.108Z" }, +] + +[[package]] +name = "s3transfer" +version = "0.16.0" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "botocore" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/05/04/74127fc843314818edfa81b5540e26dd537353b123a4edc563109d8f17dd/s3transfer-0.16.0.tar.gz", hash = "sha256:8e990f13268025792229cd52fa10cb7163744bf56e719e0b9cb925ab79abf920", size = 153827, upload-time = "2025-12-01T02:30:59.114Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/fc/51/727abb13f44c1fcf6d145979e1535a35794db0f6e450a0cb46aa24732fe2/s3transfer-0.16.0-py3-none-any.whl", hash = "sha256:18e25d66fed509e3868dc1572b3f427ff947dd2c56f844a5bf09481ad3f3b2fe", size = 86830, upload-time = "2025-12-01T02:30:57.729Z" }, +] + +[[package]] +name = "setuptools" +version = "82.0.0" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/82/f3/748f4d6f65d1756b9ae577f329c951cda23fb900e4de9f70900ced962085/setuptools-82.0.0.tar.gz", hash = "sha256:22e0a2d69474c6ae4feb01951cb69d515ed23728cf96d05513d36e42b62b37cb", size = 1144893, upload-time = "2026-02-08T15:08:40.206Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/e1/c6/76dc613121b793286a3f91621d7b75a2b493e0390ddca50f11993eadf192/setuptools-82.0.0-py3-none-any.whl", hash = "sha256:70b18734b607bd1da571d097d236cfcfacaf01de45717d59e6e04b96877532e0", size = 1003468, upload-time = "2026-02-08T15:08:38.723Z" }, +] + +[[package]] +name = "six" +version = "1.17.0" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/94/e7/b2c673351809dca68a0e064b6af791aa332cf192da575fd474ed7d6f16a2/six-1.17.0.tar.gz", hash = "sha256:ff70335d468e7eb6ec65b95b99d3a2836546063f63acc5171de367e834932a81", size = 34031, upload-time = "2024-12-04T17:35:28.174Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/b7/ce/149a00dd41f10bc29e5921b496af8b574d8413afcd5e30dfa0ed46c2cc5e/six-1.17.0-py2.py3-none-any.whl", hash = "sha256:4721f391ed90541fddacab5acf947aa0d3dc7d27b2e1e8eda2be8970586c3274", size = 11050, upload-time = "2024-12-04T17:35:26.475Z" }, +] + +[[package]] +name = "softasp" +version = "0.1.0" +source = { editable = "." } +dependencies = [ + { name = "aim" }, + { name = "hydra-core" }, + { name = "omegaconf" }, + { name = "rich" }, + { name = "softasp-core" }, + { name = "softasp-domain" }, + { name = "softasp-solver" }, +] + +[package.dev-dependencies] +dev = [ + { name = "pyright" }, + { name = "pytest" }, + { name = "ruff" }, +] + +[package.metadata] +requires-dist = [ + { name = "aim", specifier = ">=3.29.1" }, + { name = "hydra-core", specifier = ">=1.3" }, + { name = "omegaconf", specifier = ">=2.3" }, + { name = "rich", specifier = ">=13.0" }, + { name = "softasp-core", editable = "packages/softasp-core" }, + { name = "softasp-domain", editable = "packages/softasp-domain" }, + { name = "softasp-solver", editable = "packages/softasp-solver" }, +] + +[package.metadata.requires-dev] +dev = [ + { name = "pyright", specifier = ">=1.1" }, + { name = "pytest", specifier = ">=8.0" }, + { name = "ruff", specifier = ">=0.8" }, +] + +[[package]] +name = "softasp-core" +version = "0.1.0" +source = { editable = "packages/softasp-core" } +dependencies = [ + { name = "aim" }, + { name = "h5py" }, + { name = "hydra-core" }, + { name = "numpy" }, + { name = "omegaconf" }, + { name = "requests" }, + { name = "rich" }, + { name = "torch" }, +] + +[package.metadata] +requires-dist = [ + { name = "aim", specifier = ">=3.19" }, + { name = "h5py", specifier = ">=3.10" }, + { name = "hydra-core", specifier = ">=1.3" }, + { name = "numpy", specifier = ">=1.26" }, + { name = "omegaconf", specifier = ">=2.3" }, + { name = "requests", specifier = ">=2.31" }, + { name = "rich", specifier = ">=13.0" }, + { name = "torch", specifier = ">=2.2" }, +] + +[[package]] +name = "softasp-domain" +version = "0.1.0" +source = { editable = "packages/softasp-domain" } +dependencies = [ + { name = "certifi" }, + { name = "clingo" }, + { name = "pillow" }, + { name = "requests" }, + { name = "softasp-core" }, + { name = "torch" }, + { name = "torchvision" }, +] + +[package.metadata] +requires-dist = [ + { name = "certifi", specifier = ">=2024.0" }, + { name = "clingo", specifier = ">=5.7" }, + { name = "pillow", specifier = ">=10.0" }, + { name = "requests", specifier = ">=2.31" }, + { name = "softasp-core", editable = "packages/softasp-core" }, + { name = "torch", specifier = ">=2.2" }, + { name = "torchvision", specifier = ">=0.17" }, +] + +[[package]] +name = "softasp-solver" +version = "0.1.0" +source = { editable = "packages/softasp-solver" } +dependencies = [ + { name = "softasp-core" }, + { name = "torch" }, +] + +[package.metadata] +requires-dist = [ + { name = "softasp-core", editable = "packages/softasp-core" }, + { name = "torch", specifier = ">=2.2" }, +] + +[[package]] +name = "sqlalchemy" +version = "2.0.47" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "greenlet", marker = "platform_machine == 'AMD64' or platform_machine == 'WIN32' or platform_machine == 'aarch64' or platform_machine == 'amd64' or platform_machine == 'ppc64le' or platform_machine == 'win32' or platform_machine == 'x86_64'" }, + { name = "typing-extensions" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/cd/4b/1e00561093fe2cd8eef09d406da003c8a118ff02d6548498c1ae677d68d9/sqlalchemy-2.0.47.tar.gz", hash = "sha256:e3e7feb57b267fe897e492b9721ae46d5c7de6f9e8dee58aacf105dc4e154f3d", size = 9886323, upload-time = "2026-02-24T16:34:27.947Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/80/88/74eb470223ff88ea6572a132c0b8de8c1d8ed7b843d3b44a8a3c77f31d39/sqlalchemy-2.0.47-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:4fa91b19d6b9821c04cc8f7aa2476429cc8887b9687c762815aa629f5c0edec1", size = 2155687, upload-time = "2026-02-24T17:05:46.451Z" }, + { url = "https://files.pythonhosted.org/packages/ef/ba/1447d3d558971b036cb93b557595cb5dcdfe728f1c7ac4dec16505ef5756/sqlalchemy-2.0.47-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:7c5bbbd14eff577c8c79cbfe39a0771eecd20f430f3678533476f0087138f356", size = 3336978, upload-time = "2026-02-24T17:18:04.597Z" }, + { url = "https://files.pythonhosted.org/packages/8a/07/b47472d2ffd0776826f17ccf0b4d01b224c99fbd1904aeb103dffbb4b1cc/sqlalchemy-2.0.47-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:a5a6c555da8d4280a3c4c78c5b7a3f990cee2b2884e5f934f87a226191682ff7", size = 3349939, upload-time = "2026-02-24T17:27:18.937Z" }, + { url = "https://files.pythonhosted.org/packages/bb/c6/95fa32b79b57769da3e16f054cf658d90940317b5ca0ec20eac84aa19c4f/sqlalchemy-2.0.47-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:ed48a1701d24dff3bb49a5bce94d6bc84cbe33d98af2aa2d3cdcce3dea1709ec", size = 3279648, upload-time = "2026-02-24T17:18:07.038Z" }, + { url = "https://files.pythonhosted.org/packages/bb/c8/3d07e7c73928dc59a0bed40961ca4e313e797bce650b088e8d5fdd3ad939/sqlalchemy-2.0.47-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:4f3178c920ad98158f0b6309382194df04b14808fa6052ae07099fdde29d5602", size = 3314695, upload-time = "2026-02-24T17:27:20.93Z" }, + { url = "https://files.pythonhosted.org/packages/6b/d2/ed32b1611c1e19fdb028eee1adc5a9aa138c2952d09ae11f1670170f80ae/sqlalchemy-2.0.47-cp312-cp312-win32.whl", hash = "sha256:b9c11ac9934dd59ece9619fe42780a08abe2faab7b0543bb00d5eabea4f421b9", size = 2115502, upload-time = "2026-02-24T17:22:52.546Z" }, + { url = "https://files.pythonhosted.org/packages/fd/52/9de590356a4dd8e9ef5a881dbba64b2bbc4cbc71bf02bc68e775fb9b1899/sqlalchemy-2.0.47-cp312-cp312-win_amd64.whl", hash = "sha256:db43b72cf8274a99e089755c9c1e0b947159b71adbc2c83c3de2e38d5d607acb", size = 2142435, upload-time = "2026-02-24T17:22:54.268Z" }, + { url = "https://files.pythonhosted.org/packages/4a/e5/0af64ce7d8f60ec5328c10084e2f449e7912a9b8bdbefdcfb44454a25f49/sqlalchemy-2.0.47-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:456a135b790da5d3c6b53d0ef71ac7b7d280b7f41eb0c438986352bf03ca7143", size = 2152551, upload-time = "2026-02-24T17:05:47.675Z" }, + { url = "https://files.pythonhosted.org/packages/63/79/746b8d15f6940e2ac469ce22d7aa5b1124b1ab820bad9b046eb3000c88a6/sqlalchemy-2.0.47-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:09a2f7698e44b3135433387da5d8846cf7cc7c10e5425af7c05fee609df978b6", size = 3278782, upload-time = "2026-02-24T17:18:10.012Z" }, + { url = "https://files.pythonhosted.org/packages/91/b1/bd793ddb34345d1ed43b13ab2d88c95d7d4eb2e28f5b5a99128b9cc2bca2/sqlalchemy-2.0.47-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:a0bbc72e6a177c78d724f9106aaddc0d26a2ada89c6332b5935414eccf04cbd5", size = 3295155, upload-time = "2026-02-24T17:27:22.827Z" }, + { url = "https://files.pythonhosted.org/packages/97/84/7213def33f94e5ca6f5718d259bc9f29de0363134648425aa218d4356b23/sqlalchemy-2.0.47-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:75460456b043b78b6006e41bdf5b86747ee42eafaf7fffa3b24a6e9a456a2092", size = 3226834, upload-time = "2026-02-24T17:18:11.465Z" }, + { url = "https://files.pythonhosted.org/packages/ef/06/456810204f4dc29b5f025b1b0a03b4bd6b600ebf3c1040aebd90a257fa33/sqlalchemy-2.0.47-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:5d9adaa616c3bc7d80f9ded57cd84b51d6617cad6a5456621d858c9f23aaee01", size = 3265001, upload-time = "2026-02-24T17:27:24.813Z" }, + { url = "https://files.pythonhosted.org/packages/fb/20/df3920a4b2217dbd7390a5bd277c1902e0393f42baaf49f49b3c935e7328/sqlalchemy-2.0.47-cp313-cp313-win32.whl", hash = "sha256:76e09f974382a496a5ed985db9343628b1cb1ac911f27342e4cc46a8bac10476", size = 2113647, upload-time = "2026-02-24T17:22:55.747Z" }, + { url = "https://files.pythonhosted.org/packages/46/06/7873ddf69918efbfabd7211829f4bd8019739d0a719253112d305d3ba51d/sqlalchemy-2.0.47-cp313-cp313-win_amd64.whl", hash = "sha256:0664089b0bf6724a0bfb49a0cf4d4da24868a0a5c8e937cd7db356d5dcdf2c66", size = 2139425, upload-time = "2026-02-24T17:22:57.033Z" }, + { url = "https://files.pythonhosted.org/packages/54/fa/61ad9731370c90ac7ea5bf8f5eaa12c48bb4beec41c0fa0360becf4ac10d/sqlalchemy-2.0.47-cp313-cp313t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:ed0c967c701ae13da98eb220f9ddab3044ab63504c1ba24ad6a59b26826ad003", size = 3558809, upload-time = "2026-02-24T17:12:15.232Z" }, + { url = "https://files.pythonhosted.org/packages/33/d5/221fac96f0529391fe374875633804c866f2b21a9c6d3a6ca57d9c12cfd7/sqlalchemy-2.0.47-cp313-cp313t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:d3537943a61fd25b241e976426a0c6814434b93cf9b09d39e8e78f3c9eb9a487", size = 3525480, upload-time = "2026-02-24T17:27:59.602Z" }, + { url = "https://files.pythonhosted.org/packages/ec/55/8247d53998c3673e4a8d1958eba75c6f5cc3b39082029d400bb1f2a911ae/sqlalchemy-2.0.47-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:57f7e336a64a0dba686c66392d46b9bc7af2c57d55ce6dc1697b4ef32b043ceb", size = 3466569, upload-time = "2026-02-24T17:12:16.94Z" }, + { url = "https://files.pythonhosted.org/packages/6b/b5/c1f0eea1bac6790845f71420a7fe2f2a0566203aa57543117d4af3b77d1c/sqlalchemy-2.0.47-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:dff735a621858680217cb5142b779bad40ef7322ddbb7c12062190db6879772e", size = 3475770, upload-time = "2026-02-24T17:28:02.034Z" }, + { url = "https://files.pythonhosted.org/packages/c5/ed/2f43f92474ea0c43c204657dc47d9d002cd738b96ca2af8e6d29a9b5e42d/sqlalchemy-2.0.47-cp313-cp313t-win32.whl", hash = "sha256:3893dc096bb3cca9608ea3487372ffcea3ae9b162f40e4d3c51dd49db1d1b2dc", size = 2141300, upload-time = "2026-02-24T17:14:37.024Z" }, + { url = "https://files.pythonhosted.org/packages/cc/a9/8b73f9f1695b6e92f7aaf1711135a1e3bbeb78bca9eded35cb79180d3c6d/sqlalchemy-2.0.47-cp313-cp313t-win_amd64.whl", hash = "sha256:b5103427466f4b3e61f04833ae01f9a914b1280a2a8bcde3a9d7ab11f3755b42", size = 2173053, upload-time = "2026-02-24T17:14:38.688Z" }, + { url = "https://files.pythonhosted.org/packages/c1/30/98243209aae58ed80e090ea988d5182244ca7ab3ff59e6d850c3dfc7651e/sqlalchemy-2.0.47-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:b03010a5a5dfe71676bc83f2473ebe082478e32d77e6f082c8fe15a31c3b42a6", size = 2154355, upload-time = "2026-02-24T17:05:48.959Z" }, + { url = "https://files.pythonhosted.org/packages/ab/62/12ca6ea92055fe486d6558a2a4efe93e194ff597463849c01f88e5adb99d/sqlalchemy-2.0.47-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:f8e3371aa9024520883a415a09cc20c33cfd3eeccf9e0f4f4c367f940b9cbd44", size = 3274486, upload-time = "2026-02-24T17:18:13.659Z" }, + { url = "https://files.pythonhosted.org/packages/97/88/7dfbdeaa8d42b1584e65d6cc713e9d33b6fa563e0d546d5cb87e545bb0e5/sqlalchemy-2.0.47-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:c9449f747e50d518c6e1b40cc379e48bfc796453c47b15e627ea901c201e48a6", size = 3279481, upload-time = "2026-02-24T17:27:26.491Z" }, + { url = "https://files.pythonhosted.org/packages/d0/b7/75e1c1970616a9dd64a8a6fd788248da2ddaf81c95f4875f2a1e8aee4128/sqlalchemy-2.0.47-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:21410f60d5cac1d6bfe360e05bd91b179be4fa0aa6eea6be46054971d277608f", size = 3224269, upload-time = "2026-02-24T17:18:15.078Z" }, + { url = "https://files.pythonhosted.org/packages/31/ac/eec1a13b891df9a8bc203334caf6e6aac60b02f61b018ef3b4124b8c4120/sqlalchemy-2.0.47-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:819841dd5bb4324c284c09e2874cf96fe6338bfb57a64548d9b81a4e39c9871f", size = 3246262, upload-time = "2026-02-24T17:27:27.986Z" }, + { url = "https://files.pythonhosted.org/packages/c9/b0/661b0245b06421058610da39f8ceb34abcc90b49f90f256380968d761dbe/sqlalchemy-2.0.47-cp314-cp314-win32.whl", hash = "sha256:e255ee44821a7ef45649c43064cf94e74f81f61b4df70547304b97a351e9b7db", size = 2116528, upload-time = "2026-02-24T17:22:59.363Z" }, + { url = "https://files.pythonhosted.org/packages/aa/ef/1035a90d899e61810791c052004958be622a2cf3eb3df71c3fe20778c5d0/sqlalchemy-2.0.47-cp314-cp314-win_amd64.whl", hash = "sha256:209467ff73ea1518fe1a5aaed9ba75bb9e33b2666e2553af9ccd13387bf192cb", size = 2142181, upload-time = "2026-02-24T17:23:01.001Z" }, + { url = "https://files.pythonhosted.org/packages/76/bb/17a1dd09cbba91258218ceb582225f14b5364d2683f9f5a274f72f2d764f/sqlalchemy-2.0.47-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:e78fd9186946afaa287f8a1fe147ead06e5d566b08c0afcb601226e9c7322a64", size = 3563477, upload-time = "2026-02-24T17:12:18.46Z" }, + { url = "https://files.pythonhosted.org/packages/66/8f/1a03d24c40cc321ef2f2231f05420d140bb06a84f7047eaa7eaa21d230ba/sqlalchemy-2.0.47-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:5740e2f31b5987ed9619d6912ae5b750c03637f2078850da3002934c9532f172", size = 3528568, upload-time = "2026-02-24T17:28:03.732Z" }, + { url = "https://files.pythonhosted.org/packages/fd/53/d56a213055d6b038a5384f0db5ece7343334aca230ff3f0fa1561106f22c/sqlalchemy-2.0.47-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:fb9ac00d03de93acb210e8ec7243fefe3e012515bf5fd2f0898c8dff38bc77a4", size = 3472284, upload-time = "2026-02-24T17:12:20.319Z" }, + { url = "https://files.pythonhosted.org/packages/ff/19/c235d81b9cfdd6130bf63143b7bade0dc4afa46c4b634d5d6b2a96bea233/sqlalchemy-2.0.47-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:c72a0b9eb2672d70d112cb149fbaf172d466bc691014c496aaac594f1988e706", size = 3478410, upload-time = "2026-02-24T17:28:05.892Z" }, + { url = "https://files.pythonhosted.org/packages/0e/db/cafdeca5ecdaa3bb0811ba5449501da677ce0d83be8d05c5822da72d2e86/sqlalchemy-2.0.47-cp314-cp314t-win32.whl", hash = "sha256:c200db1128d72a71dc3c31c24b42eb9fd85b2b3e5a3c9ba1e751c11ac31250ff", size = 2147164, upload-time = "2026-02-24T17:14:40.783Z" }, + { url = "https://files.pythonhosted.org/packages/fc/5e/ff41a010e9e0f76418b02ad352060a4341bb15f0af66cedc924ab376c7c6/sqlalchemy-2.0.47-cp314-cp314t-win_amd64.whl", hash = "sha256:669837759b84e575407355dcff912835892058aea9b80bd1cb76d6a151cf37f7", size = 2182154, upload-time = "2026-02-24T17:14:43.205Z" }, + { url = "https://files.pythonhosted.org/packages/15/9f/7c378406b592fcf1fc157248607b495a40e3202ba4a6f1372a2ba6447717/sqlalchemy-2.0.47-py3-none-any.whl", hash = "sha256:e2647043599297a1ef10e720cf310846b7f31b6c841fee093d2b09d81215eb93", size = 1940159, upload-time = "2026-02-24T17:15:07.158Z" }, +] + +[[package]] +name = "starlette" +version = "0.52.1" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "anyio" }, + { name = "typing-extensions", marker = "python_full_version < '3.13'" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/c4/68/79977123bb7be889ad680d79a40f339082c1978b5cfcf62c2d8d196873ac/starlette-0.52.1.tar.gz", hash = "sha256:834edd1b0a23167694292e94f597773bc3f89f362be6effee198165a35d62933", size = 2653702, upload-time = "2026-01-18T13:34:11.062Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/81/0d/13d1d239a25cbfb19e740db83143e95c772a1fe10202dda4b76792b114dd/starlette-0.52.1-py3-none-any.whl", hash = "sha256:0029d43eb3d273bc4f83a08720b4912ea4b071087a3b48db01b7c839f7954d74", size = 74272, upload-time = "2026-01-18T13:34:09.188Z" }, +] + +[[package]] +name = "sympy" +version = "1.14.0" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "mpmath" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/83/d3/803453b36afefb7c2bb238361cd4ae6125a569b4db67cd9e79846ba2d68c/sympy-1.14.0.tar.gz", hash = "sha256:d3d3fe8df1e5a0b42f0e7bdf50541697dbe7d23746e894990c030e2b05e72517", size = 7793921, upload-time = "2025-04-27T18:05:01.611Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/a2/09/77d55d46fd61b4a135c444fc97158ef34a095e5681d0a6c10b75bf356191/sympy-1.14.0-py3-none-any.whl", hash = "sha256:e091cc3e99d2141a0ba2847328f5479b05d94a6635cb96148ccb3f34671bd8f5", size = 6299353, upload-time = "2025-04-27T18:04:59.103Z" }, +] + +[[package]] +name = "torch" +version = "2.10.0" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "cuda-bindings", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, + { name = "filelock" }, + { name = "fsspec" }, + { name = "jinja2" }, + { name = "networkx" }, + { name = "nvidia-cublas-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, + { name = "nvidia-cuda-cupti-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, + { name = "nvidia-cuda-nvrtc-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, + { name = "nvidia-cuda-runtime-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, + { name = "nvidia-cudnn-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, + { name = "nvidia-cufft-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, + { name = "nvidia-cufile-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, + { name = "nvidia-curand-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, + { name = "nvidia-cusolver-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, + { name = "nvidia-cusparse-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, + { name = "nvidia-cusparselt-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, + { name = "nvidia-nccl-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, + { name = "nvidia-nvjitlink-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, + { name = "nvidia-nvshmem-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, + { name = "nvidia-nvtx-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, + { name = "setuptools" }, + { name = "sympy" }, + { name = "triton", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, + { name = "typing-extensions" }, +] +wheels = [ + { url = "https://files.pythonhosted.org/packages/d3/54/a2ba279afcca44bbd320d4e73675b282fcee3d81400ea1b53934efca6462/torch-2.10.0-2-cp312-none-macosx_11_0_arm64.whl", hash = "sha256:13ec4add8c3faaed8d13e0574f5cd4a323c11655546f91fbe6afa77b57423574", size = 79498202, upload-time = "2026-02-10T21:44:52.603Z" }, + { url = "https://files.pythonhosted.org/packages/ec/23/2c9fe0c9c27f7f6cb865abcea8a4568f29f00acaeadfc6a37f6801f84cb4/torch-2.10.0-2-cp313-none-macosx_11_0_arm64.whl", hash = "sha256:e521c9f030a3774ed770a9c011751fb47c4d12029a3d6522116e48431f2ff89e", size = 79498254, upload-time = "2026-02-10T21:44:44.095Z" }, + { url = "https://files.pythonhosted.org/packages/b3/7a/abada41517ce0011775f0f4eacc79659bc9bc6c361e6bfe6f7052a6b9363/torch-2.10.0-3-cp312-cp312-manylinux_2_28_x86_64.whl", hash = "sha256:98c01b8bb5e3240426dcde1446eed6f40c778091c8544767ef1168fc663a05a6", size = 915622781, upload-time = "2026-03-11T14:17:11.354Z" }, + { url = "https://files.pythonhosted.org/packages/ab/c6/4dfe238342ffdcec5aef1c96c457548762d33c40b45a1ab7033bb26d2ff2/torch-2.10.0-3-cp313-cp313-manylinux_2_28_x86_64.whl", hash = "sha256:80b1b5bfe38eb0e9f5ff09f206dcac0a87aadd084230d4a36eea5ec5232c115b", size = 915627275, upload-time = "2026-03-11T14:16:11.325Z" }, + { url = "https://files.pythonhosted.org/packages/d8/f0/72bf18847f58f877a6a8acf60614b14935e2f156d942483af1ffc081aea0/torch-2.10.0-3-cp313-cp313t-manylinux_2_28_x86_64.whl", hash = "sha256:46b3574d93a2a8134b3f5475cfb98e2eb46771794c57015f6ad1fb795ec25e49", size = 915523474, upload-time = "2026-03-11T14:17:44.422Z" }, + { url = "https://files.pythonhosted.org/packages/f4/39/590742415c3030551944edc2ddc273ea1fdfe8ffb2780992e824f1ebee98/torch-2.10.0-3-cp314-cp314-manylinux_2_28_x86_64.whl", hash = "sha256:b1d5e2aba4eb7f8e87fbe04f86442887f9167a35f092afe4c237dfcaaef6e328", size = 915632474, upload-time = "2026-03-11T14:15:13.666Z" }, + { url = "https://files.pythonhosted.org/packages/b6/8e/34949484f764dde5b222b7fe3fede43e4a6f0da9d7f8c370bb617d629ee2/torch-2.10.0-3-cp314-cp314t-manylinux_2_28_x86_64.whl", hash = "sha256:0228d20b06701c05a8f978357f657817a4a63984b0c90745def81c18aedfa591", size = 915523882, upload-time = "2026-03-11T14:14:46.311Z" }, + { url = "https://files.pythonhosted.org/packages/cc/af/758e242e9102e9988969b5e621d41f36b8f258bb4a099109b7a4b4b50ea4/torch-2.10.0-cp312-cp312-manylinux_2_28_aarch64.whl", hash = "sha256:5fd4117d89ffd47e3dcc71e71a22efac24828ad781c7e46aaaf56bf7f2796acf", size = 145996088, upload-time = "2026-01-21T16:24:44.171Z" }, + { url = "https://files.pythonhosted.org/packages/23/8e/3c74db5e53bff7ed9e34c8123e6a8bfef718b2450c35eefab85bb4a7e270/torch-2.10.0-cp312-cp312-manylinux_2_28_x86_64.whl", hash = "sha256:787124e7db3b379d4f1ed54dd12ae7c741c16a4d29b49c0226a89bea50923ffb", size = 915711952, upload-time = "2026-01-21T16:23:53.503Z" }, + { url = "https://files.pythonhosted.org/packages/6e/01/624c4324ca01f66ae4c7cd1b74eb16fb52596dce66dbe51eff95ef9e7a4c/torch-2.10.0-cp312-cp312-win_amd64.whl", hash = "sha256:2c66c61f44c5f903046cc696d088e21062644cbe541c7f1c4eaae88b2ad23547", size = 113757972, upload-time = "2026-01-21T16:24:39.516Z" }, + { url = "https://files.pythonhosted.org/packages/c9/5c/dee910b87c4d5c0fcb41b50839ae04df87c1cfc663cf1b5fca7ea565eeaa/torch-2.10.0-cp312-none-macosx_11_0_arm64.whl", hash = "sha256:6d3707a61863d1c4d6ebba7be4ca320f42b869ee657e9b2c21c736bf17000294", size = 79498198, upload-time = "2026-01-21T16:24:34.704Z" }, + { url = "https://files.pythonhosted.org/packages/c9/6f/f2e91e34e3fcba2e3fc8d8f74e7d6c22e74e480bbd1db7bc8900fdf3e95c/torch-2.10.0-cp313-cp313-manylinux_2_28_aarch64.whl", hash = "sha256:5c4d217b14741e40776dd7074d9006fd28b8a97ef5654db959d8635b2fe5f29b", size = 146004247, upload-time = "2026-01-21T16:24:29.335Z" }, + { url = "https://files.pythonhosted.org/packages/98/fb/5160261aeb5e1ee12ee95fe599d0541f7c976c3701d607d8fc29e623229f/torch-2.10.0-cp313-cp313-manylinux_2_28_x86_64.whl", hash = "sha256:6b71486353fce0f9714ca0c9ef1c850a2ae766b409808acd58e9678a3edb7738", size = 915716445, upload-time = "2026-01-21T16:22:45.353Z" }, + { url = "https://files.pythonhosted.org/packages/6a/16/502fb1b41e6d868e8deb5b0e3ae926bbb36dab8ceb0d1b769b266ad7b0c3/torch-2.10.0-cp313-cp313-win_amd64.whl", hash = "sha256:c2ee399c644dc92ef7bc0d4f7e74b5360c37cdbe7c5ba11318dda49ffac2bc57", size = 113757050, upload-time = "2026-01-21T16:24:19.204Z" }, + { url = "https://files.pythonhosted.org/packages/1a/0b/39929b148f4824bc3ad6f9f72a29d4ad865bcf7ebfc2fa67584773e083d2/torch-2.10.0-cp313-cp313t-macosx_14_0_arm64.whl", hash = "sha256:3202429f58309b9fa96a614885eace4b7995729f44beb54d3e4a47773649d382", size = 79851305, upload-time = "2026-01-21T16:24:09.209Z" }, + { url = "https://files.pythonhosted.org/packages/d8/14/21fbce63bc452381ba5f74a2c0a959fdf5ad5803ccc0c654e752e0dbe91a/torch-2.10.0-cp313-cp313t-manylinux_2_28_aarch64.whl", hash = "sha256:aae1b29cd68e50a9397f5ee897b9c24742e9e306f88a807a27d617f07adb3bd8", size = 146005472, upload-time = "2026-01-21T16:22:29.022Z" }, + { url = "https://files.pythonhosted.org/packages/54/fd/b207d1c525cb570ef47f3e9f836b154685011fce11a2f444ba8a4084d042/torch-2.10.0-cp313-cp313t-manylinux_2_28_x86_64.whl", hash = "sha256:6021db85958db2f07ec94e1bc77212721ba4920c12a18dc552d2ae36a3eb163f", size = 915612644, upload-time = "2026-01-21T16:21:47.019Z" }, + { url = "https://files.pythonhosted.org/packages/36/53/0197f868c75f1050b199fe58f9bf3bf3aecac9b4e85cc9c964383d745403/torch-2.10.0-cp313-cp313t-win_amd64.whl", hash = "sha256:ff43db38af76fda183156153983c9a096fc4c78d0cd1e07b14a2314c7f01c2c8", size = 113997015, upload-time = "2026-01-21T16:23:00.767Z" }, + { url = "https://files.pythonhosted.org/packages/0e/13/e76b4d9c160e89fff48bf16b449ea324bda84745d2ab30294c37c2434c0d/torch-2.10.0-cp313-none-macosx_11_0_arm64.whl", hash = "sha256:cdf2a523d699b70d613243211ecaac14fe9c5df8a0b0a9c02add60fb2a413e0f", size = 79498248, upload-time = "2026-01-21T16:23:09.315Z" }, + { url = "https://files.pythonhosted.org/packages/4f/93/716b5ac0155f1be70ed81bacc21269c3ece8dba0c249b9994094110bfc51/torch-2.10.0-cp314-cp314-macosx_14_0_arm64.whl", hash = "sha256:bf0d9ff448b0218e0433aeb198805192346c4fd659c852370d5cc245f602a06a", size = 79464992, upload-time = "2026-01-21T16:23:05.162Z" }, + { url = "https://files.pythonhosted.org/packages/69/2b/51e663ff190c9d16d4a8271203b71bc73a16aa7619b9f271a69b9d4a936b/torch-2.10.0-cp314-cp314-manylinux_2_28_aarch64.whl", hash = "sha256:233aed0659a2503b831d8a67e9da66a62c996204c0bba4f4c442ccc0c68a3f60", size = 146018567, upload-time = "2026-01-21T16:22:23.393Z" }, + { url = "https://files.pythonhosted.org/packages/5e/cd/4b95ef7f293b927c283db0b136c42be91c8ec6845c44de0238c8c23bdc80/torch-2.10.0-cp314-cp314-manylinux_2_28_x86_64.whl", hash = "sha256:682497e16bdfa6efeec8cde66531bc8d1fbbbb4d8788ec6173c089ed3cc2bfe5", size = 915721646, upload-time = "2026-01-21T16:21:16.983Z" }, + { url = "https://files.pythonhosted.org/packages/56/97/078a007208f8056d88ae43198833469e61a0a355abc0b070edd2c085eb9a/torch-2.10.0-cp314-cp314-win_amd64.whl", hash = "sha256:6528f13d2a8593a1a412ea07a99812495bec07e9224c28b2a25c0a30c7da025c", size = 113752373, upload-time = "2026-01-21T16:22:13.471Z" }, + { url = "https://files.pythonhosted.org/packages/d8/94/71994e7d0d5238393df9732fdab607e37e2b56d26a746cb59fdb415f8966/torch-2.10.0-cp314-cp314t-macosx_14_0_arm64.whl", hash = "sha256:f5ab4ba32383061be0fb74bda772d470140a12c1c3b58a0cfbf3dae94d164c28", size = 79850324, upload-time = "2026-01-21T16:22:09.494Z" }, + { url = "https://files.pythonhosted.org/packages/e2/65/1a05346b418ea8ccd10360eef4b3e0ce688fba544e76edec26913a8d0ee0/torch-2.10.0-cp314-cp314t-manylinux_2_28_aarch64.whl", hash = "sha256:716b01a176c2a5659c98f6b01bf868244abdd896526f1c692712ab36dbaf9b63", size = 146006482, upload-time = "2026-01-21T16:22:18.42Z" }, + { url = "https://files.pythonhosted.org/packages/1d/b9/5f6f9d9e859fc3235f60578fa64f52c9c6e9b4327f0fe0defb6de5c0de31/torch-2.10.0-cp314-cp314t-manylinux_2_28_x86_64.whl", hash = "sha256:d8f5912ba938233f86361e891789595ff35ca4b4e2ac8fe3670895e5976731d6", size = 915613050, upload-time = "2026-01-21T16:20:49.035Z" }, + { url = "https://files.pythonhosted.org/packages/66/4d/35352043ee0eaffdeff154fad67cd4a31dbed7ff8e3be1cc4549717d6d51/torch-2.10.0-cp314-cp314t-win_amd64.whl", hash = "sha256:71283a373f0ee2c89e0f0d5f446039bdabe8dbc3c9ccf35f0f784908b0acd185", size = 113995816, upload-time = "2026-01-21T16:22:05.312Z" }, +] + +[[package]] +name = "torchvision" +version = "0.25.0" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "numpy" }, + { name = "pillow" }, + { name = "torch" }, +] +wheels = [ + { url = "https://files.pythonhosted.org/packages/56/3a/6ea0d73f49a9bef38a1b3a92e8dd455cea58470985d25635beab93841748/torchvision-0.25.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:c2abe430c90b1d5e552680037d68da4eb80a5852ebb1c811b2b89d299b10573b", size = 1874920, upload-time = "2026-01-21T16:27:45.348Z" }, + { url = "https://files.pythonhosted.org/packages/51/f8/c0e1ef27c66e15406fece94930e7d6feee4cb6374bbc02d945a630d6426e/torchvision-0.25.0-cp312-cp312-manylinux_2_28_aarch64.whl", hash = "sha256:b75deafa2dfea3e2c2a525559b04783515e3463f6e830cb71de0fb7ea36fe233", size = 2344556, upload-time = "2026-01-21T16:27:40.125Z" }, + { url = "https://files.pythonhosted.org/packages/68/2f/f24b039169db474e8688f649377de082a965fbf85daf4e46c44412f1d15a/torchvision-0.25.0-cp312-cp312-manylinux_2_28_x86_64.whl", hash = "sha256:f25aa9e380865b11ea6e9d99d84df86b9cc959f1a007cd966fc6f1ab2ed0e248", size = 8072351, upload-time = "2026-01-21T16:27:21.074Z" }, + { url = "https://files.pythonhosted.org/packages/ad/16/8f650c2e288977cf0f8f85184b90ee56ed170a4919347fc74ee99286ed6f/torchvision-0.25.0-cp312-cp312-win_amd64.whl", hash = "sha256:f9c55ae8d673ab493325d1267cbd285bb94d56f99626c00ac4644de32a59ede3", size = 4303059, upload-time = "2026-01-21T16:27:11.08Z" }, + { url = "https://files.pythonhosted.org/packages/f5/5b/1562a04a6a5a4cf8cf40016a0cdeda91ede75d6962cff7f809a85ae966a5/torchvision-0.25.0-cp313-cp313-macosx_12_0_arm64.whl", hash = "sha256:24e11199e4d84ba9c5ee7825ebdf1cd37ce8deec225117f10243cae984ced3ec", size = 1874918, upload-time = "2026-01-21T16:27:39.02Z" }, + { url = "https://files.pythonhosted.org/packages/36/b1/3d6c42f62c272ce34fcce609bb8939bdf873dab5f1b798fd4e880255f129/torchvision-0.25.0-cp313-cp313-manylinux_2_28_aarch64.whl", hash = "sha256:5f271136d2d2c0b7a24c5671795c6e4fd8da4e0ea98aeb1041f62bc04c4370ef", size = 2309106, upload-time = "2026-01-21T16:27:30.624Z" }, + { url = "https://files.pythonhosted.org/packages/c7/60/59bb9c8b67cce356daeed4cb96a717caa4f69c9822f72e223a0eae7a9bd9/torchvision-0.25.0-cp313-cp313-manylinux_2_28_x86_64.whl", hash = "sha256:855c0dc6d37f462482da7531c6788518baedca1e0847f3df42a911713acdfe52", size = 8071522, upload-time = "2026-01-21T16:27:29.392Z" }, + { url = "https://files.pythonhosted.org/packages/32/a5/9a9b1de0720f884ea50dbf9acb22cbe5312e51d7b8c4ac6ba9b51efd9bba/torchvision-0.25.0-cp313-cp313-win_amd64.whl", hash = "sha256:cef0196be31be421f6f462d1e9da1101be7332d91984caa6f8022e6c78a5877f", size = 4321911, upload-time = "2026-01-21T16:27:35.195Z" }, + { url = "https://files.pythonhosted.org/packages/52/99/dca81ed21ebaeff2b67cc9f815a20fdaa418b69f5f9ea4c6ed71721470db/torchvision-0.25.0-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:a8f8061284395ce31bcd460f2169013382ccf411148ceb2ee38e718e9860f5a7", size = 1896209, upload-time = "2026-01-21T16:27:32.159Z" }, + { url = "https://files.pythonhosted.org/packages/28/cc/2103149761fdb4eaed58a53e8437b2d716d48f05174fab1d9fcf1e2a2244/torchvision-0.25.0-cp313-cp313t-manylinux_2_28_aarch64.whl", hash = "sha256:146d02c9876858420adf41f3189fe90e3d6a409cbfa65454c09f25fb33bf7266", size = 2310735, upload-time = "2026-01-21T16:27:22.327Z" }, + { url = "https://files.pythonhosted.org/packages/76/ad/f4c985ad52ddd3b22711c588501be1b330adaeaf6850317f66751711b78c/torchvision-0.25.0-cp313-cp313t-manylinux_2_28_x86_64.whl", hash = "sha256:c4d395cb2c4a2712f6eb93a34476cdf7aae74bb6ea2ea1917f858e96344b00aa", size = 8089557, upload-time = "2026-01-21T16:27:27.666Z" }, + { url = "https://files.pythonhosted.org/packages/63/cc/0ea68b5802e5e3c31f44b307e74947bad5a38cc655231d845534ed50ddb8/torchvision-0.25.0-cp313-cp313t-win_amd64.whl", hash = "sha256:5e6b449e9fa7d642142c0e27c41e5a43b508d57ed8e79b7c0a0c28652da8678c", size = 4344260, upload-time = "2026-01-21T16:27:17.018Z" }, + { url = "https://files.pythonhosted.org/packages/9e/1f/fa839532660e2602b7e704d65010787c5bb296258b44fa8b9c1cd6175e7d/torchvision-0.25.0-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:620a236288d594dcec7634c754484542dc0a5c1b0e0b83a34bda5e91e9b7c3a1", size = 1896193, upload-time = "2026-01-21T16:27:24.785Z" }, + { url = "https://files.pythonhosted.org/packages/80/ed/d51889da7ceaf5ff7a0574fb28f9b6b223df19667265395891f81b364ab3/torchvision-0.25.0-cp314-cp314-manylinux_2_28_aarch64.whl", hash = "sha256:0b5e7f50002a8145a98c5694a018e738c50e2972608310c7e88e1bd4c058f6ce", size = 2309331, upload-time = "2026-01-21T16:27:19.97Z" }, + { url = "https://files.pythonhosted.org/packages/90/a5/f93fcffaddd8f12f9e812256830ec9c9ca65abbf1bc369379f9c364d1ff4/torchvision-0.25.0-cp314-cp314-manylinux_2_28_x86_64.whl", hash = "sha256:632db02300e83793812eee4f61ae6a2686dab10b4cfd628b620dc47747aa9d03", size = 8088713, upload-time = "2026-01-21T16:27:15.281Z" }, + { url = "https://files.pythonhosted.org/packages/1f/eb/d0096eed5690d962853213f2ee00d91478dfcb586b62dbbb449fb8abc3a6/torchvision-0.25.0-cp314-cp314-win_amd64.whl", hash = "sha256:d1abd5ed030c708f5dbf4812ad5f6fbe9384b63c40d6bd79f8df41a4a759a917", size = 4325058, upload-time = "2026-01-21T16:27:26.165Z" }, + { url = "https://files.pythonhosted.org/packages/97/36/96374a4c7ab50dea9787ce987815614ccfe988a42e10ac1a2e3e5b60319a/torchvision-0.25.0-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:ad9a8a5877782944d99186e4502a614770fe906626d76e9cd32446a0ac3075f2", size = 1896207, upload-time = "2026-01-21T16:27:23.383Z" }, + { url = "https://files.pythonhosted.org/packages/b5/e2/7abb10a867db79b226b41da419b63b69c0bd5b82438c4a4ed50e084c552f/torchvision-0.25.0-cp314-cp314t-manylinux_2_28_aarch64.whl", hash = "sha256:40a122c3cf4d14b651f095e0f672b688dde78632783fc5cd3d4d5e4f6a828563", size = 2310741, upload-time = "2026-01-21T16:27:18.712Z" }, + { url = "https://files.pythonhosted.org/packages/08/e6/0927784e6ffc340b6676befde1c60260bd51641c9c574b9298d791a9cda4/torchvision-0.25.0-cp314-cp314t-manylinux_2_28_x86_64.whl", hash = "sha256:846890161b825b38aa85fc37fb3ba5eea74e7091ff28bab378287111483b6443", size = 8089772, upload-time = "2026-01-21T16:27:14.048Z" }, + { url = "https://files.pythonhosted.org/packages/b6/37/e7ca4ec820d434c0f23f824eb29f0676a0c3e7a118f1514f5b949c3356da/torchvision-0.25.0-cp314-cp314t-win_amd64.whl", hash = "sha256:f07f01d27375ad89d72aa2b3f2180f07da95dd9d2e4c758e015c0acb2da72977", size = 4425879, upload-time = "2026-01-21T16:27:12.579Z" }, +] + +[[package]] +name = "tqdm" +version = "4.67.3" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "colorama", marker = "sys_platform == 'win32'" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/09/a9/6ba95a270c6f1fbcd8dac228323f2777d886cb206987444e4bce66338dd4/tqdm-4.67.3.tar.gz", hash = "sha256:7d825f03f89244ef73f1d4ce193cb1774a8179fd96f31d7e1dcde62092b960bb", size = 169598, upload-time = "2026-02-03T17:35:53.048Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/16/e1/3079a9ff9b8e11b846c6ac5c8b5bfb7ff225eee721825310c91b3b50304f/tqdm-4.67.3-py3-none-any.whl", hash = "sha256:ee1e4c0e59148062281c49d80b25b67771a127c85fc9676d3be5f243206826bf", size = 78374, upload-time = "2026-02-03T17:35:50.982Z" }, +] + +[[package]] +name = "triton" +version = "3.6.0" +source = { registry = "https://pypi.org/simple" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/ab/a8/cdf8b3e4c98132f965f88c2313a4b493266832ad47fb52f23d14d4f86bb5/triton-3.6.0-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:74caf5e34b66d9f3a429af689c1c7128daba1d8208df60e81106b115c00d6fca", size = 188266850, upload-time = "2026-01-20T16:00:43.041Z" }, + { url = "https://files.pythonhosted.org/packages/f9/0b/37d991d8c130ce81a8728ae3c25b6e60935838e9be1b58791f5997b24a54/triton-3.6.0-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:10c7f76c6e72d2ef08df639e3d0d30729112f47a56b0c81672edc05ee5116ac9", size = 188289450, upload-time = "2026-01-20T16:00:49.136Z" }, + { url = "https://files.pythonhosted.org/packages/35/f8/9c66bfc55361ec6d0e4040a0337fb5924ceb23de4648b8a81ae9d33b2b38/triton-3.6.0-cp313-cp313t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:d002e07d7180fd65e622134fbd980c9a3d4211fb85224b56a0a0efbd422ab72f", size = 188400296, upload-time = "2026-01-20T16:00:56.042Z" }, + { url = "https://files.pythonhosted.org/packages/df/3d/9e7eee57b37c80cec63322c0231bb6da3cfe535a91d7a4d64896fcb89357/triton-3.6.0-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:a17a5d5985f0ac494ed8a8e54568f092f7057ef60e1b0fa09d3fd1512064e803", size = 188273063, upload-time = "2026-01-20T16:01:07.278Z" }, + { url = "https://files.pythonhosted.org/packages/f6/56/6113c23ff46c00aae423333eb58b3e60bdfe9179d542781955a5e1514cb3/triton-3.6.0-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:46bd1c1af4b6704e554cad2eeb3b0a6513a980d470ccfa63189737340c7746a7", size = 188397994, upload-time = "2026-01-20T16:01:14.236Z" }, +] + +[[package]] +name = "typing-extensions" +version = "4.15.0" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/72/94/1a15dd82efb362ac84269196e94cf00f187f7ed21c242792a923cdb1c61f/typing_extensions-4.15.0.tar.gz", hash = "sha256:0cea48d173cc12fa28ecabc3b837ea3cf6f38c6d1136f85cbaaf598984861466", size = 109391, upload-time = "2025-08-25T13:49:26.313Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/18/67/36e9267722cc04a6b9f15c7f3441c2363321a3ea07da7ae0c0707beb2a9c/typing_extensions-4.15.0-py3-none-any.whl", hash = "sha256:f0fa19c6845758ab08074a0cfa8b7aecb71c999ca73d62883bc25cc018c4e548", size = 44614, upload-time = "2025-08-25T13:49:24.86Z" }, +] + +[[package]] +name = "typing-inspection" +version = "0.4.2" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "typing-extensions" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/55/e3/70399cb7dd41c10ac53367ae42139cf4b1ca5f36bb3dc6c9d33acdb43655/typing_inspection-0.4.2.tar.gz", hash = "sha256:ba561c48a67c5958007083d386c3295464928b01faa735ab8547c5692e87f464", size = 75949, upload-time = "2025-10-01T02:14:41.687Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/dc/9b/47798a6c91d8bdb567fe2698fe81e0c6b7cb7ef4d13da4114b41d239f65d/typing_inspection-0.4.2-py3-none-any.whl", hash = "sha256:4ed1cacbdc298c220f1bd249ed5287caa16f34d44ef4e9c3d0cbad5b521545e7", size = 14611, upload-time = "2025-10-01T02:14:40.154Z" }, +] + +[[package]] +name = "urllib3" +version = "2.6.3" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/c7/24/5f1b3bdffd70275f6661c76461e25f024d5a38a46f04aaca912426a2b1d3/urllib3-2.6.3.tar.gz", hash = "sha256:1b62b6884944a57dbe321509ab94fd4d3b307075e0c2eae991ac71ee15ad38ed", size = 435556, upload-time = "2026-01-07T16:24:43.925Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/39/08/aaaad47bc4e9dc8c725e68f9d04865dbcb2052843ff09c97b08904852d84/urllib3-2.6.3-py3-none-any.whl", hash = "sha256:bf272323e553dfb2e87d9bfd225ca7b0f467b919d7bbd355436d3fd37cb0acd4", size = 131584, upload-time = "2026-01-07T16:24:42.685Z" }, +] + +[[package]] +name = "uvicorn" +version = "0.41.0" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "click" }, + { name = "h11" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/32/ce/eeb58ae4ac36fe09e3842eb02e0eb676bf2c53ae062b98f1b2531673efdd/uvicorn-0.41.0.tar.gz", hash = "sha256:09d11cf7008da33113824ee5a1c6422d89fbc2ff476540d69a34c87fab8b571a", size = 82633, upload-time = "2026-02-16T23:07:24.1Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/83/e4/d04a086285c20886c0daad0e026f250869201013d18f81d9ff5eada73a88/uvicorn-0.41.0-py3-none-any.whl", hash = "sha256:29e35b1d2c36a04b9e180d4007ede3bcb32a85fbdfd6c6aeb3f26839de088187", size = 68783, upload-time = "2026-02-16T23:07:22.357Z" }, +] + +[[package]] +name = "watchdog" +version = "6.0.0" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/db/7d/7f3d619e951c88ed75c6037b246ddcf2d322812ee8ea189be89511721d54/watchdog-6.0.0.tar.gz", hash = "sha256:9ddf7c82fda3ae8e24decda1338ede66e1c99883db93711d8fb941eaa2d8c282", size = 131220, upload-time = "2024-11-01T14:07:13.037Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/39/ea/3930d07dafc9e286ed356a679aa02d777c06e9bfd1164fa7c19c288a5483/watchdog-6.0.0-cp312-cp312-macosx_10_13_universal2.whl", hash = "sha256:bdd4e6f14b8b18c334febb9c4425a878a2ac20efd1e0b231978e7b150f92a948", size = 96471, upload-time = "2024-11-01T14:06:37.745Z" }, + { url = "https://files.pythonhosted.org/packages/12/87/48361531f70b1f87928b045df868a9fd4e253d9ae087fa4cf3f7113be363/watchdog-6.0.0-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:c7c15dda13c4eb00d6fb6fc508b3c0ed88b9d5d374056b239c4ad1611125c860", size = 88449, upload-time = "2024-11-01T14:06:39.748Z" }, + { url = "https://files.pythonhosted.org/packages/5b/7e/8f322f5e600812e6f9a31b75d242631068ca8f4ef0582dd3ae6e72daecc8/watchdog-6.0.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:6f10cb2d5902447c7d0da897e2c6768bca89174d0c6e1e30abec5421af97a5b0", size = 89054, upload-time = "2024-11-01T14:06:41.009Z" }, + { url = "https://files.pythonhosted.org/packages/68/98/b0345cabdce2041a01293ba483333582891a3bd5769b08eceb0d406056ef/watchdog-6.0.0-cp313-cp313-macosx_10_13_universal2.whl", hash = "sha256:490ab2ef84f11129844c23fb14ecf30ef3d8a6abafd3754a6f75ca1e6654136c", size = 96480, upload-time = "2024-11-01T14:06:42.952Z" }, + { url = "https://files.pythonhosted.org/packages/85/83/cdf13902c626b28eedef7ec4f10745c52aad8a8fe7eb04ed7b1f111ca20e/watchdog-6.0.0-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:76aae96b00ae814b181bb25b1b98076d5fc84e8a53cd8885a318b42b6d3a5134", size = 88451, upload-time = "2024-11-01T14:06:45.084Z" }, + { url = "https://files.pythonhosted.org/packages/fe/c4/225c87bae08c8b9ec99030cd48ae9c4eca050a59bf5c2255853e18c87b50/watchdog-6.0.0-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:a175f755fc2279e0b7312c0035d52e27211a5bc39719dd529625b1930917345b", size = 89057, upload-time = "2024-11-01T14:06:47.324Z" }, + { url = "https://files.pythonhosted.org/packages/a9/c7/ca4bf3e518cb57a686b2feb4f55a1892fd9a3dd13f470fca14e00f80ea36/watchdog-6.0.0-py3-none-manylinux2014_aarch64.whl", hash = "sha256:7607498efa04a3542ae3e05e64da8202e58159aa1fa4acddf7678d34a35d4f13", size = 79079, upload-time = "2024-11-01T14:06:59.472Z" }, + { url = "https://files.pythonhosted.org/packages/5c/51/d46dc9332f9a647593c947b4b88e2381c8dfc0942d15b8edc0310fa4abb1/watchdog-6.0.0-py3-none-manylinux2014_armv7l.whl", hash = "sha256:9041567ee8953024c83343288ccc458fd0a2d811d6a0fd68c4c22609e3490379", size = 79078, upload-time = "2024-11-01T14:07:01.431Z" }, + { url = "https://files.pythonhosted.org/packages/d4/57/04edbf5e169cd318d5f07b4766fee38e825d64b6913ca157ca32d1a42267/watchdog-6.0.0-py3-none-manylinux2014_i686.whl", hash = "sha256:82dc3e3143c7e38ec49d61af98d6558288c415eac98486a5c581726e0737c00e", size = 79076, upload-time = "2024-11-01T14:07:02.568Z" }, + { url = "https://files.pythonhosted.org/packages/ab/cc/da8422b300e13cb187d2203f20b9253e91058aaf7db65b74142013478e66/watchdog-6.0.0-py3-none-manylinux2014_ppc64.whl", hash = "sha256:212ac9b8bf1161dc91bd09c048048a95ca3a4c4f5e5d4a7d1b1a7d5752a7f96f", size = 79077, upload-time = "2024-11-01T14:07:03.893Z" }, + { url = "https://files.pythonhosted.org/packages/2c/3b/b8964e04ae1a025c44ba8e4291f86e97fac443bca31de8bd98d3263d2fcf/watchdog-6.0.0-py3-none-manylinux2014_ppc64le.whl", hash = "sha256:e3df4cbb9a450c6d49318f6d14f4bbc80d763fa587ba46ec86f99f9e6876bb26", size = 79078, upload-time = "2024-11-01T14:07:05.189Z" }, + { url = "https://files.pythonhosted.org/packages/62/ae/a696eb424bedff7407801c257d4b1afda455fe40821a2be430e173660e81/watchdog-6.0.0-py3-none-manylinux2014_s390x.whl", hash = "sha256:2cce7cfc2008eb51feb6aab51251fd79b85d9894e98ba847408f662b3395ca3c", size = 79077, upload-time = "2024-11-01T14:07:06.376Z" }, + { url = "https://files.pythonhosted.org/packages/b5/e8/dbf020b4d98251a9860752a094d09a65e1b436ad181faf929983f697048f/watchdog-6.0.0-py3-none-manylinux2014_x86_64.whl", hash = "sha256:20ffe5b202af80ab4266dcd3e91aae72bf2da48c0d33bdb15c66658e685e94e2", size = 79078, upload-time = "2024-11-01T14:07:07.547Z" }, + { url = "https://files.pythonhosted.org/packages/07/f6/d0e5b343768e8bcb4cda79f0f2f55051bf26177ecd5651f84c07567461cf/watchdog-6.0.0-py3-none-win32.whl", hash = "sha256:07df1fdd701c5d4c8e55ef6cf55b8f0120fe1aef7ef39a1c6fc6bc2e606d517a", size = 79065, upload-time = "2024-11-01T14:07:09.525Z" }, + { url = "https://files.pythonhosted.org/packages/db/d9/c495884c6e548fce18a8f40568ff120bc3a4b7b99813081c8ac0c936fa64/watchdog-6.0.0-py3-none-win_amd64.whl", hash = "sha256:cbafb470cf848d93b5d013e2ecb245d4aa1c8fd0504e863ccefa32445359d680", size = 79070, upload-time = "2024-11-01T14:07:10.686Z" }, + { url = "https://files.pythonhosted.org/packages/33/e8/e40370e6d74ddba47f002a32919d91310d6074130fe4e17dabcafc15cbf1/watchdog-6.0.0-py3-none-win_ia64.whl", hash = "sha256:a1914259fa9e1454315171103c6a30961236f508b9b623eae470268bbcc6a22f", size = 79067, upload-time = "2024-11-01T14:07:11.845Z" }, +] + +[[package]] +name = "websockets" +version = "16.0" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/04/24/4b2031d72e840ce4c1ccb255f693b15c334757fc50023e4db9537080b8c4/websockets-16.0.tar.gz", hash = "sha256:5f6261a5e56e8d5c42a4497b364ea24d94d9563e8fbd44e78ac40879c60179b5", size = 179346, upload-time = "2026-01-10T09:23:47.181Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/84/7b/bac442e6b96c9d25092695578dda82403c77936104b5682307bd4deb1ad4/websockets-16.0-cp312-cp312-macosx_10_13_universal2.whl", hash = "sha256:71c989cbf3254fbd5e84d3bff31e4da39c43f884e64f2551d14bb3c186230f00", size = 177365, upload-time = "2026-01-10T09:22:46.787Z" }, + { url = "https://files.pythonhosted.org/packages/b0/fe/136ccece61bd690d9c1f715baaeefd953bb2360134de73519d5df19d29ca/websockets-16.0-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:8b6e209ffee39ff1b6d0fa7bfef6de950c60dfb91b8fcead17da4ee539121a79", size = 175038, upload-time = "2026-01-10T09:22:47.999Z" }, + { url = "https://files.pythonhosted.org/packages/40/1e/9771421ac2286eaab95b8575b0cb701ae3663abf8b5e1f64f1fd90d0a673/websockets-16.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:86890e837d61574c92a97496d590968b23c2ef0aeb8a9bc9421d174cd378ae39", size = 175328, upload-time = "2026-01-10T09:22:49.809Z" }, + { url = "https://files.pythonhosted.org/packages/18/29/71729b4671f21e1eaa5d6573031ab810ad2936c8175f03f97f3ff164c802/websockets-16.0-cp312-cp312-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:9b5aca38b67492ef518a8ab76851862488a478602229112c4b0d58d63a7a4d5c", size = 184915, upload-time = "2026-01-10T09:22:51.071Z" }, + { url = "https://files.pythonhosted.org/packages/97/bb/21c36b7dbbafc85d2d480cd65df02a1dc93bf76d97147605a8e27ff9409d/websockets-16.0-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:e0334872c0a37b606418ac52f6ab9cfd17317ac26365f7f65e203e2d0d0d359f", size = 186152, upload-time = "2026-01-10T09:22:52.224Z" }, + { url = "https://files.pythonhosted.org/packages/4a/34/9bf8df0c0cf88fa7bfe36678dc7b02970c9a7d5e065a3099292db87b1be2/websockets-16.0-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:a0b31e0b424cc6b5a04b8838bbaec1688834b2383256688cf47eb97412531da1", size = 185583, upload-time = "2026-01-10T09:22:53.443Z" }, + { url = "https://files.pythonhosted.org/packages/47/88/4dd516068e1a3d6ab3c7c183288404cd424a9a02d585efbac226cb61ff2d/websockets-16.0-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:485c49116d0af10ac698623c513c1cc01c9446c058a4e61e3bf6c19dff7335a2", size = 184880, upload-time = "2026-01-10T09:22:55.033Z" }, + { url = "https://files.pythonhosted.org/packages/91/d6/7d4553ad4bf1c0421e1ebd4b18de5d9098383b5caa1d937b63df8d04b565/websockets-16.0-cp312-cp312-win32.whl", hash = "sha256:eaded469f5e5b7294e2bdca0ab06becb6756ea86894a47806456089298813c89", size = 178261, upload-time = "2026-01-10T09:22:56.251Z" }, + { url = "https://files.pythonhosted.org/packages/c3/f0/f3a17365441ed1c27f850a80b2bc680a0fa9505d733fe152fdf5e98c1c0b/websockets-16.0-cp312-cp312-win_amd64.whl", hash = "sha256:5569417dc80977fc8c2d43a86f78e0a5a22fee17565d78621b6bb264a115d4ea", size = 178693, upload-time = "2026-01-10T09:22:57.478Z" }, + { url = "https://files.pythonhosted.org/packages/cc/9c/baa8456050d1c1b08dd0ec7346026668cbc6f145ab4e314d707bb845bf0d/websockets-16.0-cp313-cp313-macosx_10_13_universal2.whl", hash = "sha256:878b336ac47938b474c8f982ac2f7266a540adc3fa4ad74ae96fea9823a02cc9", size = 177364, upload-time = "2026-01-10T09:22:59.333Z" }, + { url = "https://files.pythonhosted.org/packages/7e/0c/8811fc53e9bcff68fe7de2bcbe75116a8d959ac699a3200f4847a8925210/websockets-16.0-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:52a0fec0e6c8d9a784c2c78276a48a2bdf099e4ccc2a4cad53b27718dbfd0230", size = 175039, upload-time = "2026-01-10T09:23:01.171Z" }, + { url = "https://files.pythonhosted.org/packages/aa/82/39a5f910cb99ec0b59e482971238c845af9220d3ab9fa76dd9162cda9d62/websockets-16.0-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:e6578ed5b6981005df1860a56e3617f14a6c307e6a71b4fff8c48fdc50f3ed2c", size = 175323, upload-time = "2026-01-10T09:23:02.341Z" }, + { url = "https://files.pythonhosted.org/packages/bd/28/0a25ee5342eb5d5f297d992a77e56892ecb65e7854c7898fb7d35e9b33bd/websockets-16.0-cp313-cp313-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:95724e638f0f9c350bb1c2b0a7ad0e83d9cc0c9259f3ea94e40d7b02a2179ae5", size = 184975, upload-time = "2026-01-10T09:23:03.756Z" }, + { url = "https://files.pythonhosted.org/packages/f9/66/27ea52741752f5107c2e41fda05e8395a682a1e11c4e592a809a90c6a506/websockets-16.0-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:c0204dc62a89dc9d50d682412c10b3542d748260d743500a85c13cd1ee4bde82", size = 186203, upload-time = "2026-01-10T09:23:05.01Z" }, + { url = "https://files.pythonhosted.org/packages/37/e5/8e32857371406a757816a2b471939d51c463509be73fa538216ea52b792a/websockets-16.0-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:52ac480f44d32970d66763115edea932f1c5b1312de36df06d6b219f6741eed8", size = 185653, upload-time = "2026-01-10T09:23:06.301Z" }, + { url = "https://files.pythonhosted.org/packages/9b/67/f926bac29882894669368dc73f4da900fcdf47955d0a0185d60103df5737/websockets-16.0-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:6e5a82b677f8f6f59e8dfc34ec06ca6b5b48bc4fcda346acd093694cc2c24d8f", size = 184920, upload-time = "2026-01-10T09:23:07.492Z" }, + { url = "https://files.pythonhosted.org/packages/3c/a1/3d6ccdcd125b0a42a311bcd15a7f705d688f73b2a22d8cf1c0875d35d34a/websockets-16.0-cp313-cp313-win32.whl", hash = "sha256:abf050a199613f64c886ea10f38b47770a65154dc37181bfaff70c160f45315a", size = 178255, upload-time = "2026-01-10T09:23:09.245Z" }, + { url = "https://files.pythonhosted.org/packages/6b/ae/90366304d7c2ce80f9b826096a9e9048b4bb760e44d3b873bb272cba696b/websockets-16.0-cp313-cp313-win_amd64.whl", hash = "sha256:3425ac5cf448801335d6fdc7ae1eb22072055417a96cc6b31b3861f455fbc156", size = 178689, upload-time = "2026-01-10T09:23:10.483Z" }, + { url = "https://files.pythonhosted.org/packages/f3/1d/e88022630271f5bd349ed82417136281931e558d628dd52c4d8621b4a0b2/websockets-16.0-cp314-cp314-macosx_10_15_universal2.whl", hash = "sha256:8cc451a50f2aee53042ac52d2d053d08bf89bcb31ae799cb4487587661c038a0", size = 177406, upload-time = "2026-01-10T09:23:12.178Z" }, + { url = "https://files.pythonhosted.org/packages/f2/78/e63be1bf0724eeb4616efb1ae1c9044f7c3953b7957799abb5915bffd38e/websockets-16.0-cp314-cp314-macosx_10_15_x86_64.whl", hash = "sha256:daa3b6ff70a9241cf6c7fc9e949d41232d9d7d26fd3522b1ad2b4d62487e9904", size = 175085, upload-time = "2026-01-10T09:23:13.511Z" }, + { url = "https://files.pythonhosted.org/packages/bb/f4/d3c9220d818ee955ae390cf319a7c7a467beceb24f05ee7aaaa2414345ba/websockets-16.0-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:fd3cb4adb94a2a6e2b7c0d8d05cb94e6f1c81a0cf9dc2694fb65c7e8d94c42e4", size = 175328, upload-time = "2026-01-10T09:23:14.727Z" }, + { url = "https://files.pythonhosted.org/packages/63/bc/d3e208028de777087e6fb2b122051a6ff7bbcca0d6df9d9c2bf1dd869ae9/websockets-16.0-cp314-cp314-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:781caf5e8eee67f663126490c2f96f40906594cb86b408a703630f95550a8c3e", size = 185044, upload-time = "2026-01-10T09:23:15.939Z" }, + { url = "https://files.pythonhosted.org/packages/ad/6e/9a0927ac24bd33a0a9af834d89e0abc7cfd8e13bed17a86407a66773cc0e/websockets-16.0-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:caab51a72c51973ca21fa8a18bd8165e1a0183f1ac7066a182ff27107b71e1a4", size = 186279, upload-time = "2026-01-10T09:23:17.148Z" }, + { url = "https://files.pythonhosted.org/packages/b9/ca/bf1c68440d7a868180e11be653c85959502efd3a709323230314fda6e0b3/websockets-16.0-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:19c4dc84098e523fd63711e563077d39e90ec6702aff4b5d9e344a60cb3c0cb1", size = 185711, upload-time = "2026-01-10T09:23:18.372Z" }, + { url = "https://files.pythonhosted.org/packages/c4/f8/fdc34643a989561f217bb477cbc47a3a07212cbda91c0e4389c43c296ebf/websockets-16.0-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:a5e18a238a2b2249c9a9235466b90e96ae4795672598a58772dd806edc7ac6d3", size = 184982, upload-time = "2026-01-10T09:23:19.652Z" }, + { url = "https://files.pythonhosted.org/packages/dd/d1/574fa27e233764dbac9c52730d63fcf2823b16f0856b3329fc6268d6ae4f/websockets-16.0-cp314-cp314-win32.whl", hash = "sha256:a069d734c4a043182729edd3e9f247c3b2a4035415a9172fd0f1b71658a320a8", size = 177915, upload-time = "2026-01-10T09:23:21.458Z" }, + { url = "https://files.pythonhosted.org/packages/8a/f1/ae6b937bf3126b5134ce1f482365fde31a357c784ac51852978768b5eff4/websockets-16.0-cp314-cp314-win_amd64.whl", hash = "sha256:c0ee0e63f23914732c6d7e0cce24915c48f3f1512ec1d079ed01fc629dab269d", size = 178381, upload-time = "2026-01-10T09:23:22.715Z" }, + { url = "https://files.pythonhosted.org/packages/06/9b/f791d1db48403e1f0a27577a6beb37afae94254a8c6f08be4a23e4930bc0/websockets-16.0-cp314-cp314t-macosx_10_15_universal2.whl", hash = "sha256:a35539cacc3febb22b8f4d4a99cc79b104226a756aa7400adc722e83b0d03244", size = 177737, upload-time = "2026-01-10T09:23:24.523Z" }, + { url = "https://files.pythonhosted.org/packages/bd/40/53ad02341fa33b3ce489023f635367a4ac98b73570102ad2cdd770dacc9a/websockets-16.0-cp314-cp314t-macosx_10_15_x86_64.whl", hash = "sha256:b784ca5de850f4ce93ec85d3269d24d4c82f22b7212023c974c401d4980ebc5e", size = 175268, upload-time = "2026-01-10T09:23:25.781Z" }, + { url = "https://files.pythonhosted.org/packages/74/9b/6158d4e459b984f949dcbbb0c5d270154c7618e11c01029b9bbd1bb4c4f9/websockets-16.0-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:569d01a4e7fba956c5ae4fc988f0d4e187900f5497ce46339c996dbf24f17641", size = 175486, upload-time = "2026-01-10T09:23:27.033Z" }, + { url = "https://files.pythonhosted.org/packages/e5/2d/7583b30208b639c8090206f95073646c2c9ffd66f44df967981a64f849ad/websockets-16.0-cp314-cp314t-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:50f23cdd8343b984957e4077839841146f67a3d31ab0d00e6b824e74c5b2f6e8", size = 185331, upload-time = "2026-01-10T09:23:28.259Z" }, + { url = "https://files.pythonhosted.org/packages/45/b0/cce3784eb519b7b5ad680d14b9673a31ab8dcb7aad8b64d81709d2430aa8/websockets-16.0-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:152284a83a00c59b759697b7f9e9cddf4e3c7861dd0d964b472b70f78f89e80e", size = 186501, upload-time = "2026-01-10T09:23:29.449Z" }, + { url = "https://files.pythonhosted.org/packages/19/60/b8ebe4c7e89fb5f6cdf080623c9d92789a53636950f7abacfc33fe2b3135/websockets-16.0-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:bc59589ab64b0022385f429b94697348a6a234e8ce22544e3681b2e9331b5944", size = 186062, upload-time = "2026-01-10T09:23:31.368Z" }, + { url = "https://files.pythonhosted.org/packages/88/a8/a080593f89b0138b6cba1b28f8df5673b5506f72879322288b031337c0b8/websockets-16.0-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:32da954ffa2814258030e5a57bc73a3635463238e797c7375dc8091327434206", size = 185356, upload-time = "2026-01-10T09:23:32.627Z" }, + { url = "https://files.pythonhosted.org/packages/c2/b6/b9afed2afadddaf5ebb2afa801abf4b0868f42f8539bfe4b071b5266c9fe/websockets-16.0-cp314-cp314t-win32.whl", hash = "sha256:5a4b4cc550cb665dd8a47f868c8d04c8230f857363ad3c9caf7a0c3bf8c61ca6", size = 178085, upload-time = "2026-01-10T09:23:33.816Z" }, + { url = "https://files.pythonhosted.org/packages/9f/3e/28135a24e384493fa804216b79a6a6759a38cc4ff59118787b9fb693df93/websockets-16.0-cp314-cp314t-win_amd64.whl", hash = "sha256:b14dc141ed6d2dde437cddb216004bcac6a1df0935d79656387bd41632ba0bbd", size = 178531, upload-time = "2026-01-10T09:23:35.016Z" }, + { url = "https://files.pythonhosted.org/packages/6f/28/258ebab549c2bf3e64d2b0217b973467394a9cea8c42f70418ca2c5d0d2e/websockets-16.0-py3-none-any.whl", hash = "sha256:1637db62fad1dc833276dded54215f2c7fa46912301a24bd94d45d46a011ceec", size = 171598, upload-time = "2026-01-10T09:23:45.395Z" }, +]