extend abbreviations list in Faroese tokenizer exceptions (#13366)

Merge main into `develop`

.github/FUNDING.yml

@@ -0,0 +1 @@
+custom: [https://explosion.ai/merch, https://explosion.ai/tailored-solutions]

.github/workflows/tests.yml

@@ -58,10 +58,8 @@ jobs:
       fail-fast: true
       matrix:
         os: [ubuntu-latest, windows-latest, macos-latest]
-        python_version: ["3.11"]
+        python_version: ["3.12"]
         include:
-          - os: ubuntu-20.04
-            python_version: "3.6"
           - os: windows-latest
             python_version: "3.7"
           - os: macos-latest
@@ -70,6 +68,8 @@ jobs:
             python_version: "3.9"
           - os: windows-latest
             python_version: "3.10"
+          - os: macos-latest
+            python_version: "3.11"
 
     runs-on: ${{ matrix.os }}
 
@@ -95,7 +95,7 @@ jobs:
       - name: Run mypy
         run: |
           python -m mypy spacy
-        if: matrix.python_version != '3.6'
+        if: matrix.python_version != '3.7'
 
       - name: Delete source directory and .egg-info
         run: |

LICENSE

@@ -1,6 +1,6 @@
 The MIT License (MIT)
 
-Copyright (C) 2016-2022 ExplosionAI GmbH, 2016 spaCy GmbH, 2015 Matthew Honnibal
+Copyright (C) 2016-2023 ExplosionAI GmbH, 2016 spaCy GmbH, 2015 Matthew Honnibal
 
 Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal

README.md

@@ -6,23 +6,20 @@ spaCy is a library for **advanced Natural Language Processing** in Python and
 Cython. It's built on the very latest research, and was designed from day one to
 be used in real products.
 
-spaCy comes with
-[pretrained pipelines](https://spacy.io/models) and
-currently supports tokenization and training for **70+ languages**. It features
-state-of-the-art speed and **neural network models** for tagging,
-parsing, **named entity recognition**, **text classification** and more,
-multi-task learning with pretrained **transformers** like BERT, as well as a
+spaCy comes with [pretrained pipelines](https://spacy.io/models) and currently
+supports tokenization and training for **70+ languages**. It features
+state-of-the-art speed and **neural network models** for tagging, parsing,
+**named entity recognition**, **text classification** and more, multi-task
+learning with pretrained **transformers** like BERT, as well as a
 production-ready [**training system**](https://spacy.io/usage/training) and easy
 model packaging, deployment and workflow management. spaCy is commercial
-open-source software, released under the [MIT license](https://github.com/explosion/spaCy/blob/master/LICENSE).
+open-source software, released under the
+[MIT license](https://github.com/explosion/spaCy/blob/master/LICENSE).
 
-💥 **We'd love to hear more about your experience with spaCy!**
-[Fill out our survey here.](https://form.typeform.com/to/aMel9q9f)
-
-💫 **Version 3.5 out now!**
+💫 **Version 3.7 out now!**
 [Check out the release notes here.](https://github.com/explosion/spaCy/releases)
 
-[![Azure Pipelines](https://img.shields.io/azure-devops/build/explosion-ai/public/8/master.svg?logo=azure-pipelines&style=flat-square&label=build)](https://dev.azure.com/explosion-ai/public/_build?definitionId=8)
+[![tests](https://github.com/explosion/spaCy/actions/workflows/tests.yml/badge.svg)](https://github.com/explosion/spaCy/actions/workflows/tests.yml)
 [![Current Release Version](https://img.shields.io/github/release/explosion/spacy.svg?style=flat-square&logo=github)](https://github.com/explosion/spaCy/releases)
 [![pypi Version](https://img.shields.io/pypi/v/spacy.svg?style=flat-square&logo=pypi&logoColor=white)](https://pypi.org/project/spacy/)
 [![conda Version](https://img.shields.io/conda/vn/conda-forge/spacy.svg?style=flat-square&logo=conda-forge&logoColor=white)](https://anaconda.org/conda-forge/spacy)
@@ -35,35 +32,42 @@ open-source software, released under the [MIT license](https://github.com/explos
 
 ## 📖 Documentation
 
-| Documentation                 |                                                                        |
-| ----------------------------- | ---------------------------------------------------------------------- |
-| ⭐️ **[spaCy 101]**           | New to spaCy? Here's everything you need to know!                      |
-| 📚 **[Usage Guides]**         | How to use spaCy and its features.                                     |
-| 🚀 **[New in v3.0]**          | New features, backwards incompatibilities and migration guide.         |
-| 🪐 **[Project Templates]**    | End-to-end workflows you can clone, modify and run.                    |
-| 🎛 **[API Reference]**         | The detailed reference for spaCy's API.                                |
-| 📦 **[Models]**               | Download trained pipelines for spaCy.                                  |
-| 🌌 **[Universe]**             | Plugins, extensions, demos and books from the spaCy ecosystem.         |
-| ⚙️ **[spaCy VS Code Extension]** | Additional tooling and features for working with spaCy's config files. |
-| 👩‍🏫 **[Online Course]** | Learn spaCy in this free and interactive online course. |
-| 📺 **[Videos]** | Our YouTube channel with video tutorials, talks and more. |
-| 🛠 **[Changelog]** | Changes and version history. |
-| 💝 **[Contribute]** | How to contribute to the spaCy project and code base. |
-| <a href="https://explosion.ai/spacy-tailored-pipelines"><img src="https://user-images.githubusercontent.com/13643239/152853098-1c761611-ccb0-4ec6-9066-b234552831fe.png" width="125" alt="spaCy Tailored Pipelines"/></a> | Get a custom spaCy pipeline, tailor-made for your NLP problem by spaCy's core developers. Streamlined, production-ready, predictable and maintainable. Start by completing our 5-minute questionnaire to tell us what you need and we'll be in touch! **[Learn more &rarr;](https://explosion.ai/spacy-tailored-pipelines)** |
-| <a href="https://explosion.ai/spacy-tailored-analysis"><img src="https://user-images.githubusercontent.com/1019791/206151300-b00cd189-e503-4797-aa1e-1bb6344062c5.png" width="125" alt="spaCy Tailored Pipelines"/></a> | Bespoke advice for problem solving, strategy and analysis for applied NLP projects. Services include data strategy, code reviews, pipeline design and annotation coaching. Curious? Fill in our 5-minute questionnaire to tell us what you need and we'll be in touch! **[Learn more &rarr;](https://explosion.ai/spacy-tailored-analysis)** |
+| Documentation                                                                                                                                                                                                             |                                                                                                                                                                                                                                                                                                                                              |
+| ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| ⭐️ **[spaCy 101]**                                                                                                                                                                                                       | New to spaCy? Here's everything you need to know!                                                                                                                                                                                                                                                                                            |
+| 📚 **[Usage Guides]**                                                                                                                                                                                                     | How to use spaCy and its features.                                                                                                                                                                                                                                                                                                           |
+| 🚀 **[New in v3.0]**                                                                                                                                                                                                      | New features, backwards incompatibilities and migration guide.                                                                                                                                                                                                                                                                               |
+| 🪐 **[Project Templates]**                                                                                                                                                                                                | End-to-end workflows you can clone, modify and run.                                                                                                                                                                                                                                                                                          |
+| 🎛 **[API Reference]**                                                                                                                                                                                                     | The detailed reference for spaCy's API.                                                                                                                                                                                                                                                                                                      |
+| ⏩ **[GPU Processing]**                                                                                                                                                                                                    | Use spaCy with CUDA-compatible GPU processing.                                                                                                                                                                                                                                                                                               |
+| 📦 **[Models]**                                                                                                                                                                                                           | Download trained pipelines for spaCy.                                                                                                                                                                                                                                                                                                        |
+| 🦙 **[Large Language Models]**                                                                                                                                                                                            | Integrate LLMs into spaCy pipelines.                                                                                                                                                                                                                                                                                                        |
+| 🌌 **[Universe]**                                                                                                                                                                                                         | Plugins, extensions, demos and books from the spaCy ecosystem.                                                                                                                                                                                                                                                                               |
+| ⚙️ **[spaCy VS Code Extension]**                                                                                                                                                                                          | Additional tooling and features for working with spaCy's config files.                                                                                                                                                                                                                                                                       |
+| 👩‍🏫 **[Online Course]**                                                                                                                                                                                                    | Learn spaCy in this free and interactive online course.                                                                                                                                                                                                                                                                                      |
+| 📰 **[Blog]**                                                                                                                                                                                                             | Read about current spaCy and Prodigy development, releases, talks and more from Explosion.                                                                                                                                                                                                                 |
+| 📺 **[Videos]**                                                                                                                                                                                                           | Our YouTube channel with video tutorials, talks and more.                                                                                                                                                                                                                                                                                    |
+| 🛠 **[Changelog]**                                                                                                                                                                                                         | Changes and version history.                                                                                                                                                                                                                                                                                                                 |
+| 💝 **[Contribute]**                                                                                                                                                                                                       | How to contribute to the spaCy project and code base.                                                                                                                                                                                                                                                                                        |
+| 👕 **[Swag]**                                                                                                                                                                                                             | Support us and our work with unique, custom-designed swag!                                                                                                                                                                                                                                                                                   |
+| <a href="https://explosion.ai/tailored-solutions"><img src="https://github.com/explosion/spaCy/assets/13643239/36d2a42e-98c0-4599-90e1-788ef75181be" width="150" alt="Tailored Solutions"/></a> | Custom NLP consulting, implementation and strategic advice by spaCy’s core development team. Streamlined, production-ready, predictable and maintainable. Send us an email or take our 5-minute questionnaire, and well'be in touch! **[Learn more &rarr;](https://explosion.ai/tailored-solutions)**                 |
 
 [spacy 101]: https://spacy.io/usage/spacy-101
 [new in v3.0]: https://spacy.io/usage/v3
 [usage guides]: https://spacy.io/usage/
 [api reference]: https://spacy.io/api/
+[gpu processing]: https://spacy.io/usage#gpu
 [models]: https://spacy.io/models
+[large language models]: https://spacy.io/usage/large-language-models
 [universe]: https://spacy.io/universe
-[spaCy VS Code Extension]: https://github.com/explosion/spacy-vscode
+[spacy vs code extension]: https://github.com/explosion/spacy-vscode
 [videos]: https://www.youtube.com/c/ExplosionAI
 [online course]: https://course.spacy.io
+[blog]: https://explosion.ai
 [project templates]: https://github.com/explosion/projects
 [changelog]: https://spacy.io/usage#changelog
 [contribute]: https://github.com/explosion/spaCy/blob/master/CONTRIBUTING.md
+[swag]: https://explosion.ai/merch
 
 ## 💬 Where to ask questions
 
@@ -92,7 +96,9 @@ more people can benefit from it.
 - State-of-the-art speed
 - Production-ready **training system**
 - Linguistically-motivated **tokenization**
-- Components for named **entity recognition**, part-of-speech-tagging, dependency parsing, sentence segmentation, **text classification**, lemmatization, morphological analysis, entity linking and more
+- Components for named **entity recognition**, part-of-speech-tagging,
+  dependency parsing, sentence segmentation, **text classification**,
+  lemmatization, morphological analysis, entity linking and more
 - Easily extensible with **custom components** and attributes
 - Support for custom models in **PyTorch**, **TensorFlow** and other frameworks
 - Built in **visualizers** for syntax and NER
@@ -109,7 +115,7 @@ For detailed installation instructions, see the
 
 - **Operating system**: macOS / OS X · Linux · Windows (Cygwin, MinGW, Visual
   Studio)
-- **Python version**: Python 3.6+ (only 64 bit)
+- **Python version**: Python 3.7+ (only 64 bit)
 - **Package managers**: [pip] · [conda] (via `conda-forge`)
 
 [pip]: https://pypi.org/project/spacy/
@@ -118,8 +124,8 @@ For detailed installation instructions, see the
 ### pip
 
 Using pip, spaCy releases are available as source packages and binary wheels.
-Before you install spaCy and its dependencies, make sure that
-your `pip`, `setuptools` and `wheel` are up to date.
+Before you install spaCy and its dependencies, make sure that your `pip`,
+`setuptools` and `wheel` are up to date.
 
 ```bash
 pip install -U pip setuptools wheel
@@ -174,9 +180,9 @@ with the new version.
 
 ## 📦 Download model packages
 
-Trained pipelines for spaCy can be installed as **Python packages**. This
-means that they're a component of your application, just like any other module.
-Models can be installed using spaCy's [`download`](https://spacy.io/api/cli#download)
+Trained pipelines for spaCy can be installed as **Python packages**. This means
+that they're a component of your application, just like any other module. Models
+can be installed using spaCy's [`download`](https://spacy.io/api/cli#download)
 command, or manually by pointing pip to a path or URL.
 
 | Documentation              |                                                                  |
@@ -242,8 +248,7 @@ do that depends on your system.
 | **Mac**     | Install a recent version of [XCode](https://developer.apple.com/xcode/), including the so-called "Command Line Tools". macOS and OS X ship with Python and git preinstalled.                                                                                        |
 | **Windows** | Install a version of the [Visual C++ Build Tools](https://visualstudio.microsoft.com/visual-cpp-build-tools/) or [Visual Studio Express](https://visualstudio.microsoft.com/vs/express/) that matches the version that was used to compile your Python interpreter. |
 
-For more details
-and instructions, see the documentation on
+For more details and instructions, see the documentation on
 [compiling spaCy from source](https://spacy.io/usage#source) and the
 [quickstart widget](https://spacy.io/usage#section-quickstart) to get the right
 commands for your platform and Python version.

build-constraints.txt

@@ -1,9 +1,6 @@
-# build version constraints for use with wheelwright + multibuild
-numpy==1.15.0; python_version<='3.7' and platform_machine!='aarch64'
-numpy==1.19.2; python_version<='3.7' and platform_machine=='aarch64'
+# build version constraints for use with wheelwright
+numpy==1.15.0; python_version=='3.7' and platform_machine!='aarch64'
+numpy==1.19.2; python_version=='3.7' and platform_machine=='aarch64'
 numpy==1.17.3; python_version=='3.8' and platform_machine!='aarch64'
 numpy==1.19.2; python_version=='3.8' and platform_machine=='aarch64'
-numpy==1.19.3; python_version=='3.9'
-numpy==1.21.3; python_version=='3.10'
-numpy==1.23.2; python_version=='3.11'
-numpy; python_version>='3.12'
+numpy>=1.25.0; python_version>='3.9'

extra/DEVELOPER_DOCS/Listeners.md

@@ -1,14 +1,17 @@
 # Listeners
 
-1. [Overview](#1-overview)
-2. [Initialization](#2-initialization)
-   - [A. Linking listeners to the embedding component](#2a-linking-listeners-to-the-embedding-component)
-   - [B. Shape inference](#2b-shape-inference)
-3. [Internal communication](#3-internal-communication)
-   - [A. During prediction](#3a-during-prediction)
-   - [B. During training](#3b-during-training)
-   - [C. Frozen components](#3c-frozen-components)
-4. [Replacing listener with standalone](#4-replacing-listener-with-standalone)
+- [1. Overview](#1-overview)
+- [2. Initialization](#2-initialization)
+  - [2A. Linking listeners to the embedding component](#2a-linking-listeners-to-the-embedding-component)
+  - [2B. Shape inference](#2b-shape-inference)
+- [3. Internal communication](#3-internal-communication)
+  - [3A. During prediction](#3a-during-prediction)
+  - [3B. During training](#3b-during-training)
+    - [Training with multiple listeners](#training-with-multiple-listeners)
+  - [3C. Frozen components](#3c-frozen-components)
+    - [The Tok2Vec or Transformer is frozen](#the-tok2vec-or-transformer-is-frozen)
+    - [The upstream component is frozen](#the-upstream-component-is-frozen)
+- [4. Replacing listener with standalone](#4-replacing-listener-with-standalone)
 
 ## 1. Overview
 
@@ -62,7 +65,7 @@ of this `find_listener()` method will specifically identify sublayers of a model
 
 If it's a Transformer-based pipeline, a
 [`transformer` component](https://github.com/explosion/spacy-transformers/blob/master/spacy_transformers/pipeline_component.py)
-has a similar implementation but its `find_listener()` function will specifically look for `TransformerListener` 
+has a similar implementation but its `find_listener()` function will specifically look for `TransformerListener`
 sublayers of downstream components.
 
 ### 2B. Shape inference
@@ -154,7 +157,7 @@ as a tagger or a parser. This used to be impossible before 3.1, but has become s
 embedding component in the [`annotating_components`](https://spacy.io/usage/training#annotating-components)
 list of the config. This works like any other "annotating component" because it relies on the `Doc` attributes.
 
-However, if the `Tok2Vec` or `Transformer` is frozen, and not present in `annotating_components`, and a related 
+However, if the `Tok2Vec` or `Transformer` is frozen, and not present in `annotating_components`, and a related
 listener isn't frozen, then a `W086` warning is shown and further training of the pipeline will likely end with `E954`.
 
 #### The upstream component is frozen
@@ -216,5 +219,17 @@ new_model = tok2vec_model.attrs["replace_listener"](new_model)
 ```
 
 The new config and model are then properly stored on the `nlp` object.
-Note that this functionality (running the replacement for a transformer listener) was broken prior to 
+Note that this functionality (running the replacement for a transformer listener) was broken prior to
 `spacy-transformers` 1.0.5.
+
+In spaCy 3.7, `Language.replace_listeners` was updated to pass the following additional arguments to the `replace_listener` callback:
+the listener to be replaced and the `tok2vec`/`transformer` pipe from which the new model was copied. To maintain backwards-compatiblity,
+the method only passes these extra arguments for callbacks that support them:
+
+```
+def replace_listener_pre_37(copied_tok2vec_model):
+  ...
+
+def replace_listener_post_37(copied_tok2vec_model, replaced_listener, tok2vec_pipe):
+  ...
+```

licenses/3rd_party_licenses.txt

@@ -158,3 +158,45 @@ AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
 LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
 OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 SOFTWARE.
+
+
+SciPy
+-----
+
+* Files: scorer.py
+
+The implementation of trapezoid() is adapted from SciPy, which is distributed
+under the following license:
+
+New BSD License
+
+Copyright (c) 2001-2002 Enthought, Inc. 2003-2023, SciPy Developers.
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions
+are met:
+
+1. Redistributions of source code must retain the above copyright
+   notice, this list of conditions and the following disclaimer.
+
+2. Redistributions in binary form must reproduce the above
+   copyright notice, this list of conditions and the following
+   disclaimer in the documentation and/or other materials provided
+   with the distribution.
+
+3. Neither the name of the copyright holder nor the names of its
+   contributors may be used to endorse or promote products derived
+   from this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

pyproject.toml

@@ -5,8 +5,9 @@ requires = [
     "cymem>=2.0.2,<2.1.0",
     "preshed>=3.0.2,<3.1.0",
     "murmurhash>=0.28.0,<1.1.0",
-    "thinc>=8.1.8,<8.2.0",
-    "numpy>=1.15.0",
+    "thinc>=8.2.2,<8.3.0",
+    "numpy>=1.15.0; python_version < '3.9'",
+    "numpy>=1.25.0; python_version >= '3.9'",
 ]
 build-backend = "setuptools.build_meta"
 

requirements.txt

@@ -3,20 +3,21 @@ spacy-legacy>=3.0.11,<3.1.0
 spacy-loggers>=1.0.0,<2.0.0
 cymem>=2.0.2,<2.1.0
 preshed>=3.0.2,<3.1.0
-thinc>=8.1.8,<8.2.0
+thinc>=8.2.2,<8.3.0
 ml_datasets>=0.2.0,<0.3.0
 murmurhash>=0.28.0,<1.1.0
 wasabi>=0.9.1,<1.2.0
 srsly>=2.4.3,<3.0.0
 catalogue>=2.0.6,<2.1.0
 typer>=0.3.0,<0.10.0
-pathy>=0.10.0
 smart-open>=5.2.1,<7.0.0
+weasel>=0.1.0,<0.4.0
 # Third party dependencies
-numpy>=1.15.0
+numpy>=1.15.0; python_version < "3.9"
+numpy>=1.19.0; python_version >= "3.9"
 requests>=2.13.0,<3.0.0
 tqdm>=4.38.0,<5.0.0
-pydantic>=1.7.4,!=1.8,!=1.8.1,<1.11.0
+pydantic>=1.7.4,!=1.8,!=1.8.1,<3.0.0
 jinja2
 langcodes>=3.2.0,<4.0.0
 # Official Python utilities
@@ -31,12 +32,11 @@ pytest-timeout>=1.3.0,<2.0.0
 mock>=2.0.0,<3.0.0
 flake8>=3.8.0,<6.0.0
 hypothesis>=3.27.0,<7.0.0
-mypy>=0.990,<1.1.0; platform_machine != "aarch64" and python_version >= "3.7"
-types-dataclasses>=0.1.3; python_version < "3.7"
+mypy>=1.5.0,<1.6.0; platform_machine != "aarch64" and python_version >= "3.8"
 types-mock>=0.1.1
 types-setuptools>=57.0.0
 types-requests
 types-setuptools>=57.0.0
 black==22.3.0
-cython-lint>=0.15.0; python_version >= "3.7"
+cython-lint>=0.15.0
 isort>=5.0,<6.0

setup.cfg

@@ -17,7 +17,6 @@ classifiers =
     Operating System :: Microsoft :: Windows
     Programming Language :: Cython
     Programming Language :: Python :: 3
-    Programming Language :: Python :: 3.6
     Programming Language :: Python :: 3.7
     Programming Language :: Python :: 3.8
     Programming Language :: Python :: 3.9
@@ -31,15 +30,18 @@ project_urls =
 [options]
 zip_safe = false
 include_package_data = true
-python_requires = >=3.6
+python_requires = >=3.7
+# NOTE: This section is superseded by pyproject.toml and will be removed in
+# spaCy v4
 setup_requires =
     cython>=0.25,<3.0
-    numpy>=1.15.0
+    numpy>=1.15.0; python_version < "3.9"
+    numpy>=1.19.0; python_version >= "3.9"
     # We also need our Cython packages here to compile against
     cymem>=2.0.2,<2.1.0
     preshed>=3.0.2,<3.1.0
     murmurhash>=0.28.0,<1.1.0
-    thinc>=8.1.8,<8.2.0
+    thinc>=8.2.2,<8.3.0
 install_requires =
     # Our libraries
     spacy-legacy>=3.0.11,<3.1.0
@@ -47,18 +49,19 @@ install_requires =
     murmurhash>=0.28.0,<1.1.0
     cymem>=2.0.2,<2.1.0
     preshed>=3.0.2,<3.1.0
-    thinc>=8.1.8,<8.2.0
+    thinc>=8.2.2,<8.3.0
     wasabi>=0.9.1,<1.2.0
     srsly>=2.4.3,<3.0.0
     catalogue>=2.0.6,<2.1.0
+    weasel>=0.1.0,<0.4.0
     # Third-party dependencies
     typer>=0.3.0,<0.10.0
-    pathy>=0.10.0
     smart-open>=5.2.1,<7.0.0
     tqdm>=4.38.0,<5.0.0
-    numpy>=1.15.0
+    numpy>=1.15.0; python_version < "3.9"
+    numpy>=1.19.0; python_version >= "3.9"
     requests>=2.13.0,<3.0.0
-    pydantic>=1.7.4,!=1.8,!=1.8.1,<1.11.0
+    pydantic>=1.7.4,!=1.8,!=1.8.1,<3.0.0
     jinja2
     # Official Python utilities
     setuptools
@@ -74,9 +77,7 @@ console_scripts =
 lookups =
     spacy_lookups_data>=1.0.3,<1.1.0
 transformers =
-    spacy_transformers>=1.1.2,<1.3.0
-ray =
-    spacy_ray>=0.1.0,<1.0.0
+    spacy_transformers>=1.1.2,<1.4.0
 cuda =
     cupy>=5.0.0b4,<13.0.0
 cuda80 =
@@ -111,6 +112,8 @@ cuda117 =
     cupy-cuda117>=5.0.0b4,<13.0.0
 cuda11x =
     cupy-cuda11x>=11.0.0,<13.0.0
+cuda12x =
+    cupy-cuda12x>=11.5.0,<13.0.0
 cuda-autodetect =
     cupy-wheel>=11.0.0,<13.0.0
 apple =

setup.py

@@ -1,10 +1,9 @@
 #!/usr/bin/env python
 from setuptools import Extension, setup, find_packages
 import sys
-import platform
 import numpy
-from distutils.command.build_ext import build_ext
-from distutils.sysconfig import get_python_inc
+from setuptools.command.build_ext import build_ext
+from sysconfig import get_path
 from pathlib import Path
 import shutil
 from Cython.Build import cythonize
@@ -79,6 +78,7 @@ COMPILER_DIRECTIVES = {
     "language_level": -3,
     "embedsignature": True,
     "annotation_typing": False,
+    "profile": sys.version_info < (3, 12),
 }
 # Files to copy into the package that are otherwise not included
 COPY_FILES = {
@@ -88,30 +88,6 @@ COPY_FILES = {
 }
 
 
-def is_new_osx():
-    """Check whether we're on OSX >= 10.7"""
-    if sys.platform != "darwin":
-        return False
-    mac_ver = platform.mac_ver()[0]
-    if mac_ver.startswith("10"):
-        minor_version = int(mac_ver.split(".")[1])
-        if minor_version >= 7:
-            return True
-        else:
-            return False
-    return False
-
-
-if is_new_osx():
-    # On Mac, use libc++ because Apple deprecated use of
-    # libstdc
-    COMPILE_OPTIONS["other"].append("-stdlib=libc++")
-    LINK_OPTIONS["other"].append("-lc++")
-    # g++ (used by unix compiler on mac) links to libstdc++ as a default lib.
-    # See: https://stackoverflow.com/questions/1653047/avoid-linking-to-libstdc
-    LINK_OPTIONS["other"].append("-nodefaultlibs")
-
-
 # By subclassing build_extensions we have the actual compiler that will be used which is really known only after finalize_options
 # http://stackoverflow.com/questions/724664/python-distutils-how-to-get-a-compiler-that-is-going-to-be-used
 class build_ext_options:
@@ -204,7 +180,7 @@ def setup_package():
 
     include_dirs = [
         numpy.get_include(),
-        get_python_inc(plat_specific=True),
+        get_path("include"),
     ]
     ext_modules = []
     ext_modules.append(

spacy/about.py

@@ -1,7 +1,5 @@
 # fmt: off
 __title__ = "spacy"
-__version__ = "3.6.0"
+__version__ = "3.7.2"
 __download_url__ = "https://github.com/explosion/spacy-models/releases/download"
 __compatibility__ = "https://raw.githubusercontent.com/explosion/spacy-models/master/compatibility.json"
-__projects__ = "https://github.com/explosion/projects"
-__projects_branch__ = "v3"

spacy/attrs.pyx

@@ -1,3 +1,4 @@
+# cython: profile=False
 from .errors import Errors
 
 IOB_STRINGS = ("", "I", "O", "B")

spacy/cli/__init__.py

@@ -14,6 +14,7 @@ from .debug_diff import debug_diff  # noqa: F401
 from .debug_model import debug_model  # noqa: F401
 from .download import download  # noqa: F401
 from .evaluate import evaluate  # noqa: F401
+from .find_function import find_function  # noqa: F401
 from .find_threshold import find_threshold  # noqa: F401
 from .info import info  # noqa: F401
 from .init_config import fill_config, init_config  # noqa: F401
@@ -21,15 +22,17 @@ from .init_pipeline import init_pipeline_cli  # noqa: F401
 from .package import package  # noqa: F401
 from .pretrain import pretrain  # noqa: F401
 from .profile import profile  # noqa: F401
-from .project.assets import project_assets  # noqa: F401
-from .project.clone import project_clone  # noqa: F401
-from .project.document import project_document  # noqa: F401
-from .project.dvc import project_update_dvc  # noqa: F401
-from .project.pull import project_pull  # noqa: F401
-from .project.push import project_push  # noqa: F401
-from .project.run import project_run  # noqa: F401
-from .train import train_cli  # noqa: F401
-from .validate import validate  # noqa: F401
+from .project.assets import project_assets  # type: ignore[attr-defined]  # noqa: F401
+from .project.clone import project_clone  # type: ignore[attr-defined]  # noqa: F401
+from .project.document import (  # type: ignore[attr-defined]  # noqa: F401
+    project_document,
+)
+from .project.dvc import project_update_dvc  # type: ignore[attr-defined]  # noqa: F401
+from .project.pull import project_pull  # type: ignore[attr-defined]  # noqa: F401
+from .project.push import project_push  # type: ignore[attr-defined]  # noqa: F401
+from .project.run import project_run  # type: ignore[attr-defined]  # noqa: F401
+from .train import train_cli  # type: ignore[attr-defined]  # noqa: F401
+from .validate import validate  # type: ignore[attr-defined]  # noqa: F401
 
 
 @app.command("link", no_args_is_help=True, deprecated=True, hidden=True)

spacy/cli/_util.py

@@ -25,10 +25,11 @@ from thinc.api import Config, ConfigValidationError, require_gpu
 from thinc.util import gpu_is_available
 from typer.main import get_command
 from wasabi import Printer, msg
+from weasel import app as project_cli
 
 from .. import about
 from ..compat import Literal
-from ..schemas import ProjectConfigSchema, validate
+from ..schemas import validate
 from ..util import (
     ENV_VARS,
     SimpleFrozenDict,
@@ -40,15 +41,10 @@ from ..util import (
     run_command,
 )
 
-if TYPE_CHECKING:
-    from pathy import FluidPath  # noqa: F401
-
-
 SDIST_SUFFIX = ".tar.gz"
 WHEEL_SUFFIX = "-py3-none-any.whl"
 
 PROJECT_FILE = "project.yml"
-PROJECT_LOCK = "project.lock"
 COMMAND = "python -m spacy"
 NAME = "spacy"
 HELP = """spaCy Command-line Interface
@@ -74,11 +70,10 @@ Opt = typer.Option
 
 app = typer.Typer(name=NAME, help=HELP)
 benchmark_cli = typer.Typer(name="benchmark", help=BENCHMARK_HELP, no_args_is_help=True)
-project_cli = typer.Typer(name="project", help=PROJECT_HELP, no_args_is_help=True)
 debug_cli = typer.Typer(name="debug", help=DEBUG_HELP, no_args_is_help=True)
 init_cli = typer.Typer(name="init", help=INIT_HELP, no_args_is_help=True)
 
-app.add_typer(project_cli)
+app.add_typer(project_cli, name="project", help=PROJECT_HELP, no_args_is_help=True)
 app.add_typer(debug_cli)
 app.add_typer(benchmark_cli)
 app.add_typer(init_cli)
@@ -153,148 +148,6 @@ def _parse_override(value: Any) -> Any:
         return str(value)
 
 
-def load_project_config(
-    path: Path, interpolate: bool = True, overrides: Dict[str, Any] = SimpleFrozenDict()
-) -> Dict[str, Any]:
-    """Load the project.yml file from a directory and validate it. Also make
-    sure that all directories defined in the config exist.
-
-    path (Path): The path to the project directory.
-    interpolate (bool): Whether to substitute project variables.
-    overrides (Dict[str, Any]): Optional config overrides.
-    RETURNS (Dict[str, Any]): The loaded project.yml.
-    """
-    config_path = path / PROJECT_FILE
-    if not config_path.exists():
-        msg.fail(f"Can't find {PROJECT_FILE}", config_path, exits=1)
-    invalid_err = f"Invalid {PROJECT_FILE}. Double-check that the YAML is correct."
-    try:
-        config = srsly.read_yaml(config_path)
-    except ValueError as e:
-        msg.fail(invalid_err, e, exits=1)
-    errors = validate(ProjectConfigSchema, config)
-    if errors:
-        msg.fail(invalid_err)
-        print("\n".join(errors))
-        sys.exit(1)
-    validate_project_version(config)
-    validate_project_commands(config)
-    if interpolate:
-        err = f"{PROJECT_FILE} validation error"
-        with show_validation_error(title=err, hint_fill=False):
-            config = substitute_project_variables(config, overrides)
-    # Make sure directories defined in config exist
-    for subdir in config.get("directories", []):
-        dir_path = path / subdir
-        if not dir_path.exists():
-            dir_path.mkdir(parents=True)
-    return config
-
-
-def substitute_project_variables(
-    config: Dict[str, Any],
-    overrides: Dict[str, Any] = SimpleFrozenDict(),
-    key: str = "vars",
-    env_key: str = "env",
-) -> Dict[str, Any]:
-    """Interpolate variables in the project file using the config system.
-
-    config (Dict[str, Any]): The project config.
-    overrides (Dict[str, Any]): Optional config overrides.
-    key (str): Key containing variables in project config.
-    env_key (str): Key containing environment variable mapping in project config.
-    RETURNS (Dict[str, Any]): The interpolated project config.
-    """
-    config.setdefault(key, {})
-    config.setdefault(env_key, {})
-    # Substitute references to env vars with their values
-    for config_var, env_var in config[env_key].items():
-        config[env_key][config_var] = _parse_override(os.environ.get(env_var, ""))
-    # Need to put variables in the top scope again so we can have a top-level
-    # section "project" (otherwise, a list of commands in the top scope wouldn't)
-    # be allowed by Thinc's config system
-    cfg = Config({"project": config, key: config[key], env_key: config[env_key]})
-    cfg = Config().from_str(cfg.to_str(), overrides=overrides)
-    interpolated = cfg.interpolate()
-    return dict(interpolated["project"])
-
-
-def validate_project_version(config: Dict[str, Any]) -> None:
-    """If the project defines a compatible spaCy version range, chec that it's
-    compatible with the current version of spaCy.
-
-    config (Dict[str, Any]): The loaded config.
-    """
-    spacy_version = config.get("spacy_version", None)
-    if spacy_version and not is_compatible_version(about.__version__, spacy_version):
-        err = (
-            f"The {PROJECT_FILE} specifies a spaCy version range ({spacy_version}) "
-            f"that's not compatible with the version of spaCy you're running "
-            f"({about.__version__}). You can edit version requirement in the "
-            f"{PROJECT_FILE} to load it, but the project may not run as expected."
-        )
-        msg.fail(err, exits=1)
-
-
-def validate_project_commands(config: Dict[str, Any]) -> None:
-    """Check that project commands and workflows are valid, don't contain
-    duplicates, don't clash  and only refer to commands that exist.
-
-    config (Dict[str, Any]): The loaded config.
-    """
-    command_names = [cmd["name"] for cmd in config.get("commands", [])]
-    workflows = config.get("workflows", {})
-    duplicates = set([cmd for cmd in command_names if command_names.count(cmd) > 1])
-    if duplicates:
-        err = f"Duplicate commands defined in {PROJECT_FILE}: {', '.join(duplicates)}"
-        msg.fail(err, exits=1)
-    for workflow_name, workflow_steps in workflows.items():
-        if workflow_name in command_names:
-            err = f"Can't use workflow name '{workflow_name}': name already exists as a command"
-            msg.fail(err, exits=1)
-        for step in workflow_steps:
-            if step not in command_names:
-                msg.fail(
-                    f"Unknown command specified in workflow '{workflow_name}': {step}",
-                    f"Workflows can only refer to commands defined in the 'commands' "
-                    f"section of the {PROJECT_FILE}.",
-                    exits=1,
-                )
-
-
-def get_hash(data, exclude: Iterable[str] = tuple()) -> str:
-    """Get the hash for a JSON-serializable object.
-
-    data: The data to hash.
-    exclude (Iterable[str]): Top-level keys to exclude if data is a dict.
-    RETURNS (str): The hash.
-    """
-    if isinstance(data, dict):
-        data = {k: v for k, v in data.items() if k not in exclude}
-    data_str = srsly.json_dumps(data, sort_keys=True).encode("utf8")
-    return hashlib.md5(data_str).hexdigest()
-
-
-def get_checksum(path: Union[Path, str]) -> str:
-    """Get the checksum for a file or directory given its file path. If a
-    directory path is provided, this uses all files in that directory.
-
-    path (Union[Path, str]): The file or directory path.
-    RETURNS (str): The checksum.
-    """
-    path = Path(path)
-    if not (path.is_file() or path.is_dir()):
-        msg.fail(f"Can't get checksum for {path}: not a file or directory", exits=1)
-    if path.is_file():
-        return hashlib.md5(Path(path).read_bytes()).hexdigest()
-    else:
-        # TODO: this is currently pretty slow
-        dir_checksum = hashlib.md5()
-        for sub_file in sorted(fp for fp in path.rglob("*") if fp.is_file()):
-            dir_checksum.update(sub_file.read_bytes())
-        return dir_checksum.hexdigest()
-
-
 @contextmanager
 def show_validation_error(
     file_path: Optional[Union[str, Path]] = None,
@@ -352,166 +205,10 @@ def import_code(code_path: Optional[Union[Path, str]]) -> None:
             msg.fail(f"Couldn't load Python code: {code_path}", e, exits=1)
 
 
-def upload_file(src: Path, dest: Union[str, "FluidPath"]) -> None:
-    """Upload a file.
-
-    src (Path): The source path.
-    url (str): The destination URL to upload to.
-    """
-    import smart_open
-
-    # Create parent directories for local paths
-    if isinstance(dest, Path):
-        if not dest.parent.exists():
-            dest.parent.mkdir(parents=True)
-
-    dest = str(dest)
-    with smart_open.open(dest, mode="wb") as output_file:
-        with src.open(mode="rb") as input_file:
-            output_file.write(input_file.read())
-
-
-def download_file(
-    src: Union[str, "FluidPath"], dest: Path, *, force: bool = False
-) -> None:
-    """Download a file using smart_open.
-
-    url (str): The URL of the file.
-    dest (Path): The destination path.
-    force (bool): Whether to force download even if file exists.
-        If False, the download will be skipped.
-    """
-    import smart_open
-
-    if dest.exists() and not force:
-        return None
-    src = str(src)
-    with smart_open.open(src, mode="rb", compression="disable") as input_file:
-        with dest.open(mode="wb") as output_file:
-            shutil.copyfileobj(input_file, output_file)
-
-
-def ensure_pathy(path):
-    """Temporary helper to prevent importing Pathy globally (which can cause
-    slow and annoying Google Cloud warning)."""
-    from pathy import Pathy  # noqa: F811
-
-    return Pathy.fluid(path)
-
-
-def git_checkout(
-    repo: str, subpath: str, dest: Path, *, branch: str = "master", sparse: bool = False
-):
-    git_version = get_git_version()
-    if dest.exists():
-        msg.fail("Destination of checkout must not exist", exits=1)
-    if not dest.parent.exists():
-        msg.fail("Parent of destination of checkout must exist", exits=1)
-    if sparse and git_version >= (2, 22):
-        return git_sparse_checkout(repo, subpath, dest, branch)
-    elif sparse:
-        # Only show warnings if the user explicitly wants sparse checkout but
-        # the Git version doesn't support it
-        err_old = (
-            f"You're running an old version of Git (v{git_version[0]}.{git_version[1]}) "
-            f"that doesn't fully support sparse checkout yet."
-        )
-        err_unk = "You're running an unknown version of Git, so sparse checkout has been disabled."
-        msg.warn(
-            f"{err_unk if git_version == (0, 0) else err_old} "
-            f"This means that more files than necessary may be downloaded "
-            f"temporarily. To only download the files needed, make sure "
-            f"you're using Git v2.22 or above."
-        )
-    with make_tempdir() as tmp_dir:
-        cmd = f"git -C {tmp_dir} clone {repo} . -b {branch}"
-        run_command(cmd, capture=True)
-        # We need Path(name) to make sure we also support subdirectories
-        try:
-            source_path = tmp_dir / Path(subpath)
-            if not is_subpath_of(tmp_dir, source_path):
-                err = f"'{subpath}' is a path outside of the cloned repository."
-                msg.fail(err, repo, exits=1)
-            shutil.copytree(str(source_path), str(dest))
-        except FileNotFoundError:
-            err = f"Can't clone {subpath}. Make sure the directory exists in the repo (branch '{branch}')"
-            msg.fail(err, repo, exits=1)
-
-
-def git_sparse_checkout(repo, subpath, dest, branch):
-    # We're using Git, partial clone and sparse checkout to
-    # only clone the files we need
-    # This ends up being RIDICULOUS. omg.
-    # So, every tutorial and SO post talks about 'sparse checkout'...But they
-    # go and *clone* the whole repo. Worthless. And cloning part of a repo
-    # turns out to be completely broken. The only way to specify a "path" is..
-    # a path *on the server*? The contents of which, specifies the paths. Wat.
-    # Obviously this is hopelessly broken and insecure, because you can query
-    # arbitrary paths on the server! So nobody enables this.
-    # What we have to do is disable *all* files. We could then just checkout
-    # the path, and it'd "work", but be hopelessly slow...Because it goes and
-    # transfers every missing object one-by-one. So the final piece is that we
-    # need to use some weird git internals to fetch the missings in bulk, and
-    # *that* we can do by path.
-    # We're using Git and sparse checkout to only clone the files we need
-    with make_tempdir() as tmp_dir:
-        # This is the "clone, but don't download anything" part.
-        cmd = (
-            f"git clone {repo} {tmp_dir} --no-checkout --depth 1 "
-            f"-b {branch} --filter=blob:none"
-        )
-        run_command(cmd)
-        # Now we need to find the missing filenames for the subpath we want.
-        # Looking for this 'rev-list' command in the git --help? Hah.
-        cmd = f"git -C {tmp_dir} rev-list --objects --all --missing=print -- {subpath}"
-        ret = run_command(cmd, capture=True)
-        git_repo = _http_to_git(repo)
-        # Now pass those missings into another bit of git internals
-        missings = " ".join([x[1:] for x in ret.stdout.split() if x.startswith("?")])
-        if not missings:
-            err = (
-                f"Could not find any relevant files for '{subpath}'. "
-                f"Did you specify a correct and complete path within repo '{repo}' "
-                f"and branch {branch}?"
-            )
-            msg.fail(err, exits=1)
-        cmd = f"git -C {tmp_dir} fetch-pack {git_repo} {missings}"
-        run_command(cmd, capture=True)
-        # And finally, we can checkout our subpath
-        cmd = f"git -C {tmp_dir} checkout {branch} {subpath}"
-        run_command(cmd, capture=True)
-
-        # Get a subdirectory of the cloned path, if appropriate
-        source_path = tmp_dir / Path(subpath)
-        if not is_subpath_of(tmp_dir, source_path):
-            err = f"'{subpath}' is a path outside of the cloned repository."
-            msg.fail(err, repo, exits=1)
-
-        shutil.move(str(source_path), str(dest))
-
-
-def git_repo_branch_exists(repo: str, branch: str) -> bool:
-    """Uses 'git ls-remote' to check if a repository and branch exists
-
-    repo (str): URL to get repo.
-    branch (str): Branch on repo to check.
-    RETURNS (bool): True if repo:branch exists.
-    """
-    get_git_version()
-    cmd = f"git ls-remote {repo} {branch}"
-    # We might be tempted to use `--exit-code` with `git ls-remote`, but
-    # `run_command` handles the `returncode` for us, so we'll rely on
-    # the fact that stdout returns '' if the requested branch doesn't exist
-    ret = run_command(cmd, capture=True)
-    exists = ret.stdout != ""
-    return exists
-
-
 def get_git_version(
     error: str = "Could not run 'git'. Make sure it's installed and the executable is available.",
 ) -> Tuple[int, int]:
     """Get the version of git and raise an error if calling 'git --version' fails.
-
     error (str): The error message to show.
     RETURNS (Tuple[int, int]): The version as a (major, minor) tuple. Returns
         (0, 0) if the version couldn't be determined.
@@ -527,30 +224,6 @@ def get_git_version(
     return int(version[0]), int(version[1])
 
 
-def _http_to_git(repo: str) -> str:
-    if repo.startswith("http://"):
-        repo = repo.replace(r"http://", r"https://")
-    if repo.startswith(r"https://"):
-        repo = repo.replace("https://", "git@").replace("/", ":", 1)
-        if repo.endswith("/"):
-            repo = repo[:-1]
-        repo = f"{repo}.git"
-    return repo
-
-
-def is_subpath_of(parent, child):
-    """
-    Check whether `child` is a path contained within `parent`.
-    """
-    # Based on https://stackoverflow.com/a/37095733 .
-
-    # In Python 3.9, the `Path.is_relative_to()` method will supplant this, so
-    # we can stop using crusty old os.path functions.
-    parent_realpath = os.path.realpath(parent)
-    child_realpath = os.path.realpath(child)
-    return os.path.commonpath([parent_realpath, child_realpath]) == parent_realpath
-
-
 @overload
 def string_to_list(value: str, intify: Literal[False] = ...) -> List[str]:
     ...

spacy/cli/apply.py

@@ -133,7 +133,9 @@ def apply(
     if len(text_files) > 0:
         streams.append(_stream_texts(text_files))
     datagen = cast(DocOrStrStream, chain(*streams))
-    for doc in tqdm.tqdm(nlp.pipe(datagen, batch_size=batch_size, n_process=n_process)):
+    for doc in tqdm.tqdm(
+        nlp.pipe(datagen, batch_size=batch_size, n_process=n_process), disable=None
+    ):
         docbin.add(doc)
     if output_file.suffix == "":
         output_file = output_file.with_suffix(".spacy")

spacy/cli/assemble.py

@@ -40,7 +40,8 @@ def assemble_cli(
 
     DOCS: https://spacy.io/api/cli#assemble
     """
-    util.logger.setLevel(logging.DEBUG if verbose else logging.INFO)
+    if verbose:
+        util.logger.setLevel(logging.DEBUG)
     # Make sure all files and paths exists if they are needed
     if not config_path or (str(config_path) != "-" and not config_path.exists()):
         msg.fail("Config file not found", config_path, exits=1)

spacy/cli/benchmark_speed.py

@@ -13,7 +13,7 @@ from .. import util
 from ..language import Language
 from ..tokens import Doc
 from ..training import Corpus
-from ._util import Arg, Opt, benchmark_cli, setup_gpu
+from ._util import Arg, Opt, benchmark_cli, import_code, setup_gpu
 
 
 @benchmark_cli.command(
@@ -30,12 +30,14 @@ def benchmark_speed_cli(
     use_gpu: int = Opt(-1, "--gpu-id", "-g", help="GPU ID or -1 for CPU"),
     n_batches: int = Opt(50, "--batches", help="Minimum number of batches to benchmark", min=30,),
     warmup_epochs: int = Opt(3, "--warmup", "-w", min=0, help="Number of iterations over the data for warmup"),
+    code_path: Optional[Path] = Opt(None, "--code", "-c", help="Path to Python file with additional code (registered functions) to be imported"),
     # fmt: on
 ):
     """
     Benchmark a pipeline. Expects a loadable spaCy pipeline and benchmark
     data in the binary .spacy format.
     """
+    import_code(code_path)
     setup_gpu(use_gpu=use_gpu, silent=False)
 
     nlp = util.load_model(model)
@@ -89,7 +91,7 @@ class Quartiles:
 def annotate(
     nlp: Language, docs: List[Doc], batch_size: Optional[int]
 ) -> numpy.ndarray:
-    docs = nlp.pipe(tqdm(docs, unit="doc"), batch_size=batch_size)
+    docs = nlp.pipe(tqdm(docs, unit="doc", disable=None), batch_size=batch_size)
     wps = []
     while True:
         with time_context() as elapsed:
@@ -171,5 +173,5 @@ def print_outliers(sample: numpy.ndarray):
 def warmup(
     nlp: Language, docs: List[Doc], warmup_epochs: int, batch_size: Optional[int]
 ) -> numpy.ndarray:
-    docs = warmup_epochs * docs
+    docs = [doc.copy() for doc in docs * warmup_epochs]
     return annotate(nlp, docs, batch_size)

spacy/cli/download.py

@@ -7,7 +7,14 @@ from wasabi import msg
 
 from .. import about
 from ..errors import OLD_MODEL_SHORTCUTS
-from ..util import get_minor_version, is_package, is_prerelease_version, run_command
+from ..util import (
+    get_minor_version,
+    is_in_interactive,
+    is_in_jupyter,
+    is_package,
+    is_prerelease_version,
+    run_command,
+)
 from ._util import SDIST_SUFFIX, WHEEL_SUFFIX, Arg, Opt, app
 
 
@@ -77,6 +84,27 @@ def download(
         "Download and installation successful",
         f"You can now load the package via spacy.load('{model_name}')",
     )
+    if is_in_jupyter():
+        reload_deps_msg = (
+            "If you are in a Jupyter or Colab notebook, you may need to "
+            "restart Python in order to load all the package's dependencies. "
+            "You can do this by selecting the 'Restart kernel' or 'Restart "
+            "runtime' option."
+        )
+        msg.warn(
+            "Restart to reload dependencies",
+            reload_deps_msg,
+        )
+    elif is_in_interactive():
+        reload_deps_msg = (
+            "If you are in an interactive Python session, you may need to "
+            "exit and restart Python to load all the package's dependencies. "
+            "You can exit with Ctrl-D (or Ctrl-Z and Enter on Windows)."
+        )
+        msg.warn(
+            "Restart to reload dependencies",
+            reload_deps_msg,
+        )
 
 
 def get_model_filename(model_name: str, version: str, sdist: bool = False) -> str:

spacy/cli/evaluate.py

@@ -28,6 +28,7 @@ def evaluate_cli(
     displacy_path: Optional[Path] = Opt(None, "--displacy-path", "-dp", help="Directory to output rendered parses as HTML", exists=True, file_okay=False),
     displacy_limit: int = Opt(25, "--displacy-limit", "-dl", help="Limit of parses to render as HTML"),
     per_component: bool = Opt(False, "--per-component", "-P", help="Return scores per component, only applicable when an output JSON file is specified."),
+    spans_key: str = Opt("sc", "--spans-key", "-sk", help="Spans key to use when evaluating Doc.spans"),
     # fmt: on
 ):
     """
@@ -53,6 +54,7 @@ def evaluate_cli(
         displacy_limit=displacy_limit,
         per_component=per_component,
         silent=False,
+        spans_key=spans_key,
     )
 
 

spacy/cli/find_function.py

@@ -0,0 +1,69 @@
+from typing import Optional, Tuple
+
+from catalogue import RegistryError
+from wasabi import msg
+
+from ..util import registry
+from ._util import Arg, Opt, app
+
+
+@app.command("find-function")
+def find_function_cli(
+    # fmt: off
+    func_name: str = Arg(..., help="Name of the registered function."),
+    registry_name: Optional[str] = Opt(None, "--registry", "-r", help="Name of the catalogue registry."),
+    # fmt: on
+):
+    """
+    Find the module, path and line number to the file the registered
+    function is defined in, if available.
+
+    func_name (str): Name of the registered function.
+    registry_name (Optional[str]): Name of the catalogue registry.
+
+    DOCS: https://spacy.io/api/cli#find-function
+    """
+    if not registry_name:
+        registry_names = registry.get_registry_names()
+        for name in registry_names:
+            if registry.has(name, func_name):
+                registry_name = name
+                break
+
+    if not registry_name:
+        msg.fail(
+            f"Couldn't find registered function: '{func_name}'",
+            exits=1,
+        )
+
+    assert registry_name is not None
+    find_function(func_name, registry_name)
+
+
+def find_function(func_name: str, registry_name: str) -> Tuple[str, int]:
+    registry_desc = None
+    try:
+        registry_desc = registry.find(registry_name, func_name)
+    except RegistryError as e:
+        msg.fail(
+            f"Couldn't find registered function: '{func_name}' in registry '{registry_name}'",
+        )
+        msg.fail(f"{e}", exits=1)
+    assert registry_desc is not None
+
+    registry_path = None
+    line_no = None
+    if registry_desc["file"]:
+        registry_path = registry_desc["file"]
+        line_no = registry_desc["line_no"]
+
+    if not registry_path or not line_no:
+        msg.fail(
+            f"Couldn't find path to registered function: '{func_name}' in registry '{registry_name}'",
+            exits=1,
+        )
+    assert registry_path is not None
+    assert line_no is not None
+
+    msg.good(f"Found registered function '{func_name}' at {registry_path}:{line_no}")
+    return str(registry_path), int(line_no)

spacy/cli/find_threshold.py

@@ -52,8 +52,8 @@ def find_threshold_cli(
 
     DOCS: https://spacy.io/api/cli#find-threshold
     """
-
-    util.logger.setLevel(logging.DEBUG if verbose else logging.INFO)
+    if verbose:
+        util.logger.setLevel(logging.DEBUG)
     import_code(code_path)
     find_threshold(
         model=model,

spacy/cli/init_pipeline.py

@@ -39,7 +39,8 @@ def init_vectors_cli(
     you can use in the [initialize] block of your config to initialize
     a model with vectors.
     """
-    util.logger.setLevel(logging.DEBUG if verbose else logging.INFO)
+    if verbose:
+        util.logger.setLevel(logging.DEBUG)
     msg.info(f"Creating blank nlp object for language '{lang}'")
     nlp = util.get_lang_class(lang)()
     if jsonl_loc is not None:
@@ -87,7 +88,8 @@ def init_pipeline_cli(
     use_gpu: int = Opt(-1, "--gpu-id", "-g", help="GPU ID or -1 for CPU")
     # fmt: on
 ):
-    util.logger.setLevel(logging.DEBUG if verbose else logging.INFO)
+    if verbose:
+        util.logger.setLevel(logging.DEBUG)
     overrides = parse_config_overrides(ctx.args)
     import_code(code_path)
     setup_gpu(use_gpu)
@@ -116,7 +118,8 @@ def init_labels_cli(
     """Generate JSON files for the labels in the data. This helps speed up the
     training process, since spaCy won't have to preprocess the data to
     extract the labels."""
-    util.logger.setLevel(logging.DEBUG if verbose else logging.INFO)
+    if verbose:
+        util.logger.setLevel(logging.DEBUG)
     if not output_path.exists():
         output_path.mkdir(parents=True)
     overrides = parse_config_overrides(ctx.args)

spacy/cli/package.py

@@ -1,5 +1,7 @@
+import os
 import re
 import shutil
+import subprocess
 import sys
 from collections import defaultdict
 from pathlib import Path
@@ -11,6 +13,7 @@ from thinc.api import Config
 from wasabi import MarkdownRenderer, Printer, get_raw_input
 
 from .. import about, util
+from ..compat import importlib_metadata
 from ..schemas import ModelMetaSchema, validate
 from ._util import SDIST_SUFFIX, WHEEL_SUFFIX, Arg, Opt, app, string_to_list
 
@@ -35,7 +38,7 @@ def package_cli(
     specified output directory, and the data will be copied over. If
     --create-meta is set and a meta.json already exists in the output directory,
     the existing values will be used as the defaults in the command-line prompt.
-    After packaging, "python setup.py sdist" is run in the package directory,
+    After packaging, "python -m build --sdist" is run in the package directory,
     which will create a .tar.gz archive that can be installed via "pip install".
 
     If additional code files are provided (e.g. Python files containing custom
@@ -78,9 +81,17 @@ def package(
     input_path = util.ensure_path(input_dir)
     output_path = util.ensure_path(output_dir)
     meta_path = util.ensure_path(meta_path)
-    if create_wheel and not has_wheel():
-        err = "Generating a binary .whl file requires wheel to be installed"
-        msg.fail(err, "pip install wheel", exits=1)
+    if create_wheel and not has_wheel() and not has_build():
+        err = (
+            "Generating wheels requires 'build' or 'wheel' (deprecated) to be installed"
+        )
+        msg.fail(err, "pip install build", exits=1)
+    if not has_build():
+        msg.warn(
+            "Generating packages without the 'build' package is deprecated and "
+            "will not be supported in the future. To install 'build': pip "
+            "install build"
+        )
     if not input_path or not input_path.exists():
         msg.fail("Can't locate pipeline data", input_path, exits=1)
     if not output_path or not output_path.exists():
@@ -184,12 +195,37 @@ def package(
     msg.good(f"Successfully created package directory '{model_name_v}'", main_path)
     if create_sdist:
         with util.working_dir(main_path):
-            util.run_command([sys.executable, "setup.py", "sdist"], capture=False)
+            # run directly, since util.run_command is not designed to continue
+            # after a command fails
+            ret = subprocess.run(
+                [sys.executable, "-m", "build", ".", "--sdist"],
+                env=os.environ.copy(),
+            )
+            if ret.returncode != 0:
+                msg.warn(
+                    "Creating sdist with 'python -m build' failed. Falling "
+                    "back to deprecated use of 'python setup.py sdist'"
+                )
+                util.run_command([sys.executable, "setup.py", "sdist"], capture=False)
         zip_file = main_path / "dist" / f"{model_name_v}{SDIST_SUFFIX}"
         msg.good(f"Successfully created zipped Python package", zip_file)
     if create_wheel:
         with util.working_dir(main_path):
-            util.run_command([sys.executable, "setup.py", "bdist_wheel"], capture=False)
+            # run directly, since util.run_command is not designed to continue
+            # after a command fails
+            ret = subprocess.run(
+                [sys.executable, "-m", "build", ".", "--wheel"],
+                env=os.environ.copy(),
+            )
+            if ret.returncode != 0:
+                msg.warn(
+                    "Creating wheel with 'python -m build' failed. Falling "
+                    "back to deprecated use of 'wheel' with "
+                    "'python setup.py bdist_wheel'"
+                )
+                util.run_command(
+                    [sys.executable, "setup.py", "bdist_wheel"], capture=False
+                )
         wheel_name_squashed = re.sub("_+", "_", model_name_v)
         wheel = main_path / "dist" / f"{wheel_name_squashed}{WHEEL_SUFFIX}"
         msg.good(f"Successfully created binary wheel", wheel)
@@ -209,6 +245,17 @@ def has_wheel() -> bool:
         return False
 
 
+def has_build() -> bool:
+    # it's very likely that there is a local directory named build/ (especially
+    # in an editable install), so an import check is not sufficient; instead
+    # check that there is a package version
+    try:
+        importlib_metadata.version("build")
+        return True
+    except importlib_metadata.PackageNotFoundError:  # type: ignore[attr-defined]
+        return False
+
+
 def get_third_party_dependencies(
     config: Config, exclude: List[str] = util.SimpleFrozenList()
 ) -> List[str]:
@@ -403,7 +450,7 @@ def _format_sources(data: Any) -> str:
         if author:
             result += " ({})".format(author)
         sources.append(result)
-    return "<br />".join(sources)
+    return "<br>".join(sources)
 
 
 def _format_accuracy(data: Dict[str, Any], exclude: List[str] = ["speed"]) -> str:

spacy/cli/profile.py

@@ -71,7 +71,7 @@ def profile(model: str, inputs: Optional[Path] = None, n_texts: int = 10000) ->
 
 
 def parse_texts(nlp: Language, texts: Sequence[str]) -> None:
-    for doc in nlp.pipe(tqdm.tqdm(texts), batch_size=16):
+    for doc in nlp.pipe(tqdm.tqdm(texts, disable=None), batch_size=16):
         pass
 
 

spacy/cli/project/assets.py

@@ -1,217 +1 @@
-import os
-import re
-import shutil
-from pathlib import Path
-from typing import Any, Dict, Optional
-
-import requests
-import typer
-from wasabi import msg
-
-from ...util import ensure_path, working_dir
-from .._util import (
-    PROJECT_FILE,
-    Arg,
-    Opt,
-    SimpleFrozenDict,
-    download_file,
-    get_checksum,
-    get_git_version,
-    git_checkout,
-    load_project_config,
-    parse_config_overrides,
-    project_cli,
-)
-
-# Whether assets are extra if `extra` is not set.
-EXTRA_DEFAULT = False
-
-
-@project_cli.command(
-    "assets",
-    context_settings={"allow_extra_args": True, "ignore_unknown_options": True},
-)
-def project_assets_cli(
-    # fmt: off
-    ctx: typer.Context,  # This is only used to read additional arguments
-    project_dir: Path = Arg(Path.cwd(), help="Path to cloned project. Defaults to current working directory.", exists=True, file_okay=False),
-    sparse_checkout: bool = Opt(False, "--sparse", "-S", help="Use sparse checkout for assets provided via Git, to only check out and clone the files needed. Requires Git v22.2+."),
-    extra: bool = Opt(False, "--extra", "-e", help="Download all assets, including those marked as 'extra'.")
-    # fmt: on
-):
-    """Fetch project assets like datasets and pretrained weights. Assets are
-    defined in the "assets" section of the project.yml. If a checksum is
-    provided in the project.yml, the file is only downloaded if no local file
-    with the same checksum exists.
-
-    DOCS: https://spacy.io/api/cli#project-assets
-    """
-    overrides = parse_config_overrides(ctx.args)
-    project_assets(
-        project_dir,
-        overrides=overrides,
-        sparse_checkout=sparse_checkout,
-        extra=extra,
-    )
-
-
-def project_assets(
-    project_dir: Path,
-    *,
-    overrides: Dict[str, Any] = SimpleFrozenDict(),
-    sparse_checkout: bool = False,
-    extra: bool = False,
-) -> None:
-    """Fetch assets for a project using DVC if possible.
-
-    project_dir (Path): Path to project directory.
-    sparse_checkout (bool): Use sparse checkout for assets provided via Git, to only check out and clone the files
-                            needed.
-    extra (bool): Whether to download all assets, including those marked as 'extra'.
-    """
-    project_path = ensure_path(project_dir)
-    config = load_project_config(project_path, overrides=overrides)
-    assets = [
-        asset
-        for asset in config.get("assets", [])
-        if extra or not asset.get("extra", EXTRA_DEFAULT)
-    ]
-    if not assets:
-        msg.warn(
-            f"No assets specified in {PROJECT_FILE} (if assets are marked as extra, download them with --extra)",
-            exits=0,
-        )
-    msg.info(f"Fetching {len(assets)} asset(s)")
-
-    for asset in assets:
-        dest = (project_dir / asset["dest"]).resolve()
-        checksum = asset.get("checksum")
-        if "git" in asset:
-            git_err = (
-                f"Cloning spaCy project templates requires Git and the 'git' command. "
-                f"Make sure it's installed and that the executable is available."
-            )
-            get_git_version(error=git_err)
-            if dest.exists():
-                # If there's already a file, check for checksum
-                if checksum and checksum == get_checksum(dest):
-                    msg.good(
-                        f"Skipping download with matching checksum: {asset['dest']}"
-                    )
-                    continue
-                else:
-                    if dest.is_dir():
-                        shutil.rmtree(dest)
-                    else:
-                        dest.unlink()
-            if "repo" not in asset["git"] or asset["git"]["repo"] is None:
-                msg.fail(
-                    "A git asset must include 'repo', the repository address.", exits=1
-                )
-            if "path" not in asset["git"] or asset["git"]["path"] is None:
-                msg.fail(
-                    "A git asset must include 'path' - use \"\" to get the entire repository.",
-                    exits=1,
-                )
-            git_checkout(
-                asset["git"]["repo"],
-                asset["git"]["path"],
-                dest,
-                branch=asset["git"].get("branch"),
-                sparse=sparse_checkout,
-            )
-            msg.good(f"Downloaded asset {dest}")
-        else:
-            url = asset.get("url")
-            if not url:
-                # project.yml defines asset without URL that the user has to place
-                check_private_asset(dest, checksum)
-                continue
-            fetch_asset(project_path, url, dest, checksum)
-
-
-def check_private_asset(dest: Path, checksum: Optional[str] = None) -> None:
-    """Check and validate assets without a URL (private assets that the user
-    has to provide themselves) and give feedback about the checksum.
-
-    dest (Path): Destination path of the asset.
-    checksum (Optional[str]): Optional checksum of the expected file.
-    """
-    if not Path(dest).exists():
-        err = f"No URL provided for asset. You need to add this file yourself: {dest}"
-        msg.warn(err)
-    else:
-        if not checksum:
-            msg.good(f"Asset already exists: {dest}")
-        elif checksum == get_checksum(dest):
-            msg.good(f"Asset exists with matching checksum: {dest}")
-        else:
-            msg.fail(f"Asset available but with incorrect checksum: {dest}")
-
-
-def fetch_asset(
-    project_path: Path, url: str, dest: Path, checksum: Optional[str] = None
-) -> None:
-    """Fetch an asset from a given URL or path. If a checksum is provided and a
-    local file exists, it's only re-downloaded if the checksum doesn't match.
-
-    project_path (Path): Path to project directory.
-    url (str): URL or path to asset.
-    checksum (Optional[str]): Optional expected checksum of local file.
-    RETURNS (Optional[Path]): The path to the fetched asset or None if fetching
-        the asset failed.
-    """
-    dest_path = (project_path / dest).resolve()
-    if dest_path.exists():
-        # If there's already a file, check for checksum
-        if checksum:
-            if checksum == get_checksum(dest_path):
-                msg.good(f"Skipping download with matching checksum: {dest}")
-                return
-        else:
-            # If there's not a checksum, make sure the file is a possibly valid size
-            if os.path.getsize(dest_path) == 0:
-                msg.warn(f"Asset exists but with size of 0 bytes, deleting: {dest}")
-                os.remove(dest_path)
-    # We might as well support the user here and create parent directories in
-    # case the asset dir isn't listed as a dir to create in the project.yml
-    if not dest_path.parent.exists():
-        dest_path.parent.mkdir(parents=True)
-    with working_dir(project_path):
-        url = convert_asset_url(url)
-        try:
-            download_file(url, dest_path)
-            msg.good(f"Downloaded asset {dest}")
-        except requests.exceptions.RequestException as e:
-            if Path(url).exists() and Path(url).is_file():
-                # If it's a local file, copy to destination
-                shutil.copy(url, str(dest_path))
-                msg.good(f"Copied local asset {dest}")
-            else:
-                msg.fail(f"Download failed: {dest}", e)
-    if checksum and checksum != get_checksum(dest_path):
-        msg.fail(f"Checksum doesn't match value defined in {PROJECT_FILE}: {dest}")
-
-
-def convert_asset_url(url: str) -> str:
-    """Check and convert the asset URL if needed.
-
-    url (str): The asset URL.
-    RETURNS (str): The converted URL.
-    """
-    # If the asset URL is a regular GitHub URL it's likely a mistake
-    if (
-        re.match(r"(http(s?)):\/\/github.com", url)
-        and "releases/download" not in url
-        and "/raw/" not in url
-    ):
-        converted = url.replace("github.com", "raw.githubusercontent.com")
-        converted = re.sub(r"/(tree|blob)/", "/", converted)
-        msg.warn(
-            "Downloading from a regular GitHub URL. This will only download "
-            "the source of the page, not the actual file. Converting the URL "
-            "to a raw URL.",
-            converted,
-        )
-        return converted
-    return url
+from weasel.cli.assets import *

spacy/cli/project/clone.py

@@ -1,124 +1 @@
-import re
-import subprocess
-from pathlib import Path
-from typing import Optional
-
-from wasabi import msg
-
-from ... import about
-from ...util import ensure_path
-from .._util import (
-    COMMAND,
-    PROJECT_FILE,
-    Arg,
-    Opt,
-    get_git_version,
-    git_checkout,
-    git_repo_branch_exists,
-    project_cli,
-)
-
-DEFAULT_REPO = about.__projects__
-DEFAULT_PROJECTS_BRANCH = about.__projects_branch__
-DEFAULT_BRANCHES = ["main", "master"]
-
-
-@project_cli.command("clone")
-def project_clone_cli(
-    # fmt: off
-    name: str = Arg(..., help="The name of the template to clone"),
-    dest: Optional[Path] = Arg(None, help="Where to clone the project. Defaults to current working directory", exists=False),
-    repo: str = Opt(DEFAULT_REPO, "--repo", "-r", help="The repository to clone from"),
-    branch: Optional[str] = Opt(None, "--branch", "-b", help=f"The branch to clone from. If not provided, will attempt {', '.join(DEFAULT_BRANCHES)}"),
-    sparse_checkout: bool = Opt(False, "--sparse", "-S", help="Use sparse Git checkout to only check out and clone the files needed. Requires Git v22.2+.")
-    # fmt: on
-):
-    """Clone a project template from a repository. Calls into "git" and will
-    only download the files from the given subdirectory. The GitHub repo
-    defaults to the official spaCy template repo, but can be customized
-    (including using a private repo).
-
-    DOCS: https://spacy.io/api/cli#project-clone
-    """
-    if dest is None:
-        dest = Path.cwd() / Path(name).parts[-1]
-    if repo == DEFAULT_REPO and branch is None:
-        branch = DEFAULT_PROJECTS_BRANCH
-
-    if branch is None:
-        for default_branch in DEFAULT_BRANCHES:
-            if git_repo_branch_exists(repo, default_branch):
-                branch = default_branch
-                break
-        if branch is None:
-            default_branches_msg = ", ".join(f"'{b}'" for b in DEFAULT_BRANCHES)
-            msg.fail(
-                "No branch provided and attempted default "
-                f"branches {default_branches_msg} do not exist.",
-                exits=1,
-            )
-    else:
-        if not git_repo_branch_exists(repo, branch):
-            msg.fail(f"repo: {repo} (branch: {branch}) does not exist.", exits=1)
-    assert isinstance(branch, str)
-    project_clone(name, dest, repo=repo, branch=branch, sparse_checkout=sparse_checkout)
-
-
-def project_clone(
-    name: str,
-    dest: Path,
-    *,
-    repo: str = about.__projects__,
-    branch: str = about.__projects_branch__,
-    sparse_checkout: bool = False,
-) -> None:
-    """Clone a project template from a repository.
-
-    name (str): Name of subdirectory to clone.
-    dest (Path): Destination path of cloned project.
-    repo (str): URL of Git repo containing project templates.
-    branch (str): The branch to clone from
-    """
-    dest = ensure_path(dest)
-    check_clone(name, dest, repo)
-    project_dir = dest.resolve()
-    repo_name = re.sub(r"(http(s?)):\/\/github.com/", "", repo)
-    try:
-        git_checkout(repo, name, dest, branch=branch, sparse=sparse_checkout)
-    except subprocess.CalledProcessError:
-        err = f"Could not clone '{name}' from repo '{repo_name}' (branch '{branch}')"
-        msg.fail(err, exits=1)
-    msg.good(f"Cloned '{name}' from '{repo_name}' (branch '{branch}')", project_dir)
-    if not (project_dir / PROJECT_FILE).exists():
-        msg.warn(f"No {PROJECT_FILE} found in directory")
-    else:
-        msg.good(f"Your project is now ready!")
-        print(f"To fetch the assets, run:\n{COMMAND} project assets {dest}")
-
-
-def check_clone(name: str, dest: Path, repo: str) -> None:
-    """Check and validate that the destination path can be used to clone. Will
-    check that Git is available and that the destination path is suitable.
-
-    name (str): Name of the directory to clone from the repo.
-    dest (Path): Local destination of cloned directory.
-    repo (str): URL of the repo to clone from.
-    """
-    git_err = (
-        f"Cloning spaCy project templates requires Git and the 'git' command. "
-        f"To clone a project without Git, copy the files from the '{name}' "
-        f"directory in the {repo} to {dest} manually."
-    )
-    get_git_version(error=git_err)
-    if not dest:
-        msg.fail(f"Not a valid directory to clone project: {dest}", exits=1)
-    if dest.exists():
-        # Directory already exists (not allowed, clone needs to create it)
-        msg.fail(f"Can't clone project, directory already exists: {dest}", exits=1)
-    if not dest.parent.exists():
-        # We're not creating parents, parent dir should exist
-        msg.fail(
-            f"Can't clone project, parent directory doesn't exist: {dest.parent}. "
-            f"Create the necessary folder(s) first before continuing.",
-            exits=1,
-        )
+from weasel.cli.clone import *

spacy/cli/project/document.py

@@ -1,115 +1 @@
-from pathlib import Path
-
-from wasabi import MarkdownRenderer, msg
-
-from ...util import working_dir
-from .._util import PROJECT_FILE, Arg, Opt, load_project_config, project_cli
-
-DOCS_URL = "https://spacy.io"
-INTRO_PROJECT = f"""The [`{PROJECT_FILE}`]({PROJECT_FILE}) defines the data assets required by the
-project, as well as the available commands and workflows. For details, see the
-[spaCy projects documentation]({DOCS_URL}/usage/projects)."""
-INTRO_COMMANDS = f"""The following commands are defined by the project. They
-can be executed using [`spacy project run [name]`]({DOCS_URL}/api/cli#project-run).
-Commands are only re-run if their inputs have changed."""
-INTRO_WORKFLOWS = f"""The following workflows are defined by the project. They
-can be executed using [`spacy project run [name]`]({DOCS_URL}/api/cli#project-run)
-and will run the specified commands in order. Commands are only re-run if their
-inputs have changed."""
-INTRO_ASSETS = f"""The following assets are defined by the project. They can
-be fetched by running [`spacy project assets`]({DOCS_URL}/api/cli#project-assets)
-in the project directory."""
-# These markers are added to the Markdown and can be used to update the file in
-# place if it already exists. Only the auto-generated part will be replaced.
-MARKER_START = "<!-- SPACY PROJECT: AUTO-GENERATED DOCS START (do not remove) -->"
-MARKER_END = "<!-- SPACY PROJECT: AUTO-GENERATED DOCS END (do not remove) -->"
-# If this marker is used in an existing README, it's ignored and not replaced
-MARKER_IGNORE = "<!-- SPACY PROJECT: IGNORE -->"
-
-
-@project_cli.command("document")
-def project_document_cli(
-    # fmt: off
-    project_dir: Path = Arg(Path.cwd(), help="Path to cloned project. Defaults to current working directory.", exists=True, file_okay=False),
-    output_file: Path = Opt("-", "--output", "-o", help="Path to output Markdown file for output. Defaults to - for standard output"),
-    no_emoji: bool = Opt(False, "--no-emoji", "-NE", help="Don't use emoji")
-    # fmt: on
-):
-    """
-    Auto-generate a README.md for a project. If the content is saved to a file,
-    hidden markers are added so you can add custom content before or after the
-    auto-generated section and only the auto-generated docs will be replaced
-    when you re-run the command.
-
-    DOCS: https://spacy.io/api/cli#project-document
-    """
-    project_document(project_dir, output_file, no_emoji=no_emoji)
-
-
-def project_document(
-    project_dir: Path, output_file: Path, *, no_emoji: bool = False
-) -> None:
-    is_stdout = str(output_file) == "-"
-    config = load_project_config(project_dir)
-    md = MarkdownRenderer(no_emoji=no_emoji)
-    md.add(MARKER_START)
-    title = config.get("title")
-    description = config.get("description")
-    md.add(md.title(1, f"spaCy Project{f': {title}' if title else ''}", "🪐"))
-    if description:
-        md.add(description)
-    md.add(md.title(2, PROJECT_FILE, "📋"))
-    md.add(INTRO_PROJECT)
-    # Commands
-    cmds = config.get("commands", [])
-    data = [(md.code(cmd["name"]), cmd.get("help", "")) for cmd in cmds]
-    if data:
-        md.add(md.title(3, "Commands", "⏯"))
-        md.add(INTRO_COMMANDS)
-        md.add(md.table(data, ["Command", "Description"]))
-    # Workflows
-    wfs = config.get("workflows", {}).items()
-    data = [(md.code(n), " &rarr; ".join(md.code(w) for w in stp)) for n, stp in wfs]
-    if data:
-        md.add(md.title(3, "Workflows", "⏭"))
-        md.add(INTRO_WORKFLOWS)
-        md.add(md.table(data, ["Workflow", "Steps"]))
-    # Assets
-    assets = config.get("assets", [])
-    data = []
-    for a in assets:
-        source = "Git" if a.get("git") else "URL" if a.get("url") else "Local"
-        dest_path = a["dest"]
-        dest = md.code(dest_path)
-        if source == "Local":
-            # Only link assets if they're in the repo
-            with working_dir(project_dir) as p:
-                if (p / dest_path).exists():
-                    dest = md.link(dest, dest_path)
-        data.append((dest, source, a.get("description", "")))
-    if data:
-        md.add(md.title(3, "Assets", "🗂"))
-        md.add(INTRO_ASSETS)
-        md.add(md.table(data, ["File", "Source", "Description"]))
-    md.add(MARKER_END)
-    # Output result
-    if is_stdout:
-        print(md.text)
-    else:
-        content = md.text
-        if output_file.exists():
-            with output_file.open("r", encoding="utf8") as f:
-                existing = f.read()
-            if MARKER_IGNORE in existing:
-                msg.warn("Found ignore marker in existing file: skipping", output_file)
-                return
-            if MARKER_START in existing and MARKER_END in existing:
-                msg.info("Found existing file: only replacing auto-generated docs")
-                before = existing.split(MARKER_START)[0]
-                after = existing.split(MARKER_END)[1]
-                content = f"{before}{content}{after}"
-            else:
-                msg.warn("Replacing existing file")
-        with output_file.open("w", encoding="utf8") as f:
-            f.write(content)
-        msg.good("Saved project documentation", output_file)
+from weasel.cli.document import *

spacy/cli/project/dvc.py

@@ -1,220 +1 @@
-"""This module contains helpers and subcommands for integrating spaCy projects
-with Data Version Controk (DVC). https://dvc.org"""
-import subprocess
-from pathlib import Path
-from typing import Any, Dict, Iterable, List, Optional
-
-from wasabi import msg
-
-from ...util import (
-    SimpleFrozenList,
-    join_command,
-    run_command,
-    split_command,
-    working_dir,
-)
-from .._util import (
-    COMMAND,
-    NAME,
-    PROJECT_FILE,
-    Arg,
-    Opt,
-    get_hash,
-    load_project_config,
-    project_cli,
-)
-
-DVC_CONFIG = "dvc.yaml"
-DVC_DIR = ".dvc"
-UPDATE_COMMAND = "dvc"
-DVC_CONFIG_COMMENT = f"""# This file is auto-generated by spaCy based on your {PROJECT_FILE}. If you've
-# edited your {PROJECT_FILE}, you can regenerate this file by running:
-# {COMMAND} project {UPDATE_COMMAND}"""
-
-
-@project_cli.command(UPDATE_COMMAND)
-def project_update_dvc_cli(
-    # fmt: off
-    project_dir: Path = Arg(Path.cwd(), help="Location of project directory. Defaults to current working directory.", exists=True, file_okay=False),
-    workflow: Optional[str] = Arg(None, help=f"Name of workflow defined in {PROJECT_FILE}. Defaults to first workflow if not set."),
-    verbose: bool = Opt(False, "--verbose", "-V", help="Print more info"),
-    quiet: bool = Opt(False, "--quiet", "-q", help="Print less info"),
-    force: bool = Opt(False, "--force", "-F", help="Force update DVC config"),
-    # fmt: on
-):
-    """Auto-generate Data Version Control (DVC) config. A DVC
-    project can only define one pipeline, so you need to specify one workflow
-    defined in the project.yml. If no workflow is specified, the first defined
-    workflow is used. The DVC config will only be updated if the project.yml
-    changed.
-
-    DOCS: https://spacy.io/api/cli#project-dvc
-    """
-    project_update_dvc(project_dir, workflow, verbose=verbose, quiet=quiet, force=force)
-
-
-def project_update_dvc(
-    project_dir: Path,
-    workflow: Optional[str] = None,
-    *,
-    verbose: bool = False,
-    quiet: bool = False,
-    force: bool = False,
-) -> None:
-    """Update the auto-generated Data Version Control (DVC) config file. A DVC
-    project can only define one pipeline, so you need to specify one workflow
-    defined in the project.yml. Will only update the file if the checksum changed.
-
-    project_dir (Path): The project directory.
-    workflow (Optional[str]): Optional name of workflow defined in project.yml.
-        If not set, the first workflow will be used.
-    verbose (bool): Print more info.
-    quiet (bool): Print less info.
-    force (bool): Force update DVC config.
-    """
-    config = load_project_config(project_dir)
-    updated = update_dvc_config(
-        project_dir, config, workflow, verbose=verbose, quiet=quiet, force=force
-    )
-    help_msg = "To execute the workflow with DVC, run: dvc repro"
-    if updated:
-        msg.good(f"Updated DVC config from {PROJECT_FILE}", help_msg)
-    else:
-        msg.info(f"No changes found in {PROJECT_FILE}, no update needed", help_msg)
-
-
-def update_dvc_config(
-    path: Path,
-    config: Dict[str, Any],
-    workflow: Optional[str] = None,
-    verbose: bool = False,
-    quiet: bool = False,
-    force: bool = False,
-) -> bool:
-    """Re-run the DVC commands in dry mode and update dvc.yaml file in the
-    project directory. The file is auto-generated based on the config. The
-    first line of the auto-generated file specifies the hash of the config
-    dict, so if any of the config values change, the DVC config is regenerated.
-
-    path (Path): The path to the project directory.
-    config (Dict[str, Any]): The loaded project.yml.
-    verbose (bool): Whether to print additional info (via DVC).
-    quiet (bool): Don't output anything (via DVC).
-    force (bool): Force update, even if hashes match.
-    RETURNS (bool): Whether the DVC config file was updated.
-    """
-    ensure_dvc(path)
-    workflows = config.get("workflows", {})
-    workflow_names = list(workflows.keys())
-    check_workflows(workflow_names, workflow)
-    if not workflow:
-        workflow = workflow_names[0]
-    config_hash = get_hash(config)
-    path = path.resolve()
-    dvc_config_path = path / DVC_CONFIG
-    if dvc_config_path.exists():
-        # Check if the file was generated using the current config, if not, redo
-        with dvc_config_path.open("r", encoding="utf8") as f:
-            ref_hash = f.readline().strip().replace("# ", "")
-        if ref_hash == config_hash and not force:
-            return False  # Nothing has changed in project.yml, don't need to update
-        dvc_config_path.unlink()
-    dvc_commands = []
-    config_commands = {cmd["name"]: cmd for cmd in config.get("commands", [])}
-
-    # some flags that apply to every command
-    flags = []
-    if verbose:
-        flags.append("--verbose")
-    if quiet:
-        flags.append("--quiet")
-
-    for name in workflows[workflow]:
-        command = config_commands[name]
-        deps = command.get("deps", [])
-        outputs = command.get("outputs", [])
-        outputs_no_cache = command.get("outputs_no_cache", [])
-        if not deps and not outputs and not outputs_no_cache:
-            continue
-        # Default to the working dir as the project path since dvc.yaml is auto-generated
-        # and we don't want arbitrary paths in there
-        project_cmd = ["python", "-m", NAME, "project", "run", name]
-        deps_cmd = [c for cl in [["-d", p] for p in deps] for c in cl]
-        outputs_cmd = [c for cl in [["-o", p] for p in outputs] for c in cl]
-        outputs_nc_cmd = [c for cl in [["-O", p] for p in outputs_no_cache] for c in cl]
-
-        dvc_cmd = ["run", *flags, "-n", name, "-w", str(path), "--no-exec"]
-        if command.get("no_skip"):
-            dvc_cmd.append("--always-changed")
-        full_cmd = [*dvc_cmd, *deps_cmd, *outputs_cmd, *outputs_nc_cmd, *project_cmd]
-        dvc_commands.append(join_command(full_cmd))
-
-    if not dvc_commands:
-        # If we don't check for this, then there will be an error when reading the
-        # config, since DVC wouldn't create it.
-        msg.fail(
-            "No usable commands for DVC found. This can happen if none of your "
-            "commands have dependencies or outputs.",
-            exits=1,
-        )
-
-    with working_dir(path):
-        for c in dvc_commands:
-            dvc_command = "dvc " + c
-            run_command(dvc_command)
-    with dvc_config_path.open("r+", encoding="utf8") as f:
-        content = f.read()
-        f.seek(0, 0)
-        f.write(f"# {config_hash}\n{DVC_CONFIG_COMMENT}\n{content}")
-    return True
-
-
-def check_workflows(workflows: List[str], workflow: Optional[str] = None) -> None:
-    """Validate workflows provided in project.yml and check that a given
-    workflow can be used to generate a DVC config.
-
-    workflows (List[str]): Names of the available workflows.
-    workflow (Optional[str]): The name of the workflow to convert.
-    """
-    if not workflows:
-        msg.fail(
-            f"No workflows defined in {PROJECT_FILE}. To generate a DVC config, "
-            f"define at least one list of commands.",
-            exits=1,
-        )
-    if workflow is not None and workflow not in workflows:
-        msg.fail(
-            f"Workflow '{workflow}' not defined in {PROJECT_FILE}. "
-            f"Available workflows: {', '.join(workflows)}",
-            exits=1,
-        )
-    if not workflow:
-        msg.warn(
-            f"No workflow specified for DVC pipeline. Using the first workflow "
-            f"defined in {PROJECT_FILE}: '{workflows[0]}'"
-        )
-
-
-def ensure_dvc(project_dir: Path) -> None:
-    """Ensure that the "dvc" command is available and that the current project
-    directory is an initialized DVC project.
-    """
-    try:
-        subprocess.run(["dvc", "--version"], stdout=subprocess.DEVNULL)
-    except Exception:
-        msg.fail(
-            "To use spaCy projects with DVC (Data Version Control), DVC needs "
-            "to be installed and the 'dvc' command needs to be available",
-            "You can install the Python package from pip (pip install dvc) or "
-            "conda (conda install -c conda-forge dvc). For more details, see the "
-            "documentation: https://dvc.org/doc/install",
-            exits=1,
-        )
-    if not (project_dir / ".dvc").exists():
-        msg.fail(
-            "Project not initialized as a DVC project",
-            "To initialize a DVC project, you can run 'dvc init' in the project "
-            "directory. For more details, see the documentation: "
-            "https://dvc.org/doc/command-reference/init",
-            exits=1,
-        )
+from weasel.cli.dvc import *

spacy/cli/project/pull.py

@@ -1,67 +1 @@
-from pathlib import Path
-
-from wasabi import msg
-
-from .._util import Arg, load_project_config, logger, project_cli
-from .remote_storage import RemoteStorage, get_command_hash
-from .run import update_lockfile
-
-
-@project_cli.command("pull")
-def project_pull_cli(
-    # fmt: off
-    remote: str = Arg("default", help="Name or path of remote storage"),
-    project_dir: Path = Arg(Path.cwd(), help="Location of project directory. Defaults to current working directory.", exists=True, file_okay=False),
-    # fmt: on
-):
-    """Retrieve available precomputed outputs from a remote storage.
-    You can alias remotes in your project.yml by mapping them to storage paths.
-    A storage can be anything that the smart-open library can upload to, e.g.
-    AWS, Google Cloud Storage, SSH, local directories etc.
-
-    DOCS: https://spacy.io/api/cli#project-pull
-    """
-    for url, output_path in project_pull(project_dir, remote):
-        if url is not None:
-            msg.good(f"Pulled {output_path} from {url}")
-
-
-def project_pull(project_dir: Path, remote: str, *, verbose: bool = False):
-    # TODO: We don't have tests for this :(. It would take a bit of mockery to
-    # set up. I guess see if it breaks first?
-    config = load_project_config(project_dir)
-    if remote in config.get("remotes", {}):
-        remote = config["remotes"][remote]
-    storage = RemoteStorage(project_dir, remote)
-    commands = list(config.get("commands", []))
-    # We use a while loop here because we don't know how the commands
-    # will be ordered. A command might need dependencies from one that's later
-    # in the list.
-    while commands:
-        for i, cmd in enumerate(list(commands)):
-            logger.debug("CMD: %s.", cmd["name"])
-            deps = [project_dir / dep for dep in cmd.get("deps", [])]
-            if all(dep.exists() for dep in deps):
-                cmd_hash = get_command_hash("", "", deps, cmd["script"])
-                for output_path in cmd.get("outputs", []):
-                    url = storage.pull(output_path, command_hash=cmd_hash)
-                    logger.debug(
-                        "URL: %s for %s with command hash %s",
-                        url,
-                        output_path,
-                        cmd_hash,
-                    )
-                    yield url, output_path
-
-                out_locs = [project_dir / out for out in cmd.get("outputs", [])]
-                if all(loc.exists() for loc in out_locs):
-                    update_lockfile(project_dir, cmd)
-                # We remove the command from the list here, and break, so that
-                # we iterate over the loop again.
-                commands.pop(i)
-                break
-            else:
-                logger.debug("Dependency missing. Skipping %s outputs.", cmd["name"])
-        else:
-            # If we didn't break the for loop, break the while loop.
-            break
+from weasel.cli.pull import *

spacy/cli/project/push.py

@@ -1,69 +1 @@
-from pathlib import Path
-
-from wasabi import msg
-
-from .._util import Arg, load_project_config, logger, project_cli
-from .remote_storage import RemoteStorage, get_command_hash, get_content_hash
-
-
-@project_cli.command("push")
-def project_push_cli(
-    # fmt: off
-    remote: str = Arg("default", help="Name or path of remote storage"),
-    project_dir: Path = Arg(Path.cwd(), help="Location of project directory. Defaults to current working directory.", exists=True, file_okay=False),
-    # fmt: on
-):
-    """Persist outputs to a remote storage. You can alias remotes in your
-    project.yml by mapping them to storage paths. A storage can be anything that
-    the smart-open library can upload to, e.g. AWS, Google Cloud Storage, SSH,
-    local directories etc.
-
-    DOCS: https://spacy.io/api/cli#project-push
-    """
-    for output_path, url in project_push(project_dir, remote):
-        if url is None:
-            msg.info(f"Skipping {output_path}")
-        else:
-            msg.good(f"Pushed {output_path} to {url}")
-
-
-def project_push(project_dir: Path, remote: str):
-    """Persist outputs to a remote storage. You can alias remotes in your project.yml
-    by mapping them to storage paths. A storage can be anything that the smart-open
-    library can upload to, e.g. gcs, aws, ssh, local directories etc
-    """
-    config = load_project_config(project_dir)
-    if remote in config.get("remotes", {}):
-        remote = config["remotes"][remote]
-    storage = RemoteStorage(project_dir, remote)
-    for cmd in config.get("commands", []):
-        logger.debug("CMD: %s", cmd["name"])
-        deps = [project_dir / dep for dep in cmd.get("deps", [])]
-        if any(not dep.exists() for dep in deps):
-            logger.debug("Dependency missing. Skipping %s outputs", cmd["name"])
-            continue
-        cmd_hash = get_command_hash(
-            "", "", [project_dir / dep for dep in cmd.get("deps", [])], cmd["script"]
-        )
-        logger.debug("CMD_HASH: %s", cmd_hash)
-        for output_path in cmd.get("outputs", []):
-            output_loc = project_dir / output_path
-            if output_loc.exists() and _is_not_empty_dir(output_loc):
-                url = storage.push(
-                    output_path,
-                    command_hash=cmd_hash,
-                    content_hash=get_content_hash(output_loc),
-                )
-                logger.debug(
-                    "URL: %s for output %s with cmd_hash %s", url, output_path, cmd_hash
-                )
-                yield output_path, url
-
-
-def _is_not_empty_dir(loc: Path):
-    if not loc.is_dir():
-        return True
-    elif any(_is_not_empty_dir(child) for child in loc.iterdir()):
-        return True
-    else:
-        return False
+from weasel.cli.push import *

spacy/cli/project/remote_storage.py

@@ -1,212 +1 @@
-import hashlib
-import os
-import site
-import tarfile
-import urllib.parse
-from pathlib import Path
-from typing import TYPE_CHECKING, Dict, List, Optional
-
-from wasabi import msg
-
-from ... import about
-from ...errors import Errors
-from ...git_info import GIT_VERSION
-from ...util import ENV_VARS, check_bool_env_var, get_minor_version
-from .._util import (
-    download_file,
-    ensure_pathy,
-    get_checksum,
-    get_hash,
-    make_tempdir,
-    upload_file,
-)
-
-if TYPE_CHECKING:
-    from pathy import FluidPath  # noqa: F401
-
-
-class RemoteStorage:
-    """Push and pull outputs to and from a remote file storage.
-
-    Remotes can be anything that `smart-open` can support: AWS, GCS, file system,
-    ssh, etc.
-    """
-
-    def __init__(self, project_root: Path, url: str, *, compression="gz"):
-        self.root = project_root
-        self.url = ensure_pathy(url)
-        self.compression = compression
-
-    def push(self, path: Path, command_hash: str, content_hash: str) -> "FluidPath":
-        """Compress a file or directory within a project and upload it to a remote
-        storage. If an object exists at the full URL, nothing is done.
-
-        Within the remote storage, files are addressed by their project path
-        (url encoded) and two user-supplied hashes, representing their creation
-        context and their file contents. If the URL already exists, the data is
-        not uploaded. Paths are archived and compressed prior to upload.
-        """
-        loc = self.root / path
-        if not loc.exists():
-            raise IOError(f"Cannot push {loc}: does not exist.")
-        url = self.make_url(path, command_hash, content_hash)
-        if url.exists():
-            return url
-        tmp: Path
-        with make_tempdir() as tmp:
-            tar_loc = tmp / self.encode_name(str(path))
-            mode_string = f"w:{self.compression}" if self.compression else "w"
-            with tarfile.open(tar_loc, mode=mode_string) as tar_file:
-                tar_file.add(str(loc), arcname=str(path))
-            upload_file(tar_loc, url)
-        return url
-
-    def pull(
-        self,
-        path: Path,
-        *,
-        command_hash: Optional[str] = None,
-        content_hash: Optional[str] = None,
-    ) -> Optional["FluidPath"]:
-        """Retrieve a file from the remote cache. If the file already exists,
-        nothing is done.
-
-        If the command_hash and/or content_hash are specified, only matching
-        results are returned. If no results are available, an error is raised.
-        """
-        dest = self.root / path
-        if dest.exists():
-            return None
-        url = self.find(path, command_hash=command_hash, content_hash=content_hash)
-        if url is None:
-            return url
-        else:
-            # Make sure the destination exists
-            if not dest.parent.exists():
-                dest.parent.mkdir(parents=True)
-            tmp: Path
-            with make_tempdir() as tmp:
-                tar_loc = tmp / url.parts[-1]
-                download_file(url, tar_loc)
-                mode_string = f"r:{self.compression}" if self.compression else "r"
-                with tarfile.open(tar_loc, mode=mode_string) as tar_file:
-                    # This requires that the path is added correctly, relative
-                    # to root. This is how we set things up in push()
-
-                    # Disallow paths outside the current directory for the tar
-                    # file (CVE-2007-4559, directory traversal vulnerability)
-                    def is_within_directory(directory, target):
-                        abs_directory = os.path.abspath(directory)
-                        abs_target = os.path.abspath(target)
-                        prefix = os.path.commonprefix([abs_directory, abs_target])
-                        return prefix == abs_directory
-
-                    def safe_extract(tar, path):
-                        for member in tar.getmembers():
-                            member_path = os.path.join(path, member.name)
-                            if not is_within_directory(path, member_path):
-                                raise ValueError(Errors.E852)
-                        tar.extractall(path)
-
-                    safe_extract(tar_file, self.root)
-        return url
-
-    def find(
-        self,
-        path: Path,
-        *,
-        command_hash: Optional[str] = None,
-        content_hash: Optional[str] = None,
-    ) -> Optional["FluidPath"]:
-        """Find the best matching version of a file within the storage,
-        or `None` if no match can be found. If both the creation and content hash
-        are specified, only exact matches will be returned. Otherwise, the most
-        recent matching file is preferred.
-        """
-        name = self.encode_name(str(path))
-        urls = []
-        if command_hash is not None and content_hash is not None:
-            url = self.url / name / command_hash / content_hash
-            urls = [url] if url.exists() else []
-        elif command_hash is not None:
-            if (self.url / name / command_hash).exists():
-                urls = list((self.url / name / command_hash).iterdir())
-        else:
-            if (self.url / name).exists():
-                for sub_dir in (self.url / name).iterdir():
-                    urls.extend(sub_dir.iterdir())
-                if content_hash is not None:
-                    urls = [url for url in urls if url.parts[-1] == content_hash]
-        if len(urls) >= 2:
-            try:
-                urls.sort(key=lambda x: x.stat().last_modified)  # type: ignore
-            except Exception:
-                msg.warn(
-                    "Unable to sort remote files by last modified. The file(s) "
-                    "pulled from the cache may not be the most recent."
-                )
-        return urls[-1] if urls else None
-
-    def make_url(self, path: Path, command_hash: str, content_hash: str) -> "FluidPath":
-        """Construct a URL from a subpath, a creation hash and a content hash."""
-        return self.url / self.encode_name(str(path)) / command_hash / content_hash
-
-    def encode_name(self, name: str) -> str:
-        """Encode a subpath into a URL-safe name."""
-        return urllib.parse.quote_plus(name)
-
-
-def get_content_hash(loc: Path) -> str:
-    return get_checksum(loc)
-
-
-def get_command_hash(
-    site_hash: str, env_hash: str, deps: List[Path], cmd: List[str]
-) -> str:
-    """Create a hash representing the execution of a command. This includes the
-    currently installed packages, whatever environment variables have been marked
-    as relevant, and the command.
-    """
-    if check_bool_env_var(ENV_VARS.PROJECT_USE_GIT_VERSION):
-        spacy_v = GIT_VERSION
-    else:
-        spacy_v = str(get_minor_version(about.__version__) or "")
-    dep_checksums = [get_checksum(dep) for dep in sorted(deps)]
-    hashes = [spacy_v, site_hash, env_hash] + dep_checksums
-    hashes.extend(cmd)
-    creation_bytes = "".join(hashes).encode("utf8")
-    return hashlib.md5(creation_bytes).hexdigest()
-
-
-def get_site_hash():
-    """Hash the current Python environment's site-packages contents, including
-    the name and version of the libraries. The list we're hashing is what
-    `pip freeze` would output.
-    """
-    site_dirs = site.getsitepackages()
-    if site.ENABLE_USER_SITE:
-        site_dirs.extend(site.getusersitepackages())
-    packages = set()
-    for site_dir in site_dirs:
-        site_dir = Path(site_dir)
-        for subpath in site_dir.iterdir():
-            if subpath.parts[-1].endswith("dist-info"):
-                packages.add(subpath.parts[-1].replace(".dist-info", ""))
-    package_bytes = "".join(sorted(packages)).encode("utf8")
-    return hashlib.md5sum(package_bytes).hexdigest()
-
-
-def get_env_hash(env: Dict[str, str]) -> str:
-    """Construct a hash of the environment variables that will be passed into
-    the commands.
-
-    Values in the env dict may be references to the current os.environ, using
-    the syntax $ENV_VAR to mean os.environ[ENV_VAR]
-    """
-    env_vars = {}
-    for key, value in env.items():
-        if value.startswith("$"):
-            env_vars[key] = os.environ.get(value[1:], "")
-        else:
-            env_vars[key] = value
-    return get_hash(env_vars)
+from weasel.cli.remote_storage import *

spacy/cli/project/run.py

@@ -1,379 +1 @@
-import os.path
-import sys
-from pathlib import Path
-from typing import Any, Dict, Iterable, List, Optional, Sequence, Tuple
-
-import srsly
-import typer
-from wasabi import msg
-from wasabi.util import locale_escape
-
-from ... import about
-from ...git_info import GIT_VERSION
-from ...util import (
-    ENV_VARS,
-    SimpleFrozenDict,
-    SimpleFrozenList,
-    check_bool_env_var,
-    is_cwd,
-    is_minor_version_match,
-    join_command,
-    run_command,
-    split_command,
-    working_dir,
-)
-from .._util import (
-    COMMAND,
-    PROJECT_FILE,
-    PROJECT_LOCK,
-    Arg,
-    Opt,
-    get_checksum,
-    get_hash,
-    load_project_config,
-    parse_config_overrides,
-    project_cli,
-)
-
-
-@project_cli.command(
-    "run", context_settings={"allow_extra_args": True, "ignore_unknown_options": True}
-)
-def project_run_cli(
-    # fmt: off
-    ctx: typer.Context,  # This is only used to read additional arguments
-    subcommand: str = Arg(None, help=f"Name of command defined in the {PROJECT_FILE}"),
-    project_dir: Path = Arg(Path.cwd(), help="Location of project directory. Defaults to current working directory.", exists=True, file_okay=False),
-    force: bool = Opt(False, "--force", "-F", help="Force re-running steps, even if nothing changed"),
-    dry: bool = Opt(False, "--dry", "-D", help="Perform a dry run and don't execute scripts"),
-    show_help: bool = Opt(False, "--help", help="Show help message and available subcommands")
-    # fmt: on
-):
-    """Run a named command or workflow defined in the project.yml. If a workflow
-    name is specified, all commands in the workflow are run, in order. If
-    commands define dependencies and/or outputs, they will only be re-run if
-    state has changed.
-
-    DOCS: https://spacy.io/api/cli#project-run
-    """
-    if show_help or not subcommand:
-        print_run_help(project_dir, subcommand)
-    else:
-        overrides = parse_config_overrides(ctx.args)
-        project_run(project_dir, subcommand, overrides=overrides, force=force, dry=dry)
-
-
-def project_run(
-    project_dir: Path,
-    subcommand: str,
-    *,
-    overrides: Dict[str, Any] = SimpleFrozenDict(),
-    force: bool = False,
-    dry: bool = False,
-    capture: bool = False,
-    skip_requirements_check: bool = False,
-) -> None:
-    """Run a named script defined in the project.yml. If the script is part
-    of the default pipeline (defined in the "run" section), DVC is used to
-    execute the command, so it can determine whether to rerun it. It then
-    calls into "exec" to execute it.
-
-    project_dir (Path): Path to project directory.
-    subcommand (str): Name of command to run.
-    overrides (Dict[str, Any]): Optional config overrides.
-    force (bool): Force re-running, even if nothing changed.
-    dry (bool): Perform a dry run and don't execute commands.
-    capture (bool): Whether to capture the output and errors of individual commands.
-        If False, the stdout and stderr will not be redirected, and if there's an error,
-        sys.exit will be called with the return code. You should use capture=False
-        when you want to turn over execution to the command, and capture=True
-        when you want to run the command more like a function.
-    skip_requirements_check (bool): Whether to skip the requirements check.
-    """
-    config = load_project_config(project_dir, overrides=overrides)
-    commands = {cmd["name"]: cmd for cmd in config.get("commands", [])}
-    workflows = config.get("workflows", {})
-    validate_subcommand(list(commands.keys()), list(workflows.keys()), subcommand)
-
-    req_path = project_dir / "requirements.txt"
-    if not skip_requirements_check:
-        if config.get("check_requirements", True) and os.path.exists(req_path):
-            with req_path.open() as requirements_file:
-                _check_requirements([req.strip() for req in requirements_file])
-
-    if subcommand in workflows:
-        msg.info(f"Running workflow '{subcommand}'")
-        for cmd in workflows[subcommand]:
-            project_run(
-                project_dir,
-                cmd,
-                overrides=overrides,
-                force=force,
-                dry=dry,
-                capture=capture,
-                skip_requirements_check=True,
-            )
-    else:
-        cmd = commands[subcommand]
-        for dep in cmd.get("deps", []):
-            if not (project_dir / dep).exists():
-                err = f"Missing dependency specified by command '{subcommand}': {dep}"
-                err_help = "Maybe you forgot to run the 'project assets' command or a previous step?"
-                err_exits = 1 if not dry else None
-                msg.fail(err, err_help, exits=err_exits)
-        check_spacy_commit = check_bool_env_var(ENV_VARS.PROJECT_USE_GIT_VERSION)
-        with working_dir(project_dir) as current_dir:
-            msg.divider(subcommand)
-            rerun = check_rerun(current_dir, cmd, check_spacy_commit=check_spacy_commit)
-            if not rerun and not force:
-                msg.info(f"Skipping '{cmd['name']}': nothing changed")
-            else:
-                run_commands(cmd["script"], dry=dry, capture=capture)
-                if not dry:
-                    update_lockfile(current_dir, cmd)
-
-
-def print_run_help(project_dir: Path, subcommand: Optional[str] = None) -> None:
-    """Simulate a CLI help prompt using the info available in the project.yml.
-
-    project_dir (Path): The project directory.
-    subcommand (Optional[str]): The subcommand or None. If a subcommand is
-        provided, the subcommand help is shown. Otherwise, the top-level help
-        and a list of available commands is printed.
-    """
-    config = load_project_config(project_dir)
-    config_commands = config.get("commands", [])
-    commands = {cmd["name"]: cmd for cmd in config_commands}
-    workflows = config.get("workflows", {})
-    project_loc = "" if is_cwd(project_dir) else project_dir
-    if subcommand:
-        validate_subcommand(list(commands.keys()), list(workflows.keys()), subcommand)
-        print(f"Usage: {COMMAND} project run {subcommand} {project_loc}")
-        if subcommand in commands:
-            help_text = commands[subcommand].get("help")
-            if help_text:
-                print(f"\n{help_text}\n")
-        elif subcommand in workflows:
-            steps = workflows[subcommand]
-            print(f"\nWorkflow consisting of {len(steps)} commands:")
-            steps_data = [
-                (f"{i + 1}. {step}", commands[step].get("help", ""))
-                for i, step in enumerate(steps)
-            ]
-            msg.table(steps_data)
-            help_cmd = f"{COMMAND} project run [COMMAND] {project_loc} --help"
-            print(f"For command details, run: {help_cmd}")
-    else:
-        print("")
-        title = config.get("title")
-        if title:
-            print(f"{locale_escape(title)}\n")
-        if config_commands:
-            print(f"Available commands in {PROJECT_FILE}")
-            print(f"Usage: {COMMAND} project run [COMMAND] {project_loc}")
-            msg.table([(cmd["name"], cmd.get("help", "")) for cmd in config_commands])
-        if workflows:
-            print(f"Available workflows in {PROJECT_FILE}")
-            print(f"Usage: {COMMAND} project run [WORKFLOW] {project_loc}")
-            msg.table([(name, " -> ".join(steps)) for name, steps in workflows.items()])
-
-
-def run_commands(
-    commands: Iterable[str] = SimpleFrozenList(),
-    silent: bool = False,
-    dry: bool = False,
-    capture: bool = False,
-) -> None:
-    """Run a sequence of commands in a subprocess, in order.
-
-    commands (List[str]): The string commands.
-    silent (bool): Don't print the commands.
-    dry (bool): Perform a dry run and don't execut anything.
-    capture (bool): Whether to capture the output and errors of individual commands.
-        If False, the stdout and stderr will not be redirected, and if there's an error,
-        sys.exit will be called with the return code. You should use capture=False
-        when you want to turn over execution to the command, and capture=True
-        when you want to run the command more like a function.
-    """
-    for c in commands:
-        command = split_command(c)
-        # Not sure if this is needed or a good idea. Motivation: users may often
-        # use commands in their config that reference "python" and we want to
-        # make sure that it's always executing the same Python that spaCy is
-        # executed with and the pip in the same env, not some other Python/pip.
-        # Also ensures cross-compatibility if user 1 writes "python3" (because
-        # that's how it's set up on their system), and user 2 without the
-        # shortcut tries to re-run the command.
-        if len(command) and command[0] in ("python", "python3"):
-            command[0] = sys.executable
-        elif len(command) and command[0] in ("pip", "pip3"):
-            command = [sys.executable, "-m", "pip", *command[1:]]
-        if not silent:
-            print(f"Running command: {join_command(command)}")
-        if not dry:
-            run_command(command, capture=capture)
-
-
-def validate_subcommand(
-    commands: Sequence[str], workflows: Sequence[str], subcommand: str
-) -> None:
-    """Check that a subcommand is valid and defined. Raises an error otherwise.
-
-    commands (Sequence[str]): The available commands.
-    subcommand (str): The subcommand.
-    """
-    if not commands and not workflows:
-        msg.fail(f"No commands or workflows defined in {PROJECT_FILE}", exits=1)
-    if subcommand not in commands and subcommand not in workflows:
-        help_msg = []
-        if subcommand in ["assets", "asset"]:
-            help_msg.append("Did you mean to run: python -m spacy project assets?")
-        if commands:
-            help_msg.append(f"Available commands: {', '.join(commands)}")
-        if workflows:
-            help_msg.append(f"Available workflows: {', '.join(workflows)}")
-        msg.fail(
-            f"Can't find command or workflow '{subcommand}' in {PROJECT_FILE}",
-            ". ".join(help_msg),
-            exits=1,
-        )
-
-
-def check_rerun(
-    project_dir: Path,
-    command: Dict[str, Any],
-    *,
-    check_spacy_version: bool = True,
-    check_spacy_commit: bool = False,
-) -> bool:
-    """Check if a command should be rerun because its settings or inputs/outputs
-    changed.
-
-    project_dir (Path): The current project directory.
-    command (Dict[str, Any]): The command, as defined in the project.yml.
-    strict_version (bool):
-    RETURNS (bool): Whether to re-run the command.
-    """
-    # Always rerun if no-skip is set
-    if command.get("no_skip", False):
-        return True
-    lock_path = project_dir / PROJECT_LOCK
-    if not lock_path.exists():  # We don't have a lockfile, run command
-        return True
-    data = srsly.read_yaml(lock_path)
-    if command["name"] not in data:  # We don't have info about this command
-        return True
-    entry = data[command["name"]]
-    # Always run commands with no outputs (otherwise they'd always be skipped)
-    if not entry.get("outs", []):
-        return True
-    # Always rerun if spaCy version or commit hash changed
-    spacy_v = entry.get("spacy_version")
-    commit = entry.get("spacy_git_version")
-    if check_spacy_version and not is_minor_version_match(spacy_v, about.__version__):
-        info = f"({spacy_v} in {PROJECT_LOCK}, {about.__version__} current)"
-        msg.info(f"Re-running '{command['name']}': spaCy minor version changed {info}")
-        return True
-    if check_spacy_commit and commit != GIT_VERSION:
-        info = f"({commit} in {PROJECT_LOCK}, {GIT_VERSION} current)"
-        msg.info(f"Re-running '{command['name']}': spaCy commit changed {info}")
-        return True
-    # If the entry in the lockfile matches the lockfile entry that would be
-    # generated from the current command, we don't rerun because it means that
-    # all inputs/outputs, hashes and scripts are the same and nothing changed
-    lock_entry = get_lock_entry(project_dir, command)
-    exclude = ["spacy_version", "spacy_git_version"]
-    return get_hash(lock_entry, exclude=exclude) != get_hash(entry, exclude=exclude)
-
-
-def update_lockfile(project_dir: Path, command: Dict[str, Any]) -> None:
-    """Update the lockfile after running a command. Will create a lockfile if
-    it doesn't yet exist and will add an entry for the current command, its
-    script and dependencies/outputs.
-
-    project_dir (Path): The current project directory.
-    command (Dict[str, Any]): The command, as defined in the project.yml.
-    """
-    lock_path = project_dir / PROJECT_LOCK
-    if not lock_path.exists():
-        srsly.write_yaml(lock_path, {})
-        data = {}
-    else:
-        data = srsly.read_yaml(lock_path)
-    data[command["name"]] = get_lock_entry(project_dir, command)
-    srsly.write_yaml(lock_path, data)
-
-
-def get_lock_entry(project_dir: Path, command: Dict[str, Any]) -> Dict[str, Any]:
-    """Get a lockfile entry for a given command. An entry includes the command,
-    the script (command steps) and a list of dependencies and outputs with
-    their paths and file hashes, if available. The format is based on the
-    dvc.lock files, to keep things consistent.
-
-    project_dir (Path): The current project directory.
-    command (Dict[str, Any]): The command, as defined in the project.yml.
-    RETURNS (Dict[str, Any]): The lockfile entry.
-    """
-    deps = get_fileinfo(project_dir, command.get("deps", []))
-    outs = get_fileinfo(project_dir, command.get("outputs", []))
-    outs_nc = get_fileinfo(project_dir, command.get("outputs_no_cache", []))
-    return {
-        "cmd": f"{COMMAND} run {command['name']}",
-        "script": command["script"],
-        "deps": deps,
-        "outs": [*outs, *outs_nc],
-        "spacy_version": about.__version__,
-        "spacy_git_version": GIT_VERSION,
-    }
-
-
-def get_fileinfo(project_dir: Path, paths: List[str]) -> List[Dict[str, Optional[str]]]:
-    """Generate the file information for a list of paths (dependencies, outputs).
-    Includes the file path and the file's checksum.
-
-    project_dir (Path): The current project directory.
-    paths (List[str]): The file paths.
-    RETURNS (List[Dict[str, str]]): The lockfile entry for a file.
-    """
-    data = []
-    for path in paths:
-        file_path = project_dir / path
-        md5 = get_checksum(file_path) if file_path.exists() else None
-        data.append({"path": path, "md5": md5})
-    return data
-
-
-def _check_requirements(requirements: List[str]) -> Tuple[bool, bool]:
-    """Checks whether requirements are installed and free of version conflicts.
-    requirements (List[str]): List of requirements.
-    RETURNS (Tuple[bool, bool]): Whether (1) any packages couldn't be imported, (2) any packages with version conflicts
-        exist.
-    """
-    import pkg_resources
-
-    failed_pkgs_msgs: List[str] = []
-    conflicting_pkgs_msgs: List[str] = []
-
-    for req in requirements:
-        try:
-            pkg_resources.require(req)
-        except pkg_resources.DistributionNotFound as dnf:
-            failed_pkgs_msgs.append(dnf.report())
-        except pkg_resources.VersionConflict as vc:
-            conflicting_pkgs_msgs.append(vc.report())
-        except Exception:
-            msg.warn(
-                f"Unable to check requirement: {req} "
-                "Checks are currently limited to requirement specifiers "
-                "(PEP 508)"
-            )
-
-    if len(failed_pkgs_msgs) or len(conflicting_pkgs_msgs):
-        msg.warn(
-            title="Missing requirements or requirement conflicts detected. Make sure your Python environment is set up "
-            "correctly and you installed all requirements specified in your project's requirements.txt: "
-        )
-        for pgk_msg in failed_pkgs_msgs + conflicting_pkgs_msgs:
-            msg.text(pgk_msg)
-
-    return len(failed_pkgs_msgs) > 0, len(conflicting_pkgs_msgs) > 0
+from weasel.cli.run import *

spacy/cli/templates/quickstart_training.jinja

@@ -271,8 +271,9 @@ grad_factor = 1.0
 @layers = "reduce_mean.v1"
 
 [components.textcat.model.linear_model]
-@architectures = "spacy.TextCatBOW.v2"
+@architectures = "spacy.TextCatBOW.v3"
 exclusive_classes = true
+length = 262144
 ngram_size = 1
 no_output_layer = false
 
@@ -308,8 +309,9 @@ grad_factor = 1.0
 @layers = "reduce_mean.v1"
 
 [components.textcat_multilabel.model.linear_model]
-@architectures = "spacy.TextCatBOW.v2"
+@architectures = "spacy.TextCatBOW.v3"
 exclusive_classes = false
+length = 262144
 ngram_size = 1
 no_output_layer = false
 
@@ -542,14 +544,15 @@ nO = null
 width = ${components.tok2vec.model.encode.width}
 
 [components.textcat.model.linear_model]
-@architectures = "spacy.TextCatBOW.v2"
+@architectures = "spacy.TextCatBOW.v3"
 exclusive_classes = true
+length = 262144
 ngram_size = 1
 no_output_layer = false
 
 {% else -%}
 [components.textcat.model]
-@architectures = "spacy.TextCatBOW.v2"
+@architectures = "spacy.TextCatBOW.v3"
 exclusive_classes = true
 ngram_size = 1
 no_output_layer = false
@@ -570,15 +573,17 @@ nO = null
 width = ${components.tok2vec.model.encode.width}
 
 [components.textcat_multilabel.model.linear_model]
-@architectures = "spacy.TextCatBOW.v2"
+@architectures = "spacy.TextCatBOW.v3"
 exclusive_classes = false
+length = 262144
 ngram_size = 1
 no_output_layer = false
 
 {% else -%}
 [components.textcat_multilabel.model]
-@architectures = "spacy.TextCatBOW.v2"
+@architectures = "spacy.TextCatBOW.v3"
 exclusive_classes = false
+length = 262144
 ngram_size = 1
 no_output_layer = false
 {%- endif %}

spacy/cli/train.py

@@ -47,7 +47,8 @@ def train_cli(
 
     DOCS: https://spacy.io/api/cli#train
     """
-    util.logger.setLevel(logging.DEBUG if verbose else logging.INFO)
+    if verbose:
+        util.logger.setLevel(logging.DEBUG)
     overrides = parse_config_overrides(ctx.args)
     import_code(code_path)
     train(config_path, output_path, use_gpu=use_gpu, overrides=overrides)

spacy/default_config.cfg

@@ -26,6 +26,9 @@ batch_size = 1000
 [nlp.tokenizer]
 @tokenizers = "spacy.Tokenizer.v1"
 
+[nlp.vectors]
+@vectors = "spacy.Vectors.v1"
+
 # The pipeline components and their models
 [components]
 

spacy/displacy/render.py

@@ -142,7 +142,25 @@ class SpanRenderer:
         spans (list): Individual entity spans and their start, end, label, kb_id and kb_url.
         title (str / None): Document title set in Doc.user_data['title'].
         """
-        per_token_info = []
+        per_token_info = self._assemble_per_token_info(tokens, spans)
+        markup = self._render_markup(per_token_info)
+        markup = TPL_SPANS.format(content=markup, dir=self.direction)
+        if title:
+            markup = TPL_TITLE.format(title=title) + markup
+        return markup
+
+    @staticmethod
+    def _assemble_per_token_info(
+        tokens: List[str], spans: List[Dict[str, Any]]
+    ) -> List[Dict[str, List[Dict[str, Any]]]]:
+        """Assembles token info used to generate markup in render_spans().
+        tokens (List[str]): Tokens in text.
+        spans (List[Dict[str, Any]]): Spans in text.
+        RETURNS (List[Dict[str, List[Dict, str, Any]]]): Per token info needed to render HTML markup for given tokens
+            and spans.
+        """
+        per_token_info: List[Dict[str, List[Dict[str, Any]]]] = []
+
         # we must sort so that we can correctly describe when spans need to "stack"
         # which is determined by their start token, then span length (longer spans on top),
         # then break any remaining ties with the span label
@@ -154,21 +172,22 @@ class SpanRenderer:
                 s["label"],
             ),
         )
+
         for s in spans:
             # this is the vertical 'slot' that the span will be rendered in
             # vertical_position = span_label_offset + (offset_step * (slot - 1))
             s["render_slot"] = 0
+
         for idx, token in enumerate(tokens):
             # Identify if a token belongs to a Span (and which) and if it's a
             # start token of said Span. We'll use this for the final HTML render
             token_markup: Dict[str, Any] = {}
             token_markup["text"] = token
-            concurrent_spans = 0
+            intersecting_spans: List[Dict[str, Any]] = []
             entities = []
             for span in spans:
                 ent = {}
                 if span["start_token"] <= idx < span["end_token"]:
-                    concurrent_spans += 1
                     span_start = idx == span["start_token"]
                     ent["label"] = span["label"]
                     ent["is_start"] = span_start
@@ -176,7 +195,12 @@ class SpanRenderer:
                         # When the span starts, we need to know how many other
                         # spans are on the 'span stack' and will be rendered.
                         # This value becomes the vertical render slot for this entire span
-                        span["render_slot"] = concurrent_spans
+                        span["render_slot"] = (
+                            intersecting_spans[-1]["render_slot"]
+                            if len(intersecting_spans)
+                            else 0
+                        ) + 1
+                    intersecting_spans.append(span)
                     ent["render_slot"] = span["render_slot"]
                     kb_id = span.get("kb_id", "")
                     kb_url = span.get("kb_url", "#")
@@ -193,11 +217,8 @@ class SpanRenderer:
                     span["render_slot"] = 0
             token_markup["entities"] = entities
             per_token_info.append(token_markup)
-        markup = self._render_markup(per_token_info)
-        markup = TPL_SPANS.format(content=markup, dir=self.direction)
-        if title:
-            markup = TPL_TITLE.format(title=title) + markup
-        return markup
+
+        return per_token_info
 
     def _render_markup(self, per_token_info: List[Dict[str, Any]]) -> str:
         """Render the markup from per-token information"""
@@ -313,6 +334,8 @@ class DependencyRenderer:
                 self.lang = settings.get("lang", DEFAULT_LANG)
             render_id = f"{id_prefix}-{i}"
             svg = self.render_svg(render_id, p["words"], p["arcs"])
+            if p.get("title"):
+                svg = TPL_TITLE.format(title=p.get("title")) + svg
             rendered.append(svg)
         if page:
             content = "".join([TPL_FIGURE.format(content=svg) for svg in rendered])
@@ -565,7 +588,7 @@ class EntityRenderer:
             for i, fragment in enumerate(fragments):
                 markup += escape_html(fragment)
                 if len(fragments) > 1 and i != len(fragments) - 1:
-                    markup += "</br>"
+                    markup += "<br>"
             if self.ents is None or label.upper() in self.ents:
                 color = self.colors.get(label.upper(), self.default_color)
                 ent_settings = {
@@ -583,7 +606,7 @@ class EntityRenderer:
         for i, fragment in enumerate(fragments):
             markup += escape_html(fragment)
             if len(fragments) > 1 and i != len(fragments) - 1:
-                markup += "</br>"
+                markup += "<br>"
         markup = TPL_ENTS.format(content=markup, dir=self.direction)
         if title:
             markup = TPL_TITLE.format(title=title) + markup

spacy/errors.py

@@ -219,6 +219,7 @@ class Warnings(metaclass=ErrorsWithCodes):
     W125 = ("The StaticVectors key_attr is no longer used. To set a custom "
             "key attribute for vectors, configure it through Vectors(attr=) or "
             "'spacy init vectors --attr'")
+    W126 = ("These keys are unsupported: {unsupported}")
 
 
 class Errors(metaclass=ErrorsWithCodes):
@@ -226,7 +227,6 @@ class Errors(metaclass=ErrorsWithCodes):
     E002 = ("Can't find factory for '{name}' for language {lang} ({lang_code}). "
             "This usually happens when spaCy calls `nlp.{method}` with a custom "
             "component name that's not registered on the current language class. "
-            "If you're using a Transformer, make sure to install 'spacy-transformers'. "
             "If you're using a custom component, make sure you've added the "
             "decorator `@Language.component` (for function components) or "
             "`@Language.factory` (for class components).\n\nAvailable "
@@ -553,12 +553,12 @@ class Errors(metaclass=ErrorsWithCodes):
             "during training, make sure to include it in 'annotating components'")
 
     # New errors added in v3.x
+    E849 = ("The vocab only supports {method} for vectors of type "
+            "spacy.vectors.Vectors, not {vectors_type}.")
     E850 = ("The PretrainVectors objective currently only supports default or "
             "floret vectors, not {mode} vectors.")
     E851 = ("The 'textcat' component labels should only have values of 0 or 1, "
             "but found value of '{val}'.")
-    E852 = ("The tar file pulled from the remote attempted an unsafe path "
-            "traversal.")
     E853 = ("Unsupported component factory name '{name}'. The character '.' is "
             "not permitted in factory names.")
     E854 = ("Unable to set doc.ents. Check that the 'ents_filter' does not "
@@ -981,6 +981,12 @@ class Errors(metaclass=ErrorsWithCodes):
              " 'min_length': {min_length}, 'max_length': {max_length}")
     E1054 = ("The text, including whitespace, must match between reference and "
              "predicted docs when training {component}.")
+    E1055 = ("The 'replace_listener' callback expects {num_params} parameters, "
+             "but only callbacks with one or three parameters are supported")
+    E1056 = ("The `TextCatBOW` architecture expects a length of at least 1, was {length}.")
+    E1057 = ("The `TextCatReduce` architecture must be used with at least one "
+             "reduction. Please enable one of `use_reduce_first`, "
+             "`use_reduce_last`, `use_reduce_max` or `use_reduce_mean`.")
 
 
 # Deprecated model shortcuts, only used in errors and warnings

spacy/kb/__init__.py

@@ -1,3 +1,11 @@
 from .candidate import Candidate, get_candidates, get_candidates_batch
 from .kb import KnowledgeBase
 from .kb_in_memory import InMemoryLookupKB
+
+__all__ = [
+    "Candidate",
+    "KnowledgeBase",
+    "InMemoryLookupKB",
+    "get_candidates",
+    "get_candidates_batch",
+]

spacy/kb/candidate.pyx

@@ -1,4 +1,4 @@
-# cython: infer_types=True, profile=True
+# cython: infer_types=True
 
 from typing import Iterable
 

spacy/kb/kb.pyx

@@ -1,4 +1,4 @@
-# cython: infer_types=True, profile=True
+# cython: infer_types=True
 
 from pathlib import Path
 from typing import Iterable, Tuple, Union

spacy/kb/kb_in_memory.pyx

@@ -1,4 +1,4 @@
-# cython: infer_types=True, profile=True
+# cython: infer_types=True
 from typing import Any, Callable, Dict, Iterable
 
 import srsly

spacy/lang/en/lex_attrs.py

@@ -6,7 +6,8 @@ _num_words = [
     "nine", "ten", "eleven", "twelve", "thirteen", "fourteen", "fifteen",
     "sixteen", "seventeen", "eighteen", "nineteen", "twenty", "thirty", "forty",
     "fifty", "sixty", "seventy", "eighty", "ninety", "hundred", "thousand",
-    "million", "billion", "trillion", "quadrillion", "gajillion", "bazillion"
+    "million", "billion", "trillion", "quadrillion", "quintillion", "sextillion",
+    "septillion", "octillion", "nonillion", "decillion", "gajillion", "bazillion"
 ]
 _ordinal_words = [
     "first", "second", "third", "fourth", "fifth", "sixth", "seventh", "eighth",
@@ -14,7 +15,8 @@ _ordinal_words = [
     "fifteenth", "sixteenth", "seventeenth", "eighteenth", "nineteenth",
     "twentieth", "thirtieth", "fortieth", "fiftieth", "sixtieth", "seventieth",
     "eightieth", "ninetieth", "hundredth", "thousandth", "millionth", "billionth",
-    "trillionth", "quadrillionth", "gajillionth", "bazillionth"
+    "trillionth", "quadrillionth", "quintillionth", "sextillionth", "septillionth",
+    "octillionth", "nonillionth", "decillionth", "gajillionth", "bazillionth"
 ]
 # fmt: on
 

spacy/lang/es/lemmatizer.py

@@ -163,7 +163,7 @@ class SpanishLemmatizer(Lemmatizer):
         for old, new in self.lookups.get_table("lemma_rules").get("det", []):
             if word == old:
                 return [new]
-        # If none of the specfic rules apply, search in the common rules for
+        # If none of the specific rules apply, search in the common rules for
         # determiners and pronouns that follow a unique pattern for
         # lemmatization. If the word is in the list, return the corresponding
         # lemma.
@@ -291,7 +291,7 @@ class SpanishLemmatizer(Lemmatizer):
         for old, new in self.lookups.get_table("lemma_rules").get("pron", []):
             if word == old:
                 return [new]
-        # If none of the specfic rules apply, search in the common rules for
+        # If none of the specific rules apply, search in the common rules for
         # determiners and pronouns that follow a unique pattern for
         # lemmatization. If the word is in the list, return the corresponding
         # lemma.

spacy/lang/fo/__init__.py

@@ -0,0 +1,18 @@
+from ...language import BaseDefaults, Language
+from ..punctuation import TOKENIZER_INFIXES, TOKENIZER_PREFIXES, TOKENIZER_SUFFIXES
+from .tokenizer_exceptions import TOKENIZER_EXCEPTIONS
+
+
+class FaroeseDefaults(BaseDefaults):
+    tokenizer_exceptions = TOKENIZER_EXCEPTIONS
+    infixes = TOKENIZER_INFIXES
+    suffixes = TOKENIZER_SUFFIXES
+    prefixes = TOKENIZER_PREFIXES
+
+
+class Faroese(Language):
+    lang = "fo"
+    Defaults = FaroeseDefaults
+
+
+__all__ = ["Faroese"]

spacy/lang/fo/tokenizer_exceptions.py

@@ -0,0 +1,157 @@
+from ...symbols import ORTH
+from ...util import update_exc
+from ..tokenizer_exceptions import BASE_EXCEPTIONS
+
+_exc = {}
+
+for orth in [
+    "Chr.",
+    "apr.",
+    "atm.",
+    "aug.",
+    "avgr.",
+    "árg.",
+    "ávís.",
+    "beinl.",
+    "blkv.",
+    "blaðkv.",
+    "blm.",
+    "blaðm.",
+    "blaðstj.",
+    "blkv.",
+    "blm.",
+    "bls.",
+    "blstj.",
+    "blaðstj.",
+    "cand.",
+    "dagf.",
+    "des.",
+    "dkr.",
+    "dr.",
+    "e.Kr.",
+    "eint.",
+    "ex.",
+    "exam.",
+    "f.",
+    "f.Kr.",
+    "fa.",
+    "fam.",
+    "feb.",
+    "febr.",
+    "ff.",
+    "fl.",
+    "form.",
+    "frí.",
+    "fyrrv.",
+    "góðk.",
+    "h.m.",
+    "hósd.",
+    "innt.",
+    "jan.",
+    "kap.",
+    "kgl.",
+    "kl.",
+    "kr.",
+    "leyg.",
+    "m.a.",
+    "mðr.",
+    "m.o.",
+    "m.ø.",
+    "mia.",
+    "mik.",
+    "min.",
+    "mió.",
+    "mán.",
+    "mðr.",
+    "nov.",
+    "nr.",
+    "nto.",
+    "nov.",
+    "nút.",
+    "o.a.",
+    "o.a.m.",
+    "o.a.tíl.",
+    "o.fl.",
+    "ff.",
+    "o.m.a.",
+    "o.o.",
+    "o.s.fr.",
+    "o.tíl.",
+    "o.u.",
+    "o.ø.",
+    "okt.",
+    "omf.",
+    "ph.d.",
+    "phil.",
+    "pr.",
+    "pst.",
+    "ritstj.",
+    "s.",
+    "sb.",
+    "sbr.",
+    "sbrt.",
+    "sek.",
+    "sep.",
+    "sept.",
+    "serst.",
+    "smb.",
+    "smbr.",
+    "sms.",
+    "smst.",
+    "smb.",
+    "sb.",
+    "sbrt.",
+    "sp.",
+    "sept.",
+    "spf.",
+    "spsk.",
+    "stk.",
+    "sunnud.",
+    "t.",
+    "t.d.",
+    "t.e.",
+    "t.o.v.",
+    "t.s.",
+    "t.s.s.",
+    "tlf.",
+    "t.v.s",
+    "t.v.s.",
+    "tel.",
+    "tils.",
+    "tlf.",
+    "tsk.",
+    "t.o.v.",
+    "t.d.",
+    "tús.",
+    "týs.",
+    "uml.",
+    "ums.",
+    "uppl.",
+    "upprfr.",
+    "uppr.",
+    "útg.",
+    "útl.",
+    "útr.",
+    "vanl.",
+    "upprfr.",
+    "v.",
+    "v.h.",
+    "v.m.",
+    "v.ø.o.",
+    "vanl.",
+    "viðm.",
+    "viðv.",
+    "vm.",
+    "v.m.",
+    "á.Kr.",
+    "árg.",
+    "ávís.",
+    "útg.",
+    "útl.",
+    "útr.",
+]:
+    _exc[orth] = [{ORTH: orth}]
+    capitalized = orth.capitalize()
+    _exc[capitalized] = [{ORTH: capitalized}]
+
+TOKENIZER_EXCEPTIONS = update_exc(BASE_EXCEPTIONS, _exc)

spacy/lang/grc/punctuation.py

@@ -15,6 +15,7 @@ _prefixes = (
     [
         "†",
         "⸏",
+        "〈",
     ]
     + LIST_PUNCT
     + LIST_ELLIPSES
@@ -31,6 +32,7 @@ _suffixes = (
     + [
         "†",
         "⸎",
+        "〉",
         r"(?<=[\u1F00-\u1FFF\u0370-\u03FF])[\-\.⸏]",
     ]
 )

spacy/lang/nn/__init__.py

@@ -0,0 +1,20 @@
+from ...language import BaseDefaults, Language
+from ..nb import SYNTAX_ITERATORS
+from .punctuation import TOKENIZER_INFIXES, TOKENIZER_PREFIXES, TOKENIZER_SUFFIXES
+from .tokenizer_exceptions import TOKENIZER_EXCEPTIONS
+
+
+class NorwegianNynorskDefaults(BaseDefaults):
+    tokenizer_exceptions = TOKENIZER_EXCEPTIONS
+    prefixes = TOKENIZER_PREFIXES
+    infixes = TOKENIZER_INFIXES
+    suffixes = TOKENIZER_SUFFIXES
+    syntax_iterators = SYNTAX_ITERATORS
+
+
+class NorwegianNynorsk(Language):
+    lang = "nn"
+    Defaults = NorwegianNynorskDefaults
+
+
+__all__ = ["NorwegianNynorsk"]

spacy/lang/nn/examples.py

@@ -0,0 +1,15 @@
+"""
+Example sentences to test spaCy and its language models.
+
+>>> from spacy.lang.nn.examples import sentences
+>>> docs = nlp.pipe(sentences)
+"""
+
+
+# sentences taken from Omsetjingsminne frå Nynorsk pressekontor 2022 (https://www.nb.no/sprakbanken/en/resource-catalogue/oai-nb-no-sbr-80/)
+sentences = [
+    "Konseptet går ut på at alle tre omgangar tel, alle hopparar må stille i kvalifiseringa og poengsummen skal telje.",
+    "Det er ein meir enn i same periode i fjor.",
+    "Det har lava ned enorme snømengder i store delar av Europa den siste tida.",
+    "Akhtar Chaudhry er ikkje innstilt på Oslo-lista til SV, men utfordrar Heikki Holmås om førsteplassen.",
+]

spacy/lang/nn/punctuation.py

@@ -0,0 +1,74 @@
+from ..char_classes import (
+    ALPHA,
+    ALPHA_LOWER,
+    ALPHA_UPPER,
+    CONCAT_QUOTES,
+    CURRENCY,
+    LIST_CURRENCY,
+    LIST_ELLIPSES,
+    LIST_ICONS,
+    LIST_PUNCT,
+    LIST_QUOTES,
+    PUNCT,
+    UNITS,
+)
+from ..punctuation import TOKENIZER_SUFFIXES
+
+_quotes = CONCAT_QUOTES.replace("'", "")
+_list_punct = [x for x in LIST_PUNCT if x != "#"]
+_list_icons = [x for x in LIST_ICONS if x != "°"]
+_list_icons = [x.replace("\\u00B0", "") for x in _list_icons]
+_list_quotes = [x for x in LIST_QUOTES if x != "\\'"]
+
+
+_prefixes = (
+    ["§", "%", "=", "—", "–", r"\+(?![0-9])"]
+    + _list_punct
+    + LIST_ELLIPSES
+    + LIST_QUOTES
+    + LIST_CURRENCY
+    + LIST_ICONS
+)
+
+
+_infixes = (
+    LIST_ELLIPSES
+    + _list_icons
+    + [
+        r"(?<=[{al}])\.(?=[{au}])".format(al=ALPHA_LOWER, au=ALPHA_UPPER),
+        r"(?<=[{a}])[,!?](?=[{a}])".format(a=ALPHA),
+        r"(?<=[{a}])[:<>=/](?=[{a}])".format(a=ALPHA),
+        r"(?<=[{a}]),(?=[{a}])".format(a=ALPHA),
+        r"(?<=[{a}])([{q}\)\]\(\[])(?=[{a}])".format(a=ALPHA, q=_quotes),
+        r"(?<=[{a}])--(?=[{a}])".format(a=ALPHA),
+    ]
+)
+
+_suffixes = (
+    LIST_PUNCT
+    + LIST_ELLIPSES
+    + _list_quotes
+    + _list_icons
+    + ["—", "–"]
+    + [
+        r"(?<=[0-9])\+",
+        r"(?<=°[FfCcKk])\.",
+        r"(?<=[0-9])(?:{c})".format(c=CURRENCY),
+        r"(?<=[0-9])(?:{u})".format(u=UNITS),
+        r"(?<=[{al}{e}{p}(?:{q})])\.".format(
+            al=ALPHA_LOWER, e=r"%²\-\+", q=_quotes, p=PUNCT
+        ),
+        r"(?<=[{au}][{au}])\.".format(au=ALPHA_UPPER),
+    ]
+    + [r"(?<=[^sSxXzZ])'"]
+)
+_suffixes += [
+    suffix
+    for suffix in TOKENIZER_SUFFIXES
+    if suffix not in ["'s", "'S", "’s", "’S", r"\'"]
+]
+
+
+TOKENIZER_PREFIXES = _prefixes
+TOKENIZER_INFIXES = _infixes
+TOKENIZER_SUFFIXES = _suffixes

spacy/lang/nn/tokenizer_exceptions.py

@@ -0,0 +1,228 @@
+from ...symbols import NORM, ORTH
+from ...util import update_exc
+from ..tokenizer_exceptions import BASE_EXCEPTIONS
+
+_exc = {}
+
+
+for exc_data in [
+    {ORTH: "jan.", NORM: "januar"},
+    {ORTH: "feb.", NORM: "februar"},
+    {ORTH: "mar.", NORM: "mars"},
+    {ORTH: "apr.", NORM: "april"},
+    {ORTH: "jun.", NORM: "juni"},
+    # note: "jul." is in the simple list below without a NORM exception
+    {ORTH: "aug.", NORM: "august"},
+    {ORTH: "sep.", NORM: "september"},
+    {ORTH: "okt.", NORM: "oktober"},
+    {ORTH: "nov.", NORM: "november"},
+    {ORTH: "des.", NORM: "desember"},
+]:
+    _exc[exc_data[ORTH]] = [exc_data]
+
+
+for orth in [
+    "Ap.",
+    "Aq.",
+    "Ca.",
+    "Chr.",
+    "Co.",
+    "Dr.",
+    "F.eks.",
+    "Fr.p.",
+    "Frp.",
+    "Grl.",
+    "Kr.",
+    "Kr.F.",
+    "Kr.F.s",
+    "Mr.",
+    "Mrs.",
+    "Pb.",
+    "Pr.",
+    "Sp.",
+    "St.",
+    "a.m.",
+    "ad.",
+    "adm.dir.",
+    "adr.",
+    "b.c.",
+    "bl.a.",
+    "bla.",
+    "bm.",
+    "bnr.",
+    "bto.",
+    "c.c.",
+    "ca.",
+    "cand.mag.",
+    "co.",
+    "d.d.",
+    "d.m.",
+    "d.y.",
+    "dept.",
+    "dr.",
+    "dr.med.",
+    "dr.philos.",
+    "dr.psychol.",
+    "dss.",
+    "dvs.",
+    "e.Kr.",
+    "e.l.",
+    "eg.",
+    "eig.",
+    "ekskl.",
+    "el.",
+    "et.",
+    "etc.",
+    "etg.",
+    "ev.",
+    "evt.",
+    "f.",
+    "f.Kr.",
+    "f.eks.",
+    "f.o.m.",
+    "fhv.",
+    "fk.",
+    "foreg.",
+    "fork.",
+    "fv.",
+    "fvt.",
+    "g.",
+    "gl.",
+    "gno.",
+    "gnr.",
+    "grl.",
+    "gt.",
+    "h.r.adv.",
+    "hhv.",
+    "hoh.",
+    "hr.",
+    "ifb.",
+    "ifm.",
+    "iht.",
+    "inkl.",
+    "istf.",
+    "jf.",
+    "jr.",
+    "jul.",
+    "juris.",
+    "kfr.",
+    "kgl.",
+    "kgl.res.",
+    "kl.",
+    "komm.",
+    "kr.",
+    "kst.",
+    "lat.",
+    "lø.",
+    "m.a.",
+    "m.a.o.",
+    "m.fl.",
+    "m.m.",
+    "m.v.",
+    "ma.",
+    "mag.art.",
+    "md.",
+    "mfl.",
+    "mht.",
+    "mill.",
+    "min.",
+    "mnd.",
+    "moh.",
+    "mrd.",
+    "muh.",
+    "mv.",
+    "mva.",
+    "n.å.",
+    "ndf.",
+    "nr.",
+    "nto.",
+    "nyno.",
+    "o.a.",
+    "o.l.",
+    "obl.",
+    "off.",
+    "ofl.",
+    "on.",
+    "op.",
+    "org.",
+    "osv.",
+    "ovf.",
+    "p.",
+    "p.a.",
+    "p.g.a.",
+    "p.m.",
+    "p.t.",
+    "pga.",
+    "ph.d.",
+    "pkt.",
+    "pr.",
+    "pst.",
+    "pt.",
+    "red.anm.",
+    "ref.",
+    "res.",
+    "res.kap.",
+    "resp.",
+    "rv.",
+    "s.",
+    "s.d.",
+    "s.k.",
+    "s.u.",
+    "s.å.",
+    "sen.",
+    "sep.",
+    "siviling.",
+    "sms.",
+    "snr.",
+    "spm.",
+    "sr.",
+    "sst.",
+    "st.",
+    "st.meld.",
+    "st.prp.",
+    "stip.",
+    "stk.",
+    "stud.",
+    "sv.",
+    "såk.",
+    "sø.",
+    "t.d.",
+    "t.h.",
+    "t.o.m.",
+    "t.v.",
+    "temp.",
+    "ti.",
+    "tils.",
+    "tilsv.",
+    "tl;dr",
+    "tlf.",
+    "to.",
+    "ult.",
+    "utg.",
+    "v.",
+    "vedk.",
+    "vedr.",
+    "vg.",
+    "vgs.",
+    "vha.",
+    "vit.ass.",
+    "vn.",
+    "vol.",
+    "vs.",
+    "vsa.",
+    "§§",
+    "©NTB",
+    "årg.",
+    "årh.",
+]:
+    _exc[orth] = [{ORTH: orth}]
+
+# Dates
+for h in range(1, 31 + 1):
+    for period in ["."]:
+        _exc[f"{h}{period}"] = [{ORTH: f"{h}."}]
+
+_custom_base_exc = {"i.": [{ORTH: "i", NORM: "i"}, {ORTH: "."}]}
+_exc.update(_custom_base_exc)
+
+TOKENIZER_EXCEPTIONS = update_exc(BASE_EXCEPTIONS, _exc)

spacy/lang/tr/examples.py

@@ -15,4 +15,7 @@ sentences = [
     "Türkiye'nin başkenti neresi?",
     "Bakanlar Kurulu 180 günlük eylem planını açıkladı.",
     "Merkez Bankası, beklentiler doğrultusunda faizlerde değişikliğe gitmedi.",
+    "Cemal Sureya kimdir?",
+    "Bunlari Biliyor muydunuz?",
+    "Altinoluk Turkiye haritasinin neresinde yer alir?",
 ]

spacy/language.py

@@ -1,4 +1,5 @@
 import functools
+import inspect
 import itertools
 import multiprocessing as mp
 import random
@@ -64,6 +65,7 @@ from .util import (
     registry,
     warn_if_jupyter_cupy,
 )
+from .vectors import BaseVectors
 from .vocab import Vocab, create_vocab
 
 PipeCallable = Callable[[Doc], Doc]
@@ -157,6 +159,7 @@ class Language:
         max_length: int = 10**6,
         meta: Dict[str, Any] = {},
         create_tokenizer: Optional[Callable[["Language"], Callable[[str], Doc]]] = None,
+        create_vectors: Optional[Callable[["Vocab"], BaseVectors]] = None,
         batch_size: int = 1000,
         **kwargs,
     ) -> None:
@@ -197,6 +200,10 @@ class Language:
         if vocab is True:
             vectors_name = meta.get("vectors", {}).get("name")
             vocab = create_vocab(self.lang, self.Defaults, vectors_name=vectors_name)
+            if not create_vectors:
+                vectors_cfg = {"vectors": self._config["nlp"]["vectors"]}
+                create_vectors = registry.resolve(vectors_cfg)["vectors"]
+            vocab.vectors = create_vectors(vocab)
         else:
             if (self.lang and vocab.lang) and (self.lang != vocab.lang):
                 raise ValueError(Errors.E150.format(nlp=self.lang, vocab=vocab.lang))
@@ -1676,6 +1683,12 @@ class Language:
         for proc in procs:
             proc.start()
 
+        # Close writing-end of channels. This is needed to avoid that reading
+        # from the channel blocks indefinitely when the worker closes the
+        # channel.
+        for tx in bytedocs_send_ch:
+            tx.close()
+
         # Cycle channels not to break the order of docs.
         # The received object is a batch of byte-encoded docs, so flatten them with chain.from_iterable.
         byte_tuples = chain.from_iterable(
@@ -1698,8 +1711,23 @@ class Language:
                     # tell `sender` that one batch was consumed.
                     sender.step()
         finally:
+            # If we are stopping in an orderly fashion, the workers' queues
+            # are empty. Put the sentinel in their queues to signal that work
+            # is done, so that they can exit gracefully.
+            for q in texts_q:
+                q.put(_WORK_DONE_SENTINEL)
+
+            # Otherwise, we are stopping because the error handler raised an
+            # exception. The sentinel will be last to go out of the queue.
+            # To avoid doing unnecessary work or hanging on platforms that
+            # block on sending (Windows), we'll close our end of the channel.
+            # This signals to the worker that it can exit the next time it
+            # attempts to send data down the channel.
+            for r in bytedocs_recv_ch:
+                r.close()
+
             for proc in procs:
-                proc.terminate()
+                proc.join()
 
     def _link_components(self) -> None:
         """Register 'listeners' within pipeline components, to allow them to
@@ -1764,6 +1792,10 @@ class Language:
             ).merge(config)
         if "nlp" not in config:
             raise ValueError(Errors.E985.format(config=config))
+        # fill in [nlp.vectors] if not present (as a narrower alternative to
+        # auto-filling [nlp] from the default config)
+        if "vectors" not in config["nlp"]:
+            config["nlp"]["vectors"] = {"@vectors": "spacy.Vectors.v1"}
         config_lang = config["nlp"].get("lang")
         if config_lang is not None and config_lang != cls.lang:
             raise ValueError(
@@ -1795,6 +1827,7 @@ class Language:
             filled["nlp"], validate=validate, schema=ConfigSchemaNlp
         )
         create_tokenizer = resolved_nlp["tokenizer"]
+        create_vectors = resolved_nlp["vectors"]
         before_creation = resolved_nlp["before_creation"]
         after_creation = resolved_nlp["after_creation"]
         after_pipeline_creation = resolved_nlp["after_pipeline_creation"]
@@ -1815,7 +1848,12 @@ class Language:
         # inside stuff like the spacy train function. If we loaded them here,
         # then we would load them twice at runtime: once when we make from config,
         # and then again when we load from disk.
-        nlp = lang_cls(vocab=vocab, create_tokenizer=create_tokenizer, meta=meta)
+        nlp = lang_cls(
+            vocab=vocab,
+            create_tokenizer=create_tokenizer,
+            create_vectors=create_vectors,
+            meta=meta,
+        )
         if after_creation is not None:
             nlp = after_creation(nlp)
             if not isinstance(nlp, cls):
@@ -2032,8 +2070,20 @@ class Language:
             # Go over the listener layers and replace them
             for listener in pipe_listeners:
                 new_model = tok2vec_model.copy()
-                if "replace_listener" in tok2vec_model.attrs:
-                    new_model = tok2vec_model.attrs["replace_listener"](new_model)
+                replace_listener_func = tok2vec_model.attrs.get("replace_listener")
+                if replace_listener_func is not None:
+                    # Pass the extra args to the callback without breaking compatibility with
+                    # old library versions that only expect a single parameter.
+                    num_params = len(
+                        inspect.signature(replace_listener_func).parameters
+                    )
+                    if num_params == 1:
+                        new_model = replace_listener_func(new_model)
+                    elif num_params == 3:
+                        new_model = replace_listener_func(new_model, listener, tok2vec)
+                    else:
+                        raise ValueError(Errors.E1055.format(num_params=num_params))
+
                 util.replace_model_node(pipe.model, listener, new_model)  # type: ignore[attr-defined]
                 tok2vec.remove_listener(listener, pipe_name)
 
@@ -2294,6 +2344,11 @@ def _apply_pipes(
     while True:
         try:
             texts_with_ctx = receiver.get()
+
+            # Stop working if we encounter the end-of-work sentinel.
+            if isinstance(texts_with_ctx, _WorkDoneSentinel):
+                return
+
             docs = (
                 ensure_doc(doc_like, context) for doc_like, context in texts_with_ctx
             )
@@ -2302,11 +2357,21 @@ def _apply_pipes(
             # Connection does not accept unpickable objects, so send list.
             byte_docs = [(doc.to_bytes(), doc._context, None) for doc in docs]
             padding = [(None, None, None)] * (len(texts_with_ctx) - len(byte_docs))
-            sender.send(byte_docs + padding)  # type: ignore[operator]
+            data: Sequence[Tuple[Optional[bytes], Optional[Any], Optional[bytes]]] = (
+                byte_docs + padding  # type: ignore[operator]
+            )
         except Exception:
             error_msg = [(None, None, srsly.msgpack_dumps(traceback.format_exc()))]
             padding = [(None, None, None)] * (len(texts_with_ctx) - 1)
-            sender.send(error_msg + padding)
+            data = error_msg + padding
+
+        try:
+            sender.send(data)
+        except BrokenPipeError:
+            # Parent has closed the pipe prematurely. This happens when a
+            # worker encounters an error and the error handler is set to
+            # stop processing.
+            return
 
 
 class _Sender:
@@ -2336,3 +2401,10 @@ class _Sender:
         if self.count >= self.chunk_size:
             self.count = 0
             self.send()
+
+
+class _WorkDoneSentinel:
+    pass
+
+
+_WORK_DONE_SENTINEL = _WorkDoneSentinel()

spacy/lexeme.pyx

@@ -1,4 +1,5 @@
 # cython: embedsignature=True
+# cython: profile=False
 # Compiler crashes on memory view coercion without this. Should report bug.
 cimport numpy as np
 from libc.string cimport memset

spacy/matcher/__init__.py

@@ -3,4 +3,4 @@ from .levenshtein import levenshtein
 from .matcher import Matcher
 from .phrasematcher import PhraseMatcher
 
-__all__ = ["Matcher", "PhraseMatcher", "DependencyMatcher", "levenshtein"]
+__all__ = ["DependencyMatcher", "Matcher", "PhraseMatcher", "levenshtein"]

spacy/matcher/dependencymatcher.pyx

@@ -1,4 +1,4 @@
-# cython: infer_types=True, profile=True
+# cython: infer_types=True
 import warnings
 from collections import defaultdict
 from itertools import product
@@ -129,6 +129,7 @@ cdef class DependencyMatcher:
             else:
                 required_keys = {"RIGHT_ID", "RIGHT_ATTRS", "REL_OP", "LEFT_ID"}
                 relation_keys = set(relation.keys())
+                # Identify required keys that have not been specified
                 missing = required_keys - relation_keys
                 if missing:
                     missing_txt = ", ".join(list(missing))
@@ -136,6 +137,13 @@ cdef class DependencyMatcher:
                         required=required_keys,
                         missing=missing_txt
                     ))
+                # Identify additional, unsupported keys
+                unsupported = relation_keys - required_keys
+                if unsupported:
+                    unsupported_txt = ", ".join(list(unsupported))
+                    warnings.warn(Warnings.W126.format(
+                        unsupported=unsupported_txt
+                    ))
                 if (
                     relation["RIGHT_ID"] in visited_nodes
                     or relation["LEFT_ID"] not in visited_nodes

spacy/matcher/levenshtein.pyx

@@ -1,4 +1,4 @@
-# cython: profile=True, binding=True, infer_types=True
+# cython: binding=True, infer_types=True
 from cpython.object cimport PyObject
 from libc.stdint cimport int64_t
 

spacy/matcher/matcher.pyx

@@ -1,4 +1,4 @@
-# cython: binding=True, infer_types=True, profile=True
+# cython: binding=True, infer_types=True
 from typing import Iterable, List
 
 from cymem.cymem cimport Pool

spacy/matcher/phrasematcher.pyx

@@ -1,4 +1,4 @@
-# cython: infer_types=True, profile=True
+# cython: infer_types=True
 from preshed.maps cimport map_clear, map_get, map_init, map_iter, map_set
 
 import warnings

spacy/ml/models/textcat.py

@@ -1,21 +1,27 @@
 from functools import partial
-from typing import List, Optional, cast
+from typing import List, Optional, Tuple, cast
 
 from thinc.api import (
     Dropout,
+    Gelu,
     LayerNorm,
     Linear,
     Logistic,
     Maxout,
     Model,
     ParametricAttention,
+    ParametricAttention_v2,
     Relu,
     Softmax,
     SparseLinear,
+    SparseLinear_v2,
     chain,
     clone,
     concatenate,
     list2ragged,
+    reduce_first,
+    reduce_last,
+    reduce_max,
     reduce_mean,
     reduce_sum,
     residual,
@@ -25,9 +31,10 @@ from thinc.api import (
 )
 from thinc.layers.chain import init as init_chain
 from thinc.layers.resizable import resize_linear_weighted, resize_model
-from thinc.types import Floats2d
+from thinc.types import ArrayXd, Floats2d
 
 from ...attrs import ORTH
+from ...errors import Errors
 from ...tokens import Doc
 from ...util import registry
 from ..extract_ngrams import extract_ngrams
@@ -47,39 +54,15 @@ def build_simple_cnn_text_classifier(
     outputs sum to 1. If exclusive_classes=False, a logistic non-linearity
     is applied instead, so that outputs are in the range [0, 1].
     """
-    fill_defaults = {"b": 0, "W": 0}
-    with Model.define_operators({">>": chain}):
-        cnn = tok2vec >> list2ragged() >> reduce_mean()
-        nI = tok2vec.maybe_get_dim("nO")
-        if exclusive_classes:
-            output_layer = Softmax(nO=nO, nI=nI)
-            fill_defaults["b"] = NEG_VALUE
-            resizable_layer: Model = resizable(
-                output_layer,
-                resize_layer=partial(
-                    resize_linear_weighted, fill_defaults=fill_defaults
-                ),
-            )
-            model = cnn >> resizable_layer
-        else:
-            output_layer = Linear(nO=nO, nI=nI)
-            resizable_layer = resizable(
-                output_layer,
-                resize_layer=partial(
-                    resize_linear_weighted, fill_defaults=fill_defaults
-                ),
-            )
-            model = cnn >> resizable_layer >> Logistic()
-        model.set_ref("output_layer", output_layer)
-        model.attrs["resize_output"] = partial(
-            resize_and_set_ref,
-            resizable_layer=resizable_layer,
-        )
-    model.set_ref("tok2vec", tok2vec)
-    if nO is not None:
-        model.set_dim("nO", cast(int, nO))
-    model.attrs["multi_label"] = not exclusive_classes
-    return model
+    return build_reduce_text_classifier(
+        tok2vec=tok2vec,
+        exclusive_classes=exclusive_classes,
+        use_reduce_first=False,
+        use_reduce_last=False,
+        use_reduce_max=False,
+        use_reduce_mean=True,
+        nO=nO,
+    )
 
 
 def resize_and_set_ref(model, new_nO, resizable_layer):
@@ -95,10 +78,48 @@ def build_bow_text_classifier(
     ngram_size: int,
     no_output_layer: bool,
     nO: Optional[int] = None,
+) -> Model[List[Doc], Floats2d]:
+    return _build_bow_text_classifier(
+        exclusive_classes=exclusive_classes,
+        ngram_size=ngram_size,
+        no_output_layer=no_output_layer,
+        nO=nO,
+        sparse_linear=SparseLinear(nO=nO),
+    )
+
+
+@registry.architectures("spacy.TextCatBOW.v3")
+def build_bow_text_classifier_v3(
+    exclusive_classes: bool,
+    ngram_size: int,
+    no_output_layer: bool,
+    length: int = 262144,
+    nO: Optional[int] = None,
+) -> Model[List[Doc], Floats2d]:
+    if length < 1:
+        raise ValueError(Errors.E1056.format(length=length))
+
+    # Find k such that 2**(k-1) < length <= 2**k.
+    length = 2 ** (length - 1).bit_length()
+
+    return _build_bow_text_classifier(
+        exclusive_classes=exclusive_classes,
+        ngram_size=ngram_size,
+        no_output_layer=no_output_layer,
+        nO=nO,
+        sparse_linear=SparseLinear_v2(nO=nO, length=length),
+    )
+
+
+def _build_bow_text_classifier(
+    exclusive_classes: bool,
+    ngram_size: int,
+    no_output_layer: bool,
+    sparse_linear: Model[Tuple[ArrayXd, ArrayXd, ArrayXd], ArrayXd],
+    nO: Optional[int] = None,
 ) -> Model[List[Doc], Floats2d]:
     fill_defaults = {"b": 0, "W": 0}
     with Model.define_operators({">>": chain}):
-        sparse_linear = SparseLinear(nO=nO)
         output_layer = None
         if not no_output_layer:
             fill_defaults["b"] = NEG_VALUE
@@ -127,6 +148,9 @@ def build_text_classifier_v2(
     linear_model: Model[List[Doc], Floats2d],
     nO: Optional[int] = None,
 ) -> Model[List[Doc], Floats2d]:
+    # TODO: build the model with _build_parametric_attention_with_residual_nonlinear
+    # in spaCy v4. We don't do this in spaCy v3 to preserve model
+    # compatibility.
     exclusive_classes = not linear_model.attrs["multi_label"]
     with Model.define_operators({">>": chain, "|": concatenate}):
         width = tok2vec.maybe_get_dim("nO")
@@ -190,3 +214,145 @@ def build_text_classifier_lowdata(
             model = model >> Dropout(dropout)
         model = model >> Logistic()
     return model
+
+
+@registry.architectures("spacy.TextCatParametricAttention.v1")
+def build_textcat_parametric_attention_v1(
+    tok2vec: Model[List[Doc], List[Floats2d]],
+    exclusive_classes: bool,
+    nO: Optional[int] = None,
+) -> Model[List[Doc], Floats2d]:
+    width = tok2vec.maybe_get_dim("nO")
+    parametric_attention = _build_parametric_attention_with_residual_nonlinear(
+        tok2vec=tok2vec,
+        nonlinear_layer=Maxout(nI=width, nO=width),
+        key_transform=Gelu(nI=width, nO=width),
+    )
+    with Model.define_operators({">>": chain}):
+        if exclusive_classes:
+            output_layer = Softmax(nO=nO)
+        else:
+            output_layer = Linear(nO=nO) >> Logistic()
+        model = parametric_attention >> output_layer
+    if model.has_dim("nO") is not False and nO is not None:
+        model.set_dim("nO", cast(int, nO))
+    model.set_ref("output_layer", output_layer)
+    model.attrs["multi_label"] = not exclusive_classes
+
+    return model
+
+
+def _build_parametric_attention_with_residual_nonlinear(
+    *,
+    tok2vec: Model[List[Doc], List[Floats2d]],
+    nonlinear_layer: Model[Floats2d, Floats2d],
+    key_transform: Optional[Model[Floats2d, Floats2d]] = None,
+) -> Model[List[Doc], Floats2d]:
+    with Model.define_operators({">>": chain, "|": concatenate}):
+        width = tok2vec.maybe_get_dim("nO")
+        attention_layer = ParametricAttention_v2(nO=width, key_transform=key_transform)
+        norm_layer = LayerNorm(nI=width)
+        parametric_attention = (
+            tok2vec
+            >> list2ragged()
+            >> attention_layer
+            >> reduce_sum()
+            >> residual(nonlinear_layer >> norm_layer >> Dropout(0.0))
+        )
+
+        parametric_attention.init = _init_parametric_attention_with_residual_nonlinear
+
+        parametric_attention.set_ref("tok2vec", tok2vec)
+        parametric_attention.set_ref("attention_layer", attention_layer)
+        parametric_attention.set_ref("nonlinear_layer", nonlinear_layer)
+        parametric_attention.set_ref("norm_layer", norm_layer)
+
+        return parametric_attention
+
+
+def _init_parametric_attention_with_residual_nonlinear(model, X, Y) -> Model:
+    tok2vec_width = get_tok2vec_width(model)
+    model.get_ref("attention_layer").set_dim("nO", tok2vec_width)
+    model.get_ref("nonlinear_layer").set_dim("nO", tok2vec_width)
+    model.get_ref("nonlinear_layer").set_dim("nI", tok2vec_width)
+    model.get_ref("norm_layer").set_dim("nI", tok2vec_width)
+    model.get_ref("norm_layer").set_dim("nO", tok2vec_width)
+    init_chain(model, X, Y)
+    return model
+
+
+@registry.architectures("spacy.TextCatReduce.v1")
+def build_reduce_text_classifier(
+    tok2vec: Model,
+    exclusive_classes: bool,
+    use_reduce_first: bool,
+    use_reduce_last: bool,
+    use_reduce_max: bool,
+    use_reduce_mean: bool,
+    nO: Optional[int] = None,
+) -> Model[List[Doc], Floats2d]:
+    """Build a model that classifies pooled `Doc` representations.
+
+    Pooling is performed using reductions. Reductions are concatenated when
+    multiple reductions are used.
+
+    tok2vec (Model): the tok2vec layer to pool over.
+    exclusive_classes (bool): Whether or not classes are mutually exclusive.
+    use_reduce_first (bool): Pool by using the hidden representation of the
+        first token of a `Doc`.
+    use_reduce_last (bool): Pool by using the hidden representation of the
+        last token of a `Doc`.
+    use_reduce_max (bool): Pool by taking the maximum values of the hidden
+        representations of a `Doc`.
+    use_reduce_mean (bool): Pool by taking the mean of all hidden
+        representations of a `Doc`.
+    nO (Optional[int]): Number of classes.
+    """
+
+    fill_defaults = {"b": 0, "W": 0}
+    reductions = []
+    if use_reduce_first:
+        reductions.append(reduce_first())
+    if use_reduce_last:
+        reductions.append(reduce_last())
+    if use_reduce_max:
+        reductions.append(reduce_max())
+    if use_reduce_mean:
+        reductions.append(reduce_mean())
+
+    if not len(reductions):
+        raise ValueError(Errors.E1057)
+
+    with Model.define_operators({">>": chain}):
+        cnn = tok2vec >> list2ragged() >> concatenate(*reductions)
+        nO_tok2vec = tok2vec.maybe_get_dim("nO")
+        nI = nO_tok2vec * len(reductions) if nO_tok2vec is not None else None
+        if exclusive_classes:
+            output_layer = Softmax(nO=nO, nI=nI)
+            fill_defaults["b"] = NEG_VALUE
+            resizable_layer: Model = resizable(
+                output_layer,
+                resize_layer=partial(
+                    resize_linear_weighted, fill_defaults=fill_defaults
+                ),
+            )
+            model = cnn >> resizable_layer
+        else:
+            output_layer = Linear(nO=nO, nI=nI)
+            resizable_layer = resizable(
+                output_layer,
+                resize_layer=partial(
+                    resize_linear_weighted, fill_defaults=fill_defaults
+                ),
+            )
+            model = cnn >> resizable_layer >> Logistic()
+        model.set_ref("output_layer", output_layer)
+        model.attrs["resize_output"] = partial(
+            resize_and_set_ref,
+            resizable_layer=resizable_layer,
+        )
+    model.set_ref("tok2vec", tok2vec)
+    if nO is not None:
+        model.set_dim("nO", cast(int, nO))
+    model.attrs["multi_label"] = not exclusive_classes
+    return model

spacy/ml/models/tok2vec.py

@@ -67,8 +67,8 @@ def build_hash_embed_cnn_tok2vec(
         are between 2 and 8.
     window_size (int): The number of tokens on either side to concatenate during
         the convolutions. The receptive field of the CNN will be
-        depth * (window_size * 2 + 1), so a 4-layer network with window_size of
-        2 will be sensitive to 20 words at a time. Recommended value is 1.
+        depth * window_size * 2 + 1, so a 4-layer network with window_size of
+        2 will be sensitive to 17 words at a time. Recommended value is 1.
     embed_size (int): The number of rows in the hash embedding tables. This can
         be surprisingly small, due to the use of the hash embeddings. Recommended
         values are between 2000 and 10000.

spacy/ml/parser_model.pyx

@@ -1,4 +1,5 @@
 # cython: infer_types=True, cdivision=True, boundscheck=False
+# cython: profile=False
 cimport numpy as np
 from libc.math cimport exp
 from libc.stdlib cimport calloc, free, realloc

spacy/ml/staticvectors.py

@@ -9,7 +9,7 @@ from thinc.util import partial
 from ..attrs import ORTH
 from ..errors import Errors, Warnings
 from ..tokens import Doc
-from ..vectors import Mode
+from ..vectors import Mode, Vectors
 from ..vocab import Vocab
 
 
@@ -48,11 +48,14 @@ def forward(
     key_attr: int = getattr(vocab.vectors, "attr", ORTH)
     keys = model.ops.flatten([cast(Ints1d, doc.to_array(key_attr)) for doc in docs])
     W = cast(Floats2d, model.ops.as_contig(model.get_param("W")))
-    if vocab.vectors.mode == Mode.default:
+    if isinstance(vocab.vectors, Vectors) and vocab.vectors.mode == Mode.default:
         V = model.ops.asarray(vocab.vectors.data)
         rows = vocab.vectors.find(keys=keys)
         V = model.ops.as_contig(V[rows])
-    elif vocab.vectors.mode == Mode.floret:
+    elif isinstance(vocab.vectors, Vectors) and vocab.vectors.mode == Mode.floret:
+        V = vocab.vectors.get_batch(keys)
+        V = model.ops.as_contig(V)
+    elif hasattr(vocab.vectors, "get_batch"):
         V = vocab.vectors.get_batch(keys)
         V = model.ops.as_contig(V)
     else:
@@ -61,7 +64,7 @@ def forward(
         vectors_data = model.ops.gemm(V, W, trans2=True)
     except ValueError:
         raise RuntimeError(Errors.E896)
-    if vocab.vectors.mode == Mode.default:
+    if isinstance(vocab.vectors, Vectors) and vocab.vectors.mode == Mode.default:
         # Convert negative indices to 0-vectors
         # TODO: more options for UNK tokens
         vectors_data[rows < 0] = 0

spacy/morphology.pyx

@@ -1,4 +1,5 @@
 # cython: infer_types
+# cython: profile=False
 import warnings
 
 import numpy

spacy/parts_of_speech.pyx

@@ -1,4 +1,4 @@
-
+# cython: profile=False
 IDS = {
     "": NO_TAG,
     "ADJ": ADJ,

spacy/pipeline/__init__.py

@@ -22,6 +22,7 @@ from .trainable_pipe import TrainablePipe
 __all__ = [
     "AttributeRuler",
     "DependencyParser",
+    "EditTreeLemmatizer",
     "EntityLinker",
     "EntityRecognizer",
     "EntityRuler",

spacy/pipeline/_edit_tree_internals/edit_trees.pyx

@@ -1,4 +1,5 @@
 # cython: infer_types=True, binding=True
+# cython: profile=False
 from cython.operator cimport dereference as deref
 from libc.stdint cimport UINT32_MAX, uint32_t
 from libc.string cimport memset

spacy/pipeline/_edit_tree_internals/schemas.py

@@ -1,8 +1,12 @@
 from collections import defaultdict
 from typing import Any, Dict, List, Union
 
-from pydantic import BaseModel, Field, ValidationError
-from pydantic.types import StrictBool, StrictInt, StrictStr
+try:
+    from pydantic.v1 import BaseModel, Field, ValidationError
+    from pydantic.v1.types import StrictBool, StrictInt, StrictStr
+except ImportError:
+    from pydantic import BaseModel, Field, ValidationError  # type: ignore
+    from pydantic.types import StrictBool, StrictInt, StrictStr  # type: ignore
 
 
 class MatchNodeSchema(BaseModel):

spacy/pipeline/_parser_internals/_beam_utils.pyx

@@ -1,5 +1,4 @@
 # cython: infer_types=True
-# cython: profile=True
 import numpy
 
 from thinc.extra.search cimport Beam

spacy/pipeline/_parser_internals/_state.pyx

@@ -0,0 +1 @@
+# cython: profile=False

spacy/pipeline/_parser_internals/arc_eager.pyx

@@ -1,4 +1,4 @@
-# cython: profile=True, cdivision=True, infer_types=True
+# cython: cdivision=True, infer_types=True
 from cymem.cymem cimport Address, Pool
 from libc.stdint cimport int32_t
 from libcpp.vector cimport vector

spacy/pipeline/_parser_internals/ner.pyx

@@ -1,3 +1,4 @@
+# cython: profile=False
 from cymem.cymem cimport Pool
 from libc.stdint cimport int32_t
 

spacy/pipeline/_parser_internals/nonproj.pyx

@@ -1,4 +1,4 @@
-# cython: profile=True, infer_types=True
+# cython: infer_types=True
 """Implements the projectivize/deprojectivize mechanism in Nivre & Nilsson 2005
 for doing pseudo-projective parsing implementation uses the HEAD decoration
 scheme.

spacy/pipeline/_parser_internals/stateclass.pyx

@@ -1,4 +1,5 @@
 # cython: infer_types=True
+# cython: profile=False
 from libcpp.vector cimport vector
 
 from ...tokens.doc cimport Doc
@@ -28,7 +29,7 @@ cdef class StateClass:
         return [self.B(i) for i in range(self.c.buffer_length())]
 
     @property
-    def token_vector_lenth(self):
+    def token_vector_length(self):
         return self.doc.tensor.shape[1]
 
     @property

spacy/pipeline/_parser_internals/transition_system.pyx

@@ -1,4 +1,5 @@
 # cython: infer_types=True
+# cython: profile=False
 from __future__ import print_function
 
 from cymem.cymem cimport Pool

spacy/pipeline/dep_parser.pyx

@@ -1,4 +1,4 @@
-# cython: infer_types=True, profile=True, binding=True
+# cython: infer_types=True, binding=True
 from collections import defaultdict
 from typing import Callable, Optional
 

spacy/pipeline/morphologizer.pyx

@@ -1,4 +1,4 @@
-# cython: infer_types=True, profile=True, binding=True
+# cython: infer_types=True, binding=True
 from itertools import islice
 from typing import Callable, Dict, Optional, Union
 

spacy/pipeline/multitask.pyx

@@ -1,4 +1,4 @@
-# cython: infer_types=True, profile=True, binding=True
+# cython: infer_types=True, binding=True
 from typing import Optional
 
 import numpy

spacy/pipeline/ner.pyx

@@ -1,4 +1,4 @@
-# cython: infer_types=True, profile=True, binding=True
+# cython: infer_types=True, binding=True
 from collections import defaultdict
 from typing import Callable, Optional
 

spacy/pipeline/pipe.pyx

@@ -1,4 +1,4 @@
-# cython: infer_types=True, profile=True, binding=True
+# cython: infer_types=True, binding=True
 import warnings
 from typing import Callable, Dict, Iterable, Iterator, Tuple, Union
 

spacy/pipeline/sentencizer.pyx

@@ -1,4 +1,4 @@
-# cython: infer_types=True, profile=True, binding=True
+# cython: infer_types=True, binding=True
 from typing import Callable, List, Optional
 
 import srsly

spacy/pipeline/senter.pyx

@@ -1,4 +1,4 @@
-# cython: infer_types=True, profile=True, binding=True
+# cython: infer_types=True, binding=True
 from itertools import islice
 from typing import Callable, Optional
 

spacy/pipeline/tagger.pyx

@@ -1,4 +1,4 @@
-# cython: infer_types=True, profile=True, binding=True
+# cython: infer_types=True, binding=True
 from itertools import islice
 from typing import Callable, Optional
 

spacy/pipeline/textcat.py

@@ -36,8 +36,9 @@ maxout_pieces = 3
 depth = 2
 
 [model.linear_model]
-@architectures = "spacy.TextCatBOW.v2"
+@architectures = "spacy.TextCatBOW.v3"
 exclusive_classes = true
+length = 262144
 ngram_size = 1
 no_output_layer = false
 """
@@ -45,16 +46,21 @@ DEFAULT_SINGLE_TEXTCAT_MODEL = Config().from_str(single_label_default_config)["m
 
 single_label_bow_config = """
 [model]
-@architectures = "spacy.TextCatBOW.v2"
+@architectures = "spacy.TextCatBOW.v3"
 exclusive_classes = true
+length = 262144
 ngram_size = 1
 no_output_layer = false
 """
 
 single_label_cnn_config = """
 [model]
-@architectures = "spacy.TextCatCNN.v2"
+@architectures = "spacy.TextCatReduce.v1"
 exclusive_classes = true
+use_reduce_first = false
+use_reduce_last = false
+use_reduce_max = false
+use_reduce_mean = true
 
 [model.tok2vec]
 @architectures = "spacy.HashEmbedCNN.v2"

spacy/pipeline/textcat_multilabel.py

@@ -35,8 +35,9 @@ maxout_pieces = 3
 depth = 2
 
 [model.linear_model]
-@architectures = "spacy.TextCatBOW.v2"
+@architectures = "spacy.TextCatBOW.v3"
 exclusive_classes = false
+length = 262144
 ngram_size = 1
 no_output_layer = false
 """
@@ -44,7 +45,7 @@ DEFAULT_MULTI_TEXTCAT_MODEL = Config().from_str(multi_label_default_config)["mod
 
 multi_label_bow_config = """
 [model]
-@architectures = "spacy.TextCatBOW.v2"
+@architectures = "spacy.TextCatBOW.v3"
 exclusive_classes = false
 ngram_size = 1
 no_output_layer = false
@@ -52,8 +53,12 @@ no_output_layer = false
 
 multi_label_cnn_config = """
 [model]
-@architectures = "spacy.TextCatCNN.v2"
+@architectures = "spacy.TextCatReduce.v1"
 exclusive_classes = false
+use_reduce_first = false
+use_reduce_last = false
+use_reduce_max = false
+use_reduce_mean = true
 
 [model.tok2vec]
 @architectures = "spacy.HashEmbedCNN.v2"

spacy/pipeline/trainable_pipe.pyx

@@ -1,4 +1,4 @@
-# cython: infer_types=True, profile=True, binding=True
+# cython: infer_types=True, binding=True
 from typing import Callable, Dict, Iterable, Iterator, Optional, Tuple
 
 import srsly

spacy/pipeline/transition_parser.pyx

@@ -1,4 +1,5 @@
 # cython: infer_types=True, cdivision=True, boundscheck=False, binding=True
+# cython: profile=False
 from __future__ import print_function
 
 cimport numpy as np

spacy/schemas.py

@@ -16,19 +16,34 @@ from typing import (
     Union,
 )
 
-from pydantic import (
-    BaseModel,
-    ConstrainedStr,
-    Field,
-    StrictBool,
-    StrictFloat,
-    StrictInt,
-    StrictStr,
-    ValidationError,
-    create_model,
-    validator,
-)
-from pydantic.main import ModelMetaclass
+try:
+    from pydantic.v1 import (
+        BaseModel,
+        ConstrainedStr,
+        Field,
+        StrictBool,
+        StrictFloat,
+        StrictInt,
+        StrictStr,
+        ValidationError,
+        create_model,
+        validator,
+    )
+    from pydantic.v1.main import ModelMetaclass
+except ImportError:
+    from pydantic import (  # type: ignore
+        BaseModel,
+        ConstrainedStr,
+        Field,
+        StrictBool,
+        StrictFloat,
+        StrictInt,
+        StrictStr,
+        ValidationError,
+        create_model,
+        validator,
+    )
+    from pydantic.main import ModelMetaclass  # type: ignore
 from thinc.api import ConfigValidationError, Model, Optimizer
 from thinc.config import Promise
 
@@ -397,6 +412,7 @@ class ConfigSchemaNlp(BaseModel):
     after_creation: Optional[Callable[["Language"], "Language"]] = Field(..., title="Optional callback to modify nlp object after creation and before the pipeline is constructed")
     after_pipeline_creation: Optional[Callable[["Language"], "Language"]] = Field(..., title="Optional callback to modify nlp object after the pipeline is constructed")
     batch_size: Optional[int] = Field(..., title="Default batch size")
+    vectors: Callable = Field(..., title="Vectors implementation")
     # fmt: on
 
     class Config:
@@ -465,66 +481,6 @@ CONFIG_SCHEMAS = {
     "initialize": ConfigSchemaInit,
 }
 
-
-# Project config Schema
-
-
-class ProjectConfigAssetGitItem(BaseModel):
-    # fmt: off
-    repo: StrictStr = Field(..., title="URL of Git repo to download from")
-    path: StrictStr = Field(..., title="File path or sub-directory to download (used for sparse checkout)")
-    branch: StrictStr = Field("master", title="Branch to clone from")
-    # fmt: on
-
-
-class ProjectConfigAssetURL(BaseModel):
-    # fmt: off
-    dest: StrictStr = Field(..., title="Destination of downloaded asset")
-    url: Optional[StrictStr] = Field(None, title="URL of asset")
-    checksum: Optional[str] = Field(None, title="MD5 hash of file", regex=r"([a-fA-F\d]{32})")
-    description: StrictStr = Field("", title="Description of asset")
-    # fmt: on
-
-
-class ProjectConfigAssetGit(BaseModel):
-    # fmt: off
-    git: ProjectConfigAssetGitItem = Field(..., title="Git repo information")
-    checksum: Optional[str] = Field(None, title="MD5 hash of file", regex=r"([a-fA-F\d]{32})")
-    description: Optional[StrictStr] = Field(None, title="Description of asset")
-    # fmt: on
-
-
-class ProjectConfigCommand(BaseModel):
-    # fmt: off
-    name: StrictStr = Field(..., title="Name of command")
-    help: Optional[StrictStr] = Field(None, title="Command description")
-    script: List[StrictStr] = Field([], title="List of CLI commands to run, in order")
-    deps: List[StrictStr] = Field([], title="File dependencies required by this command")
-    outputs: List[StrictStr] = Field([], title="Outputs produced by this command")
-    outputs_no_cache: List[StrictStr] = Field([], title="Outputs not tracked by DVC (DVC only)")
-    no_skip: bool = Field(False, title="Never skip this command, even if nothing changed")
-    # fmt: on
-
-    class Config:
-        title = "A single named command specified in a project config"
-        extra = "forbid"
-
-
-class ProjectConfigSchema(BaseModel):
-    # fmt: off
-    vars: Dict[StrictStr, Any] = Field({}, title="Optional variables to substitute in commands")
-    env: Dict[StrictStr, Any] = Field({}, title="Optional variable names to substitute in commands, mapped to environment variable names")
-    assets: List[Union[ProjectConfigAssetURL, ProjectConfigAssetGit]] = Field([], title="Data assets")
-    workflows: Dict[StrictStr, List[StrictStr]] = Field({}, title="Named workflows, mapped to list of project commands to run in order")
-    commands: List[ProjectConfigCommand] = Field([], title="Project command shortucts")
-    title: Optional[str] = Field(None, title="Project title")
-    spacy_version: Optional[StrictStr] = Field(None, title="spaCy version range that the project is compatible with")
-    # fmt: on
-
-    class Config:
-        title = "Schema for project configuration file"
-
-
 # Recommendations for init config workflows
 
 

spacy/scorer.py

@@ -802,6 +802,140 @@ def get_ner_prf(examples: Iterable[Example], **kwargs) -> Dict[str, Any]:
         }
 
 
+# The following implementation of trapezoid() is adapted from SciPy,
+# which is distributed under the New BSD License.
+# Copyright (c) 2001-2002 Enthought, Inc. 2003-2023, SciPy Developers.
+# See licenses/3rd_party_licenses.txt
+def trapezoid(y, x=None, dx=1.0, axis=-1):
+    r"""
+    Integrate along the given axis using the composite trapezoidal rule.
+
+    If `x` is provided, the integration happens in sequence along its
+    elements - they are not sorted.
+
+    Integrate `y` (`x`) along each 1d slice on the given axis, compute
+    :math:`\int y(x) dx`.
+    When `x` is specified, this integrates along the parametric curve,
+    computing :math:`\int_t y(t) dt =
+    \int_t y(t) \left.\frac{dx}{dt}\right|_{x=x(t)} dt`.
+
+    Parameters
+    ----------
+    y : array_like
+        Input array to integrate.
+    x : array_like, optional
+        The sample points corresponding to the `y` values. If `x` is None,
+        the sample points are assumed to be evenly spaced `dx` apart. The
+        default is None.
+    dx : scalar, optional
+        The spacing between sample points when `x` is None. The default is 1.
+    axis : int, optional
+        The axis along which to integrate.
+
+    Returns
+    -------
+    trapezoid : float or ndarray
+        Definite integral of `y` = n-dimensional array as approximated along
+        a single axis by the trapezoidal rule. If `y` is a 1-dimensional array,
+        then the result is a float. If `n` is greater than 1, then the result
+        is an `n`-1 dimensional array.
+
+    See Also
+    --------
+    cumulative_trapezoid, simpson, romb
+
+    Notes
+    -----
+    Image [2]_ illustrates trapezoidal rule -- y-axis locations of points
+    will be taken from `y` array, by default x-axis distances between
+    points will be 1.0, alternatively they can be provided with `x` array
+    or with `dx` scalar.  Return value will be equal to combined area under
+    the red lines.
+
+    References
+    ----------
+    .. [1] Wikipedia page: https://en.wikipedia.org/wiki/Trapezoidal_rule
+
+    .. [2] Illustration image:
+           https://en.wikipedia.org/wiki/File:Composite_trapezoidal_rule_illustration.png
+
+    Examples
+    --------
+    Use the trapezoidal rule on evenly spaced points:
+
+    >>> import numpy as np
+    >>> from scipy import integrate
+    >>> integrate.trapezoid([1, 2, 3])
+    4.0
+
+    The spacing between sample points can be selected by either the
+    ``x`` or ``dx`` arguments:
+
+    >>> integrate.trapezoid([1, 2, 3], x=[4, 6, 8])
+    8.0
+    >>> integrate.trapezoid([1, 2, 3], dx=2)
+    8.0
+
+    Using a decreasing ``x`` corresponds to integrating in reverse:
+
+    >>> integrate.trapezoid([1, 2, 3], x=[8, 6, 4])
+    -8.0
+
+    More generally ``x`` is used to integrate along a parametric curve. We can
+    estimate the integral :math:`\int_0^1 x^2 = 1/3` using:
+
+    >>> x = np.linspace(0, 1, num=50)
+    >>> y = x**2
+    >>> integrate.trapezoid(y, x)
+    0.33340274885464394
+
+    Or estimate the area of a circle, noting we repeat the sample which closes
+    the curve:
+
+    >>> theta = np.linspace(0, 2 * np.pi, num=1000, endpoint=True)
+    >>> integrate.trapezoid(np.cos(theta), x=np.sin(theta))
+    3.141571941375841
+
+    ``trapezoid`` can be applied along a specified axis to do multiple
+    computations in one call:
+
+    >>> a = np.arange(6).reshape(2, 3)
+    >>> a
+    array([[0, 1, 2],
+           [3, 4, 5]])
+    >>> integrate.trapezoid(a, axis=0)
+    array([1.5, 2.5, 3.5])
+    >>> integrate.trapezoid(a, axis=1)
+    array([2.,  8.])
+    """
+    y = np.asanyarray(y)
+    if x is None:
+        d = dx
+    else:
+        x = np.asanyarray(x)
+        if x.ndim == 1:
+            d = np.diff(x)
+            # reshape to correct shape
+            shape = [1] * y.ndim
+            shape[axis] = d.shape[0]
+            d = d.reshape(shape)
+        else:
+            d = np.diff(x, axis=axis)
+    nd = y.ndim
+    slice1 = [slice(None)] * nd
+    slice2 = [slice(None)] * nd
+    slice1[axis] = slice(1, None)
+    slice2[axis] = slice(None, -1)
+    try:
+        ret = (d * (y[tuple(slice1)] + y[tuple(slice2)]) / 2.0).sum(axis)
+    except ValueError:
+        # Operations didn't work, cast to ndarray
+        d = np.asarray(d)
+        y = np.asarray(y)
+        ret = np.add.reduce(d * (y[tuple(slice1)] + y[tuple(slice2)]) / 2.0, axis)
+    return ret
+
+
 # The following implementation of roc_auc_score() is adapted from
 # scikit-learn, which is distributed under the New BSD License.
 # Copyright (c) 2007–2019 The scikit-learn developers.
@@ -1024,9 +1158,9 @@ def _auc(x, y):
         else:
             raise ValueError(Errors.E164.format(x=x))
 
-    area = direction * np.trapz(y, x)
+    area = direction * trapezoid(y, x)
     if isinstance(area, np.memmap):
-        # Reductions such as .sum used internally in np.trapz do not return a
+        # Reductions such as .sum used internally in trapezoid do not return a
         # scalar by default for numpy.memmap instances contrary to
         # regular numpy.ndarray instances.
         area = area.dtype.type(area)

spacy/strings.pyx

@@ -1,4 +1,5 @@
 # cython: infer_types=True
+# cython: profile=False
 cimport cython
 from libc.stdint cimport uint32_t
 from libc.string cimport memcpy

spacy/symbols.pyx

@@ -1,4 +1,5 @@
 # cython: optimize.unpack_method_calls=False
+# cython: profile=False
 IDS = {
     "": NIL,
     "IS_ALPHA": IS_ALPHA,

spacy/tests/conftest.py

@@ -162,6 +162,11 @@ def fi_tokenizer():
     return get_lang_class("fi")().tokenizer
 
 
+@pytest.fixture(scope="session")
+def fo_tokenizer():
+    return get_lang_class("fo")().tokenizer
+
+
 @pytest.fixture(scope="session")
 def fr_tokenizer():
     return get_lang_class("fr")().tokenizer
@@ -317,6 +322,11 @@ def nl_tokenizer():
     return get_lang_class("nl")().tokenizer
 
 
+@pytest.fixture(scope="session")
+def nn_tokenizer():
+    return get_lang_class("nn")().tokenizer
+
+
 @pytest.fixture(scope="session")
 def pl_tokenizer():
     return get_lang_class("pl")().tokenizer

spacy/tests/doc/test_span.py

@@ -731,3 +731,12 @@ def test_for_no_ent_sents():
     sents = list(doc.ents[0].sents)
     assert len(sents) == 1
     assert str(sents[0]) == str(doc.ents[0].sent) == "ENTITY"
+
+
+def test_span_api_richcmp_other(en_tokenizer):
+    doc1 = en_tokenizer("a b")
+    doc2 = en_tokenizer("b c")
+    assert not doc1[1:2] == doc1[1]
+    assert not doc1[1:2] == doc2[0]
+    assert not doc1[1:2] == doc2[0:1]
+    assert not doc1[0:1] == doc2

spacy/tests/doc/test_token_api.py

@@ -294,3 +294,12 @@ def test_missing_head_dep(en_vocab):
     assert aligned_heads[0] == ref_heads[0]
     assert aligned_deps[5] == ref_deps[5]
     assert aligned_heads[5] == ref_heads[5]
+
+
+def test_token_api_richcmp_other(en_tokenizer):
+    doc1 = en_tokenizer("a b")
+    doc2 = en_tokenizer("b c")
+    assert not doc1[1] == doc1[0:1]
+    assert not doc1[1] == doc2[1:2]
+    assert not doc1[1] == doc2[0]
+    assert not doc1[0] == doc2

spacy/tests/lang/fo/test_tokenizer.py

@@ -0,0 +1,26 @@
+import pytest
+
+# examples taken from Basic LAnguage Resource Kit 1.0 for Faroese (https://maltokni.fo/en/resources) licensed with CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/)
+# fmt: off
+FO_TOKEN_EXCEPTION_TESTS = [
+    (
+        "Eftir løgtingslóg um samsýning og eftirløn landsstýrismanna v.m., skulu løgmaður og landsstýrismenn vanliga siga frá sær størv í almennari tænastu ella privatum virkjum, samtøkum ella stovnum. ",
+        [
+            "Eftir", "løgtingslóg", "um", "samsýning", "og", "eftirløn", "landsstýrismanna", "v.m.", ",", "skulu", "løgmaður", "og", "landsstýrismenn", "vanliga", "siga", "frá", "sær", "størv", "í", "almennari", "tænastu", "ella", "privatum", "virkjum", ",", "samtøkum", "ella", "stovnum", ".",
+        ],
+    ),
+    (
+        "Sambandsflokkurin gongur aftur við 2,7 prosentum í mun til valið í 1994, tá flokkurin fekk undirtøku frá 23,4 prosent av veljarunum.",
+        [
+            "Sambandsflokkurin", "gongur", "aftur", "við", "2,7", "prosentum", "í", "mun", "til", "valið", "í", "1994", ",", "tá", "flokkurin", "fekk", "undirtøku", "frá", "23,4", "prosent", "av", "veljarunum", ".",
+        ],
+    ),
+]
+# fmt: on
+
+
+@pytest.mark.parametrize("text,expected_tokens", FO_TOKEN_EXCEPTION_TESTS)
+def test_fo_tokenizer_handles_exception_cases(fo_tokenizer, text, expected_tokens):
+    tokens = fo_tokenizer(text)
+    token_list = [token.text for token in tokens if not token.is_space]
+    assert expected_tokens == token_list

spacy/tests/lang/nn/test_tokenizer.py

@@ -0,0 +1,38 @@
+import pytest
+
+# examples taken from Omsetjingsminne frå Nynorsk pressekontor 2022 (https://www.nb.no/sprakbanken/en/resource-catalogue/oai-nb-no-sbr-80/)
+# fmt: off
+NN_TOKEN_EXCEPTION_TESTS = [
+    (
+        "Målet til direktoratet er at alle skal bli tilbydd jobb i politiet så raskt som mogleg i 2014.",
+        [
+            "Målet", "til", "direktoratet", "er", "at", "alle", "skal", "bli", "tilbydd", "jobb", "i", "politiet", "så", "raskt", "som", "mogleg", "i", "2014", ".",
+        ],
+    ),
+    (
+        "Han ønskjer ikkje at staten skal vere med på å finansiere slik undervisning, men dette er rektor på skulen ueinig i.",
+        [
+            "Han", "ønskjer", "ikkje", "at", "staten", "skal", "vere", "med", "på", "å", "finansiere", "slik", "undervisning", ",", "men", "dette", "er", "rektor", "på", "skulen", "ueinig", "i", ".",
+        ],
+    ),
+    (
+        "Ifølgje China Daily vart det 8.848 meter høge fjellet flytta 3 centimeter sørvestover under jordskjelvet, som vart målt til 7,8.",
+        [
+            "Ifølgje", "China", "Daily", "vart", "det", "8.848", "meter", "høge", "fjellet", "flytta", "3", "centimeter", "sørvestover", "under", "jordskjelvet", ",", "som", "vart", "målt", "til", "7,8", ".",
+        ],
+    ),
+    (
+        "Brukssesongen er frå nov. til mai, med ein topp i mars.",
+        [
+            "Brukssesongen", "er", "frå", "nov.", "til", "mai", ",", "med", "ein", "topp", "i", "mars", ".",
+        ],
+    ),
+]
+# fmt: on
+
+
+@pytest.mark.parametrize("text,expected_tokens", NN_TOKEN_EXCEPTION_TESTS)
+def test_nn_tokenizer_handles_exception_cases(nn_tokenizer, text, expected_tokens):
+    tokens = nn_tokenizer(text)
+    token_list = [token.text for token in tokens if not token.is_space]
+    assert expected_tokens == token_list

spacy/tests/matcher/test_dependency_matcher.py

@@ -216,6 +216,11 @@ def test_dependency_matcher_pattern_validation(en_vocab):
         pattern2 = copy.deepcopy(pattern)
         pattern2[1]["RIGHT_ID"] = "fox"
         matcher.add("FOUNDED", [pattern2])
+    # invalid key
+    with pytest.warns(UserWarning):
+        pattern2 = copy.deepcopy(pattern)
+        pattern2[1]["FOO"] = "BAR"
+        matcher.add("FOUNDED", [pattern2])
 
 
 def test_dependency_matcher_callback(en_vocab, doc):

spacy/tests/package/test_requirements.py

@@ -4,8 +4,8 @@ from pathlib import Path
 
 def test_build_dependencies():
     # Check that library requirements are pinned exactly the same across different setup files.
-    # TODO: correct checks for numpy rather than ignoring
     libs_ignore_requirements = [
+        "numpy",
         "pytest",
         "pytest-timeout",
         "mock",
@@ -23,6 +23,7 @@ def test_build_dependencies():
     ]
     # ignore language-specific packages that shouldn't be installed by all
     libs_ignore_setup = [
+        "numpy",
         "fugashi",
         "natto-py",
         "pythainlp",