automatic import of python-mllint

3 files changed, 301 insertions, 0 deletions

diff --git a/.gitignore b/.gitignore
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+++ b/.gitignore
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+/mllint-0.12.2.tar.gz
diff --git a/python-mllint.spec b/python-mllint.spec
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+%global _empty_manifest_terminate_build 0
+Name:		python-mllint
+Version:	0.12.2
+Release:	1
+Summary:	Software Quality Linter for Machine Learning projects
+License:	GPLv3
+URL:		https://github.com/bvobart/mllint
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/b7/95/624ac7b4d14cc01c49728cd7ecd717ef756502e548eeaaf9e077635fedf6/mllint-0.12.2.tar.gz
+
+Requires:	python3-dvc
+Requires:	python3-pylint
+Requires:	python3-mypy
+Requires:	python3-black
+Requires:	python3-isort
+Requires:	python3-bandit
+
+%description
+## Installation
+`mllint` is compiled for Linux, MacOS and Windows, both 64 and 32 bit x86 (MacOS 64-bit only), as well as 64-bit ARM on Linux and MacOS (Apple M1).
+`mllint` is published to [PyPI](https://pypi.org/project/mllint/), so it can be installed globally or in your current environment using `pip`:
+```sh
+pip install --upgrade mllint
+```
+Alternatively, to add `mllint` to an existing project, if your project uses Poetry for its dependencies:
+```sh
+poetry add --dev mllint
+```
+Or if your project uses Pipenv:
+```sh
+pipenv install --dev mllint
+```
+### Tools
+`mllint` has a soft dependency on several Python tools that it uses for its analysis. While `mllint` will recommend that you place these tools in your project's development dependencies, these tools are listed as optional dependencies of `mllint` and can be installed along with `mllint` using:
+```sh
+pip install --upgrade mllint[tools]
+```
+### Docker
+There are also `mllint` Docker containers available on [Docker Hub](https://hub.docker.com/r/bvobart/mllint) at `bvobart/mllint` for Python 3.6, 3.7, 3.8 and 3.9. These may particularly be helpful when running `mllint` in CI environments, such as Gitlab CI or Github Actions. See the Docker Hub for a full list of available tags that can be used.
+The Docker containers require that you mount the folder with your project onto the container as a volume on `/app`. Here is an example of how to use this Docker container, assuming that your project is in the current folder. Replace `$(pwd)` with the full path to your project folder if it is somewhere else.
+```sh
+docker run -it --rm -v $(pwd):/app bvobart/mllint:latest
+```
+## Usage
+`mllint` is designed to be used both on your personal computer as well as on CI systems. So, open a terminal in your project folder and run one of the following commands, or add it to your project's CI script.
+To run `mllint` on the project in the current folder, simply run:
+```sh
+mllint
+```
+To run `mllint` on a project in another folder, simply run:
+```sh
+mllint path/to/my-ml-project
+```
+`mllint` will analyse your project and create a Markdown-formatted report of its analysis. By default, this will be pretty printed to your terminal. 
+If you instead prefer to export the raw Markdown text to a file, which may be particularly useful when running on CI, the `--output` or `-o` flag and provide a filename. `mllint` does not overwrite the destination file if it already exists, unless `--force` or `-f` is used. For example:
+```sh
+mllint --output report.md
+```
+Using `-` (a dash) as the filename prints the raw Markdown directly to your terminal:
+```sh
+mllint -o -
+```
+In CI scripts, such raw markdown output (whether as a file or printed to the standard output) can be used to e.g. make comments on pull/merge requests or create Wiki pages on your repository.
+See [docs/example-report.md](docs/example-report.md) for an example of a report that `mllint` generates, or explore those generated for the [example projects](https://github.com/bvobart/mllint-example-projects).
+Of course, feel free to explore `mllint help` for more information about its commands and to discover additional flags that can be used.
+### Linters, Categories and Rules
+`mllint` analyses your project by evaluating several categories of linting rules. Each category, as well as each rule, has a 'slug', i.e., a lowercased piece of text with dashes or slashes for spaces, e.g., `code-quality/pylint/no-issues`. This slug identifies a rule and is often (if not always) displayed next to the category or rule that it references.
+#### Command-line
+To list all available (implemented) categories and linting rules, run:
+```sh
+mllint list all
+```
+To list all enabled linting rules, run (optionally providing the path to the project's folder):
+```sh
+mllint list enabled
+```
+By default, all of `mllint`'s rules are enabled. See [Configuration](#configuration) to learn how to selectively disable certain rules.
+To learn more about a certain rule or category, use `mllint describe` along with the slug of the category or rule:
+```sh
+# Describe the Version Control category. This will also list the rules that it checks.
+mllint describe version-control
+# Use the exact slug of a rule to describe one rule,
+# e.g., the rule on DVC usage in the Version Control category
+mllint describe version-control/data/dvc
+# Use a partial slug to describe all rules whose slug starts with this snippet, 
+# e.g., all rules about version controlling data
+mllint describe version-control/data
+```
+#### Online Documentation
+Alternatively, visit the [Categories](https://bvobart.github.io/mllint/docs/categories/) and [Rules](https://bvobart.github.io/mllint/docs/rules/) pages on [`mllint`'s website](https://bvobart.github.io/mllint/) to view the latest online documentation of these rules.
+### Custom linting rules
+It is also possible to define your own custom linting rules by implementing a script or program that `mllint` will run while performing its analysis.
+
+%package -n python3-mllint
+Summary:	Software Quality Linter for Machine Learning projects
+Provides:	python-mllint
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+BuildRequires:	python3-cffi
+BuildRequires:	gcc
+BuildRequires:	gdb
+%description -n python3-mllint
+## Installation
+`mllint` is compiled for Linux, MacOS and Windows, both 64 and 32 bit x86 (MacOS 64-bit only), as well as 64-bit ARM on Linux and MacOS (Apple M1).
+`mllint` is published to [PyPI](https://pypi.org/project/mllint/), so it can be installed globally or in your current environment using `pip`:
+```sh
+pip install --upgrade mllint
+```
+Alternatively, to add `mllint` to an existing project, if your project uses Poetry for its dependencies:
+```sh
+poetry add --dev mllint
+```
+Or if your project uses Pipenv:
+```sh
+pipenv install --dev mllint
+```
+### Tools
+`mllint` has a soft dependency on several Python tools that it uses for its analysis. While `mllint` will recommend that you place these tools in your project's development dependencies, these tools are listed as optional dependencies of `mllint` and can be installed along with `mllint` using:
+```sh
+pip install --upgrade mllint[tools]
+```
+### Docker
+There are also `mllint` Docker containers available on [Docker Hub](https://hub.docker.com/r/bvobart/mllint) at `bvobart/mllint` for Python 3.6, 3.7, 3.8 and 3.9. These may particularly be helpful when running `mllint` in CI environments, such as Gitlab CI or Github Actions. See the Docker Hub for a full list of available tags that can be used.
+The Docker containers require that you mount the folder with your project onto the container as a volume on `/app`. Here is an example of how to use this Docker container, assuming that your project is in the current folder. Replace `$(pwd)` with the full path to your project folder if it is somewhere else.
+```sh
+docker run -it --rm -v $(pwd):/app bvobart/mllint:latest
+```
+## Usage
+`mllint` is designed to be used both on your personal computer as well as on CI systems. So, open a terminal in your project folder and run one of the following commands, or add it to your project's CI script.
+To run `mllint` on the project in the current folder, simply run:
+```sh
+mllint
+```
+To run `mllint` on a project in another folder, simply run:
+```sh
+mllint path/to/my-ml-project
+```
+`mllint` will analyse your project and create a Markdown-formatted report of its analysis. By default, this will be pretty printed to your terminal. 
+If you instead prefer to export the raw Markdown text to a file, which may be particularly useful when running on CI, the `--output` or `-o` flag and provide a filename. `mllint` does not overwrite the destination file if it already exists, unless `--force` or `-f` is used. For example:
+```sh
+mllint --output report.md
+```
+Using `-` (a dash) as the filename prints the raw Markdown directly to your terminal:
+```sh
+mllint -o -
+```
+In CI scripts, such raw markdown output (whether as a file or printed to the standard output) can be used to e.g. make comments on pull/merge requests or create Wiki pages on your repository.
+See [docs/example-report.md](docs/example-report.md) for an example of a report that `mllint` generates, or explore those generated for the [example projects](https://github.com/bvobart/mllint-example-projects).
+Of course, feel free to explore `mllint help` for more information about its commands and to discover additional flags that can be used.
+### Linters, Categories and Rules
+`mllint` analyses your project by evaluating several categories of linting rules. Each category, as well as each rule, has a 'slug', i.e., a lowercased piece of text with dashes or slashes for spaces, e.g., `code-quality/pylint/no-issues`. This slug identifies a rule and is often (if not always) displayed next to the category or rule that it references.
+#### Command-line
+To list all available (implemented) categories and linting rules, run:
+```sh
+mllint list all
+```
+To list all enabled linting rules, run (optionally providing the path to the project's folder):
+```sh
+mllint list enabled
+```
+By default, all of `mllint`'s rules are enabled. See [Configuration](#configuration) to learn how to selectively disable certain rules.
+To learn more about a certain rule or category, use `mllint describe` along with the slug of the category or rule:
+```sh
+# Describe the Version Control category. This will also list the rules that it checks.
+mllint describe version-control
+# Use the exact slug of a rule to describe one rule,
+# e.g., the rule on DVC usage in the Version Control category
+mllint describe version-control/data/dvc
+# Use a partial slug to describe all rules whose slug starts with this snippet, 
+# e.g., all rules about version controlling data
+mllint describe version-control/data
+```
+#### Online Documentation
+Alternatively, visit the [Categories](https://bvobart.github.io/mllint/docs/categories/) and [Rules](https://bvobart.github.io/mllint/docs/rules/) pages on [`mllint`'s website](https://bvobart.github.io/mllint/) to view the latest online documentation of these rules.
+### Custom linting rules
+It is also possible to define your own custom linting rules by implementing a script or program that `mllint` will run while performing its analysis.
+
+%package help
+Summary:	Development documents and examples for mllint
+Provides:	python3-mllint-doc
+%description help
+## Installation
+`mllint` is compiled for Linux, MacOS and Windows, both 64 and 32 bit x86 (MacOS 64-bit only), as well as 64-bit ARM on Linux and MacOS (Apple M1).
+`mllint` is published to [PyPI](https://pypi.org/project/mllint/), so it can be installed globally or in your current environment using `pip`:
+```sh
+pip install --upgrade mllint
+```
+Alternatively, to add `mllint` to an existing project, if your project uses Poetry for its dependencies:
+```sh
+poetry add --dev mllint
+```
+Or if your project uses Pipenv:
+```sh
+pipenv install --dev mllint
+```
+### Tools
+`mllint` has a soft dependency on several Python tools that it uses for its analysis. While `mllint` will recommend that you place these tools in your project's development dependencies, these tools are listed as optional dependencies of `mllint` and can be installed along with `mllint` using:
+```sh
+pip install --upgrade mllint[tools]
+```
+### Docker
+There are also `mllint` Docker containers available on [Docker Hub](https://hub.docker.com/r/bvobart/mllint) at `bvobart/mllint` for Python 3.6, 3.7, 3.8 and 3.9. These may particularly be helpful when running `mllint` in CI environments, such as Gitlab CI or Github Actions. See the Docker Hub for a full list of available tags that can be used.
+The Docker containers require that you mount the folder with your project onto the container as a volume on `/app`. Here is an example of how to use this Docker container, assuming that your project is in the current folder. Replace `$(pwd)` with the full path to your project folder if it is somewhere else.
+```sh
+docker run -it --rm -v $(pwd):/app bvobart/mllint:latest
+```
+## Usage
+`mllint` is designed to be used both on your personal computer as well as on CI systems. So, open a terminal in your project folder and run one of the following commands, or add it to your project's CI script.
+To run `mllint` on the project in the current folder, simply run:
+```sh
+mllint
+```
+To run `mllint` on a project in another folder, simply run:
+```sh
+mllint path/to/my-ml-project
+```
+`mllint` will analyse your project and create a Markdown-formatted report of its analysis. By default, this will be pretty printed to your terminal. 
+If you instead prefer to export the raw Markdown text to a file, which may be particularly useful when running on CI, the `--output` or `-o` flag and provide a filename. `mllint` does not overwrite the destination file if it already exists, unless `--force` or `-f` is used. For example:
+```sh
+mllint --output report.md
+```
+Using `-` (a dash) as the filename prints the raw Markdown directly to your terminal:
+```sh
+mllint -o -
+```
+In CI scripts, such raw markdown output (whether as a file or printed to the standard output) can be used to e.g. make comments on pull/merge requests or create Wiki pages on your repository.
+See [docs/example-report.md](docs/example-report.md) for an example of a report that `mllint` generates, or explore those generated for the [example projects](https://github.com/bvobart/mllint-example-projects).
+Of course, feel free to explore `mllint help` for more information about its commands and to discover additional flags that can be used.
+### Linters, Categories and Rules
+`mllint` analyses your project by evaluating several categories of linting rules. Each category, as well as each rule, has a 'slug', i.e., a lowercased piece of text with dashes or slashes for spaces, e.g., `code-quality/pylint/no-issues`. This slug identifies a rule and is often (if not always) displayed next to the category or rule that it references.
+#### Command-line
+To list all available (implemented) categories and linting rules, run:
+```sh
+mllint list all
+```
+To list all enabled linting rules, run (optionally providing the path to the project's folder):
+```sh
+mllint list enabled
+```
+By default, all of `mllint`'s rules are enabled. See [Configuration](#configuration) to learn how to selectively disable certain rules.
+To learn more about a certain rule or category, use `mllint describe` along with the slug of the category or rule:
+```sh
+# Describe the Version Control category. This will also list the rules that it checks.
+mllint describe version-control
+# Use the exact slug of a rule to describe one rule,
+# e.g., the rule on DVC usage in the Version Control category
+mllint describe version-control/data/dvc
+# Use a partial slug to describe all rules whose slug starts with this snippet, 
+# e.g., all rules about version controlling data
+mllint describe version-control/data
+```
+#### Online Documentation
+Alternatively, visit the [Categories](https://bvobart.github.io/mllint/docs/categories/) and [Rules](https://bvobart.github.io/mllint/docs/rules/) pages on [`mllint`'s website](https://bvobart.github.io/mllint/) to view the latest online documentation of these rules.
+### Custom linting rules
+It is also possible to define your own custom linting rules by implementing a script or program that `mllint` will run while performing its analysis.
+
+%prep
+%autosetup -n mllint-0.12.2
+
+%build
+%py3_build
+
+%install
+%py3_install
+install -d -m755 %{buildroot}/%{_pkgdocdir}
+if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
+if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
+if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
+if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
+pushd %{buildroot}
+if [ -d usr/lib ]; then
+	find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/lib64 ]; then
+	find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/bin ]; then
+	find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/sbin ]; then
+	find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+touch doclist.lst
+if [ -d usr/share/man ]; then
+	find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
+fi
+popd
+mv %{buildroot}/filelist.lst .
+mv %{buildroot}/doclist.lst .
+
+%files -n python3-mllint -f filelist.lst
+%dir %{python3_sitearch}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Tue Apr 11 2023 Python_Bot <Python_Bot@openeuler.org> - 0.12.2-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..66871e3
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+7fe298cee3b3927b7aa5ca1d10e83f5d  mllint-0.12.2.tar.gz