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chore: remove unused scripts (#2400)
Co-authored-by: Owl Bot <gcf-owl-bot[bot]@users.noreply.github.com>
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.dockerignore

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.git
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.gitignore
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# Docker scaffolding
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Dockerfile
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.dockerignore
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# Python scaffolding
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*.py[cod]
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*.egg

Dockerfile

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docker-entrypoint.sh

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docs/getting-started/_verifying.rst

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Verifying the Library
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---------------------
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Once you have compiled a client library, whether using a Docker image,
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local installation or bazel, it is time for the fun part: actually running it!
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Once you have compiled a client library, whether using a local installation
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or bazel, it is time for the fun part: actually running it!
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Create a virtual environment for the library:
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docs/getting-started/docker-shortcut.rst

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docs/getting-started/docker.rst

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docs/getting-started/index.rst

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for `protocol buffers`_, and will run in any environment that Python 3.7+ and
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protocol buffers do.
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Because dependency management and such can be a significant undertaking, we
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offer a Docker image and interface which requires you only to have Docker
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installed and provide the protos for your API. Alternatively, the generator is
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also invocable via bazel rules.
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It is recommended to install the tool locally and run it through ``protoc``.
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It is also possible to install the tool locally and run it through ``protoc``,
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and this approach is fully supported.
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.. note::
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The Docker approach is recommended for users new to this ecosystem, or
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those which do not have a robust Python environment available. If you want
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to experiment with generating client libraries but do not want to make
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changes to the generator itself, try the Docker image first.
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The bazel approach is recommended for established pipelines. It is more
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lightweight than the Docker image but may take some more effort to set up.
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.. _protocol buffers: https://developers.google.com/protocol-buffers/
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.. toctree::
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:maxdepth: 4
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docker
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local
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bazel

docs/getting-started/local.rst

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Local Installation
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==================
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If you are just getting started with code generation for protobuf-based APIs,
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or if you do not have a robust Python environment already available, it is
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probably easier to get started using Docker: :ref:`getting-started/docker`
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However, this tool offers first-class support for local execution using
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This tool offers first-class support for local execution using
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``protoc``. It is still reasonably easy, but initial setup will take a bit
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longer.
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docs/templates.rst

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--python_gapic_opt="python-gapic-templates=/path/to/templates"
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--python_gapic_opt="python-gapic-templates=DEFAULT"
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Building with Docker
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~~~~~~~~~~~~~~~~~~~~
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When building with Docker, you instead provide the ``--python-gapic-templates``
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argument after the ``docker run`` command:
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.. code-block:: shell
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$ docker run \
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--mount type=bind,source=google/cloud/vision/v1/,destination=/in/google/cloud/vision/v1/,readonly \
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--mount type=bind,source=dest/,destination=/out/ \
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--mount type=bind,source=/path/to/templates,destination=/templates/,readonly \
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--rm \
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--user $UID \
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gcr.io/gapic-images/gapic-generator-python \
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--python-gapic-templates /templates/ \
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--python-gapic-templates DEFAULT
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As before, to provide more than one location for templates, specify the
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argument more than once.
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.. warning::
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If you are using custom templates with Docker, be sure to also mount
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the directory with the templates into the Docker image; otherwise
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the generator will not be able to read that directory. When specifying
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the ``--python-gapic-templates`` argument, it is the path *inside*
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the Docker image that matters!
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.. _Jinja: http://jinja.pocoo.org/docs/2.10/

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