Add files using upload-large-folder tool

.gitattributes

@@ -74,3 +74,5 @@ testbed/scverse__scanpy/scanpy/tests/_data/10x-10k-subset.zarr/X/3.0 filter=lfs
 testbed/scverse__scanpy/scanpy/tests/_data/10x-10k-subset.zarr/X/0.0 filter=lfs diff=lfs merge=lfs -text
 testbed/scverse__scanpy/scanpy/tests/_data/10x-10k-subset.zarr/X/4.0 filter=lfs diff=lfs merge=lfs -text
 testbed/scverse__scanpy/scanpy/datasets/10x_pbmc68k_reduced.h5ad filter=lfs diff=lfs merge=lfs -text
+testbed/scikit-learn__scikit-learn/doc/logos/identity.pdf filter=lfs diff=lfs merge=lfs -text
+testbed/pyvista__pyvista/tests/plotting/fonts/Mplus2-Regular.ttf filter=lfs diff=lfs merge=lfs -text

testbed/googleapis__python-aiplatform/.coveragerc

@@ -0,0 +1,16 @@
+[run]
+branch = True
+
+[report]
+show_missing = True
+omit =
+    google/cloud/aiplatform/vizier/pyvizier/*
+    google/cloud/aiplatform_v1/*
+    google/cloud/aiplatform_v1beta1/*
+    google/cloud/aiplatform/v1/schema/*
+    google/cloud/aiplatform/v1beta1/schema/*
+exclude_lines =
+    # Re-enable the standard pragma
+    pragma: NO COVER
+    # Ignore debug-only repr
+    def __repr__

testbed/googleapis__python-aiplatform/.flake8

@@ -0,0 +1,33 @@
+# -*- coding: utf-8 -*-
+#
+# Copyright 2023 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# Generated by synthtool. DO NOT EDIT!
+[flake8]
+ignore = E203, E231, E266, E501, W503
+exclude =
+  # Exclude generated code.
+  **/proto/**
+  **/gapic/**
+  **/services/**
+  **/types/**
+  *_pb2.py
+
+  # Standard linting exemptions.
+  **/.nox/**
+  __pycache__,
+  .git,
+  *.pyc,
+  conf.py

testbed/googleapis__python-aiplatform/.gitignore

@@ -0,0 +1,64 @@
+*.py[cod]
+*.sw[op]
+
+# C extensions
+*.so
+
+# Packages
+*.egg
+*.egg-info
+dist
+build
+eggs
+.eggs
+parts
+bin
+var
+sdist
+develop-eggs
+.installed.cfg
+lib
+lib64
+__pycache__
+
+# Installer logs
+pip-log.txt
+
+# Unit test / coverage reports
+.coverage
+.nox
+.cache
+.pytest_cache
+
+
+# Mac
+.DS_Store
+
+# JetBrains
+.idea
+
+# VS Code
+.vscode
+
+# emacs
+*~
+
+# Built documentation
+docs/_build
+bigquery/docs/generated
+docs.metadata
+
+# Virtual environment
+env/
+venv/
+
+# Test logs
+coverage.xml
+*sponge_log.xml
+
+# System test environment variables.
+system_tests/local_test_setup
+
+# Make sure a generated file isn't accidentally committed.
+pylintrc
+pylintrc.test

testbed/googleapis__python-aiplatform/.pre-commit-config.yaml

@@ -0,0 +1,31 @@
+# Copyright 2021 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# See https://pre-commit.com for more information
+# See https://pre-commit.com/hooks.html for more hooks
+repos:
+-   repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v4.0.1
+    hooks:
+    -   id: trailing-whitespace
+    -   id: end-of-file-fixer
+    -   id: check-yaml
+-   repo: https://github.com/psf/black
+    rev: 22.3.0
+    hooks:
+    - id: black
+-   repo: https://github.com/pycqa/flake8
+    rev: 6.1.0
+    hooks:
+    - id: flake8

testbed/googleapis__python-aiplatform/.release-please-manifest.json

@@ -0,0 +1,3 @@
+{
+    ".": "1.75.0"
+}

testbed/googleapis__python-aiplatform/.repo-metadata.json

@@ -0,0 +1,14 @@
+{
+    "name": "aiplatform",
+    "name_pretty": "AI Platform",
+    "product_documentation": "https://cloud.google.com/ai-platform",
+    "client_documentation": "https://cloud.google.com/python/docs/reference/aiplatform/latest",
+    "issue_tracker": "https://issuetracker.google.com/savedsearches/559744",
+    "release_level": "stable",
+    "language": "python",
+    "library_type": "GAPIC_COMBO",
+    "repo": "googleapis/python-aiplatform",
+    "distribution_name": "google-cloud-aiplatform",
+    "api_id": "aiplatform.googleapis.com",
+    "api_shortname": "aiplatform"
+}

testbed/googleapis__python-aiplatform/.trampolinerc

@@ -0,0 +1,61 @@
+# Copyright 2023 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# Add required env vars here.
+required_envvars+=(
+)
+
+# Add env vars which are passed down into the container here.
+pass_down_envvars+=(
+    "NOX_SESSION"
+    ###############
+    # Docs builds
+    ###############
+    "STAGING_BUCKET"
+    "V2_STAGING_BUCKET"
+    ##################
+    # Samples builds
+    ##################
+    "INSTALL_LIBRARY_FROM_SOURCE"
+    "RUN_TESTS_SESSION"
+    "BUILD_SPECIFIC_GCLOUD_PROJECT"
+    # Target directories.
+    "RUN_TESTS_DIRS"
+    # The nox session to run.
+    "RUN_TESTS_SESSION"
+)
+
+# Prevent unintentional override on the default image.
+if [[ "${TRAMPOLINE_IMAGE_UPLOAD:-false}" == "true" ]] && \
+   [[ -z "${TRAMPOLINE_IMAGE:-}" ]]; then
+   echo "Please set TRAMPOLINE_IMAGE if you want to upload the Docker image."
+   exit 1
+fi
+
+# Define the default value if it makes sense.
+if [[ -z "${TRAMPOLINE_IMAGE_UPLOAD:-}" ]]; then
+    TRAMPOLINE_IMAGE_UPLOAD=""
+fi
+
+if [[ -z "${TRAMPOLINE_IMAGE:-}" ]]; then
+    TRAMPOLINE_IMAGE=""
+fi
+
+if [[ -z "${TRAMPOLINE_DOCKERFILE:-}" ]]; then
+    TRAMPOLINE_DOCKERFILE=""
+fi
+
+if [[ -z "${TRAMPOLINE_BUILD_FILE:-}" ]]; then
+    TRAMPOLINE_BUILD_FILE=""
+fi

testbed/googleapis__python-aiplatform/CODE_OF_CONDUCT.md

@@ -0,0 +1,95 @@
+<!-- # Generated by synthtool. DO NOT EDIT! !-->
+# Code of Conduct
+
+## Our Pledge
+
+In the interest of fostering an open and welcoming environment, we as
+contributors and maintainers pledge to making participation in our project and
+our community a harassment-free experience for everyone, regardless of age, body
+size, disability, ethnicity, gender identity and expression, level of
+experience, education, socio-economic status, nationality, personal appearance,
+race, religion, or sexual identity and orientation.
+
+## Our Standards
+
+Examples of behavior that contributes to creating a positive environment
+include:
+
+*   Using welcoming and inclusive language
+*   Being respectful of differing viewpoints and experiences
+*   Gracefully accepting constructive criticism
+*   Focusing on what is best for the community
+*   Showing empathy towards other community members
+
+Examples of unacceptable behavior by participants include:
+
+*   The use of sexualized language or imagery and unwelcome sexual attention or
+    advances
+*   Trolling, insulting/derogatory comments, and personal or political attacks
+*   Public or private harassment
+*   Publishing others' private information, such as a physical or electronic
+    address, without explicit permission
+*   Other conduct which could reasonably be considered inappropriate in a
+    professional setting
+
+## Our Responsibilities
+
+Project maintainers are responsible for clarifying the standards of acceptable
+behavior and are expected to take appropriate and fair corrective action in
+response to any instances of unacceptable behavior.
+
+Project maintainers have the right and responsibility to remove, edit, or reject
+comments, commits, code, wiki edits, issues, and other contributions that are
+not aligned to this Code of Conduct, or to ban temporarily or permanently any
+contributor for other behaviors that they deem inappropriate, threatening,
+offensive, or harmful.
+
+## Scope
+
+This Code of Conduct applies both within project spaces and in public spaces
+when an individual is representing the project or its community. Examples of
+representing a project or community include using an official project e-mail
+address, posting via an official social media account, or acting as an appointed
+representative at an online or offline event. Representation of a project may be
+further defined and clarified by project maintainers.
+
+This Code of Conduct also applies outside the project spaces when the Project
+Steward has a reasonable belief that an individual's behavior may have a
+negative impact on the project or its community.
+
+## Conflict Resolution
+
+We do not believe that all conflict is bad; healthy debate and disagreement
+often yield positive results. However, it is never okay to be disrespectful or
+to engage in behavior that violates the project’s code of conduct.
+
+If you see someone violating the code of conduct, you are encouraged to address
+the behavior directly with those involved. Many issues can be resolved quickly
+and easily, and this gives people more control over the outcome of their
+dispute. If you are unable to resolve the matter for any reason, or if the
+behavior is threatening or harassing, report it. We are dedicated to providing
+an environment where participants feel welcome and safe.
+
+
+Reports should be directed to *googleapis-stewards@google.com*, the
+Project Steward(s) for *Google Cloud Client Libraries*. It is the Project Steward’s duty to
+receive and address reported violations of the code of conduct. They will then
+work with a committee consisting of representatives from the Open Source
+Programs Office and the Google Open Source Strategy team. If for any reason you
+are uncomfortable reaching out to the Project Steward, please email
+opensource@google.com.
+
+We will investigate every complaint, but you may not receive a direct response.
+We will use our discretion in determining when and how to follow up on reported
+incidents, which may range from not taking action to permanent expulsion from
+the project and project-sponsored spaces. We will notify the accused of the
+report and provide them an opportunity to discuss it before any action is taken.
+The identity of the reporter will be omitted from the details of the report
+supplied to the accused. In potentially harmful situations, such as ongoing
+harassment or threats to anyone's safety, we may take action without notice.
+
+## Attribution
+
+This Code of Conduct is adapted from the Contributor Covenant, version 1.4,
+available at
+https://www.contributor-covenant.org/version/1/4/code-of-conduct.html

testbed/googleapis__python-aiplatform/CONTRIBUTING.rst

@@ -0,0 +1,281 @@
+.. Generated by synthtool. DO NOT EDIT!
+############
+Contributing
+############
+
+#. **Please sign one of the contributor license agreements below.**
+#. Fork the repo, develop and test your code changes, add docs.
+#. Make sure that your commit messages clearly describe the changes.
+#. Send a pull request. (Please Read: `Faster Pull Request Reviews`_)
+
+.. _Faster Pull Request Reviews: https://github.com/kubernetes/community/blob/master/contributors/guide/pull-requests.md#best-practices-for-faster-reviews
+
+.. contents:: Here are some guidelines for hacking on the Google Cloud Client libraries.
+
+***************
+Adding Features
+***************
+
+In order to add a feature:
+
+- The feature must be documented in both the API and narrative
+  documentation.
+
+- The feature must work fully on the following CPython versions:
+  3.8, 3.9, 3.10, 3.11 and 3.12 on both UNIX and Windows.
+
+- The feature must not add unnecessary dependencies (where
+  "unnecessary" is of course subjective, but new dependencies should
+  be discussed).
+
+****************************
+Using a Development Checkout
+****************************
+
+You'll have to create a development environment using a Git checkout:
+
+- While logged into your GitHub account, navigate to the
+  ``python-aiplatform`` `repo`_ on GitHub.
+
+- Fork and clone the ``python-aiplatform`` repository to your GitHub account by
+  clicking the "Fork" button.
+
+- Clone your fork of ``python-aiplatform`` from your GitHub account to your local
+  computer, substituting your account username and specifying the destination
+  as ``hack-on-python-aiplatform``.  E.g.::
+
+   $ cd ${HOME}
+   $ git clone git@github.com:USERNAME/python-aiplatform.git hack-on-python-aiplatform
+   $ cd hack-on-python-aiplatform
+   # Configure remotes such that you can pull changes from the googleapis/python-aiplatform
+   # repository into your local repository.
+   $ git remote add upstream git@github.com:googleapis/python-aiplatform.git
+   # fetch and merge changes from upstream into main
+   $ git fetch upstream
+   $ git merge upstream/main
+
+Now your local repo is set up such that you will push changes to your GitHub
+repo, from which you can submit a pull request.
+
+To work on the codebase and run the tests, we recommend using ``nox``,
+but you can also use a ``virtualenv`` of your own creation.
+
+.. _repo: https://github.com/googleapis/python-aiplatform
+
+Using ``nox``
+=============
+
+We use `nox <https://nox.readthedocs.io/en/latest/>`__ to instrument our tests.
+
+- To test your changes, run unit tests with ``nox``::
+    $ nox -s unit
+
+- To run a single unit test::
+
+    $ nox -s unit-3.12 -- -k <name of test>
+
+
+  .. note::
+
+    The unit tests and system tests are described in the
+    ``noxfile.py`` files in each directory.
+
+.. nox: https://pypi.org/project/nox/
+
+*****************************************
+I'm getting weird errors... Can you help?
+*****************************************
+
+If the error mentions ``Python.h`` not being found,
+install ``python-dev`` and try again.
+On Debian/Ubuntu::
+
+  $ sudo apt-get install python-dev
+
+************
+Coding Style
+************
+- We use the automatic code formatter ``black``. You can run it using
+  the nox session ``blacken``. This will eliminate many lint errors. Run via::
+
+   $ nox -s blacken
+
+- PEP8 compliance is required, with exceptions defined in the linter configuration.
+  If you have ``nox`` installed, you can test that you have not introduced
+  any non-compliant code via::
+
+   $ nox -s lint
+
+- In order to make ``nox -s lint`` run faster, you can set some environment
+  variables::
+
+   export GOOGLE_CLOUD_TESTING_REMOTE="upstream"
+   export GOOGLE_CLOUD_TESTING_BRANCH="main"
+
+  By doing this, you are specifying the location of the most up-to-date
+  version of ``python-aiplatform``. The
+  remote name ``upstream`` should point to the official ``googleapis``
+  checkout and the branch should be the default branch on that remote (``main``).
+
+- This repository contains configuration for the
+  `pre-commit <https://pre-commit.com/>`__ tool, which automates checking
+  our linters during a commit.  If you have it installed on your ``$PATH``,
+  you can enable enforcing those checks via:
+
+.. code-block:: bash
+
+   $ pre-commit install
+   pre-commit installed at .git/hooks/pre-commit
+
+Exceptions to PEP8:
+
+- Many unit tests use a helper method, ``_call_fut`` ("FUT" is short for
+  "Function-Under-Test"), which is PEP8-incompliant, but more readable.
+  Some also use a local variable, ``MUT`` (short for "Module-Under-Test").
+
+********************
+Running System Tests
+********************
+
+- To run system tests, you can execute::
+
+   # Run all system tests
+   $ nox -s system
+
+   # Run a single system test
+   $ nox -s system-3.8 -- -k <name of test>
+
+
+  .. note::
+
+      System tests are only configured to run under Python 3.8.
+      For expediency, we do not run them in older versions of Python 3.
+
+  This alone will not run the tests. You'll need to change some local
+  auth settings and change some configuration in your project to
+  run all the tests.
+
+- System tests will be run against an actual project. You should use local credentials from gcloud when possible. See `Best practices for application authentication <https://cloud.google.com/docs/authentication/best-practices-applications#local_development_and_testing_with_the>`__. Some tests require a service account. For those tests see `Authenticating as a service account <https://cloud.google.com/docs/authentication/production>`__.
+
+*************
+Test Coverage
+*************
+
+- The codebase *must* have 100% test statement coverage after each commit.
+  You can test coverage via ``nox -s cover``.
+
+******************************************************
+Documentation Coverage and Building HTML Documentation
+******************************************************
+
+If you fix a bug, and the bug requires an API or behavior modification, all
+documentation in this package which references that API or behavior must be
+changed to reflect the bug fix, ideally in the same commit that fixes the bug
+or adds the feature.
+
+Build the docs via:
+
+   $ nox -s docs
+
+*************************
+Samples and code snippets
+*************************
+
+Code samples and snippets live in the `samples/` catalogue. Feel free to
+provide more examples, but make sure to write tests for those examples.
+Each folder containing example code requires its own `noxfile.py` script
+which automates testing. If you decide to create a new folder, you can
+base it on the `samples/snippets` folder (providing `noxfile.py` and
+the requirements files).
+
+The tests will run against a real Google Cloud Project, so you should
+configure them just like the System Tests.
+
+- To run sample tests, you can execute::
+
+   # Run all tests in a folder
+   $ cd samples/snippets
+   $ nox -s py-3.8
+
+   # Run a single sample test
+   $ cd samples/snippets
+   $ nox -s py-3.8 -- -k <name of test>
+
+********************************************
+Note About ``README`` as it pertains to PyPI
+********************************************
+
+The `description on PyPI`_ for the project comes directly from the
+``README``. Due to the reStructuredText (``rst``) parser used by
+PyPI, relative links which will work on GitHub (e.g. ``CONTRIBUTING.rst``
+instead of
+``https://github.com/googleapis/python-aiplatform/blob/main/CONTRIBUTING.rst``)
+may cause problems creating links or rendering the description.
+
+.. _description on PyPI: https://pypi.org/project/google-cloud-aiplatform
+
+
+*************************
+Supported Python Versions
+*************************
+
+We support:
+
+-  `Python 3.8`_
+-  `Python 3.9`_
+-  `Python 3.10`_
+-  `Python 3.11`_
+-  `Python 3.12`_
+
+.. _Python 3.8: https://docs.python.org/3.8/
+.. _Python 3.9: https://docs.python.org/3.9/
+.. _Python 3.10: https://docs.python.org/3.10/
+.. _Python 3.11: https://docs.python.org/3.11/
+.. _Python 3.12: https://docs.python.org/3.12/
+
+
+Supported versions can be found in our ``noxfile.py`` `config`_.
+
+.. _config: https://github.com/googleapis/python-aiplatform/blob/main/noxfile.py
+
+
+We also explicitly decided to support Python 3 beginning with version 3.8.
+Reasons for this include:
+
+-  Encouraging use of newest versions of Python 3
+-  Taking the lead of `prominent`_ open-source `projects`_
+-  `Unicode literal support`_ which allows for a cleaner codebase that
+   works in both Python 2 and Python 3
+
+.. _prominent: https://docs.djangoproject.com/en/1.9/faq/install/#what-python-version-can-i-use-with-django
+.. _projects: http://flask.pocoo.org/docs/0.10/python3/
+.. _Unicode literal support: https://www.python.org/dev/peps/pep-0414/
+
+**********
+Versioning
+**********
+
+This library follows `Semantic Versioning`_.
+
+.. _Semantic Versioning: http://semver.org/
+
+Some packages are currently in major version zero (``0.y.z``), which means that
+anything may change at any time and the public API should not be considered
+stable.
+
+******************************
+Contributor License Agreements
+******************************
+
+Before we can accept your pull requests you'll need to sign a Contributor
+License Agreement (CLA):
+
+- **If you are an individual writing original source code** and **you own the
+  intellectual property**, then you'll need to sign an
+  `individual CLA <https://developers.google.com/open-source/cla/individual>`__.
+- **If you work for a company that wants to allow you to contribute your work**,
+  then you'll need to sign a
+  `corporate CLA <https://developers.google.com/open-source/cla/corporate>`__.
+
+You can sign these electronically (just scroll to the bottom). After that,
+we'll be able to accept your pull requests.

testbed/googleapis__python-aiplatform/LICENSE

@@ -0,0 +1,202 @@
+
+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+   1. Definitions.
+
+      "License" shall mean the terms and conditions for use, reproduction,
+      and distribution as defined by Sections 1 through 9 of this document.
+
+      "Licensor" shall mean the copyright owner or entity authorized by
+      the copyright owner that is granting the License.
+
+      "Legal Entity" shall mean the union of the acting entity and all
+      other entities that control, are controlled by, or are under common
+      control with that entity. For the purposes of this definition,
+      "control" means (i) the power, direct or indirect, to cause the
+      direction or management of such entity, whether by contract or
+      otherwise, or (ii) ownership of fifty percent (50%) or more of the
+      outstanding shares, or (iii) beneficial ownership of such entity.
+
+      "You" (or "Your") shall mean an individual or Legal Entity
+      exercising permissions granted by this License.
+
+      "Source" form shall mean the preferred form for making modifications,
+      including but not limited to software source code, documentation
+      source, and configuration files.
+
+      "Object" form shall mean any form resulting from mechanical
+      transformation or translation of a Source form, including but
+      not limited to compiled object code, generated documentation,
+      and conversions to other media types.
+
+      "Work" shall mean the work of authorship, whether in Source or
+      Object form, made available under the License, as indicated by a
+      copyright notice that is included in or attached to the work
+      (an example is provided in the Appendix below).
+
+      "Derivative Works" shall mean any work, whether in Source or Object
+      form, that is based on (or derived from) the Work and for which the
+      editorial revisions, annotations, elaborations, or other modifications
+      represent, as a whole, an original work of authorship. For the purposes
+      of this License, Derivative Works shall not include works that remain
+      separable from, or merely link (or bind by name) to the interfaces of,
+      the Work and Derivative Works thereof.
+
+      "Contribution" shall mean any work of authorship, including
+      the original version of the Work and any modifications or additions
+      to that Work or Derivative Works thereof, that is intentionally
+      submitted to Licensor for inclusion in the Work by the copyright owner
+      or by an individual or Legal Entity authorized to submit on behalf of
+      the copyright owner. For the purposes of this definition, "submitted"
+      means any form of electronic, verbal, or written communication sent
+      to the Licensor or its representatives, including but not limited to
+      communication on electronic mailing lists, source code control systems,
+      and issue tracking systems that are managed by, or on behalf of, the
+      Licensor for the purpose of discussing and improving the Work, but
+      excluding communication that is conspicuously marked or otherwise
+      designated in writing by the copyright owner as "Not a Contribution."
+
+      "Contributor" shall mean Licensor and any individual or Legal Entity
+      on behalf of whom a Contribution has been received by Licensor and
+      subsequently incorporated within the Work.
+
+   2. Grant of Copyright License. Subject to the terms and conditions of
+      this License, each Contributor hereby grants to You a perpetual,
+      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+      copyright license to reproduce, prepare Derivative Works of,
+      publicly display, publicly perform, sublicense, and distribute the
+      Work and such Derivative Works in Source or Object form.
+
+   3. Grant of Patent License. Subject to the terms and conditions of
+      this License, each Contributor hereby grants to You a perpetual,
+      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+      (except as stated in this section) patent license to make, have made,
+      use, offer to sell, sell, import, and otherwise transfer the Work,
+      where such license applies only to those patent claims licensable
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testbed/googleapis__python-aiplatform/MANIFEST.in

@@ -0,0 +1,25 @@
+# -*- coding: utf-8 -*-
+#
+# Copyright 2023 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# Generated by synthtool. DO NOT EDIT!
+include README.rst LICENSE
+recursive-include google *.json *.proto py.typed
+recursive-include tests *
+global-exclude *.py[co]
+global-exclude __pycache__
+
+# Exclude scripts for samples readmegen
+prune scripts/readme-gen

testbed/googleapis__python-aiplatform/README.rst

@@ -0,0 +1,573 @@
+Vertex AI SDK for Python
+=================================================
+
+
+Gemini API and Generative AI on Vertex AI
+-----------------------------------------
+
+.. note::
+
+   For Gemini API and Generative AI on Vertex AI, please reference `Vertex Generative AI SDK for Python`_
+.. _Vertex Generative AI SDK for Python: https://cloud.google.com/vertex-ai/generative-ai/docs/reference/python/latest
+
+-----------------------------------------
+
+|GA| |pypi| |versions| |unit-tests| |system-tests| |sample-tests|
+
+`Vertex AI`_: Google Vertex AI is an integrated suite of machine learning tools and services for building and using ML models with AutoML or custom code. It offers both novices and experts the best workbench for the entire machine learning development lifecycle.
+
+- `Client Library Documentation`_
+- `Product Documentation`_
+
+.. |GA| image:: https://img.shields.io/badge/support-ga-gold.svg
+   :target: https://github.com/googleapis/google-cloud-python/blob/main/README.rst#general-availability
+.. |pypi| image:: https://img.shields.io/pypi/v/google-cloud-aiplatform.svg
+   :target: https://pypi.org/project/google-cloud-aiplatform/
+.. |versions| image:: https://img.shields.io/pypi/pyversions/google-cloud-aiplatform.svg
+   :target: https://pypi.org/project/google-cloud-aiplatform/
+.. |unit-tests| image:: https://storage.googleapis.com/cloud-devrel-public/python-aiplatform/badges/sdk-unit-tests.svg
+   :target: https://storage.googleapis.com/cloud-devrel-public/python-aiplatform/badges/sdk-unit-tests.html
+.. |system-tests| image:: https://storage.googleapis.com/cloud-devrel-public/python-aiplatform/badges/sdk-system-tests.svg
+   :target: https://storage.googleapis.com/cloud-devrel-public/python-aiplatform/badges/sdk-system-tests.html
+.. |sample-tests| image:: https://storage.googleapis.com/cloud-devrel-public/python-aiplatform/badges/sdk-sample-tests.svg
+   :target: https://storage.googleapis.com/cloud-devrel-public/python-aiplatform/badges/sdk-sample-tests.html
+.. _Vertex AI: https://cloud.google.com/vertex-ai/docs
+.. _Client Library Documentation: https://cloud.google.com/python/docs/reference/aiplatform/latest
+.. _Product Documentation:  https://cloud.google.com/vertex-ai/docs
+
+Quick Start
+-----------
+
+In order to use this library, you first need to go through the following steps:
+
+1. `Select or create a Cloud Platform project.`_
+2. `Enable billing for your project.`_
+3. `Enable the Vertex AI API.`_
+4. `Setup Authentication.`_
+
+.. _Select or create a Cloud Platform project.: https://console.cloud.google.com/project
+.. _Enable billing for your project.: https://cloud.google.com/billing/docs/how-to/modify-project#enable_billing_for_a_project
+.. _Enable the Vertex AI API.:  https://cloud.google.com/vertex-ai/docs/start/use-vertex-ai-python-sdk
+.. _Setup Authentication.: https://googleapis.dev/python/google-api-core/latest/auth.html
+
+Installation
+~~~~~~~~~~~~
+
+Install this library in a `virtualenv`_ using pip. `virtualenv`_ is a tool to
+create isolated Python environments. The basic problem it addresses is one of
+dependencies and versions, and indirectly permissions.
+
+With `virtualenv`_, it's possible to install this library without needing system
+install permissions, and without clashing with the installed system
+dependencies.
+
+.. _virtualenv: https://virtualenv.pypa.io/en/latest/
+
+
+Mac/Linux
+^^^^^^^^^
+
+.. code-block:: console
+
+    pip install virtualenv
+    virtualenv <your-env>
+    source <your-env>/bin/activate
+    <your-env>/bin/pip install google-cloud-aiplatform
+
+
+Windows
+^^^^^^^
+
+.. code-block:: console
+
+    pip install virtualenv
+    virtualenv <your-env>
+    <your-env>\Scripts\activate
+    <your-env>\Scripts\pip.exe install google-cloud-aiplatform
+
+
+Supported Python Versions
+^^^^^^^^^^^^^^^^^^^^^^^^^
+Python >= 3.8
+
+Deprecated Python Versions
+^^^^^^^^^^^^^^^^^^^^^^^^^^
+Python <= 3.7.
+
+The last version of this library compatible with Python 3.6 is google-cloud-aiplatform==1.12.1.
+
+Overview
+~~~~~~~~
+This section provides a brief overview of the Vertex AI SDK for Python. You can also reference the notebooks in `vertex-ai-samples`_ for examples.
+
+.. _vertex-ai-samples: https://github.com/GoogleCloudPlatform/vertex-ai-samples/tree/main/notebooks/community/sdk
+
+All publicly available SDK features can be found in the :code:`google/cloud/aiplatform` directory.
+Under the hood, Vertex SDK builds on top of GAPIC, which stands for Google API CodeGen.
+The GAPIC library code sits in :code:`google/cloud/aiplatform_v1` and :code:`google/cloud/aiplatform_v1beta1`,
+and it is auto-generated from Google's service proto files.
+
+For most developers' programmatic needs, they can follow these steps to figure out which libraries to import:
+
+1. Look through :code:`google/cloud/aiplatform` first -- Vertex SDK's APIs will almost always be easier to use and more concise comparing with GAPIC
+2. If the feature that you are looking for cannot be found there, look through :code:`aiplatform_v1` to see if it's available in GAPIC
+3. If it is still in beta phase, it will be available in :code:`aiplatform_v1beta1`
+
+If none of the above scenarios could help you find the right tools for your task, please feel free to open a github issue and send us a feature request.
+
+Importing
+^^^^^^^^^
+Vertex AI SDK resource based functionality can be used by importing the following namespace:
+
+.. code-block:: Python
+
+    from google.cloud import aiplatform
+
+Initialization
+^^^^^^^^^^^^^^
+Initialize the SDK to store common configurations that you use with the SDK.
+
+.. code-block:: Python
+
+    aiplatform.init(
+        # your Google Cloud Project ID or number
+        # environment default used is not set
+        project='my-project',
+
+        # the Vertex AI region you will use
+        # defaults to us-central1
+        location='us-central1',
+
+        # Google Cloud Storage bucket in same region as location
+        # used to stage artifacts
+        staging_bucket='gs://my_staging_bucket',
+
+        # custom google.auth.credentials.Credentials
+        # environment default credentials used if not set
+        credentials=my_credentials,
+
+        # customer managed encryption key resource name
+        # will be applied to all Vertex AI resources if set
+        encryption_spec_key_name=my_encryption_key_name,
+
+        # the name of the experiment to use to track
+        # logged metrics and parameters
+        experiment='my-experiment',
+
+        # description of the experiment above
+        experiment_description='my experiment description'
+    )
+
+Datasets
+^^^^^^^^
+Vertex AI provides managed tabular, text, image, and video datasets. In the SDK, datasets can be used downstream to
+train models.
+
+To create a tabular dataset:
+
+.. code-block:: Python
+
+    my_dataset = aiplatform.TabularDataset.create(
+        display_name="my-dataset", gcs_source=['gs://path/to/my/dataset.csv'])
+
+You can also create and import a dataset in separate steps:
+
+.. code-block:: Python
+
+    from google.cloud import aiplatform
+
+    my_dataset = aiplatform.TextDataset.create(
+        display_name="my-dataset")
+
+    my_dataset.import_data(
+        gcs_source=['gs://path/to/my/dataset.csv'],
+        import_schema_uri=aiplatform.schema.dataset.ioformat.text.multi_label_classification
+    )
+
+To get a previously created Dataset:
+
+.. code-block:: Python
+
+  dataset = aiplatform.ImageDataset('projects/my-project/location/us-central1/datasets/{DATASET_ID}')
+
+Vertex AI supports a variety of dataset schemas. References to these schemas are available under the
+:code:`aiplatform.schema.dataset` namespace. For more information on the supported dataset schemas please refer to the
+`Preparing data docs`_.
+
+.. _Preparing data docs: https://cloud.google.com/ai-platform-unified/docs/datasets/prepare
+
+Training
+^^^^^^^^
+The Vertex AI SDK for Python allows you train Custom and AutoML Models.
+
+You can train custom models using a custom Python script, custom Python package, or container.
+
+**Preparing Your Custom Code**
+
+Vertex AI custom training enables you to train on Vertex AI datasets and produce Vertex AI models. To do so your
+script must adhere to the following contract:
+
+It must read datasets from the environment variables populated by the training service:
+
+.. code-block:: Python
+
+  os.environ['AIP_DATA_FORMAT']  # provides format of data
+  os.environ['AIP_TRAINING_DATA_URI']  # uri to training split
+  os.environ['AIP_VALIDATION_DATA_URI']  # uri to validation split
+  os.environ['AIP_TEST_DATA_URI']  # uri to test split
+
+Please visit `Using a managed dataset in a custom training application`_ for a detailed overview.
+
+.. _Using a managed dataset in a custom training application: https://cloud.google.com/vertex-ai/docs/training/using-managed-datasets
+
+It must write the model artifact to the environment variable populated by the training service:
+
+.. code-block:: Python
+
+  os.environ['AIP_MODEL_DIR']
+
+**Running Training**
+
+.. code-block:: Python
+
+  job = aiplatform.CustomTrainingJob(
+      display_name="my-training-job",
+      script_path="training_script.py",
+      container_uri="us-docker.pkg.dev/vertex-ai/training/tf-cpu.2-2:latest",
+      requirements=["gcsfs==0.7.1"],
+      model_serving_container_image_uri="us-docker.pkg.dev/vertex-ai/prediction/tf2-cpu.2-2:latest",
+  )
+
+  model = job.run(my_dataset,
+                  replica_count=1,
+                  machine_type="n1-standard-4",
+                  accelerator_type='NVIDIA_TESLA_K80',
+                  accelerator_count=1)
+
+In the code block above `my_dataset` is managed dataset created in the `Dataset` section above. The `model` variable is a managed Vertex AI model that can be deployed or exported.
+
+
+AutoMLs
+-------
+The Vertex AI SDK for Python supports AutoML tabular, image, text, video, and forecasting.
+
+To train an AutoML tabular model:
+
+.. code-block:: Python
+
+  dataset = aiplatform.TabularDataset('projects/my-project/location/us-central1/datasets/{DATASET_ID}')
+
+  job = aiplatform.AutoMLTabularTrainingJob(
+    display_name="train-automl",
+    optimization_prediction_type="regression",
+    optimization_objective="minimize-rmse",
+  )
+
+  model = job.run(
+      dataset=dataset,
+      target_column="target_column_name",
+      training_fraction_split=0.6,
+      validation_fraction_split=0.2,
+      test_fraction_split=0.2,
+      budget_milli_node_hours=1000,
+      model_display_name="my-automl-model",
+      disable_early_stopping=False,
+  )
+
+
+Models
+------
+To get a model:
+
+
+.. code-block:: Python
+
+  model = aiplatform.Model('/projects/my-project/locations/us-central1/models/{MODEL_ID}')
+
+
+
+To upload a model:
+
+.. code-block:: Python
+
+  model = aiplatform.Model.upload(
+      display_name='my-model',
+      artifact_uri="gs://python/to/my/model/dir",
+      serving_container_image_uri="us-docker.pkg.dev/vertex-ai/prediction/tf2-cpu.2-2:latest",
+  )
+
+
+
+To deploy a model:
+
+
+.. code-block:: Python
+
+  endpoint = model.deploy(machine_type="n1-standard-4",
+                          min_replica_count=1,
+                          max_replica_count=5
+                          machine_type='n1-standard-4',
+                          accelerator_type='NVIDIA_TESLA_K80',
+                          accelerator_count=1)
+
+
+Please visit `Importing models to Vertex AI`_ for a detailed overview:
+
+.. _Importing models to Vertex AI: https://cloud.google.com/vertex-ai/docs/general/import-model
+
+Model Evaluation
+----------------
+
+The Vertex AI SDK for Python currently supports getting model evaluation metrics for all AutoML models.
+
+To list all model evaluations for a model:
+
+.. code-block:: Python
+
+  model = aiplatform.Model('projects/my-project/locations/us-central1/models/{MODEL_ID}')
+
+  evaluations = model.list_model_evaluations()
+
+
+To get the model evaluation resource for a given model:
+
+.. code-block:: Python
+
+  model = aiplatform.Model('projects/my-project/locations/us-central1/models/{MODEL_ID}')
+
+  # returns the first evaluation with no arguments, you can also pass the evaluation ID
+  evaluation = model.get_model_evaluation()
+
+  eval_metrics = evaluation.metrics
+
+
+You can also create a reference to your model evaluation directly by passing in the resource name of the model evaluation:
+
+.. code-block:: Python
+
+  evaluation = aiplatform.ModelEvaluation(
+    evaluation_name='projects/my-project/locations/us-central1/models/{MODEL_ID}/evaluations/{EVALUATION_ID}')
+
+Alternatively, you can create a reference to your evaluation by passing in the model and evaluation IDs:
+
+.. code-block:: Python
+
+  evaluation = aiplatform.ModelEvaluation(
+    evaluation_name={EVALUATION_ID},
+    model_id={MODEL_ID})
+
+
+Batch Prediction
+----------------
+
+To create a batch prediction job:
+
+.. code-block:: Python
+
+  model = aiplatform.Model('/projects/my-project/locations/us-central1/models/{MODEL_ID}')
+
+  batch_prediction_job = model.batch_predict(
+    job_display_name='my-batch-prediction-job',
+    instances_format='csv',
+    machine_type='n1-standard-4',
+    gcs_source=['gs://path/to/my/file.csv'],
+    gcs_destination_prefix='gs://path/to/my/batch_prediction/results/',
+    service_account='my-sa@my-project.iam.gserviceaccount.com'
+  )
+
+You can also create a batch prediction job asynchronously by including the `sync=False` argument:
+
+.. code-block:: Python
+
+  batch_prediction_job = model.batch_predict(..., sync=False)
+
+  # wait for resource to be created
+  batch_prediction_job.wait_for_resource_creation()
+
+  # get the state
+  batch_prediction_job.state
+
+  # block until job is complete
+  batch_prediction_job.wait()
+
+
+Endpoints
+---------
+
+To create an endpoint:
+
+.. code-block:: Python
+
+  endpoint = aiplatform.Endpoint.create(display_name='my-endpoint')
+
+To deploy a model to a created endpoint:
+
+.. code-block:: Python
+
+  model = aiplatform.Model('/projects/my-project/locations/us-central1/models/{MODEL_ID}')
+
+  endpoint.deploy(model,
+                  min_replica_count=1,
+                  max_replica_count=5,
+                  machine_type='n1-standard-4',
+                  accelerator_type='NVIDIA_TESLA_K80',
+                  accelerator_count=1)
+
+To get predictions from endpoints:
+
+.. code-block:: Python
+
+  endpoint.predict(instances=[[6.7, 3.1, 4.7, 1.5], [4.6, 3.1, 1.5, 0.2]])
+
+To undeploy models from an endpoint:
+
+.. code-block:: Python
+
+  endpoint.undeploy_all()
+
+To delete an endpoint:
+
+.. code-block:: Python
+
+  endpoint.delete()
+
+
+Pipelines
+---------
+
+To create a Vertex AI Pipeline run and monitor until completion:
+
+.. code-block:: Python
+
+  # Instantiate PipelineJob object
+  pl = PipelineJob(
+      display_name="My first pipeline",
+
+      # Whether or not to enable caching
+      # True = always cache pipeline step result
+      # False = never cache pipeline step result
+      # None = defer to cache option for each pipeline component in the pipeline definition
+      enable_caching=False,
+
+      # Local or GCS path to a compiled pipeline definition
+      template_path="pipeline.json",
+
+      # Dictionary containing input parameters for your pipeline
+      parameter_values=parameter_values,
+
+      # GCS path to act as the pipeline root
+      pipeline_root=pipeline_root,
+  )
+
+  # Execute pipeline in Vertex AI and monitor until completion
+  pl.run(
+    # Email address of service account to use for the pipeline run
+    # You must have iam.serviceAccounts.actAs permission on the service account to use it
+    service_account=service_account,
+
+    # Whether this function call should be synchronous (wait for pipeline run to finish before terminating)
+    # or asynchronous (return immediately)
+    sync=True
+  )
+
+To create a Vertex AI Pipeline without monitoring until completion, use `submit` instead of `run`:
+
+.. code-block:: Python
+
+  # Instantiate PipelineJob object
+  pl = PipelineJob(
+      display_name="My first pipeline",
+
+      # Whether or not to enable caching
+      # True = always cache pipeline step result
+      # False = never cache pipeline step result
+      # None = defer to cache option for each pipeline component in the pipeline definition
+      enable_caching=False,
+
+      # Local or GCS path to a compiled pipeline definition
+      template_path="pipeline.json",
+
+      # Dictionary containing input parameters for your pipeline
+      parameter_values=parameter_values,
+
+      # GCS path to act as the pipeline root
+      pipeline_root=pipeline_root,
+  )
+
+  # Submit the Pipeline to Vertex AI
+  pl.submit(
+    # Email address of service account to use for the pipeline run
+    # You must have iam.serviceAccounts.actAs permission on the service account to use it
+    service_account=service_account,
+  )
+
+
+Explainable AI: Get Metadata
+----------------------------
+
+To get metadata in dictionary format from TensorFlow 1 models:
+
+.. code-block:: Python
+
+  from google.cloud.aiplatform.explain.metadata.tf.v1 import saved_model_metadata_builder
+
+  builder = saved_model_metadata_builder.SavedModelMetadataBuilder(
+            'gs://python/to/my/model/dir', tags=[tf.saved_model.tag_constants.SERVING]
+        )
+  generated_md = builder.get_metadata()
+
+To get metadata in dictionary format from TensorFlow 2 models:
+
+.. code-block:: Python
+
+  from google.cloud.aiplatform.explain.metadata.tf.v2 import saved_model_metadata_builder
+
+  builder = saved_model_metadata_builder.SavedModelMetadataBuilder('gs://python/to/my/model/dir')
+  generated_md = builder.get_metadata()
+
+To use Explanation Metadata in endpoint deployment and model upload:
+
+.. code-block:: Python
+
+  explanation_metadata = builder.get_metadata_protobuf()
+
+  # To deploy a model to an endpoint with explanation
+  model.deploy(..., explanation_metadata=explanation_metadata)
+
+  # To deploy a model to a created endpoint with explanation
+  endpoint.deploy(..., explanation_metadata=explanation_metadata)
+
+  # To upload a model with explanation
+  aiplatform.Model.upload(..., explanation_metadata=explanation_metadata)
+
+
+Cloud Profiler
+----------------------------
+
+Cloud Profiler allows you to profile your remote Vertex AI Training jobs on demand and visualize the results in Vertex AI Tensorboard.
+
+To start using the profiler with TensorFlow, update your training script to include the following:
+
+.. code-block:: Python
+
+    from google.cloud.aiplatform.training_utils import cloud_profiler
+    ...
+    cloud_profiler.init()
+
+Next, run the job with with a Vertex AI TensorBoard instance. For full details on how to do this, visit https://cloud.google.com/vertex-ai/docs/experiments/tensorboard-overview
+
+Finally, visit your TensorBoard in your Google Cloud Console, navigate to the "Profile" tab, and click the `Capture Profile` button. This will allow users to capture profiling statistics for the running jobs.
+
+
+Next Steps
+~~~~~~~~~~
+
+-  Read the `Client Library Documentation`_ for Vertex AI
+   API to see other available methods on the client.
+-  Read the `Vertex AI API Product documentation`_ to learn
+   more about the product and see How-to Guides.
+-  View this `README`_ to see the full list of Cloud
+   APIs that we cover.
+
+.. _Vertex AI API Product documentation:  https://cloud.google.com/vertex-ai/docs
+.. _README: https://github.com/googleapis/google-cloud-python/blob/main/README.rst

testbed/googleapis__python-aiplatform/SECURITY.md

@@ -0,0 +1,7 @@
+# Security Policy
+
+To report a security issue, please use [g.co/vulnz](https://g.co/vulnz).
+
+The Google Security Team will respond within 5 working days of your report on g.co/vulnz.
+
+We use g.co/vulnz for our intake, and do coordination and disclosure here using GitHub Security Advisory to privately discuss and fix the issue.

testbed/googleapis__python-aiplatform/gemini_docs/README.md

@@ -0,0 +1,326 @@
+# Vertex Generative AI SDK for Python
+The Vertex Generative AI SDK helps developers use Google's generative AI
+[Gemini models](http://cloud.google.com/vertex-ai/docs/generative-ai/multimodal/overview)
+to build AI-powered features and applications.
+The SDKs support use cases like the following:
+
+- Generate text from texts, images and videos (multimodal generation)
+- Build stateful multi-turn conversations (chat)
+- Function calling
+
+## Installation
+
+To install the
+[google-cloud-aiplatform](https://pypi.org/project/google-cloud-aiplatform/)
+Python package, run the following command:
+
+```shell
+pip3 install --upgrade --user "google-cloud-aiplatform>=1.38"
+```
+
+## Usage
+
+For detailed instructions, see [quickstart](http://cloud.google.com/vertex-ai/docs/generative-ai/start/quickstarts/quickstart-multimodal) and [Introduction to multimodal classes in the Vertex AI SDK](http://cloud.google.com/vertex-ai/docs/generative-ai/multimodal/sdk-for-gemini/gemini-sdk-overview-reference).
+
+#### Imports:
+```python
+import vertexai
+```
+
+#### Initialization:
+
+```python
+vertexai.init(project='my-project', location='us-central1')
+```
+
+#### Basic generation:
+```python
+from vertexai.generative_models import GenerativeModel
+model = GenerativeModel("gemini-pro")
+print(model.generate_content("Why is sky blue?"))
+```
+
+#### Using images and videos
+```python
+from vertexai.generative_models import GenerativeModel, Image
+vision_model = GenerativeModel("gemini-pro-vision")
+
+# Local image
+image = Image.load_from_file("image.jpg")
+print(vision_model.generate_content(["What is shown in this image?", image]))
+
+# Image from Cloud Storage
+image_part = generative_models.Part.from_uri("gs://download.tensorflow.org/example_images/320px-Felis_catus-cat_on_snow.jpg", mime_type="image/jpeg")
+print(vision_model.generate_content([image_part, "Describe this image?"]))
+
+# Text and video
+video_part = Part.from_uri("gs://cloud-samples-data/video/animals.mp4", mime_type="video/mp4")
+print(vision_model.generate_content(["What is in the video? ", video_part]))
+```
+
+#### Chat
+```python
+from vertexai.generative_models import GenerativeModel, Image
+vision_model = GenerativeModel("gemini-ultra-vision")
+vision_chat = vision_model.start_chat()
+image = Image.load_from_file("image.jpg")
+print(vision_chat.send_message(["I like this image.", image]))
+print(vision_chat.send_message("What things do I like?."))
+```
+
+#### System instructions
+```python
+from vertexai.generative_models import GenerativeModel
+model = GenerativeModel(
+    "gemini-1.0-pro",
+    system_instruction=[
+        "Talk like a pirate.",
+        "Don't use rude words.",
+    ],
+)
+print(model.generate_content("Why is sky blue?"))
+```
+
+#### Function calling
+
+```python
+# First, create tools that the model is can use to answer your questions.
+# Describe a function by specifying it's schema (JsonSchema format)
+get_current_weather_func = generative_models.FunctionDeclaration(
+    name="get_current_weather",
+    description="Get the current weather in a given location",
+    parameters={
+        "type": "object",
+        "properties": {
+            "location": {
+                "type": "string",
+                "description": "The city and state, e.g. San Francisco, CA"
+            },
+            "unit": {
+                "type": "string",
+                "enum": [
+                    "celsius",
+                    "fahrenheit",
+                ]
+            }
+        },
+        "required": [
+            "location"
+        ]
+    },
+)
+# Tool is a collection of related functions
+weather_tool = generative_models.Tool(
+    function_declarations=[get_current_weather_func],
+)
+
+# Use tools in chat:
+model = GenerativeModel(
+    "gemini-pro",
+    # You can specify tools when creating a model to avoid having to send them with every request.
+    tools=[weather_tool],
+)
+chat = model.start_chat()
+# Send a message to the model. The model will respond with a function call.
+print(chat.send_message("What is the weather like in Boston?"))
+# Then send a function response to the model. The model will use it to answer.
+print(chat.send_message(
+    Part.from_function_response(
+        name="get_current_weather",
+        response={
+            "content": {"weather": "super nice"},
+        }
+    ),
+))
+```
+
+
+#### Automatic Function calling
+
+Note: The `FunctionDeclaration.from_func` converter does not support nested types for parameters. Please provide full `FunctionDeclaration` instead.
+
+```python
+from vertexai.preview.generative_models import GenerativeModel, Tool, FunctionDeclaration, AutomaticFunctionCallingResponder
+
+# First, create functions that the model can use to answer your questions.
+def get_current_weather(location: str, unit: str = "centigrade"):
+    """Gets weather in the specified location.
+
+    Args:
+        location: The location for which to get the weather.
+        unit: Optional. Temperature unit. Can be Centigrade or Fahrenheit. Defaults to Centigrade.
+    """
+    return dict(
+        location=location,
+        unit=unit,
+        weather="Super nice, but maybe a bit hot.",
+    )
+
+# Infer function schema
+get_current_weather_func = FunctionDeclaration.from_func(get_current_weather)
+# Tool is a collection of related functions
+weather_tool = Tool(
+    function_declarations=[get_current_weather_func],
+)
+
+# Use tools in chat:
+model = GenerativeModel(
+    "gemini-pro",
+    # You can specify tools when creating a model to avoid having to send them with every request.
+    tools=[weather_tool],
+)
+
+# Activate automatic function calling:
+afc_responder = AutomaticFunctionCallingResponder(
+    # Optional:
+    max_automatic_function_calls=5,
+)
+chat = model.start_chat(responder=afc_responder)
+# Send a message to the model. The model will respond with a function call.
+# The SDK will automatically call the requested function and respond to the model.
+# The model will use the function call response to answer the original question.
+print(chat.send_message("What is the weather like in Boston?"))
+```
+
+#### Evaluation
+
+-  To perform bring-your-own-response(BYOR) evaluation, provide the model responses in the `response` column in the dataset. If a pairwise metric is used for BYOR evaluation, provide the baseline model responses in the `baseline_model_response` column.
+
+```python
+import pandas as pd
+from vertexai.evaluation import EvalTask, MetricPromptTemplateExamples
+
+eval_dataset = pd.DataFrame({
+        "prompt"  : [...],
+        "reference": [...],
+        "response" : [...],
+        "baseline_model_response": [...],
+})
+eval_task = EvalTask(
+    dataset=eval_dataset,
+    metrics=[
+            "bleu",
+            "rouge_l_sum",
+            MetricPromptTemplateExamples.Pointwise.FLUENCY,
+            MetricPromptTemplateExamples.Pairwise.SAFETY
+    ],
+    experiment="my-experiment",
+)
+eval_result = eval_task.evaluate(experiment_run_name="eval-experiment-run")
+```
+-  To perform evaluation with Gemini model inference, specify the `model` parameter with a `GenerativeModel` instance.  The input column name to the model is `prompt` and must be present in the dataset.
+
+```python
+from vertexai.evaluation import EvalTask
+from vertexai.generative_models import GenerativeModel
+
+eval_dataset = pd.DataFrame({
+    "reference": [...],
+    "prompt"  : [...],
+})
+result = EvalTask(
+    dataset=eval_dataset,
+    metrics=["exact_match", "bleu", "rouge_1", "rouge_l_sum"],
+    experiment="my-experiment",
+).evaluate(
+    model=GenerativeModel("gemini-1.5-pro"),
+    experiment_run_name="gemini-eval-run"
+)
+```
+
+- If a `prompt_template` is specified, the `prompt` column is not required. Prompts can be assembled from the evaluation dataset, and all prompt template variable names must be present in the dataset columns.
+
+```python
+import pandas as pd
+from vertexai.evaluation import EvalTask, MetricPromptTemplateExamples
+from vertexai.generative_models import GenerativeModel
+
+eval_dataset = pd.DataFrame({
+    "context"    : [...],
+    "instruction": [...],
+})
+result = EvalTask(
+    dataset=eval_dataset,
+    metrics=[MetricPromptTemplateExamples.Pointwise.SUMMARIZATION_QUALITY],
+).evaluate(
+    model=GenerativeModel("gemini-1.5-pro"),
+    prompt_template="{instruction}. Article: {context}. Summary:",
+)
+```
+
+- To perform evaluation with custom model inference, specify the `model`
+parameter with a custom inference function. The input column name to the
+custom inference function is `prompt` and must be present in the dataset.
+
+```python
+from openai import OpenAI
+from vertexai.evaluation import EvalTask, MetricPromptTemplateExamples
+
+
+client = OpenAI()
+def custom_model_fn(input: str) -> str:
+    response = client.chat.completions.create(
+        model="gpt-3.5-turbo",
+        messages=[
+        {"role": "user", "content": input}
+        ]
+    )
+    return response.choices[0].message.content
+
+eval_dataset = pd.DataFrame({
+    "prompt"  : [...],
+    "reference": [...],
+})
+result = EvalTask(
+    dataset=eval_dataset,
+    metrics=[MetricPromptTemplateExamples.Pointwise.SAFETY],
+    experiment="my-experiment",
+).evaluate(
+    model=custom_model_fn,
+    experiment_run_name="gpt-eval-run"
+)
+```
+
+- To perform pairwise metric evaluation with model inference step, specify
+the `baseline_model` input to a `PairwiseMetric` instance and the candidate
+`model` input to the `EvalTask.evaluate()` function. The input column name
+to both models is `prompt` and must be present in the dataset.
+
+```python
+import pandas as pd
+from vertexai.evaluation import EvalTask, MetricPromptTemplateExamples, PairwiseMetric
+from vertexai.generative_models import GenerativeModel
+
+baseline_model = GenerativeModel("gemini-1.0-pro")
+candidate_model = GenerativeModel("gemini-1.5-pro")
+
+pairwise_groundedness = PairwiseMetric(
+    metric_prompt_template=MetricPromptTemplateExamples.get_prompt_template(
+        "pairwise_groundedness"
+    ),
+    baseline_model=baseline_model,
+)
+eval_dataset = pd.DataFrame({
+    "prompt"  : [...],
+})
+result = EvalTask(
+    dataset=eval_dataset,
+    metrics=[pairwise_groundedness],
+    experiment="my-pairwise-experiment",
+).evaluate(
+    model=candidate_model,
+    experiment_run_name="gemini-pairwise-eval-run",
+)
+```
+
+## Documentation
+
+You can find complete documentation for the Vertex AI SDKs and the Gemini model in the Google Cloud [documentation](https://cloud.google.com/vertex-ai/docs/generative-ai/learn/overview)
+
+## Contributing
+
+See [Contributing](https://github.com/googleapis/python-aiplatform/blob/main/CONTRIBUTING.rst) for more information on contributing to the Vertex AI Python SDK.
+
+## License
+
+The contents of this repository are licensed under the [Apache License, version 2.0](http://www.apache.org/licenses/LICENSE-2.0).

testbed/googleapis__python-aiplatform/gemini_docs/conf.py

@@ -0,0 +1,440 @@
+# -*- coding: utf-8 -*-
+# Copyright 2021 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# google-cloud-aiplatform documentation build configuration file
+#
+# This file is execfile()d with the current directory set to its
+# containing dir.
+#
+# Note that not all possible configuration values are present in this
+# autogenerated file.
+#
+# All configuration values have a default; values that are commented out
+# serve to show the default.
+
+import sys
+import os
+import shlex
+
+# If extensions (or modules to document with autodoc) are in another directory,
+# add these directories to sys.path here. If the directory is relative to the
+# documentation root, use os.path.abspath to make it absolute, like shown here.
+sys.path.insert(0, os.path.abspath(".."))
+
+# For plugins that can not read conf.py.
+# See also: https://github.com/docascode/sphinx-docfx-yaml/issues/85
+sys.path.insert(0, os.path.abspath("."))
+
+__version__ = ""
+
+# -- General configuration ------------------------------------------------
+
+# If your documentation needs a minimal Sphinx version, state it here.
+needs_sphinx = "1.5.5"
+
+# Add any Sphinx extension module names here, as strings. They can be
+# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
+# ones.
+extensions = [
+    "sphinx.ext.autodoc",
+    "sphinx.ext.autosummary",
+    "sphinx.ext.intersphinx",
+    "sphinx.ext.coverage",
+    "sphinx.ext.doctest",
+    "sphinx.ext.napoleon",
+    "sphinx.ext.todo",
+    "sphinx.ext.viewcode",
+    "recommonmark",
+]
+
+# autodoc/autosummary flags
+autoclass_content = "both"
+autodoc_default_options = {"members": True}
+autosummary_generate = True
+
+
+# Add any paths that contain templates here, relative to this directory.
+templates_path = ["_templates"]
+
+# The suffix(es) of source filenames.
+# You can specify multiple suffix as a list of string:
+# source_suffix = ['.rst', '.md']
+source_suffix = [".rst", ".md"]
+
+# The encoding of source files.
+# source_encoding = 'utf-8-sig'
+
+# The root toctree document.
+root_doc = "index"
+
+# General information about the project.
+project = "google-cloud-vertexai"
+copyright = "2019, Google"
+author = "Google APIs"
+
+# The version info for the project you're documenting, acts as replacement for
+# |version| and |release|, also used in various other places throughout the
+# built documents.
+#
+# The full version, including alpha/beta/rc tags.
+release = __version__
+# The short X.Y version.
+version = ".".join(release.split(".")[0:2])
+
+# The language for content autogenerated by Sphinx. Refer to documentation
+# for a list of supported languages.
+#
+# This is also used if you do content translation via gettext catalogs.
+# Usually you set "language" from the command line for these cases.
+language = None
+
+# There are two options for replacing |today|: either, you set today to some
+# non-false value, then it is used:
+# today = ''
+# Else, today_fmt is used as the format for a strftime call.
+# today_fmt = '%B %d, %Y'
+
+# List of patterns, relative to source directory, that match files and
+# directories to ignore when looking for source files.
+exclude_patterns = [
+    "_build",
+    "**/.nox/**/*",
+    "samples/AUTHORING_GUIDE.md",
+    "samples/CONTRIBUTING.md",
+    "samples/snippets/README.rst",
+]
+
+# The reST default role (used for this markup: `text`) to use for all
+# documents.
+# default_role = None
+
+# If true, '()' will be appended to :func: etc. cross-reference text.
+# add_function_parentheses = True
+
+# If true, the current module name will be prepended to all description
+# unit titles (such as .. function::).
+# add_module_names = True
+
+# If true, sectionauthor and moduleauthor directives will be shown in the
+# output. They are ignored by default.
+# show_authors = False
+
+# The name of the Pygments (syntax highlighting) style to use.
+pygments_style = "sphinx"
+
+# A list of ignored prefixes for module index sorting.
+# modindex_common_prefix = []
+
+# If true, keep warnings as "system message" paragraphs in the built documents.
+# keep_warnings = False
+
+# If true, `todo` and `todoList` produce output, else they produce nothing.
+todo_include_todos = True
+
+
+# -- Options for HTML output ----------------------------------------------
+
+# The theme to use for HTML and HTML Help pages.  See the documentation for
+# a list of builtin themes.
+html_theme = "alabaster"
+
+# Theme options are theme-specific and customize the look and feel of a theme
+# further.  For a list of options available for each theme, see the
+# documentation.
+html_theme_options = {
+    "description": "Google Cloud Client Libraries for google-cloud-aiplatform",
+    "github_user": "googleapis",
+    "github_repo": "python-aiplatform",
+    "github_banner": True,
+    "font_family": "'Roboto', Georgia, sans",
+    "head_font_family": "'Roboto', Georgia, serif",
+    "code_font_family": "'Roboto Mono', 'Consolas', monospace",
+}
+
+# Add any paths that contain custom themes here, relative to this directory.
+# html_theme_path = []
+
+# The name for this set of Sphinx documents.  If None, it defaults to
+# "<project> v<release> documentation".
+# html_title = None
+
+# A shorter title for the navigation bar.  Default is the same as html_title.
+# html_short_title = None
+
+# The name of an image file (relative to this directory) to place at the top
+# of the sidebar.
+# html_logo = None
+
+# The name of an image file (within the static path) to use as favicon of the
+# docs.  This file should be a Windows icon file (.ico) being 16x16 or 32x32
+# pixels large.
+# html_favicon = None
+
+# Add any paths that contain custom static files (such as style sheets) here,
+# relative to this directory. They are copied after the builtin static files,
+# so a file named "default.css" will overwrite the builtin "default.css".
+html_static_path = ["_static"]
+
+# Add any extra paths that contain custom files (such as robots.txt or
+# .htaccess) here, relative to this directory. These files are copied
+# directly to the root of the documentation.
+# html_extra_path = []
+
+# If not '', a 'Last updated on:' timestamp is inserted at every page bottom,
+# using the given strftime format.
+# html_last_updated_fmt = '%b %d, %Y'
+
+# If true, SmartyPants will be used to convert quotes and dashes to
+# typographically correct entities.
+# html_use_smartypants = True
+
+# Custom sidebar templates, maps document names to template names.
+# html_sidebars = {}
+
+# Additional templates that should be rendered to pages, maps page names to
+# template names.
+# html_additional_pages = {}
+
+# If false, no module index is generated.
+# html_domain_indices = True
+
+# If false, no index is generated.
+# html_use_index = True
+
+# If true, the index is split into individual pages for each letter.
+# html_split_index = False
+
+# If true, links to the reST sources are added to the pages.
+# html_show_sourcelink = True
+
+# If true, "Created using Sphinx" is shown in the HTML footer. Default is True.
+# html_show_sphinx = True
+
+# If true, "(C) Copyright ..." is shown in the HTML footer. Default is True.
+# html_show_copyright = True
+
+# If true, an OpenSearch description file will be output, and all pages will
+# contain a <link> tag referring to it.  The value of this option must be the
+# base URL from which the finished HTML is served.
+# html_use_opensearch = ''
+
+# This is the file name suffix for HTML files (e.g. ".xhtml").
+# html_file_suffix = None
+
+# Language to be used for generating the HTML full-text search index.
+# Sphinx supports the following languages:
+#   'da', 'de', 'en', 'es', 'fi', 'fr', 'hu', 'it', 'ja'
+#   'nl', 'no', 'pt', 'ro', 'ru', 'sv', 'tr'
+# html_search_language = 'en'
+
+# A dictionary with options for the search language support, empty by default.
+# Now only 'ja' uses this config value
+# html_search_options = {'type': 'default'}
+
+# The name of a javascript file (relative to the configuration directory) that
+# implements a search results scorer. If empty, the default will be used.
+# html_search_scorer = 'scorer.js'
+
+# Output file base name for HTML help builder.
+htmlhelp_basename = "google-cloud-aiplatform-doc"
+
+# -- Options for warnings ------------------------------------------------------
+
+
+suppress_warnings = [
+    # Temporarily suppress this to avoid "more than one target found for
+    # cross-reference" warning, which are intractable for us to avoid while in
+    # a mono-repo.
+    # See https://github.com/sphinx-doc/sphinx/blob
+    # /2a65ffeef5c107c19084fabdd706cdff3f52d93c/sphinx/domains/python.py#L843
+    "ref.python"
+]
+
+# -- Options for LaTeX output ---------------------------------------------
+
+latex_elements = {
+    # The paper size ('letterpaper' or 'a4paper').
+    #'papersize': 'letterpaper',
+    # The font size ('10pt', '11pt' or '12pt').
+    #'pointsize': '10pt',
+    # Additional stuff for the LaTeX preamble.
+    #'preamble': '',
+    # Latex figure (float) alignment
+    #'figure_align': 'htbp',
+}
+
+# Grouping the document tree into LaTeX files. List of tuples
+# (source start file, target name, title,
+#  author, documentclass [howto, manual, or own class]).
+latex_documents = [
+    (
+        root_doc,
+        "google-cloud-aiplatform.tex",
+        "google-cloud-aiplatform Documentation",
+        author,
+        "manual",
+    )
+]
+
+# The name of an image file (relative to this directory) to place at the top of
+# the title page.
+# latex_logo = None
+
+# For "manual" documents, if this is true, then toplevel headings are parts,
+# not chapters.
+# latex_use_parts = False
+
+# If true, show page references after internal links.
+# latex_show_pagerefs = False
+
+# If true, show URL addresses after external links.
+# latex_show_urls = False
+
+# Documents to append as an appendix to all manuals.
+# latex_appendices = []
+
+# If false, no module index is generated.
+# latex_domain_indices = True
+
+
+# -- Options for manual page output ---------------------------------------
+
+# One entry per manual page. List of tuples
+# (source start file, name, description, authors, manual section).
+man_pages = [
+    (
+        root_doc,
+        "google-cloud-aiplatform",
+        "google-cloud-aiplatform Documentation",
+        [author],
+        1,
+    )
+]
+
+# If true, show URL addresses after external links.
+# man_show_urls = False
+
+
+# -- Options for Texinfo output -------------------------------------------
+
+# Grouping the document tree into Texinfo files. List of tuples
+# (source start file, target name, title, author,
+#  dir menu entry, description, category)
+texinfo_documents = [
+    (
+        root_doc,
+        "google-cloud-aiplatform",
+        "google-cloud-aiplatform Documentation",
+        author,
+        "google-cloud-aiplatform",
+        "google-cloud-aiplatform Library",
+        "APIs",
+    )
+]
+
+# Documents to append as an appendix to all manuals.
+# texinfo_appendices = []
+
+# If false, no module index is generated.
+# texinfo_domain_indices = True
+
+# How to display URL addresses: 'footnote', 'no', or 'inline'.
+# texinfo_show_urls = 'footnote'
+
+# If true, do not generate a @detailmenu in the "Top" node's menu.
+# texinfo_no_detailmenu = False
+
+
+# Example configuration for intersphinx: refer to the Python standard library.
+intersphinx_mapping = {
+    "python": ("https://python.readthedocs.org/en/latest/", None),
+    "google-auth": ("https://googleapis.dev/python/google-auth/latest/", None),
+    "google.api_core": (
+        "https://googleapis.dev/python/google-api-core/latest/",
+        None,
+    ),
+    "grpc": ("https://grpc.github.io/grpc/python/", None),
+    "proto-plus": ("https://proto-plus-python.readthedocs.io/en/latest/", None),
+    "protobuf": ("https://googleapis.dev/python/protobuf/latest/", None),
+}
+
+
+# Napoleon settings
+napoleon_google_docstring = True
+napoleon_numpy_docstring = True
+napoleon_include_private_with_doc = False
+napoleon_include_special_with_doc = True
+napoleon_use_admonition_for_examples = False
+napoleon_use_admonition_for_notes = False
+napoleon_use_admonition_for_references = False
+napoleon_use_ivar = False
+napoleon_use_param = True
+napoleon_use_rtype = True
+
+
+def adopt_members_reexported_from_private_modules(public_module: str):
+    """Remaps the module items that come from internal modules.
+
+    A public module might be exporting items that are imported from private modules.
+    This function changes the `__module__` of such items to the public module.
+
+    Example:
+        `package/public.py`:
+
+        ```
+        from package._private import _PrivateClass as PublicClass
+        __all__ = ["PublicClass"]
+        ```
+
+        Calling this function on the `package.public` module will change:
+        ```
+        package._private._PrivateClass.__name__ = "PublicClass"
+        package._private._PrivateClass.__module__ = "package.public"
+        ```
+    """
+    for name, cls in public_module.__dict__.items():
+        if name in public_module.__all__:
+            if "._" in cls.__module__:
+                cls.__name__ = name
+                cls.__module__ = public_module.__name__
+
+
+def setup(*args, **kwargs):
+    # 1. Giving pretty module names to the GA and preview classes
+    # 2. Giving pretty class names to the preview classes
+    # 3. Making Sphinx automodule render the class members instead of
+    #     dismissing the exported private classes as "Alias of".
+    from vertexai import evaluation
+    from vertexai import language_models
+    from vertexai import vision_models
+    from vertexai.preview import (
+        language_models as preview_language_models,
+    )
+    from vertexai.preview import (
+        vision_models as preview_vision_models,
+    )
+
+    # There are many possible ways to select which classes to fix.
+    # We select the publicly exported members that have an internal module ("*._*").
+
+    # Setting the modules of the GA classes
+    adopt_members_reexported_from_private_modules(evaluation)
+    adopt_members_reexported_from_private_modules(language_models)
+    adopt_members_reexported_from_private_modules(vision_models)
+
+    # Setting the modules of the public preview classes
+    # Selecting the members that still have an internal module after the GA fixes.
+    adopt_members_reexported_from_private_modules(preview_language_models)
+    adopt_members_reexported_from_private_modules(preview_vision_models)

testbed/googleapis__python-aiplatform/gemini_docs/index.rst

@@ -0,0 +1,8 @@
+.. include:: README.md
+
+API Reference
+-------------
+.. toctree::
+    :maxdepth: 2
+
+    vertexai/vertexai

testbed/googleapis__python-aiplatform/gemini_docs/vertexai/vertexai.rst

@@ -0,0 +1,97 @@
+Vertex AI SDK
+=============================================
+
+.. automodule:: vertexai
+    :members:
+    :show-inheritance:
+    :inherited-members:
+
+.. automodule:: vertexai.generative_models
+    :members:
+    :show-inheritance:
+    :inherited-members:
+
+.. automodule:: vertexai.preview.generative_models
+    :members:
+    :show-inheritance:
+    :inherited-members:
+
+.. automodule:: vertexai.preview.prompts
+    :members:
+    :show-inheritance:
+    :inherited-members:
+
+.. automodule:: vertexai.prompts._prompts
+    :members:
+    :show-inheritance:
+    :inherited-members:
+
+.. automodule:: vertexai.prompts._prompt_management
+    :members:
+    :show-inheritance:
+    :inherited-members:
+
+.. automodule:: vertexai.language_models
+    :members:
+    :show-inheritance:
+    :inherited-members:
+
+.. automodule:: vertexai.language_models._language_models
+    :no-members:
+    :private-members: _TunableModelMixin
+
+.. automodule:: vertexai.preview
+    :members:
+    :show-inheritance:
+    :inherited-members:
+
+.. automodule:: vertexai.preview.language_models
+    :members:
+    :show-inheritance:
+    :inherited-members:
+
+.. automodule:: vertexai.vision_models
+    :members:
+    :show-inheritance:
+    :inherited-members:
+
+.. automodule:: vertexai.preview.vision_models
+    :members:
+    :show-inheritance:
+    :inherited-members:
+
+.. automodule:: vertexai.preview.tuning
+    :members:
+    :show-inheritance:
+    :inherited-members:
+
+.. automodule:: vertexai.preview.tuning.sft
+    :members:
+    :show-inheritance:
+    :inherited-members:
+
+.. automodule:: vertexai.evaluation
+    :members:
+    :show-inheritance:
+    :inherited-members:
+
+.. automodule:: vertexai.preview.reasoning_engines
+    :members:
+    :show-inheritance:
+    :inherited-members:
+
+.. automodule:: vertexai.resources
+    :no-members:
+
+.. automodule:: vertexai.resources.preview
+    :no-members:
+
+.. automodule:: vertexai.resources.preview.ml_monitoring
+    :members:
+    :show-inheritance:
+    :inherited-members:
+
+.. automodule:: vertexai.resources.preview.ml_monitoring.spec
+    :members:
+    :show-inheritance:
+    :inherited-members:

testbed/googleapis__python-aiplatform/mypy.ini

@@ -0,0 +1,3 @@
+[mypy]
+python_version = 3.7
+namespace_packages = True

testbed/googleapis__python-aiplatform/noxfile.py

@@ -0,0 +1,591 @@
+# -*- coding: utf-8 -*-
+#
+# Copyright 2018 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# Generated by synthtool. DO NOT EDIT!
+
+from __future__ import absolute_import
+import os
+import pathlib
+import re
+import shutil
+import warnings
+
+import nox
+
+FLAKE8_VERSION = "flake8==6.1.0"
+BLACK_VERSION = "black==22.3.0"
+ISORT_VERSION = "isort==5.10.1"
+LINT_PATHS = ["docs", "google", "vertexai", "tests", "noxfile.py", "setup.py"]
+
+DEFAULT_PYTHON_VERSION = "3.8"
+
+DOCS_DEPENDENCIES = (
+    "sphinx==5.0.2",
+    "alabaster",
+    "google-cloud-aiplatform[evaluation]",
+    "recommonmark",
+)
+
+DOCFX_DEPENDENCIES = (
+    "gcp-sphinx-docfx-yaml",
+    "sphinxcontrib-applehelp==1.0.4",
+    "sphinxcontrib-devhelp==1.0.2",
+    "sphinxcontrib-htmlhelp==2.0.1",
+    "sphinxcontrib-qthelp==1.0.3",
+    "sphinxcontrib-serializinghtml==1.1.5",
+    "alabaster",
+    "google-cloud-aiplatform[evaluation]",
+    "recommonmark",
+)
+
+UNIT_TEST_PYTHON_VERSIONS = ["3.8", "3.9", "3.10", "3.11", "3.12"]
+UNIT_TEST_LANGCHAIN_PYTHON_VERSIONS = ["3.9", "3.10", "3.11", "3.12"]
+UNIT_TEST_STANDARD_DEPENDENCIES = [
+    "mock",
+    "asyncmock",
+    "pytest",
+    "pytest-cov",
+    "pytest-asyncio",
+    # Preventing: py.test: error: unrecognized arguments: -n=auto --dist=loadscope
+    "pytest-xdist",
+]
+UNIT_TEST_EXTERNAL_DEPENDENCIES = []
+UNIT_TEST_LOCAL_DEPENDENCIES = []
+UNIT_TEST_DEPENDENCIES = []
+UNIT_TEST_EXTRAS = [
+    "testing",
+]
+UNIT_TEST_EXTRAS_BY_PYTHON = {}
+
+SYSTEM_TEST_PYTHON_VERSIONS = ["3.10"]
+SYSTEM_TEST_STANDARD_DEPENDENCIES = [
+    "mock",
+    "pytest",
+    "google-cloud-testutils",
+]
+SYSTEM_TEST_EXTERNAL_DEPENDENCIES = []
+SYSTEM_TEST_LOCAL_DEPENDENCIES = []
+SYSTEM_TEST_DEPENDENCIES = []
+SYSTEM_TEST_EXTRAS = [
+    "testing",
+]
+SYSTEM_TEST_EXTRAS_BY_PYTHON = {}
+
+CURRENT_DIRECTORY = pathlib.Path(__file__).parent.absolute()
+
+# 'docfx' is excluded since it only needs to run in 'docs-presubmit'
+nox.options.sessions = [
+    "unit",
+    "unit_ray",
+    "unit_langchain",
+    "system",
+    "cover",
+    "lint",
+    "lint_setup_py",
+    "blacken",
+    "docs",
+]
+
+# Error if a python version is missing
+nox.options.error_on_missing_interpreters = True
+
+
+@nox.session(python=DEFAULT_PYTHON_VERSION)
+def lint(session):
+    """Run linters.
+
+    Returns a failure if the linters find linting errors or sufficiently
+    serious code quality issues.
+    """
+    session.install(FLAKE8_VERSION, BLACK_VERSION)
+    session.run(
+        "black",
+        "--check",
+        "--diff",
+        *LINT_PATHS,
+    )
+    session.run("flake8", *LINT_PATHS)
+
+
+@nox.session(python=DEFAULT_PYTHON_VERSION)
+def blacken(session):
+    """Run black. Format code to uniform standard."""
+    session.install(BLACK_VERSION)
+    session.run(
+        "black",
+        *LINT_PATHS,
+    )
+
+
+@nox.session(python=DEFAULT_PYTHON_VERSION)
+def format(session):
+    """
+    Run isort to sort imports. Then run black
+    to format code to uniform standard.
+    """
+    session.install(BLACK_VERSION, ISORT_VERSION)
+    # Use the --fss option to sort imports using strict alphabetical order.
+    # See https://pycqa.github.io/isort/docs/configuration/options.html#force-sort-within-sections
+    session.run(
+        "isort",
+        "--fss",
+        *LINT_PATHS,
+    )
+    session.run(
+        "black",
+        *LINT_PATHS,
+    )
+
+
+@nox.session(python=DEFAULT_PYTHON_VERSION)
+def lint_setup_py(session):
+    """Verify that setup.py is valid (including RST check)."""
+    session.install("docutils", "pygments")
+    session.run("python", "setup.py", "check", "--restructuredtext", "--strict")
+
+
+def install_unittest_dependencies(session, *constraints):
+    standard_deps = UNIT_TEST_STANDARD_DEPENDENCIES + UNIT_TEST_DEPENDENCIES
+    session.install(*standard_deps, *constraints)
+
+    if UNIT_TEST_EXTERNAL_DEPENDENCIES:
+        warnings.warn(
+            "'unit_test_external_dependencies' is deprecated. Instead, please "
+            "use 'unit_test_dependencies' or 'unit_test_local_dependencies'.",
+            DeprecationWarning,
+        )
+        session.install(*UNIT_TEST_EXTERNAL_DEPENDENCIES, *constraints)
+
+    if UNIT_TEST_LOCAL_DEPENDENCIES:
+        session.install(*UNIT_TEST_LOCAL_DEPENDENCIES, *constraints)
+
+    if UNIT_TEST_EXTRAS_BY_PYTHON:
+        extras = UNIT_TEST_EXTRAS_BY_PYTHON.get(session.python, [])
+    elif UNIT_TEST_EXTRAS:
+        extras = UNIT_TEST_EXTRAS
+    else:
+        extras = []
+
+    if extras:
+        session.install("-e", f".[{','.join(extras)}]", *constraints)
+    else:
+        session.install("-e", ".", *constraints)
+
+
+def default(session):
+    # Install all test dependencies, then install this package in-place.
+
+    constraints_path = str(
+        CURRENT_DIRECTORY / "testing" / f"constraints-{session.python}.txt"
+    )
+    install_unittest_dependencies(session, "-c", constraints_path)
+
+    # Run py.test against the unit tests.
+    session.run(
+        "py.test",
+        "--quiet",
+        f"--junitxml=unit_{session.python}_sponge_log.xml",
+        "--cov=google",
+        "--cov-append",
+        "--cov-config=.coveragerc",
+        "--cov-report=",
+        "--cov-fail-under=0",
+        "--ignore=tests/unit/vertex_ray",
+        "--ignore=tests/unit/vertex_langchain",
+        "--ignore=tests/unit/architecture",
+        os.path.join("tests", "unit"),
+        *session.posargs,
+    )
+
+    # Run tests that require isolation.
+    session.run(
+        "py.test",
+        "--quiet",
+        f"--junitxml=unit_{session.python}_test_vertexai_import_sponge_log.xml",
+        os.path.join("tests", "unit", "architecture", "test_vertexai_import.py"),
+        *session.posargs,
+    )
+
+
+@nox.session(python=UNIT_TEST_PYTHON_VERSIONS)
+def unit(session):
+    """Run the unit test suite."""
+    # First run the minimal GenAI tests
+    unit_genai_minimal_dependencies(session)
+
+    # Then run the default full test suite
+    default(session)
+
+
+def unit_genai_minimal_dependencies(session):
+    # Install minimal test dependencies, then install this package in-place.
+
+    standard_deps = UNIT_TEST_STANDARD_DEPENDENCIES + UNIT_TEST_DEPENDENCIES
+    session.install(*standard_deps)
+    session.install("-e", ".")
+
+    # Run py.test against the unit tests.
+    session.run(
+        "py.test",
+        "--quiet",
+        f"--junitxml=unit_{session.python}_sponge_log.xml",
+        # These tests require the PIL module
+        # "--ignore=TestGenerativeModels::test_image_mime_types",
+        os.path.join("tests", "unit", "vertexai", "test_generative_models.py"),
+        *session.posargs,
+    )
+
+
+@nox.session(python="3.10")
+@nox.parametrize("ray", ["2.9.3", "2.33.0"])
+def unit_ray(session, ray):
+    # Install all test dependencies, then install this package in-place.
+
+    constraints_path = str(CURRENT_DIRECTORY / "testing" / f"constraints-ray-{ray}.txt")
+    standard_deps = UNIT_TEST_STANDARD_DEPENDENCIES + UNIT_TEST_DEPENDENCIES
+    session.install(*standard_deps, "-c", constraints_path)
+
+    # Install ray extras
+    session.install("-e", ".[ray_testing]", "-c", constraints_path)
+
+    # Run py.test against the unit tests.
+    session.run(
+        "py.test",
+        "--quiet",
+        f"--junitxml=unit_ray_{ray}_sponge_log.xml",
+        "--cov=google",
+        "--cov-append",
+        "--cov-config=.coveragerc",
+        "--cov-report=",
+        "--cov-fail-under=0",
+        os.path.join("tests", "unit", "vertex_ray"),
+        *session.posargs,
+    )
+
+
+@nox.session(python=UNIT_TEST_LANGCHAIN_PYTHON_VERSIONS)
+def unit_langchain(session):
+    # Install all test dependencies, then install this package in-place.
+
+    constraints_path = str(CURRENT_DIRECTORY / "testing" / "constraints-langchain.txt")
+    standard_deps = UNIT_TEST_STANDARD_DEPENDENCIES + UNIT_TEST_DEPENDENCIES
+    session.install(*standard_deps, "-c", constraints_path)
+
+    # Install langchain extras
+    session.install("-e", ".[langchain_testing]", "-c", constraints_path)
+
+    # Run py.test against the unit tests.
+    session.run(
+        "py.test",
+        "--quiet",
+        "--junitxml=unit_langchain_sponge_log.xml",
+        "--cov=google",
+        "--cov-append",
+        "--cov-config=.coveragerc",
+        "--cov-report=",
+        "--cov-fail-under=0",
+        os.path.join("tests", "unit", "vertex_langchain"),
+        *session.posargs,
+    )
+
+
+def install_systemtest_dependencies(session, *constraints):
+    # Use pre-release gRPC for system tests.
+    # Exclude version 1.52.0rc1 which has a known issue.
+    # See https://github.com/grpc/grpc/issues/32163
+    session.install("--pre", "grpcio!=1.52.0rc1")
+
+    session.install(*SYSTEM_TEST_STANDARD_DEPENDENCIES, *constraints)
+
+    if SYSTEM_TEST_EXTERNAL_DEPENDENCIES:
+        session.install(*SYSTEM_TEST_EXTERNAL_DEPENDENCIES, *constraints)
+
+    if SYSTEM_TEST_LOCAL_DEPENDENCIES:
+        session.install("-e", *SYSTEM_TEST_LOCAL_DEPENDENCIES, *constraints)
+
+    if SYSTEM_TEST_DEPENDENCIES:
+        session.install("-e", *SYSTEM_TEST_DEPENDENCIES, *constraints)
+
+    if SYSTEM_TEST_EXTRAS_BY_PYTHON:
+        extras = SYSTEM_TEST_EXTRAS_BY_PYTHON.get(session.python, [])
+    elif SYSTEM_TEST_EXTRAS:
+        extras = SYSTEM_TEST_EXTRAS
+    else:
+        extras = []
+
+    if extras:
+        session.install("-e", f".[{','.join(extras)}]", *constraints)
+    else:
+        session.install("-e", ".", *constraints)
+
+
+@nox.session(python=SYSTEM_TEST_PYTHON_VERSIONS)
+def system(session):
+    """Run the system test suite."""
+    constraints_path = str(
+        CURRENT_DIRECTORY / "testing" / f"constraints-{session.python}.txt"
+    )
+    system_test_path = os.path.join("tests", "system.py")
+    system_test_folder_path = os.path.join("tests", "system")
+
+    # Check the value of `RUN_SYSTEM_TESTS` env var. It defaults to true.
+    if os.environ.get("RUN_SYSTEM_TESTS", "true") == "false":
+        session.skip("RUN_SYSTEM_TESTS is set to false, skipping")
+    # Install pyopenssl for mTLS testing.
+    if os.environ.get("GOOGLE_API_USE_CLIENT_CERTIFICATE", "false") == "true":
+        session.install("pyopenssl")
+
+    system_test_exists = os.path.exists(system_test_path)
+    system_test_folder_exists = os.path.exists(system_test_folder_path)
+    # Sanity check: only run tests if found.
+    if not system_test_exists and not system_test_folder_exists:
+        session.skip("System tests were not found")
+
+    install_systemtest_dependencies(session, "-c", constraints_path)
+
+    # Run py.test against the system tests.
+    if system_test_exists:
+        session.run(
+            "py.test",
+            "--quiet",
+            f"--junitxml=system_{session.python}_sponge_log.xml",
+            system_test_path,
+            *session.posargs,
+        )
+    if system_test_folder_exists:
+        session.run(
+            "py.test",
+            "-v",
+            f"--junitxml=system_{session.python}_sponge_log.xml",
+            system_test_folder_path,
+            *session.posargs,
+        )
+
+
+@nox.session(python=DEFAULT_PYTHON_VERSION)
+def cover(session):
+    """Run the final coverage report.
+
+    This outputs the coverage report aggregating coverage from the unit
+    test runs (not system test runs), and then erases coverage data.
+    """
+    session.install("coverage", "pytest-cov")
+    session.run("coverage", "report", "--show-missing", "--fail-under=85")
+
+    session.run("coverage", "erase")
+
+
+@nox.session(python="3.9")
+def docs(session):
+    """Build the docs for this library."""
+
+    session.install("-e", ".")
+    session.install(
+        *DOCS_DEPENDENCIES,
+        "google-cloud-aiplatform[prediction]",
+    )
+
+    shutil.rmtree(os.path.join("docs", "_build"), ignore_errors=True)
+    session.run(
+        "sphinx-build",
+        "-T",  # show full traceback on exception
+        "-N",  # no colors
+        "-b",
+        "html",
+        "-d",
+        os.path.join("docs", "_build", "doctrees", ""),
+        os.path.join("docs", ""),
+        os.path.join("docs", "_build", "html", ""),
+    )
+
+
+@nox.session(python="3.10")
+def docfx(session):
+    """Build the docfx yaml files for this library."""
+
+    session.install("-e", ".")
+    session.install(
+        *DOCFX_DEPENDENCIES,
+        "google-cloud-aiplatform[prediction]",
+    )
+
+    shutil.rmtree(os.path.join("docs", "_build"), ignore_errors=True)
+    session.run(
+        "sphinx-build",
+        "-T",  # show full traceback on exception
+        "-N",  # no colors
+        "-D",
+        (
+            "extensions=sphinx.ext.autodoc,"
+            "sphinx.ext.autosummary,"
+            "docfx_yaml.extension,"
+            "sphinx.ext.intersphinx,"
+            "sphinx.ext.coverage,"
+            "sphinx.ext.napoleon,"
+            "sphinx.ext.todo,"
+            "sphinx.ext.viewcode,"
+            "recommonmark"
+        ),
+        "-b",
+        "html",
+        "-d",
+        os.path.join("docs", "_build", "doctrees", ""),
+        os.path.join("docs", ""),
+        os.path.join("docs", "_build", "html", ""),
+    )
+
+
+@nox.session(python="3.9")
+def gemini_docs(session):
+    """Build the docs for library related to Gemini."""
+
+    session.install("-e", ".")
+    session.install(*DOCS_DEPENDENCIES)
+
+    shutil.rmtree(os.path.join("docs", "_build"), ignore_errors=True)
+    session.run(
+        "sphinx-build",
+        "-T",  # show full traceback on exception
+        "-N",  # no colors
+        "-b",
+        "html",
+        "-d",
+        os.path.join("gemini_docs", "_build", "doctrees", ""),
+        os.path.join("gemini_docs", ""),
+        os.path.join("gemini_docs", "_build", "html", ""),
+    )
+
+
+@nox.session(python="3.10")
+def gemini_docfx(session):
+    """Build the docfx yaml files for library related to Gemini."""
+
+    session.install("-e", ".")
+    session.install(*DOCFX_DEPENDENCIES)
+
+    shutil.rmtree(os.path.join("docs", "_build"), ignore_errors=True)
+    session.run(
+        "sphinx-build",
+        "-T",  # show full traceback on exception
+        "-N",  # no colors
+        "-D",
+        (
+            "extensions=sphinx.ext.autodoc,"
+            "sphinx.ext.autosummary,"
+            "docfx_yaml.extension,"
+            "sphinx.ext.intersphinx,"
+            "sphinx.ext.coverage,"
+            "sphinx.ext.napoleon,"
+            "sphinx.ext.todo,"
+            "sphinx.ext.viewcode,"
+            "recommonmark"
+        ),
+        "-b",
+        "html",
+        "-d",
+        os.path.join("gemini_docs", "_build", "doctrees", ""),
+        os.path.join("gemini_docs", ""),
+        os.path.join("gemini_docs", "_build", "html", ""),
+    )
+
+
+@nox.session(python=SYSTEM_TEST_PYTHON_VERSIONS)
+def prerelease_deps(session):
+    """Run all tests with prerelease versions of dependencies installed."""
+
+    # Install all dependencies
+    session.install("-e", ".[all, tests, tracing]")
+    unit_deps_all = UNIT_TEST_STANDARD_DEPENDENCIES + UNIT_TEST_EXTERNAL_DEPENDENCIES
+    session.install(*unit_deps_all)
+    system_deps_all = (
+        SYSTEM_TEST_STANDARD_DEPENDENCIES + SYSTEM_TEST_EXTERNAL_DEPENDENCIES
+    )
+    session.install(*system_deps_all)
+
+    # Because we test minimum dependency versions on the minimum Python
+    # version, the first version we test with in the unit tests sessions has a
+    # constraints file containing all dependencies and extras.
+    with open(
+        CURRENT_DIRECTORY
+        / "testing"
+        / f"constraints-{UNIT_TEST_PYTHON_VERSIONS[0]}.txt",
+        encoding="utf-8",
+    ) as constraints_file:
+        constraints_text = constraints_file.read()
+
+    # Ignore leading whitespace and comment lines.
+    constraints_deps = [
+        match.group(1)
+        for match in re.finditer(
+            r"^\s*(\S+)(?===\S+)", constraints_text, flags=re.MULTILINE
+        )
+    ]
+
+    session.install(*constraints_deps)
+
+    prerel_deps = [
+        "protobuf",
+        # dependency of grpc
+        "six",
+        "googleapis-common-protos",
+        # Exclude version 1.52.0rc1 which has a known issue. See https://github.com/grpc/grpc/issues/32163
+        "grpcio!=1.52.0rc1",
+        "grpcio-status",
+        "google-api-core",
+        "proto-plus",
+        "google-cloud-testutils",
+        # dependencies of google-cloud-testutils"
+        "click",
+    ]
+
+    for dep in prerel_deps:
+        session.install("--pre", "--no-deps", "--upgrade", dep)
+
+    # Remaining dependencies
+    other_deps = [
+        "requests",
+        "google-auth",
+    ]
+    session.install(*other_deps)
+
+    # Print out prerelease package versions
+    session.run(
+        "python", "-c", "import google.protobuf; print(google.protobuf.__version__)"
+    )
+    session.run("python", "-c", "import grpc; print(grpc.__version__)")
+
+    session.run("py.test", "tests/unit")
+
+    system_test_path = os.path.join("tests", "system.py")
+    system_test_folder_path = os.path.join("tests", "system")
+
+    # Only run system tests if found.
+    if os.path.exists(system_test_path):
+        session.run(
+            "py.test",
+            "--verbose",
+            f"--junitxml=system_{session.python}_sponge_log.xml",
+            system_test_path,
+            *session.posargs,
+        )
+    if os.path.exists(system_test_folder_path):
+        session.run(
+            "py.test",
+            "--verbose",
+            f"--junitxml=system_{session.python}_sponge_log.xml",
+            system_test_folder_path,
+            *session.posargs,
+        )

testbed/googleapis__python-aiplatform/owlbot.py

@@ -0,0 +1,205 @@
+# Copyright 2020 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""This script is used to synthesize generated parts of this library."""
+
+import re
+
+import synthtool as s
+import synthtool.gcp as gcp
+from synthtool.languages import python
+
+common = gcp.CommonTemplates()
+
+default_version = "v1"
+
+has_generator_updates = False
+for library in s.get_staging_dirs(default_version):
+    # ---------------------------------------------------------------------
+    # Patch each version of the library
+    # ---------------------------------------------------------------------
+
+    # https://github.com/googleapis/gapic-generator-python/issues/413
+    s.replace(
+        library
+        / f"google/cloud/aiplatform_{library.name}/services/prediction_service/client.py",
+        "request.instances = instances",
+        "request.instances.extend(instances)",
+    )
+
+    # Remove test_predict_flattened/test_predict_flattened_async due to gapic generator bug
+    # https://github.com/googleapis/gapic-generator-python/issues/414
+    s.replace(
+        library
+        / f"tests/unit/gapic/aiplatform_{library.name}/test_prediction_service.py",
+        """def test_predict_flattened.*?def test_predict_flattened_error""",
+        "def test_predict_flattened_error",
+        flags=re.MULTILINE | re.DOTALL,
+    )
+
+    # Remove test_explain_flattened/test_explain_flattened_async due to gapic generator bug
+    # https://github.com/googleapis/gapic-generator-python/issues/414
+    s.replace(
+        library
+        / f"tests/unit/gapic/aiplatform_{library.name}/test_prediction_service.py",
+        """def test_explain_flattened.*?def test_explain_flattened_error""",
+        "def test_explain_flattened_error",
+        flags=re.MULTILINE | re.DOTALL,
+    )
+
+    s.move(
+        library,
+        excludes=[
+            ".coveragerc",
+            ".pre-commit-config.yaml",
+            "setup.py",
+            "README.rst",
+            "docs/index.rst",
+            "docs/summary_overview.md",
+            f"docs/definition_{library.name}/services.rst",
+            f"docs/instance_{library.name}/services.rst",
+            f"docs/params_{library.name}/services.rst",
+            f"docs/prediction_{library.name}/services.rst",
+            f"scripts/fixup_aiplatform_{library.name}_keywords.py",
+            f"scripts/fixup_definition_{library.name}_keywords.py",
+            f"scripts/fixup_instance_{library.name}_keywords.py",
+            f"scripts/fixup_params_{library.name}_keywords.py",
+            f"scripts/fixup_prediction_{library.name}_keywords.py",
+            "google/cloud/aiplatform/__init__.py",
+            f"google/cloud/aiplatform/{library.name}/schema/**/services/",
+            "**/gapic_version.py",  # exclude gapic_version.py to avoid reverting the version to 0.1.0
+            ".kokoro/samples",
+            "noxfile.py",
+            "testing",
+            "docs/conf.py",
+        ],
+    )
+    has_generator_updates = True
+
+s.remove_staging_dirs()
+
+# only run post processor when there are changes to the generated code
+if has_generator_updates:
+    # ----------------------------------------------------------------------------
+    # Add templated files
+    # ----------------------------------------------------------------------------
+
+    templated_files = common.py_library(
+        cov_level=98,
+        system_test_python_versions=["3.8"],
+        unit_test_python_versions=["3.8", "3.9", "3.10", "3.11", "3.12"],
+        unit_test_extras=["testing"],
+        system_test_extras=["testing"],
+        microgenerator=True,
+    )
+    s.move(
+        templated_files,
+        excludes=[
+            ".coveragerc",
+            ".pre-commit-config.yaml",
+            ".kokoro/continuous/common.cfg",
+            ".kokoro/presubmit/presubmit.cfg",
+            ".kokoro/continuous/prerelease-deps.cfg",
+            ".kokoro/presubmit/prerelease-deps.cfg",
+            ".kokoro/docs/docs-presubmit.cfg",
+            ".kokoro/build.sh",
+            ".kokoro/release.sh",
+            ".kokoro/release/common.cfg",
+            ".kokoro/requirements*",
+            # exclude sample configs so periodic samples are tested against main
+            # instead of pypi
+            ".kokoro/samples/python3.7/common.cfg",
+            ".kokoro/samples/python3.8/common.cfg",
+            ".kokoro/samples/python3.9/common.cfg",
+            ".kokoro/samples/python3.10/common.cfg",
+            ".kokoro/samples/python3.11/common.cfg",
+            ".kokoro/samples/python3.12/common.cfg",
+            ".kokoro/samples/python3.7/periodic.cfg",
+            ".kokoro/samples/python3.8/periodic.cfg",
+            ".kokoro/samples/python3.9/periodic.cfg",
+            ".kokoro/samples/python3.10/periodic.cfg",
+            ".kokoro/samples/python3.11/periodic.cfg",
+            ".kokoro/samples/python3.12/periodic.cfg",
+            ".github/CODEOWNERS",
+            ".github/PULL_REQUEST_TEMPLATE.md",
+            ".github/workflows",  # exclude gh actions as credentials are needed for tests
+            "README.rst",
+            ".github/release-please.yml",  # use release please manifest
+            "noxfile.py",
+            "testing",
+            "docs/conf.py",
+        ],
+    )  # the microgenerator has a good coveragerc file
+
+    python.py_samples(skip_readmes=True)
+
+    python.configure_previous_major_version_branches()
+
+    # Update samples config to use `ucaip-sample-tests` project
+    s.replace(
+        ".kokoro/samples/python3.*/common.cfg",
+        """env_vars: \{
+        key: "BUILD_SPECIFIC_GCLOUD_PROJECT"
+        value: "python-docs-samples-tests-.*?"
+    \}""",
+        """env_vars: {
+        key: "BUILD_SPECIFIC_GCLOUD_PROJECT"
+        value: "ucaip-sample-tests"
+    }""",
+    )
+
+    s.replace(
+        ".kokoro/test-samples-impl.sh",
+        "python3.9",
+        "python3",
+    )
+
+    # Update publish-docs to include gemini docs workflow.
+    s.replace(
+        ".kokoro/publish-docs.sh",
+        "# build docs",
+        """\
+# build Gemini docs
+nox -s gemini_docs
+# create metadata
+python3 -m docuploader create-metadata \\
+  --name="vertexai" \\
+  --version=$(python3 setup.py --version) \\
+  --language=$(jq --raw-output '.language // empty' .repo-metadata.json) \\
+  --distribution-name="google-cloud-vertexai" \\
+  --product-page=$(jq --raw-output '.product_documentation // empty' .repo-metadata.json) \\
+  --github-repository=$(jq --raw-output '.repo // empty' .repo-metadata.json) \\
+  --issue-tracker=$(jq --raw-output '.issue_tracker // empty' .repo-metadata.json)
+cat docs.metadata
+# upload docs
+python3 -m docuploader upload gemini_docs/_build/html --metadata-file docs.metadata --staging-bucket "${STAGING_BUCKET}"
+# Gemini docfx yaml files
+nox -s gemini_docfx
+# create metadata.
+python3 -m docuploader create-metadata \\
+  --name="vertexai" \\
+  --version=$(python3 setup.py --version) \\
+  --language=$(jq --raw-output '.language // empty' .repo-metadata.json) \\
+  --distribution-name="google-cloud-vertexai" \\
+  --product-page=$(jq --raw-output '.product_documentation // empty' .repo-metadata.json) \\
+  --github-repository=$(jq --raw-output '.repo // empty' .repo-metadata.json) \\
+  --issue-tracker=$(jq --raw-output '.issue_tracker // empty' .repo-metadata.json) \\
+  --stem="/vertex-ai/generative-ai/docs/reference/python"
+cat docs.metadata
+# upload docs
+python3 -m docuploader upload gemini_docs/_build/html/docfx_yaml --metadata-file docs.metadata --destination-prefix docfx --staging-bucket "${V2_STAGING_BUCKET}"
+# build docs""",
+    )
+
+    s.shell.run(["nox", "-s", "blacken"], hide_output=False)

testbed/googleapis__python-aiplatform/pypi/README.md

@@ -0,0 +1,4 @@
+# vertexai package
+
+The pypi package in the `_vertex_ai_placeholder` directory is being used to
+upload a package with `vertexai` namespace on PyPi which depends on `google-cloud-aiplatform`.

testbed/googleapis__python-aiplatform/pypi/_vertex_ai_placeholder/LICENSE

@@ -0,0 +1,202 @@
+
+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+   1. Definitions.
+
+      "License" shall mean the terms and conditions for use, reproduction,
+      and distribution as defined by Sections 1 through 9 of this document.
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+
+      "Legal Entity" shall mean the union of the acting entity and all
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+      direction or management of such entity, whether by contract or
+      otherwise, or (ii) ownership of fifty percent (50%) or more of the
+      outstanding shares, or (iii) beneficial ownership of such entity.
+
+      "You" (or "Your") shall mean an individual or Legal Entity
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+      any Contribution intentionally submitted for inclusion in the Work
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+      this License, without any additional terms or conditions.
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+      the terms of any separate license agreement you may have executed
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+   6. Trademarks. This License does not grant permission to use the trade
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+      on Your own behalf and on Your sole responsibility, not on behalf
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testbed/googleapis__python-aiplatform/pypi/_vertex_ai_placeholder/README.md

@@ -0,0 +1,6 @@
+vertexai
+========
+
+To use the Vertex GAPIC clients, please install the `google-cloud-aiplatform` PyPi package by running `pip install google-cloud-aiplatform`.
+
+To use the Vertex AI SDK, please install the `vertexai` PyPi package by running `pip install vertexai`.

testbed/googleapis__python-aiplatform/pypi/_vertex_ai_placeholder/pyproject.toml

@@ -0,0 +1,17 @@
+[build-system]
+requires = ["setuptools>=61.0"]
+build-backend = "setuptools.build_meta"
+
+[project]
+name = "vertexai"
+dynamic = ["version", "dependencies", "optional-dependencies"]
+authors = [
+  { name="Google LLC", email="googleapis-packages@google.com" },
+]
+license = {text = "Apache 2.0"}
+description = "Please run pip install vertexai to use the Vertex SDK."
+readme = "README.md"
+requires-python = ">=3.8"
+
+[project.urls]
+repository = "https://github.com/googleapis/python-aiplatform.git"

testbed/googleapis__python-aiplatform/pypi/_vertex_ai_placeholder/setup.py

@@ -0,0 +1,247 @@
+# -*- coding: utf-8 -*-
+
+# Copyright 2024 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import io
+import os
+
+import setuptools  # type: ignore
+
+name = "vertexai"
+description = "Vertex AI API client library"
+
+package_root = os.path.abspath(os.path.dirname(__file__))
+readme_filename = os.path.join(package_root, "README.md")
+with io.open(readme_filename, encoding="utf-8") as readme_file:
+    readme = readme_file.read()
+
+version = {}
+with open(os.path.join(package_root, "version.py")) as fp:
+    exec(fp.read(), version)
+version = version["__version__"]
+
+tensorboard_extra_require = ["tensorflow >=2.3.0, <3.0.0dev; python_version<='3.11'"]
+metadata_extra_require = ["pandas >= 1.0.0", "numpy>=1.15.0"]
+xai_extra_require = ["tensorflow >=2.3.0, <3.0.0dev"]
+lit_extra_require = [
+    "tensorflow >= 2.3.0, <3.0.0dev",
+    "pandas >= 1.0.0",
+    "lit-nlp == 0.4.0",
+    "explainable-ai-sdk >= 1.0.0",
+]
+profiler_extra_require = [
+    "tensorboard-plugin-profile >= 2.4.0, <3.0.0dev",
+    "werkzeug >= 2.0.0, <2.1.0dev",
+    "tensorflow >=2.4.0, <3.0.0dev",
+]
+featurestore_extra_require = [
+    "google-cloud-bigquery-storage",
+    "pandas >= 1.0.0",
+    "pyarrow >= 6.0.1",
+]
+pipelines_extra_require = [
+    "pyyaml>=5.3.1,<7",
+]
+datasets_extra_require = [
+    "pyarrow >= 3.0.0, < 8.0dev; python_version<'3.11'",
+    "pyarrow >= 10.0.1; python_version=='3.11'",
+    "pyarrow >= 14.0.0; python_version>='3.12'",
+]
+
+vizier_extra_require = [
+    "google-vizier>=0.1.6",
+]
+
+prediction_extra_require = [
+    "docker >= 5.0.3",
+    "fastapi >= 0.71.0, <=0.109.1",
+    "httpx >=0.23.0, <0.25.0",  # Optional dependency of fastapi
+    "starlette >= 0.17.1",
+    "uvicorn[standard] >= 0.16.0",
+]
+
+endpoint_extra_require = ["requests >= 2.28.1"]
+
+private_endpoints_extra_require = [
+    "urllib3 >=1.21.1, <1.27",
+    "requests >= 2.28.1",
+]
+
+autologging_extra_require = ["mlflow>=1.27.0,<=2.1.1"]
+
+preview_extra_require = [
+    "cloudpickle < 3.0",
+    "google-cloud-logging < 4.0",
+]
+
+ray_extra_require = [
+    # Cluster only supports 2.4.0 and 2.9.3
+    (
+        "ray[default] >= 2.4, <= 2.9.3,!= 2.5.*,!= 2.6.*,!= 2.7.*,!="
+        " 2.8.*,!=2.9.0,!=2.9.1,!=2.9.2; python_version<'3.11'"
+    ),
+    # Ray Data v2.4 in Python 3.11 is broken, but got fixed in Ray v2.5.
+    "ray[default] >= 2.5, <= 2.9.3; python_version=='3.11'",
+    "google-cloud-bigquery-storage",
+    "google-cloud-bigquery",
+    "pandas >= 1.0.0, < 2.2.0",
+    "pyarrow >= 6.0.1",
+    # Workaround for https://github.com/ray-project/ray/issues/36990.
+    # TODO(b/295406381): Remove this pin when we drop support of ray<=2.5.
+    "pydantic < 2",
+    "immutabledict",
+]
+
+genai_requires = (
+    "pydantic < 3",
+    "docstring_parser < 1",
+)
+
+ray_testing_extra_require = ray_extra_require + [
+    "pytest-xdist",
+    # ray train extras required for prediction tests
+    (
+        "ray[train] >= 2.4, <= 2.9.3,!= 2.5.*,!= 2.6.*,!= 2.7.*,!="
+        " 2.8.*,!=2.9.0,!=2.9.1,!=2.9.2"
+    ),
+    # Framework version constraints copied from testing_extra_require
+    "scikit-learn",
+    "tensorflow",
+    "torch >= 2.0.0, < 2.1.0",
+    "xgboost",
+    "xgboost_ray",
+]
+
+reasoning_engine_extra_require = [
+    "cloudpickle >= 2.2.1, < 3.0",
+    "pydantic < 3",
+]
+
+rapid_evaluation_extra_require = [
+    "nest_asyncio >= 1.0.0, < 1.6.0",
+    "pandas >= 1.0.0, < 2.2.0",
+]
+
+langchain_extra_require = [
+    "langchain >= 0.1.13, < 0.2",
+    "langchain-core < 0.2",
+    "langchain-google-vertexai < 0.2",
+]
+
+langchain_testing_extra_require = langchain_extra_require + [
+    "pytest-xdist",
+]
+
+full_extra_require = list(
+    set(
+        tensorboard_extra_require
+        + metadata_extra_require
+        + xai_extra_require
+        + lit_extra_require
+        + featurestore_extra_require
+        + pipelines_extra_require
+        + datasets_extra_require
+        + endpoint_extra_require
+        + vizier_extra_require
+        + prediction_extra_require
+        + private_endpoints_extra_require
+        + autologging_extra_require
+        + preview_extra_require
+        + ray_extra_require
+        + reasoning_engine_extra_require
+        + rapid_evaluation_extra_require
+    )
+)
+testing_extra_require = (
+    full_extra_require
+    + profiler_extra_require
+    + [
+        "bigframes; python_version>='3.10'",
+        # google-api-core 2.x is required since kfp requires protobuf > 4
+        "google-api-core >= 2.11, < 3.0.0",
+        "grpcio-testing",
+        "ipython",
+        "kfp >= 2.6.0, < 3.0.0",
+        "pyfakefs",
+        "pytest-asyncio",
+        "pytest-xdist",
+        "scikit-learn",
+        # Lazy import requires > 2.12.0
+        "tensorflow == 2.13.0; python_version<='3.11'",
+        "tensorflow == 2.16.1; python_version>'3.11'",
+        # TODO(jayceeli) torch 2.1.0 has conflict with pyfakefs, will check if
+        # future versions fix this issue
+        "torch >= 2.0.0, < 2.1.0; python_version<='3.11'",
+        "torch >= 2.2.0; python_version>'3.11'",
+        "requests-toolbelt < 1.0.0",
+        "immutabledict",
+        "xgboost",
+    ]
+)
+
+
+setuptools.setup(
+    name=name,
+    version=version,
+    description=description,
+    long_description=readme,
+    author="Google LLC",
+    author_email="vertex-sdk-dev-pypi@google.com",
+    license="Apache 2.0",
+    url="https://github.com/googleapis/python-aiplatform",
+    platforms="Posix; MacOS X; Windows",
+    include_package_data=True,
+    install_requires=[f"google-cloud-aiplatform[all] == {version}"],
+    extras_require={
+        "endpoint": endpoint_extra_require,
+        "full": full_extra_require,
+        "metadata": metadata_extra_require,
+        "tensorboard": tensorboard_extra_require,
+        "testing": testing_extra_require,
+        "xai": xai_extra_require,
+        "lit": lit_extra_require,
+        "cloud_profiler": profiler_extra_require,
+        "pipelines": pipelines_extra_require,
+        "vizier": vizier_extra_require,
+        "prediction": prediction_extra_require,
+        "datasets": datasets_extra_require,
+        "private_endpoints": private_endpoints_extra_require,
+        "autologging": autologging_extra_require,
+        "preview": preview_extra_require,
+        "ray": ray_extra_require,
+        "ray_testing": ray_testing_extra_require,
+        "reasoningengine": reasoning_engine_extra_require,
+        "rapid_evaluation": rapid_evaluation_extra_require,
+        "langchain": langchain_extra_require,
+        "langchain_testing": langchain_testing_extra_require,
+    },
+    python_requires=">=3.8",
+    classifiers=[
+        "Development Status :: 5 - Production/Stable",
+        "Intended Audience :: Developers",
+        "Operating System :: OS Independent",
+        "Programming Language :: Python",
+        "Programming Language :: Python :: 3",
+        "Programming Language :: Python :: 3.8",
+        "Programming Language :: Python :: 3.9",
+        "Programming Language :: Python :: 3.10",
+        "Programming Language :: Python :: 3.11",
+        "Programming Language :: Python :: 3.12",
+        "Topic :: Internet",
+        "Topic :: Software Development :: Libraries :: Python Modules",
+    ],
+    zip_safe=False,
+)

testbed/googleapis__python-aiplatform/pypi/_vertex_ai_placeholder/version.py

@@ -0,0 +1,18 @@
+# -*- coding: utf-8 -*-
+
+# Copyright 2024 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+__version__ = "1.75.0"

testbed/googleapis__python-aiplatform/release-please-config.json

@@ -0,0 +1,48 @@
+{
+    "$schema": "https://raw.githubusercontent.com/googleapis/release-please/main/schemas/config.json",
+    "packages": {
+      ".": {
+        "release-type": "python",
+        "extra-files": [
+          "google/cloud/aiplatform/version.py",
+          "google/cloud/aiplatform/gapic_version.py",
+          "google/cloud/aiplatform_v1/gapic_version.py",
+          "google/cloud/aiplatform/v1/schema/trainingjob/definition_v1/gapic_version.py",
+          "google/cloud/aiplatform/v1/schema/trainingjob/definition/gapic_version.py",
+          "google/cloud/aiplatform/v1/schema/predict/instance/gapic_version.py",
+          "google/cloud/aiplatform/v1/schema/predict/instance_v1/gapic_version.py",
+          "google/cloud/aiplatform/v1/schema/predict/prediction/gapic_version.py",
+          "google/cloud/aiplatform/v1/schema/predict/prediction_v1/gapic_version.py",
+          "google/cloud/aiplatform/v1/schema/predict/params/gapic_version.py",
+          "google/cloud/aiplatform/v1/schema/predict/params_v1/gapic_version.py",
+          "google/cloud/aiplatform/v1beta1/schema/trainingjob/definition/gapic_version.py",
+          "google/cloud/aiplatform/v1beta1/schema/trainingjob/definition_v1beta1/gapic_version.py",
+          "google/cloud/aiplatform/v1beta1/schema/predict/instance/gapic_version.py",
+          "google/cloud/aiplatform/v1beta1/schema/predict/prediction_v1beta1/gapic_version.py",
+          "google/cloud/aiplatform/v1beta1/schema/predict/prediction/gapic_version.py",
+          "google/cloud/aiplatform/v1beta1/schema/predict/params/gapic_version.py",
+          "google/cloud/aiplatform/v1beta1/schema/predict/params_v1beta1/gapic_version.py",
+          "google/cloud/aiplatform/v1beta1/schema/predict/instance_v1beta1/gapic_version.py",
+          "google/cloud/aiplatform_v1beta1/gapic_version.py",
+          "pypi/_vertex_ai_placeholder/version.py",
+          {
+            "type": "json",
+            "path": "samples/generated_samples/snippet_metadata_google.cloud.aiplatform.v1beta1.json",
+            "jsonpath": "$.clientLibrary.version"
+          },
+          {
+            "type": "json",
+            "path": "samples/generated_samples/snippet_metadata_google.cloud.aiplatform.v1.json",
+            "jsonpath": "$.clientLibrary.version"
+          }
+        ]
+      }
+    },
+    "release-type": "python",
+    "plugins": [
+      {
+        "type": "sentence-case"
+      }
+    ],
+    "initial-version": "0.1.0"
+}

testbed/googleapis__python-aiplatform/renovate.json

@@ -0,0 +1,12 @@
+{
+  "extends": [
+    "config:base",
+    "group:all",
+    ":preserveSemverRanges",
+    ":disableDependencyDashboard"
+  ],
+  "ignorePaths": [".pre-commit-config.yaml", ".kokoro/requirements.txt", "setup.py"],
+  "pip_requirements": {
+    "fileMatch": ["requirements-test.txt", "samples/[\\S/]*constraints.txt", "samples/[\\S/]*constraints-test.txt"]
+  }
+}

testbed/googleapis__python-aiplatform/sdk_schema_tests/__init__.py

@@ -0,0 +1,14 @@
+# Copyright 2024 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#

testbed/googleapis__python-aiplatform/sdk_schema_tests/common_contract.py

@@ -0,0 +1,24 @@
+# Copyright 2024 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+expected_generate_content_common_arg_keys = (
+    "self",
+    "contents",
+    "generation_config",
+    "safety_settings",
+    "tools",
+    "tool_config",
+    "stream",
+)

testbed/googleapis__python-aiplatform/sdk_schema_tests/method_signature_tests/__init__.py

@@ -0,0 +1,14 @@
+# Copyright 2024 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#

testbed/googleapis__python-aiplatform/sdk_schema_tests/method_signature_tests/method_signature_tests.py

@@ -0,0 +1,54 @@
+"""Tests for method_signature."""
+
+from inspect import signature
+import unittest
+
+from vertexai.generative_models import GenerativeModel as VertexAIGenerativeModel
+from google.generativeai import GenerativeModel as GoogleAIGenerativeModel
+from sdk_schema_tests import common_contract
+
+
+_VERTEX_AI_SDK_NAME = "Vertex AI SDK"
+_GOOGLE_AI_SDK_NAME = "Google AI SDK"
+
+
+class TestGenerativeModelMethodSignatures(unittest.TestCase):
+    """Tests for method signatures of GenerativeModel."""
+
+    def _test_method_argument_key_in_both_sdks(
+        self,
+        method_under_test,
+        expected_method_arg_keys,
+        sdk_name
+    ):
+        method_signature = signature(method_under_test)
+        actual_method_arg_keys = method_signature.parameters.keys()
+        for expected_arg_key in expected_method_arg_keys:
+            self.assertIn(
+                member=expected_arg_key,
+                container=actual_method_arg_keys,
+                msg=(
+                    f"[{sdk_name}][method {method_under_test.__name__}]: expected"
+                    f" common arugment {expected_arg_key} not found in actual arugment"
+                    f" list: {actual_method_arg_keys}"
+                ),
+            )
+
+    def test_generate_content_method_signature(self):
+        expected_common_arg_keys = (
+            common_contract.expected_generate_content_common_arg_keys
+        )
+        test_arguments = [
+            {
+                "method_under_test": VertexAIGenerativeModel.generate_content,
+                "expected_method_arg_keys": expected_common_arg_keys,
+                "sdk_name": _VERTEX_AI_SDK_NAME,
+            },
+            {
+                "method_under_test": GoogleAIGenerativeModel.generate_content,
+                "expected_method_arg_keys": expected_common_arg_keys,
+                "sdk_name": _GOOGLE_AI_SDK_NAME,
+            },
+        ]
+        for test_argument in test_arguments:
+            self._test_method_argument_key_in_both_sdks(**test_argument)

testbed/googleapis__python-aiplatform/setup.cfg

@@ -0,0 +1,19 @@
+# -*- coding: utf-8 -*-
+#
+# Copyright 2023 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# Generated by synthtool. DO NOT EDIT!
+[bdist_wheel]
+universal = 1

testbed/googleapis__python-aiplatform/setup.py

@@ -0,0 +1,300 @@
+# -*- coding: utf-8 -*-
+
+# Copyright 2022 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import io
+import os
+
+import setuptools  # type: ignore
+
+name = "google-cloud-aiplatform"
+description = "Vertex AI API client library"
+
+package_root = os.path.abspath(os.path.dirname(__file__))
+readme_filename = os.path.join(package_root, "README.rst")
+with io.open(readme_filename, encoding="utf-8") as readme_file:
+    readme = readme_file.read()
+
+version = {}
+with open(os.path.join(package_root, "google/cloud/aiplatform/version.py")) as fp:
+    exec(fp.read(), version)
+version = version["__version__"]
+
+packages = [
+    package
+    for package in setuptools.PEP420PackageFinder.find()
+    if package.startswith("google") or package.startswith("vertexai")
+]
+
+# Add vertex_ray relative packages
+packages += [
+    package.replace("google.cloud.aiplatform.vertex_ray", "vertex_ray")
+    for package in setuptools.PEP420PackageFinder.find()
+    if package.startswith("google.cloud.aiplatform.vertex_ray")
+]
+
+profiler_extra_require = [
+    "tensorboard-plugin-profile >= 2.4.0, <2.18.0",  # <3.0.0dev",
+    "werkzeug >= 2.0.0, <2.1.0dev",
+    "tensorflow >=2.4.0, <3.0.0dev",
+]
+tensorboard_extra_require = [
+    "tensorflow >=2.3.0, <3.0.0dev; python_version<='3.11'"
+] + profiler_extra_require
+
+metadata_extra_require = ["pandas >= 1.0.0", "numpy>=1.15.0"]
+xai_extra_require = ["tensorflow >=2.3.0, <3.0.0dev"]
+lit_extra_require = [
+    "tensorflow >= 2.3.0, <3.0.0dev",
+    "pandas >= 1.0.0",
+    "lit-nlp == 0.4.0",
+    "explainable-ai-sdk >= 1.0.0",
+]
+featurestore_extra_require = [
+    "google-cloud-bigquery-storage",
+    "pandas >= 1.0.0",
+    "pyarrow >= 6.0.1",
+]
+pipelines_extra_require = [
+    "pyyaml>=5.3.1,<7",
+]
+datasets_extra_require = [
+    "pyarrow >= 3.0.0, < 8.0dev; python_version<'3.11'",
+    "pyarrow >= 10.0.1; python_version=='3.11'",
+    "pyarrow >= 14.0.0; python_version>='3.12'",
+]
+
+vizier_extra_require = [
+    "google-vizier>=0.1.6",
+]
+
+prediction_extra_require = [
+    "docker >= 5.0.3",
+    "fastapi >= 0.71.0, <=0.114.0",
+    "httpx >=0.23.0, <0.25.0",  # Optional dependency of fastapi
+    "starlette >= 0.17.1",
+    "uvicorn[standard] >= 0.16.0",
+]
+
+endpoint_extra_require = ["requests >= 2.28.1"]
+
+private_endpoints_extra_require = [
+    "urllib3 >=1.21.1, <1.27",
+    "requests >= 2.28.1",
+]
+
+autologging_extra_require = ["mlflow>=1.27.0,<=2.16.0"]
+
+preview_extra_require = []
+
+ray_extra_require = [
+    # Cluster only supports 2.9.3 and 2.33.0. Keep 2.4.0 for our testing environment.
+    # Note that testing is submiting a job in a cluster with Ray 2.9.3 remotely.
+    (
+        "ray[default] >= 2.4, <= 2.33.0,!= 2.5.*,!= 2.6.*,!= 2.7.*,!="
+        " 2.8.*,!=2.9.0,!=2.9.1,!=2.9.2, !=2.10.*, !=2.11.*, !=2.12.*, !=2.13.*, !="
+        " 2.14.*, !=2.15.*, !=2.16.*, !=2.17.*, !=2.18.*, !=2.19.*, !=2.20.*, !="
+        " 2.21.*, !=2.22.*, !=2.23.*, !=2.24.*, !=2.25.*, !=2.26.*, !=2.27.*, !="
+        " 2.28.*, !=2.29.*, !=2.30.*, !=2.31.*, !=2.32.*; python_version<'3.11'"
+    ),
+    # To avoid  ImportError: cannot import name 'packaging' from 'pkg_resources'
+    "setuptools < 70.0.0",
+    # Ray Data v2.4 in Python 3.11 is broken, but got fixed in Ray v2.5.
+    "ray[default] >= 2.5, <= 2.33.0; python_version=='3.11'",
+    "google-cloud-bigquery-storage",
+    "google-cloud-bigquery",
+    "pandas >= 1.0.0",
+    "pyarrow >= 6.0.1",
+    "immutabledict",
+]
+
+genai_requires = (
+    "pydantic < 3",
+    "typing_extensions",
+    "docstring_parser < 1",
+)
+
+ray_testing_extra_require = ray_extra_require + [
+    "pytest-xdist",
+    # ray train extras required for prediction tests
+    "ray[train]",
+    # Framework version constraints copied from testing_extra_require
+    "scikit-learn<1.6.0",
+    "tensorflow",
+    "torch >= 2.0.0, < 2.1.0",
+    "xgboost",
+    "xgboost_ray",
+]
+
+reasoning_engine_extra_require = [
+    "cloudpickle >= 3.0, < 4.0",
+    "google-cloud-trace < 2",
+    "opentelemetry-sdk < 2",
+    "opentelemetry-exporter-gcp-trace < 2",
+    "pydantic >= 2.6.3, < 3",
+    "typing_extensions",
+]
+
+evaluation_extra_require = [
+    "pandas >= 1.0.0",
+    "tqdm>=4.23.0",
+]
+
+langchain_extra_require = [
+    "langchain >= 0.1.16, < 0.4",
+    "langchain-core < 0.4",
+    "langchain-google-vertexai < 3",
+    "openinference-instrumentation-langchain >= 0.1.19, < 0.2",
+]
+
+langchain_testing_extra_require = list(
+    set(
+        langchain_extra_require
+        + reasoning_engine_extra_require
+        + ["absl-py", "pytest-xdist"]
+    )
+)
+
+tokenization_extra_require = ["sentencepiece >= 0.2.0"]
+tokenization_testing_extra_require = tokenization_extra_require + ["nltk"]
+
+full_extra_require = list(
+    set(
+        tensorboard_extra_require
+        + metadata_extra_require
+        + xai_extra_require
+        + lit_extra_require
+        + featurestore_extra_require
+        + pipelines_extra_require
+        + datasets_extra_require
+        + endpoint_extra_require
+        + vizier_extra_require
+        + prediction_extra_require
+        + private_endpoints_extra_require
+        + autologging_extra_require
+        + preview_extra_require
+        + ray_extra_require
+        + evaluation_extra_require
+    )
+)
+testing_extra_require = (
+    full_extra_require
+    + profiler_extra_require
+    + tokenization_testing_extra_require
+    + [
+        # aiohttp is required for async rest tests (need google-auth[aiohttp],
+        # but can't specify extras in constraints files)
+        "aiohttp",
+        "bigframes; python_version>='3.10'",
+        # google-api-core 2.x is required since kfp requires protobuf > 4
+        "google-api-core >= 2.11, < 3.0.0",
+        "grpcio-testing",
+        "ipython",
+        "kfp >= 2.6.0, < 3.0.0",
+        "pytest-asyncio",
+        "pytest-xdist",
+        "scikit-learn<1.6.0; python_version<='3.10'",
+        "scikit-learn; python_version>'3.10'",
+        # Lazy import requires > 2.12.0
+        "tensorflow == 2.13.0; python_version<='3.11'",
+        "tensorflow == 2.16.1; python_version>'3.11'",
+        # TODO(jayceeli) torch 2.1.0 has conflict with pyfakefs, will check if
+        # future versions fix this issue
+        "torch >= 2.0.0, < 2.1.0; python_version<='3.11'",
+        "torch >= 2.2.0; python_version>'3.11'",
+        "requests-toolbelt < 1.0.0",
+        "immutabledict",
+        "xgboost",
+    ]
+)
+
+
+setuptools.setup(
+    name=name,
+    version=version,
+    description=description,
+    long_description=readme,
+    packages=packages,
+    package_dir={"vertex_ray": "google/cloud/aiplatform/vertex_ray"},
+    package_data={"": ["*.html.j2"]},
+    entry_points={
+        "console_scripts": [
+            "tb-gcp-uploader=google.cloud.aiplatform.tensorboard.uploader_main:run_main"
+        ],
+    },
+    namespace_packages=("google", "google.cloud"),
+    author="Google LLC",
+    author_email="googleapis-packages@google.com",
+    license="Apache 2.0",
+    url="https://github.com/googleapis/python-aiplatform",
+    platforms="Posix; MacOS X; Windows",
+    include_package_data=True,
+    install_requires=(
+        (
+            "google-api-core[grpc] >= 1.34.1,"
+            " <3.0.0dev,!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.*,!=2.4.*,!=2.5.*,!=2.6.*,!=2.7.*"
+        ),
+        "google-auth >= 2.14.1, <3.0.0dev",
+        "proto-plus >= 1.22.3, <2.0.0dev",
+        "protobuf>=3.20.2,<6.0.0dev,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5",
+        "packaging >= 14.3",
+        "google-cloud-storage >= 1.32.0, < 3.0.0dev",
+        "google-cloud-bigquery >= 1.15.0, < 4.0.0dev, !=3.20.0",
+        "google-cloud-resource-manager >= 1.3.3, < 3.0.0dev",
+        "shapely < 3.0.0dev",
+    )
+    + genai_requires,
+    extras_require={
+        "endpoint": endpoint_extra_require,
+        "full": full_extra_require,
+        "metadata": metadata_extra_require,
+        "tensorboard": tensorboard_extra_require,
+        "testing": testing_extra_require,
+        "xai": xai_extra_require,
+        "lit": lit_extra_require,
+        "cloud_profiler": profiler_extra_require,
+        "pipelines": pipelines_extra_require,
+        "vizier": vizier_extra_require,
+        "prediction": prediction_extra_require,
+        "datasets": datasets_extra_require,
+        "private_endpoints": private_endpoints_extra_require,
+        "autologging": autologging_extra_require,
+        "preview": preview_extra_require,
+        "ray": ray_extra_require,
+        "ray_testing": ray_testing_extra_require,
+        "reasoningengine": reasoning_engine_extra_require,
+        "evaluation": evaluation_extra_require,
+        "langchain": langchain_extra_require,
+        "langchain_testing": langchain_testing_extra_require,
+        "tokenization": tokenization_extra_require,
+    },
+    python_requires=">=3.8",
+    classifiers=[
+        "Development Status :: 5 - Production/Stable",
+        "Intended Audience :: Developers",
+        "Operating System :: OS Independent",
+        "Programming Language :: Python",
+        "Programming Language :: Python :: 3",
+        "Programming Language :: Python :: 3.8",
+        "Programming Language :: Python :: 3.9",
+        "Programming Language :: Python :: 3.10",
+        "Programming Language :: Python :: 3.11",
+        "Programming Language :: Python :: 3.12",
+        "Topic :: Internet",
+        "Topic :: Software Development :: Libraries :: Python Modules",
+    ],
+    zip_safe=False,
+)

testbed/googleapis__python-aiplatform/testing/constraints-langchain.txt

@@ -0,0 +1,3 @@
+langchain
+langchain-core
+langchain-google-vertexai

testbed/googleapis__python-aiplatform/testing/constraints-ray-2.33.0.txt

@@ -0,0 +1,13 @@
+ray==2.33.0
+# Below constraints are inherited from constraints-3.10.txt
+google-api-core
+proto-plus==1.22.3
+protobuf
+mock==4.0.2
+google-cloud-storage==2.2.1 # Increased for kfp 2.0 compatibility
+packaging==24.1 # Increased to unbreak canonicalize_version error (b/377774673)
+grpcio-testing==1.34.0
+mlflow==1.30.1 # Pinned to speed up installation
+pytest-xdist==3.3.1 # Pinned to unbreak unit tests
+IPython # Added to test supernova rich html buttons
+

testbed/googleapis__python-aiplatform/testing/constraints-ray-2.4.0.txt

@@ -0,0 +1,13 @@
+ray==2.4.0
+# Below constraints are inherited from constraints-3.10.txt
+google-api-core
+proto-plus==1.22.3
+protobuf
+mock==4.0.2
+google-cloud-storage==2.2.1 # Increased for kfp 2.0 compatibility
+packaging==20.0 # Increased for compatibility with MLFlow
+grpcio-testing==1.34.0
+mlflow==1.30.1 # Pinned to speed up installation
+pytest-xdist==3.3.1 # Pinned to unbreak unit tests
+IPython # Added to test supernova rich html buttons
+

testbed/googleapis__python-aiplatform/tests/system/__init__.py

@@ -0,0 +1,15 @@
+# -*- coding: utf-8 -*-
+# Copyright 2022 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#

testbed/googleapis__python-aiplatform/tests/system/aiplatform/e2e_base.py

@@ -0,0 +1,216 @@
+# -*- coding: utf-8 -*-
+
+# Copyright 2021 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import abc
+import asyncio
+import importlib
+import logging
+import os
+import pytest
+import uuid
+
+from typing import Any, Dict, Generator
+
+from google.api_core import exceptions
+from google.cloud import aiplatform
+import vertexai
+from google.cloud import bigquery
+from google.cloud import resourcemanager
+from google.cloud import storage
+from google.cloud.aiplatform import initializer
+
+_PROJECT = os.getenv("BUILD_SPECIFIC_GCLOUD_PROJECT")
+_VPC_NETWORK_URI = os.getenv("_VPC_NETWORK_URI")
+_LOCATION = "us-central1"
+
+
+class TestEndToEnd(metaclass=abc.ABCMeta):
+    @property
+    @classmethod
+    @abc.abstractmethod
+    def _temp_prefix(cls) -> str:
+        """Prefix to staging bucket and display names created by this end-to-end test.
+        Keep the string as short as possible and use kebab case, starting with a lowercase letter.
+
+        Example: `"temp-vertex-hpt-test"`
+        """
+        pass
+
+    @classmethod
+    def _make_display_name(cls, key: str) -> str:
+        """Helper method to make unique display_names.
+
+        Args:
+            key (str): Required. Identifier for the display name.
+        Returns:
+            Unique display name.
+        """
+        return f"{cls._temp_prefix}-{key}-{uuid.uuid4()}"
+
+    def setup_method(self):
+        importlib.reload(initializer)
+        importlib.reload(aiplatform)
+        importlib.reload(vertexai)
+
+    @pytest.fixture(scope="class")
+    def shared_state(self) -> Generator[Dict[str, Any], None, None]:
+        shared_state = {}
+        yield shared_state
+
+    @pytest.fixture(scope="class")
+    def prepare_staging_bucket(
+        self, shared_state: Dict[str, Any]
+    ) -> Generator[storage.bucket.Bucket, None, None]:
+        """Create a staging bucket and store bucket resource object in shared state."""
+
+        staging_bucket_name = f"{self._temp_prefix.lower()}-{uuid.uuid4()}"[:63]
+        shared_state["staging_bucket_name"] = staging_bucket_name
+
+        storage_client = storage.Client(project=_PROJECT)
+        shared_state["storage_client"] = storage_client
+
+        bucket = storage_client.create_bucket(
+            staging_bucket_name, project=_PROJECT, location=_LOCATION
+        )
+
+        # TODO(#1415) Once PR Is merged, use the added utilities to
+        # provide create/view access to Pipeline's default service account (compute)
+        project_number = (
+            resourcemanager.ProjectsClient()
+            .get_project(name=f"projects/{_PROJECT}")
+            .name.split("/", 1)[1]
+        )
+
+        service_account = f"{project_number}-compute@developer.gserviceaccount.com"
+        bucket_iam_policy = bucket.get_iam_policy()
+        bucket_iam_policy.setdefault("roles/storage.objectCreator", set()).add(
+            f"serviceAccount:{service_account}"
+        )
+        bucket_iam_policy.setdefault("roles/storage.objectViewer", set()).add(
+            f"serviceAccount:{service_account}"
+        )
+        bucket.set_iam_policy(bucket_iam_policy)
+
+        shared_state["bucket"] = bucket
+        yield
+
+    @pytest.fixture(scope="class")
+    def delete_staging_bucket(self, shared_state: Dict[str, Any]):
+        """Delete the staging bucket and all it's contents"""
+
+        yield
+
+        # Get the staging bucket used for testing and wipe it
+        bucket = shared_state["bucket"]
+        bucket.delete(force=True)
+
+    @pytest.fixture(scope="class")
+    def prepare_bigquery_dataset(
+        self, shared_state: Dict[str, Any]
+    ) -> Generator[bigquery.dataset.Dataset, None, None]:
+        """Create a bigquery dataset and store bigquery resource object in shared state."""
+
+        bigquery_client = bigquery.Client(project=_PROJECT)
+        shared_state["bigquery_client"] = bigquery_client
+
+        dataset_name = f"{self._temp_prefix.lower()}_{uuid.uuid4()}".replace("-", "_")
+        dataset_id = f"{_PROJECT}.{dataset_name}"
+        shared_state["bigquery_dataset_id"] = dataset_id
+
+        dataset = bigquery.Dataset(dataset_id)
+        dataset.location = _LOCATION
+        shared_state["bigquery_dataset"] = bigquery_client.create_dataset(dataset)
+
+        yield
+
+    @pytest.fixture(scope="class")
+    def delete_bigquery_dataset(self, shared_state: Dict[str, Any]):
+        """Delete the bigquery dataset"""
+
+        yield
+
+        # Get the bigquery dataset id used for testing and wipe it
+        bigquery_dataset = shared_state["bigquery_dataset"]
+        bigquery_client = shared_state["bigquery_client"]
+        bigquery_client.delete_dataset(
+            bigquery_dataset.dataset_id, delete_contents=True, not_found_ok=True
+        )  # Make an API request.
+
+    @pytest.fixture(scope="class")
+    def bigquery_dataset(self) -> Generator[bigquery.dataset.Dataset, None, None]:
+        """Create a bigquery dataset and store bigquery resource object in shared state."""
+
+        bigquery_client = bigquery.Client(project=_PROJECT)
+
+        dataset_name = f"{self._temp_prefix.lower()}_{uuid.uuid4()}".replace("-", "_")
+        dataset_id = f"{_PROJECT}.{dataset_name}"
+
+        dataset = bigquery.Dataset(dataset_id)
+        dataset.location = _LOCATION
+        dataset = bigquery_client.create_dataset(dataset)
+
+        yield dataset
+
+        bigquery_client.delete_dataset(
+            dataset.dataset_id, delete_contents=True, not_found_ok=True
+        )  # Make an API request.
+
+    @pytest.fixture(scope="class")
+    def tear_down_resources(self, shared_state: Dict[str, Any]):
+        """Delete every Vertex AI resource created during test"""
+
+        yield
+
+        if "resources" not in shared_state:
+            return
+
+        # TODO(b/218310362): Add resource deletion system tests
+        # Bring all Endpoints to the front of the list
+        # Ensures Models are undeployed first before we attempt deletion
+        shared_state["resources"].sort(
+            key=lambda r: 1
+            if isinstance(r, aiplatform.Endpoint)
+            or isinstance(r, aiplatform.MatchingEngineIndexEndpoint)
+            or isinstance(r, aiplatform.Experiment)
+            else 2
+        )
+
+        for resource in shared_state["resources"]:
+            try:
+                if isinstance(
+                    resource,
+                    (
+                        aiplatform.Endpoint,
+                        aiplatform.Featurestore,
+                        aiplatform.MatchingEngineIndexEndpoint,
+                    ),
+                ):
+                    # For endpoint, undeploy model then delete endpoint
+                    # For featurestore, force delete its entity_types and features with the featurestore
+                    resource.delete(force=True)
+                elif isinstance(resource, aiplatform.Experiment):
+                    resource.delete(delete_backing_tensorboard_runs=True)
+                else:
+                    resource.delete()
+            except (exceptions.GoogleAPIError, RuntimeError) as e:
+                logging.exception(f"Could not delete resource: {resource} due to: {e}")
+
+    @pytest.fixture(scope="session")
+    def event_loop(event_loop):
+        loop = asyncio.get_event_loop()
+        yield loop
+        loop.close()

testbed/googleapis__python-aiplatform/tests/system/aiplatform/test_custom_job.py

@@ -0,0 +1,185 @@
+# -*- coding: utf-8 -*-
+
+# Copyright 2023 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import os
+
+import pytest
+from unittest import mock
+
+from google.cloud import aiplatform
+from google.cloud.aiplatform.constants import base as constants
+from google.cloud.aiplatform.utils import resource_manager_utils
+from google.cloud.aiplatform.compat.types import job_state as gca_job_state
+from tests.system.aiplatform import e2e_base
+
+_PREBUILT_CONTAINER_IMAGE = (
+    "us-docker.pkg.dev/vertex-ai/training/sklearn-cpu.1-0:latest"
+)
+_CUSTOM_CONTAINER_IMAGE = "python:3.8"
+
+_DIR_NAME = os.path.dirname(os.path.abspath(__file__))
+_LOCAL_TRAINING_SCRIPT_PATH = os.path.join(
+    _DIR_NAME, "test_resources/custom_job_script.py"
+)
+
+
+@mock.patch.object(
+    constants,
+    "AIPLATFORM_DEPENDENCY_PATH",
+    "google-cloud-aiplatform @ git+https://github.com/googleapis/"
+    f"python-aiplatform.git@{os.environ['KOKORO_GIT_COMMIT']}#egg=google-cloud-aiplatform"
+    if os.environ.get("KOKORO_GIT_COMMIT")
+    else constants.AIPLATFORM_DEPENDENCY_PATH,
+)
+@mock.patch.object(
+    constants,
+    "AIPLATFORM_AUTOLOG_DEPENDENCY_PATH",
+    "google-cloud-aiplatform[autologging] @ git+https://github.com/googleapis/"
+    f"python-aiplatform.git@{os.environ['KOKORO_GIT_COMMIT']}#egg=google-cloud-aiplatform"
+    if os.environ.get("KOKORO_GIT_COMMIT")
+    else constants.AIPLATFORM_AUTOLOG_DEPENDENCY_PATH,
+)
+@pytest.mark.usefixtures(
+    "prepare_staging_bucket", "delete_staging_bucket", "tear_down_resources"
+)
+class TestCustomJob(e2e_base.TestEndToEnd):
+
+    _temp_prefix = "temp-vertex-sdk-custom-job"
+
+    def setup_class(cls):
+        cls._experiment_name = cls._make_display_name("experiment")[:60]
+        cls._experiment_run_name = cls._make_display_name("experiment-run")[:60]
+
+        project_number = resource_manager_utils.get_project_number(e2e_base._PROJECT)
+        cls._service_account = f"{project_number}-compute@developer.gserviceaccount.com"
+
+    def test_from_local_script_prebuilt_container(self, shared_state):
+        shared_state["resources"] = []
+
+        aiplatform.init(
+            project=e2e_base._PROJECT,
+            location=e2e_base._LOCATION,
+            staging_bucket=shared_state["staging_bucket_name"],
+        )
+
+        display_name = self._make_display_name("custom-job")
+
+        custom_job = aiplatform.CustomJob.from_local_script(
+            display_name=display_name,
+            script_path=_LOCAL_TRAINING_SCRIPT_PATH,
+            container_uri=_PREBUILT_CONTAINER_IMAGE,
+            requirements=["scikit-learn", "pandas"],
+        )
+        try:
+            custom_job.run()
+        finally:
+            shared_state["resources"].append(custom_job)
+
+        assert custom_job.state == gca_job_state.JobState.JOB_STATE_SUCCEEDED
+
+    def test_from_local_script_custom_container(self, shared_state):
+
+        aiplatform.init(
+            project=e2e_base._PROJECT,
+            location=e2e_base._LOCATION,
+            staging_bucket=shared_state["staging_bucket_name"],
+        )
+
+        display_name = self._make_display_name("custom-job")
+
+        custom_job = aiplatform.CustomJob.from_local_script(
+            display_name=display_name,
+            script_path=_LOCAL_TRAINING_SCRIPT_PATH,
+            container_uri=_CUSTOM_CONTAINER_IMAGE,
+            requirements=["scikit-learn", "pandas"],
+        )
+        try:
+            custom_job.run()
+        finally:
+            shared_state["resources"].append(custom_job)
+
+        assert custom_job.state == gca_job_state.JobState.JOB_STATE_SUCCEEDED
+
+    def test_from_local_script_enable_autolog_prebuilt_container(self, shared_state):
+
+        aiplatform.init(
+            project=e2e_base._PROJECT,
+            location=e2e_base._LOCATION,
+            staging_bucket=shared_state["staging_bucket_name"],
+            experiment=self._experiment_name,
+        )
+
+        shared_state["resources"].append(
+            aiplatform.metadata.metadata._experiment_tracker.experiment
+        )
+
+        display_name = self._make_display_name("custom-job")
+
+        custom_job = aiplatform.CustomJob.from_local_script(
+            display_name=display_name,
+            script_path=_LOCAL_TRAINING_SCRIPT_PATH,
+            container_uri=_PREBUILT_CONTAINER_IMAGE,
+            requirements=["scikit-learn", "pandas"],
+            enable_autolog=True,
+        )
+
+        try:
+            with aiplatform.start_run(self._experiment_run_name) as run:
+                shared_state["resources"].append(run)
+                custom_job.run(
+                    experiment=self._experiment_name,
+                    experiment_run=run,
+                    service_account=self._service_account,
+                )
+        finally:
+            shared_state["resources"].append(custom_job)
+
+        assert custom_job.state == gca_job_state.JobState.JOB_STATE_SUCCEEDED
+
+    def test_from_local_script_enable_autolog_custom_container(self, shared_state):
+
+        aiplatform.init(
+            project=e2e_base._PROJECT,
+            location=e2e_base._LOCATION,
+            staging_bucket=shared_state["staging_bucket_name"],
+        )
+
+        display_name = self._make_display_name("custom-job")
+
+        custom_job = aiplatform.CustomJob.from_local_script(
+            display_name=display_name,
+            script_path=_LOCAL_TRAINING_SCRIPT_PATH,
+            container_uri=_CUSTOM_CONTAINER_IMAGE,
+            requirements=["scikit-learn", "pandas"],
+            enable_autolog=True,
+        )
+
+        # Let the job auto-create the experiment run.
+        try:
+            custom_job.run(
+                experiment=self._experiment_name,
+                service_account=self._service_account,
+            )
+        finally:
+            shared_state["resources"].append(custom_job)
+            experiment_run_resource = aiplatform.Context.get(
+                custom_job.job_spec.experiment_run
+            )
+            if experiment_run_resource:
+                shared_state["resources"].append(experiment_run_resource)
+
+        assert custom_job.state == gca_job_state.JobState.JOB_STATE_SUCCEEDED

testbed/googleapis__python-aiplatform/tests/system/aiplatform/test_dataset.py

@@ -0,0 +1,436 @@
+# -*- coding: utf-8 -*-
+
+# Copyright 2022 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import os
+import uuid
+import pytest
+import importlib
+
+import pandas as pd
+import re
+
+from datetime import datetime
+
+from google.api_core import exceptions
+from google.api_core import client_options
+
+from google.cloud import aiplatform
+from google.cloud import bigquery
+from google.cloud import storage
+from google.cloud.aiplatform import utils
+from google.cloud.aiplatform import initializer
+from google.cloud.aiplatform.compat.services import (
+    dataset_service_client_v1 as dataset_service,
+)
+
+from test_utils.vpcsc_config import vpcsc_config
+
+from tests.system.aiplatform import e2e_base
+
+_TEST_PROJECT = e2e_base._PROJECT
+_TEST_LOCATION = e2e_base._LOCATION
+TEST_BUCKET = os.environ.get(
+    "GCLOUD_TEST_SAMPLES_BUCKET", "cloud-samples-data-us-central1"
+)
+
+_TEST_PARENT = f"projects/{_TEST_PROJECT}/locations/{_TEST_LOCATION}"
+_TEST_API_ENDPOINT = f"{_TEST_LOCATION}-aiplatform.googleapis.com"
+_TEST_IMAGE_DATASET_ID = "1997950066622464000"  # permanent_50_flowers_dataset
+_TEST_TEXT_DATASET_ID = (
+    "6203215905493614592"  # permanent_text_entity_extraction_dataset
+)
+_TEST_DATASET_DISPLAY_NAME = "permanent_50_flowers_dataset"
+_TEST_DATASET_LABELS = {"test": "labels"}
+_TEST_DATASET_DESCRIPTION = "test description"
+_TEST_TABULAR_CLASSIFICATION_GCS_SOURCE = "gs://ucaip-sample-resources/iris_1000.csv"
+_TEST_FORECASTING_BQ_SOURCE = (
+    "bq://ucaip-sample-tests:ucaip_test_us_central1.2020_sales_train"
+)
+_TEST_TEXT_ENTITY_EXTRACTION_GCS_SOURCE = "gs://ucaip-samples-us-central1/sdk_system_test_resources/text_entity_extraction_dataset_small.jsonl"
+_TEST_IMAGE_OBJECT_DETECTION_GCS_SOURCE = "gs://cloud-samples-data-us-central1/ai-platform-unified/datasets/images/isg_data.jsonl"
+_TEST_TEXT_ENTITY_IMPORT_SCHEMA = "gs://google-cloud-aiplatform/schema/dataset/ioformat/text_extraction_io_format_1.0.0.yaml"
+_TEST_IMAGE_OBJ_DET_SEGMENTATION_IMPORT_SCHEMA = "gs://google-cloud-aiplatform/schema/dataset/ioformat/image_segmentation_io_format_1.0.0.yaml"
+
+# create_from_dataframe
+_TEST_BOOL_COL = "bool_col"
+_TEST_BOOL_ARR_COL = "bool_array_col"
+_TEST_DOUBLE_COL = "double_col"
+_TEST_DOUBLE_ARR_COL = "double_array_col"
+_TEST_INT_COL = "int64_col"
+_TEST_INT_ARR_COL = "int64_array_col"
+_TEST_STR_COL = "string_col"
+_TEST_STR_ARR_COL = "string_array_col"
+_TEST_BYTES_COL = "bytes_col"
+_TEST_TIMESTAMP_COL = "timestamp_col"
+_TEST_DATETIME_COL = "datetime_col"
+_TEST_DF_COLUMN_NAMES = [
+    _TEST_BOOL_COL,
+    _TEST_BOOL_ARR_COL,
+    _TEST_DOUBLE_COL,
+    _TEST_DOUBLE_ARR_COL,
+    _TEST_INT_COL,
+    _TEST_INT_ARR_COL,
+    _TEST_STR_COL,
+    _TEST_STR_ARR_COL,
+    _TEST_BYTES_COL,
+    _TEST_TIMESTAMP_COL,
+    _TEST_DATETIME_COL,
+]
+
+_TEST_TIME_NOW = datetime.now()
+_TEST_TIMESTAMP_WITH_TIMEZONE = pd.Timestamp(_TEST_TIME_NOW, tz="US/Pacific")
+_TEST_TIMESTAMP_WITHOUT_TIMEZONE = pd.Timestamp(_TEST_TIME_NOW)
+
+_TEST_DATAFRAME = pd.DataFrame(
+    data=[
+        [
+            False,
+            [True, False],
+            1.2,
+            [1.2, 3.4],
+            1,
+            [1, 2],
+            "test",
+            ["test1", "test2"],
+            b"1",
+            _TEST_TIMESTAMP_WITH_TIMEZONE,
+            _TEST_TIMESTAMP_WITHOUT_TIMEZONE,
+        ],
+        [
+            True,
+            [True, True],
+            2.2,
+            [2.2, 4.4],
+            2,
+            [2, 3],
+            "test1",
+            ["test2", "test3"],
+            b"0",
+            _TEST_TIMESTAMP_WITH_TIMEZONE,
+            _TEST_TIMESTAMP_WITHOUT_TIMEZONE,
+        ],
+    ],
+    columns=_TEST_DF_COLUMN_NAMES,
+)
+_TEST_DATAFRAME_BQ_SCHEMA = [
+    bigquery.SchemaField(name="bool_col", field_type="BOOL"),
+    bigquery.SchemaField(name="bool_array_col", field_type="BOOL", mode="REPEATED"),
+    bigquery.SchemaField(name="double_col", field_type="FLOAT"),
+    bigquery.SchemaField(name="double_array_col", field_type="FLOAT", mode="REPEATED"),
+    bigquery.SchemaField(name="int64_col", field_type="INTEGER"),
+    bigquery.SchemaField(name="int64_array_col", field_type="INTEGER", mode="REPEATED"),
+    bigquery.SchemaField(name="string_col", field_type="STRING"),
+    bigquery.SchemaField(name="string_array_col", field_type="STRING", mode="REPEATED"),
+    bigquery.SchemaField(name="bytes_col", field_type="STRING"),
+    bigquery.SchemaField(name="timestamp_col", field_type="TIMESTAMP"),
+    bigquery.SchemaField(name="datetime_col", field_type="DATETIME"),
+]
+
+
+class TestDataset(e2e_base.TestEndToEnd):
+
+    _temp_prefix = "temp-vertex-sdk-dataset-test"
+
+    def setup_method(self):
+        importlib.reload(initializer)
+        importlib.reload(aiplatform)
+
+        aiplatform.init(project=_TEST_PROJECT, location=_TEST_LOCATION)
+
+    @pytest.fixture()
+    def storage_client(self):
+        yield storage.Client(project=_TEST_PROJECT)
+
+    @pytest.fixture()
+    def staging_bucket(self, storage_client):
+        new_staging_bucket = f"temp-sdk-integration-{uuid.uuid4()}"
+        bucket = storage_client.create_bucket(new_staging_bucket)
+
+        yield bucket
+
+        bucket.delete(force=True)
+
+    @pytest.fixture()
+    def dataset_gapic_client(self):
+        gapic_client = dataset_service.DatasetServiceClient(
+            client_options=client_options.ClientOptions(api_endpoint=_TEST_API_ENDPOINT)
+        )
+
+        yield gapic_client
+
+    # TODO(vinnys): Remove pytest skip once persistent resources are accessible
+    @pytest.mark.skip(reason="System tests cannot access persistent test resources")
+    def test_get_existing_dataset(self):
+        """Retrieve a known existing dataset, ensure SDK successfully gets the
+        dataset resource."""
+
+        flowers_dataset = aiplatform.ImageDataset(dataset_name=_TEST_IMAGE_DATASET_ID)
+        assert flowers_dataset.name == _TEST_IMAGE_DATASET_ID
+        assert flowers_dataset.display_name == _TEST_DATASET_DISPLAY_NAME
+
+    def test_get_nonexistent_dataset(self):
+        """Ensure attempting to retrieve a dataset that doesn't exist raises
+        a Google API core 404 exception."""
+
+        # AI Platform service returns 404
+        with pytest.raises(exceptions.NotFound):
+            aiplatform.ImageDataset(dataset_name="0")
+
+    def test_get_new_dataset_and_import(self, dataset_gapic_client):
+        """Retrieve new, empty dataset and import a text dataset using import().
+        Then verify data items were successfully imported."""
+
+        try:
+            text_dataset = aiplatform.TextDataset.create(
+                display_name=self._make_display_name(key="get_new_dataset_and_import"),
+            )
+
+            my_dataset = aiplatform.TextDataset(dataset_name=text_dataset.name)
+
+            data_items_pre_import = dataset_gapic_client.list_data_items(
+                parent=my_dataset.resource_name
+            )
+
+            assert len(list(data_items_pre_import)) == 0
+
+            # Blocking call to import
+            my_dataset.import_data(
+                gcs_source=_TEST_TEXT_ENTITY_EXTRACTION_GCS_SOURCE,
+                import_schema_uri=_TEST_TEXT_ENTITY_IMPORT_SCHEMA,
+                import_request_timeout=500,
+            )
+
+            data_items_post_import = dataset_gapic_client.list_data_items(
+                parent=my_dataset.resource_name
+            )
+
+            assert len(list(data_items_post_import)) == 51
+        finally:
+            text_dataset.delete()
+
+    @vpcsc_config.skip_if_inside_vpcsc
+    def test_create_and_import_image_dataset(self, dataset_gapic_client):
+        """Use the Dataset.create() method to create a new image obj detection
+        dataset and import images. Then confirm images were successfully imported."""
+
+        try:
+            img_dataset = aiplatform.ImageDataset.create(
+                display_name=self._make_display_name(key="create_image_dataset"),
+                gcs_source=_TEST_IMAGE_OBJECT_DETECTION_GCS_SOURCE,
+                import_schema_uri=_TEST_IMAGE_OBJ_DET_SEGMENTATION_IMPORT_SCHEMA,
+                create_request_timeout=None,
+            )
+
+        finally:
+            if img_dataset is not None:
+                img_dataset.delete()
+
+    def test_create_tabular_dataset(self):
+        """Use the Dataset.create() method to create a new tabular dataset.
+        Then confirm the dataset was successfully created and references GCS source."""
+
+        try:
+            tabular_dataset = aiplatform.TabularDataset.create(
+                display_name=self._make_display_name(key="create_tabular_dataset"),
+                gcs_source=[_TEST_TABULAR_CLASSIFICATION_GCS_SOURCE],
+                create_request_timeout=None,
+            )
+
+            gapic_metadata = tabular_dataset.to_dict()["metadata"]
+            gcs_source_uris = gapic_metadata["inputConfig"]["gcsSource"]["uri"]
+
+            assert len(gcs_source_uris) == 1
+            assert _TEST_TABULAR_CLASSIFICATION_GCS_SOURCE == gcs_source_uris[0]
+            assert (
+                tabular_dataset.metadata_schema_uri
+                == aiplatform.schema.dataset.metadata.tabular
+            )
+
+        finally:
+            if tabular_dataset is not None:
+                tabular_dataset.delete()
+
+    def test_create_tabular_dataset_from_dataframe(self, bigquery_dataset):
+        table_id = f"test_table{uuid.uuid4()}"
+        bq_staging_table = (
+            f"bq://{_TEST_PROJECT}.{bigquery_dataset.dataset_id}.{table_id}"
+        )
+        try:
+            tabular_dataset = aiplatform.TabularDataset.create_from_dataframe(
+                df_source=_TEST_DATAFRAME,
+                staging_path=bq_staging_table,
+                display_name=self._make_display_name(
+                    key="create_and_import_dataset_from_dataframe"
+                ),
+            )
+
+            """Use the Dataset.create_from_dataframe() method to create a new tabular dataset.
+            Then confirm the dataset was successfully created and references the BQ source."""
+            gapic_metadata = tabular_dataset.to_dict()["metadata"]
+            bq_source = gapic_metadata["inputConfig"]["bigquerySource"]["uri"]
+
+            assert bq_staging_table == bq_source
+            assert (
+                tabular_dataset.metadata_schema_uri
+                == aiplatform.schema.dataset.metadata.tabular
+            )
+            bigquery_client = bigquery.Client(
+                project=_TEST_PROJECT,
+                credentials=initializer.global_config.credentials,
+            )
+            table = bigquery_client.get_table(
+                f"{_TEST_PROJECT}.{bigquery_dataset.dataset_id}.{table_id}"
+            )
+            assert (
+                table.schema[-1]
+                == bigquery.SchemaField(name="datetime_col", field_type="DATETIME")
+                if re.match(
+                    r"3.*",
+                    bigquery.__version__,
+                )
+                else bigquery.SchemaField(name="datetime_col", field_type="TIMESTAMP")
+            )
+        finally:
+            if tabular_dataset is not None:
+                tabular_dataset.delete()
+
+    def test_create_tabular_dataset_from_dataframe_with_provided_schema(
+        self, bigquery_dataset
+    ):
+        """Use the Dataset.create_from_dataframe() method to create a new tabular dataset,
+        passing in the optional `bq_schema` argument. Then confirm the dataset was successfully
+        created and references the BQ source."""
+
+        try:
+            bq_staging_table = f"bq://{_TEST_PROJECT}.{bigquery_dataset.dataset_id}.test_table{uuid.uuid4()}"
+
+            tabular_dataset = aiplatform.TabularDataset.create_from_dataframe(
+                df_source=_TEST_DATAFRAME,
+                staging_path=bq_staging_table,
+                display_name=self._make_display_name(
+                    key="create_and_import_dataset_from_dataframe"
+                ),
+                bq_schema=_TEST_DATAFRAME_BQ_SCHEMA,
+            )
+
+            gapic_metadata = tabular_dataset.to_dict()["metadata"]
+            bq_source = gapic_metadata["inputConfig"]["bigquerySource"]["uri"]
+
+            assert bq_staging_table == bq_source
+            assert (
+                tabular_dataset.metadata_schema_uri
+                == aiplatform.schema.dataset.metadata.tabular
+            )
+        finally:
+            tabular_dataset.delete()
+
+    def test_create_time_series_dataset(self):
+        """Use the Dataset.create() method to create a new time series dataset.
+        Then confirm the dataset was successfully created and references GCS source."""
+
+        try:
+            time_series_dataset = aiplatform.TimeSeriesDataset.create(
+                display_name=self._make_display_name(key="create_time_series_dataset"),
+                bq_source=[_TEST_FORECASTING_BQ_SOURCE],
+                create_request_timeout=None,
+            )
+
+            gapic_metadata = time_series_dataset.to_dict()["metadata"]
+            bq_source_uri = gapic_metadata["inputConfig"]["bigquerySource"]["uri"]
+
+            assert _TEST_FORECASTING_BQ_SOURCE == bq_source_uri
+            assert (
+                time_series_dataset.metadata_schema_uri
+                == aiplatform.schema.dataset.metadata.time_series
+            )
+
+        finally:
+            if time_series_dataset is not None:
+                time_series_dataset.delete()
+
+    def test_export_data(self, storage_client, staging_bucket):
+        """Get an existing dataset, export data to a newly created folder in
+        Google Cloud Storage, then verify data was successfully exported."""
+
+        dataset = aiplatform.TextDataset(dataset_name=_TEST_TEXT_DATASET_ID)
+
+        exported_files = dataset.export_data(output_dir=f"gs://{staging_bucket.name}")
+
+        assert len(exported_files)  # Ensure at least one GCS path was returned
+
+        exported_file = exported_files[0]
+        bucket, prefix = utils.extract_bucket_and_prefix_from_gcs_path(exported_file)
+
+        bucket = storage_client.get_bucket(bucket)
+        blob = bucket.get_blob(prefix)
+
+        assert blob  # Verify the returned GCS export path exists
+
+    def test_export_data_for_custom_training(self, staging_bucket):
+        """Get an existing dataset, export data to a newly created folder in
+        Google Cloud Storage, then verify data was successfully exported."""
+
+        # pylint: disable=protected-access
+        # Custom training data export should be generic, hence using the base
+        # _Dataset class here in test. In practice, users shuold be able to
+        # use this function in any inhericted classes of _Dataset.
+        dataset = aiplatform.datasets._Dataset(dataset_name=_TEST_IMAGE_DATASET_ID)
+
+        split = {
+            "training_filter": "labels.aiplatform.googleapis.com/ml_use=training",
+            "validation_filter": "labels.aiplatform.googleapis.com/ml_use=validation",
+            "test_filter": "labels.aiplatform.googleapis.com/ml_use=test",
+        }
+
+        export_data_response = dataset.export_data_for_custom_training(
+            output_dir=f"gs://{staging_bucket.name}",
+            annotation_schema_uri="gs://google-cloud-aiplatform/schema/dataset/annotation/image_classification_1.0.0.yaml",
+            split=split,
+        )
+
+        # Ensure three output paths (training, validation and test) are provided
+        assert len(export_data_response["exportedFiles"]) == 3
+        # Ensure data stats are calculated and correct
+        assert int(export_data_response["dataStats"]["trainingDataItemsCount"]) == 40
+        assert int(export_data_response["dataStats"]["validationDataItemsCount"]) == 5
+        assert int(export_data_response["dataStats"]["testDataItemsCount"]) == 5
+        assert int(export_data_response["dataStats"]["trainingAnnotationsCount"]) == 40
+        assert int(export_data_response["dataStats"]["validationAnnotationsCount"]) == 5
+        assert int(export_data_response["dataStats"]["testAnnotationsCount"]) == 5
+
+    def test_update_dataset(self):
+        """Create a new dataset and use update() method to change its display_name, labels, and description.
+        Then confirm these fields of the dataset was successfully modifed."""
+
+        try:
+            dataset = aiplatform.ImageDataset.create()
+            labels = dataset.labels
+
+            dataset = dataset.update(
+                display_name=_TEST_DATASET_DISPLAY_NAME,
+                labels=_TEST_DATASET_LABELS,
+                description=_TEST_DATASET_DESCRIPTION,
+                update_request_timeout=None,
+            )
+            labels.update(_TEST_DATASET_LABELS)
+
+            assert dataset.display_name == _TEST_DATASET_DISPLAY_NAME
+            assert dataset.labels == labels
+            assert dataset.gca_resource.description == _TEST_DATASET_DESCRIPTION
+
+        finally:
+            dataset.delete()

testbed/googleapis__python-aiplatform/tests/system/aiplatform/test_e2e_forecasting.py

@@ -0,0 +1,395 @@
+# -*- coding: utf-8 -*-
+
+# Copyright 2022 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+from google.cloud import aiplatform
+from google.cloud.aiplatform import training_jobs
+
+from google.cloud.aiplatform.compat.types import job_state
+from google.cloud.aiplatform.compat.types import pipeline_state
+import pytest
+from tests.system.aiplatform import e2e_base
+
+_TRAINING_DATASET_BQ_PATH = (
+    "bq://ucaip-sample-tests:ucaip_test_us_central1.2020_sales_train"
+)
+_PREDICTION_DATASET_BQ_PATH = (
+    "bq://ucaip-sample-tests:ucaip_test_us_central1.2021_sales_predict"
+)
+
+
+@pytest.mark.usefixtures("prepare_staging_bucket", "delete_staging_bucket")
+class TestEndToEndForecasting1(e2e_base.TestEndToEnd):
+    """End to end system test of the Vertex SDK with forecasting data."""
+
+    _temp_prefix = "temp-vertex-sdk-e2e-forecasting"
+
+    @pytest.mark.parametrize(
+        "training_job",
+        [
+            training_jobs.AutoMLForecastingTrainingJob,
+        ],
+    )
+    def test_end_to_end_forecasting(self, shared_state, training_job):
+        """Builds a dataset, trains models, and gets batch predictions."""
+        resources = []
+
+        aiplatform.init(
+            project=e2e_base._PROJECT,
+            location=e2e_base._LOCATION,
+            staging_bucket=shared_state["staging_bucket_name"],
+        )
+        try:
+            ds = aiplatform.TimeSeriesDataset.create(
+                display_name=self._make_display_name("dataset"),
+                bq_source=[_TRAINING_DATASET_BQ_PATH],
+                sync=False,
+                create_request_timeout=180.0,
+            )
+            resources.append(ds)
+
+            time_column = "date"
+            time_series_identifier_column = "store_name"
+            target_column = "sale_dollars"
+            column_specs = {
+                time_column: "timestamp",
+                target_column: "numeric",
+                "city": "categorical",
+                "zip_code": "categorical",
+                "county": "categorical",
+            }
+
+            job = training_job(
+                display_name=self._make_display_name("train-housing-forecasting"),
+                optimization_objective="minimize-rmse",
+                column_specs=column_specs,
+            )
+            resources.append(job)
+
+            model = job.run(
+                dataset=ds,
+                target_column=target_column,
+                time_column=time_column,
+                time_series_identifier_column=time_series_identifier_column,
+                available_at_forecast_columns=[time_column],
+                unavailable_at_forecast_columns=[target_column],
+                time_series_attribute_columns=["city", "zip_code", "county"],
+                forecast_horizon=30,
+                context_window=30,
+                data_granularity_unit="day",
+                data_granularity_count=1,
+                budget_milli_node_hours=1000,
+                holiday_regions=["GLOBAL"],
+                hierarchy_group_total_weight=1,
+                window_stride_length=1,
+                model_display_name=self._make_display_name("forecasting-liquor-model"),
+                sync=False,
+            )
+            resources.append(model)
+
+            batch_prediction_job = model.batch_predict(
+                job_display_name=self._make_display_name("forecasting-liquor-model"),
+                instances_format="bigquery",
+                predictions_format="csv",
+                machine_type="n1-standard-4",
+                bigquery_source=_PREDICTION_DATASET_BQ_PATH,
+                gcs_destination_prefix=(
+                    f'gs://{shared_state["staging_bucket_name"]}/bp_results/'
+                ),
+                sync=False,
+            )
+            resources.append(batch_prediction_job)
+
+            batch_prediction_job.wait()
+            model.wait()
+            assert job.state == pipeline_state.PipelineState.PIPELINE_STATE_SUCCEEDED
+            assert batch_prediction_job.state == job_state.JobState.JOB_STATE_SUCCEEDED
+        finally:
+            for resource in resources:
+                resource.delete()
+
+
+@pytest.mark.usefixtures("prepare_staging_bucket", "delete_staging_bucket")
+class TestEndToEndForecasting2(e2e_base.TestEndToEnd):
+    """End to end system test of the Vertex SDK with forecasting data."""
+
+    _temp_prefix = "temp-vertex-sdk-e2e-forecasting"
+
+    @pytest.mark.parametrize(
+        "training_job",
+        [
+            training_jobs.SequenceToSequencePlusForecastingTrainingJob,
+        ],
+    )
+    def test_end_to_end_forecasting(self, shared_state, training_job):
+        """Builds a dataset, trains models, and gets batch predictions."""
+        resources = []
+
+        aiplatform.init(
+            project=e2e_base._PROJECT,
+            location=e2e_base._LOCATION,
+            staging_bucket=shared_state["staging_bucket_name"],
+        )
+        try:
+            ds = aiplatform.TimeSeriesDataset.create(
+                display_name=self._make_display_name("dataset"),
+                bq_source=[_TRAINING_DATASET_BQ_PATH],
+                sync=False,
+                create_request_timeout=180.0,
+            )
+            resources.append(ds)
+
+            time_column = "date"
+            time_series_identifier_column = "store_name"
+            target_column = "sale_dollars"
+            column_specs = {
+                time_column: "timestamp",
+                target_column: "numeric",
+                "city": "categorical",
+                "zip_code": "categorical",
+                "county": "categorical",
+            }
+
+            job = training_job(
+                display_name=self._make_display_name("train-housing-forecasting"),
+                optimization_objective="minimize-rmse",
+                column_specs=column_specs,
+            )
+            resources.append(job)
+
+            model = job.run(
+                dataset=ds,
+                target_column=target_column,
+                time_column=time_column,
+                time_series_identifier_column=time_series_identifier_column,
+                available_at_forecast_columns=[time_column],
+                unavailable_at_forecast_columns=[target_column],
+                time_series_attribute_columns=["city", "zip_code", "county"],
+                forecast_horizon=30,
+                context_window=30,
+                data_granularity_unit="day",
+                data_granularity_count=1,
+                budget_milli_node_hours=1000,
+                holiday_regions=["GLOBAL"],
+                hierarchy_group_total_weight=1,
+                window_stride_length=1,
+                model_display_name=self._make_display_name("forecasting-liquor-model"),
+                sync=False,
+            )
+            resources.append(model)
+
+            batch_prediction_job = model.batch_predict(
+                job_display_name=self._make_display_name("forecasting-liquor-model"),
+                instances_format="bigquery",
+                predictions_format="csv",
+                machine_type="n1-standard-4",
+                bigquery_source=_PREDICTION_DATASET_BQ_PATH,
+                gcs_destination_prefix=(
+                    f'gs://{shared_state["staging_bucket_name"]}/bp_results/'
+                ),
+                sync=False,
+            )
+            resources.append(batch_prediction_job)
+
+            batch_prediction_job.wait()
+            model.wait()
+            assert job.state == pipeline_state.PipelineState.PIPELINE_STATE_SUCCEEDED
+            assert batch_prediction_job.state == job_state.JobState.JOB_STATE_SUCCEEDED
+        finally:
+            for resource in resources:
+                resource.delete()
+
+
+@pytest.mark.usefixtures("prepare_staging_bucket", "delete_staging_bucket")
+class TestEndToEndForecasting3(e2e_base.TestEndToEnd):
+    """End to end system test of the Vertex SDK with forecasting data."""
+
+    _temp_prefix = "temp-vertex-sdk-e2e-forecasting"
+
+    @pytest.mark.parametrize(
+        "training_job",
+        [
+            training_jobs.TemporalFusionTransformerForecastingTrainingJob,
+        ],
+    )
+    def test_end_to_end_forecasting(self, shared_state, training_job):
+        """Builds a dataset, trains models, and gets batch predictions."""
+        resources = []
+
+        aiplatform.init(
+            project=e2e_base._PROJECT,
+            location=e2e_base._LOCATION,
+            staging_bucket=shared_state["staging_bucket_name"],
+        )
+        try:
+            ds = aiplatform.TimeSeriesDataset.create(
+                display_name=self._make_display_name("dataset"),
+                bq_source=[_TRAINING_DATASET_BQ_PATH],
+                sync=False,
+                create_request_timeout=180.0,
+            )
+            resources.append(ds)
+
+            time_column = "date"
+            time_series_identifier_column = "store_name"
+            target_column = "sale_dollars"
+            column_specs = {
+                time_column: "timestamp",
+                target_column: "numeric",
+                "city": "categorical",
+                "zip_code": "categorical",
+                "county": "categorical",
+            }
+
+            job = training_job(
+                display_name=self._make_display_name("train-housing-forecasting"),
+                optimization_objective="minimize-rmse",
+                column_specs=column_specs,
+            )
+            resources.append(job)
+
+            model = job.run(
+                dataset=ds,
+                target_column=target_column,
+                time_column=time_column,
+                time_series_identifier_column=time_series_identifier_column,
+                available_at_forecast_columns=[time_column],
+                unavailable_at_forecast_columns=[target_column],
+                time_series_attribute_columns=["city", "zip_code", "county"],
+                forecast_horizon=30,
+                context_window=30,
+                data_granularity_unit="day",
+                data_granularity_count=1,
+                budget_milli_node_hours=1000,
+                holiday_regions=["GLOBAL"],
+                hierarchy_group_total_weight=1,
+                window_stride_length=1,
+                model_display_name=self._make_display_name("forecasting-liquor-model"),
+                sync=False,
+            )
+            resources.append(model)
+
+            batch_prediction_job = model.batch_predict(
+                job_display_name=self._make_display_name("forecasting-liquor-model"),
+                instances_format="bigquery",
+                predictions_format="csv",
+                machine_type="n1-standard-4",
+                bigquery_source=_PREDICTION_DATASET_BQ_PATH,
+                gcs_destination_prefix=(
+                    f'gs://{shared_state["staging_bucket_name"]}/bp_results/'
+                ),
+                sync=False,
+            )
+            resources.append(batch_prediction_job)
+
+            batch_prediction_job.wait()
+            model.wait()
+            assert job.state == pipeline_state.PipelineState.PIPELINE_STATE_SUCCEEDED
+            assert batch_prediction_job.state == job_state.JobState.JOB_STATE_SUCCEEDED
+        finally:
+            for resource in resources:
+                resource.delete()
+
+
+@pytest.mark.usefixtures("prepare_staging_bucket", "delete_staging_bucket")
+class TestEndToEndForecasting4(e2e_base.TestEndToEnd):
+    """End to end system test of the Vertex SDK with forecasting data."""
+
+    _temp_prefix = "temp-vertex-sdk-e2e-forecasting"
+
+    @pytest.mark.parametrize(
+        "training_job",
+        [
+            training_jobs.TimeSeriesDenseEncoderForecastingTrainingJob,
+        ],
+    )
+    def test_end_to_end_forecasting(self, shared_state, training_job):
+        """Builds a dataset, trains models, and gets batch predictions."""
+        resources = []
+
+        aiplatform.init(
+            project=e2e_base._PROJECT,
+            location=e2e_base._LOCATION,
+            staging_bucket=shared_state["staging_bucket_name"],
+        )
+        try:
+            ds = aiplatform.TimeSeriesDataset.create(
+                display_name=self._make_display_name("dataset"),
+                bq_source=[_TRAINING_DATASET_BQ_PATH],
+                sync=False,
+                create_request_timeout=180.0,
+            )
+            resources.append(ds)
+
+            time_column = "date"
+            time_series_identifier_column = "store_name"
+            target_column = "sale_dollars"
+            column_specs = {
+                time_column: "timestamp",
+                target_column: "numeric",
+                "city": "categorical",
+                "zip_code": "categorical",
+                "county": "categorical",
+            }
+
+            job = training_job(
+                display_name=self._make_display_name("train-housing-forecasting"),
+                optimization_objective="minimize-rmse",
+                column_specs=column_specs,
+            )
+            resources.append(job)
+
+            model = job.run(
+                dataset=ds,
+                target_column=target_column,
+                time_column=time_column,
+                time_series_identifier_column=time_series_identifier_column,
+                available_at_forecast_columns=[time_column],
+                unavailable_at_forecast_columns=[target_column],
+                time_series_attribute_columns=["city", "zip_code", "county"],
+                forecast_horizon=30,
+                context_window=30,
+                data_granularity_unit="day",
+                data_granularity_count=1,
+                budget_milli_node_hours=1000,
+                holiday_regions=["GLOBAL"],
+                hierarchy_group_total_weight=1,
+                window_stride_length=1,
+                model_display_name=self._make_display_name("forecasting-liquor-model"),
+                sync=False,
+            )
+            resources.append(model)
+
+            batch_prediction_job = model.batch_predict(
+                job_display_name=self._make_display_name("forecasting-liquor-model"),
+                instances_format="bigquery",
+                predictions_format="csv",
+                machine_type="n1-standard-4",
+                bigquery_source=_PREDICTION_DATASET_BQ_PATH,
+                gcs_destination_prefix=(
+                    f'gs://{shared_state["staging_bucket_name"]}/bp_results/'
+                ),
+                sync=False,
+            )
+            resources.append(batch_prediction_job)
+
+            batch_prediction_job.wait()
+            model.wait()
+            assert job.state == pipeline_state.PipelineState.PIPELINE_STATE_SUCCEEDED
+            assert batch_prediction_job.state == job_state.JobState.JOB_STATE_SUCCEEDED
+        finally:
+            for resource in resources:
+                resource.delete()

testbed/googleapis__python-aiplatform/tests/system/aiplatform/test_e2e_metadata_schema.py

@@ -0,0 +1,121 @@
+# -*- coding: utf-8 -*-
+
+# Copyright 2022 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+import json
+
+import pytest
+
+from google.cloud import aiplatform
+from google.cloud.aiplatform.metadata.schema.google import (
+    artifact_schema as google_artifact_schema,
+)
+from google.cloud.aiplatform.metadata.schema.system import (
+    artifact_schema as system_artifact_schema,
+)
+from google.cloud.aiplatform.metadata.schema.system import (
+    execution_schema as system_execution_schema,
+)
+from tests.system.aiplatform import e2e_base
+
+
+@pytest.mark.usefixtures("tear_down_resources")
+class TestMetadataSchema(e2e_base.TestEndToEnd):
+
+    _temp_prefix = "tmpvrtxmlmdsdk-e2e"
+
+    def setup_class(cls):
+        # Truncating the name because of resource id constraints from the service
+        cls.artifact_display_name = cls._make_display_name("base-artifact")[:30]
+        cls.artifact_id = cls._make_display_name("base-artifact-id")[:30]
+        cls.artifact_uri = cls._make_display_name("base-uri")
+        cls.artifact_metadata = {"test_property": "test_value"}
+        cls.artifact_description = cls._make_display_name("base-description")
+        cls.execution_display_name = cls._make_display_name("base-execution")[:30]
+        cls.execution_description = cls._make_display_name("base-description")
+
+    def test_system_dataset_artifact_create(self, shared_state):
+
+        aiplatform.init(
+            project=e2e_base._PROJECT,
+            location=e2e_base._LOCATION,
+        )
+
+        artifact = system_artifact_schema.Dataset(
+            display_name=self.artifact_display_name,
+            uri=self.artifact_uri,
+            metadata=self.artifact_metadata,
+            description=self.artifact_description,
+        ).create()
+
+        shared_state["resources"] = [artifact]
+
+        assert artifact.display_name == self.artifact_display_name
+        assert json.dumps(artifact.metadata, sort_keys=True) == json.dumps(
+            self.artifact_metadata, sort_keys=True
+        )
+        assert artifact.schema_title == "system.Dataset"
+        assert artifact.description == self.artifact_description
+        assert "/metadataStores/default/artifacts/" in artifact.resource_name
+
+    def test_google_dataset_artifact_create(self, shared_state):
+
+        aiplatform.init(
+            project=e2e_base._PROJECT,
+            location=e2e_base._LOCATION,
+        )
+        vertex_dataset_name = f"projects/{e2e_base._PROJECT}/locations/{e2e_base._LOCATION}/datasets/dataset"
+        artifact = google_artifact_schema.VertexDataset(
+            vertex_dataset_name=vertex_dataset_name,
+            display_name=self.artifact_display_name,
+            metadata=self.artifact_metadata,
+            description=self.artifact_description,
+        ).create()
+
+        shared_state["resources"].append(artifact)
+
+        expected_metadata = self.artifact_metadata.copy()
+        expected_metadata["resourceName"] = vertex_dataset_name
+
+        assert artifact.display_name == self.artifact_display_name
+        assert json.dumps(artifact.metadata, sort_keys=True) == json.dumps(
+            expected_metadata, sort_keys=True
+        )
+        assert artifact.schema_title == "google.VertexDataset"
+        assert artifact.description == self.artifact_description
+        assert "/metadataStores/default/artifacts/" in artifact.resource_name
+        assert (
+            artifact.uri
+            == f"https://{e2e_base._LOCATION}-aiplatform.googleapis.com/v1/{vertex_dataset_name}"
+        )
+
+    def test_execution_create_using_system_schema_class(self, shared_state):
+
+        aiplatform.init(
+            project=e2e_base._PROJECT,
+            location=e2e_base._LOCATION,
+        )
+
+        execution = system_execution_schema.CustomJobExecution(
+            display_name=self.execution_display_name,
+            description=self.execution_description,
+        ).create()
+
+        shared_state["resources"].append(execution)
+
+        assert execution.display_name == self.execution_display_name
+        assert execution.schema_title == "system.CustomJobExecution"
+        assert execution.description == self.execution_description
+        assert "/metadataStores/default/executions/" in execution.resource_name

testbed/googleapis__python-aiplatform/tests/system/aiplatform/test_e2e_tabular.py

@@ -0,0 +1,221 @@
+# -*- coding: utf-8 -*-
+
+# Copyright 2023 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import os
+
+import pytest
+
+from google.cloud import storage
+
+from google.cloud import aiplatform
+from google.cloud.aiplatform.compat.types import (
+    job_state as gca_job_state,
+    pipeline_state as gca_pipeline_state,
+)
+from tests.system.aiplatform import e2e_base
+
+
+_DATASET_TRAINING_SRC = "gs://cloud-samples-data-us-central1/vertex-ai/structured_data/california_housing/california-housing-data.csv"
+_DATASET_BATCH_PREDICT_SRC = "gs://cloud-samples-data-us-central1/vertex-ai/batch-prediction/california_housing_batch_predict.jsonl"
+_DIR_NAME = os.path.dirname(os.path.abspath(__file__))
+_LOCAL_TRAINING_SCRIPT_PATH = os.path.join(
+    _DIR_NAME, "test_resources/california_housing_training_script.py"
+)
+_INSTANCE = {
+    "longitude": -124.35,
+    "latitude": 40.54,
+    "housing_median_age": 52.0,
+    "total_rooms": 1820.0,
+    "total_bedrooms": 300.0,
+    "population": 806,
+    "households": 270.0,
+    "median_income": 3.014700,
+}
+
+
+@pytest.mark.usefixtures(
+    "prepare_staging_bucket", "delete_staging_bucket", "tear_down_resources"
+)
+class TestEndToEndTabular(e2e_base.TestEndToEnd):
+    """End to end system test of the Vertex SDK with tabular data adapted from
+    reference notebook http://shortn/_eyoNx3SN0X"""
+
+    _temp_prefix = "temp-vertex-sdk-e2e-tabular"
+
+    def test_end_to_end_tabular(self, shared_state):
+        """Build dataset, train a custom and AutoML model, deploy, and get predictions"""
+
+        # Collection of resources generated by this test, to be deleted during teardown
+        shared_state["resources"] = []
+
+        aiplatform.init(
+            project=e2e_base._PROJECT,
+            location=e2e_base._LOCATION,
+            staging_bucket=shared_state["staging_bucket_name"],
+        )
+
+        # Create and import to single managed dataset for both training jobs
+
+        ds = aiplatform.TabularDataset.create(
+            display_name=self._make_display_name("dataset"),
+            gcs_source=[_DATASET_TRAINING_SRC],
+            sync=False,
+            create_request_timeout=180.0,
+        )
+
+        shared_state["resources"].extend([ds])
+
+        # Define both training jobs
+
+        custom_job = aiplatform.CustomTrainingJob(
+            display_name=self._make_display_name("train-housing-custom"),
+            script_path=_LOCAL_TRAINING_SCRIPT_PATH,
+            container_uri="gcr.io/cloud-aiplatform/training/tf-cpu.2-2:latest",
+            requirements=["gcsfs==0.7.1"],
+            model_serving_container_image_uri="gcr.io/cloud-aiplatform/prediction/tf2-cpu.2-2:latest",
+        )
+
+        automl_job = aiplatform.AutoMLTabularTrainingJob(
+            display_name=self._make_display_name("train-housing-automl"),
+            optimization_prediction_type="regression",
+            optimization_objective="minimize-rmse",
+        )
+
+        # Kick off both training jobs, AutoML job will take approx one hour to run
+
+        custom_model = custom_job.run(
+            ds,
+            replica_count=1,
+            model_display_name=self._make_display_name("custom-housing-model"),
+            timeout=1234,
+            restart_job_on_worker_restart=True,
+            enable_web_access=True,
+            sync=False,
+            create_request_timeout=None,
+            disable_retries=True,
+        )
+
+        automl_model = automl_job.run(
+            dataset=ds,
+            target_column="median_house_value",
+            model_display_name=self._make_display_name("automl-housing-model"),
+            sync=False,
+        )
+
+        shared_state["resources"].extend(
+            [automl_job, automl_model, custom_job, custom_model]
+        )
+
+        # Deploy both models after training completes
+        custom_endpoint = custom_model.deploy(machine_type="n1-standard-4", sync=False)
+        automl_endpoint = automl_model.deploy(machine_type="n1-standard-4", sync=False)
+        shared_state["resources"].extend([automl_endpoint, custom_endpoint])
+
+        custom_batch_prediction_job = custom_model.batch_predict(
+            job_display_name=self._make_display_name("custom-housing-model"),
+            instances_format="jsonl",
+            machine_type="n1-standard-4",
+            gcs_source=_DATASET_BATCH_PREDICT_SRC,
+            gcs_destination_prefix=f'gs://{shared_state["staging_bucket_name"]}/bp_results/',
+            sync=False,
+        )
+
+        shared_state["resources"].append(custom_batch_prediction_job)
+
+        in_progress_done_check = custom_job.done()
+        custom_job.wait_for_resource_creation()
+
+        automl_job.wait_for_resource_creation()
+        # custom_batch_prediction_job.wait_for_resource_creation()
+
+        # Send online prediction with same instance to both deployed models
+        # This sample is taken from an observation where median_house_value = 94600
+        custom_endpoint.wait()
+
+        # Check scheduling is correctly set
+        assert (
+            custom_job._gca_resource.training_task_inputs["scheduling"]["timeout"]
+            == "1234s"
+        )
+        assert (
+            custom_job._gca_resource.training_task_inputs["scheduling"][
+                "restartJobOnWorkerRestart"
+            ]
+            is True
+        )
+
+        custom_prediction = custom_endpoint.predict([_INSTANCE], timeout=180.0)
+
+        custom_batch_prediction_job.wait()
+
+        automl_endpoint.wait()
+        automl_prediction = automl_endpoint.predict(
+            [{k: str(v) for k, v in _INSTANCE.items()}],  # Cast int values to strings
+            timeout=180.0,
+        )
+
+        # Test lazy loading of Endpoint, check getter was never called after predict()
+        custom_endpoint = aiplatform.Endpoint(custom_endpoint.resource_name)
+        custom_endpoint.predict([_INSTANCE])
+
+        completion_done_check = custom_job.done()
+        assert custom_endpoint._skipped_getter_call()
+
+        assert (
+            custom_job.state
+            == gca_pipeline_state.PipelineState.PIPELINE_STATE_SUCCEEDED
+        )
+        assert (
+            automl_job.state
+            == gca_pipeline_state.PipelineState.PIPELINE_STATE_SUCCEEDED
+        )
+        assert (
+            custom_batch_prediction_job.state
+            == gca_job_state.JobState.JOB_STATE_SUCCEEDED
+        )
+
+        # Ensure batch prediction errors output file is empty
+        batch_predict_gcs_output_path = (
+            custom_batch_prediction_job.output_info.gcs_output_directory
+        )
+        client = storage.Client()
+
+        for blob in client.list_blobs(
+            bucket_or_name=shared_state["staging_bucket_name"],
+            prefix=f"bp_results/{batch_predict_gcs_output_path.split('/')[-1]}",
+        ):
+            # There are always 2 files in this output path: 1 with errors, 1 with predictions
+            if "errors" in blob.name:
+                error_output_filestr = blob.download_as_string().decode()
+                assert not error_output_filestr
+
+        # Ensure a single prediction was returned
+        assert len(custom_prediction.predictions) == 1
+        assert len(automl_prediction.predictions) == 1
+
+        # Ensure the models are remotely accurate
+        try:
+            automl_result = automl_prediction.predictions[0]["value"]
+            custom_result = custom_prediction.predictions[0][0]
+            assert 200000 > automl_result > 50000
+            assert 200000 > custom_result > 50000
+        except KeyError as e:
+            raise RuntimeError("Unexpected prediction response structure:", e)
+
+        # Check done() method works correctly
+        assert in_progress_done_check is False
+        assert completion_done_check is True

testbed/googleapis__python-aiplatform/tests/system/aiplatform/test_experiments.py

@@ -0,0 +1,769 @@
+# -*- coding: utf-8 -*-
+
+# Copyright 2023 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+import tempfile
+
+import uuid
+import pytest
+
+from google.api_core import exceptions
+from google.cloud import storage
+
+from google.cloud import aiplatform
+from google.cloud.aiplatform.utils import rest_utils
+from google.cloud.aiplatform.metadata.schema.google import (
+    artifact_schema as google_artifact_schema,
+)
+from tests.system.aiplatform import e2e_base
+from tests.system.aiplatform import test_model_upload
+
+import numpy as np
+import sklearn
+from sklearn.linear_model import LinearRegression
+
+
+_RUN = "run-1"
+_PARAMS = {"sdk-param-test-1": 0.1, "sdk-param-test-2": 0.2}
+_METRICS = {"sdk-metric-test-1": 0.8, "sdk-metric-test-2": 100.0}
+
+_RUN_2 = "run-2"
+_PARAMS_2 = {"sdk-param-test-1": 0.2, "sdk-param-test-2": 0.4}
+_METRICS_2 = {"sdk-metric-test-1": 1.6, "sdk-metric-test-2": 200.0}
+
+_READ_TIME_SERIES_BATCH_SIZE = 20
+
+_TIME_SERIES_METRIC_KEY = "accuracy"
+
+_CLASSIFICATION_METRICS = {
+    "display_name": "my-classification-metrics",
+    "labels": ["cat", "dog"],
+    "matrix": [[9, 1], [1, 9]],
+    "fpr": [0.1, 0.5, 0.9],
+    "tpr": [0.1, 0.7, 0.9],
+    "threshold": [0.9, 0.5, 0.1],
+}
+
+
+@pytest.mark.usefixtures(
+    "prepare_staging_bucket", "delete_staging_bucket", "tear_down_resources"
+)
+class TestExperiments(e2e_base.TestEndToEnd):
+
+    _temp_prefix = "tmpvrtxsdk-e2e"
+
+    def setup_class(cls):
+        cls._experiment_name = cls._make_display_name("")[:64]
+        cls._experiment_name_2 = cls._make_display_name("")[:64]
+        cls._experiment_model_name = cls._make_display_name("sklearn-model")[:64]
+        cls._dataset_artifact_name = cls._make_display_name("")[:64]
+        cls._dataset_artifact_uri = cls._make_display_name("ds-uri")
+        cls._pipeline_job_id = cls._make_display_name("job-id")
+
+    def test_create_experiment(self, shared_state):
+
+        # Truncating the name because of resource id constraints from the service
+        tensorboard = aiplatform.Tensorboard.create(
+            project=e2e_base._PROJECT,
+            location=e2e_base._LOCATION,
+            display_name=self._experiment_name,
+        )
+
+        shared_state["resources"] = [tensorboard]
+
+        aiplatform.init(
+            project=e2e_base._PROJECT,
+            location=e2e_base._LOCATION,
+            experiment=self._experiment_name,
+            experiment_tensorboard=tensorboard,
+        )
+
+        shared_state["resources"].append(
+            aiplatform.metadata.metadata._experiment_tracker.experiment
+        )
+
+    def test_get_experiment(self):
+        experiment = aiplatform.Experiment(
+            experiment_name=self._experiment_name,
+            project=e2e_base._PROJECT,
+            location=e2e_base._LOCATION,
+        )
+        assert experiment.name == self._experiment_name
+
+    def test_start_run(self):
+        aiplatform.init(
+            project=e2e_base._PROJECT,
+            location=e2e_base._LOCATION,
+            experiment=self._experiment_name,
+        )
+        run = aiplatform.start_run(_RUN)
+        assert run.name == _RUN
+
+    def test_get_run(self):
+        run = aiplatform.ExperimentRun(
+            run_name=_RUN,
+            experiment=self._experiment_name,
+            project=e2e_base._PROJECT,
+            location=e2e_base._LOCATION,
+        )
+        assert run.name == _RUN
+        assert run.state == aiplatform.gapic.Execution.State.RUNNING
+
+    def test_log_params(self):
+        aiplatform.init(
+            project=e2e_base._PROJECT,
+            location=e2e_base._LOCATION,
+            experiment=self._experiment_name,
+        )
+        aiplatform.start_run(_RUN, resume=True)
+        aiplatform.log_params(_PARAMS)
+        run = aiplatform.ExperimentRun(run_name=_RUN, experiment=self._experiment_name)
+        assert run.get_params() == _PARAMS
+
+    def test_log_metrics(self):
+        aiplatform.init(
+            project=e2e_base._PROJECT,
+            location=e2e_base._LOCATION,
+            experiment=self._experiment_name,
+        )
+        aiplatform.start_run(_RUN, resume=True)
+        aiplatform.log_metrics(_METRICS)
+        run = aiplatform.ExperimentRun(run_name=_RUN, experiment=self._experiment_name)
+        assert run.get_metrics() == _METRICS
+
+    def test_log_time_series_metrics(self):
+        aiplatform.init(
+            project=e2e_base._PROJECT,
+            location=e2e_base._LOCATION,
+            experiment=self._experiment_name,
+        )
+
+        aiplatform.start_run(_RUN, resume=True)
+
+        for i in range(5):
+            aiplatform.log_time_series_metrics({_TIME_SERIES_METRIC_KEY: i})
+
+        run = aiplatform.ExperimentRun(run_name=_RUN, experiment=self._experiment_name)
+
+        time_series_result = run.get_time_series_data_frame()[
+            [_TIME_SERIES_METRIC_KEY, "step"]
+        ].to_dict("list")
+
+        assert time_series_result == {
+            "step": list(range(1, 6)),
+            _TIME_SERIES_METRIC_KEY: [float(value) for value in range(5)],
+        }
+
+    def test_get_time_series_data_frame_batch_read_success(self, shared_state):
+        tensorboard = aiplatform.Tensorboard.create(
+            project=e2e_base._PROJECT,
+            location=e2e_base._LOCATION,
+            display_name=self._experiment_name_2,
+        )
+        shared_state["resources"] = [tensorboard]
+        aiplatform.init(
+            project=e2e_base._PROJECT,
+            location=e2e_base._LOCATION,
+            experiment=self._experiment_name_2,
+            experiment_tensorboard=tensorboard,
+        )
+        shared_state["resources"].append(
+            aiplatform.metadata.metadata._experiment_tracker.experiment
+        )
+        aiplatform.start_run(_RUN)
+        for i in range(_READ_TIME_SERIES_BATCH_SIZE + 1):
+            aiplatform.log_time_series_metrics({f"{_TIME_SERIES_METRIC_KEY}-{i}": 1})
+
+        run = aiplatform.ExperimentRun(
+            run_name=_RUN, experiment=self._experiment_name_2
+        )
+        time_series_result = run.get_time_series_data_frame()
+
+        assert len(time_series_result) > _READ_TIME_SERIES_BATCH_SIZE
+
+    def test_log_classification_metrics(self, shared_state):
+        aiplatform.init(
+            project=e2e_base._PROJECT,
+            location=e2e_base._LOCATION,
+            experiment=self._experiment_name,
+        )
+        aiplatform.start_run(_RUN, resume=True)
+        classification_metrics = aiplatform.log_classification_metrics(
+            display_name=_CLASSIFICATION_METRICS["display_name"],
+            labels=_CLASSIFICATION_METRICS["labels"],
+            matrix=_CLASSIFICATION_METRICS["matrix"],
+            fpr=_CLASSIFICATION_METRICS["fpr"],
+            tpr=_CLASSIFICATION_METRICS["tpr"],
+            threshold=_CLASSIFICATION_METRICS["threshold"],
+        )
+
+        run = aiplatform.ExperimentRun(run_name=_RUN, experiment=self._experiment_name)
+        metrics = run.get_classification_metrics()[0]
+        metric_artifact = aiplatform.Artifact(metrics.pop("id"))
+        assert metrics == _CLASSIFICATION_METRICS
+        assert isinstance(
+            classification_metrics, google_artifact_schema.ClassificationMetrics
+        )
+        metric_artifact.delete()
+
+    def test_log_model(self, shared_state):
+        aiplatform.init(
+            project=e2e_base._PROJECT,
+            location=e2e_base._LOCATION,
+            experiment=self._experiment_name,
+        )
+        aiplatform.start_run(_RUN, resume=True)
+
+        train_x = np.array([[1, 1], [1, 2], [2, 2], [2, 3]])
+        train_y = np.dot(train_x, np.array([1, 2])) + 3
+        model = LinearRegression()
+        model.fit(train_x, train_y)
+
+        model_artifact = aiplatform.log_model(
+            model=model,
+            artifact_id=self._experiment_model_name,
+            uri=f"gs://{shared_state['staging_bucket_name']}/sklearn-model",
+            input_example=train_x,
+        )
+        shared_state["resources"].append(model_artifact)
+
+        run = aiplatform.ExperimentRun(run_name=_RUN, experiment=self._experiment_name)
+        experiment_model = run.get_experiment_models()[0]
+        assert "sklearn-model" in experiment_model.name
+        assert (
+            experiment_model.uri
+            == f"gs://{shared_state['staging_bucket_name']}/sklearn-model"
+        )
+        assert experiment_model.get_model_info() == {
+            "model_class": "sklearn.linear_model._base.LinearRegression",
+            "framework_name": "sklearn",
+            "framework_version": sklearn.__version__,
+            "input_example": {
+                "type": "numpy.ndarray",
+                "data": train_x.tolist(),
+            },
+        }
+        experiment_model.delete()
+
+    def test_create_artifact(self, shared_state):
+        ds = aiplatform.Artifact.create(
+            schema_title="system.Dataset",
+            resource_id=self._dataset_artifact_name,
+            uri=self._dataset_artifact_uri,
+            project=e2e_base._PROJECT,
+            location=e2e_base._LOCATION,
+        )
+
+        shared_state["resources"].append(ds)
+        assert ds.uri == self._dataset_artifact_uri
+
+    def test_get_artifact_by_uri(self):
+        ds = aiplatform.Artifact.get_with_uri(
+            uri=self._dataset_artifact_uri,
+            project=e2e_base._PROJECT,
+            location=e2e_base._LOCATION,
+        )
+
+        assert ds.uri == self._dataset_artifact_uri
+        assert ds.name == self._dataset_artifact_name
+
+    def test_log_execution_and_artifact(self, shared_state):
+        aiplatform.init(
+            project=e2e_base._PROJECT,
+            location=e2e_base._LOCATION,
+            experiment=self._experiment_name,
+        )
+        aiplatform.start_run(_RUN, resume=True)
+
+        with aiplatform.start_execution(
+            schema_title="system.ContainerExecution",
+            resource_id=self._make_display_name("execution"),
+        ) as execution:
+
+            shared_state["resources"].append(execution)
+
+            ds = aiplatform.Artifact(
+                artifact_name=self._dataset_artifact_name,
+            )
+            execution.assign_input_artifacts([ds])
+
+            model = aiplatform.Artifact.create(schema_title="system.Model")
+            shared_state["resources"].append(model)
+
+            storage_client = storage.Client(project=e2e_base._PROJECT)
+            model_blob = storage.Blob.from_string(
+                uri=test_model_upload._XGBOOST_MODEL_URI, client=storage_client
+            )
+            model_path = tempfile.mktemp() + ".my_model.xgb"
+            model_blob.download_to_filename(filename=model_path)
+
+            vertex_model = aiplatform.Model.upload_xgboost_model_file(
+                display_name=self._make_display_name("model"),
+                model_file_path=model_path,
+            )
+            shared_state["resources"].append(vertex_model)
+
+            execution.assign_output_artifacts([model, vertex_model])
+
+        input_artifacts = execution.get_input_artifacts()
+        assert input_artifacts[0].name == ds.name
+
+        output_artifacts = execution.get_output_artifacts()
+        # system.Model, google.VertexModel
+        output_artifacts.sort(key=lambda artifact: artifact.schema_title, reverse=True)
+
+        shared_state["resources"].append(output_artifacts[-1])
+
+        assert output_artifacts[0].name == model.name
+        assert output_artifacts[1].uri == rest_utils.make_gcp_resource_rest_url(
+            resource=vertex_model
+        )
+
+        run = aiplatform.ExperimentRun(run_name=_RUN, experiment=self._experiment_name)
+        executions = run.get_executions()
+        assert executions[0].name == execution.name
+
+        artifacts = run.get_artifacts()
+
+        # system.Model, system.Dataset, google.VertexTensorboardRun, google.VertexModel
+        artifacts.sort(key=lambda artifact: artifact.schema_title, reverse=True)
+        assert artifacts.pop().uri == rest_utils.make_gcp_resource_rest_url(
+            resource=vertex_model
+        )
+
+        # tensorboard run artifact is also included
+        assert sorted([artifact.name for artifact in artifacts]) == sorted(
+            [ds.name, model.name, run._tensorboard_run_id(run.resource_id)]
+        )
+
+    def test_end_run(self):
+        aiplatform.init(
+            project=e2e_base._PROJECT,
+            location=e2e_base._LOCATION,
+            experiment=self._experiment_name,
+        )
+        aiplatform.start_run(_RUN, resume=True)
+        aiplatform.end_run()
+        run = aiplatform.ExperimentRun(run_name=_RUN, experiment=self._experiment_name)
+        assert run.state == aiplatform.gapic.Execution.State.COMPLETE
+
+    def test_run_context_manager(self):
+        aiplatform.init(
+            project=e2e_base._PROJECT,
+            location=e2e_base._LOCATION,
+            experiment=self._experiment_name,
+        )
+        with aiplatform.start_run(_RUN_2) as run:
+            run.log_params(_PARAMS_2)
+            run.log_metrics(_METRICS_2)
+            assert run.state == aiplatform.gapic.Execution.State.RUNNING
+
+        assert run.state == aiplatform.gapic.Execution.State.COMPLETE
+
+    def test_add_pipeline_job_to_experiment(self, shared_state):
+        import kfp.v2.dsl as dsl
+        import kfp.v2.compiler as compiler
+        from kfp.v2.dsl import component, Metrics, Output
+
+        @component
+        def trainer(
+            learning_rate: float, dropout_rate: float, metrics: Output[Metrics]
+        ):
+            metrics.log_metric("accuracy", 0.8)
+            metrics.log_metric("mse", 1.2)
+
+        @dsl.pipeline(name=self._make_display_name("pipeline"))
+        def pipeline(learning_rate: float, dropout_rate: float):
+            trainer(learning_rate=learning_rate, dropout_rate=dropout_rate)
+
+        compiler.Compiler().compile(
+            pipeline_func=pipeline, package_path="pipeline.json"
+        )
+
+        job = aiplatform.PipelineJob(
+            display_name=self._make_display_name("experiment pipeline job"),
+            template_path="pipeline.json",
+            job_id=self._pipeline_job_id,
+            pipeline_root=f'gs://{shared_state["staging_bucket_name"]}',
+            parameter_values={"learning_rate": 0.1, "dropout_rate": 0.2},
+            project=e2e_base._PROJECT,
+            location=e2e_base._LOCATION,
+        )
+
+        job.submit(
+            experiment=self._experiment_name,
+        )
+
+        shared_state["resources"].append(job)
+
+        job.wait()
+
+        test_experiment = job.get_associated_experiment()
+
+        assert test_experiment.name == self._experiment_name
+
+    def test_get_experiments_df(self):
+        aiplatform.init(
+            project=e2e_base._PROJECT,
+            location=e2e_base._LOCATION,
+            experiment=self._experiment_name,
+        )
+
+        df = aiplatform.get_experiment_df()
+
+        pipelines_param_and_metrics = {
+            "param.dropout_rate": 0.2,
+            "param.learning_rate": 0.1,
+            "metric.accuracy": 0.8,
+            "metric.mse": 1.2,
+        }
+
+        true_df_dict_1 = {f"metric.{key}": value for key, value in _METRICS.items()}
+        for key, value in _PARAMS.items():
+            true_df_dict_1[f"param.{key}"] = value
+
+        true_df_dict_1["experiment_name"] = self._experiment_name
+        true_df_dict_1["run_name"] = _RUN
+        true_df_dict_1["state"] = aiplatform.gapic.Execution.State.COMPLETE.name
+        true_df_dict_1["run_type"] = aiplatform.metadata.constants.SYSTEM_EXPERIMENT_RUN
+        true_df_dict_1[f"time_series_metric.{_TIME_SERIES_METRIC_KEY}"] = 4.0
+
+        true_df_dict_2 = {f"metric.{key}": value for key, value in _METRICS_2.items()}
+        for key, value in _PARAMS_2.items():
+            true_df_dict_2[f"param.{key}"] = value
+
+        true_df_dict_2["experiment_name"] = self._experiment_name
+        true_df_dict_2["run_name"] = _RUN_2
+        true_df_dict_2["state"] = aiplatform.gapic.Execution.State.COMPLETE.name
+        true_df_dict_2["run_type"] = aiplatform.metadata.constants.SYSTEM_EXPERIMENT_RUN
+        true_df_dict_2[f"time_series_metric.{_TIME_SERIES_METRIC_KEY}"] = 0.0
+        true_df_dict_2.update(pipelines_param_and_metrics)
+
+        true_df_dict_3 = {
+            "experiment_name": self._experiment_name,
+            "run_name": self._pipeline_job_id,
+            "run_type": aiplatform.metadata.constants.SYSTEM_PIPELINE_RUN,
+            "state": aiplatform.gapic.Execution.State.COMPLETE.name,
+            "time_series_metric.accuracy": 0.0,
+        }
+
+        true_df_dict_3.update(pipelines_param_and_metrics)
+
+        for key in pipelines_param_and_metrics.keys():
+            true_df_dict_1[key] = 0.0
+            true_df_dict_2[key] = 0.0
+
+        for key in _PARAMS.keys():
+            true_df_dict_3[f"param.{key}"] = 0.0
+
+        for key in _METRICS.keys():
+            true_df_dict_3[f"metric.{key}"] = 0.0
+
+        assert sorted(
+            [true_df_dict_1, true_df_dict_2, true_df_dict_3],
+            key=lambda d: d["run_name"],
+        ) == sorted(df.fillna(0.0).to_dict("records"), key=lambda d: d["run_name"])
+
+    def test_get_experiments_df_include_time_series_false(self):
+        aiplatform.init(
+            project=e2e_base._PROJECT,
+            location=e2e_base._LOCATION,
+            experiment=self._experiment_name,
+        )
+
+        df = aiplatform.get_experiment_df(include_time_series=False)
+
+        pipelines_param_and_metrics = {
+            "param.dropout_rate": 0.2,
+            "param.learning_rate": 0.1,
+            "metric.accuracy": 0.8,
+            "metric.mse": 1.2,
+        }
+
+        true_df_dict_1 = {f"metric.{key}": value for key, value in _METRICS.items()}
+        for key, value in _PARAMS.items():
+            true_df_dict_1[f"param.{key}"] = value
+
+        true_df_dict_1["experiment_name"] = self._experiment_name
+        true_df_dict_1["run_name"] = _RUN
+        true_df_dict_1["state"] = aiplatform.gapic.Execution.State.COMPLETE.name
+        true_df_dict_1["run_type"] = aiplatform.metadata.constants.SYSTEM_EXPERIMENT_RUN
+
+        true_df_dict_2 = {f"metric.{key}": value for key, value in _METRICS_2.items()}
+        for key, value in _PARAMS_2.items():
+            true_df_dict_2[f"param.{key}"] = value
+
+        true_df_dict_2["experiment_name"] = self._experiment_name
+        true_df_dict_2["run_name"] = _RUN_2
+        true_df_dict_2["state"] = aiplatform.gapic.Execution.State.COMPLETE.name
+        true_df_dict_2["run_type"] = aiplatform.metadata.constants.SYSTEM_EXPERIMENT_RUN
+        true_df_dict_2.update(pipelines_param_and_metrics)
+
+        true_df_dict_3 = {
+            "experiment_name": self._experiment_name,
+            "run_name": self._pipeline_job_id,
+            "run_type": aiplatform.metadata.constants.SYSTEM_PIPELINE_RUN,
+            "state": aiplatform.gapic.Execution.State.COMPLETE.name,
+        }
+
+        true_df_dict_3.update(pipelines_param_and_metrics)
+
+        for key in pipelines_param_and_metrics.keys():
+            true_df_dict_1[key] = 0.0
+            true_df_dict_2[key] = 0.0
+
+        for key in _PARAMS.keys():
+            true_df_dict_3[f"param.{key}"] = 0.0
+
+        for key in _METRICS.keys():
+            true_df_dict_3[f"metric.{key}"] = 0.0
+
+        assert sorted(
+            [true_df_dict_1, true_df_dict_2, true_df_dict_3],
+            key=lambda d: d["run_name"],
+        ) == sorted(df.fillna(0.0).to_dict("records"), key=lambda d: d["run_name"])
+
+    def test_delete_run_does_not_exist_raises_exception(self):
+        run = aiplatform.ExperimentRun(
+            run_name=_RUN,
+            experiment=self._experiment_name,
+            project=e2e_base._PROJECT,
+            location=e2e_base._LOCATION,
+        )
+        run.delete(delete_backing_tensorboard_run=True)
+
+        with pytest.raises(exceptions.NotFound):
+            aiplatform.ExperimentRun(run_name=_RUN, experiment=self._experiment_name)
+
+    def test_delete_run_success(self):
+        aiplatform.init(
+            project=e2e_base._PROJECT,
+            location=e2e_base._LOCATION,
+            experiment=self._experiment_name,
+        )
+        aiplatform.start_run(_RUN)
+        run = aiplatform.ExperimentRun(
+            run_name=_RUN,
+            experiment=self._experiment_name,
+            project=e2e_base._PROJECT,
+            location=e2e_base._LOCATION,
+        )
+        aiplatform.end_run()
+
+        run.delete(delete_backing_tensorboard_run=True)
+
+        with pytest.raises(exceptions.NotFound):
+            aiplatform.ExperimentRun(
+                run_name=_RUN,
+                experiment=self._experiment_name,
+                project=e2e_base._PROJECT,
+                location=e2e_base._LOCATION,
+            )
+
+    def test_reuse_run_success(self):
+        aiplatform.init(
+            project=e2e_base._PROJECT,
+            location=e2e_base._LOCATION,
+            experiment=self._experiment_name,
+        )
+        aiplatform.start_run(_RUN)
+        run = aiplatform.ExperimentRun(
+            run_name=_RUN,
+            experiment=self._experiment_name,
+            project=e2e_base._PROJECT,
+            location=e2e_base._LOCATION,
+        )
+        aiplatform.end_run()
+        run.delete(delete_backing_tensorboard_run=True)
+
+        aiplatform.start_run(_RUN)
+        aiplatform.end_run()
+
+        run = aiplatform.ExperimentRun(
+            run_name=_RUN,
+            experiment=self._experiment_name,
+            project=e2e_base._PROJECT,
+            location=e2e_base._LOCATION,
+        )
+        assert run.name == _RUN
+
+    def test_delete_run_then_tensorboard_success(self):
+        aiplatform.init(
+            project=e2e_base._PROJECT,
+            location=e2e_base._LOCATION,
+            experiment=self._experiment_name,
+        )
+        aiplatform.start_run(_RUN, resume=True)
+        run = aiplatform.ExperimentRun(
+            run_name=_RUN,
+            experiment=self._experiment_name,
+            project=e2e_base._PROJECT,
+            location=e2e_base._LOCATION,
+        )
+        aiplatform.end_run()
+        run.delete()
+        tensorboard_run_artifact = aiplatform.metadata.artifact.Artifact(
+            artifact_name=f"{self._experiment_name}-{_RUN}-tb-run"
+        )
+        tensorboard_run_resource = aiplatform.TensorboardRun(
+            tensorboard_run_artifact.metadata["resourceName"]
+        )
+        tensorboard_run_resource.delete()
+        tensorboard_run_artifact.delete()
+
+        aiplatform.start_run(_RUN)
+        aiplatform.end_run()
+
+        run = aiplatform.ExperimentRun(
+            run_name=_RUN,
+            experiment=self._experiment_name,
+            project=e2e_base._PROJECT,
+            location=e2e_base._LOCATION,
+        )
+        assert run.name == _RUN
+
+    def test_delete_wout_backing_tensorboard_reuse_run_raises_exception(self):
+        aiplatform.init(
+            project=e2e_base._PROJECT,
+            location=e2e_base._LOCATION,
+            experiment=self._experiment_name,
+        )
+        aiplatform.start_run(_RUN, resume=True)
+        run = aiplatform.ExperimentRun(
+            run_name=_RUN,
+            experiment=self._experiment_name,
+            project=e2e_base._PROJECT,
+            location=e2e_base._LOCATION,
+        )
+        aiplatform.end_run()
+        run.delete()
+
+        with pytest.raises(ValueError):
+            aiplatform.start_run(_RUN)
+
+    def test_delete_experiment_does_not_exist_raises_exception(self):
+        experiment = aiplatform.Experiment(
+            experiment_name=self._experiment_name,
+            project=e2e_base._PROJECT,
+            location=e2e_base._LOCATION,
+        )
+        experiment.delete(delete_backing_tensorboard_runs=True)
+
+        with pytest.raises(exceptions.NotFound):
+            aiplatform.Experiment(experiment_name=self._experiment_name)
+
+    def test_init_associates_global_tensorboard_to_experiment(self, shared_state):
+
+        tensorboard = aiplatform.Tensorboard.create(
+            project=e2e_base._PROJECT,
+            location=e2e_base._LOCATION,
+            display_name=self._make_display_name("")[:64],
+        )
+
+        shared_state["resources"] = [tensorboard]
+
+        aiplatform.init(
+            project=e2e_base._PROJECT,
+            location=e2e_base._LOCATION,
+            experiment_tensorboard=tensorboard,
+        )
+
+        assert (
+            aiplatform.metadata.metadata._experiment_tracker._global_tensorboard
+            == tensorboard
+        )
+
+        new_experiment_name = self._make_display_name("")[:64]
+        new_experiment_resource = aiplatform.Experiment.create(
+            experiment_name=new_experiment_name
+        )
+
+        shared_state["resources"].append(new_experiment_resource)
+
+        aiplatform.init(
+            project=e2e_base._PROJECT,
+            location=e2e_base._LOCATION,
+            experiment=new_experiment_name,
+        )
+
+        assert (
+            new_experiment_resource._lookup_backing_tensorboard().resource_name
+            == tensorboard.resource_name
+        )
+
+        assert (
+            new_experiment_resource._metadata_context.metadata.get(
+                aiplatform.metadata.constants._BACKING_TENSORBOARD_RESOURCE_KEY
+            )
+            == tensorboard.resource_name
+        )
+
+    def test_get_backing_tensorboard_resource_returns_tensorboard(self, shared_state):
+        tensorboard = aiplatform.Tensorboard.create(
+            project=e2e_base._PROJECT,
+            location=e2e_base._LOCATION,
+            display_name=self._make_display_name("")[:64],
+        )
+        shared_state["resources"] = [tensorboard]
+        aiplatform.init(
+            project=e2e_base._PROJECT,
+            location=e2e_base._LOCATION,
+            experiment=self._experiment_name,
+            experiment_tensorboard=tensorboard,
+        )
+        experiment = aiplatform.Experiment(
+            self._experiment_name,
+            project=e2e_base._PROJECT,
+            location=e2e_base._LOCATION,
+        )
+
+        assert (
+            experiment.get_backing_tensorboard_resource().resource_name
+            == tensorboard.resource_name
+        )
+
+    def test_get_backing_tensorboard_resource_returns_none(self):
+        new_experiment_name = f"example-{uuid.uuid1()}"
+        aiplatform.init(
+            project=e2e_base._PROJECT,
+            location=e2e_base._LOCATION,
+            experiment=new_experiment_name,
+            experiment_tensorboard=False,
+        )
+        new_experiment = aiplatform.Experiment(
+            new_experiment_name,
+            project=e2e_base._PROJECT,
+            location=e2e_base._LOCATION,
+        )
+
+        assert new_experiment.get_backing_tensorboard_resource() is None
+
+    def test_delete_backing_tensorboard_experiment_run_success(self):
+        aiplatform.init(
+            project=e2e_base._PROJECT,
+            location=e2e_base._LOCATION,
+            experiment=self._experiment_name,
+        )
+        experiment = aiplatform.Experiment(
+            self._experiment_name,
+            project=e2e_base._PROJECT,
+            location=e2e_base._LOCATION,
+        )
+        experiment.get_backing_tensorboard_resource().delete()
+        run = aiplatform.start_run(_RUN)
+        aiplatform.end_run()
+
+        assert experiment.get_backing_tensorboard_resource() is None
+        assert run.name == _RUN

testbed/googleapis__python-aiplatform/tests/system/aiplatform/test_featurestore.py

@@ -0,0 +1,714 @@
+# -*- coding: utf-8 -*-
+
+# Copyright 2021 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import datetime
+import logging
+import pytest
+
+from google.cloud import aiplatform
+from tests.system.aiplatform import e2e_base
+
+import pandas as pd
+
+_TEST_USERS_ENTITY_TYPE_GCS_SRC = (
+    "gs://cloud-samples-data-us-central1/vertex-ai/feature-store/datasets/users.avro"
+)
+
+_TEST_READ_INSTANCE_SRC = "gs://cloud-samples-data-us-central1/vertex-ai/feature-store/datasets/movie_prediction.csv"
+
+_TEST_FEATURESTORE_ID = "movie_prediction"
+_TEST_USER_ENTITY_TYPE_ID = "users"
+_TEST_MOVIE_ENTITY_TYPE_ID = "movies"
+_TEST_MOVIE_ENTITY_TYPE_UPDATE_LABELS = {"my_key_update": "my_value_update"}
+
+_TEST_USER_AGE_FEATURE_ID = "age"
+_TEST_USER_GENDER_FEATURE_ID = "gender"
+_TEST_USER_LIKED_GENRES_FEATURE_ID = "liked_genres"
+
+_TEST_MOVIE_TITLE_FEATURE_ID = "title"
+_TEST_MOVIE_GENRES_FEATURE_ID = "genres"
+_TEST_MOVIE_AVERAGE_RATING_FEATURE_ID = "average_rating"
+
+
+@pytest.mark.usefixtures(
+    "prepare_staging_bucket",
+    "delete_staging_bucket",
+    "prepare_bigquery_dataset",
+    "delete_bigquery_dataset",
+    "tear_down_resources",
+)
+class TestFeaturestore(e2e_base.TestEndToEnd):
+
+    _temp_prefix = "temp_vertex_sdk_e2e_featurestore_test"
+
+    def test_create_get_list_featurestore(self, shared_state):
+        aiplatform.init(
+            project=e2e_base._PROJECT,
+            location=e2e_base._LOCATION,
+        )
+
+        featurestore_id = self._make_display_name(key=_TEST_FEATURESTORE_ID).replace(
+            "-", "_"
+        )[:60]
+        featurestore = aiplatform.Featurestore.create(
+            featurestore_id=featurestore_id, online_store_fixed_node_count=1
+        )
+
+        shared_state["resources"] = [featurestore]
+        shared_state["featurestore"] = featurestore
+        shared_state["featurestore_name"] = featurestore.resource_name
+
+        get_featurestore = aiplatform.Featurestore(
+            featurestore_name=featurestore.resource_name
+        )
+        assert featurestore.resource_name == get_featurestore.resource_name
+
+        list_featurestores = aiplatform.Featurestore.list()
+        assert get_featurestore.resource_name in [
+            featurestore.resource_name for featurestore in list_featurestores
+        ]
+
+    def test_create_get_list_entity_types(self, shared_state):
+
+        assert shared_state["featurestore"]
+        assert shared_state["featurestore_name"]
+
+        featurestore = shared_state["featurestore"]
+        featurestore_name = shared_state["featurestore_name"]
+
+        aiplatform.init(
+            project=e2e_base._PROJECT,
+            location=e2e_base._LOCATION,
+        )
+
+        # Users
+        user_entity_type = featurestore.create_entity_type(
+            entity_type_id=_TEST_USER_ENTITY_TYPE_ID
+        )
+        shared_state["user_entity_type"] = user_entity_type
+        shared_state["user_entity_type_name"] = user_entity_type.resource_name
+
+        get_user_entity_type = featurestore.get_entity_type(
+            entity_type_id=_TEST_USER_ENTITY_TYPE_ID
+        )
+        assert user_entity_type.resource_name == get_user_entity_type.resource_name
+
+        # Movies
+        movie_entity_type = aiplatform.EntityType.create(
+            entity_type_id=_TEST_MOVIE_ENTITY_TYPE_ID,
+            featurestore_name=featurestore_name,
+        )
+        shared_state["movie_entity_type"] = movie_entity_type
+        shared_state["movie_entity_type_name"] = movie_entity_type.resource_name
+
+        get_movie_entity_type = aiplatform.EntityType(
+            entity_type_name=movie_entity_type.resource_name
+        )
+        assert movie_entity_type.resource_name == get_movie_entity_type.resource_name
+
+        list_entity_types = aiplatform.EntityType.list(
+            featurestore_name=featurestore_name
+        )
+        assert get_movie_entity_type.resource_name in [
+            entity_type.resource_name for entity_type in list_entity_types
+        ]
+
+        # Update information about the movie entity type.
+        assert movie_entity_type.labels != _TEST_MOVIE_ENTITY_TYPE_UPDATE_LABELS
+
+        movie_entity_type.update(
+            labels=_TEST_MOVIE_ENTITY_TYPE_UPDATE_LABELS,
+        )
+
+        assert movie_entity_type.labels == _TEST_MOVIE_ENTITY_TYPE_UPDATE_LABELS
+
+    def test_create_get_list_features(self, shared_state):
+
+        assert shared_state["user_entity_type"]
+        assert shared_state["user_entity_type_name"]
+        user_entity_type = shared_state["user_entity_type"]
+        user_entity_type_name = shared_state["user_entity_type_name"]
+
+        aiplatform.init(
+            project=e2e_base._PROJECT,
+            location=e2e_base._LOCATION,
+        )
+
+        # User Features
+        user_age_feature = user_entity_type.create_feature(
+            feature_id=_TEST_USER_AGE_FEATURE_ID, value_type="INT64"
+        )
+        shared_state["user_age_feature_resource_name"] = user_age_feature.resource_name
+        get_user_age_feature = user_entity_type.get_feature(
+            feature_id=_TEST_USER_AGE_FEATURE_ID
+        )
+        assert user_age_feature.resource_name == get_user_age_feature.resource_name
+
+        user_gender_feature = aiplatform.Feature.create(
+            feature_id=_TEST_USER_GENDER_FEATURE_ID,
+            value_type="STRING",
+            entity_type_name=user_entity_type_name,
+        )
+        shared_state[
+            "user_gender_feature_resource_name"
+        ] = user_gender_feature.resource_name
+
+        get_user_gender_feature = aiplatform.Feature(
+            feature_name=user_gender_feature.resource_name
+        )
+        assert (
+            user_gender_feature.resource_name == get_user_gender_feature.resource_name
+        )
+
+        user_liked_genres_feature = user_entity_type.create_feature(
+            feature_id=_TEST_USER_LIKED_GENRES_FEATURE_ID,
+            value_type="STRING_ARRAY",
+        )
+        shared_state[
+            "user_liked_genres_feature_resource_name"
+        ] = user_liked_genres_feature.resource_name
+
+        get_user_liked_genres_feature = aiplatform.Feature(
+            feature_name=user_liked_genres_feature.resource_name
+        )
+        assert (
+            user_liked_genres_feature.resource_name
+            == get_user_liked_genres_feature.resource_name
+        )
+
+        list_user_features = user_entity_type.list_features()
+        list_user_feature_resource_names = [
+            feature.resource_name for feature in list_user_features
+        ]
+
+        assert get_user_age_feature.resource_name in list_user_feature_resource_names
+        assert get_user_gender_feature.resource_name in list_user_feature_resource_names
+        assert (
+            get_user_liked_genres_feature.resource_name
+            in list_user_feature_resource_names
+        )
+
+    def test_ingest_feature_values(self, shared_state, caplog):
+
+        assert shared_state["user_entity_type"]
+        user_entity_type = shared_state["user_entity_type"]
+
+        caplog.set_level(logging.INFO)
+
+        aiplatform.init(
+            project=e2e_base._PROJECT,
+            location=e2e_base._LOCATION,
+        )
+
+        user_entity_type.ingest_from_gcs(
+            feature_ids=[
+                _TEST_USER_AGE_FEATURE_ID,
+                _TEST_USER_GENDER_FEATURE_ID,
+                _TEST_USER_LIKED_GENRES_FEATURE_ID,
+            ],
+            feature_time="update_time",
+            gcs_source_uris=_TEST_USERS_ENTITY_TYPE_GCS_SRC,
+            gcs_source_type="avro",
+            entity_id_field="user_id",
+            worker_count=1,
+        )
+
+        assert "EntityType feature values imported." in caplog.text
+
+        caplog.clear()
+
+    def test_batch_create_features(self, shared_state):
+        assert shared_state["movie_entity_type"]
+        movie_entity_type = shared_state["movie_entity_type"]
+
+        aiplatform.init(
+            project=e2e_base._PROJECT,
+            location=e2e_base._LOCATION,
+        )
+
+        movie_feature_configs = {
+            _TEST_MOVIE_TITLE_FEATURE_ID: {"value_type": "STRING"},
+            _TEST_MOVIE_GENRES_FEATURE_ID: {"value_type": "STRING_ARRAY"},
+            _TEST_MOVIE_AVERAGE_RATING_FEATURE_ID: {"value_type": "DOUBLE"},
+        }
+
+        movie_entity_type.batch_create_features(feature_configs=movie_feature_configs)
+
+        get_movie_title_feature = movie_entity_type.get_feature(
+            feature_id=_TEST_MOVIE_TITLE_FEATURE_ID
+        )
+        get_movie_genres_feature = movie_entity_type.get_feature(
+            feature_id=_TEST_MOVIE_GENRES_FEATURE_ID
+        )
+        get_movie_avg_rating_feature = movie_entity_type.get_feature(
+            feature_id=_TEST_MOVIE_AVERAGE_RATING_FEATURE_ID
+        )
+
+        list_movie_features = movie_entity_type.list_features()
+        movie_feature_resource_names = [
+            feature.resource_name for feature in list_movie_features
+        ]
+
+        assert get_movie_title_feature.resource_name in movie_feature_resource_names
+        assert get_movie_genres_feature.resource_name in movie_feature_resource_names
+        assert (
+            get_movie_avg_rating_feature.resource_name in movie_feature_resource_names
+        )
+
+    def test_ingest_feature_values_from_df_using_feature_time_column_and_online_read_multiple_entities(
+        self, shared_state, caplog
+    ):
+
+        assert shared_state["movie_entity_type"]
+        movie_entity_type = shared_state["movie_entity_type"]
+
+        caplog.set_level(logging.INFO)
+
+        aiplatform.init(
+            project=e2e_base._PROJECT,
+            location=e2e_base._LOCATION,
+        )
+
+        read_feature_ids = ["average_rating", "title", "genres"]
+
+        movie_entity_views_df_before_ingest = movie_entity_type.read(
+            entity_ids=["movie_01", "movie_02"],
+            feature_ids=read_feature_ids,
+        )
+        expected_data_before_ingest = [
+            {
+                "entity_id": "movie_01",
+                "average_rating": None,
+                "title": None,
+                "genres": None,
+            },
+            {
+                "entity_id": "movie_02",
+                "average_rating": None,
+                "title": None,
+                "genres": None,
+            },
+        ]
+        expected_movie_entity_views_df_before_ingest = pd.DataFrame(
+            data=expected_data_before_ingest, columns=read_feature_ids
+        )
+
+        movie_entity_views_df_before_ingest.equals(
+            expected_movie_entity_views_df_before_ingest
+        )
+
+        movies_df = pd.DataFrame(
+            data=[
+                {
+                    "movie_id": "movie_01",
+                    "average_rating": 4.9,
+                    "title": "The Shawshank Redemption",
+                    "genres": ["Drama"],
+                    "update_time": "2021-08-20 20:44:11.094375+00:00",
+                },
+                {
+                    "movie_id": "movie_02",
+                    "average_rating": 4.2,
+                    "title": "The Shining",
+                    "genres": ["Horror"],
+                    "update_time": "2021-08-20 20:44:11.094375+00:00",
+                },
+            ],
+            columns=["movie_id", "average_rating", "title", "genres", "update_time"],
+        )
+        movies_df["update_time"] = pd.to_datetime(movies_df["update_time"], utc=True)
+        feature_time_column = "update_time"
+
+        movie_entity_type.ingest_from_df(
+            feature_ids=[
+                _TEST_MOVIE_TITLE_FEATURE_ID,
+                _TEST_MOVIE_GENRES_FEATURE_ID,
+                _TEST_MOVIE_AVERAGE_RATING_FEATURE_ID,
+            ],
+            feature_time=feature_time_column,
+            df_source=movies_df,
+            entity_id_field="movie_id",
+        )
+
+        movie_entity_views_df_after_ingest = movie_entity_type.read(
+            entity_ids=["movie_01", "movie_02"],
+            feature_ids=read_feature_ids,
+        )
+        expected_data_after_ingest = [
+            {
+                "movie_id": "movie_01",
+                "average_rating": 4.9,
+                "title": "The Shawshank Redemption",
+                "genres": ["Drama"],
+            },
+            {
+                "movie_id": "movie_02",
+                "average_rating": 4.2,
+                "title": "The Shining",
+                "genres": ["Horror"],
+            },
+        ]
+        expected_movie_entity_views_df_after_ingest = pd.DataFrame(
+            data=expected_data_after_ingest, columns=read_feature_ids
+        )
+
+        movie_entity_views_df_after_ingest.equals(
+            expected_movie_entity_views_df_after_ingest
+        )
+
+        assert "EntityType feature values imported." in caplog.text
+        caplog.clear()
+
+    def test_ingest_feature_values_from_df_using_feature_time_datetime_and_online_read_single_entity(
+        self, shared_state, caplog
+    ):
+        assert shared_state["movie_entity_type"]
+        movie_entity_type = shared_state["movie_entity_type"]
+
+        caplog.set_level(logging.INFO)
+
+        aiplatform.init(
+            project=e2e_base._PROJECT,
+            location=e2e_base._LOCATION,
+        )
+
+        movies_df = pd.DataFrame(
+            data=[
+                {
+                    "movie_id": "movie_03",
+                    "average_rating": 4.5,
+                    "title": "Cinema Paradiso",
+                    "genres": ["Romance"],
+                },
+                {
+                    "movie_id": "movie_04",
+                    "average_rating": 4.6,
+                    "title": "The Dark Knight",
+                    "genres": ["Action"],
+                },
+            ],
+            columns=["movie_id", "average_rating", "title", "genres"],
+        )
+
+        feature_time_datetime_str = datetime.datetime.now().isoformat(
+            sep=" ", timespec="milliseconds"
+        )
+        feature_time_datetime = datetime.datetime.strptime(
+            feature_time_datetime_str, "%Y-%m-%d %H:%M:%S.%f"
+        )
+
+        movie_entity_type.ingest_from_df(
+            feature_ids=[
+                _TEST_MOVIE_TITLE_FEATURE_ID,
+                _TEST_MOVIE_GENRES_FEATURE_ID,
+                _TEST_MOVIE_AVERAGE_RATING_FEATURE_ID,
+            ],
+            feature_time=feature_time_datetime,
+            df_source=movies_df,
+            entity_id_field="movie_id",
+        )
+
+        movie_entity_views_df_avg_rating = movie_entity_type.read(
+            entity_ids="movie_04",
+            feature_ids="average_rating",
+        )
+        expected_data_avg_rating = [
+            {"movie_id": "movie_04", "average_rating": 4.6},
+        ]
+        expected_movie_entity_views_df_avg_rating = pd.DataFrame(
+            data=expected_data_avg_rating, columns=["average_rating"]
+        )
+
+        movie_entity_views_df_avg_rating.equals(
+            expected_movie_entity_views_df_avg_rating
+        )
+
+        assert "EntityType feature values imported." in caplog.text
+
+        caplog.clear()
+
+    def test_write_features(self, shared_state, caplog):
+        assert shared_state["movie_entity_type"]
+        movie_entity_type = shared_state["movie_entity_type"]
+
+        caplog.set_level(logging.INFO)
+
+        aiplatform.init(
+            project=e2e_base._PROJECT,
+            location=e2e_base._LOCATION,
+        )
+
+        # Create pandas DataFrame
+        movies_df = pd.DataFrame(
+            data=[
+                {
+                    "entity_id": "movie_01",
+                    "average_rating": 4.9,
+                    "title": "The Shawshank Redemption",
+                    "genres": ["Drama", "Action"],
+                },
+                {
+                    "entity_id": "movie_02",
+                    "average_rating": 4.4,
+                    "title": "The Shining",
+                    "genres": ["Horror", "Action"],
+                },
+            ],
+            columns=["entity_id", "average_rating", "title", "genres"],
+        )
+        movies_df = movies_df.set_index("entity_id")
+
+        # Write feature values
+        movie_entity_type.preview.write_feature_values(instances=movies_df)
+        movie_entity_type.write_feature_values(
+            instances={"movie_02": {"average_rating": 4.5}}
+        )
+
+        # Ensure writing feature values overwrites previous values
+        movie_entity_df_avg_rating_genres = movie_entity_type.read(
+            entity_ids="movie_02", feature_ids=["average_rating", "genres"]
+        )
+        expected_data_avg_rating = [
+            {
+                "entity_id": "movie_02",
+                "average_rating": 4.5,
+                "genres": ["Horror", "Action"],
+            },
+        ]
+        expected_movie_entity_df_avg_rating_genres = pd.DataFrame(
+            data=expected_data_avg_rating,
+            columns=["entity_id", "average_rating", "genres"],
+        )
+        expected_movie_entity_df_avg_rating_genres.equals(
+            movie_entity_df_avg_rating_genres
+        )
+
+        assert "EntityType feature values written." in caplog.text
+
+        caplog.clear()
+
+    def test_search_features(self, shared_state):
+
+        aiplatform.init(
+            project=e2e_base._PROJECT,
+            location=e2e_base._LOCATION,
+        )
+
+        list_searched_features = aiplatform.Feature.search()
+        assert len(list_searched_features) >= 1
+
+    def test_batch_serve_to_df(self, shared_state, caplog):
+
+        assert shared_state["featurestore"]
+        assert shared_state["user_age_feature_resource_name"]
+        assert shared_state["user_gender_feature_resource_name"]
+        assert shared_state["user_liked_genres_feature_resource_name"]
+
+        featurestore = shared_state["featurestore"]
+
+        user_age_feature_resource_name = shared_state["user_age_feature_resource_name"]
+        user_gender_feature_resource_name = shared_state[
+            "user_gender_feature_resource_name"
+        ]
+        user_liked_genres_feature_resource_name = shared_state[
+            "user_liked_genres_feature_resource_name"
+        ]
+
+        aiplatform.init(
+            project=e2e_base._PROJECT,
+            location=e2e_base._LOCATION,
+        )
+
+        caplog.set_level(logging.INFO)
+
+        read_instances_df = pd.DataFrame(
+            data=[
+                ["alice", "movie_01", "2021-09-15T08:28:14Z"],
+                ["bob", "movie_02", "2021-09-15T08:28:14Z"],
+                ["dav", "movie_03", "2021-09-15T08:28:14Z"],
+                ["eve", "movie_04", "2021-09-15T08:28:14Z"],
+                ["alice", "movie_03", "2021-09-14T09:35:15Z"],
+                ["bob", "movie_04", "2020-02-14T09:35:15Z"],
+            ],
+            columns=["users", "movies", "timestamp"],
+        )
+        read_instances_df["timestamp"] = pd.to_datetime(
+            read_instances_df["timestamp"], utc=True
+        )
+
+        df = featurestore.batch_serve_to_df(
+            serving_feature_ids={
+                _TEST_USER_ENTITY_TYPE_ID: [
+                    _TEST_USER_AGE_FEATURE_ID,
+                    _TEST_USER_GENDER_FEATURE_ID,
+                    _TEST_USER_LIKED_GENRES_FEATURE_ID,
+                ],
+                _TEST_MOVIE_ENTITY_TYPE_ID: [
+                    _TEST_MOVIE_TITLE_FEATURE_ID,
+                    _TEST_MOVIE_GENRES_FEATURE_ID,
+                    _TEST_MOVIE_AVERAGE_RATING_FEATURE_ID,
+                ],
+            },
+            read_instances_df=read_instances_df,
+            feature_destination_fields={
+                user_age_feature_resource_name: "user_age_dest",
+                user_gender_feature_resource_name: "user_gender_dest",
+                user_liked_genres_feature_resource_name: "user_liked_genres_dest",
+            },
+        )
+
+        expected_df_columns = [
+            "timestamp",
+            "entity_type_users",
+            "user_age_dest",
+            "user_gender_dest",
+            "user_liked_genres_dest",
+            "entity_type_movies",
+            "title",
+            "genres",
+            "average_rating",
+        ]
+
+        assert isinstance(df, pd.DataFrame)
+        assert list(df.columns) == expected_df_columns
+        assert df.size == 54
+        assert "Featurestore feature values served." in caplog.text
+
+        caplog.clear()
+
+    def test_batch_serve_to_gcs(self, shared_state, caplog):
+
+        assert shared_state["featurestore"]
+        assert shared_state["bucket"]
+        assert shared_state["user_age_feature_resource_name"]
+        assert shared_state["user_gender_feature_resource_name"]
+        assert shared_state["user_liked_genres_feature_resource_name"]
+
+        featurestore = shared_state["featurestore"]
+        bucket_name = shared_state["staging_bucket_name"]
+        user_age_feature_resource_name = shared_state["user_age_feature_resource_name"]
+        user_gender_feature_resource_name = shared_state[
+            "user_gender_feature_resource_name"
+        ]
+        user_liked_genres_feature_resource_name = shared_state[
+            "user_liked_genres_feature_resource_name"
+        ]
+
+        aiplatform.init(
+            project=e2e_base._PROJECT,
+            location=e2e_base._LOCATION,
+        )
+
+        caplog.set_level(logging.INFO)
+
+        featurestore.batch_serve_to_gcs(
+            serving_feature_ids={
+                _TEST_USER_ENTITY_TYPE_ID: [
+                    _TEST_USER_AGE_FEATURE_ID,
+                    _TEST_USER_GENDER_FEATURE_ID,
+                    _TEST_USER_LIKED_GENRES_FEATURE_ID,
+                ],
+                _TEST_MOVIE_ENTITY_TYPE_ID: [
+                    _TEST_MOVIE_TITLE_FEATURE_ID,
+                    _TEST_MOVIE_GENRES_FEATURE_ID,
+                    _TEST_MOVIE_AVERAGE_RATING_FEATURE_ID,
+                ],
+            },
+            read_instances_uri=_TEST_READ_INSTANCE_SRC,
+            feature_destination_fields={
+                user_age_feature_resource_name: "user_age_dest",
+                user_gender_feature_resource_name: "user_gender_dest",
+                user_liked_genres_feature_resource_name: "user_liked_genres_dest",
+            },
+            gcs_destination_output_uri_prefix=f"gs://{bucket_name}/featurestore_test/tfrecord",
+            gcs_destination_type="tfrecord",
+        )
+        assert "Featurestore feature values served." in caplog.text
+
+        caplog.clear()
+
+    def test_batch_serve_to_bq(self, shared_state, caplog):
+
+        assert shared_state["featurestore"]
+        assert shared_state["bigquery_dataset"]
+        assert shared_state["user_age_feature_resource_name"]
+        assert shared_state["user_gender_feature_resource_name"]
+        assert shared_state["user_liked_genres_feature_resource_name"]
+
+        featurestore = shared_state["featurestore"]
+        bigquery_dataset_id = shared_state["bigquery_dataset_id"]
+        user_age_feature_resource_name = shared_state["user_age_feature_resource_name"]
+        user_gender_feature_resource_name = shared_state[
+            "user_gender_feature_resource_name"
+        ]
+        user_liked_genres_feature_resource_name = shared_state[
+            "user_liked_genres_feature_resource_name"
+        ]
+
+        aiplatform.init(
+            project=e2e_base._PROJECT,
+            location=e2e_base._LOCATION,
+        )
+
+        caplog.set_level(logging.INFO)
+
+        featurestore.batch_serve_to_bq(
+            serving_feature_ids={
+                _TEST_USER_ENTITY_TYPE_ID: [
+                    _TEST_USER_AGE_FEATURE_ID,
+                    _TEST_USER_GENDER_FEATURE_ID,
+                    _TEST_USER_LIKED_GENRES_FEATURE_ID,
+                ],
+                _TEST_MOVIE_ENTITY_TYPE_ID: [
+                    _TEST_MOVIE_TITLE_FEATURE_ID,
+                    _TEST_MOVIE_GENRES_FEATURE_ID,
+                    _TEST_MOVIE_AVERAGE_RATING_FEATURE_ID,
+                ],
+            },
+            read_instances_uri=_TEST_READ_INSTANCE_SRC,
+            feature_destination_fields={
+                user_age_feature_resource_name: "user_age_dest",
+                user_gender_feature_resource_name: "user_gender_dest",
+                user_liked_genres_feature_resource_name: "user_liked_genres_dest",
+            },
+            bq_destination_output_uri=f"bq://{bigquery_dataset_id}.test_table",
+        )
+
+        assert "Featurestore feature values served." in caplog.text
+        caplog.clear()
+
+    def test_online_reads(self, shared_state):
+        assert shared_state["user_entity_type"]
+        assert shared_state["movie_entity_type"]
+
+        user_entity_type = shared_state["user_entity_type"]
+        movie_entity_type = shared_state["movie_entity_type"]
+
+        user_entity_views = user_entity_type.read(entity_ids="alice")
+        assert isinstance(user_entity_views, pd.DataFrame)
+
+        movie_entity_views = movie_entity_type.read(
+            entity_ids=["movie_01", "movie_04"],
+            feature_ids=[_TEST_MOVIE_TITLE_FEATURE_ID, _TEST_MOVIE_GENRES_FEATURE_ID],
+        )
+        assert isinstance(movie_entity_views, pd.DataFrame)
+
+        movie_entity_views = movie_entity_type.read(
+            entity_ids="movie_01",
+            feature_ids=[_TEST_MOVIE_TITLE_FEATURE_ID, _TEST_MOVIE_GENRES_FEATURE_ID],
+        )
+        assert isinstance(movie_entity_views, pd.DataFrame)

testbed/googleapis__python-aiplatform/tests/system/aiplatform/test_initializer.py

@@ -0,0 +1,59 @@
+# -*- coding: utf-8 -*-
+
+# Copyright 2023 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import pytest
+
+from google.auth import credentials as auth_credentials
+
+from google.cloud import aiplatform
+from google.cloud.aiplatform import initializer as aiplatform_initializer
+from tests.system.aiplatform import e2e_base
+
+
+class TestInitializer(e2e_base.TestEndToEnd):
+    """Tests the _set_google_auth_default() functionality in initializer._Config."""
+
+    _temp_prefix = "test_initializer_"
+
+    def test_init_calls_set_google_auth_default(self):
+        aiplatform.init(project=e2e_base._PROJECT)
+
+        # init() with only creds shouldn't overwrite the project
+        creds = auth_credentials.AnonymousCredentials()
+        aiplatform.init(credentials=creds)
+
+        assert aiplatform.initializer.global_config.project == e2e_base._PROJECT
+        assert aiplatform.initializer.global_config.credentials == creds
+
+        # init() with only project shouldn't overwrite creds
+        aiplatform.init(project=e2e_base._PROJECT)
+        assert aiplatform.initializer.global_config.credentials == creds
+
+    def test_init_rest_async_incorrect_credentials(self):
+        # Async REST credentials must be explicitly set using
+        # _set_async_rest_credentials() for async REST transport.
+        creds = auth_credentials.AnonymousCredentials()
+        aiplatform.init(
+            project=e2e_base._PROJECT,
+            location=e2e_base._LOCATION,
+            api_transport="rest",
+        )
+
+        # System tests are run on Python 3.10 which has async deps.
+        with pytest.raises(ValueError):
+            # Expect a ValueError for passing in sync credentials.
+            aiplatform_initializer._set_async_rest_credentials(credentials=creds)

testbed/googleapis__python-aiplatform/tests/system/aiplatform/test_language_models.py

@@ -0,0 +1,559 @@
+# -*- coding: utf-8 -*-
+
+# Copyright 2023 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+# pylint: disable=protected-access, g-multiple-import
+
+import pytest
+
+from google import auth
+from google.cloud import aiplatform
+from google.cloud.aiplatform.compat.types import (
+    job_state as gca_job_state,
+)
+from tests.system.aiplatform import e2e_base
+from google.cloud.aiplatform.utils import gcs_utils
+from vertexai import language_models
+from vertexai.preview import (
+    language_models as preview_language_models,
+)
+from vertexai.preview.language_models import (
+    ChatModel,
+    CodeGenerationModel,
+    InputOutputTextPair,
+    TextGenerationModel,
+    TextGenerationResponse,
+    TextEmbeddingModel,
+)
+
+STAGING_DIR_URI = "gs://ucaip-samples-us-central1/tmp/staging"
+
+
+class TestLanguageModels(e2e_base.TestEndToEnd):
+    """System tests for language models."""
+
+    _temp_prefix = "temp_language_models_test_"
+
+    @pytest.mark.parametrize("api_transport", ["grpc", "rest"])
+    def test_text_generation(self, api_transport):
+        aiplatform.init(
+            project=e2e_base._PROJECT,
+            location=e2e_base._LOCATION,
+            api_transport=api_transport,
+        )
+
+        model = TextGenerationModel.from_pretrained("google/text-bison@001")
+        grounding_source = language_models.GroundingSource.WebSearch()
+        response = model.predict(
+            "What is the best recipe for cupcakes? Recipe:",
+            max_output_tokens=128,
+            temperature=0.0,
+            top_p=1.0,
+            top_k=5,
+            stop_sequences=["# %%"],
+            grounding_source=grounding_source,
+        )
+        assert response.text or response.is_blocked
+
+    @pytest.mark.parametrize("api_transport", ["grpc", "rest"])
+    def test_text_generation_preview_count_tokens(self, api_transport):
+        aiplatform.init(
+            project=e2e_base._PROJECT,
+            location=e2e_base._LOCATION,
+            api_transport=api_transport,
+        )
+
+        model = preview_language_models.TextGenerationModel.from_pretrained(
+            "google/text-bison@001"
+        )
+
+        response = model.count_tokens(["How are you doing?"])
+
+        assert response.total_tokens
+        assert response.total_billable_characters
+
+    @pytest.mark.asyncio
+    async def test_text_generation_model_predict_async(self):
+        aiplatform.init(
+            project=e2e_base._PROJECT,
+            location=e2e_base._LOCATION,
+        )
+
+        model = TextGenerationModel.from_pretrained("google/text-bison@001")
+        grounding_source = language_models.GroundingSource.WebSearch()
+        response = await model.predict_async(
+            "What is the best recipe for cupcakes? Recipe:",
+            max_output_tokens=128,
+            temperature=0.0,
+            top_p=1.0,
+            top_k=5,
+            stop_sequences=["# %%"],
+            grounding_source=grounding_source,
+        )
+        assert response.text or response.is_blocked
+
+    @pytest.mark.parametrize("api_transport", ["grpc", "rest"])
+    def test_text_generation_streaming(self, api_transport):
+        aiplatform.init(
+            project=e2e_base._PROJECT,
+            location=e2e_base._LOCATION,
+            api_transport=api_transport,
+        )
+
+        model = TextGenerationModel.from_pretrained("google/text-bison@001")
+
+        for response in model.predict_streaming(
+            "What is the best recipe for cupcakes? Recipe:",
+            max_output_tokens=128,
+            temperature=0.0,
+            top_p=1.0,
+            top_k=5,
+        ):
+            assert response.text or response.is_blocked
+
+    @pytest.mark.parametrize("api_transport", ["grpc", "rest"])
+    def test_preview_text_generation_from_pretrained(self, api_transport):
+        aiplatform.init(
+            project=e2e_base._PROJECT,
+            location=e2e_base._LOCATION,
+            api_transport=api_transport,
+        )
+
+        model = preview_language_models.TextGenerationModel.from_pretrained(
+            "google/text-bison@001"
+        )
+
+        response = model.predict(
+            "What is the best recipe for cupcakes? Recipe:",
+            max_output_tokens=128,
+            temperature=0.0,
+            top_p=1.0,
+            top_k=5,
+            stop_sequences=["# %%"],
+        )
+        assert response.text or response.is_blocked
+
+        assert isinstance(model, preview_language_models.TextGenerationModel)
+
+    @pytest.mark.parametrize("api_transport", ["grpc", "rest"])
+    def test_chat_on_chat_model(self, api_transport):
+        aiplatform.init(
+            project=e2e_base._PROJECT,
+            location=e2e_base._LOCATION,
+            api_transport=api_transport,
+        )
+        chat_model = ChatModel.from_pretrained("google/chat-bison@001")
+        grounding_source = language_models.GroundingSource.WebSearch()
+        chat = chat_model.start_chat(
+            context="My name is Ned. You are my personal assistant. My favorite movies are Lord of the Rings and Hobbit.",
+            examples=[
+                InputOutputTextPair(
+                    input_text="Who do you work for?",
+                    output_text="I work for Ned.",
+                ),
+                InputOutputTextPair(
+                    input_text="What do I like?",
+                    output_text="Ned likes watching movies.",
+                ),
+            ],
+            temperature=0.0,
+            stop_sequences=["# %%"],
+        )
+
+        message1 = "Are my favorite movies based on a book series?"
+        response1 = chat.send_message(
+            message1,
+            grounding_source=grounding_source,
+        )
+        assert response1.text
+        assert response1.grounding_metadata
+        assert len(chat.message_history) == 2
+        assert chat.message_history[0].author == chat.USER_AUTHOR
+        assert chat.message_history[0].content == message1
+        assert chat.message_history[1].author == chat.MODEL_AUTHOR
+
+        message2 = "When were these books published?"
+        response2 = chat.send_message(
+            message2, temperature=0.1, grounding_source=grounding_source
+        )
+        assert response2.text
+        assert response2.grounding_metadata
+        assert len(chat.message_history) == 4
+        assert chat.message_history[2].author == chat.USER_AUTHOR
+        assert chat.message_history[2].content == message2
+        assert chat.message_history[3].author == chat.MODEL_AUTHOR
+
+    @pytest.mark.parametrize("api_transport", ["grpc", "rest"])
+    def test_chat_model_preview_count_tokens(self, api_transport):
+        aiplatform.init(
+            project=e2e_base._PROJECT,
+            location=e2e_base._LOCATION,
+            api_transport=api_transport,
+        )
+
+        chat_model = ChatModel.from_pretrained("google/chat-bison@001")
+
+        chat = chat_model.start_chat()
+
+        chat.send_message("What should I do today?")
+
+        response_with_history = chat.count_tokens("Any ideas?")
+
+        response_without_history = chat_model.start_chat().count_tokens(
+            "What should I do today?"
+        )
+
+        assert (
+            response_with_history.total_tokens > response_without_history.total_tokens
+        )
+        assert (
+            response_with_history.total_billable_characters
+            > response_without_history.total_billable_characters
+        )
+
+    @pytest.mark.asyncio
+    async def test_chat_model_async(self):
+        aiplatform.init(
+            project=e2e_base._PROJECT,
+            location=e2e_base._LOCATION,
+        )
+
+        chat_model = ChatModel.from_pretrained("google/chat-bison@001")
+        grounding_source = language_models.GroundingSource.WebSearch()
+        chat = chat_model.start_chat(
+            context="My name is Ned. You are my personal assistant. My favorite movies are Lord of the Rings and Hobbit.",
+            examples=[
+                InputOutputTextPair(
+                    input_text="Who do you work for?",
+                    output_text="I work for Ned.",
+                ),
+                InputOutputTextPair(
+                    input_text="What do I like?",
+                    output_text="Ned likes watching movies.",
+                ),
+            ],
+            temperature=0.0,
+            stop_sequences=["# %%"],
+        )
+
+        message1 = "Are my favorite movies based on a book series?"
+        response1 = await chat.send_message_async(
+            message1,
+            grounding_source=grounding_source,
+        )
+        assert response1.text
+        assert response1.grounding_metadata
+        assert len(chat.message_history) == 2
+        assert chat.message_history[0].author == chat.USER_AUTHOR
+        assert chat.message_history[0].content == message1
+        assert chat.message_history[1].author == chat.MODEL_AUTHOR
+
+        message2 = "When were these books published?"
+        response2 = await chat.send_message_async(
+            message2,
+            temperature=0.1,
+            grounding_source=grounding_source,
+        )
+        assert response2.text
+        assert response2.grounding_metadata
+        assert len(chat.message_history) == 4
+        assert chat.message_history[2].author == chat.USER_AUTHOR
+        assert chat.message_history[2].content == message2
+        assert chat.message_history[3].author == chat.MODEL_AUTHOR
+
+    @pytest.mark.parametrize("api_transport", ["grpc", "rest"])
+    def test_chat_model_send_message_streaming(self, api_transport):
+        aiplatform.init(
+            project=e2e_base._PROJECT,
+            location=e2e_base._LOCATION,
+            api_transport=api_transport,
+        )
+
+        chat_model = ChatModel.from_pretrained("google/chat-bison@001")
+        chat = chat_model.start_chat(
+            context="My name is Ned. You are my personal assistant. My favorite movies are Lord of the Rings and Hobbit.",
+            examples=[
+                InputOutputTextPair(
+                    input_text="Who do you work for?",
+                    output_text="I work for Ned.",
+                ),
+                InputOutputTextPair(
+                    input_text="What do I like?",
+                    output_text="Ned likes watching movies.",
+                ),
+            ],
+            temperature=0.0,
+        )
+
+        message1 = "Are my favorite movies based on a book series?"
+        for response in chat.send_message_streaming(message1):
+            assert isinstance(response, TextGenerationResponse)
+        assert len(chat.message_history) == 2
+        assert chat.message_history[0].author == chat.USER_AUTHOR
+        assert chat.message_history[0].content == message1
+        assert chat.message_history[1].author == chat.MODEL_AUTHOR
+
+        message2 = "When were these books published?"
+        for response2 in chat.send_message_streaming(
+            message2,
+            temperature=0.1,
+        ):
+            assert isinstance(response2, TextGenerationResponse)
+        assert len(chat.message_history) == 4
+        assert chat.message_history[2].author == chat.USER_AUTHOR
+        assert chat.message_history[2].content == message2
+        assert chat.message_history[3].author == chat.MODEL_AUTHOR
+
+    @pytest.mark.parametrize("api_transport", ["grpc", "rest"])
+    def test_text_embedding(self, api_transport):
+        aiplatform.init(
+            project=e2e_base._PROJECT,
+            location=e2e_base._LOCATION,
+            api_transport=api_transport,
+        )
+
+        model = TextEmbeddingModel.from_pretrained("google/textembedding-gecko@001")
+        # One short text, one llong text (to check truncation)
+        texts = ["What is life?", "What is life?" * 1000]
+        embeddings = model.get_embeddings(texts)
+        assert len(embeddings) == 2
+        assert len(embeddings[0].values) == 768
+        assert embeddings[0].statistics.token_count > 0
+        assert not embeddings[0].statistics.truncated
+
+        assert len(embeddings[1].values) == 768
+        assert embeddings[1].statistics.token_count > 1000
+        assert embeddings[1].statistics.truncated
+
+    @pytest.mark.asyncio
+    async def test_text_embedding_async(self):
+        aiplatform.init(
+            project=e2e_base._PROJECT,
+            location=e2e_base._LOCATION,
+        )
+
+        model = TextEmbeddingModel.from_pretrained("google/textembedding-gecko@001")
+        # One short text, one llong text (to check truncation)
+        texts = ["What is life?", "What is life?" * 1000]
+        embeddings = await model.get_embeddings_async(texts)
+        assert len(embeddings) == 2
+        assert len(embeddings[0].values) == 768
+        assert embeddings[0].statistics.token_count > 0
+        assert not embeddings[0].statistics.truncated
+
+        assert len(embeddings[1].values) == 768
+        assert embeddings[1].statistics.token_count > 1000
+        assert embeddings[1].statistics.truncated
+
+    # TODO(b/339907038): Re-enable test after timeout issue is fixed.
+    @pytest.mark.skip(reason="Causes system tests timeout")
+    @pytest.mark.parametrize("api_transport", ["grpc", "rest"])
+    def test_tuning(self, shared_state, api_transport):
+        """Test tuning, listing and loading models."""
+        credentials, _ = auth.default(
+            scopes=["https://www.googleapis.com/auth/cloud-platform"]
+        )
+        aiplatform.init(
+            project=e2e_base._PROJECT,
+            location=e2e_base._LOCATION,
+            credentials=credentials,
+            api_transport=api_transport,
+        )
+
+        model = language_models.TextGenerationModel.from_pretrained("text-bison@001")
+
+        import pandas
+
+        training_data = pandas.DataFrame(
+            data=[
+                {"input_text": "Input 0", "output_text": "Output 0"},
+                {"input_text": "Input 1", "output_text": "Output 1"},
+                {"input_text": "Input 2", "output_text": "Output 2"},
+                {"input_text": "Input 3", "output_text": "Output 3"},
+                {"input_text": "Input 4", "output_text": "Output 4"},
+                {"input_text": "Input 5", "output_text": "Output 5"},
+                {"input_text": "Input 6", "output_text": "Output 6"},
+                {"input_text": "Input 7", "output_text": "Output 7"},
+                {"input_text": "Input 8", "output_text": "Output 8"},
+                {"input_text": "Input 9", "output_text": "Output 9"},
+            ]
+        )
+
+        dataset_uri = (
+            STAGING_DIR_URI + "/veretx_llm_tuning_training_data.text-bison.dummy.jsonl"
+        )
+        gcs_utils._upload_pandas_df_to_gcs(
+            df=training_data, upload_gcs_path=dataset_uri
+        )
+
+        tuning_job = model.tune_model(
+            training_data=training_data,
+            train_steps=1,
+            tuning_job_location="europe-west4",
+            tuned_model_location="us-central1",
+            learning_rate_multiplier=2.0,
+            tuning_evaluation_spec=preview_language_models.TuningEvaluationSpec(
+                evaluation_data=dataset_uri,
+                evaluation_interval=37,
+                enable_early_stopping=True,
+            ),
+        )
+        tuned_model1 = tuning_job.get_tuned_model()
+
+        # According to the Pipelines design, external resources created by a pipeline
+        # must not be modified or deleted. Otherwise caching will break next pipeline runs.
+        shared_state.setdefault("resources", [])
+        shared_state["resources"].append(tuned_model1._endpoint)
+        shared_state["resources"].extend(
+            aiplatform.Model(model_name=deployed_model.model)
+            for deployed_model in tuned_model1._endpoint.list_models()
+        )
+        # Deleting the Endpoint is a little less bad since the LLM SDK will recreate it, but it's not advised for the same reason.
+
+        # Testing the new model returned by the `tuning_job.get_tuned_model` method
+        response1 = tuned_model1.predict(
+            "What is the best recipe for cupcakes? Recipe:",
+            max_output_tokens=128,
+            temperature=0.0,
+            top_p=1.0,
+            top_k=5,
+        )
+        assert response1.text or response1.is_blocked
+
+        # Testing listing and getting tuned models
+        tuned_model_names = model.list_tuned_model_names()
+        assert tuned_model_names
+        tuned_model_name = tuned_model_names[0]
+
+        tuned_model = TextGenerationModel.get_tuned_model(tuned_model_name)
+
+        tuned_model_response = tuned_model.predict(
+            "What is the best recipe for cupcakes? Recipe:",
+            max_output_tokens=128,
+            temperature=0.0,
+            top_p=1.0,
+            top_k=5,
+        )
+        assert tuned_model_response.text or tuned_model_response.is_blocked
+
+    @pytest.mark.parametrize("api_transport", ["grpc", "rest"])
+    def test_batch_prediction_for_text_generation(self, api_transport):
+        source_uri = "gs://ucaip-samples-us-central1/model/llm/batch_prediction/batch_prediction_prompts1.jsonl"
+        destination_uri_prefix = "gs://ucaip-samples-us-central1/model/llm/batch_prediction/predictions/text-bison@001_"
+
+        aiplatform.init(
+            project=e2e_base._PROJECT,
+            location=e2e_base._LOCATION,
+            api_transport=api_transport,
+        )
+
+        model = TextGenerationModel.from_pretrained("text-bison@001")
+        job = model.batch_predict(
+            dataset=source_uri,
+            destination_uri_prefix=destination_uri_prefix,
+            model_parameters={"temperature": 0, "top_p": 1, "top_k": 5},
+        )
+
+        job.wait_for_resource_creation()
+        job.wait()
+        gapic_job = job._gca_resource
+        job.delete()
+
+        assert gapic_job.state == gca_job_state.JobState.JOB_STATE_SUCCEEDED
+
+    @pytest.mark.parametrize("api_transport", ["grpc", "rest"])
+    def test_batch_prediction_for_textembedding(self, api_transport):
+        source_uri = "gs://ucaip-samples-us-central1/model/llm/batch_prediction/batch_prediction_prompts_textembedding_dummy1.jsonl"
+        destination_uri_prefix = "gs://ucaip-samples-us-central1/model/llm/batch_prediction/predictions/textembedding-gecko@001_"
+
+        aiplatform.init(
+            project=e2e_base._PROJECT,
+            location=e2e_base._LOCATION,
+            api_transport=api_transport,
+        )
+
+        model = TextEmbeddingModel.from_pretrained("textembedding-gecko@001")
+        job = model.batch_predict(
+            dataset=source_uri,
+            destination_uri_prefix=destination_uri_prefix,
+            model_parameters={},
+        )
+
+        job.wait_for_resource_creation()
+        job.wait()
+        gapic_job = job._gca_resource
+        job.delete()
+
+        assert gapic_job.state == gca_job_state.JobState.JOB_STATE_SUCCEEDED
+
+    @pytest.mark.parametrize("api_transport", ["grpc", "rest"])
+    def test_batch_prediction_for_code_generation(self, api_transport):
+        source_uri = "gs://ucaip-samples-us-central1/model/llm/batch_prediction/code-bison.batch_prediction_prompts.1.jsonl"
+        destination_uri_prefix = "gs://ucaip-samples-us-central1/model/llm/batch_prediction/predictions/code-bison@001_"
+
+        aiplatform.init(
+            project=e2e_base._PROJECT,
+            location=e2e_base._LOCATION,
+            api_transport=api_transport,
+        )
+
+        model = CodeGenerationModel.from_pretrained("code-bison@001")
+        job = model.batch_predict(
+            dataset=source_uri,
+            destination_uri_prefix=destination_uri_prefix,
+            model_parameters={"temperature": 0},
+        )
+
+        job.wait_for_resource_creation()
+        job.wait()
+        gapic_job = job._gca_resource
+        job.delete()
+
+        assert gapic_job.state == gca_job_state.JobState.JOB_STATE_SUCCEEDED
+
+    @pytest.mark.parametrize("api_transport", ["grpc", "rest"])
+    def test_code_generation_streaming(self, api_transport):
+        aiplatform.init(
+            project=e2e_base._PROJECT,
+            location=e2e_base._LOCATION,
+            api_transport=api_transport,
+        )
+
+        model = language_models.CodeGenerationModel.from_pretrained("code-bison@001")
+
+        for response in model.predict_streaming(
+            prefix="def reverse_string(s):",
+            # code-bison does not support suffix
+            # suffix="    return s",
+            max_output_tokens=128,
+            temperature=0.0,
+        ):
+            assert response.text
+
+    @pytest.mark.parametrize("api_transport", ["grpc", "rest"])
+    def test_code_chat_model_send_message_streaming(self, api_transport):
+        aiplatform.init(
+            project=e2e_base._PROJECT,
+            location=e2e_base._LOCATION,
+            api_transport=api_transport,
+        )
+
+        chat_model = language_models.CodeChatModel.from_pretrained("codechat-bison@001")
+        chat = chat_model.start_chat()
+
+        message1 = "Please help write a function to calculate the max of two numbers"
+        for response in chat.send_message_streaming(message1):
+            assert response.text