Delete models:syntaxnet (#8170)

* remove tensorrt as the example repo has been moved for a while

* delete syntax net to reduce repo size as syntax net is going to move to google-research/

* Update README.md

Delete syntaxnet from readme.

* Update CODEOWNERS

Delete syntaxnet from codeowners

.gitmodules

-[submodule "tensorflow"]
-	path = research/syntaxnet/tensorflow
-	url = https://github.com/tensorflow/tensorflow.git

CODEOWNERS

@@ -49,7 +49,6 @@
 /research/street/ @theraysmith
 /research/struct2depth/ @aneliaangelova
 /research/swivel/ @waterson
-/research/syntaxnet/ @calberti @andorardo @bogatyy @markomernick
 /research/tcn/ @coreylynch @sermanet
 /research/tensorrt/ @karmel
 /research/textsum/ @panyx0718 @peterjliu

research/README.md

@@ -72,7 +72,6 @@ request.
     using a Deep RNN.
 -   [struct2depth](struct2depth): unsupervised learning of depth and ego-motion.
 -   [swivel](swivel): the Swivel algorithm for generating word embeddings.
--   [syntaxnet](syntaxnet): neural models of natural language syntax.
 -   [tcn](tcn): Self-supervised representation learning from multi-view video.
 -   [textsum](textsum): sequence-to-sequence with attention model for text
     summarization.

research/syntaxnet/.dockerignore

-.git
-bazel/
-Dockerfile*
-tensorflow/.git

research/syntaxnet/.gitignore

-/bazel-bin
-/bazel-genfiles
-/bazel-out
-/bazel-tensorflow
-/bazel-testlogs
-/bazel-tf
-/bazel-syntaxnet

research/syntaxnet/Dockerfile

-FROM ubuntu:16.04
-
-ENV SYNTAXNETDIR=/opt/tensorflow PATH=$PATH:/root/bin
-
-# Install system packages. This doesn't include everything the TensorFlow
-# dockerfile specifies, so if anything goes awry, maybe install more packages
-# from there. Also, running apt-get clean before further commands will make the
-# Docker images smaller.
-RUN mkdir -p $SYNTAXNETDIR \
-    && cd $SYNTAXNETDIR \
-    && apt-get update \
-    && apt-get install -y \
-          file \
-          git \
-          graphviz \
-          libcurl3-dev \
-          libfreetype6-dev \
-          libgraphviz-dev \
-          liblapack-dev \
-          libopenblas-dev \
-          libpng-dev \
-          libxft-dev \
-          openjdk-8-jdk \
-          python-dev \
-          python-mock \
-          python-pip \
-          python2.7 \
-          swig \
-          unzip \
-          vim \
-          wget \
-          zlib1g-dev \
-    && apt-get clean \
-    && (rm -f /var/cache/apt/archives/*.deb \
-        /var/cache/apt/archives/partial/*.deb /var/cache/apt/*.bin || true)
-
-# Install common Python dependencies. Similar to above, remove caches
-# afterwards to help keep Docker images smaller.
-RUN pip install --ignore-installed pip \
-    && python -m pip install numpy \
-    && rm -rf /root/.cache/pip /tmp/pip*
-RUN python -m pip install \
-          asciitree \
-          ipykernel \
-          jupyter \
-          matplotlib \
-          pandas \
-          protobuf \
-          scipy \
-          sklearn \
-    && python -m ipykernel.kernelspec \
-    && python -m pip install pygraphviz \
-          --install-option="--include-path=/usr/include/graphviz" \
-          --install-option="--library-path=/usr/lib/graphviz/" \
-    && python -m jupyter_core.command nbextension enable \
-          --py --sys-prefix widgetsnbextension \
-    && rm -rf /root/.cache/pip /tmp/pip*
-
-# Installs Bazel.
-RUN wget --quiet https://github.com/bazelbuild/bazel/releases/download/0.11.1/bazel-0.11.1-installer-linux-x86_64.sh \
-    && chmod +x bazel-0.11.1-installer-linux-x86_64.sh \
-    && ./bazel-0.11.1-installer-linux-x86_64.sh \
-    && rm ./bazel-0.11.1-installer-linux-x86_64.sh
-
-COPY WORKSPACE $SYNTAXNETDIR/syntaxnet/WORKSPACE
-COPY tools/bazel.rc $SYNTAXNETDIR/syntaxnet/tools/bazel.rc
-COPY tensorflow $SYNTAXNETDIR/syntaxnet/tensorflow
-
-# Compile common TensorFlow targets, which don't depend on DRAGNN / SyntaxNet
-# source. This makes it more convenient to re-compile DRAGNN / SyntaxNet for
-# development (though not as convenient as the docker-devel scripts).
-RUN cd $SYNTAXNETDIR/syntaxnet/tensorflow \
-    && tensorflow/tools/ci_build/builds/configured CPU \
-    && cd $SYNTAXNETDIR/syntaxnet \
-    && bazel build -c opt @org_tensorflow//tensorflow:tensorflow_py
-
-# Build the codez.
-WORKDIR $SYNTAXNETDIR/syntaxnet
-COPY dragnn $SYNTAXNETDIR/syntaxnet/dragnn
-COPY syntaxnet $SYNTAXNETDIR/syntaxnet/syntaxnet
-COPY third_party $SYNTAXNETDIR/syntaxnet/third_party
-COPY util/utf8 $SYNTAXNETDIR/syntaxnet/util/utf8
-RUN bazel build -c opt //dragnn/python:all //dragnn/tools:all
-
-# This makes the IP exposed actually "*"; we'll do host restrictions by passing
-# a hostname to the `docker run` command.
-COPY tensorflow/tensorflow/tools/docker/jupyter_notebook_config.py /root/.jupyter/
-EXPOSE 8888
-
-# This does not need to be compiled, only copied.
-COPY examples $SYNTAXNETDIR/syntaxnet/examples
-# Todo: Move this earlier in the file (don't want to invalidate caches for now).
-
-CMD /bin/bash -c "bazel-bin/dragnn/tools/oss_notebook_launcher notebook --debug --notebook-dir=/opt/tensorflow/syntaxnet/examples"

research/syntaxnet/README.md

-# SyntaxNet: Neural Models of Syntax.
-
-*A TensorFlow toolkit for deep learning powered natural language understanding
-(NLU).*
-
-**CoNLL**: See [here](g3doc/conll2017/README.md) for instructions for using the
-SyntaxNet/DRAGNN baseline for the CoNLL2017 Shared Task.
-
-At Google, we spend a lot of time thinking about how computer systems can read
-and understand human language in order to process it in intelligent ways. We are
-excited to share the fruits of our research with the broader community by
-releasing SyntaxNet, an open-source neural network framework for
-[TensorFlow](http://www.tensorflow.org) that provides a foundation for Natural
-Language Understanding (NLU) systems. Our release includes all the code needed
-to train new SyntaxNet models on your own data, as well as a suite of models
-that we have trained for you, and that you can use to analyze text in over 40
-languages.
-
-This repository is largely divided into two sub-packages:
-
-1.  **DRAGNN:
-    [code](https://github.com/tensorflow/models/tree/master/research/syntaxnet/dragnn),
-    [documentation](g3doc/DRAGNN.md),
-    [paper](https://arxiv.org/pdf/1703.04474.pdf)** implements Dynamic Recurrent
-    Acyclic Graphical Neural Networks (DRAGNN), a framework for building
-    multi-task, fully dynamically constructed computation graphs. Practically,
-    we use DRAGNN to extend our prior work from [Andor et al.
-    (2016)](http://arxiv.org/abs/1603.06042) with end-to-end, deep recurrent
-    models and to provide a much easier to use interface to SyntaxNet. *DRAGNN
-    is designed first and foremost as a Python library, and therefore much
-    easier to use than the original SyntaxNet implementation.*
-
-1.  **SyntaxNet:
-    [code](https://github.com/tensorflow/models/tree/master/research/syntaxnet/syntaxnet),
-    [documentation](g3doc/syntaxnet-tutorial.md)** is a transition-based
-    framework for natural language processing, with core functionality for
-    feature extraction, representing annotated data, and evaluation. As of the
-    DRAGNN release, it is recommended to train and deploy SyntaxNet models using
-    the DRAGNN framework.
-
-## How to use this library
-
-There are three ways to use SyntaxNet:
-
-*   See [here](g3doc/conll2017/README.md) for instructions for using the
-    SyntaxNet/DRAGNN baseline for the CoNLL2017 Shared Task, and running the
-    ParseySaurus models.
-*   You can use DRAGNN to train your NLP models for other tasks and dataset. See
-    "Getting started with DRAGNN" below.
-*   You can continue to use the Parsey McParseface family of pre-trained
-    SyntaxNet models. See "Pre-trained NLP models" below.
-
-## Installation
-
-### Docker installation
-
-_This process takes ~10 minutes._
-
-The simplest way to get started with DRAGNN is by loading our Docker container.
-[Here](g3doc/CLOUD.md) is a tutorial for running the DRAGNN container on
-[GCP](https://cloud.google.com) (just as applicable to your own computer).
-
-### Ubuntu 16.04+ binary installation
-
-_This process takes ~5 minutes, but is only compatible with Linux using GNU libc
-3.4.22 and above (e.g. Ubuntu 16.04)._
-
-Binary wheel packages are provided for TensorFlow and SyntaxNet. If you do not
-need to write new binary TensorFlow ops, these should suffice.
-
-*   `apt-get install -y graphviz libgraphviz-dev libopenblas-base libpng16-16
-    libxft2 python-pip python-mock`
-*   `pip install pygraphviz
-    --install-option="--include-path=/usr/include/graphviz"
-    --install-option="--library-path=/usr/lib/graphviz/"`
-*   `pip install 'ipython<6.0' protobuf numpy scipy jupyter
-    syntaxnet-with-tensorflow`
-*   `python -m jupyter_core.command nbextension enable --py --sys-prefix
-    widgetsnbextension`
-
-You can test that binary modules can be successfully imported by running,
-
-*   `python -c 'import dragnn.python.load_dragnn_cc_impl,
-    syntaxnet.load_parser_ops'`
-
-### Manual installation
-
-_This process takes 1-2 hours._
-
-Running and training SyntaxNet/DRAGNN models requires building this package from
-source. You'll need to install:
-
-*   python 2.7:
-    *   Python 3 support is not available yet
-*   bazel 0.11.1:
-    *   Follow the instructions [here](http://bazel.build/docs/install.html)
-    *   Alternately, Download bazel 0.11.1 <.deb> from
-        [https://github.com/bazelbuild/bazel/releases](https://github.com/bazelbuild/bazel/releases)
-        for your system configuration.
-    *   Install it using the command: sudo dpkg -i <.deb file>
-    *   Check for the bazel version by typing: bazel version
-*   swig:
-    *   `apt-get install swig` on Ubuntu
-    *   `brew install swig` on OSX
-*   protocol buffers, with a version supported by TensorFlow:
-    *   check your protobuf version with `pip freeze | grep protobuf`
-    *   upgrade to a supported version with `pip install -U protobuf==3.3.0`
-*   autograd, with a version supported by TensorFlow:
-    *   `pip install -U autograd==1.1.13`
-*   mock, the testing package:
-    *   `pip install mock`
-*   asciitree, to draw parse trees on the console for the demo:
-    *   `pip install asciitree`
-*   numpy, package for scientific computing:
-    *   `pip install numpy`
-*   pygraphviz to visualize traces and parse trees:
-    *   `apt-get install -y graphviz libgraphviz-dev`
-    *   `pip install pygraphviz
-        --install-option="--include-path=/usr/include/graphviz"
-        --install-option="--library-path=/usr/lib/graphviz/"`
-
-Once you completed the above steps, you can build and test SyntaxNet with the
-following commands:
-
-```shell
-  git clone --recursive https://github.com/tensorflow/models.git
-  cd models/research/syntaxnet/tensorflow
-  ./configure
-  cd ..
-  bazel test ...
-  # On Mac, run the following:
-  bazel test --linkopt=-headerpad_max_install_names \
-    dragnn/... syntaxnet/... util/utf8/...
-```
-
-Bazel should complete reporting all tests passed.
-
-Now you can install the SyntaxNet and DRAGNN Python modules with the following
-commands:
-
-```shell
-  mkdir /tmp/syntaxnet_pkg
-  bazel-bin/dragnn/tools/build_pip_package --output-dir=/tmp/syntaxnet_pkg
-  #  The filename of the .whl depends on your platform.
-  sudo pip install /tmp/syntaxnet_pkg/syntaxnet-x.xx-none-any.whl
-```
-
-To build SyntaxNet with GPU support please refer to the instructions in
-[issues/248](https://github.com/tensorflow/models/issues/248).
-
-**Note:** If you are running Docker on OSX, make sure that you have enough
-memory allocated for your Docker VM.
-
-## Getting Started
-
-We have a few guides on this README, as well as more extensive
-[documentation](g3doc/).
-
-### Learning the DRAGNN framework
-
-![DRAGNN](g3doc/unrolled-dragnn.png)
-
-An easy and visual way to get started with DRAGNN is to run our Jupyter
-notebooks for [interactive
-debugging](examples/dragnn/interactive_text_analyzer.ipynb) and [training a new
-model](examples/dragnn/trainer_tutorial.ipynb). Our tutorial
-[here](g3doc/CLOUD.md) explains how to start it up from the Docker container.
-Once you have DRAGNN installed and running, try out the
-[ParseySaurus](g3doc/conll2017) models.
-
-### Using the Pre-trained NLP models
-
-We are happy to release *Parsey McParseface*, an English parser that we have
-trained for you, and that you can use to analyze English text, along with
-[trained models for 40 languages](g3doc/universal.md) and support for text
-segmentation and morphological analysis.
-
-Once you have successfully built SyntaxNet, you can start parsing text right
-away with Parsey McParseface, located under `syntaxnet/models`. The easiest
-thing is to use or modify the included script `syntaxnet/demo.sh`, which shows a
-basic setup to parse English taking plain text as input.
-
-You can also skip right away to the [detailed SyntaxNet
-tutorial](g3doc/syntaxnet-tutorial.md).
-
-How accurate is Parsey McParseface? For the initial release, we tried to balance
-a model that runs fast enough to be useful on a single machine (e.g. ~600
-words/second on a modern desktop) and that is also the most accurate parser
-available. Here's how Parsey McParseface compares to the academic literature on
-several different English domains: (all numbers are % correct head assignments
-in the tree, or unlabelled attachment score)
-
-Model                                                                                                           | News  | Web   | Questions
---------------------------------------------------------------------------------------------------------------- | :---: | :---: | :-------:
-[Martins et al. (2013)](http://www.cs.cmu.edu/~ark/TurboParser/)                                                | 93.10 | 88.23 | 94.21
-[Zhang and McDonald (2014)](http://research.google.com/pubs/archive/38148.pdf)                                  | 93.32 | 88.65 | 93.37
-[Weiss et al. (2015)](http://static.googleusercontent.com/media/research.google.com/en//pubs/archive/43800.pdf) | 93.91 | 89.29 | 94.17
-[Andor et al. (2016)](http://arxiv.org/abs/1603.06042)*                                                         | 94.44 | 90.17 | 95.40
-Parsey McParseface                                                                                              | 94.15 | 89.08 | 94.77
-
-We see that Parsey McParseface is state-of-the-art; more importantly, with
-SyntaxNet you can train larger networks with more hidden units and bigger beam
-sizes if you want to push the accuracy even further: [Andor et al.
-(2016)](http://arxiv.org/abs/1603.06042)* is simply a SyntaxNet model with a
-larger beam and network. For futher information on the datasets, see that paper
-under the section "Treebank Union".
-
-Parsey McParseface is also state-of-the-art for part-of-speech (POS) tagging
-(numbers below are per-token accuracy):
-
-Model                                                                      | News  | Web   | Questions
--------------------------------------------------------------------------- | :---: | :---: | :-------:
-[Ling et al. (2015)](http://www.cs.cmu.edu/~lingwang/papers/emnlp2015.pdf) | 97.44 | 94.03 | 96.18
-[Andor et al. (2016)](http://arxiv.org/abs/1603.06042)*                    | 97.77 | 94.80 | 96.86
-Parsey McParseface                                                         | 97.52 | 94.24 | 96.45
-
-#### Parsing from Standard Input
-
-Simply pass one sentence per line of text into the script at
-`syntaxnet/demo.sh`. The script will break the text into words, run the POS
-tagger, run the parser, and then generate an ASCII version of the parse tree:
-
-```shell
-echo 'Bob brought the pizza to Alice.' | syntaxnet/demo.sh
-
-Input: Bob brought the pizza to Alice .
-Parse:
-brought VBD ROOT
- +-- Bob NNP nsubj
- +-- pizza NN dobj
- |   +-- the DT det
- +-- to IN prep
- |   +-- Alice NNP pobj
- +-- . . punct
-```
-
-The ASCII tree shows the text organized as in the parse, not left-to-right as
-visualized in our tutorial graphs. In this example, we see that the verb
-"brought" is the root of the sentence, with the subject "Bob", the object
-"pizza", and the prepositional phrase "to Alice".
-
-If you want to feed in tokenized, CONLL-formatted text, you can run `demo.sh
---conll`.
-
-#### Annotating a Corpus
-
-To change the pipeline to read and write to specific files (as opposed to piping
-through stdin and stdout), we have to modify the `demo.sh` to point to the files
-we want. The SyntaxNet models are configured via a combination of run-time flags
-(which are easy to change) and a text format `TaskSpec` protocol buffer. The
-spec file used in the demo is in
-`syntaxnet/models/parsey_mcparseface/context.pbtxt`.
-
-To use corpora instead of stdin/stdout, we have to:
-
-1.  Create or modify an `input` field inside the `TaskSpec`, with the
-    `file_pattern` specifying the location we want. If the input corpus is in
-    CONLL format, make sure to put `record_format: 'conll-sentence'`.
-1.  Change the `--input` and/or `--output` flag to use the name of the resource
-    as the output, instead of `stdin` and `stdout`.
-
-E.g., if we wanted to POS tag the CONLL corpus `./wsj.conll`, we would create
-two entries, one for the input and one for the output:
-
-```protosame
-input {
-  name: 'wsj-data'
-  record_format: 'conll-sentence'
-  Part {
-    file_pattern: './wsj.conll'
-  }
-}
-input {
-  name: 'wsj-data-tagged'
-  record_format: 'conll-sentence'
-  Part {
-    file_pattern: './wsj-tagged.conll'
-  }
-}
-```
-
-Then we can use `--input=wsj-data --output=wsj-data-tagged` on the command line
-to specify reading and writing to these files.
-
-#### Configuring the Python Scripts
-
-As mentioned above, the python scripts are configured in two ways:
-
-1.  **Run-time flags** are used to point to the `TaskSpec` file, switch between
-    inputs for reading and writing, and set various run-time model parameters.
-    At training time, these flags are used to set the learning rate, hidden
-    layer sizes, and other key parameters.
-1.  The **`TaskSpec` proto** stores configuration about the transition system,
-    the features, and a set of named static resources required by the parser. It
-    is specified via the `--task_context` flag. A few key notes to remember:
-
-    -   The `Parameter` settings in the `TaskSpec` have a prefix: either
-        `brain_pos` (they apply to the tagger) or `brain_parser` (they apply to
-        the parser). The `--prefix` run-time flag switches between reading from
-        the two configurations.
-    -   The resources will be created and/or modified during multiple stages of
-        training. As described above, the resources can also be used at
-        evaluation time to read or write to specific files. These resources are
-        also separate from the model parameters, which are saved separately via
-        calls to TensorFlow ops, and loaded via the `--model_path` flag.
-    -   Because the `TaskSpec` contains file path, remember that copying around
-        this file is not enough to relocate a trained model: you need to move
-        and update all the paths as well.
-
-Note that some run-time flags need to be consistent between training and testing
-(e.g. the number of hidden units).
-
-### Next Steps
-
-There are many ways to extend this framework, e.g. adding new features, changing
-the model structure, training on other languages, etc. We suggest reading the
-detailed tutorial below to get a handle on the rest of the framework.
-
-## Contact
-
-To ask questions or report issues please post on Stack Overflow with the tag
-[syntaxnet](http://stackoverflow.com/questions/tagged/syntaxnet) or open an
-issue on the tensorflow/models [issues
-tracker](https://github.com/tensorflow/models/issues). Please assign SyntaxNet
-issues to @calberti or @andorardo.
-
-## Credits
-
-Original authors of the code in this package include (in alphabetical order):
-
-*   Alessandro Presta
-*   Aliaksei Severyn
-*   Andy Golding
-*   Bernd Bohnet
-*   Chayut Thanapirom
-*   Chris Alberti
-*   Daniel Andor
-*   David Weiss
-*   Emily Pitler
-*   Greg Coppola
-*   Ivan Bogatyy
-*   Ji Ma
-*   Keith Hall
-*   Kuzman Ganchev
-*   Lingpeng Kong
-*   Livio Baldini Soares
-*   Mark Omernick
-*   Michael Collins
-*   Michael Ringgaard
-*   Ryan McDonald
-*   Slav Petrov
-*   Stefan Istrate
-*   Terry Koo
-*   Tim Credo
-*   Zora Tung

research/syntaxnet/WORKSPACE

-local_repository(
-    name = "org_tensorflow",
-    path = "tensorflow",
-)
-
-# We need to pull in @io_bazel_rules_closure for TensorFlow. Bazel design
-# documentation states that this verbosity is intentional, to prevent
-# TensorFlow/SyntaxNet from depending on different versions of
-# @io_bazel_rules_closure.
-http_archive(
-    name = "io_bazel_rules_closure",
-    sha256 = "6691c58a2cd30a86776dd9bb34898b041e37136f2dc7e24cadaeaf599c95c657",
-    strip_prefix = "rules_closure-08039ba8ca59f64248bb3b6ae016460fe9c9914f",
-    urls = [
-        "http://bazel-mirror.storage.googleapis.com/github.com/bazelbuild/rules_closure/archive/08039ba8ca59f64248bb3b6ae016460fe9c9914f.tar.gz",
-        "https://github.com/bazelbuild/rules_closure/archive/08039ba8ca59f64248bb3b6ae016460fe9c9914f.tar.gz",
-    ],
-)
-
-load("@org_tensorflow//tensorflow:workspace.bzl", "tf_workspace")
-
-tf_workspace(
-    path_prefix = "",
-    tf_repo_name = "org_tensorflow",
-)
-
-http_archive(
-    name = "sling",
-    sha256 = "f1ce597476cb024808ca0a371a01db9dda4e0c58fb34a4f9c4ea91796f437b10",
-    strip_prefix = "sling-e3ae9d94eb1d9ee037a851070d54ed2eefaa928a",
-    urls = [
-        "http://bazel-mirror.storage.googleapis.com/github.com/google/sling/archive/e3ae9d94eb1d9ee037a851070d54ed2eefaa928a.tar.gz",
-        "https://github.com/google/sling/archive/e3ae9d94eb1d9ee037a851070d54ed2eefaa928a.tar.gz",
-    ],
-)
-
-# Used by SLING.
-bind(
-    name = "zlib",
-    actual = "@zlib_archive//:zlib",
-)

research/syntaxnet/docker-devel/Dockerfile-test

-FROM dragnn-oss-test-base:latest
-
-RUN rm -rf \
-  $SYNTAXNETDIR/syntaxnet/dragnn \
-  $SYNTAXNETDIR/syntaxnet/syntaxnet \
-  $SYNTAXNETDIR/syntaxnet/third_party \
-  $SYNTAXNETDIR/syntaxnet/util/utf8
-COPY dragnn $SYNTAXNETDIR/syntaxnet/dragnn
-COPY syntaxnet $SYNTAXNETDIR/syntaxnet/syntaxnet
-COPY third_party $SYNTAXNETDIR/syntaxnet/third_party
-COPY util/utf8 $SYNTAXNETDIR/syntaxnet/util/utf8
-COPY WORKSPACE $SYNTAXNETDIR/syntaxnet/WORKSPACE

research/syntaxnet/docker-devel/Dockerfile-test-base

-FROM ubuntu:16.04
-
-ENV SYNTAXNETDIR=/opt/tensorflow PATH=$PATH:/root/bin
-
-# Install system packages. This doesn't include everything the TensorFlow
-# dockerfile specifies, so if anything goes awry, maybe install more packages
-# from there. Also, running apt-get clean before further commands will make the
-# Docker images smaller.
-RUN mkdir -p $SYNTAXNETDIR \
-    && cd $SYNTAXNETDIR \
-    && apt-get update \
-    && apt-get install -y \
-          file \
-          git \
-          graphviz \
-          libcurl3-dev \
-          libfreetype6-dev \
-          libgraphviz-dev \
-          liblapack-dev \
-          libopenblas-dev \
-          libpng-dev \
-          libxft-dev \
-          openjdk-8-jdk \
-          python-dev \
-          python-mock \
-          python-pip \
-          python2.7 \
-          swig \
-          unzip \
-          vim \
-          wget \
-          zlib1g-dev \
-    && apt-get clean \
-    && (rm -f /var/cache/apt/archives/*.deb \
-        /var/cache/apt/archives/partial/*.deb /var/cache/apt/*.bin || true)
-
-# Install common Python dependencies. Similar to above, remove caches
-# afterwards to help keep Docker images smaller.
-RUN pip install --ignore-installed pip \
-    && python -m pip install numpy \
-    && rm -rf /root/.cache/pip /tmp/pip*
-RUN python -m pip install \
-          asciitree \
-          ipykernel \
-          jupyter \
-          matplotlib \
-          pandas \
-          protobuf \
-          scipy \
-          sklearn \
-    && python -m ipykernel.kernelspec \
-    && python -m pip install pygraphviz \
-          --install-option="--include-path=/usr/include/graphviz" \
-          --install-option="--library-path=/usr/lib/graphviz/" \
-    && python -m jupyter_core.command nbextension enable \
-          --py --sys-prefix widgetsnbextension \
-    && rm -rf /root/.cache/pip /tmp/pip*
-
-# Installs Bazel.
-RUN wget --quiet https://github.com/bazelbuild/bazel/releases/download/0.11.1/bazel-0.11.1-installer-linux-x86_64.sh \
-    && chmod +x bazel-0.11.1-installer-linux-x86_64.sh \
-    && JAVA_HOME=/usr/lib/jvm/java-8-openjdk-amd64/ ./bazel-0.11.1-installer-linux-x86_64.sh \
-    && rm ./bazel-0.11.1-installer-linux-x86_64.sh
-
-COPY WORKSPACE $SYNTAXNETDIR/syntaxnet/WORKSPACE
-COPY tools/bazel.rc $SYNTAXNETDIR/syntaxnet/tools/bazel.rc
-
-# Compile common TensorFlow targets, which don't depend on DRAGNN / SyntaxNet
-# source. This makes it more convenient to re-compile DRAGNN / SyntaxNet for
-# development (though not as convenient as the docker-devel scripts).
-RUN cd $SYNTAXNETDIR/syntaxnet \
-    && git clone --branch r1.8 --recurse-submodules https://github.com/tensorflow/tensorflow \
-    && cd tensorflow \
-    && tensorflow/tools/ci_build/builds/configured CPU \
-    && cd $SYNTAXNETDIR/syntaxnet \
-    && bazel build -c opt @org_tensorflow//tensorflow:tensorflow_py
-
-# Just copy the code and run tests. The build and test flags differ enough that
-# doing a normal build of TensorFlow targets doesn't save much test time.
-WORKDIR $SYNTAXNETDIR/syntaxnet
-COPY dragnn $SYNTAXNETDIR/syntaxnet/dragnn
-COPY syntaxnet $SYNTAXNETDIR/syntaxnet/syntaxnet
-COPY third_party $SYNTAXNETDIR/syntaxnet/third_party
-COPY util/utf8 $SYNTAXNETDIR/syntaxnet/util/utf8
-
-# Doesn't matter if the tests pass or not, since we're going to re-copy over the
-# code.
-RUN bazel test -c opt ... || true

research/syntaxnet/docker-devel/Dockerfile.min

-# You need to build wheels before building this image. Please consult
-# docker-devel/README.txt.
-#
-# It might be more efficient to use a minimal distribution, like Alpine. But
-# the upside of this being popular is that people might already have it.
-FROM ubuntu:16.04
-
-ENV SYNTAXNETDIR=/opt/tensorflow PATH=$PATH:/root/bin
-
-RUN apt-get update \
-    && apt-get install -y \
-          file \
-          git \
-          graphviz \
-          libcurl3 \
-          libfreetype6 \
-          libgraphviz-dev \
-          liblapack3 \
-          libopenblas-base \
-          libpng16-16 \
-          libxft2 \
-          python-dev \
-          python-mock \
-          python-pip \
-          python2.7 \
-          zlib1g-dev \
-    && apt-get clean \
-    && (rm -f /var/cache/apt/archives/*.deb \
-        /var/cache/apt/archives/partial/*.deb /var/cache/apt/*.bin || true)
-
-# Install common Python dependencies. Similar to above, remove caches
-# afterwards to help keep Docker images smaller.
-RUN pip install --ignore-installed pip \
-    && python -m pip install numpy \
-    && rm -rf /root/.cache/pip /tmp/pip*
-RUN python -m pip install \
-          asciitree \
-          ipykernel \
-          jupyter \
-          matplotlib \
-          pandas \
-          protobuf \
-          scipy \
-          sklearn \
-    && python -m ipykernel.kernelspec \
-    && python -m pip install pygraphviz \
-          --install-option="--include-path=/usr/include/graphviz" \
-          --install-option="--library-path=/usr/lib/graphviz/" \
-    && python -m jupyter_core.command nbextension enable \
-          --py --sys-prefix widgetsnbextension \
-    && rm -rf /root/.cache/pip /tmp/pip*
-
-COPY syntaxnet_with_tensorflow-0.2-cp27-cp27mu-linux_x86_64.whl $SYNTAXNETDIR/
-RUN python -m pip install \
-        $SYNTAXNETDIR/syntaxnet_with_tensorflow-0.2-cp27-cp27mu-linux_x86_64.whl \
-    && rm -rf /root/.cache/pip /tmp/pip*
-
-# This makes the IP exposed actually "*"; we'll do host restrictions by passing
-# a hostname to the `docker run` command.
-COPY tensorflow/tensorflow/tools/docker/jupyter_notebook_config.py /root/.jupyter/
-EXPOSE 8888
-
-# This does not need to be compiled, only copied.
-COPY examples $SYNTAXNETDIR/syntaxnet/examples
-# For some reason, this works if we run it in a bash shell :/ :/ :/
-CMD /bin/bash -c "python -m jupyter_core.command notebook --debug --notebook-dir=/opt/tensorflow/syntaxnet/examples --allow-root"

research/syntaxnet/docker-devel/README.txt

-Docker is used for packaging the SyntaxNet. There are three primary things we
-build with Docker,
-
-1. A development image, which contains all source built with Bazel.
-2. Python/pip wheels, built by running a command in the development container.
-3. A minified image, which only has the compiled version of TensorFlow and
-   SyntaxNet, by installing the wheel built by the above step.
-
-
-Important info (please read)
-------------------------------
-
-One thing to be wary of is that YOU CAN LOSE DATA IF YOU DEVELOP IN A DOCKER
-CONTAINER. Please be very careful to mount data you care about to Docker
-volumes, or use a volume mount so that it's mapped to your host filesystem.
-
-Another note, especially relevant to training models, is that Docker sends the
-whole source tree to the Docker daemon every time you try to build an image.
-This can take some time if you have large temporary model files lying around.
-You can exclude your model files by editing .dockerignore, or just don't store
-them in the base directory.
-
-
-Step 1: Building the development image
-------------------------------
-
-Simply run `docker build -t dragnn-oss .` in the base directory. Make sure you
-have all the source checked out correctly, including git submodules.
-
-
-Step 2: Building wheels
-------------------------------
-
-Please run,
-
-  bash ./docker-devel/build_wheels.sh
-
-This actually builds the image from Step 1 as well.
-
-
-Step 3: Building the development image
-------------------------------
-
-First, ensure you have the file
-
-  syntaxnet_with_tensorflow-0.2-cp27-cp27mu-linux_x86_64.whl
-
-in your working directory, from step 2. Then run,
-
-  docker build -t dragnn-oss:latest-minimal -f docker-devel/Dockerfile.min .
-
-If the filename changes (e.g. you are on a different architecture), just update
-Dockerfile.min.
-
-
-Developing in Docker
-------------------------------
-
-We recommend developing in Docker by using the `./docker-devel/build_devel.sh`
-script; it will set up a few volume mounts, and port mappings automatically.
-You may want to add more port mappings on your own. If you want to drop into a
-shell instead of launching the notebook, simply run,
-
-  ./docker-devel/build_devel.sh /bin/bash

research/syntaxnet/docker-devel/build_devel.sh

-#!/bin/bash
-#
-# This file puts you in a Docker sub-shell where you can build SyntaxNet
-# targets. It is intended for development, as the Dockerfile (build file) does
-# not actually build any of SyntaxNet, but instead mounts it in a volume.
-
-script_path="$(readlink -f "$0")"
-root_path="$(dirname "$(dirname "${script_path}")")"
-set -e
-
-if [[ -z "$(docker images -q dragnn-oss)" ]]; then
-  docker build -t dragnn-oss .
-else
-  echo "NOTE: dragnn-oss image already exists, not re-building." >&2
-  echo "Please run \`docker build -t dragnn-oss .\` if you need." >&2
-fi
-
-echo -e "\n\nRun bazel commands like \`bazel test syntaxnet/...\`"
-
-# NOTE: Unfortunately, we need to mount /tensorflow over /syntaxnet/tensorflow
-# (which happens via devel_entrypoint.sh). This requires privileged mode.
-syntaxnet_base="/opt/tensorflow/syntaxnet"
-docker run --rm -ti \
-  -v "${root_path}"/syntaxnet:"${syntaxnet_base}"/syntaxnet \
-  -v "${root_path}"/dragnn:"${syntaxnet_base}"/dragnn \
-  -v "${root_path}"/examples:"${syntaxnet_base}"/examples \
-  -p 127.0.0.1:8888:8888 \
-  dragnn-oss "$@"

research/syntaxnet/docker-devel/build_wheels.sh

-#!/bin/bash
-#
-# Convenience script to build wheel files in Docker, and copy them out of the
-# container.
-#
-# Usage: docker-devel/build_wheels.sh (takes no arguments; run it from the base
-# directory).
-set -e
-docker build -t dragnn-oss .
-
-# Start building the wheels.
-script="bazel run //dragnn/tools:build_pip_package \
-  -- --output-dir=/opt/tensorflow/syntaxnet; \
-  bazel run //dragnn/tools:build_pip_package \
-  -- --output-dir=/opt/tensorflow/syntaxnet --include-tensorflow"
-container_id="$(docker run -d dragnn-oss /bin/bash -c "${script}")"
-
-echo "Waiting for container ${container_id} to finish building the wheel ..."
-if [[ "$(docker wait "${container_id}")" != 0 ]]; then
-  echo "Container failed! Please run \`docker logs <id>\` to see errors." >&2
-  exit 1
-fi
-
-# The build_pip_package.py script prints lines like "Wrote x.whl". The wheel
-# names are prefixed by architecture and such, so don't guess them.
-wheels=(
-  $(docker logs "${container_id}" 2>/dev/null | grep Wrote | awk '{print $2;}'))
-for wheel in "${wheels[@]}"; do
-  output=./"$(basename "${wheel}")"
-  docker cp "${container_id}:${wheel}" "${output}"
-  echo "Wrote ${output} ($(du -h "${output}" | awk '{print $1;}'))"
-done
-
-echo "Removing ${container_id} ..."
-docker rm "${container_id}" >/dev/null

research/syntaxnet/dragnn/BUILD

-package_group(
-    name = "dragnn_visibility",
-    packages = [
-    ],
-)

research/syntaxnet/dragnn/components/stateless/BUILD

-package(
-    default_visibility = ["//visibility:public"],
-    features = ["-layering_check"],
-)
-
-cc_library(
-    name = "stateless_component",
-    srcs = ["stateless_component.cc"],
-    deps = [
-        "//dragnn/core:component_registry",
-        "//dragnn/core/interfaces:component",
-        "//dragnn/core/interfaces:transition_state",
-        "//dragnn/core/util:label",
-        "//dragnn/protos:data_proto_cc",
-        "//syntaxnet:base",
-    ],
-    alwayslink = 1,
-)
-
-cc_test(
-    name = "stateless_component_test",
-    srcs = ["stateless_component_test.cc"],
-    deps = [
-        ":stateless_component",
-        "//dragnn/core:component_registry",
-        "//dragnn/core:input_batch_cache",
-        "//dragnn/core/test:generic",
-        "//dragnn/core/test:mock_transition_state",
-        "//dragnn/io:sentence_input_batch",
-        "//syntaxnet:base",
-        "//syntaxnet:sentence_proto_cc",
-        "//syntaxnet:test_main",
-    ],
-)

research/syntaxnet/dragnn/components/stateless/stateless_component.cc

-// Copyright 2017 Google Inc. All Rights Reserved.
-//
-// 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.
-// =============================================================================
-
-#include "dragnn/core/component_registry.h"
-#include "dragnn/core/interfaces/component.h"
-#include "dragnn/core/interfaces/transition_state.h"
-#include "dragnn/core/util/label.h"
-#include "dragnn/protos/data.pb.h"
-#include "syntaxnet/base.h"
-
-namespace syntaxnet {
-namespace dragnn {
-namespace {
-
-// A component that does not create its own transition states; instead, it
-// simply forwards the states of the previous component.  Requires that some
-// previous component has converted the input batch.  Does not support all
-// methods.  Intended for "compute-only" bulk components that only use linked
-// features, which use only a small subset of DRAGNN functionality.
-class StatelessComponent : public Component {
- public:
-  void InitializeComponent(const ComponentSpec &spec) override {
-    name_ = spec.name();
-  }
-
-  // Stores the |parent_states| for forwarding to downstream components.
-  void InitializeData(
-      const std::vector<std::vector<const TransitionState *>> &parent_states,
-      int max_beam_size, InputBatchCache *input_data) override {
-    batch_size_ = input_data->Size();
-    beam_size_ = max_beam_size;
-    parent_states_ = parent_states;
-
-    // The beam should be wide enough for the previous component.
-    for (const auto &beam : parent_states) {
-      CHECK_LE(beam.size(), beam_size_);
-    }
-  }
-
-  // Forwards the states of the previous component.
-  std::vector<std::vector<const TransitionState *>> GetBeam() override {
-    return parent_states_;
-  }
-
-  // Forwards the |current_index| to the previous component.
-  int GetSourceBeamIndex(int current_index, int batch) const override {
-    return current_index;
-  }
-
-  string Name() const override { return name_; }
-  int BeamSize() const override { return beam_size_; }
-  int BatchSize() const override { return batch_size_; }
-  int StepsTaken(int batch_index) const override { return 0; }
-  bool IsReady() const override { return true; }
-  bool IsTerminal() const override { return true; }
-  void FinalizeData() override {}
-  void ResetComponent() override {}
-  void InitializeTracing() override {}
-  void DisableTracing() override {}
-  std::vector<std::vector<ComponentTrace>> GetTraceProtos() const override {
-    return {};
-  }
-
-  // Unsupported methods.
-  int GetBeamIndexAtStep(int step, int current_index,
-                         int batch) const override {
-    LOG(FATAL) << "[" << name_ << "] Method not supported";
-    return 0;
-  }
-  std::function<int(int, int, int)> GetStepLookupFunction(
-      const string &method) override {
-    LOG(FATAL) << "[" << name_ << "] Method not supported";
-    return nullptr;
-  }
-  bool AdvanceFromPrediction(const float *transition_matrix, int num_items,
-                             int num_actions) override {
-    LOG(FATAL) << "[" << name_ << "] AdvanceFromPrediction not supported";
-  }
-  void AdvanceFromOracle() override {
-    LOG(FATAL) << "[" << name_ << "] AdvanceFromOracle not supported";
-  }
-  std::vector<std::vector<std::vector<Label>>> GetOracleLabels()
-      const override {
-    LOG(FATAL) << "[" << name_ << "] Method not supported";
-  }
-  int GetFixedFeatures(std::function<int32 *(int)> allocate_indices,
-                       std::function<int64 *(int)> allocate_ids,
-                       std::function<float *(int)> allocate_weights,
-                       int channel_id) const override {
-    LOG(FATAL) << "[" << name_ << "] Method not supported";
-  }
-  int BulkGetFixedFeatures(const BulkFeatureExtractor &extractor) override {
-    LOG(FATAL) << "[" << name_ << "] Method not supported";
-  }
-  void BulkEmbedFixedFeatures(
-      int batch_size_padding, int num_steps_padding, int output_array_size,
-      const vector<const float *> &per_channel_embeddings,
-      float *embedding_output) override {
-    LOG(FATAL) << "[" << name_ << "] Method not supported";
-  }
-  void BulkEmbedDenseFixedFeatures(
-      const vector<const float *> &per_channel_embeddings,
-      float *embedding_output, int embedding_output_size,
-      int32 *offset_array_output, int offset_array_size) override {
-    LOG(FATAL) << "[" << name_ << "] Method not supported";
-  }
-  int BulkDenseFeatureSize() const override {
-    LOG(FATAL) << "Method not supported";
-  }
-  std::vector<LinkFeatures> GetRawLinkFeatures(int channel_id) const override {
-    LOG(FATAL) << "[" << name_ << "] Method not supported";
-  }
-  void AddTranslatedLinkFeaturesToTrace(
-      const std::vector<LinkFeatures> &features, int channel_id) override {
-    LOG(FATAL) << "[" << name_ << "] Method not supported";
-  }
-
- private:
-  string name_;         // component name
-  int batch_size_ = 1;  // number of sentences in current batch
-  int beam_size_ = 1;   // maximum beam size
-
-  // Parent states passed to InitializeData(), and passed along in GetBeam().
-  std::vector<std::vector<const TransitionState *>> parent_states_;
-};
-
-REGISTER_DRAGNN_COMPONENT(StatelessComponent);
-
-}  // namespace
-}  // namespace dragnn
-}  // namespace syntaxnet

research/syntaxnet/dragnn/components/stateless/stateless_component_test.cc

-// Copyright 2017 Google Inc. All Rights Reserved.
-//
-// 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.
-// =============================================================================
-
-#include "dragnn/core/component_registry.h"
-#include "dragnn/core/input_batch_cache.h"
-#include "dragnn/core/test/generic.h"
-#include "dragnn/core/test/mock_transition_state.h"
-#include "dragnn/io/sentence_input_batch.h"
-#include "dragnn/protos/data.pb.h"
-#include "syntaxnet/base.h"
-#include "syntaxnet/sentence.pb.h"
-#include "tensorflow/core/lib/core/errors.h"
-#include "tensorflow/core/lib/core/status.h"
-#include "tensorflow/core/lib/io/path.h"
-#include "tensorflow/core/platform/env.h"
-#include "tensorflow/core/platform/protobuf.h"
-#include "tensorflow/core/platform/test.h"
-
-namespace syntaxnet {
-namespace dragnn {
-namespace {
-
-const char kSentence0[] = R"(
-token {
-  word: "Sentence" start: 0 end: 7 tag: "NN" category: "NOUN" label: "ROOT"
-  break_level: NO_BREAK
-}
-token {
-  word: "0" start: 9 end: 9 head: 0 tag: "CD" category: "NUM" label: "num"
-  break_level: SPACE_BREAK
-}
-token {
-  word: "." start: 10 end: 10 head: 0 tag: "." category: "." label: "punct"
-  break_level: NO_BREAK
-}
-)";
-
-const char kSentence1[] = R"(
-token {
-  word: "Sentence" start: 0 end: 7 tag: "NN" category: "NOUN" label: "ROOT"
-  break_level: NO_BREAK
-}
-token {
-  word: "1" start: 9 end: 9 head: 0 tag: "CD" category: "NUM" label: "num"
-  break_level: SPACE_BREAK
-}
-token {
-  word: "." start: 10 end: 10 head: 0 tag: "." category: "." label: "punct"
-  break_level: NO_BREAK
-}
-)";
-
-const char kLongSentence[] = R"(
-token {
-  word: "Sentence" start: 0 end: 7 tag: "NN" category: "NOUN" label: "ROOT"
-  break_level: NO_BREAK
-}
-token {
-  word: "1" start: 9 end: 9 head: 0 tag: "CD" category: "NUM" label: "num"
-  break_level: SPACE_BREAK
-}
-token {
-  word: "2" start: 10 end: 10 head: 0 tag: "CD" category: "NUM" label: "num"
-  break_level: SPACE_BREAK
-}
-token {
-  word: "3" start: 11 end: 11 head: 0 tag: "CD" category: "NUM" label: "num"
-  break_level: SPACE_BREAK
-}
-token {
-  word: "." start: 12 end: 12 head: 0 tag: "." category: "." label: "punct"
-  break_level: NO_BREAK
-}
-)";
-
-const char kMasterSpec[] = R"(
-component {
-  name: "test"
-  transition_system {
-    registered_name: "shift-only"
-  }
-  linked_feature {
-    name: "prev"
-    fml: "input.focus"
-    embedding_dim: 32
-    size: 1
-    source_component: "prev"
-    source_translator: "identity"
-    source_layer: "last_layer"
-  }
-  backend {
-    registered_name: "StatelessComponent"
-  }
-}
-)";
-
-}  // namespace
-
-using testing::Return;
-
-class StatelessComponentTest : public ::testing::Test {
- public:
-  std::unique_ptr<Component> CreateParser(
-      int beam_size,
-      const std::vector<std::vector<const TransitionState *>> &states,
-      const std::vector<string> &data) {
-    MasterSpec master_spec;
-    CHECK(TextFormat::ParseFromString(kMasterSpec, &master_spec));
-    data_.reset(new InputBatchCache(data));
-
-    // The stateless component does not use any particular input batch type, and
-    // relies on the preceding components to convert the input batch.
-    data_->GetAs<SentenceInputBatch>();
-
-    // Create a parser component with the specified beam size.
-    std::unique_ptr<Component> parser_component(
-        Component::Create("StatelessComponent"));
-    parser_component->InitializeComponent(master_spec.component(0));
-    parser_component->InitializeData(states, beam_size, data_.get());
-    return parser_component;
-  }
-
-  std::unique_ptr<InputBatchCache> data_;
-};
-
-TEST_F(StatelessComponentTest, ForwardsTransitionStates) {
-  MockTransitionState mock_state_1, mock_state_2, mock_state_3;
-  const std::vector<std::vector<const TransitionState *>> parent_states = {
-      {}, {&mock_state_1}, {&mock_state_2, &mock_state_3}};
-
-  std::vector<string> data;
-  for (const string &textproto : {kSentence0, kSentence1, kLongSentence}) {
-    Sentence sentence;
-    CHECK(TextFormat::ParseFromString(textproto, &sentence));
-    data.emplace_back();
-    CHECK(sentence.SerializeToString(&data.back()));
-  }
-  CHECK_EQ(parent_states.size(), data.size());
-
-  const int kBeamSize = 2;
-  auto test_parser = CreateParser(kBeamSize, parent_states, data);
-
-  EXPECT_TRUE(test_parser->IsReady());
-  EXPECT_TRUE(test_parser->IsTerminal());
-  EXPECT_EQ(kBeamSize, test_parser->BeamSize());
-  EXPECT_EQ(data.size(), test_parser->BatchSize());
-  EXPECT_TRUE(test_parser->GetTraceProtos().empty());
-
-  for (int batch_index = 0; batch_index < parent_states.size(); ++batch_index) {
-    EXPECT_EQ(0, test_parser->StepsTaken(batch_index));
-    const auto &beam = parent_states[batch_index];
-    for (int beam_index = 0; beam_index < beam.size(); ++beam_index) {
-      // Expect an identity mapping.
-      EXPECT_EQ(beam_index,
-                test_parser->GetSourceBeamIndex(beam_index, batch_index));
-    }
-  }
-
-  const auto forwarded_states = test_parser->GetBeam();
-  EXPECT_EQ(parent_states, forwarded_states);
-}
-
-TEST_F(StatelessComponentTest, UnimplementedMethodsDie) {
-  MockTransitionState mock_state_1, mock_state_2, mock_state_3;
-  const std::vector<std::vector<const TransitionState *>> parent_states;
-  std::vector<string> data;
-  for (const string &textproto : {kSentence0, kSentence1, kLongSentence}) {
-    Sentence sentence;
-    CHECK(TextFormat::ParseFromString(textproto, &sentence));
-    data.emplace_back();
-    CHECK(sentence.SerializeToString(&data.back()));
-  }
-
-  const int kBeamSize = 2;
-  auto test_parser = CreateParser(kBeamSize, parent_states, data);
-
-  EXPECT_TRUE(test_parser->IsReady());
-  EXPECT_DEATH(test_parser->AdvanceFromPrediction({}, 0, 0),
-               "AdvanceFromPrediction not supported");
-  EXPECT_DEATH(test_parser->AdvanceFromOracle(),
-               "AdvanceFromOracle not supported");
-  EXPECT_DEATH(test_parser->GetOracleLabels(), "Method not supported");
-  EXPECT_DEATH(test_parser->GetFixedFeatures(nullptr, nullptr, nullptr, 0),
-               "Method not supported");
-  BulkFeatureExtractor extractor(nullptr, nullptr, nullptr);
-  EXPECT_DEATH(test_parser->BulkEmbedFixedFeatures(0, 0, 0, {nullptr}, nullptr),
-               "Method not supported");
-  EXPECT_DEATH(test_parser->BulkGetFixedFeatures(extractor),
-               "Method not supported");
-  EXPECT_DEATH(test_parser->GetRawLinkFeatures(0), "Method not supported");
-  EXPECT_DEATH(test_parser->AddTranslatedLinkFeaturesToTrace({}, 0),
-               "Method not supported");
-}
-
-}  // namespace dragnn
-}  // namespace syntaxnet

research/syntaxnet/dragnn/components/syntaxnet/BUILD

-package(
-    default_visibility = ["//visibility:public"],
-    features = ["-layering_check"],
-)
-
-cc_library(
-    name = "syntaxnet_component",
-    srcs = ["syntaxnet_component.cc"],
-    hdrs = ["syntaxnet_component.h"],
-    deps = [
-        ":syntaxnet_link_feature_extractor",
-        ":syntaxnet_transition_state",
-        "//dragnn/components/util:bulk_feature_extractor",
-        "//dragnn/core:beam",
-        "//dragnn/core:component_registry",
-        "//dragnn/core:input_batch_cache",
-        "//dragnn/core/interfaces:component",
-        "//dragnn/core/interfaces:transition_state",
-        "//dragnn/core/util:label",
-        "//dragnn/io:sentence_input_batch",
-        "//dragnn/io:syntaxnet_sentence",
-        "//dragnn/protos:data_proto_cc",
-        "//dragnn/protos:spec_proto_cc",
-        "//dragnn/protos:trace_proto_cc",
-        "//syntaxnet:base",
-        "//syntaxnet:parser_transitions",
-        "//syntaxnet:registry",
-        "//syntaxnet:sparse_proto_cc",
-        "//syntaxnet:task_context",
-        "//syntaxnet:task_spec_proto_cc",
-        "//syntaxnet:utils",
-        "//util/utf8:unicodetext",
-    ],
-    alwayslink = 1,
-)
-
-cc_library(
-    name = "syntaxnet_link_feature_extractor",
-    srcs = ["syntaxnet_link_feature_extractor.cc"],
-    hdrs = ["syntaxnet_link_feature_extractor.h"],
-    deps = [
-        "//dragnn/protos:spec_proto_cc",
-        "//syntaxnet:base",
-        "//syntaxnet:embedding_feature_extractor",
-        "//syntaxnet:parser_transitions",
-        "//syntaxnet:task_context",
-    ],
-)
-
-cc_library(
-    name = "syntaxnet_transition_state",
-    srcs = ["syntaxnet_transition_state.cc"],
-    hdrs = ["syntaxnet_transition_state.h"],
-    deps = [
-        "//dragnn/core/interfaces:cloneable_transition_state",
-        "//dragnn/core/interfaces:transition_state",
-        "//dragnn/io:syntaxnet_sentence",
-        "//dragnn/protos:trace_proto_cc",
-        "//syntaxnet:base",
-        "//syntaxnet:parser_transitions",
-    ],
-)
-
-# Test data.
-filegroup(
-    name = "testdata",
-    data = glob(["testdata/**"]),
-)
-
-# Tests.
-cc_test(
-    name = "syntaxnet_component_test",
-    srcs = ["syntaxnet_component_test.cc"],
-    data = [":testdata"],
-    deps = [
-        ":syntaxnet_component",
-        "//dragnn/core:input_batch_cache",
-        "//dragnn/core/test:generic",
-        "//dragnn/core/test:mock_transition_state",
-        "//dragnn/io:sentence_input_batch",
-        "//syntaxnet:base",
-        "//syntaxnet:sentence_proto_cc",
-        "//syntaxnet:test_main",
-    ],
-)
-
-cc_test(
-    name = "syntaxnet_link_feature_extractor_test",
-    srcs = ["syntaxnet_link_feature_extractor_test.cc"],
-    deps = [
-        ":syntaxnet_link_feature_extractor",
-        "//dragnn/core/test:generic",
-        "//dragnn/protos:spec_proto_cc",
-        "//syntaxnet:task_context",
-        "//syntaxnet:test_main",
-    ],
-)
-
-cc_test(
-    name = "syntaxnet_transition_state_test",
-    srcs = ["syntaxnet_transition_state_test.cc"],
-    data = [":testdata"],
-    deps = [
-        ":syntaxnet_component",
-        ":syntaxnet_transition_state",
-        "//dragnn/core:input_batch_cache",
-        "//dragnn/core/test:generic",
-        "//dragnn/core/test:mock_transition_state",
-        "//dragnn/io:sentence_input_batch",
-        "//dragnn/protos:spec_proto_cc",
-        "//syntaxnet:base",
-        "//syntaxnet:sentence_proto_cc",
-        "//syntaxnet:test_main",
-    ],
-)