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Tools for developing ICPC-style programming contest problems.
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readme.md

BAPCtools

BAPCtools is a tool for creating and developing problems following the CLICS (DOMjudge/Kattis) problem format specified here. It aims to implement the entire spec, but some parts may not yet work.

The aim of this tool is to run all necessary compilation, validation, and testing commands while working on an ICPC-style problem. Ideally I should never have to manually run any compilation or testing command myself.

I'm interested to know who's using this, so feel free to inform me (e.g. via an issue) if so ;) The current state is relatively stable, but things do change from time to time since I'm not aware of usage outside of BAPC yet.

Installation

For now the only way to use this is to clone the repository and install the required dependencies manually:

  • Python 3 with the yaml library via pip install pyyaml or the python-yaml Arch Linux package.
  • The argcomplete library for command line argument completion. Install via python[3]-argcomplete.
    • Note that actually using argcomplete is optional, but recommended. Detailed instructions are here.

      TL;DR: Put eval "$(register-python-argcomplete tools.py)" in your .bashrc or .zshrc.

  • The pdflatex command, provided by texlive-bin on Arch Linux and potentially some specific LaTeX packages (like tikz) provided by texlive-extra. These are only needed for building pdf files, not for run and validate and such.

For Windows, you'll need the following in your path:

  • Python for Python 3
  • g++ to compile C++
  • javac and java to compile and run java.

Note that colorized output does not work (yet?) on Windows. Resource limits (memory limit/hard cpu time limit) are also not supported.

Usage

The bin/tools.py file is the only thing you need. You can symlink it to a more convenient location if needed. I prefer bt for this.

The tool can be run either from a problem directory or a contest directory. This is automatically detected by searching for the problem.yaml file.

The most important subcommands it supports are (see --help for a few more and aliases):

  • contest: Create a new stub contest directory.
  • problem: Create a new stub problem directory filled with very simple example code.
  • run: Run given or all submissions against the given or all testcases.
  • test: Run a single submissions against the given testcases and print the output (instead of validating it).
  • validate, input, output: Validate input and/or output data files using a small c++-library.
    • Viva is supported.
    • CheckTestdata is also supported when found in the Path.
  • generate: Use a submission to create .ans files for all .in files.
  • generate_input: Use an input validator to generate random .in files.
  • constraints searches for const int <name> = <value>; numeric values in the validator file and \newcommand{<name>}{<number>} definitions in the latex statement and prints the values side by side for easy manual verification.
  • pdf, solutions: Build a (solutions) pdf for either a problem or entire contest.
  • stats: Print statistics on the number of testcases and submissions per problem.
  • zip, kattis: Create a zip file ready for uploading to DomJudge/ready for verification by Kattis problemtools.
  • samplezip: Create a zip containing all sample testcases in a contest, useful for distribution to contestant computers.
  • gitlabci: Prints commands that can be included in a .gitlab-ci.yml to set up CI for all problems and the contest pdf.

Please try the -v flag for more verbose output especially for run and validate. Specify it twice to print all external compile/run/validate commands being executed.

The tool prints the first few lines of compile errors and wrong answer diffs. -e enables full output and -E hides it completely.

Please use bt --help and bt <command> --help to see all optional arguments. There are quite a few that are not documented here.

What follows is a quick walk-through of some of the most important commands.

Create a new contest

tools.py contest example_contest

This creates a new directory example_contest with a contest.tex file containing metadata to put on the front page of the generated pdf.

new contest

Create a new problem

tools.py problem example_problem

This create a new problem directory following the DomJudge/Kattis format.

new problem

The stub files contain the problem to read and print a single integer.

Run submissions

From inside either a problem or contest directory: tools.py run [-v] [-v] [submissions] [testcases]

This runs all submissions in the problem/contest on all testdata for the problem. Use -v to make it print testcases where submissions fail.

run

You can also run one (or multiple) given submissions and see the status with -v. Note that the wrong answer verdict is green here because the submission is expected to get wrong answer. Unexpected outcomes are always printed, even without -v. If the given and expected answer are a single line only, the diff is given inline. Otherwise a small snippet is printed on the lines below.

run single submission

Generating output files

tools.py generate [-f] [submission] chooses a submission or uses the given submission to generate a .ans file for every .in file. Supply -f to overwrite changed answer files.

generate ans files

Validating input/answer/output files

tools.py validate runs all validator files in the input_validator and output_validator directories against all testcases.

Validators can be one of

  • an executable,
  • a c++ program,
  • a .ctd CheckTestData file (this needs the checktestdata executable in the PATH).
  • a .viva file.

See the Notes on Validation section further down for more info.

You can use --remove to delete all failing testdata or --move <dir> to move them to a separate directory.

validator

Building problem PDF

tools.py pdf [--web] creates a PDF of the problem statement or entire contest, depending on where you run it. The --web flag removes empty pages and makes it single sided. The output file is a example_problem/problem.pdf like this symlink to a build directory. (See the Notes on LaTeX further down.)

Running it from a contest directory creates a contest.pdf like this file in the contest directory.

  • The problem letter and time limit are extracted from domjudge-problem.ini.
  • Samples are automatically included from the data/sample directory.
  • The problem_statement/problem.en.tex file consists of:
    • \problemname{<name>}
    • (optionally) a figure
    • the problem statement
    • an Input section or environment
    • an Output section or environment

Printing problem stats and progress

tools.py stats print the number of AC/WA/TLE solutions, the number of sample/secret testcases and whether the required config files are present:

stats

Notes on compilation

All compilation output for submissions and validators goes into a temporary directory (in /tmp/bapctools_xxxxxx) created by python's tempfile.mkdtemp. All build output is stored there and reused when the source files did not change. Use --force_rebuild if needed.

Notes on LaTeX

LaTeX files are compiled using pdflatex. To keep the problem repository clean and prevent compilation on disk, all latex files are copied or symlinked into /tmp/.

The tool does not call pdflatex multiple times so it is recommended to run tools.py pdf multiple times to fix the table of contents and references to images.

Build structure (outdated)

latex/readme.md has more detailed information on the steps taken to compile a problem/contest pdf. A quick overview:

  • latex/problem.tex is the root latex file that will be compiled. It has the bapc documentclass which contains most style info. This file includes three files:
  • build/problem/problemid.tex: a file generated by tools.py setting the section counter to the right offset (for problem A, B, ...) and a variable indicating the timelimit extracted from the config files.
  • The problem statement problem_statement/problem.en.tex
  • samples.tex, a programmatically generated file containing tables of all sample input and output. We use somewhat hacky code to escape most asci into printable latex monospace characters.

Contests are build in a similar way: - The contest.tex and logo.pdf files are symlinked into the build/contest directory. - A build/contest/problems.tex file is created which for each problem includes the three files mentioned before. - The latex/contest.tex file is compiled, which includes the contest metadata file and the list of problems.

Note that to keep the latex code a bit simpler, we create a new directory for each problem/contest, but symlink it to 'problem' or 'contest' so that the exact location of the relevant files is known.

TikZ pictures

headers/tikz.h is a small helper class for writing TikZ pictures from a list of points. This can be used to create images of input and output of testcases.

Notes on validation

We use our own small C++ validator library headers/validation.h to validate: - test input - test answers - team output.

An InputValidator is always both whitespace sensitive and case sensitive, and reads from stdin. It will be called as ./input_validator < testcase.in. The InputValidator should then be used for all reading, see skel/problem/input_validators/input_validator.cpp.

The OutputValidator (e.g. skel/problem/output_validators/output_validator.cpp) should be passed the commandline arguments of the main program itself. It looks for the case_sensitive and space_change_sensitive flags to decide the mode. It will be called in strict mode when testing jury-provided .ans files, but will run in permissive mode when validating team output (in case the problem needs custom validation, as specified in problem.yaml). Again it binds to stdin to read input in strict mode.

TODO: The float_tolerance flags are not yet supported in custom validation mode, though they do work with the default validator build into tools.py itself.

The output validator will be called as ./output_validator testcase.in testcase.ans output_dir [flags] < team_output.out.

For simple .ans validation it is sufficient to read the .in file specified as the first argument for important variables and then use the OutputValidator for parsing the answer file that will be provided to stdin.

For custom validation, the code can read the input from argv[1], the answer from argv[2] and the OutputValidator will still bind to the team output on stdin.

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