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42 lines
9.2 KiB
42 lines
9.2 KiB
2 years ago
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# submission_autograder.py
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# ------------------------
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# Licensing Information: You are free to use or extend these projects for
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# educational purposes provided that (1) you do not distribute or publish
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# solutions, (2) you retain this notice, and (3) you provide clear
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# attribution to UC Berkeley, including a link to http://ai.berkeley.edu.
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#
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# Attribution Information: The Pacman AI projects were developed at UC Berkeley.
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# The core projects and autograders were primarily created by John DeNero
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# (denero@cs.berkeley.edu) and Dan Klein (klein@cs.berkeley.edu).
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# Student side autograding was added by Brad Miller, Nick Hay, and
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# Pieter Abbeel (pabbeel@cs.berkeley.edu).
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#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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from __future__ import print_function
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from codecs import open
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"""
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CS 188 Local Submission Autograder
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Written by the CS 188 Staff
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==============================================================================
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_____ _ _
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/ ____| | | |
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| (___ | |_ ___ _ __ | |
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\___ \| __/ _ \| '_ \| |
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____) | || (_) | |_) |_|
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|_____/ \__\___/| .__/(_)
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Modifying or tampering with this file is a violation of course policy.
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If you're having trouble running the autograder, please contact the staff.
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==============================================================================
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"""
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import bz2, base64
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exec(bz2.decompress(base64.b64decode('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