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How to use setupenv method in tox

Best Python code snippet using tox_python

test_basic.py

Source:test_basic.py

1import unittest2import code3from code.baseline_network import BaselineNetwork4from code.network_utils import build_mlp, np2torch5from code.policy_gradient import PolicyGradient6from code.config import get_config7import gym8import numpy as np9import torch10import builtins11# Suppress unnecessary logging12# gym.logging.disable(gym.logging.FATAL)13builtins.config = None14class TestBasic(unittest.TestCase):15    def setUp(self):16        self.pg = None17        builtins.config = None18    def setUpEnv(self, env_name):19        np.random.seed(0)20        config = get_config(env_name, True)21        self.env = gym.make(config.env_name)22        builtins.config = config23        self.pg = PolicyGradient(self.env, config, seed=1)24        self.policy = self.pg.policy25        self.baseline = BaselineNetwork(self.env, config)26        self.rand_obs = np.random.randn(10, self.pg.observation_dim)27    def test_policy_optimizer_exists(self):28        self.setUpEnv("cartpole")29        self.assertTrue(hasattr(self.pg, "optimizer"))30        self.assertTrue(isinstance(self.pg.optimizer, torch.optim.Optimizer))31    def test_baseline_optimizer_exists(self):32        self.setUpEnv("cartpole")33        self.assertTrue(hasattr(self.baseline, "optimizer"))34        self.assertTrue(isinstance(self.baseline.optimizer, torch.optim.Optimizer))35    def test_get_returns_zero(self):36        self.setUpEnv("cartpole")37        paths = [{"reward": np.zeros(11)}]38        returns = self.pg.get_returns(paths)39        expected = np.zeros(11)40        self.assertEqual(returns.shape, (11,))41        diff = np.sum((returns - expected) ** 2)42        self.assertAlmostEqual(diff, 0, delta=0.01)43    def test_get_returns_ones(self):44        self.setUpEnv("cartpole")45        paths = [{"reward": np.ones(5)}]46        returns = self.pg.get_returns(paths)47        gamma = self.pg.config.gamma48        expected = np.array(49            [50                1 + gamma + gamma ** 2 + gamma ** 3 + gamma ** 4,51                1 + gamma + gamma ** 2 + gamma ** 3,52                1 + gamma + gamma ** 2,53                1 + gamma,54                1,55            ]56        )57        diff = np.sum((returns - expected) ** 2)58        self.assertAlmostEqual(diff, 0, delta=0.001)59    def _test_sampled_actions(self):60        actions = self.policy.act(self.rand_obs)61        action_space = self.env.action_space62        discrete = isinstance(action_space, gym.spaces.Discrete)63        for action in actions:64            if discrete:65                self.assertTrue(action_space.contains(action))66            else:67                # We don't use contains because technically the Gaussian policy68                # doesn't respect the action bounds69                self.assertEqual(action_space.shape, action.shape)70    def test_cartpole_sampled_actions(self):71        self.setUpEnv("cartpole")72        self._test_sampled_actions()73    def test_pendulum_sampled_actions(self):74        self.setUpEnv("pendulum")75        self._test_sampled_actions()76    def test_cheetah_sampled_actions(self):77        self.setUpEnv("cheetah")78        self._test_sampled_actions()79    def _test_log_prob(self):80        actions = np2torch(self.policy.act(self.rand_obs))81        observations = np2torch(self.rand_obs)82        log_probs = (83            self.policy.action_distribution(observations).log_prob(actions).detach()84        )85        self.assertEqual(log_probs.shape, torch.Size([len(observations)]))86    def test_cartpole_logprob(self):87        self.setUpEnv("cartpole")88        self._test_log_prob()89    def test_pendulum_logprob(self):90        self.setUpEnv("pendulum")91        self._test_log_prob()92    def test_cheetah_logprob(self):93        self.setUpEnv("cheetah")94        self._test_log_prob()95    def test_policy_network_cartpole_logprob_value(self):96        self.setUpEnv("cartpole")97        actions = np2torch(self.policy.act(self.rand_obs))98        observations = np2torch(self.rand_obs)99        log_probs = (100            self.policy.action_distribution(observations).log_prob(actions).detach()101        )102        self.assertTrue(torch.all(log_probs < 0))103    def test_baseline_op(self):104        self.setUpEnv("cartpole")105        # make sure we can overfit!106        returns = np.random.randn(len(self.rand_obs))107        for i in range(1000):108            self.baseline.update_baseline(returns, self.rand_obs)109        advantages = self.baseline.calculate_advantage(returns, self.rand_obs)110        self.assertTrue(np.allclose(advantages, 0, atol=0.01))111    def test_adv_basic(self):112        self.setUpEnv("cartpole")113        returns = np.random.randn(5)114        observations = np.random.randn(5, 4)115        self.pg.config.use_baseline = False116        self.pg.config.normalize_advantage = False117        res = self.pg.calculate_advantage(returns, observations)...

ctfSetupper.py

Source:ctfSetupper.py

1#!/usr/bin/python3.92import optparse3import subprocess4###########################35#       by Cave 20226###########################37# Exact use of / are important here8# Suffix and prefix /'s need to be right9HOME_DIR = "/home/cave/"10CTF_FOLDER = HOME_DIR + "Documents/CTF/"11CUSTOM_TOOLING = HOME_DIR + ".tooling/custom/"12PWN_TEMPLATE = CUSTOM_TOOLING + "pwntemp.py"13with open(CUSTOM_TOOLING+"CTF_CONTEXT", "r") as f:14    CTF_CONTEXT = f.read()15def optParser():16    parser = optparse.OptionParser()17     18    # add options19    parser.add_option('-f', dest = 'file',20                      type = 'str',21                      help = 'Specify the file')22    parser.add_option('-s', dest = 'ctfContext',23                      type = 'str',24                      help = 'Specify the current ctf')25    parser.add_option('-c', dest = 'challCategory',26                      type = 'str',27                      help = 'Specify the current challenges category')28     29    (options, args) = parser.parse_args()30    if options.ctfContext:31        with open(CUSTOM_TOOLING+"CTF_CONTEXT", "w") as f:32            f.write(options.ctfContext)33            print(f"Writing {options.ctfContext} > CTF_CONTEXT")34            exit("[:)] Wrote new CTF context")35    elif options.challCategory:36        if "web" in options.challCategory.lower():37            return options.file, "web"38        elif "pwn" in options.challCategory.lower():39            return options.file, "pwn"40        elif "rev" in options.challCategory.lower():41            return options.file, "rev"42        elif "crypto" in options.challCategory.lower():43            return options.file, "crypto"44        elif "misc" in options.challCategory.lower():45            return options.file, "misc"46        elif "foren" in options.challCategory.lower():47            return options.file, "forensics"48        else:49            exit("""50        ----------51        Possible categories, you silly goose:52        web, rev, pwn, crypto, misc, and forensics53        ----------54        """)55    elif options.file:56        return options.file, None57    else:58        print(options.file)59        exit("""60        Sorry that is not a command!61        You can use -f <file> to specify a file.62        You can also use -c <category> 63        OR you can set the CTF context with64        -s <ctf>. To see all commands65        try running -h or --help!66        """)67def ctfSetup(fileName, category):68    ans = input(f"Do you want to setup? [y/n]\nCurrent context: {CTF_CONTEXT}\n") 69    if ans.lower() == "n":70        return 071    else:72        setupEnv = CTF_FOLDER+CTF_CONTEXT73        print(f"This is your ctf setup folder: {setupEnv}\nHappy hacking!\n")74        # Ensure that folder does not exist75        folderExists = subprocess.call(["/usr/bin/test", "-e", setupEnv])76        if folderExists == 1:77            subprocess.call(["/usr/bin/mkdir", setupEnv])78            subprocess.call(["/usr/bin/mkdir", setupEnv+"/web"])79            subprocess.call(["/usr/bin/mkdir", setupEnv+"/pwn"])80            subprocess.call(["/usr/bin/mkdir", setupEnv+"/rev"])81            subprocess.call(["/usr/bin/mkdir", setupEnv+"/crypto"])82            subprocess.call(["/usr/bin/mkdir", setupEnv+"/misc"])83            subprocess.call(["/usr/bin/mkdir", setupEnv+"/forensics"])84        if category:85            catFolder = setupEnv+"/"+category+"/"+fileName.split(".")[0]86            subprocess.call(["/usr/bin/mkdir", catFolder])87            subprocess.call(["/usr/bin/mv", fileName, catFolder])88            solExists = subprocess.call(["/usr/bin/test", "-e", catFolder+"/"+"pwntemp.py"])89            if solExists == 1:90                if category == "pwn":91                    subprocess.call(["/usr/bin/cp", CUSTOM_TOOLING+"pwntemp.py", catFolder])92            93                elif category == "rev":94                    subprocess.call(["/usr/bin/cp", CUSTOM_TOOLING+"pwntemp.py", catFolder])95            96            readmeExists = subprocess.call(["/usr/bin/test", "-e", catFolder+"/"+"README.md"])97            if readmeExists == 1:98                subprocess.call(["/usr/bin/touch", catFolder+"/"+"README.md"])99        else:100            subprocess.call(["/usr/bin/mv", fileName, setupEnv])101def main(fileName, category):102    ctfSetup(fileName, category)103if __name__ == "__main__":104    file, category = optParser()...

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