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()...

Automation Testing Tutorials

Learn to execute automation testing from scratch with LambdaTest Learning Hub. Right from setting up the prerequisites to run your first automation test, to following best practices and diving deeper into advanced test scenarios. LambdaTest Learning Hubs compile a list of step-by-step guides to help you be proficient with different test automation frameworks i.e. Selenium, Cypress, TestNG etc.