How to use fail_step method in grail

Best Python code snippet using grail_python

runner.py

Source:runner.py

1from os import environ2import traceback3from typing import List, Dict, Optional4from dataclasses import dataclass5from timun.model import Feature, Scenario, ScenarioOutline, ScenarioType, Step6from timun.step import StepDescriptor, StepDict7from termcolor import colored8@dataclass9class ScenarioTestReport:10    feature: Optional[Feature]11    scenario: Scenario12    fail_step: Optional[Step] = None13    exception: Optional[Exception] = None14    step_desc: Optional[StepDescriptor] = None15    message: Optional[str] = None16    show_traceback = True17    def desc_line(self) -> str:18        res = f"{colored(self.scenario.type.name, 'blue')}\t: {self.scenario.text}"19        if self.feature:20            res += f"@{self.feature.filename}:{self.scenario.idx + 1} - "21        else:22            res += f"@GENERATED SCENARIOS - "23        if self.exception:24            res += f"{colored('FAILED', 'red')}:"25        else:26            res += colored("SUCCESS", "green")27        return res28    def __str__(self) -> str:29        res = self.desc_line()30        if self.exception:31            if self.fail_step:32                res += f"\n\tStep: {self.fail_step.text}:{self.fail_step.idx}"33            if self.step_desc:34                res += f" (desc {self.step_desc.function.__name__}"35                res += f"@{self.step_desc.filename}:{self.step_desc.function.__code__.co_firstlineno})"36            if self.message == None:37                self.message = "\n".join(self.exception.args)38        if self.message:39            lines = self.message.splitlines()40            if self.show_traceback and not isinstance(self.exception, AssertionError):41                for l in lines[:-3]:42                    res += f"\n\t| {l}"43            for l in lines[-3:]:44                res += f"\n\t| {l}"45        return res46class TestRunner:47    def __init__(48        self, features: List[Feature], step_dict: Dict[str, StepDescriptor], environment49    ):50        self.features = features51        self.step_dict = step_dict52        self._indent = 053        self.environment = environment54        self.test_report: List[ScenarioTestReport] = []55    def run(self):56        self.test_report = []57        for feature in self.features:58            self.run_feature(feature)59        print("==== Failed Scenarios ====")60        for report in self.test_report:61            if report.exception != None:62                print(report)63    def run_feature(self, feature: Feature):64        print(f"==== Feature: {feature.text} @ {feature.filename}:{feature.idx+1}")65        for scenario in feature.scenarios:66            if isinstance(scenario, ScenarioOutline):67                raise Exception(68                    f"Unexpanded scenario outline ({feature.filename}:{scenario.idx+1})"69                )70            report = run_scenario(self.step_dict, feature, scenario, self.environment)71            print(report.desc_line())72            self.test_report.append(report)73def run_scenario(74    step_dict: StepDict, feature: Optional[Feature], scenario: Scenario, environment75) -> ScenarioTestReport:76    context = {"failed": False, "scenario": scenario}77    report = ScenarioTestReport(feature, scenario)78    before_scenario_hook = getattr(environment, "before_scenario", None)79    if before_scenario_hook:80        before_scenario_hook(context)81    if feature and feature.background:82        for step in feature.background.steps:83            try:84                run_step(step_dict, step, context)85            except Exception as e:86                return create_report(e, feature, scenario, step, step_dict)87    scenario_type = scenario.type88    if scenario_type == ScenarioType.FAIL_SCENARIO:89        try:90            for step in scenario.steps:91                run_step(step_dict, step, context)92            report.exception = Exception(93                "All test in this scenario passes, expected failure"94            )95        except Exception as e:96            pass97    else:98        # NORMAL SCENARIO CASE99        for step in scenario.steps:100            try:101                run_step(step_dict, step, context)102            except Exception as e:103                return create_report(e, feature, scenario, step, step_dict)104    return report105def create_report(exception, feature, scenario, step, step_dict):106    report = ScenarioTestReport(feature, scenario)107    report.fail_step = step108    report.exception = exception109    try:110        report.step_desc, _ = find_matching_step(step_dict, step)111    except:112        report.step_desc = None113        report.show_traceback = False114    report.message = traceback.format_exc().strip()115    return report116def run_step(step_dict: StepDict, step: Step, context):117    step_definition, match = find_matching_step(step_dict, step)118    step_definition.function(context, **match.named)119def find_matching_step(step_dict: StepDict, step: Step):120    matching_step: List[StepDescriptor] = []121    # find matching step descriptor122    for desc in step_dict.values():123        match_pattern = desc.pattern.parse(step.text)124        match_type = desc.type == step.type125        if match_pattern and match_type:126            matching_step.append(desc)127    if matching_step == []:128        raise Exception(129            f"Can't find matching step definition for '{step.type.name} {step.text}'"130        )131    # TODO: Raise exception when multiple matching steps found...

log_utils.py

Source:log_utils.py

1import tensorflow as tf2tf.compat.v1.enable_v2_behavior()3from tf_agents.trajectories import trajectory4def eval_fn(before_fail_step, fail_step, safe_step, after_safe_step, get_action):5  time_step, _, _ = trajectory.to_transition(before_fail_step, fail_step)6  fail_pol_ac = get_action(time_step)7  fail_ac = fail_step.action8  time_step, _, _ = trajectory.to_transition(safe_step, after_safe_step)9  init_pol_ac = get_action(time_step)10  init_ac = safe_step.action11  fail_pol_sc_input = (fail_step.observation, fail_pol_ac)12  fail_sc_input = (fail_step.observation, fail_ac)13  init_pol_sc_input = (safe_step.observation, init_pol_ac)14  init_sc_input = (safe_step.observation, init_ac)15  def eval_sc(safety_critic, step):16    p_fail_logits, _ = safety_critic(fail_pol_sc_input, fail_step.step_type,17                                     training=True)18    p_fail_onpol = tf.nn.sigmoid(p_fail_logits)19    p_fail_logits, _ = safety_critic(fail_sc_input, fail_step.step_type,20                                     training=True)21    p_fail = tf.nn.sigmoid(p_fail_logits)22    init_p_fail_logits, _ = safety_critic(init_pol_sc_input,23                                          safe_step.step_type, training=True)24    init_p_fail_onpol = tf.nn.sigmoid(init_p_fail_logits)25    init_p_fail_logits, _ = safety_critic(init_sc_input, safe_step.step_type,26                                          training=True)27    init_p_fail = tf.nn.sigmoid(init_p_fail_logits)28    tf.compat.v2.summary.histogram('p_fail_term_onpol', data=p_fail_onpol,29                                   step=step)30    tf.compat.v2.summary.histogram('p_fail_term', data=p_fail, step=step)31    tf.compat.v2.summary.histogram('p_fail_init_onpol',32                                   data=init_p_fail_onpol, step=step)33    tf.compat.v2.summary.histogram('p_fail_init', data=init_p_fail, step=step)...

pr_graph.py

Source:pr_graph.py

1from IPython import embed as shell2from core.parsing.parse_solvers_config import ParseSolversConfig3from core.parsing.parse_domain_config import ParseDomainConfig4from core.parsing.parse_problem_config import ParseProblemConfig5from Queue import PriorityQueue6from prg_search_node import HLSearchNode, LLSearchNode7"""8Many methods called in p_mod_abs have detailed documentation.9"""10def p_mod_abs(domain_config, problem_config, solvers_config, max_iter=100):11    hl_solver, ll_solver = ParseSolversConfig.parse(solvers_config, domain_config)12    domain = ParseDomainConfig.parse(domain_config)13    problem = ParseProblemConfig.parse(problem_config, domain)14    if problem.goal_test():15        return False, "Goal is already satisfied. No planning done."16    n0 = HLSearchNode(hl_solver.translate_problem(problem), domain, problem)17    Q = PriorityQueue()18    Q.put((0, n0))19    for _ in range(max_iter):20        n = Q.get()[1]21        if n.is_hl_node():22            c_plan = n.plan(hl_solver)23            c = LLSearchNode(c_plan)24            Q.put((-n.heuristic(), n))25            Q.put((-c.heuristic(), c))26        elif n.is_ll_node():27            n.plan(ll_solver)28            if n.solved():29                return n.curr_plan, None30            Q.put((-n.heuristic(), n))31            if n.gen_child():32                fail_step, fail_pred = n.get_failed_pred()33                n_problem = n.get_problem(fail_step, fail_pred)34                c = HLSearchNode(hl_solver.translate_problem(n_problem), domain, n_problem, prefix=n.curr_plan.prefix(fail_step))35                Q.put((-c.heuristic(), c))...

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