#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# Copyright 2019 The ChromiumOS Authors
# Use of this source code is governed by a BSD-style license that can be
# found in the LICENSE file.

"""Tests for afdo_prof_analysis."""


import random
import io
import unittest

from afdo_tools.bisection import afdo_prof_analysis as analysis


class AfdoProfAnalysisTest(unittest.TestCase):
    """Class for testing AFDO Profile Analysis"""

    bad_items = {"func_a": "1", "func_b": "3", "func_c": "5"}
    good_items = {"func_a": "2", "func_b": "4", "func_d": "5"}
    random.seed(13)  # 13 is an arbitrary choice. just for consistency
    # add some extra info to make tests more reflective of real scenario
    for num in range(128):
        func_name = "func_extra_%d" % num
        # 1/3 to both, 1/3 only to good, 1/3 only to bad
        rand_val = random.randint(1, 101)
        if rand_val < 67:
            bad_items[func_name] = "test_data"
        if rand_val < 34 or rand_val >= 67:
            good_items[func_name] = "test_data"

    analysis.random.seed(5)  # 5 is an arbitrary choice. For consistent testing

    def test_text_to_json(self):
        test_data = io.StringIO(
            "deflate_slow:87460059:3\n"
            " 3: 24\n"
            " 14: 54767\n"
            " 15: 664 fill_window:22\n"
            " 16: 661\n"
            " 19: 637\n"
            " 41: 36692 longest_match:36863\n"
            " 44: 36692\n"
            " 44.2: 5861\n"
            " 46: 13942\n"
            " 46.1: 14003\n"
        )
        expected = {
            "deflate_slow": ":87460059:3\n"
            " 3: 24\n"
            " 14: 54767\n"
            " 15: 664 fill_window:22\n"
            " 16: 661\n"
            " 19: 637\n"
            " 41: 36692 longest_match:36863\n"
            " 44: 36692\n"
            " 44.2: 5861\n"
            " 46: 13942\n"
            " 46.1: 14003\n"
        }
        actual = analysis.text_to_json(test_data)
        self.assertEqual(actual, expected)
        test_data.close()

    def test_text_to_json_empty_afdo(self):
        expected = {}
        actual = analysis.text_to_json("")
        self.assertEqual(actual, expected)

    def test_json_to_text(self):
        example_prof = {"func_a": ":1\ndata\n", "func_b": ":2\nmore data\n"}
        expected_text = "func_a:1\ndata\nfunc_b:2\nmore data\n"
        self.assertEqual(analysis.json_to_text(example_prof), expected_text)

    def test_bisect_profiles(self):

        # mock run of external script with arbitrarily-chosen bad profile vals
        # save_run specified and unused b/c afdo_prof_analysis.py
        # will call with argument explicitly specified
        # pylint: disable=unused-argument
        class DeciderClass(object):
            """Class for this tests's decider."""

            def run(self, prof, save_run=False):
                if "1" in prof["func_a"] or "3" in prof["func_b"]:
                    return analysis.StatusEnum.BAD_STATUS
                return analysis.StatusEnum.GOOD_STATUS

        results = analysis.bisect_profiles_wrapper(
            DeciderClass(), self.good_items, self.bad_items
        )
        self.assertEqual(results["individuals"], sorted(["func_a", "func_b"]))
        self.assertEqual(results["ranges"], [])

    def test_range_search(self):

        # arbitrarily chosen functions whose values in the bad profile constitute
        # a problematic pair
        # pylint: disable=unused-argument
        class DeciderClass(object):
            """Class for this tests's decider."""

            def run(self, prof, save_run=False):
                if "1" in prof["func_a"] and "3" in prof["func_b"]:
                    return analysis.StatusEnum.BAD_STATUS
                return analysis.StatusEnum.GOOD_STATUS

        # put the problematic combination in separate halves of the common funcs
        # so that non-bisecting search is invoked for its actual use case
        common_funcs = [
            func for func in self.good_items if func in self.bad_items
        ]
        common_funcs.remove("func_a")
        common_funcs.insert(0, "func_a")
        common_funcs.remove("func_b")
        common_funcs.append("func_b")

        problem_range = analysis.range_search(
            DeciderClass(),
            self.good_items,
            self.bad_items,
            common_funcs,
            0,
            len(common_funcs),
        )

        self.assertEqual(["func_a", "func_b"], problem_range)

    def test_check_good_not_bad(self):
        func_in_good = "func_c"

        # pylint: disable=unused-argument
        class DeciderClass(object):
            """Class for this tests's decider."""

            def run(self, prof, save_run=False):
                if func_in_good in prof:
                    return analysis.StatusEnum.GOOD_STATUS
                return analysis.StatusEnum.BAD_STATUS

        self.assertTrue(
            analysis.check_good_not_bad(
                DeciderClass(), self.good_items, self.bad_items
            )
        )

    def test_check_bad_not_good(self):
        func_in_bad = "func_d"

        # pylint: disable=unused-argument
        class DeciderClass(object):
            """Class for this tests's decider."""

            def run(self, prof, save_run=False):
                if func_in_bad in prof:
                    return analysis.StatusEnum.BAD_STATUS
                return analysis.StatusEnum.GOOD_STATUS

        self.assertTrue(
            analysis.check_bad_not_good(
                DeciderClass(), self.good_items, self.bad_items
            )
        )


if __name__ == "__main__":
    unittest.main()