{
    "file_diffs": [
        {
            "old_file_content": "import unittest\nimport numpy as np\n\nfrom Orange.data import Domain, ContinuousVariable\nfrom Orange.data.util import get_unique_names, get_unique_names_duplicates, \\\n    get_unique_names_domain, one_hot, sanitized_name\n\n\nclass TestGetUniqueNames(unittest.TestCase):\n    def test_get_unique_names(self):\n        names = [\"foo\", \"bar\", \"baz\", \"baz (3)\"]\n        self.assertEqual(get_unique_names(names, [\"qux\"]), [\"qux\"])\n        self.assertEqual(get_unique_names(names, [\"foo\"]), [\"foo (1)\"])\n        self.assertEqual(get_unique_names(names, [\"baz\"]), [\"baz (4)\"])\n        self.assertEqual(get_unique_names(names, [\"baz (3)\"]), [\"baz (3) (1)\"])\n        self.assertEqual(\n            get_unique_names(names, [\"qux\", \"quux\"]), [\"qux\", \"quux\"])\n        self.assertEqual(\n            get_unique_names(names, [\"bar\", \"baz\"]), [\"bar (4)\", \"baz (4)\"])\n        self.assertEqual(\n            get_unique_names(names, [\"qux\", \"baz\"]), [\"qux (4)\", \"baz (4)\"])\n        self.assertEqual(\n            get_unique_names(names, [\"qux\", \"bar\"]), [\"qux (1)\", \"bar (1)\"])\n\n        self.assertEqual(get_unique_names(names, \"qux\"), \"qux\")\n        self.assertEqual(get_unique_names(names, \"foo\"), \"foo (1)\")\n        self.assertEqual(get_unique_names(names, \"baz\"), \"baz (4)\")\n\n        self.assertEqual(get_unique_names(tuple(names), \"baz\"), \"baz (4)\")\n\n    def test_get_unique_names_with_domain(self):\n        a, b, c, d = map(ContinuousVariable, [\"foo\", \"bar\", \"baz\", \"baz (3)\"])\n        domain = Domain([a, b], c, [d])\n        self.assertEqual(get_unique_names(domain, [\"qux\"]), [\"qux\"])\n        self.assertEqual(get_unique_names(domain, [\"foo\"]), [\"foo (1)\"])\n        self.assertEqual(get_unique_names(domain, [\"baz\"]), [\"baz (4)\"])\n        self.assertEqual(get_unique_names(domain, [\"baz (3)\"]), [\"baz (3) (1)\"])\n        self.assertEqual(\n            get_unique_names(domain, [\"qux\", \"quux\"]), [\"qux\", \"quux\"])\n        self.assertEqual(\n            get_unique_names(domain, [\"bar\", \"baz\"]), [\"bar (4)\", \"baz (4)\"])\n        self.assertEqual(\n            get_unique_names(domain, [\"qux\", \"baz\"]), [\"qux (4)\", \"baz (4)\"])\n        self.assertEqual(\n            get_unique_names(domain, [\"qux\", \"bar\"]), [\"qux (1)\", \"bar (1)\"])\n\n        self.assertEqual(get_unique_names(domain, \"qux\"), \"qux\")\n        self.assertEqual(get_unique_names(domain, \"foo\"), \"foo (1)\")\n        self.assertEqual(get_unique_names(domain, \"baz\"), \"baz (4)\")\n\n    def test_get_unique_names_not_equal(self):\n        names = [\"foo\", \"bar\", \"baz\", \"baz (3)\"]\n        self.assertEqual(\n            get_unique_names(names, [\"qux\"], equal_numbers=False), [\"qux\"]\n        )\n        self.assertEqual(\n            get_unique_names(names, [\"foo\"], equal_numbers=False), [\"foo (1)\"]\n        )\n        self.assertEqual(\n            get_unique_names(names, [\"baz\"], equal_numbers=False), [\"baz (4)\"]\n        )\n        self.assertEqual(\n            get_unique_names(names, [\"baz (3)\"], equal_numbers=False),\n            [\"baz (3) (1)\"]\n        )\n        self.assertEqual(\n            get_unique_names(names, [\"qux\", \"quux\"], equal_numbers=False),\n            [\"qux\", \"quux\"]\n        )\n        self.assertEqual(\n            get_unique_names(names, [\"bar\", \"baz\"], equal_numbers=False),\n            [\"bar (1)\", \"baz (4)\"]\n        )\n        self.assertEqual(\n            get_unique_names(names, [\"qux\", \"baz\"], equal_numbers=False),\n            [\"qux\", \"baz (4)\"]\n        )\n        self.assertEqual(\n            get_unique_names(names, [\"qux\", \"bar\"], equal_numbers=False),\n            [\"qux\", \"bar (1)\"]\n        )\n        self.assertEqual(\n            get_unique_names(names, [\"foo\", \"bar\", \"baz\"], equal_numbers=False),\n            [\"foo (1)\", \"bar (1)\", \"baz (4)\"]\n        )\n\n        a, b, c, d = map(ContinuousVariable, [\"foo\", \"bar\", \"baz\", \"baz (3)\"])\n        domain = Domain([a, b], c, [d])\n        self.assertEqual(\n            get_unique_names(names, [\"qux\"], equal_numbers=False), [\"qux\"]\n        )\n        self.assertEqual(\n            get_unique_names(names, [\"foo\"], equal_numbers=False), [\"foo (1)\"]\n        )\n        self.assertEqual(\n            get_unique_names(names, [\"baz\"], equal_numbers=False), [\"baz (4)\"]\n        )\n        self.assertEqual(\n            get_unique_names(names, [\"baz (3)\"], equal_numbers=False),\n            [\"baz (3) (1)\"]\n        )\n        self.assertEqual(\n            get_unique_names(domain, [\"qux\", \"quux\"], equal_numbers=False),\n            [\"qux\", \"quux\"]\n        )\n        self.assertEqual(\n            get_unique_names(domain, [\"bar\", \"baz\"], equal_numbers=False),\n            [\"bar (1)\", \"baz (4)\"]\n        )\n        self.assertEqual(\n            get_unique_names(domain, [\"qux\", \"baz\"], equal_numbers=False),\n            [\"qux\", \"baz (4)\"]\n        )\n        self.assertEqual(\n            get_unique_names(domain, [\"qux\", \"bar\"], equal_numbers=False),\n            [\"qux\", \"bar (1)\"]\n        )\n        self.assertEqual(\n            get_unique_names(domain, [\"foo\", \"bar\", \"baz\"], equal_numbers=False),\n            [\"foo (1)\", \"bar (1)\", \"baz (4)\"]\n        )\n\n    def test_get_unique_names_from_duplicates(self):\n        self.assertEqual(\n            get_unique_names_duplicates([\"foo\", \"bar\", \"baz\"]),\n            [\"foo\", \"bar\", \"baz\"])\n        self.assertEqual(\n            get_unique_names_duplicates([\"foo\", \"bar\", \"baz\", \"bar\"]),\n            [\"foo\", \"bar (1)\", \"baz\", \"bar (2)\"])\n        self.assertEqual(\n            get_unique_names_duplicates([\"x\", \"x\", \"x (1)\"]),\n            [\"x (2)\", \"x (3)\", \"x (1)\"])\n        self.assertEqual(\n            get_unique_names_duplicates([\"x (2)\", \"x\", \"x\", \"x (2)\", \"x (3)\"]),\n            [\"x (2) (1)\", \"x (4)\", \"x (5)\", \"x (2) (2)\", \"x (3)\"])\n        self.assertEqual(\n                        get_unique_names_duplicates([\"iris\", \"iris\", \"iris (1)\"]),\n                        [\"iris (2)\", \"iris (3)\", \"iris (1)\"])\n\n        self.assertEqual(\n            get_unique_names_duplicates([\"foo\", \"bar\", \"baz\"], return_duplicated=True),\n            ([\"foo\", \"bar\", \"baz\"], []))\n        self.assertEqual(\n            get_unique_names_duplicates([\"foo\", \"bar\", \"baz\", \"bar\"], return_duplicated=True),\n            ([\"foo\", \"bar (1)\", \"baz\", \"bar (2)\"], [\"bar\"]))\n        self.assertEqual(\n            get_unique_names_duplicates([\"x\", \"x\", \"x (1)\"], return_duplicated=True),\n            ([\"x (2)\", \"x (3)\", \"x (1)\"], [\"x\"]))\n        self.assertEqual(\n            get_unique_names_duplicates([\"x (2)\", \"x\", \"x\", \"x (2)\", \"x (3)\"], return_duplicated=True),\n            ([\"x (2) (1)\", \"x (4)\", \"x (5)\", \"x (2) (2)\", \"x (3)\"], [\"x (2)\", \"x\"]))\n        self.assertEqual(\n            get_unique_names_duplicates([\"x\", \"\", \"\", None, None, \"x\"]),\n            [\"x (1)\", \"\", \"\", None, None, \"x (2)\"])\n        self.assertEqual(\n            get_unique_names_duplicates([\"iris\", \"iris\", \"iris (1)\", \"iris (2)\"], return_duplicated=True),\n            ([\"iris (3)\", \"iris (4)\", \"iris (1)\", \"iris (2)\"], [\"iris\"]))\n\n        self.assertEqual(\n            get_unique_names_duplicates([\"iris (1) (1)\", \"iris (1)\", \"iris (1)\"]),\n            [\"iris (1) (1)\", \"iris (1) (2)\", \"iris (1) (3)\"]\n        )\n\n        self.assertEqual(\n            get_unique_names_duplicates([\"iris (1) (1)\", \"iris (1)\", \"iris (1)\", \"iris\", \"iris\"]),\n            [\"iris (1) (1)\", \"iris (1) (2)\", \"iris (1) (3)\", \"iris (2)\", \"iris (3)\"]\n        )\n\n    def test_get_unique_names_domain(self):\n        (attrs, classes, metas), renamed = \\\n            get_unique_names_domain([\"a\", \"t\", \"c\", \"t\"], [\"t\", \"d\"], [\"d\", \"e\"])\n        self.assertEqual(attrs, [\"a\", \"t (1)\", \"c\", \"t (2)\"])\n        self.assertEqual(classes, [\"t (3)\", \"d (1)\"])\n        self.assertEqual(metas, [\"d (2)\", \"e\"])\n        self.assertEqual(renamed, [\"t\", \"d\"])\n\n        (attrs, classes, metas), renamed = \\\n            get_unique_names_domain([\"a\", \"t\", \"c\", \"t\"], [\"t\", \"d\"])\n        self.assertEqual(attrs, [\"a\", \"t (1)\", \"c\", \"t (2)\"])\n        self.assertEqual(classes, [\"t (3)\", \"d\"])\n        self.assertEqual(metas, [])\n        self.assertEqual(renamed, [\"t\"])\n\n        (attrs, classes, metas), renamed = \\\n            get_unique_names_domain([\"a\", \"t\", \"c\"])\n        self.assertEqual(attrs, [\"a\", \"t\", \"c\"])\n        self.assertEqual(classes, [])\n        self.assertEqual(metas, [])\n        self.assertEqual(renamed, [])\n\n        (attrs, classes, metas), renamed = \\\n            get_unique_names_domain([\"a\", \"t\", \"d\", \"t\"], [], [\"d\", \"e\"])\n        self.assertEqual(attrs, [\"a\", \"t (1)\", \"d (1)\", \"t (2)\"])\n        self.assertEqual(classes, [])\n        self.assertEqual(metas, [\"d (2)\", \"e\"])\n        self.assertEqual(renamed, [\"t\", \"d\"])\n\n        (attrs, classes, metas), renamed = \\\n            get_unique_names_domain([], [\"t\", \"d\"], [\"d\", \"e\"])\n        self.assertEqual(attrs, [])\n        self.assertEqual(classes, [\"t\", \"d (1)\"])\n        self.assertEqual(metas, [\"d (2)\", \"e\"])\n        self.assertEqual(renamed, [\"d\"])\n\n        (attrs, classes, metas), renamed = \\\n            get_unique_names_domain([], [\"t\", \"t\", \"d\"], [])\n        self.assertEqual(attrs, [])\n        self.assertEqual(classes, [\"t (1)\", \"t (2)\", \"d\"])\n        self.assertEqual(metas, [])\n        self.assertEqual(renamed, [\"t\"])\n\n        (attrs, classes, metas), renamed = \\\n            get_unique_names_domain([], [], [])\n        self.assertEqual(attrs, [])\n        self.assertEqual(classes, [])\n        self.assertEqual(metas, [])\n        self.assertEqual(renamed, [])\n\n\nclass TestOneHot(unittest.TestCase):\n\n    def setUp(self):\n        self.values = np.array([0, 1, 2, 1])\n\n    @staticmethod\n    def test_empty():\n        np.testing.assert_array_equal(\n            one_hot([]), np.zeros((0, 0))\n        )\n        np.testing.assert_array_equal(\n            one_hot([], dim=2), np.zeros((0, 2))\n        )\n\n    def test_one_hot(self):\n        np.testing.assert_array_equal(\n            one_hot(self.values),\n            [[1, 0, 0],\n             [0, 1, 0],\n             [0, 0, 1],\n             [0, 1, 0]]\n        )\n\n    def test_dtype(self):\n        res = one_hot(self.values)\n        self.assertEqual(res.dtype, float)\n        res = one_hot(self.values, dtype=int)\n        self.assertEqual(res.dtype, int)\n\n    def test_dim(self):\n        np.testing.assert_array_equal(\n            one_hot(self.values, dim=4),\n            [[1, 0, 0, 0],\n             [0, 1, 0, 0],\n             [0, 0, 1, 0],\n             [0, 1, 0, 0]]\n        )\n\n    def test_dim_too_low(self):\n        with self.assertRaises(ValueError):\n            one_hot(self.values, dim=2)\n\n\nclass TestSanitizedName(unittest.TestCase):\n    def test_sanitized_name(self):\n        self.assertEqual(sanitized_name(\"Foo\"), \"Foo\")\n        self.assertEqual(sanitized_name(\"Foo Bar\"), \"Foo_Bar\")\n        self.assertEqual(sanitized_name(\"0Foo\"), \"_0Foo\")\n        self.assertEqual(sanitized_name(\"1 Foo Bar\"), \"_1_Foo_Bar\")\n\n\nif __name__ == \"__main__\":\n    unittest.main()\n",
            "new_file_content": "import unittest\nimport numpy as np\n\nfrom Orange.data import Domain, ContinuousVariable\nfrom Orange.data.util import get_unique_names, get_unique_names_duplicates, \\\n    get_unique_names_domain, one_hot, sanitized_name\n\n\nclass TestGetUniqueNames(unittest.TestCase):\n    def test_get_unique_names(self):\n        names = [\"foo\", \"bar\", \"baz\", \"baz (3)\"]\n        self.assertEqual(get_unique_names(names, [\"qux\"]), [\"qux\"])\n        self.assertEqual(get_unique_names(names, [\"foo\"]), [\"foo (1)\"])\n        self.assertEqual(get_unique_names(names, [\"baz\"]), [\"baz (4)\"])\n        self.assertEqual(get_unique_names(names, [\"baz (3)\"]), [\"baz (3) (1)\"])\n        self.assertEqual(\n            get_unique_names(names, [\"qux\", \"quux\"]), [\"qux\", \"quux\"])\n        self.assertEqual(\n            get_unique_names(names, [\"bar\", \"baz\"]), [\"bar (4)\", \"baz (4)\"])\n        self.assertEqual(\n            get_unique_names(names, [\"qux\", \"baz\"]), [\"qux (4)\", \"baz (4)\"])\n        self.assertEqual(\n            get_unique_names(names, [\"qux\", \"bar\"]), [\"qux (1)\", \"bar (1)\"])\n\n        self.assertEqual(get_unique_names(names, \"qux\"), \"qux\")\n        self.assertEqual(get_unique_names(names, \"foo\"), \"foo (1)\")\n        self.assertEqual(get_unique_names(names, \"baz\"), \"baz (4)\")\n\n        self.assertEqual(get_unique_names(tuple(names), \"baz\"), \"baz (4)\")\n\n    def test_get_unique_names_with_domain(self):\n        a, b, c, d = map(ContinuousVariable, [\"foo\", \"bar\", \"baz\", \"baz (3)\"])\n        domain = Domain([a, b], c, [d])\n        self.assertEqual(get_unique_names(domain, [\"qux\"]), [\"qux\"])\n        self.assertEqual(get_unique_names(domain, [\"foo\"]), [\"foo (1)\"])\n        self.assertEqual(get_unique_names(domain, [\"baz\"]), [\"baz (4)\"])\n        self.assertEqual(get_unique_names(domain, [\"baz (3)\"]), [\"baz (3) (1)\"])\n        self.assertEqual(\n            get_unique_names(domain, [\"qux\", \"quux\"]), [\"qux\", \"quux\"])\n        self.assertEqual(\n            get_unique_names(domain, [\"bar\", \"baz\"]), [\"bar (4)\", \"baz (4)\"])\n        self.assertEqual(\n            get_unique_names(domain, [\"qux\", \"baz\"]), [\"qux (4)\", \"baz (4)\"])\n        self.assertEqual(\n            get_unique_names(domain, [\"qux\", \"bar\"]), [\"qux (1)\", \"bar (1)\"])\n\n        self.assertEqual(get_unique_names(domain, \"qux\"), \"qux\")\n        self.assertEqual(get_unique_names(domain, \"foo\"), \"foo (1)\")\n        self.assertEqual(get_unique_names(domain, \"baz\"), \"baz (4)\")\n\n    def test_get_unique_names_not_equal(self):\n        names = [\"foo\", \"bar\", \"baz\", \"baz (3)\"]\n        self.assertEqual(\n            get_unique_names(names, [\"qux\"], equal_numbers=False), [\"qux\"]\n        )\n        self.assertEqual(\n            get_unique_names(names, [\"foo\"], equal_numbers=False), [\"foo (1)\"]\n        )\n        self.assertEqual(\n            get_unique_names(names, [\"baz\"], equal_numbers=False), [\"baz (4)\"]\n        )\n        self.assertEqual(\n            get_unique_names(names, [\"baz (3)\"], equal_numbers=False),\n            [\"baz (3) (1)\"]\n        )\n        self.assertEqual(\n            get_unique_names(names, [\"qux\", \"quux\"], equal_numbers=False),\n            [\"qux\", \"quux\"]\n        )\n        self.assertEqual(\n            get_unique_names(names, [\"bar\", \"baz\"], equal_numbers=False),\n            [\"bar (1)\", \"baz (4)\"]\n        )\n        self.assertEqual(\n            get_unique_names(names, [\"qux\", \"baz\"], equal_numbers=False),\n            [\"qux\", \"baz (4)\"]\n        )\n        self.assertEqual(\n            get_unique_names(names, [\"qux\", \"bar\"], equal_numbers=False),\n            [\"qux\", \"bar (1)\"]\n        )\n        self.assertEqual(\n            get_unique_names(names, [\"foo\", \"bar\", \"baz\"], equal_numbers=False),\n            [\"foo (1)\", \"bar (1)\", \"baz (4)\"]\n        )\n\n        a, b, c, d = map(ContinuousVariable, [\"foo\", \"bar\", \"baz\", \"baz (3)\"])\n        domain = Domain([a, b], c, [d])\n        self.assertEqual(\n            get_unique_names(names, [\"qux\"], equal_numbers=False), [\"qux\"]\n        )\n        self.assertEqual(\n            get_unique_names(names, [\"foo\"], equal_numbers=False), [\"foo (1)\"]\n        )\n        self.assertEqual(\n            get_unique_names(names, [\"baz\"], equal_numbers=False), [\"baz (4)\"]\n        )\n        self.assertEqual(\n            get_unique_names(names, [\"baz (3)\"], equal_numbers=False),\n            [\"baz (3) (1)\"]\n        )\n        self.assertEqual(\n            get_unique_names(domain, [\"qux\", \"quux\"], equal_numbers=False),\n            [\"qux\", \"quux\"]\n        )\n        self.assertEqual(\n            get_unique_names(domain, [\"bar\", \"baz\"], equal_numbers=False),\n            [\"bar (1)\", \"baz (4)\"]\n        )\n        self.assertEqual(\n            get_unique_names(domain, [\"qux\", \"baz\"], equal_numbers=False),\n            [\"qux\", \"baz (4)\"]\n        )\n        self.assertEqual(\n            get_unique_names(domain, [\"qux\", \"bar\"], equal_numbers=False),\n            [\"qux\", \"bar (1)\"]\n        )\n        self.assertEqual(\n            get_unique_names(domain, [\"foo\", \"bar\", \"baz\"], equal_numbers=False),\n            [\"foo (1)\", \"bar (1)\", \"baz (4)\"]\n        )\n\n    def test_get_unique_names_duplicated_proposals(self):\n        names = [\"foo\", \"bar\", \"baz\", \"baz (3)\"]\n\n        self.assertEqual(\n            get_unique_names(names, [\"foo\", \"boo\", \"boo\"]),\n            ['foo (1)', 'boo (1)', 'boo (2)']\n        )\n        self.assertEqual(\n            get_unique_names(names, [\"foo\", \"boo\", \"boo\", \"baz\"]),\n            ['foo (4)', 'boo (4)', 'boo (5)', 'baz (4)']\n        )\n        self.assertEqual(\n            get_unique_names([], [\"foo\", \"boo\", \"boo\", \"baz\"]),\n            ['foo', 'boo (1)', 'boo (2)', 'baz']\n        )\n        self.assertEqual(\n            get_unique_names([\"foo\", \"bong\"], [\"foo\", \"boo\", \"boo\", \"baz\"]),\n            ['foo (1)', 'boo (1)', 'boo (2)', 'baz']\n        )\n\n        self.assertEqual(\n            get_unique_names(names, [\"foo\", \"boo\", \"boo\"],\n                             equal_numbers=False),\n            ['foo (1)', 'boo (1)', 'boo (2)']\n        )\n        self.assertEqual(\n            get_unique_names(names, [\"foo\", \"boo\", \"boo\", \"baz\"],\n                             equal_numbers=False),\n            ['foo (1)', 'boo (1)', 'boo (2)', 'baz (4)']\n        )\n        self.assertEqual(\n            get_unique_names([], [\"foo\", \"boo\", \"boo\", \"baz\"],\n                             equal_numbers=False),\n            ['foo', 'boo (1)', 'boo (2)', 'baz']\n        )\n        self.assertEqual(\n            get_unique_names([\"foo\", \"bong\"], [\"foo\", \"boo\", \"boo\", \"baz\"],\n                             equal_numbers=False),\n            ['foo (1)', 'boo (1)', 'boo (2)', 'baz']\n        )\n\n    def test_get_unique_names_from_duplicates(self):\n        self.assertEqual(\n            get_unique_names_duplicates([\"foo\", \"bar\", \"baz\"]),\n            [\"foo\", \"bar\", \"baz\"])\n        self.assertEqual(\n            get_unique_names_duplicates([\"foo\", \"bar\", \"baz\", \"bar\"]),\n            [\"foo\", \"bar (1)\", \"baz\", \"bar (2)\"])\n        self.assertEqual(\n            get_unique_names_duplicates([\"x\", \"x\", \"x (1)\"]),\n            [\"x (2)\", \"x (3)\", \"x (1)\"])\n        self.assertEqual(\n            get_unique_names_duplicates([\"x (2)\", \"x\", \"x\", \"x (2)\", \"x (3)\"]),\n            [\"x (2) (1)\", \"x (4)\", \"x (5)\", \"x (2) (2)\", \"x (3)\"])\n        self.assertEqual(\n                        get_unique_names_duplicates([\"iris\", \"iris\", \"iris (1)\"]),\n                        [\"iris (2)\", \"iris (3)\", \"iris (1)\"])\n\n        self.assertEqual(\n            get_unique_names_duplicates([\"foo\", \"bar\", \"baz\"], return_duplicated=True),\n            ([\"foo\", \"bar\", \"baz\"], []))\n        self.assertEqual(\n            get_unique_names_duplicates([\"foo\", \"bar\", \"baz\", \"bar\"], return_duplicated=True),\n            ([\"foo\", \"bar (1)\", \"baz\", \"bar (2)\"], [\"bar\"]))\n        self.assertEqual(\n            get_unique_names_duplicates([\"x\", \"x\", \"x (1)\"], return_duplicated=True),\n            ([\"x (2)\", \"x (3)\", \"x (1)\"], [\"x\"]))\n        self.assertEqual(\n            get_unique_names_duplicates([\"x (2)\", \"x\", \"x\", \"x (2)\", \"x (3)\"], return_duplicated=True),\n            ([\"x (2) (1)\", \"x (4)\", \"x (5)\", \"x (2) (2)\", \"x (3)\"], [\"x (2)\", \"x\"]))\n        self.assertEqual(\n            get_unique_names_duplicates([\"x\", \"\", \"\", None, None, \"x\"]),\n            [\"x (1)\", \"\", \"\", None, None, \"x (2)\"])\n        self.assertEqual(\n            get_unique_names_duplicates([\"iris\", \"iris\", \"iris (1)\", \"iris (2)\"], return_duplicated=True),\n            ([\"iris (3)\", \"iris (4)\", \"iris (1)\", \"iris (2)\"], [\"iris\"]))\n\n        self.assertEqual(\n            get_unique_names_duplicates([\"iris (1) (1)\", \"iris (1)\", \"iris (1)\"]),\n            [\"iris (1) (1)\", \"iris (1) (2)\", \"iris (1) (3)\"]\n        )\n\n        self.assertEqual(\n            get_unique_names_duplicates([\"iris (1) (1)\", \"iris (1)\", \"iris (1)\", \"iris\", \"iris\"]),\n            [\"iris (1) (1)\", \"iris (1) (2)\", \"iris (1) (3)\", \"iris (2)\", \"iris (3)\"]\n        )\n\n    def test_get_unique_names_domain(self):\n        (attrs, classes, metas), renamed = \\\n            get_unique_names_domain([\"a\", \"t\", \"c\", \"t\"], [\"t\", \"d\"], [\"d\", \"e\"])\n        self.assertEqual(attrs, [\"a\", \"t (1)\", \"c\", \"t (2)\"])\n        self.assertEqual(classes, [\"t (3)\", \"d (1)\"])\n        self.assertEqual(metas, [\"d (2)\", \"e\"])\n        self.assertEqual(renamed, [\"t\", \"d\"])\n\n        (attrs, classes, metas), renamed = \\\n            get_unique_names_domain([\"a\", \"t\", \"c\", \"t\"], [\"t\", \"d\"])\n        self.assertEqual(attrs, [\"a\", \"t (1)\", \"c\", \"t (2)\"])\n        self.assertEqual(classes, [\"t (3)\", \"d\"])\n        self.assertEqual(metas, [])\n        self.assertEqual(renamed, [\"t\"])\n\n        (attrs, classes, metas), renamed = \\\n            get_unique_names_domain([\"a\", \"t\", \"c\"])\n        self.assertEqual(attrs, [\"a\", \"t\", \"c\"])\n        self.assertEqual(classes, [])\n        self.assertEqual(metas, [])\n        self.assertEqual(renamed, [])\n\n        (attrs, classes, metas), renamed = \\\n            get_unique_names_domain([\"a\", \"t\", \"d\", \"t\"], [], [\"d\", \"e\"])\n        self.assertEqual(attrs, [\"a\", \"t (1)\", \"d (1)\", \"t (2)\"])\n        self.assertEqual(classes, [])\n        self.assertEqual(metas, [\"d (2)\", \"e\"])\n        self.assertEqual(renamed, [\"t\", \"d\"])\n\n        (attrs, classes, metas), renamed = \\\n            get_unique_names_domain([], [\"t\", \"d\"], [\"d\", \"e\"])\n        self.assertEqual(attrs, [])\n        self.assertEqual(classes, [\"t\", \"d (1)\"])\n        self.assertEqual(metas, [\"d (2)\", \"e\"])\n        self.assertEqual(renamed, [\"d\"])\n\n        (attrs, classes, metas), renamed = \\\n            get_unique_names_domain([], [\"t\", \"t\", \"d\"], [])\n        self.assertEqual(attrs, [])\n        self.assertEqual(classes, [\"t (1)\", \"t (2)\", \"d\"])\n        self.assertEqual(metas, [])\n        self.assertEqual(renamed, [\"t\"])\n\n        (attrs, classes, metas), renamed = \\\n            get_unique_names_domain([], [], [])\n        self.assertEqual(attrs, [])\n        self.assertEqual(classes, [])\n        self.assertEqual(metas, [])\n        self.assertEqual(renamed, [])\n\n\nclass TestOneHot(unittest.TestCase):\n\n    def setUp(self):\n        self.values = np.array([0, 1, 2, 1])\n\n    @staticmethod\n    def test_empty():\n        np.testing.assert_array_equal(\n            one_hot([]), np.zeros((0, 0))\n        )\n        np.testing.assert_array_equal(\n            one_hot([], dim=2), np.zeros((0, 2))\n        )\n\n    def test_one_hot(self):\n        np.testing.assert_array_equal(\n            one_hot(self.values),\n            [[1, 0, 0],\n             [0, 1, 0],\n             [0, 0, 1],\n             [0, 1, 0]]\n        )\n\n    def test_dtype(self):\n        res = one_hot(self.values)\n        self.assertEqual(res.dtype, float)\n        res = one_hot(self.values, dtype=int)\n        self.assertEqual(res.dtype, int)\n\n    def test_dim(self):\n        np.testing.assert_array_equal(\n            one_hot(self.values, dim=4),\n            [[1, 0, 0, 0],\n             [0, 1, 0, 0],\n             [0, 0, 1, 0],\n             [0, 1, 0, 0]]\n        )\n\n    def test_dim_too_low(self):\n        with self.assertRaises(ValueError):\n            one_hot(self.values, dim=2)\n\n\nclass TestSanitizedName(unittest.TestCase):\n    def test_sanitized_name(self):\n        self.assertEqual(sanitized_name(\"Foo\"), \"Foo\")\n        self.assertEqual(sanitized_name(\"Foo Bar\"), \"Foo_Bar\")\n        self.assertEqual(sanitized_name(\"0Foo\"), \"_0Foo\")\n        self.assertEqual(sanitized_name(\"1 Foo Bar\"), \"_1_Foo_Bar\")\n\n\nif __name__ == \"__main__\":\n    unittest.main()\n",
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                                "content": "            get_unique_names([], [\"foo\", \"boo\", \"boo\", \"baz\"],",
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                                "type": "added"
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                            {
                                "content": "            ['foo', 'boo (1)', 'boo (2)', 'baz']",
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                                "content": "        self.assertEqual(",
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                            "file_name": "Orange/data/tests/test_util.py",
                            "type": "class",
                            "name": "TestGetUniqueNames",
                            "content": "class TestGetUniqueNames(unittest.TestCase):\n    def test_get_unique_names(self):\n        names = [\"foo\", \"bar\", \"baz\", \"baz (3)\"]\n        self.assertEqual(get_unique_names(names, [\"qux\"]), [\"qux\"])\n        self.assertEqual(get_unique_names(names, [\"foo\"]), [\"foo (1)\"])\n        self.assertEqual(get_unique_names(names, [\"baz\"]), [\"baz (4)\"])\n        self.assertEqual(get_unique_names(names, [\"baz (3)\"]), [\"baz (3) (1)\"])\n        self.assertEqual(\n            get_unique_names(names, [\"qux\", \"quux\"]), [\"qux\", \"quux\"])\n        self.assertEqual(\n            get_unique_names(names, [\"bar\", \"baz\"]), [\"bar (4)\", \"baz (4)\"])\n        self.assertEqual(\n            get_unique_names(names, [\"qux\", \"baz\"]), [\"qux (4)\", \"baz (4)\"])\n        self.assertEqual(\n            get_unique_names(names, [\"qux\", \"bar\"]), [\"qux (1)\", \"bar (1)\"])\n\n        self.assertEqual(get_unique_names(names, \"qux\"), \"qux\")\n        self.assertEqual(get_unique_names(names, \"foo\"), \"foo (1)\")\n        self.assertEqual(get_unique_names(names, \"baz\"), \"baz (4)\")\n\n        self.assertEqual(get_unique_names(tuple(names), \"baz\"), \"baz (4)\")\n\n    def test_get_unique_names_with_domain(self):\n        a, b, c, d = map(ContinuousVariable, [\"foo\", \"bar\", \"baz\", \"baz (3)\"])\n        domain = Domain([a, b], c, [d])\n        self.assertEqual(get_unique_names(domain, [\"qux\"]), [\"qux\"])\n        self.assertEqual(get_unique_names(domain, [\"foo\"]), [\"foo (1)\"])\n        self.assertEqual(get_unique_names(domain, [\"baz\"]), [\"baz (4)\"])\n        self.assertEqual(get_unique_names(domain, [\"baz (3)\"]), [\"baz (3) (1)\"])\n        self.assertEqual(\n            get_unique_names(domain, [\"qux\", \"quux\"]), [\"qux\", \"quux\"])\n        self.assertEqual(\n            get_unique_names(domain, [\"bar\", \"baz\"]), [\"bar (4)\", \"baz (4)\"])\n        self.assertEqual(\n            get_unique_names(domain, [\"qux\", \"baz\"]), [\"qux (4)\", \"baz (4)\"])\n        self.assertEqual(\n            get_unique_names(domain, [\"qux\", \"bar\"]), [\"qux (1)\", \"bar (1)\"])\n\n        self.assertEqual(get_unique_names(domain, \"qux\"), \"qux\")\n        self.assertEqual(get_unique_names(domain, \"foo\"), \"foo (1)\")\n        self.assertEqual(get_unique_names(domain, \"baz\"), \"baz (4)\")\n\n    def test_get_unique_names_not_equal(self):\n        names = [\"foo\", \"bar\", \"baz\", \"baz (3)\"]\n        self.assertEqual(\n            get_unique_names(names, [\"qux\"], equal_numbers=False), [\"qux\"]\n        )\n        self.assertEqual(\n            get_unique_names(names, [\"foo\"], equal_numbers=False), [\"foo (1)\"]\n        )\n        self.assertEqual(\n            get_unique_names(names, [\"baz\"], equal_numbers=False), [\"baz (4)\"]\n        )\n        self.assertEqual(\n            get_unique_names(names, [\"baz (3)\"], equal_numbers=False),\n            [\"baz (3) (1)\"]\n        )\n        self.assertEqual(\n            get_unique_names(names, [\"qux\", \"quux\"], equal_numbers=False),\n            [\"qux\", \"quux\"]\n        )\n        self.assertEqual(\n            get_unique_names(names, [\"bar\", \"baz\"], equal_numbers=False),\n            [\"bar (1)\", \"baz (4)\"]\n        )\n        self.assertEqual(\n            get_unique_names(names, [\"qux\", \"baz\"], equal_numbers=False),\n            [\"qux\", \"baz (4)\"]\n        )\n        self.assertEqual(\n            get_unique_names(names, [\"qux\", \"bar\"], equal_numbers=False),\n            [\"qux\", \"bar (1)\"]\n        )\n        self.assertEqual(\n            get_unique_names(names, [\"foo\", \"bar\", \"baz\"], equal_numbers=False),\n            [\"foo (1)\", \"bar (1)\", \"baz (4)\"]\n        )\n\n        a, b, c, d = map(ContinuousVariable, [\"foo\", \"bar\", \"baz\", \"baz (3)\"])\n        domain = Domain([a, b], c, [d])\n        self.assertEqual(\n            get_unique_names(names, [\"qux\"], equal_numbers=False), [\"qux\"]\n        )\n        self.assertEqual(\n            get_unique_names(names, [\"foo\"], equal_numbers=False), [\"foo (1)\"]\n        )\n        self.assertEqual(\n            get_unique_names(names, [\"baz\"], equal_numbers=False), [\"baz (4)\"]\n        )\n        self.assertEqual(\n            get_unique_names(names, [\"baz (3)\"], equal_numbers=False),\n            [\"baz (3) (1)\"]\n        )\n        self.assertEqual(\n            get_unique_names(domain, [\"qux\", \"quux\"], equal_numbers=False),\n            [\"qux\", \"quux\"]\n        )\n        self.assertEqual(\n            get_unique_names(domain, [\"bar\", \"baz\"], equal_numbers=False),\n            [\"bar (1)\", \"baz (4)\"]\n        )\n        self.assertEqual(\n            get_unique_names(domain, [\"qux\", \"baz\"], equal_numbers=False),\n            [\"qux\", \"baz (4)\"]\n        )\n        self.assertEqual(\n            get_unique_names(domain, [\"qux\", \"bar\"], equal_numbers=False),\n            [\"qux\", \"bar (1)\"]\n        )\n        self.assertEqual(\n            get_unique_names(domain, [\"foo\", \"bar\", \"baz\"], equal_numbers=False),\n            [\"foo (1)\", \"bar (1)\", \"baz (4)\"]\n        )\n\n    def test_get_unique_names_duplicated_proposals(self):\n        names = [\"foo\", \"bar\", \"baz\", \"baz (3)\"]\n\n        self.assertEqual(\n            get_unique_names(names, [\"foo\", \"boo\", \"boo\"]),\n            ['foo (1)', 'boo (1)', 'boo (2)']\n        )\n        self.assertEqual(\n            get_unique_names(names, [\"foo\", \"boo\", \"boo\", \"baz\"]),\n            ['foo (4)', 'boo (4)', 'boo (5)', 'baz (4)']\n        )\n        self.assertEqual(\n            get_unique_names([], [\"foo\", \"boo\", \"boo\", \"baz\"]),\n            ['foo', 'boo (1)', 'boo (2)', 'baz']\n        )\n        self.assertEqual(\n            get_unique_names([\"foo\", \"bong\"], [\"foo\", \"boo\", \"boo\", \"baz\"]),\n            ['foo (1)', 'boo (1)', 'boo (2)', 'baz']\n        )\n\n        self.assertEqual(\n            get_unique_names(names, [\"foo\", \"boo\", \"boo\"],\n                             equal_numbers=False),\n            ['foo (1)', 'boo (1)', 'boo (2)']\n        )\n        self.assertEqual(\n            get_unique_names(names, [\"foo\", \"boo\", \"boo\", \"baz\"],\n                             equal_numbers=False),\n            ['foo (1)', 'boo (1)', 'boo (2)', 'baz (4)']\n        )\n        self.assertEqual(\n            get_unique_names([], [\"foo\", \"boo\", \"boo\", \"baz\"],\n                             equal_numbers=False),\n            ['foo', 'boo (1)', 'boo (2)', 'baz']\n        )\n        self.assertEqual(\n            get_unique_names([\"foo\", \"bong\"], [\"foo\", \"boo\", \"boo\", \"baz\"],\n                             equal_numbers=False),\n            ['foo (1)', 'boo (1)', 'boo (2)', 'baz']\n        )\n\n    def test_get_unique_names_from_duplicates(self):\n        self.assertEqual(\n            get_unique_names_duplicates([\"foo\", \"bar\", \"baz\"]),\n            [\"foo\", \"bar\", \"baz\"])\n        self.assertEqual(\n            get_unique_names_duplicates([\"foo\", \"bar\", \"baz\", \"bar\"]),\n            [\"foo\", \"bar (1)\", \"baz\", \"bar (2)\"])\n        self.assertEqual(\n            get_unique_names_duplicates([\"x\", \"x\", \"x (1)\"]),\n            [\"x (2)\", \"x (3)\", \"x (1)\"])\n        self.assertEqual(\n            get_unique_names_duplicates([\"x (2)\", \"x\", \"x\", \"x (2)\", \"x (3)\"]),\n            [\"x (2) (1)\", \"x (4)\", \"x (5)\", \"x (2) (2)\", \"x (3)\"])\n        self.assertEqual(\n                        get_unique_names_duplicates([\"iris\", \"iris\", \"iris (1)\"]),\n                        [\"iris (2)\", \"iris (3)\", \"iris (1)\"])\n\n        self.assertEqual(\n            get_unique_names_duplicates([\"foo\", \"bar\", \"baz\"], return_duplicated=True),\n            ([\"foo\", \"bar\", \"baz\"], []))\n        self.assertEqual(\n            get_unique_names_duplicates([\"foo\", \"bar\", \"baz\", \"bar\"], return_duplicated=True),\n            ([\"foo\", \"bar (1)\", \"baz\", \"bar (2)\"], [\"bar\"]))\n        self.assertEqual(\n            get_unique_names_duplicates([\"x\", \"x\", \"x (1)\"], return_duplicated=True),\n            ([\"x (2)\", \"x (3)\", \"x (1)\"], [\"x\"]))\n        self.assertEqual(\n            get_unique_names_duplicates([\"x (2)\", \"x\", \"x\", \"x (2)\", \"x (3)\"], return_duplicated=True),\n            ([\"x (2) (1)\", \"x (4)\", \"x (5)\", \"x (2) (2)\", \"x (3)\"], [\"x (2)\", \"x\"]))\n        self.assertEqual(\n            get_unique_names_duplicates([\"x\", \"\", \"\", None, None, \"x\"]),\n            [\"x (1)\", \"\", \"\", None, None, \"x (2)\"])\n        self.assertEqual(\n            get_unique_names_duplicates([\"iris\", \"iris\", \"iris (1)\", \"iris (2)\"], return_duplicated=True),\n            ([\"iris (3)\", \"iris (4)\", \"iris (1)\", \"iris (2)\"], [\"iris\"]))\n\n        self.assertEqual(\n            get_unique_names_duplicates([\"iris (1) (1)\", \"iris (1)\", \"iris (1)\"]),\n            [\"iris (1) (1)\", \"iris (1) (2)\", \"iris (1) (3)\"]\n        )\n\n        self.assertEqual(\n            get_unique_names_duplicates([\"iris (1) (1)\", \"iris (1)\", \"iris (1)\", \"iris\", \"iris\"]),\n            [\"iris (1) (1)\", \"iris (1) (2)\", \"iris (1) (3)\", \"iris (2)\", \"iris (3)\"]\n        )\n\n    def test_get_unique_names_domain(self):\n        (attrs, classes, metas), renamed = \\\n            get_unique_names_domain([\"a\", \"t\", \"c\", \"t\"], [\"t\", \"d\"], [\"d\", \"e\"])\n        self.assertEqual(attrs, [\"a\", \"t (1)\", \"c\", \"t (2)\"])\n        self.assertEqual(classes, [\"t (3)\", \"d (1)\"])\n        self.assertEqual(metas, [\"d (2)\", \"e\"])\n        self.assertEqual(renamed, [\"t\", \"d\"])\n\n        (attrs, classes, metas), renamed = \\\n            get_unique_names_domain([\"a\", \"t\", \"c\", \"t\"], [\"t\", \"d\"])\n        self.assertEqual(attrs, [\"a\", \"t (1)\", \"c\", \"t (2)\"])\n        self.assertEqual(classes, [\"t (3)\", \"d\"])\n        self.assertEqual(metas, [])\n        self.assertEqual(renamed, [\"t\"])\n\n        (attrs, classes, metas), renamed = \\\n            get_unique_names_domain([\"a\", \"t\", \"c\"])\n        self.assertEqual(attrs, [\"a\", \"t\", \"c\"])\n        self.assertEqual(classes, [])\n        self.assertEqual(metas, [])\n        self.assertEqual(renamed, [])\n\n        (attrs, classes, metas), renamed = \\\n            get_unique_names_domain([\"a\", \"t\", \"d\", \"t\"], [], [\"d\", \"e\"])\n        self.assertEqual(attrs, [\"a\", \"t (1)\", \"d (1)\", \"t (2)\"])\n        self.assertEqual(classes, [])\n        self.assertEqual(metas, [\"d (2)\", \"e\"])\n        self.assertEqual(renamed, [\"t\", \"d\"])\n\n        (attrs, classes, metas), renamed = \\\n            get_unique_names_domain([], [\"t\", \"d\"], [\"d\", \"e\"])\n        self.assertEqual(attrs, [])\n        self.assertEqual(classes, [\"t\", \"d (1)\"])\n        self.assertEqual(metas, [\"d (2)\", \"e\"])\n        self.assertEqual(renamed, [\"d\"])\n\n        (attrs, classes, metas), renamed = \\\n            get_unique_names_domain([], [\"t\", \"t\", \"d\"], [])\n        self.assertEqual(attrs, [])\n        self.assertEqual(classes, [\"t (1)\", \"t (2)\", \"d\"])\n        self.assertEqual(metas, [])\n        self.assertEqual(renamed, [\"t\"])\n\n        (attrs, classes, metas), renamed = \\\n            get_unique_names_domain([], [], [])\n        self.assertEqual(attrs, [])\n        self.assertEqual(classes, [])\n        self.assertEqual(metas, [])\n        self.assertEqual(renamed, [])",
                            "ast_type_str": "ClassDef",
                            "start_lineno": 9,
                            "end_lineno": 258,
                            "parent": null
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                            "file_name": "Orange/data/tests/test_util.py",
                            "type": "method",
                            "name": "TestGetUniqueNames.test_get_unique_names_duplicated_proposals",
                            "content": "class TestGetUniqueNames(unittest.TestCase):\n    def test_get_unique_names(self):\n        names = [\"foo\", \"bar\", \"baz\", \"baz (3)\"]\n        self.assertEqual(get_unique_names(names, [\"qux\"]), [\"qux\"])\n        self.assertEqual(get_unique_names(names, [\"foo\"]), [\"foo (1)\"])\n        self.assertEqual(get_unique_names(names, [\"baz\"]), [\"baz (4)\"])\n        self.assertEqual(get_unique_names(names, [\"baz (3)\"]), [\"baz (3) (1)\"])\n        self.assertEqual(\n            get_unique_names(names, [\"qux\", \"quux\"]), [\"qux\", \"quux\"])\n        self.assertEqual(\n            get_unique_names(names, [\"bar\", \"baz\"]), [\"bar (4)\", \"baz (4)\"])\n        self.assertEqual(\n            get_unique_names(names, [\"qux\", \"baz\"]), [\"qux (4)\", \"baz (4)\"])\n        self.assertEqual(\n            get_unique_names(names, [\"qux\", \"bar\"]), [\"qux (1)\", \"bar (1)\"])\n\n        self.assertEqual(get_unique_names(names, \"qux\"), \"qux\")\n        self.assertEqual(get_unique_names(names, \"foo\"), \"foo (1)\")\n        self.assertEqual(get_unique_names(names, \"baz\"), \"baz (4)\")\n\n        self.assertEqual(get_unique_names(tuple(names), \"baz\"), \"baz (4)\")\n\n    def test_get_unique_names_with_domain(self):\n        a, b, c, d = map(ContinuousVariable, [\"foo\", \"bar\", \"baz\", \"baz (3)\"])\n        domain = Domain([a, b], c, [d])\n        self.assertEqual(get_unique_names(domain, [\"qux\"]), [\"qux\"])\n        self.assertEqual(get_unique_names(domain, [\"foo\"]), [\"foo (1)\"])\n        self.assertEqual(get_unique_names(domain, [\"baz\"]), [\"baz (4)\"])\n        self.assertEqual(get_unique_names(domain, [\"baz (3)\"]), [\"baz (3) (1)\"])\n        self.assertEqual(\n            get_unique_names(domain, [\"qux\", \"quux\"]), [\"qux\", \"quux\"])\n        self.assertEqual(\n            get_unique_names(domain, [\"bar\", \"baz\"]), [\"bar (4)\", \"baz (4)\"])\n        self.assertEqual(\n            get_unique_names(domain, [\"qux\", \"baz\"]), [\"qux (4)\", \"baz (4)\"])\n        self.assertEqual(\n            get_unique_names(domain, [\"qux\", \"bar\"]), [\"qux (1)\", \"bar (1)\"])\n\n        self.assertEqual(get_unique_names(domain, \"qux\"), \"qux\")\n        self.assertEqual(get_unique_names(domain, \"foo\"), \"foo (1)\")\n        self.assertEqual(get_unique_names(domain, \"baz\"), \"baz (4)\")\n\n    def test_get_unique_names_not_equal(self):\n        names = [\"foo\", \"bar\", \"baz\", \"baz (3)\"]\n        self.assertEqual(\n            get_unique_names(names, [\"qux\"], equal_numbers=False), [\"qux\"]\n        )\n        self.assertEqual(\n            get_unique_names(names, [\"foo\"], equal_numbers=False), [\"foo (1)\"]\n        )\n        self.assertEqual(\n            get_unique_names(names, [\"baz\"], equal_numbers=False), [\"baz (4)\"]\n        )\n        self.assertEqual(\n            get_unique_names(names, [\"baz (3)\"], equal_numbers=False),\n            [\"baz (3) (1)\"]\n        )\n        self.assertEqual(\n            get_unique_names(names, [\"qux\", \"quux\"], equal_numbers=False),\n            [\"qux\", \"quux\"]\n        )\n        self.assertEqual(\n            get_unique_names(names, [\"bar\", \"baz\"], equal_numbers=False),\n            [\"bar (1)\", \"baz (4)\"]\n        )\n        self.assertEqual(\n            get_unique_names(names, [\"qux\", \"baz\"], equal_numbers=False),\n            [\"qux\", \"baz (4)\"]\n        )\n        self.assertEqual(\n            get_unique_names(names, [\"qux\", \"bar\"], equal_numbers=False),\n            [\"qux\", \"bar (1)\"]\n        )\n        self.assertEqual(\n            get_unique_names(names, [\"foo\", \"bar\", \"baz\"], equal_numbers=False),\n            [\"foo (1)\", \"bar (1)\", \"baz (4)\"]\n        )\n\n        a, b, c, d = map(ContinuousVariable, [\"foo\", \"bar\", \"baz\", \"baz (3)\"])\n        domain = Domain([a, b], c, [d])\n        self.assertEqual(\n            get_unique_names(names, [\"qux\"], equal_numbers=False), [\"qux\"]\n        )\n        self.assertEqual(\n            get_unique_names(names, [\"foo\"], equal_numbers=False), [\"foo (1)\"]\n        )\n        self.assertEqual(\n            get_unique_names(names, [\"baz\"], equal_numbers=False), [\"baz (4)\"]\n        )\n        self.assertEqual(\n            get_unique_names(names, [\"baz (3)\"], equal_numbers=False),\n            [\"baz (3) (1)\"]\n        )\n        self.assertEqual(\n            get_unique_names(domain, [\"qux\", \"quux\"], equal_numbers=False),\n            [\"qux\", \"quux\"]\n        )\n        self.assertEqual(\n            get_unique_names(domain, [\"bar\", \"baz\"], equal_numbers=False),\n            [\"bar (1)\", \"baz (4)\"]\n        )\n        self.assertEqual(\n            get_unique_names(domain, [\"qux\", \"baz\"], equal_numbers=False),\n            [\"qux\", \"baz (4)\"]\n        )\n        self.assertEqual(\n            get_unique_names(domain, [\"qux\", \"bar\"], equal_numbers=False),\n            [\"qux\", \"bar (1)\"]\n        )\n        self.assertEqual(\n            get_unique_names(domain, [\"foo\", \"bar\", \"baz\"], equal_numbers=False),\n            [\"foo (1)\", \"bar (1)\", \"baz (4)\"]\n        )\n\n    def test_get_unique_names_duplicated_proposals(self):\n        names = [\"foo\", \"bar\", \"baz\", \"baz (3)\"]\n\n        self.assertEqual(\n            get_unique_names(names, [\"foo\", \"boo\", \"boo\"]),\n            ['foo (1)', 'boo (1)', 'boo (2)']\n        )\n        self.assertEqual(\n            get_unique_names(names, [\"foo\", \"boo\", \"boo\", \"baz\"]),\n            ['foo (4)', 'boo (4)', 'boo (5)', 'baz (4)']\n        )\n        self.assertEqual(\n            get_unique_names([], [\"foo\", \"boo\", \"boo\", \"baz\"]),\n            ['foo', 'boo (1)', 'boo (2)', 'baz']\n        )\n        self.assertEqual(\n            get_unique_names([\"foo\", \"bong\"], [\"foo\", \"boo\", \"boo\", \"baz\"]),\n            ['foo (1)', 'boo (1)', 'boo (2)', 'baz']\n        )\n\n        self.assertEqual(\n            get_unique_names(names, [\"foo\", \"boo\", \"boo\"],\n                             equal_numbers=False),\n            ['foo (1)', 'boo (1)', 'boo (2)']\n        )\n        self.assertEqual(\n            get_unique_names(names, [\"foo\", \"boo\", \"boo\", \"baz\"],\n                             equal_numbers=False),\n            ['foo (1)', 'boo (1)', 'boo (2)', 'baz (4)']\n        )\n        self.assertEqual(\n            get_unique_names([], [\"foo\", \"boo\", \"boo\", \"baz\"],\n                             equal_numbers=False),\n            ['foo', 'boo (1)', 'boo (2)', 'baz']\n        )\n        self.assertEqual(\n            get_unique_names([\"foo\", \"bong\"], [\"foo\", \"boo\", \"boo\", \"baz\"],\n                             equal_numbers=False),\n            ['foo (1)', 'boo (1)', 'boo (2)', 'baz']\n        )\n\n    def test_get_unique_names_from_duplicates(self):\n        self.assertEqual(\n            get_unique_names_duplicates([\"foo\", \"bar\", \"baz\"]),\n            [\"foo\", \"bar\", \"baz\"])\n        self.assertEqual(\n            get_unique_names_duplicates([\"foo\", \"bar\", \"baz\", \"bar\"]),\n            [\"foo\", \"bar (1)\", \"baz\", \"bar (2)\"])\n        self.assertEqual(\n            get_unique_names_duplicates([\"x\", \"x\", \"x (1)\"]),\n            [\"x (2)\", \"x (3)\", \"x (1)\"])\n        self.assertEqual(\n            get_unique_names_duplicates([\"x (2)\", \"x\", \"x\", \"x (2)\", \"x (3)\"]),\n            [\"x (2) (1)\", \"x (4)\", \"x (5)\", \"x (2) (2)\", \"x (3)\"])\n        self.assertEqual(\n                        get_unique_names_duplicates([\"iris\", \"iris\", \"iris (1)\"]),\n                        [\"iris (2)\", \"iris (3)\", \"iris (1)\"])\n\n        self.assertEqual(\n            get_unique_names_duplicates([\"foo\", \"bar\", \"baz\"], return_duplicated=True),\n            ([\"foo\", \"bar\", \"baz\"], []))\n        self.assertEqual(\n            get_unique_names_duplicates([\"foo\", \"bar\", \"baz\", \"bar\"], return_duplicated=True),\n            ([\"foo\", \"bar (1)\", \"baz\", \"bar (2)\"], [\"bar\"]))\n        self.assertEqual(\n            get_unique_names_duplicates([\"x\", \"x\", \"x (1)\"], return_duplicated=True),\n            ([\"x (2)\", \"x (3)\", \"x (1)\"], [\"x\"]))\n        self.assertEqual(\n            get_unique_names_duplicates([\"x (2)\", \"x\", \"x\", \"x (2)\", \"x (3)\"], return_duplicated=True),\n            ([\"x (2) (1)\", \"x (4)\", \"x (5)\", \"x (2) (2)\", \"x (3)\"], [\"x (2)\", \"x\"]))\n        self.assertEqual(\n            get_unique_names_duplicates([\"x\", \"\", \"\", None, None, \"x\"]),\n            [\"x (1)\", \"\", \"\", None, None, \"x (2)\"])\n        self.assertEqual(\n            get_unique_names_duplicates([\"iris\", \"iris\", \"iris (1)\", \"iris (2)\"], return_duplicated=True),\n            ([\"iris (3)\", \"iris (4)\", \"iris (1)\", \"iris (2)\"], [\"iris\"]))\n\n        self.assertEqual(\n            get_unique_names_duplicates([\"iris (1) (1)\", \"iris (1)\", \"iris (1)\"]),\n            [\"iris (1) (1)\", \"iris (1) (2)\", \"iris (1) (3)\"]\n        )\n\n        self.assertEqual(\n            get_unique_names_duplicates([\"iris (1) (1)\", \"iris (1)\", \"iris (1)\", \"iris\", \"iris\"]),\n            [\"iris (1) (1)\", \"iris (1) (2)\", \"iris (1) (3)\", \"iris (2)\", \"iris (3)\"]\n        )\n\n    def test_get_unique_names_domain(self):\n        (attrs, classes, metas), renamed = \\\n            get_unique_names_domain([\"a\", \"t\", \"c\", \"t\"], [\"t\", \"d\"], [\"d\", \"e\"])\n        self.assertEqual(attrs, [\"a\", \"t (1)\", \"c\", \"t (2)\"])\n        self.assertEqual(classes, [\"t (3)\", \"d (1)\"])\n        self.assertEqual(metas, [\"d (2)\", \"e\"])\n        self.assertEqual(renamed, [\"t\", \"d\"])\n\n        (attrs, classes, metas), renamed = \\\n            get_unique_names_domain([\"a\", \"t\", \"c\", \"t\"], [\"t\", \"d\"])\n        self.assertEqual(attrs, [\"a\", \"t (1)\", \"c\", \"t (2)\"])\n        self.assertEqual(classes, [\"t (3)\", \"d\"])\n        self.assertEqual(metas, [])\n        self.assertEqual(renamed, [\"t\"])\n\n        (attrs, classes, metas), renamed = \\\n            get_unique_names_domain([\"a\", \"t\", \"c\"])\n        self.assertEqual(attrs, [\"a\", \"t\", \"c\"])\n        self.assertEqual(classes, [])\n        self.assertEqual(metas, [])\n        self.assertEqual(renamed, [])\n\n        (attrs, classes, metas), renamed = \\\n            get_unique_names_domain([\"a\", \"t\", \"d\", \"t\"], [], [\"d\", \"e\"])\n        self.assertEqual(attrs, [\"a\", \"t (1)\", \"d (1)\", \"t (2)\"])\n        self.assertEqual(classes, [])\n        self.assertEqual(metas, [\"d (2)\", \"e\"])\n        self.assertEqual(renamed, [\"t\", \"d\"])\n\n        (attrs, classes, metas), renamed = \\\n            get_unique_names_domain([], [\"t\", \"d\"], [\"d\", \"e\"])\n        self.assertEqual(attrs, [])\n        self.assertEqual(classes, [\"t\", \"d (1)\"])\n        self.assertEqual(metas, [\"d (2)\", \"e\"])\n        self.assertEqual(renamed, [\"d\"])\n\n        (attrs, classes, metas), renamed = \\\n            get_unique_names_domain([], [\"t\", \"t\", \"d\"], [])\n        self.assertEqual(attrs, [])\n        self.assertEqual(classes, [\"t (1)\", \"t (2)\", \"d\"])\n        self.assertEqual(metas, [])\n        self.assertEqual(renamed, [\"t\"])\n\n        (attrs, classes, metas), renamed = \\\n            get_unique_names_domain([], [], [])\n        self.assertEqual(attrs, [])\n        self.assertEqual(classes, [])\n        self.assertEqual(metas, [])\n        self.assertEqual(renamed, [])",
                            "ast_type_str": "ClassDef",
                            "start_lineno": 123,
                            "end_lineno": 162,
                            "parent": {
                                "file_name": "Orange/data/tests/test_util.py",
                                "type": "class",
                                "name": "TestGetUniqueNames",
                                "content": "class TestGetUniqueNames(unittest.TestCase):\n    def test_get_unique_names(self):\n        names = [\"foo\", \"bar\", \"baz\", \"baz (3)\"]\n        self.assertEqual(get_unique_names(names, [\"qux\"]), [\"qux\"])\n        self.assertEqual(get_unique_names(names, [\"foo\"]), [\"foo (1)\"])\n        self.assertEqual(get_unique_names(names, [\"baz\"]), [\"baz (4)\"])\n        self.assertEqual(get_unique_names(names, [\"baz (3)\"]), [\"baz (3) (1)\"])\n        self.assertEqual(\n            get_unique_names(names, [\"qux\", \"quux\"]), [\"qux\", \"quux\"])\n        self.assertEqual(\n            get_unique_names(names, [\"bar\", \"baz\"]), [\"bar (4)\", \"baz (4)\"])\n        self.assertEqual(\n            get_unique_names(names, [\"qux\", \"baz\"]), [\"qux (4)\", \"baz (4)\"])\n        self.assertEqual(\n            get_unique_names(names, [\"qux\", \"bar\"]), [\"qux (1)\", \"bar (1)\"])\n\n        self.assertEqual(get_unique_names(names, \"qux\"), \"qux\")\n        self.assertEqual(get_unique_names(names, \"foo\"), \"foo (1)\")\n        self.assertEqual(get_unique_names(names, \"baz\"), \"baz (4)\")\n\n        self.assertEqual(get_unique_names(tuple(names), \"baz\"), \"baz (4)\")\n\n    def test_get_unique_names_with_domain(self):\n        a, b, c, d = map(ContinuousVariable, [\"foo\", \"bar\", \"baz\", \"baz (3)\"])\n        domain = Domain([a, b], c, [d])\n        self.assertEqual(get_unique_names(domain, [\"qux\"]), [\"qux\"])\n        self.assertEqual(get_unique_names(domain, [\"foo\"]), [\"foo (1)\"])\n        self.assertEqual(get_unique_names(domain, [\"baz\"]), [\"baz (4)\"])\n        self.assertEqual(get_unique_names(domain, [\"baz (3)\"]), [\"baz (3) (1)\"])\n        self.assertEqual(\n            get_unique_names(domain, [\"qux\", \"quux\"]), [\"qux\", \"quux\"])\n        self.assertEqual(\n            get_unique_names(domain, [\"bar\", \"baz\"]), [\"bar (4)\", \"baz (4)\"])\n        self.assertEqual(\n            get_unique_names(domain, [\"qux\", \"baz\"]), [\"qux (4)\", \"baz (4)\"])\n        self.assertEqual(\n            get_unique_names(domain, [\"qux\", \"bar\"]), [\"qux (1)\", \"bar (1)\"])\n\n        self.assertEqual(get_unique_names(domain, \"qux\"), \"qux\")\n        self.assertEqual(get_unique_names(domain, \"foo\"), \"foo (1)\")\n        self.assertEqual(get_unique_names(domain, \"baz\"), \"baz (4)\")\n\n    def test_get_unique_names_not_equal(self):\n        names = [\"foo\", \"bar\", \"baz\", \"baz (3)\"]\n        self.assertEqual(\n            get_unique_names(names, [\"qux\"], equal_numbers=False), [\"qux\"]\n        )\n        self.assertEqual(\n            get_unique_names(names, [\"foo\"], equal_numbers=False), [\"foo (1)\"]\n        )\n        self.assertEqual(\n            get_unique_names(names, [\"baz\"], equal_numbers=False), [\"baz (4)\"]\n        )\n        self.assertEqual(\n            get_unique_names(names, [\"baz (3)\"], equal_numbers=False),\n            [\"baz (3) (1)\"]\n        )\n        self.assertEqual(\n            get_unique_names(names, [\"qux\", \"quux\"], equal_numbers=False),\n            [\"qux\", \"quux\"]\n        )\n        self.assertEqual(\n            get_unique_names(names, [\"bar\", \"baz\"], equal_numbers=False),\n            [\"bar (1)\", \"baz (4)\"]\n        )\n        self.assertEqual(\n            get_unique_names(names, [\"qux\", \"baz\"], equal_numbers=False),\n            [\"qux\", \"baz (4)\"]\n        )\n        self.assertEqual(\n            get_unique_names(names, [\"qux\", \"bar\"], equal_numbers=False),\n            [\"qux\", \"bar (1)\"]\n        )\n        self.assertEqual(\n            get_unique_names(names, [\"foo\", \"bar\", \"baz\"], equal_numbers=False),\n            [\"foo (1)\", \"bar (1)\", \"baz (4)\"]\n        )\n\n        a, b, c, d = map(ContinuousVariable, [\"foo\", \"bar\", \"baz\", \"baz (3)\"])\n        domain = Domain([a, b], c, [d])\n        self.assertEqual(\n            get_unique_names(names, [\"qux\"], equal_numbers=False), [\"qux\"]\n        )\n        self.assertEqual(\n            get_unique_names(names, [\"foo\"], equal_numbers=False), [\"foo (1)\"]\n        )\n        self.assertEqual(\n            get_unique_names(names, [\"baz\"], equal_numbers=False), [\"baz (4)\"]\n        )\n        self.assertEqual(\n            get_unique_names(names, [\"baz (3)\"], equal_numbers=False),\n            [\"baz (3) (1)\"]\n        )\n        self.assertEqual(\n            get_unique_names(domain, [\"qux\", \"quux\"], equal_numbers=False),\n            [\"qux\", \"quux\"]\n        )\n        self.assertEqual(\n            get_unique_names(domain, [\"bar\", \"baz\"], equal_numbers=False),\n            [\"bar (1)\", \"baz (4)\"]\n        )\n        self.assertEqual(\n            get_unique_names(domain, [\"qux\", \"baz\"], equal_numbers=False),\n            [\"qux\", \"baz (4)\"]\n        )\n        self.assertEqual(\n            get_unique_names(domain, [\"qux\", \"bar\"], equal_numbers=False),\n            [\"qux\", \"bar (1)\"]\n        )\n        self.assertEqual(\n            get_unique_names(domain, [\"foo\", \"bar\", \"baz\"], equal_numbers=False),\n            [\"foo (1)\", \"bar (1)\", \"baz (4)\"]\n        )\n\n    def test_get_unique_names_duplicated_proposals(self):\n        names = [\"foo\", \"bar\", \"baz\", \"baz (3)\"]\n\n        self.assertEqual(\n            get_unique_names(names, [\"foo\", \"boo\", \"boo\"]),\n            ['foo (1)', 'boo (1)', 'boo (2)']\n        )\n        self.assertEqual(\n            get_unique_names(names, [\"foo\", \"boo\", \"boo\", \"baz\"]),\n            ['foo (4)', 'boo (4)', 'boo (5)', 'baz (4)']\n        )\n        self.assertEqual(\n            get_unique_names([], [\"foo\", \"boo\", \"boo\", \"baz\"]),\n            ['foo', 'boo (1)', 'boo (2)', 'baz']\n        )\n        self.assertEqual(\n            get_unique_names([\"foo\", \"bong\"], [\"foo\", \"boo\", \"boo\", \"baz\"]),\n            ['foo (1)', 'boo (1)', 'boo (2)', 'baz']\n        )\n\n        self.assertEqual(\n            get_unique_names(names, [\"foo\", \"boo\", \"boo\"],\n                             equal_numbers=False),\n            ['foo (1)', 'boo (1)', 'boo (2)']\n        )\n        self.assertEqual(\n            get_unique_names(names, [\"foo\", \"boo\", \"boo\", \"baz\"],\n                             equal_numbers=False),\n            ['foo (1)', 'boo (1)', 'boo (2)', 'baz (4)']\n        )\n        self.assertEqual(\n            get_unique_names([], [\"foo\", \"boo\", \"boo\", \"baz\"],\n                             equal_numbers=False),\n            ['foo', 'boo (1)', 'boo (2)', 'baz']\n        )\n        self.assertEqual(\n            get_unique_names([\"foo\", \"bong\"], [\"foo\", \"boo\", \"boo\", \"baz\"],\n                             equal_numbers=False),\n            ['foo (1)', 'boo (1)', 'boo (2)', 'baz']\n        )\n\n    def test_get_unique_names_from_duplicates(self):\n        self.assertEqual(\n            get_unique_names_duplicates([\"foo\", \"bar\", \"baz\"]),\n            [\"foo\", \"bar\", \"baz\"])\n        self.assertEqual(\n            get_unique_names_duplicates([\"foo\", \"bar\", \"baz\", \"bar\"]),\n            [\"foo\", \"bar (1)\", \"baz\", \"bar (2)\"])\n        self.assertEqual(\n            get_unique_names_duplicates([\"x\", \"x\", \"x (1)\"]),\n            [\"x (2)\", \"x (3)\", \"x (1)\"])\n        self.assertEqual(\n            get_unique_names_duplicates([\"x (2)\", \"x\", \"x\", \"x (2)\", \"x (3)\"]),\n            [\"x (2) (1)\", \"x (4)\", \"x (5)\", \"x (2) (2)\", \"x (3)\"])\n        self.assertEqual(\n                        get_unique_names_duplicates([\"iris\", \"iris\", \"iris (1)\"]),\n                        [\"iris (2)\", \"iris (3)\", \"iris (1)\"])\n\n        self.assertEqual(\n            get_unique_names_duplicates([\"foo\", \"bar\", \"baz\"], return_duplicated=True),\n            ([\"foo\", \"bar\", \"baz\"], []))\n        self.assertEqual(\n            get_unique_names_duplicates([\"foo\", \"bar\", \"baz\", \"bar\"], return_duplicated=True),\n            ([\"foo\", \"bar (1)\", \"baz\", \"bar (2)\"], [\"bar\"]))\n        self.assertEqual(\n            get_unique_names_duplicates([\"x\", \"x\", \"x (1)\"], return_duplicated=True),\n            ([\"x (2)\", \"x (3)\", \"x (1)\"], [\"x\"]))\n        self.assertEqual(\n            get_unique_names_duplicates([\"x (2)\", \"x\", \"x\", \"x (2)\", \"x (3)\"], return_duplicated=True),\n            ([\"x (2) (1)\", \"x (4)\", \"x (5)\", \"x (2) (2)\", \"x (3)\"], [\"x (2)\", \"x\"]))\n        self.assertEqual(\n            get_unique_names_duplicates([\"x\", \"\", \"\", None, None, \"x\"]),\n            [\"x (1)\", \"\", \"\", None, None, \"x (2)\"])\n        self.assertEqual(\n            get_unique_names_duplicates([\"iris\", \"iris\", \"iris (1)\", \"iris (2)\"], return_duplicated=True),\n            ([\"iris (3)\", \"iris (4)\", \"iris (1)\", \"iris (2)\"], [\"iris\"]))\n\n        self.assertEqual(\n            get_unique_names_duplicates([\"iris (1) (1)\", \"iris (1)\", \"iris (1)\"]),\n            [\"iris (1) (1)\", \"iris (1) (2)\", \"iris (1) (3)\"]\n        )\n\n        self.assertEqual(\n            get_unique_names_duplicates([\"iris (1) (1)\", \"iris (1)\", \"iris (1)\", \"iris\", \"iris\"]),\n            [\"iris (1) (1)\", \"iris (1) (2)\", \"iris (1) (3)\", \"iris (2)\", \"iris (3)\"]\n        )\n\n    def test_get_unique_names_domain(self):\n        (attrs, classes, metas), renamed = \\\n            get_unique_names_domain([\"a\", \"t\", \"c\", \"t\"], [\"t\", \"d\"], [\"d\", \"e\"])\n        self.assertEqual(attrs, [\"a\", \"t (1)\", \"c\", \"t (2)\"])\n        self.assertEqual(classes, [\"t (3)\", \"d (1)\"])\n        self.assertEqual(metas, [\"d (2)\", \"e\"])\n        self.assertEqual(renamed, [\"t\", \"d\"])\n\n        (attrs, classes, metas), renamed = \\\n            get_unique_names_domain([\"a\", \"t\", \"c\", \"t\"], [\"t\", \"d\"])\n        self.assertEqual(attrs, [\"a\", \"t (1)\", \"c\", \"t (2)\"])\n        self.assertEqual(classes, [\"t (3)\", \"d\"])\n        self.assertEqual(metas, [])\n        self.assertEqual(renamed, [\"t\"])\n\n        (attrs, classes, metas), renamed = \\\n            get_unique_names_domain([\"a\", \"t\", \"c\"])\n        self.assertEqual(attrs, [\"a\", \"t\", \"c\"])\n        self.assertEqual(classes, [])\n        self.assertEqual(metas, [])\n        self.assertEqual(renamed, [])\n\n        (attrs, classes, metas), renamed = \\\n            get_unique_names_domain([\"a\", \"t\", \"d\", \"t\"], [], [\"d\", \"e\"])\n        self.assertEqual(attrs, [\"a\", \"t (1)\", \"d (1)\", \"t (2)\"])\n        self.assertEqual(classes, [])\n        self.assertEqual(metas, [\"d (2)\", \"e\"])\n        self.assertEqual(renamed, [\"t\", \"d\"])\n\n        (attrs, classes, metas), renamed = \\\n            get_unique_names_domain([], [\"t\", \"d\"], [\"d\", \"e\"])\n        self.assertEqual(attrs, [])\n        self.assertEqual(classes, [\"t\", \"d (1)\"])\n        self.assertEqual(metas, [\"d (2)\", \"e\"])\n        self.assertEqual(renamed, [\"d\"])\n\n        (attrs, classes, metas), renamed = \\\n            get_unique_names_domain([], [\"t\", \"t\", \"d\"], [])\n        self.assertEqual(attrs, [])\n        self.assertEqual(classes, [\"t (1)\", \"t (2)\", \"d\"])\n        self.assertEqual(metas, [])\n        self.assertEqual(renamed, [\"t\"])\n\n        (attrs, classes, metas), renamed = \\\n            get_unique_names_domain([], [], [])\n        self.assertEqual(attrs, [])\n        self.assertEqual(classes, [])\n        self.assertEqual(metas, [])\n        self.assertEqual(renamed, [])",
                                "ast_type_str": "ClassDef",
                                "start_lineno": 9,
                                "end_lineno": 258,
                                "parent": null
                            }
                        }
                    ],
                    "deleted_entities": []
                }
            ]
        },
        {
            "old_file_content": "\"\"\"\nData-manipulation utilities.\n\"\"\"\nimport re\nfrom collections import Counter\nfrom itertools import chain, count\nfrom typing import Callable, Union, List, Type\n\nimport numpy as np\nimport bottleneck as bn\nfrom scipy import sparse as sp\n\nRE_FIND_INDEX = r\"(^{})( \\((\\d{{1,}})\\))?$\"\n\n\ndef one_hot(\n        values: Union[np.ndarray, List], dtype: Type = float, dim: int = None\n) -> np.ndarray:\n    \"\"\"Return a one-hot transform of values\n\n    Parameters\n    ----------\n    values : 1d array\n        Integer values (hopefully 0-max).\n    dtype\n        dtype of result array\n    dim\n        Number of columns (attributes) in the one hot encoding. This parameter\n        is used when we need fixed number of columns and values does not\n        reflect that number correctly, e.g. not all values from the discrete\n        variable are present in values parameter.\n\n    Returns\n    -------\n    result\n        2d array with ones in respective indicator columns.\n    \"\"\"\n    dim_values = int(np.max(values) + 1 if len(values) > 0 else 0)\n    if dim is None:\n        dim = dim_values\n    elif dim < dim_values:\n        raise ValueError(\"dim must be greater than max(values)\")\n    return np.eye(dim, dtype=dtype)[np.asanyarray(values, dtype=int)]\n\n\n# pylint: disable=redefined-builtin\ndef scale(values, min=0, max=1):\n    \"\"\"Return values scaled to [min, max]\"\"\"\n    if len(values) == 0:\n        return np.array([])\n    minval = np.float_(bn.nanmin(values))\n    ptp = bn.nanmax(values) - minval\n    if ptp == 0:\n        return np.clip(values, min, max)\n    return (-minval + values) / ptp * (max - min) + min\n\n\nclass SharedComputeValue:\n    \"\"\"A base class that separates compute_value computation\n    for different variables into shared and specific parts.\n\n    Parameters\n    ----------\n    compute_shared: Callable[[Orange.data.Table], object]\n        A callable that performs computation that is shared between\n        multiple variables. Variables sharing computation need to set\n        the same instance.\n    variable: Orange.data.Variable\n        The original variable on which this compute value is set. Optional.\n    \"\"\"\n\n    def __init__(self, compute_shared, variable=None):\n        self.compute_shared = compute_shared\n        self.variable = variable\n\n    def __call__(self, data, shared_data=None):\n        \"\"\"Fallback if common parts are not passed.\"\"\"\n        if shared_data is None:\n            shared_data = self.compute_shared(data)\n        return self.compute(data, shared_data)\n\n    def compute(self, data, shared_data):\n        \"\"\"Given precomputed shared data, perform variable-specific\n        part of computation and return new variable values.\n        Subclasses need to implement this function.\"\"\"\n        raise NotImplementedError\n\n\ndef vstack(arrays):\n    \"\"\"vstack that supports sparse and dense arrays\n\n    If all arrays are dense, result is dense. Otherwise,\n    result is a sparse (csr) array.\n    \"\"\"\n    if any(sp.issparse(arr) for arr in arrays):\n        arrays = [sp.csr_matrix(arr) for arr in arrays]\n        return sp.vstack(arrays)\n    else:\n        return np.vstack(arrays)\n\n\ndef hstack(arrays):\n    \"\"\"hstack that supports sparse and dense arrays\n\n    If all arrays are dense, result is dense. Otherwise,\n    result is a sparse (csc) array.\n    \"\"\"\n    if any(sp.issparse(arr) for arr in arrays):\n        arrays = [sp.csc_matrix(arr) for arr in arrays]\n        return sp.hstack(arrays)\n    else:\n        return np.hstack(arrays)\n\n\ndef array_equal(a1, a2):\n    \"\"\"array_equal that supports sparse and dense arrays with missing values\"\"\"\n    if a1.shape != a2.shape:\n        return False\n\n    if not (sp.issparse(a1) or sp.issparse(a2)):  # Both dense: just compare\n        return np.allclose(a1, a2, equal_nan=True)\n\n    v1 = np.vstack(sp.find(a1)).T\n    v2 = np.vstack(sp.find(a2)).T\n    if not (sp.issparse(a1) and sp.issparse(a2)):  # Any dense: order indices\n        v1.sort(axis=0)\n        v2.sort(axis=0)\n    return np.allclose(v1, v2, equal_nan=True)\n\n\ndef assure_array_dense(a):\n    if sp.issparse(a):\n        a = a.toarray()\n    return np.asarray(a)\n\n\ndef assure_array_sparse(a, sparse_class: Callable = sp.csc_matrix):\n    if not sp.issparse(a):\n        # since x can be a list, cast to np.array\n        # since x can come from metas with string, cast to float\n        a = np.asarray(a).astype(float)\n    return sparse_class(a)\n\n\ndef assure_column_sparse(a):\n    # if x of shape (n, ) is passed to csc_matrix constructor or\n    # sparse matrix with shape (1, n) is passed,\n    # the resulting matrix is of shape (1, n) and hence we\n    # need to transpose it to make it a column\n    if a.ndim == 1 or a.shape[0] == 1:\n        # csr matrix becomes csc when transposed\n        return assure_array_sparse(a, sparse_class=sp.csr_matrix).T\n    else:\n        return assure_array_sparse(a, sparse_class=sp.csc_matrix)\n\n\ndef assure_column_dense(a):\n    # quick check and exit for the most common case\n    if isinstance(a, np.ndarray) and len(a.shape) == 1:\n        return a\n    a = assure_array_dense(a)\n    # column assignments must be (n, )\n    return a.reshape(-1)\n\n\ndef get_indices(names, name):\n    \"\"\"\n    Return list of indices which occur in a names list for a given name.\n    :param names: list of strings\n    :param name: str\n    :return: list of indices\n    \"\"\"\n    return [int(a.group(3) or 0) for x in filter(None, names)\n            for a in re.finditer(RE_FIND_INDEX.format(re.escape(name)), x)]\n\n\ndef get_unique_names(names, proposed, equal_numbers=True):\n    \"\"\"\n    Returns unique names for variables\n\n    Proposed is a list of names (or a string with a single name). If any name\n    already appears in `names`, the function appends an index in parentheses,\n    which is one higher than the highest index at these variables. Also, if\n    `names` contains any of the names with index in parentheses, this counts\n    as an occurence of the name. For instance, if `names` does not contain\n    `x` but it contains `x (3)`, `get_unique_names` will replace `x` with\n    `x (4)`.\n\n    If argument `names` is domain, the method observes all variables and metas.\n\n    Function returns a string if `proposed` is a string, and a list if it's a\n    list.\n\n    The method is used in widgets like MDS, which adds two variables (`x` and\n    `y`). It is desired that they have the same index. In case when\n    equal_numbers=True, if `x`, `x (1)` and `x (2)` and `y` (but no other\n    `y`'s already exist in the domain, MDS should append `x (3)` and `y (3)`,\n    not `x (3)` and y (1)`.\n\n    Args:\n        names (Domain or list of str): used names\n        proposed (str or list of str): proposed name\n        equal_numbers (bool): Add same number to all proposed names\n\n    Return:\n        str or list of str\n    \"\"\"\n    # prevent cyclic import: pylint: disable=import-outside-toplevel\n    from Orange.data import Domain\n    if isinstance(names, Domain):\n        names = [var.name for var in chain(names.variables, names.metas)]\n    if isinstance(proposed, str):\n        return get_unique_names(names, [proposed])[0]\n    indices = {name: get_indices(names, name) for name in proposed}\n    indices = {name: max(ind) + 1 for name, ind in indices.items() if ind}\n    if not (set(proposed) & set(names) or indices):\n        return proposed\n    if equal_numbers:\n        max_index = max(indices.values())\n        return [f\"{name} ({max_index})\" for name in proposed]\n    else:\n        return [f\"{name} ({indices[name]})\" if name in indices else name\n                for name in proposed]\n\n\ndef get_unique_names_duplicates(proposed: list, return_duplicated=False) -> list:\n    \"\"\"\n    Returns list of unique names. If a name is duplicated, the\n    function appends the next available index in parentheses.\n\n    For example, a proposed list of names `x`, `x` and `x (2)`\n    results in `x (3)`, `x (4)`, `x (2)`.\n    \"\"\"\n    indices = {name: count(max(get_indices(proposed, name), default=0) + 1)\n               for name, cnt in Counter(proposed).items()\n               if name and cnt > 1}\n    new_names = [f\"{name} ({next(indices[name])})\" if name in indices else name\n                 for name in proposed]\n    if return_duplicated:\n        return new_names, list(indices)\n    return new_names\n\n\ndef get_unique_names_domain(attributes, class_vars=(), metas=()):\n    \"\"\"\n    Return de-duplicated names for variables for attributes, class_vars\n    and metas. If a name appears more than once, the function appends\n    indices in parentheses.\n\n    Args:\n        attributes (list of str): proposed names for attributes\n        class_vars (list of str): proposed names for class_vars\n        metas (list of str): proposed names for metas\n\n    Returns:\n        (attributes, class_vars, metas): new names\n        renamed: list of names renamed variables; names appear in order of\n            appearance in original lists; every name appears only once\n    \"\"\"\n    all_names = list(chain(attributes, class_vars, metas))\n    unique_names = get_unique_names_duplicates(all_names)\n    # don't be smart with negative indices: they won't work for empty lists\n    attributes = unique_names[:len(attributes)]\n    class_vars = unique_names[len(attributes):len(attributes) + len(class_vars)]\n    metas = unique_names[len(attributes) + len(class_vars):]\n    # use dict, not set, to keep the order\n    renamed = list(dict.fromkeys(old\n                                 for old, new in zip(all_names, unique_names)\n                                 if new != old))\n    return (attributes, class_vars, metas), renamed\n\n\ndef sanitized_name(name: str) -> str:\n    \"\"\"\n    Replace non-alphanumeric characters and leading zero with `_`.\n\n    Args:\n        name (str): proposed name\n\n    Returns:\n        name (str): new name\n    \"\"\"\n    sanitized = re.sub(r\"\\W\", \"_\", name)\n    if sanitized[0].isdigit():\n        sanitized = \"_\" + sanitized\n    return sanitized\n",
            "new_file_content": "\"\"\"\nData-manipulation utilities.\n\"\"\"\nimport re\nfrom collections import Counter\nfrom itertools import chain, count\nfrom typing import Callable, Union, List, Type\n\nimport numpy as np\nimport bottleneck as bn\nfrom scipy import sparse as sp\n\nRE_FIND_INDEX = r\"(^{})( \\((\\d{{1,}})\\))?$\"\n\n\ndef one_hot(\n        values: Union[np.ndarray, List], dtype: Type = float, dim: int = None\n) -> np.ndarray:\n    \"\"\"Return a one-hot transform of values\n\n    Parameters\n    ----------\n    values : 1d array\n        Integer values (hopefully 0-max).\n    dtype\n        dtype of result array\n    dim\n        Number of columns (attributes) in the one hot encoding. This parameter\n        is used when we need fixed number of columns and values does not\n        reflect that number correctly, e.g. not all values from the discrete\n        variable are present in values parameter.\n\n    Returns\n    -------\n    result\n        2d array with ones in respective indicator columns.\n    \"\"\"\n    dim_values = int(np.max(values) + 1 if len(values) > 0 else 0)\n    if dim is None:\n        dim = dim_values\n    elif dim < dim_values:\n        raise ValueError(\"dim must be greater than max(values)\")\n    return np.eye(dim, dtype=dtype)[np.asanyarray(values, dtype=int)]\n\n\n# pylint: disable=redefined-builtin\ndef scale(values, min=0, max=1):\n    \"\"\"Return values scaled to [min, max]\"\"\"\n    if len(values) == 0:\n        return np.array([])\n    minval = np.float_(bn.nanmin(values))\n    ptp = bn.nanmax(values) - minval\n    if ptp == 0:\n        return np.clip(values, min, max)\n    return (-minval + values) / ptp * (max - min) + min\n\n\nclass SharedComputeValue:\n    \"\"\"A base class that separates compute_value computation\n    for different variables into shared and specific parts.\n\n    Parameters\n    ----------\n    compute_shared: Callable[[Orange.data.Table], object]\n        A callable that performs computation that is shared between\n        multiple variables. Variables sharing computation need to set\n        the same instance.\n    variable: Orange.data.Variable\n        The original variable on which this compute value is set. Optional.\n    \"\"\"\n\n    def __init__(self, compute_shared, variable=None):\n        self.compute_shared = compute_shared\n        self.variable = variable\n\n    def __call__(self, data, shared_data=None):\n        \"\"\"Fallback if common parts are not passed.\"\"\"\n        if shared_data is None:\n            shared_data = self.compute_shared(data)\n        return self.compute(data, shared_data)\n\n    def compute(self, data, shared_data):\n        \"\"\"Given precomputed shared data, perform variable-specific\n        part of computation and return new variable values.\n        Subclasses need to implement this function.\"\"\"\n        raise NotImplementedError\n\n\ndef vstack(arrays):\n    \"\"\"vstack that supports sparse and dense arrays\n\n    If all arrays are dense, result is dense. Otherwise,\n    result is a sparse (csr) array.\n    \"\"\"\n    if any(sp.issparse(arr) for arr in arrays):\n        arrays = [sp.csr_matrix(arr) for arr in arrays]\n        return sp.vstack(arrays)\n    else:\n        return np.vstack(arrays)\n\n\ndef hstack(arrays):\n    \"\"\"hstack that supports sparse and dense arrays\n\n    If all arrays are dense, result is dense. Otherwise,\n    result is a sparse (csc) array.\n    \"\"\"\n    if any(sp.issparse(arr) for arr in arrays):\n        arrays = [sp.csc_matrix(arr) for arr in arrays]\n        return sp.hstack(arrays)\n    else:\n        return np.hstack(arrays)\n\n\ndef array_equal(a1, a2):\n    \"\"\"array_equal that supports sparse and dense arrays with missing values\"\"\"\n    if a1.shape != a2.shape:\n        return False\n\n    if not (sp.issparse(a1) or sp.issparse(a2)):  # Both dense: just compare\n        return np.allclose(a1, a2, equal_nan=True)\n\n    v1 = np.vstack(sp.find(a1)).T\n    v2 = np.vstack(sp.find(a2)).T\n    if not (sp.issparse(a1) and sp.issparse(a2)):  # Any dense: order indices\n        v1.sort(axis=0)\n        v2.sort(axis=0)\n    return np.allclose(v1, v2, equal_nan=True)\n\n\ndef assure_array_dense(a):\n    if sp.issparse(a):\n        a = a.toarray()\n    return np.asarray(a)\n\n\ndef assure_array_sparse(a, sparse_class: Callable = sp.csc_matrix):\n    if not sp.issparse(a):\n        # since x can be a list, cast to np.array\n        # since x can come from metas with string, cast to float\n        a = np.asarray(a).astype(float)\n    return sparse_class(a)\n\n\ndef assure_column_sparse(a):\n    # if x of shape (n, ) is passed to csc_matrix constructor or\n    # sparse matrix with shape (1, n) is passed,\n    # the resulting matrix is of shape (1, n) and hence we\n    # need to transpose it to make it a column\n    if a.ndim == 1 or a.shape[0] == 1:\n        # csr matrix becomes csc when transposed\n        return assure_array_sparse(a, sparse_class=sp.csr_matrix).T\n    else:\n        return assure_array_sparse(a, sparse_class=sp.csc_matrix)\n\n\ndef assure_column_dense(a):\n    # quick check and exit for the most common case\n    if isinstance(a, np.ndarray) and len(a.shape) == 1:\n        return a\n    a = assure_array_dense(a)\n    # column assignments must be (n, )\n    return a.reshape(-1)\n\n\ndef get_indices(names, name):\n    \"\"\"\n    Return list of indices which occur in a names list for a given name.\n    :param names: list of strings\n    :param name: str\n    :return: list of indices\n    \"\"\"\n    return [int(a.group(3) or 0) for x in filter(None, names)\n            for a in re.finditer(RE_FIND_INDEX.format(re.escape(name)), x)]\n\n\ndef get_unique_names(names, proposed, equal_numbers=True):\n    \"\"\"\n    Returns unique names for variables\n\n    Proposed is a list of names (or a string with a single name). If any name\n    already appears in `names`, the function appends an index in parentheses,\n    which is one higher than the highest index at these variables. Also, if\n    `names` contains any of the names with index in parentheses, this counts\n    as an occurence of the name. For instance, if `names` does not contain\n    `x` but it contains `x (3)`, `get_unique_names` will replace `x` with\n    `x (4)`.\n\n    If argument `names` is domain, the method observes all variables and metas.\n\n    Function returns a string if `proposed` is a string, and a list if it's a\n    list.\n\n    The method is used in widgets like MDS, which adds two variables (`x` and\n    `y`). It is desired that they have the same index. In case when\n    equal_numbers=True, if `x`, `x (1)` and `x (2)` and `y` (but no other\n    `y`'s already exist in the domain, MDS should append `x (3)` and `y (3)`,\n    not `x (3)` and y (1)`.\n\n    Args:\n        names (Domain or list of str): used names\n        proposed (str or list of str): proposed name\n        equal_numbers (bool): Add same number to all proposed names\n\n    Return:\n        str or list of str\n    \"\"\"\n    # prevent cyclic import: pylint: disable=import-outside-toplevel\n    from Orange.data import Domain\n    if isinstance(names, Domain):\n        names = [var.name for var in chain(names.variables, names.metas)]\n    if isinstance(proposed, str):\n        return get_unique_names(names, [proposed])[0]\n    indices = {name: get_indices(names, name) for name in proposed}\n    indices = {name: max(ind) + 1 for name, ind in indices.items() if ind}\n\n    duplicated_proposed = {name for name, count in Counter(proposed).items()\n                           if count > 1}\n    if duplicated_proposed:\n        # This could be merged with the code below, but it would make it slower\n        # because it can't be done within list comprehension\n        if equal_numbers:\n            max_index = max(indices.values(), default=1)\n            indices = {name: max_index\n                       for name in chain(indices, duplicated_proposed)}\n        else:\n            indices.update({name: 1\n                            for name in duplicated_proposed - set(indices)})\n        names = []\n        for name in proposed:\n            if name in indices:\n                names.append(f\"{name} ({indices[name]})\")\n                indices[name] += 1\n            else:\n                names.append(name)\n        return names\n\n    if not (set(proposed) & set(names) or indices):\n        return proposed\n\n    if equal_numbers:\n        max_index = max(indices.values())\n        return [f\"{name} ({max_index})\" for name in proposed]\n    else:\n        return [f\"{name} ({indices[name]})\" if name in indices else name\n                for name in proposed]\n\n\ndef get_unique_names_duplicates(proposed: list, return_duplicated=False) -> list:\n    \"\"\"\n    Returns list of unique names. If a name is duplicated, the\n    function appends the next available index in parentheses.\n\n    For example, a proposed list of names `x`, `x` and `x (2)`\n    results in `x (3)`, `x (4)`, `x (2)`.\n    \"\"\"\n    indices = {name: count(max(get_indices(proposed, name), default=0) + 1)\n               for name, cnt in Counter(proposed).items()\n               if name and cnt > 1}\n    new_names = [f\"{name} ({next(indices[name])})\" if name in indices else name\n                 for name in proposed]\n    if return_duplicated:\n        return new_names, list(indices)\n    return new_names\n\n\ndef get_unique_names_domain(attributes, class_vars=(), metas=()):\n    \"\"\"\n    Return de-duplicated names for variables for attributes, class_vars\n    and metas. If a name appears more than once, the function appends\n    indices in parentheses.\n\n    Args:\n        attributes (list of str): proposed names for attributes\n        class_vars (list of str): proposed names for class_vars\n        metas (list of str): proposed names for metas\n\n    Returns:\n        (attributes, class_vars, metas): new names\n        renamed: list of names renamed variables; names appear in order of\n            appearance in original lists; every name appears only once\n    \"\"\"\n    all_names = list(chain(attributes, class_vars, metas))\n    unique_names = get_unique_names_duplicates(all_names)\n    # don't be smart with negative indices: they won't work for empty lists\n    attributes = unique_names[:len(attributes)]\n    class_vars = unique_names[len(attributes):len(attributes) + len(class_vars)]\n    metas = unique_names[len(attributes) + len(class_vars):]\n    # use dict, not set, to keep the order\n    renamed = list(dict.fromkeys(old\n                                 for old, new in zip(all_names, unique_names)\n                                 if new != old))\n    return (attributes, class_vars, metas), renamed\n\n\ndef sanitized_name(name: str) -> str:\n    \"\"\"\n    Replace non-alphanumeric characters and leading zero with `_`.\n\n    Args:\n        name (str): proposed name\n\n    Returns:\n        name (str): new name\n    \"\"\"\n    sanitized = re.sub(r\"\\W\", \"_\", name)\n    if sanitized[0].isdigit():\n        sanitized = \"_\" + sanitized\n    return sanitized\n",
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                        "section": "def get_unique_names(names, proposed, equal_numbers=True):"
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                            {
                                "content": "        return get_unique_names(names, [proposed])[0]",
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                                "content": "    indices = {name: get_indices(names, name) for name in proposed}",
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                            },
                            {
                                "content": "    indices = {name: max(ind) + 1 for name, ind in indices.items() if ind}",
                                "type": "context"
                            },
                            {
                                "content": "",
                                "type": "added"
                            },
                            {
                                "content": "    duplicated_proposed = {name for name, count in Counter(proposed).items()",
                                "type": "added"
                            },
                            {
                                "content": "                           if count > 1}",
                                "type": "added"
                            },
                            {
                                "content": "    if duplicated_proposed:",
                                "type": "added"
                            },
                            {
                                "content": "        # This could be merged with the code below, but it would make it slower",
                                "type": "added"
                            },
                            {
                                "content": "        # because it can't be done within list comprehension",
                                "type": "added"
                            },
                            {
                                "content": "        if equal_numbers:",
                                "type": "added"
                            },
                            {
                                "content": "            max_index = max(indices.values(), default=1)",
                                "type": "added"
                            },
                            {
                                "content": "            indices = {name: max_index",
                                "type": "added"
                            },
                            {
                                "content": "                       for name in chain(indices, duplicated_proposed)}",
                                "type": "added"
                            },
                            {
                                "content": "        else:",
                                "type": "added"
                            },
                            {
                                "content": "            indices.update({name: 1",
                                "type": "added"
                            },
                            {
                                "content": "                            for name in duplicated_proposed - set(indices)})",
                                "type": "added"
                            },
                            {
                                "content": "        names = []",
                                "type": "added"
                            },
                            {
                                "content": "        for name in proposed:",
                                "type": "added"
                            },
                            {
                                "content": "            if name in indices:",
                                "type": "added"
                            },
                            {
                                "content": "                names.append(f\"{name} ({indices[name]})\")",
                                "type": "added"
                            },
                            {
                                "content": "                indices[name] += 1",
                                "type": "added"
                            },
                            {
                                "content": "            else:",
                                "type": "added"
                            },
                            {
                                "content": "                names.append(name)",
                                "type": "added"
                            },
                            {
                                "content": "        return names",
                                "type": "added"
                            },
                            {
                                "content": "",
                                "type": "added"
                            },
                            {
                                "content": "    if not (set(proposed) & set(names) or indices):",
                                "type": "context"
                            },
                            {
                                "content": "        return proposed",
                                "type": "context"
                            },
                            {
                                "content": "",
                                "type": "added"
                            },
                            {
                                "content": "    if equal_numbers:",
                                "type": "context"
                            },
                            {
                                "content": "        max_index = max(indices.values())",
                                "type": "context"
                            },
                            {
                                "content": "        return [f\"{name} ({max_index})\" for name in proposed]",
                                "type": "context"
                            },
                            {
                                "content": "",
                                "type": "context"
                            }
                        ]
                    },
                    "modified_entities": [
                        {
                            "file_name": "Orange/data/util.py",
                            "type": "function",
                            "name": "get_unique_names",
                            "content": "def get_unique_names(names, proposed, equal_numbers=True):\n    \"\"\"\n    Returns unique names for variables\n\n    Proposed is a list of names (or a string with a single name). If any name\n    already appears in `names`, the function appends an index in parentheses,\n    which is one higher than the highest index at these variables. Also, if\n    `names` contains any of the names with index in parentheses, this counts\n    as an occurence of the name. For instance, if `names` does not contain\n    `x` but it contains `x (3)`, `get_unique_names` will replace `x` with\n    `x (4)`.\n\n    If argument `names` is domain, the method observes all variables and metas.\n\n    Function returns a string if `proposed` is a string, and a list if it's a\n    list.\n\n    The method is used in widgets like MDS, which adds two variables (`x` and\n    `y`). It is desired that they have the same index. In case when\n    equal_numbers=True, if `x`, `x (1)` and `x (2)` and `y` (but no other\n    `y`'s already exist in the domain, MDS should append `x (3)` and `y (3)`,\n    not `x (3)` and y (1)`.\n\n    Args:\n        names (Domain or list of str): used names\n        proposed (str or list of str): proposed name\n        equal_numbers (bool): Add same number to all proposed names\n\n    Return:\n        str or list of str\n    \"\"\"\n    # prevent cyclic import: pylint: disable=import-outside-toplevel\n    from Orange.data import Domain\n    if isinstance(names, Domain):\n        names = [var.name for var in chain(names.variables, names.metas)]\n    if isinstance(proposed, str):\n        return get_unique_names(names, [proposed])[0]\n    indices = {name: get_indices(names, name) for name in proposed}\n    indices = {name: max(ind) + 1 for name, ind in indices.items() if ind}\n\n    duplicated_proposed = {name for name, count in Counter(proposed).items()\n                           if count > 1}\n    if duplicated_proposed:\n        # This could be merged with the code below, but it would make it slower\n        # because it can't be done within list comprehension\n        if equal_numbers:\n            max_index = max(indices.values(), default=1)\n            indices = {name: max_index\n                       for name in chain(indices, duplicated_proposed)}\n        else:\n            indices.update({name: 1\n                            for name in duplicated_proposed - set(indices)})\n        names = []\n        for name in proposed:\n            if name in indices:\n                names.append(f\"{name} ({indices[name]})\")\n                indices[name] += 1\n            else:\n                names.append(name)\n        return names\n\n    if not (set(proposed) & set(names) or indices):\n        return proposed\n\n    if equal_numbers:\n        max_index = max(indices.values())\n        return [f\"{name} ({max_index})\" for name in proposed]\n    else:\n        return [f\"{name} ({indices[name]})\" if name in indices else name\n                for name in proposed]",
                            "ast_type_str": "FunctionDef",
                            "start_lineno": 177,
                            "end_lineno": 246,
                            "parent": null
                        }
                    ],
                    "added_entities": [],
                    "deleted_entities": []
                }
            ]
        }
    ],
    "old_commit_hash": "8826e2e4a3e3a6c81b51f56115264a0141036c34^",
    "new_commit_hash": "8826e2e4a3e3a6c81b51f56115264a0141036c34",
    "commit_message": "util.get_unique_names: Deduplicate duplicated proposals",
    "commit_date": "2022-05-24T18:42:38+02:00",
    "metadata": {}
}
{
    "repo_name": "orange3",
    "new_commit_hash": "8826e2e4a3e3a6c81b51f56115264a0141036c34",
    "test_file_codes": [
        "import unittest\nimport numpy as np\n\nfrom Orange.data import Domain, ContinuousVariable\nfrom Orange.data.util import get_unique_names, get_unique_names_duplicates, \\\n    get_unique_names_domain, one_hot, sanitized_name\n\n\nclass TestGetUniqueNames(unittest.TestCase):\n    def test_get_unique_names(self):\n        names = [\"foo\", \"bar\", \"baz\", \"baz (3)\"]\n        self.assertEqual(get_unique_names(names, [\"qux\"]), [\"qux\"])\n        self.assertEqual(get_unique_names(names, [\"foo\"]), [\"foo (1)\"])\n        self.assertEqual(get_unique_names(names, [\"baz\"]), [\"baz (4)\"])\n        self.assertEqual(get_unique_names(names, [\"baz (3)\"]), [\"baz (3) (1)\"])\n        self.assertEqual(\n            get_unique_names(names, [\"qux\", \"quux\"]), [\"qux\", \"quux\"])\n        self.assertEqual(\n            get_unique_names(names, [\"bar\", \"baz\"]), [\"bar (4)\", \"baz (4)\"])\n        self.assertEqual(\n            get_unique_names(names, [\"qux\", \"baz\"]), [\"qux (4)\", \"baz (4)\"])\n        self.assertEqual(\n            get_unique_names(names, [\"qux\", \"bar\"]), [\"qux (1)\", \"bar (1)\"])\n\n        self.assertEqual(get_unique_names(names, \"qux\"), \"qux\")\n        self.assertEqual(get_unique_names(names, \"foo\"), \"foo (1)\")\n        self.assertEqual(get_unique_names(names, \"baz\"), \"baz (4)\")\n\n        self.assertEqual(get_unique_names(tuple(names), \"baz\"), \"baz (4)\")\n\n    def test_get_unique_names_with_domain(self):\n        a, b, c, d = map(ContinuousVariable, [\"foo\", \"bar\", \"baz\", \"baz (3)\"])\n        domain = Domain([a, b], c, [d])\n        self.assertEqual(get_unique_names(domain, [\"qux\"]), [\"qux\"])\n        self.assertEqual(get_unique_names(domain, [\"foo\"]), [\"foo (1)\"])\n        self.assertEqual(get_unique_names(domain, [\"baz\"]), [\"baz (4)\"])\n        self.assertEqual(get_unique_names(domain, [\"baz (3)\"]), [\"baz (3) (1)\"])\n        self.assertEqual(\n            get_unique_names(domain, [\"qux\", \"quux\"]), [\"qux\", \"quux\"])\n        self.assertEqual(\n            get_unique_names(domain, [\"bar\", \"baz\"]), [\"bar (4)\", \"baz (4)\"])\n        self.assertEqual(\n            get_unique_names(domain, [\"qux\", \"baz\"]), [\"qux (4)\", \"baz (4)\"])\n        self.assertEqual(\n            get_unique_names(domain, [\"qux\", \"bar\"]), [\"qux (1)\", \"bar (1)\"])\n\n        self.assertEqual(get_unique_names(domain, \"qux\"), \"qux\")\n        self.assertEqual(get_unique_names(domain, \"foo\"), \"foo (1)\")\n        self.assertEqual(get_unique_names(domain, \"baz\"), \"baz (4)\")\n\n    def test_get_unique_names_not_equal(self):\n        names = [\"foo\", \"bar\", \"baz\", \"baz (3)\"]\n        self.assertEqual(\n            get_unique_names(names, [\"qux\"], equal_numbers=False), [\"qux\"]\n        )\n        self.assertEqual(\n            get_unique_names(names, [\"foo\"], equal_numbers=False), [\"foo (1)\"]\n        )\n        self.assertEqual(\n            get_unique_names(names, [\"baz\"], equal_numbers=False), [\"baz (4)\"]\n        )\n        self.assertEqual(\n            get_unique_names(names, [\"baz (3)\"], equal_numbers=False),\n            [\"baz (3) (1)\"]\n        )\n        self.assertEqual(\n            get_unique_names(names, [\"qux\", \"quux\"], equal_numbers=False),\n            [\"qux\", \"quux\"]\n        )\n        self.assertEqual(\n            get_unique_names(names, [\"bar\", \"baz\"], equal_numbers=False),\n            [\"bar (1)\", \"baz (4)\"]\n        )\n        self.assertEqual(\n            get_unique_names(names, [\"qux\", \"baz\"], equal_numbers=False),\n            [\"qux\", \"baz (4)\"]\n        )\n        self.assertEqual(\n            get_unique_names(names, [\"qux\", \"bar\"], equal_numbers=False),\n            [\"qux\", \"bar (1)\"]\n        )\n        self.assertEqual(\n            get_unique_names(names, [\"foo\", \"bar\", \"baz\"], equal_numbers=False),\n            [\"foo (1)\", \"bar (1)\", \"baz (4)\"]\n        )\n\n        a, b, c, d = map(ContinuousVariable, [\"foo\", \"bar\", \"baz\", \"baz (3)\"])\n        domain = Domain([a, b], c, [d])\n        self.assertEqual(\n            get_unique_names(names, [\"qux\"], equal_numbers=False), [\"qux\"]\n        )\n        self.assertEqual(\n            get_unique_names(names, [\"foo\"], equal_numbers=False), [\"foo (1)\"]\n        )\n        self.assertEqual(\n            get_unique_names(names, [\"baz\"], equal_numbers=False), [\"baz (4)\"]\n        )\n        self.assertEqual(\n            get_unique_names(names, [\"baz (3)\"], equal_numbers=False),\n            [\"baz (3) (1)\"]\n        )\n        self.assertEqual(\n            get_unique_names(domain, [\"qux\", \"quux\"], equal_numbers=False),\n            [\"qux\", \"quux\"]\n        )\n        self.assertEqual(\n            get_unique_names(domain, [\"bar\", \"baz\"], equal_numbers=False),\n            [\"bar (1)\", \"baz (4)\"]\n        )\n        self.assertEqual(\n            get_unique_names(domain, [\"qux\", \"baz\"], equal_numbers=False),\n            [\"qux\", \"baz (4)\"]\n        )\n        self.assertEqual(\n            get_unique_names(domain, [\"qux\", \"bar\"], equal_numbers=False),\n            [\"qux\", \"bar (1)\"]\n        )\n        self.assertEqual(\n            get_unique_names(domain, [\"foo\", \"bar\", \"baz\"], equal_numbers=False),\n            [\"foo (1)\", \"bar (1)\", \"baz (4)\"]\n        )\n\n    def test_get_unique_names_duplicated_proposals(self):\n        names = [\"foo\", \"bar\", \"baz\", \"baz (3)\"]\n\n        self.assertEqual(\n            get_unique_names(names, [\"foo\", \"boo\", \"boo\"]),\n            ['foo (1)', 'boo (1)', 'boo (2)']\n        )\n        self.assertEqual(\n            get_unique_names(names, [\"foo\", \"boo\", \"boo\", \"baz\"]),\n            ['foo (4)', 'boo (4)', 'boo (5)', 'baz (4)']\n        )\n        self.assertEqual(\n            get_unique_names([], [\"foo\", \"boo\", \"boo\", \"baz\"]),\n            ['foo', 'boo (1)', 'boo (2)', 'baz']\n        )\n        self.assertEqual(\n            get_unique_names([\"foo\", \"bong\"], [\"foo\", \"boo\", \"boo\", \"baz\"]),\n            ['foo (1)', 'boo (1)', 'boo (2)', 'baz']\n        )\n\n        self.assertEqual(\n            get_unique_names(names, [\"foo\", \"boo\", \"boo\"],\n                             equal_numbers=False),\n            ['foo (1)', 'boo (1)', 'boo (2)']\n        )\n        self.assertEqual(\n            get_unique_names(names, [\"foo\", \"boo\", \"boo\", \"baz\"],\n                             equal_numbers=False),\n            ['foo (1)', 'boo (1)', 'boo (2)', 'baz (4)']\n        )\n        self.assertEqual(\n            get_unique_names([], [\"foo\", \"boo\", \"boo\", \"baz\"],\n                             equal_numbers=False),\n            ['foo', 'boo (1)', 'boo (2)', 'baz']\n        )\n        self.assertEqual(\n            get_unique_names([\"foo\", \"bong\"], [\"foo\", \"boo\", \"boo\", \"baz\"],\n                             equal_numbers=False),\n            ['foo (1)', 'boo (1)', 'boo (2)', 'baz']\n        )\n\n    def test_get_unique_names_from_duplicates(self):\n        self.assertEqual(\n            get_unique_names_duplicates([\"foo\", \"bar\", \"baz\"]),\n            [\"foo\", \"bar\", \"baz\"])\n        self.assertEqual(\n            get_unique_names_duplicates([\"foo\", \"bar\", \"baz\", \"bar\"]),\n            [\"foo\", \"bar (1)\", \"baz\", \"bar (2)\"])\n        self.assertEqual(\n            get_unique_names_duplicates([\"x\", \"x\", \"x (1)\"]),\n            [\"x (2)\", \"x (3)\", \"x (1)\"])\n        self.assertEqual(\n            get_unique_names_duplicates([\"x (2)\", \"x\", \"x\", \"x (2)\", \"x (3)\"]),\n            [\"x (2) (1)\", \"x (4)\", \"x (5)\", \"x (2) (2)\", \"x (3)\"])\n        self.assertEqual(\n                        get_unique_names_duplicates([\"iris\", \"iris\", \"iris (1)\"]),\n                        [\"iris (2)\", \"iris (3)\", \"iris (1)\"])\n\n        self.assertEqual(\n            get_unique_names_duplicates([\"foo\", \"bar\", \"baz\"], return_duplicated=True),\n            ([\"foo\", \"bar\", \"baz\"], []))\n        self.assertEqual(\n            get_unique_names_duplicates([\"foo\", \"bar\", \"baz\", \"bar\"], return_duplicated=True),\n            ([\"foo\", \"bar (1)\", \"baz\", \"bar (2)\"], [\"bar\"]))\n        self.assertEqual(\n            get_unique_names_duplicates([\"x\", \"x\", \"x (1)\"], return_duplicated=True),\n            ([\"x (2)\", \"x (3)\", \"x (1)\"], [\"x\"]))\n        self.assertEqual(\n            get_unique_names_duplicates([\"x (2)\", \"x\", \"x\", \"x (2)\", \"x (3)\"], return_duplicated=True),\n            ([\"x (2) (1)\", \"x (4)\", \"x (5)\", \"x (2) (2)\", \"x (3)\"], [\"x (2)\", \"x\"]))\n        self.assertEqual(\n            get_unique_names_duplicates([\"x\", \"\", \"\", None, None, \"x\"]),\n            [\"x (1)\", \"\", \"\", None, None, \"x (2)\"])\n        self.assertEqual(\n            get_unique_names_duplicates([\"iris\", \"iris\", \"iris (1)\", \"iris (2)\"], return_duplicated=True),\n            ([\"iris (3)\", \"iris (4)\", \"iris (1)\", \"iris (2)\"], [\"iris\"]))\n\n        self.assertEqual(\n            get_unique_names_duplicates([\"iris (1) (1)\", \"iris (1)\", \"iris (1)\"]),\n            [\"iris (1) (1)\", \"iris (1) (2)\", \"iris (1) (3)\"]\n        )\n\n        self.assertEqual(\n            get_unique_names_duplicates([\"iris (1) (1)\", \"iris (1)\", \"iris (1)\", \"iris\", \"iris\"]),\n            [\"iris (1) (1)\", \"iris (1) (2)\", \"iris (1) (3)\", \"iris (2)\", \"iris (3)\"]\n        )\n\n    def test_get_unique_names_domain(self):\n        (attrs, classes, metas), renamed = \\\n            get_unique_names_domain([\"a\", \"t\", \"c\", \"t\"], [\"t\", \"d\"], [\"d\", \"e\"])\n        self.assertEqual(attrs, [\"a\", \"t (1)\", \"c\", \"t (2)\"])\n        self.assertEqual(classes, [\"t (3)\", \"d (1)\"])\n        self.assertEqual(metas, [\"d (2)\", \"e\"])\n        self.assertEqual(renamed, [\"t\", \"d\"])\n\n        (attrs, classes, metas), renamed = \\\n            get_unique_names_domain([\"a\", \"t\", \"c\", \"t\"], [\"t\", \"d\"])\n        self.assertEqual(attrs, [\"a\", \"t (1)\", \"c\", \"t (2)\"])\n        self.assertEqual(classes, [\"t (3)\", \"d\"])\n        self.assertEqual(metas, [])\n        self.assertEqual(renamed, [\"t\"])\n\n        (attrs, classes, metas), renamed = \\\n            get_unique_names_domain([\"a\", \"t\", \"c\"])\n        self.assertEqual(attrs, [\"a\", \"t\", \"c\"])\n        self.assertEqual(classes, [])\n        self.assertEqual(metas, [])\n        self.assertEqual(renamed, [])\n\n        (attrs, classes, metas), renamed = \\\n            get_unique_names_domain([\"a\", \"t\", \"d\", \"t\"], [], [\"d\", \"e\"])\n        self.assertEqual(attrs, [\"a\", \"t (1)\", \"d (1)\", \"t (2)\"])\n        self.assertEqual(classes, [])\n        self.assertEqual(metas, [\"d (2)\", \"e\"])\n        self.assertEqual(renamed, [\"t\", \"d\"])\n\n        (attrs, classes, metas), renamed = \\\n            get_unique_names_domain([], [\"t\", \"d\"], [\"d\", \"e\"])\n        self.assertEqual(attrs, [])\n        self.assertEqual(classes, [\"t\", \"d (1)\"])\n        self.assertEqual(metas, [\"d (2)\", \"e\"])\n        self.assertEqual(renamed, [\"d\"])\n\n        (attrs, classes, metas), renamed = \\\n            get_unique_names_domain([], [\"t\", \"t\", \"d\"], [])\n        self.assertEqual(attrs, [])\n        self.assertEqual(classes, [\"t (1)\", \"t (2)\", \"d\"])\n        self.assertEqual(metas, [])\n        self.assertEqual(renamed, [\"t\"])\n\n        (attrs, classes, metas), renamed = \\\n            get_unique_names_domain([], [], [])\n        self.assertEqual(attrs, [])\n        self.assertEqual(classes, [])\n        self.assertEqual(metas, [])\n        self.assertEqual(renamed, [])\n\n\nclass TestOneHot(unittest.TestCase):\n\n    def setUp(self):\n        self.values = np.array([0, 1, 2, 1])\n\n    @staticmethod\n    def test_empty():\n        np.testing.assert_array_equal(\n            one_hot([]), np.zeros((0, 0))\n        )\n        np.testing.assert_array_equal(\n            one_hot([], dim=2), np.zeros((0, 2))\n        )\n\n    def test_one_hot(self):\n        np.testing.assert_array_equal(\n            one_hot(self.values),\n            [[1, 0, 0],\n             [0, 1, 0],\n             [0, 0, 1],\n             [0, 1, 0]]\n        )\n\n    def test_dtype(self):\n        res = one_hot(self.values)\n        self.assertEqual(res.dtype, float)\n        res = one_hot(self.values, dtype=int)\n        self.assertEqual(res.dtype, int)\n\n    def test_dim(self):\n        np.testing.assert_array_equal(\n            one_hot(self.values, dim=4),\n            [[1, 0, 0, 0],\n             [0, 1, 0, 0],\n             [0, 0, 1, 0],\n             [0, 1, 0, 0]]\n        )\n\n    def test_dim_too_low(self):\n        with self.assertRaises(ValueError):\n            one_hot(self.values, dim=2)\n\n\nclass TestSanitizedName(unittest.TestCase):\n    def test_sanitized_name(self):\n        self.assertEqual(sanitized_name(\"Foo\"), \"Foo\")\n        self.assertEqual(sanitized_name(\"Foo Bar\"), \"Foo_Bar\")\n        self.assertEqual(sanitized_name(\"0Foo\"), \"_0Foo\")\n        self.assertEqual(sanitized_name(\"1 Foo Bar\"), \"_1_Foo_Bar\")\n\n\nif __name__ == \"__main__\":\n    unittest.main()\n"
    ],
    "test_file_names": [
        "test_1.py"
    ],
    "setup_res_code": 0,
    "setup_res_stdout": "Starting Orange installation attempts...\nAttempting installation with Python 3.7...\nrunning build_ext\nCompiling Orange/classification/_tree_scorers.pyx because it changed.\nCompiling Orange/data/_contingency.pyx because it changed.\nCompiling Orange/data/_io.pyx because it changed.\nCompiling Orange/data/_valuecount.pyx because it changed.\nCompiling Orange/data/_variable.pyx because it changed.\nCompiling Orange/distance/_distance.pyx because it changed.\nCompiling Orange/preprocess/_discretize.pyx because it changed.\nCompiling Orange/preprocess/_relieff.pyx because it changed.\nCompiling Orange/projection/_som.pyx because it changed.\n[1/9] Cythonizing Orange/classification/_tree_scorers.pyx\n[2/9] Cythonizing Orange/data/_contingency.pyx\n[3/9] Cythonizing Orange/data/_io.pyx\n[4/9] Cythonizing Orange/data/_valuecount.pyx\n[5/9] Cythonizing Orange/data/_variable.pyx\n[6/9] Cythonizing Orange/distance/_distance.pyx\n[7/9] Cythonizing Orange/preprocess/_discretize.pyx\n[8/9] Cythonizing Orange/preprocess/_relieff.pyx\n[9/9] Cythonizing Orange/projection/_som.pyx\nbuilding 'Orange.classification._tree_scorers' extension\ncreating build\ncreating build/temp.linux-x86_64-3.7\ncreating build/temp.linux-x86_64-3.7/Orange\ncreating build/temp.linux-x86_64-3.7/Orange/classification\ncc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -I/tools/deps/include -I/tools/deps/include/ncursesw -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/include/python3.7m -c Orange/classification/_tree_scorers.c -o build/temp.linux-x86_64-3.7/Orange/classification/_tree_scorers.o\ncreating build/lib.linux-x86_64-3.7\ncreating build/lib.linux-x86_64-3.7/Orange\ncreating build/lib.linux-x86_64-3.7/Orange/classification\ncc -pthread -shared -L/tools/deps/lib build/temp.linux-x86_64-3.7/Orange/classification/_tree_scorers.o -L/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/lib -lm -lpython3.7m -o build/lib.linux-x86_64-3.7/Orange/classification/_tree_scorers.cpython-37m-x86_64-linux-gnu.so\nbuilding 'Orange.data._contingency' extension\ncreating build/temp.linux-x86_64-3.7/Orange/data\ncc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -I/tools/deps/include -I/tools/deps/include/ncursesw -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/include/python3.7m -c Orange/data/_contingency.c -o build/temp.linux-x86_64-3.7/Orange/data/_contingency.o\ncreating build/lib.linux-x86_64-3.7/Orange/data\ncc -pthread -shared -L/tools/deps/lib build/temp.linux-x86_64-3.7/Orange/data/_contingency.o -L/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/lib -lm -lpython3.7m -o build/lib.linux-x86_64-3.7/Orange/data/_contingency.cpython-37m-x86_64-linux-gnu.so\nbuilding 'Orange.data._io' extension\ncc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -I/tools/deps/include -I/tools/deps/include/ncursesw -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/include/python3.7m -c Orange/data/_io.c -o build/temp.linux-x86_64-3.7/Orange/data/_io.o\ncc -pthread -shared -L/tools/deps/lib build/temp.linux-x86_64-3.7/Orange/data/_io.o -L/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/lib -lm -lpython3.7m -o build/lib.linux-x86_64-3.7/Orange/data/_io.cpython-37m-x86_64-linux-gnu.so\nbuilding 'Orange.data._valuecount' extension\ncc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -I/tools/deps/include -I/tools/deps/include/ncursesw -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/include/python3.7m -c Orange/data/_valuecount.c -o build/temp.linux-x86_64-3.7/Orange/data/_valuecount.o\ncc -pthread -shared -L/tools/deps/lib build/temp.linux-x86_64-3.7/Orange/data/_valuecount.o -L/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/lib -lm -lpython3.7m -o build/lib.linux-x86_64-3.7/Orange/data/_valuecount.cpython-37m-x86_64-linux-gnu.so\nbuilding 'Orange.data._variable' extension\ncc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -I/tools/deps/include -I/tools/deps/include/ncursesw -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/include/python3.7m -c Orange/data/_variable.c -o build/temp.linux-x86_64-3.7/Orange/data/_variable.o\ncc -pthread -shared -L/tools/deps/lib build/temp.linux-x86_64-3.7/Orange/data/_variable.o -L/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/lib -lm -lpython3.7m -o build/lib.linux-x86_64-3.7/Orange/data/_variable.cpython-37m-x86_64-linux-gnu.so\nbuilding 'Orange.distance._distance' extension\ncreating build/temp.linux-x86_64-3.7/Orange/distance\ncc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -I/tools/deps/include -I/tools/deps/include/ncursesw -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/include/python3.7m -c Orange/distance/_distance.c -o build/temp.linux-x86_64-3.7/Orange/distance/_distance.o\ncreating build/lib.linux-x86_64-3.7/Orange/distance\ncc -pthread -shared -L/tools/deps/lib build/temp.linux-x86_64-3.7/Orange/distance/_distance.o -L/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/lib -lm -lpython3.7m -o build/lib.linux-x86_64-3.7/Orange/distance/_distance.cpython-37m-x86_64-linux-gnu.so\nbuilding 'Orange.preprocess._discretize' extension\ncreating build/temp.linux-x86_64-3.7/Orange/preprocess\ncc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -I/tools/deps/include -I/tools/deps/include/ncursesw -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/include/python3.7m -c Orange/preprocess/_discretize.c -o build/temp.linux-x86_64-3.7/Orange/preprocess/_discretize.o\ncreating build/lib.linux-x86_64-3.7/Orange/preprocess\ncc -pthread -shared -L/tools/deps/lib build/temp.linux-x86_64-3.7/Orange/preprocess/_discretize.o -L/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/lib -lm -lpython3.7m -o build/lib.linux-x86_64-3.7/Orange/preprocess/_discretize.cpython-37m-x86_64-linux-gnu.so\nbuilding 'Orange.preprocess._relieff' extension\ncc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -I/tools/deps/include -I/tools/deps/include/ncursesw -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/include/python3.7m -c Orange/preprocess/_relieff.cpp -o build/temp.linux-x86_64-3.7/Orange/preprocess/_relieff.o\nc++ -pthread -shared -L/tools/deps/lib build/temp.linux-x86_64-3.7/Orange/preprocess/_relieff.o -L/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/lib -lm -lpython3.7m -o build/lib.linux-x86_64-3.7/Orange/preprocess/_relieff.cpython-37m-x86_64-linux-gnu.so\nbuilding 'Orange.projection._som' extension\ncreating build/temp.linux-x86_64-3.7/Orange/projection\ncc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -I/tools/deps/include -I/tools/deps/include/ncursesw -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/include/python3.7m -c Orange/projection/_som.c -o build/temp.linux-x86_64-3.7/Orange/projection/_som.o\ncreating build/lib.linux-x86_64-3.7/Orange/projection\ncc -pthread -shared -L/tools/deps/lib build/temp.linux-x86_64-3.7/Orange/projection/_som.o -L/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/lib -lm -lpython3.7m -o build/lib.linux-x86_64-3.7/Orange/projection/_som.cpython-37m-x86_64-linux-gnu.so\nbuilding 'Orange.classification._simple_tree' extension\ncc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -I/tools/deps/include -I/tools/deps/include/ncursesw -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/include/python3.7m -c Orange/classification/_simple_tree.c -o build/temp.linux-x86_64-3.7/Orange/classification/_simple_tree.o\ncc -pthread -shared -L/tools/deps/lib build/temp.linux-x86_64-3.7/Orange/classification/_simple_tree.o -L/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/lib -lm -lpython3.7m -o build/lib.linux-x86_64-3.7/Orange/classification/_simple_tree.cpython-37m-x86_64-linux-gnu.so\nbuilding 'Orange.widgets.utils._grid_density' extension\ncreating build/temp.linux-x86_64-3.7/Orange/widgets\ncreating build/temp.linux-x86_64-3.7/Orange/widgets/utils\ncc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -I/tools/deps/include -I/tools/deps/include/ncursesw -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/include/python3.7m -c Orange/widgets/utils/_grid_density.cpp -o build/temp.linux-x86_64-3.7/Orange/widgets/utils/_grid_density.o\ncreating build/lib.linux-x86_64-3.7/Orange/widgets\ncreating build/lib.linux-x86_64-3.7/Orange/widgets/utils\nc++ -pthread -shared -L/tools/deps/lib build/temp.linux-x86_64-3.7/Orange/widgets/utils/_grid_density.o -L/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/lib -lm -lpython3.7m -o build/lib.linux-x86_64-3.7/Orange/widgets/utils/_grid_density.cpython-37m-x86_64-linux-gnu.so\ncopying build/lib.linux-x86_64-3.7/Orange/classification/_tree_scorers.cpython-37m-x86_64-linux-gnu.so -> Orange/classification\ncopying build/lib.linux-x86_64-3.7/Orange/data/_contingency.cpython-37m-x86_64-linux-gnu.so -> Orange/data\ncopying build/lib.linux-x86_64-3.7/Orange/data/_io.cpython-37m-x86_64-linux-gnu.so -> Orange/data\ncopying build/lib.linux-x86_64-3.7/Orange/data/_valuecount.cpython-37m-x86_64-linux-gnu.so -> Orange/data\ncopying build/lib.linux-x86_64-3.7/Orange/data/_variable.cpython-37m-x86_64-linux-gnu.so -> Orange/data\ncopying build/lib.linux-x86_64-3.7/Orange/distance/_distance.cpython-37m-x86_64-linux-gnu.so -> Orange/distance\ncopying build/lib.linux-x86_64-3.7/Orange/preprocess/_discretize.cpython-37m-x86_64-linux-gnu.so -> Orange/preprocess\ncopying build/lib.linux-x86_64-3.7/Orange/preprocess/_relieff.cpython-37m-x86_64-linux-gnu.so -> Orange/preprocess\ncopying build/lib.linux-x86_64-3.7/Orange/projection/_som.cpython-37m-x86_64-linux-gnu.so -> Orange/projection\ncopying build/lib.linux-x86_64-3.7/Orange/classification/_simple_tree.cpython-37m-x86_64-linux-gnu.so -> Orange/classification\ncopying build/lib.linux-x86_64-3.7/Orange/widgets/utils/_grid_density.cpython-37m-x86_64-linux-gnu.so -> Orange/widgets/utils\nrunning develop\nrunning egg_info\ncreating Orange3.egg-info\nwriting Orange3.egg-info/PKG-INFO\nwriting dependency_links to Orange3.egg-info/dependency_links.txt\nwriting entry points to Orange3.egg-info/entry_points.txt\nwriting requirements to Orange3.egg-info/requires.txt\nwriting top-level names to Orange3.egg-info/top_level.txt\nwriting manifest file 'Orange3.egg-info/SOURCES.txt'\nreading manifest file 'Orange3.egg-info/SOURCES.txt'\nreading manifest template 'MANIFEST.in'\nadding license file 'LICENSE'\nwriting manifest file 'Orange3.egg-info/SOURCES.txt'\nrunning build_ext\ncopying build/lib.linux-x86_64-3.7/Orange/classification/_tree_scorers.cpython-37m-x86_64-linux-gnu.so -> Orange/classification\ncopying build/lib.linux-x86_64-3.7/Orange/data/_contingency.cpython-37m-x86_64-linux-gnu.so -> Orange/data\ncopying build/lib.linux-x86_64-3.7/Orange/data/_io.cpython-37m-x86_64-linux-gnu.so -> Orange/data\ncopying build/lib.linux-x86_64-3.7/Orange/data/_valuecount.cpython-37m-x86_64-linux-gnu.so -> Orange/data\ncopying build/lib.linux-x86_64-3.7/Orange/data/_variable.cpython-37m-x86_64-linux-gnu.so -> Orange/data\ncopying build/lib.linux-x86_64-3.7/Orange/distance/_distance.cpython-37m-x86_64-linux-gnu.so -> Orange/distance\ncopying build/lib.linux-x86_64-3.7/Orange/preprocess/_discretize.cpython-37m-x86_64-linux-gnu.so -> Orange/preprocess\ncopying build/lib.linux-x86_64-3.7/Orange/preprocess/_relieff.cpython-37m-x86_64-linux-gnu.so -> Orange/preprocess\ncopying build/lib.linux-x86_64-3.7/Orange/projection/_som.cpython-37m-x86_64-linux-gnu.so -> Orange/projection\ncopying build/lib.linux-x86_64-3.7/Orange/classification/_simple_tree.cpython-37m-x86_64-linux-gnu.so -> Orange/classification\ncopying build/lib.linux-x86_64-3.7/Orange/widgets/utils/_grid_density.cpython-37m-x86_64-linux-gnu.so -> Orange/widgets/utils\nCreating /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages/Orange3.egg-link (link to .)\nAdding Orange3 3.33.0.dev0+8826e2e to easy-install.pth file\nInstalling orange-canvas script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/bin\n\nInstalled /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34\nProcessing dependencies for Orange3==3.33.0.dev0+8826e2e\nSearching for XlsxWriter==3.2.0\nBest match: XlsxWriter 3.2.0\nAdding XlsxWriter 3.2.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages\nSearching for xlrd==2.0.1\nBest match: xlrd 2.0.1\nAdding xlrd 2.0.1 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages\nSearching for setuptools==59.8.0\nBest match: setuptools 59.8.0\nAdding setuptools 59.8.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages\nSearching for serverfiles==0.3.1\nBest match: serverfiles 0.3.1\nAdding serverfiles 0.3.1 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages\nSearching for scipy==1.7.3\nBest match: scipy 1.7.3\nAdding scipy 1.7.3 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages\nSearching for scikit-learn==1.0.2\nBest match: scikit-learn 1.0.2\nAdding scikit-learn 1.0.2 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages\nSearching for requests==2.31.0\nBest match: requests 2.31.0\nAdding requests 2.31.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages\nSearching for qtconsole==5.4.4\nBest match: qtconsole 5.4.4\nAdding qtconsole 5.4.4 to easy-install.pth file\nInstalling jupyter-qtconsole script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages\nSearching for PyYAML==6.0.1\nBest match: PyYAML 6.0.1\nAdding PyYAML 6.0.1 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages\nSearching for python-louvain==0.16\nBest match: python-louvain 0.16\nAdding python-louvain 0.16 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages\nSearching for pyqtgraph==0.12.3\nBest match: pyqtgraph 0.12.3\nAdding pyqtgraph 0.12.3 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages\nSearching for pygments==2.17.2\nBest match: pygments 2.17.2\nAdding pygments 2.17.2 to easy-install.pth file\nInstalling pygmentize script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages\nSearching for pip==24.0\nBest match: pip 24.0\nAdding pip 24.0 to easy-install.pth file\nInstalling pip script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/bin\nInstalling pip3 script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/bin\nInstalling pip3.10 script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages\nSearching for pandas==1.3.5\nBest match: pandas 1.3.5\nAdding pandas 1.3.5 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages\nSearching for orange-widget-base==4.22.0\nBest match: orange-widget-base 4.22.0\nAdding orange-widget-base 4.22.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages\nSearching for orange-canvas-core==0.1.35\nBest match: orange-canvas-core 0.1.35\nAdding orange-canvas-core 0.1.35 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages\nSearching for openpyxl==3.1.3\nBest match: openpyxl 3.1.3\nAdding openpyxl 3.1.3 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages\nSearching for openTSNE==1.0.1\nBest match: openTSNE 1.0.1\nAdding openTSNE 1.0.1 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages\nSearching for numpy==1.17.5\nBest match: numpy 1.17.5\nAdding numpy 1.17.5 to easy-install.pth file\nInstalling f2py script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/bin\nInstalling f2py3 script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/bin\nInstalling f2py3.7 script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages\nSearching for networkx==2.6.3\nBest match: networkx 2.6.3\nAdding networkx 2.6.3 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages\nSearching for matplotlib==3.5.3\nBest match: matplotlib 3.5.3\nAdding matplotlib 3.5.3 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages\nSearching for keyrings.alt==4.2.0\nBest match: keyrings.alt 4.2.0\nAdding keyrings.alt 4.2.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages\nSearching for keyring==24.1.1\nBest match: keyring 24.1.1\nAdding keyring 24.1.1 to easy-install.pth file\nInstalling keyring script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages\nSearching for joblib==1.3.2\nBest match: joblib 1.3.2\nAdding joblib 1.3.2 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages\nSearching for httpx==0.24.1\nBest match: httpx 0.24.1\nAdding httpx 0.24.1 to easy-install.pth file\nInstalling httpx script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages\nSearching for chardet==5.2.0\nBest match: chardet 5.2.0\nAdding chardet 5.2.0 to easy-install.pth file\nInstalling chardetect script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages\nSearching for Bottleneck==1.4.0\nBest match: Bottleneck 1.4.0\nAdding Bottleneck 1.4.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages\nSearching for baycomp==1.0.3\nBest match: baycomp 1.0.3\nAdding baycomp 1.0.3 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages\nSearching for PyQtWebEngine==5.15.6\nBest match: PyQtWebEngine 5.15.6\nAdding PyQtWebEngine 5.15.6 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages\nSearching for PyQt5==5.15.10\nBest match: PyQt5 5.15.10\nAdding PyQt5 5.15.10 to easy-install.pth file\nInstalling pylupdate5 script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/bin\nInstalling pyrcc5 script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/bin\nInstalling pyuic5 script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages\nSearching for AnyQt==0.2.0\nBest match: AnyQt 0.2.0\nAdding AnyQt 0.2.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages\nSearching for threadpoolctl==3.1.0\nBest match: threadpoolctl 3.1.0\nAdding threadpoolctl 3.1.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages\nSearching for idna==3.10\nBest match: idna 3.10\nAdding idna 3.10 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages\nSearching for certifi==2024.12.14\nBest match: certifi 2024.12.14\nAdding certifi 2024.12.14 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages\nSearching for charset-normalizer==3.4.1\nBest match: charset-normalizer 3.4.1\nAdding charset-normalizer 3.4.1 to easy-install.pth file\nInstalling normalizer script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages\nSearching for urllib3==2.0.7\nBest match: urllib3 2.0.7\nAdding urllib3 2.0.7 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages\nSearching for packaging==24.0\nBest match: packaging 24.0\nAdding packaging 24.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages\nSearching for QtPy==2.4.2\nBest match: QtPy 2.4.2\nAdding QtPy 2.4.2 to easy-install.pth file\nInstalling qtpy script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages\nSearching for ipython-genutils==0.2.0\nBest match: ipython-genutils 0.2.0\nAdding ipython-genutils 0.2.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages\nSearching for traitlets==5.9.0\nBest match: traitlets 5.9.0\nAdding traitlets 5.9.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages\nSearching for jupyter-client==7.4.9\nBest match: jupyter-client 7.4.9\nAdding jupyter-client 7.4.9 to easy-install.pth file\nInstalling jupyter-kernel script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/bin\nInstalling jupyter-kernelspec script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/bin\nInstalling jupyter-run script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages\nSearching for ipykernel==6.16.2\nBest match: ipykernel 6.16.2\nAdding ipykernel 6.16.2 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages\nSearching for jupyter-core==4.12.0\nBest match: jupyter-core 4.12.0\nAdding jupyter-core 4.12.0 to easy-install.pth file\nInstalling jupyter script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/bin\nInstalling jupyter-migrate script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/bin\nInstalling jupyter-troubleshoot script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages\nSearching for pyzmq==26.2.0\nBest match: pyzmq 26.2.0\nAdding pyzmq 26.2.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages\nSearching for python-dateutil==2.9.0.post0\nBest match: python-dateutil 2.9.0.post0\nAdding python-dateutil 2.9.0.post0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages\nSearching for pytz==2024.2\nBest match: pytz 2024.2\nAdding pytz 2024.2 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages\nSearching for typing-extensions==4.7.1\nBest match: typing-extensions 4.7.1\nAdding typing-extensions 4.7.1 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages\nSearching for commonmark==0.9.1\nBest match: commonmark 0.9.1\nAdding commonmark 0.9.1 to easy-install.pth file\nInstalling cmark script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages\nSearching for dictdiffer==0.9.0\nBest match: dictdiffer 0.9.0\nAdding dictdiffer 0.9.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages\nSearching for requests-cache==1.1.1\nBest match: requests-cache 1.1.1\nAdding requests-cache 1.1.1 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages\nSearching for docutils==0.20.1\nBest match: docutils 0.20.1\nAdding docutils 0.20.1 to easy-install.pth file\nInstalling docutils script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages\nSearching for importlib-metadata==6.7.0\nBest match: importlib-metadata 6.7.0\nAdding importlib-metadata 6.7.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages\nSearching for qasync==0.25.0\nBest match: qasync 0.25.0\nAdding qasync 0.25.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages\nSearching for et-xmlfile==1.1.0\nBest match: et-xmlfile 1.1.0\nAdding et-xmlfile 1.1.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages\nSearching for fonttools==4.38.0\nBest match: fonttools 4.38.0\nAdding fonttools 4.38.0 to easy-install.pth file\nInstalling fonttools script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/bin\nInstalling pyftmerge script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/bin\nInstalling pyftsubset script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/bin\nInstalling ttx script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages\nSearching for cycler==0.11.0\nBest match: cycler 0.11.0\nAdding cycler 0.11.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages\nSearching for kiwisolver==1.4.5\nBest match: kiwisolver 1.4.5\nAdding kiwisolver 1.4.5 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages\nSearching for pyparsing==3.1.4\nBest match: pyparsing 3.1.4\nAdding pyparsing 3.1.4 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages\nSearching for Pillow==9.5.0\nBest match: Pillow 9.5.0\nAdding Pillow 9.5.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages\nSearching for jaraco.classes==3.2.3\nBest match: jaraco.classes 3.2.3\nAdding jaraco.classes 3.2.3 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages\nSearching for importlib-resources==5.12.0\nBest match: importlib-resources 5.12.0\nAdding importlib-resources 5.12.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages\nSearching for jeepney==0.8.0\nBest match: jeepney 0.8.0\nAdding jeepney 0.8.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages\nSearching for SecretStorage==3.3.3\nBest match: SecretStorage 3.3.3\nAdding SecretStorage 3.3.3 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages\nSearching for httpcore==0.17.3\nBest match: httpcore 0.17.3\nAdding httpcore 0.17.3 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages\nSearching for sniffio==1.3.1\nBest match: sniffio 1.3.1\nAdding sniffio 1.3.1 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages\nSearching for PyQtWebEngine-Qt5==5.15.16\nBest match: PyQtWebEngine-Qt5 5.15.16\nAdding PyQtWebEngine-Qt5 5.15.16 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages\nSearching for PyQt5-sip==12.13.0\nBest match: PyQt5-sip 12.13.0\nAdding PyQt5-sip 12.13.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages\nSearching for PyQt5-Qt5==5.15.16\nBest match: PyQt5-Qt5 5.15.16\nAdding PyQt5-Qt5 5.15.16 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages\nSearching for entrypoints==0.4\nBest match: entrypoints 0.4\nAdding entrypoints 0.4 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages\nSearching for tornado==6.2\nBest match: tornado 6.2\nAdding tornado 6.2 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages\nSearching for nest-asyncio==1.6.0\nBest match: nest-asyncio 1.6.0\nAdding nest-asyncio 1.6.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages\nSearching for psutil==6.1.1\nBest match: psutil 6.1.1\nAdding psutil 6.1.1 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages\nSearching for debugpy==1.7.0\nBest match: debugpy 1.7.0\nAdding debugpy 1.7.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages\nSearching for ipython==7.34.0\nBest match: ipython 7.34.0\nAdding ipython 7.34.0 to easy-install.pth file\nInstalling iptest script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/bin\nInstalling iptest3 script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/bin\nInstalling ipython script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/bin\nInstalling ipython3 script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages\nSearching for matplotlib-inline==0.1.6\nBest match: matplotlib-inline 0.1.6\nAdding matplotlib-inline 0.1.6 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages\nSearching for six==1.17.0\nBest match: six 1.17.0\nAdding six 1.17.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages\nSearching for attrs==24.2.0\nBest match: attrs 24.2.0\nAdding attrs 24.2.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages\nSearching for platformdirs==4.0.0\nBest match: platformdirs 4.0.0\nAdding platformdirs 4.0.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages\nSearching for url-normalize==1.4.3\nBest match: url-normalize 1.4.3\nAdding url-normalize 1.4.3 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages\nSearching for cattrs==23.1.2\nBest match: cattrs 23.1.2\nAdding cattrs 23.1.2 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages\nSearching for zipp==3.15.0\nBest match: zipp 3.15.0\nAdding zipp 3.15.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages\nSearching for more-itertools==9.1.0\nBest match: more-itertools 9.1.0\nAdding more-itertools 9.1.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages\nSearching for cryptography==44.0.0\nBest match: cryptography 44.0.0\nAdding cryptography 44.0.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages\nSearching for anyio==3.7.1\nBest match: anyio 3.7.1\nAdding anyio 3.7.1 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages\nSearching for h11==0.14.0\nBest match: h11 0.14.0\nAdding h11 0.14.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages\nSearching for backcall==0.2.0\nBest match: backcall 0.2.0\nAdding backcall 0.2.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages\nSearching for decorator==5.1.1\nBest match: decorator 5.1.1\nAdding decorator 5.1.1 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages\nSearching for jedi==0.19.2\nBest match: jedi 0.19.2\nAdding jedi 0.19.2 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages\nSearching for pexpect==4.9.0\nBest match: pexpect 4.9.0\nAdding pexpect 4.9.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages\nSearching for prompt-toolkit==3.0.48\nBest match: prompt-toolkit 3.0.48\nAdding prompt-toolkit 3.0.48 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages\nSearching for pickleshare==0.7.5\nBest match: pickleshare 0.7.5\nAdding pickleshare 0.7.5 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages\nSearching for exceptiongroup==1.2.2\nBest match: exceptiongroup 1.2.2\nAdding exceptiongroup 1.2.2 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages\nSearching for cffi==1.15.1\nBest match: cffi 1.15.1\nAdding cffi 1.15.1 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages\nSearching for parso==0.8.4\nBest match: parso 0.8.4\nAdding parso 0.8.4 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages\nSearching for ptyprocess==0.7.0\nBest match: ptyprocess 0.7.0\nAdding ptyprocess 0.7.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages\nSearching for wcwidth==0.2.13\nBest match: wcwidth 0.2.13\nAdding wcwidth 0.2.13 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages\nSearching for pycparser==2.21\nBest match: pycparser 2.21\nAdding pycparser 2.21 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages\nFinished processing dependencies for Orange3==3.33.0.dev0+8826e2e\nVerifying Orange installation...\n✅ Orange installation successful!\nSuccessfully installed orange using Python 3.7\n",
    "setup_res_stderr": "Using CPython 3.7.9\nCreating virtual environment at: .venv\nActivate with: source .venv/bin/activate\nResolved 18 packages in 223ms\nPrepared 18 packages in 2ms\nInstalled 18 packages in 708ms\n + cython==0.29.37\n + exceptiongroup==1.2.2\n + importlib-metadata==6.7.0\n + iniconfig==2.0.0\n + numpy==1.17.5\n + packaging==24.0\n + pluggy==1.2.0\n + pyqt5==5.15.10\n + pyqt5-qt5==5.15.16\n + pyqt5-sip==12.13.0\n + pyqtwebengine==5.15.6\n + pyqtwebengine-qt5==5.15.16\n + pytest==7.4.4\n + setuptools==59.8.0\n + tomli==2.0.1\n + typing-extensions==4.7.1\n + wheel==0.42.0\n + zipp==3.15.0\nResolved 54 packages in 296ms\nInstalled 47 packages in 543ms\n + anyio==3.7.1\n + baycomp==1.0.3\n + bottleneck==1.4.0\n + certifi==2024.12.14\n + cffi==1.15.1\n + chardet==5.2.0\n + charset-normalizer==3.4.1\n + cryptography==44.0.0\n + cycler==0.11.0\n + et-xmlfile==1.1.0\n + fonttools==4.38.0\n + h11==0.14.0\n + httpcore==0.17.3\n + httpx==0.24.1\n + idna==3.10\n + importlib-resources==5.12.0\n + jaraco-classes==3.2.3\n + jeepney==0.8.0\n + joblib==1.3.2\n + keyring==24.1.1\n + keyrings-alt==4.2.0\n + kiwisolver==1.4.5\n + matplotlib==3.5.3\n + more-itertools==9.1.0\n + networkx==2.6.3\n + openpyxl==3.1.3\n + opentsne==1.0.1\n + pandas==1.3.5\n + pillow==9.5.0\n + pip==24.0\n + pycparser==2.21\n + pyparsing==3.1.4\n + python-dateutil==2.9.0.post0\n + python-louvain==0.16\n + pytz==2024.2\n + pyyaml==6.0.1\n + requests==2.31.0\n + scikit-learn==1.0.2\n + scipy==1.7.3\n + secretstorage==3.3.3\n + serverfiles==0.3.1\n + six==1.17.0\n + sniffio==1.3.1\n + threadpoolctl==3.1.0\n + urllib3==2.0.7\n + xlrd==2.0.1\n + xlsxwriter==3.2.0\nResolved 59 packages in 304ms\nInstalled 38 packages in 266ms\n + anyqt==0.2.0\n + attrs==24.2.0\n + backcall==0.2.0\n + cattrs==23.1.2\n + commonmark==0.9.1\n + debugpy==1.7.0\n + decorator==5.1.1\n + dictdiffer==0.9.0\n + docutils==0.20.1\n + entrypoints==0.4\n + ipykernel==6.16.2\n + ipython==7.34.0\n + ipython-genutils==0.2.0\n + jedi==0.19.2\n + jupyter-client==7.4.9\n + jupyter-core==4.12.0\n + matplotlib-inline==0.1.6\n + nest-asyncio==1.6.0\n + orange-canvas-core==0.1.35\n + orange-widget-base==4.22.0\n + parso==0.8.4\n + pexpect==4.9.0\n + pickleshare==0.7.5\n + platformdirs==4.0.0\n + prompt-toolkit==3.0.48\n + psutil==6.1.1\n + ptyprocess==0.7.0\n + pygments==2.17.2\n + pyqtgraph==0.12.3\n + pyzmq==26.2.0\n + qasync==0.25.0\n + qtconsole==5.4.4\n + qtpy==2.4.2\n + requests-cache==1.1.1\n + tornado==6.2\n + traitlets==5.9.0\n + url-normalize==1.4.3\n + wcwidth==0.2.13\nResolved 2 packages in 4ms\nInstalled 2 packages in 7ms\n + psycopg2==2.9.9\n + pymssql==2.3.2\nResolved 19 packages in 27ms\nInstalled 5 packages in 1.09s\n + catboost==1.2.7\n + graphviz==0.20.1\n + plotly==5.18.0\n + tenacity==8.2.3\n + xgboost==1.6.2\nAudited 2 packages in 1ms\nIn file included from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,\n                 from Orange/classification/_tree_scorers.c:775:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:17:2: warning: #warning \"Using deprecated NumPy API, disable it with \" \"#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION\" [-Wcpp]\n   17 | #warning \"Using deprecated NumPy API, disable it with \" \\\n      |  ^~~~~~~\nOrange/classification/_tree_scorers.c: In function ‘__pyx_pf_6Orange_14classification_13_tree_scorers_2find_threshold_entropy’:\nOrange/classification/_tree_scorers.c:3427:27: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n 3427 |   __pyx_t_7 = ((__pyx_v_N <= __pyx_v_min_leaf) != 0);\n      |                           ^~\nOrange/classification/_tree_scorers.c: In function ‘__pyx_pf_6Orange_14classification_13_tree_scorers_6find_threshold_MSE’:\nOrange/classification/_tree_scorers.c:4883:27: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n 4883 |   __pyx_t_1 = ((__pyx_v_N <= __pyx_v_min_leaf) != 0);\n      |                           ^~\nOrange/classification/_tree_scorers.c: In function ‘__pyx_pf_6Orange_14classification_13_tree_scorers_8find_binarization_MSE’:\nOrange/classification/_tree_scorers.c:5629:43: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n 5629 |           for (__pyx_t_19 = 0; __pyx_t_19 < __pyx_t_18; __pyx_t_19+=1) {\n      |                                           ^\nOrange/classification/_tree_scorers.c:5750:39: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n 5750 |           __pyx_t_20 = ((__pyx_v_left >= __pyx_v_min_leaf) != 0);\n      |                                       ^~\nOrange/classification/_tree_scorers.c:5756:53: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n 5756 |           __pyx_t_20 = (((__pyx_v_N - __pyx_v_left) >= __pyx_v_min_leaf) != 0);\n      |                                                     ^~\nOrange/classification/_tree_scorers.c: In function ‘__pyx_pf_6Orange_14classification_13_tree_scorers_14compute_predictions_csr’:\nOrange/classification/_tree_scorers.c:7217:41: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n 7217 |         for (__pyx_t_12 = 0; __pyx_t_12 < __pyx_t_11; __pyx_t_12+=1) {\n      |                                         ^\nOrange/classification/_tree_scorers.c: In function ‘__pyx_pf_6Orange_14classification_13_tree_scorers_16compute_predictions_csc’:\nOrange/classification/_tree_scorers.c:7874:41: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n 7874 |         for (__pyx_t_12 = 0; __pyx_t_12 < __pyx_t_11; __pyx_t_12+=1) {\n      |                                         ^\nOrange/classification/_tree_scorers.c:7934:150: warning: comparison of integer expressions of different signedness: ‘__pyx_t_5numpy_int32_t’ {aka ‘int’} and ‘unsigned int’ [-Wsign-compare]\n 7934 |               __pyx_t_16 = (((*((__pyx_t_5numpy_int32_t const  *) ( /* dim=0 */ (__pyx_v_indices.data + __pyx_t_15 * __pyx_v_indices.strides[0]) ))) != __pyx_v_i) != 0);\n      |                                                                                                                                                      ^~\nIn file included from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,\n                 from Orange/data/_contingency.c:775:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:17:2: warning: #warning \"Using deprecated NumPy API, disable it with \" \"#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION\" [-Wcpp]\n   17 | #warning \"Using deprecated NumPy API, disable it with \" \\\n      |  ^~~~~~~\nOrange/data/_io.c: In function ‘__pyx_pf_6Orange_4data_3_io_sparse_read_float.constprop’:\nOrange/data/_io.c:4120:43: warning: ‘__pyx_v_decs’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 4120 |           __pyx_v_value = ((__pyx_v_value * __pyx_v_decs) + (__pyx_v_c & 0xf));\n      |                            ~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~\nOrange/data/_io.c:4318:24: warning: ‘__pyx_v_value’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 4318 |           __pyx_t_10 = PyFloat_FromDouble(__pyx_v_value); if (unlikely(!__pyx_t_10)) __PYX_ERR(0, 223, __pyx_L5_error)\n      |                        ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nOrange/data/_io.c:683:40: warning: ‘__pyx_v_attr_index’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n  683 |   #define PyInt_FromLong               PyLong_FromLong\n      |                                        ^~~~~~~~~~~~~~~\nOrange/data/_io.c:1590:7: note: ‘__pyx_v_attr_index’ was declared here\n 1590 |   int __pyx_v_attr_index;\n      |       ^~~~~~~~~~~~~~~~~~\nOrange/data/_io.c:3183:25: warning: ‘__pyx_v_atomp’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 3183 |           __pyx_v_atomp = (__pyx_v_atomp + 1);\n      |           ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~\nIn file included from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,\n                 from Orange/data/_valuecount.c:775:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:17:2: warning: #warning \"Using deprecated NumPy API, disable it with \" \"#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION\" [-Wcpp]\n   17 | #warning \"Using deprecated NumPy API, disable it with \" \\\n      |  ^~~~~~~\nIn file included from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,\n                 from Orange/distance/_distance.c:775:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:17:2: warning: #warning \"Using deprecated NumPy API, disable it with \" \"#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION\" [-Wcpp]\n   17 | #warning \"Using deprecated NumPy API, disable it with \" \\\n      |  ^~~~~~~\nIn file included from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,\n                 from Orange/preprocess/_discretize.c:774:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:17:2: warning: #warning \"Using deprecated NumPy API, disable it with \" \"#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION\" [-Wcpp]\n   17 | #warning \"Using deprecated NumPy API, disable it with \" \\\n      |  ^~~~~~~\nIn file included from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,\n                 from Orange/preprocess/_relieff.cpp:790:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:17:2: warning: #warning \"Using deprecated NumPy API, disable it with \" \"#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION\" [-Wcpp]\n   17 | #warning \"Using deprecated NumPy API, disable it with \" \\\n      |  ^~~~~~~\nIn file included from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,\n                 from Orange/projection/_som.c:775:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:17:2: warning: #warning \"Using deprecated NumPy API, disable it with \" \"#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION\" [-Wcpp]\n   17 | #warning \"Using deprecated NumPy API, disable it with \" \\\n      |  ^~~~~~~\nOrange/projection/_som.c: In function ‘__pyx_pw_6Orange_10projection_4_som_5update_hex’:\nOrange/projection/_som.c:3863:7: warning: ‘__pyx_v_win_y’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 3863 |   int __pyx_v_win_y;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c:3862:7: warning: ‘__pyx_v_win_x’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 3862 |   int __pyx_v_win_x;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c: In function ‘__pyx_pf_6Orange_10projection_4_som_8update_sparse.constprop’:\nOrange/projection/_som.c:4934:7: warning: ‘__pyx_v_win_y’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 4934 |   int __pyx_v_win_y;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c:4933:7: warning: ‘__pyx_v_win_x’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 4933 |   int __pyx_v_win_x;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c: In function ‘__pyx_pw_6Orange_10projection_4_som_7get_winners_sparse’:\nOrange/projection/_som.c:4732:223: warning: ‘__pyx_v_win_y’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 4732 |           *__Pyx_BufPtrStrided2d(__pyx_t_5numpy_int16_t *, __pyx_pybuffernd_winners.rcbuffer->pybuffer.buf, __pyx_t_26, __pyx_pybuffernd_winners.diminfo[0].strides, __pyx_t_27, __pyx_pybuffernd_winners.diminfo[1].strides) = __pyx_v_win_y;\n      |           ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~\nOrange/projection/_som.c:4307:7: note: ‘__pyx_v_win_y’ was declared here\n 4307 |   int __pyx_v_win_y;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c:4721:223: warning: ‘__pyx_v_win_x’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 4721 |           *__Pyx_BufPtrStrided2d(__pyx_t_5numpy_int16_t *, __pyx_pybuffernd_winners.rcbuffer->pybuffer.buf, __pyx_t_27, __pyx_pybuffernd_winners.diminfo[0].strides, __pyx_t_26, __pyx_pybuffernd_winners.diminfo[1].strides) = __pyx_v_win_x;\n      |           ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~\nOrange/projection/_som.c:4306:7: note: ‘__pyx_v_win_x’ was declared here\n 4306 |   int __pyx_v_win_x;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c: In function ‘__pyx_pf_6Orange_10projection_4_som_10update_sparse_hex.constprop’:\nOrange/projection/_som.c:5682:7: warning: ‘__pyx_v_win_y’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 5682 |   int __pyx_v_win_y;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c:5681:7: warning: ‘__pyx_v_win_x’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 5681 |   int __pyx_v_win_x;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c: In function ‘__pyx_pw_6Orange_10projection_4_som_1get_winners’:\nOrange/projection/_som.c:3184:223: warning: ‘__pyx_v_win_y’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 3184 |           *__Pyx_BufPtrStrided2d(__pyx_t_5numpy_int16_t *, __pyx_pybuffernd_winners.rcbuffer->pybuffer.buf, __pyx_t_22, __pyx_pybuffernd_winners.diminfo[0].strides, __pyx_t_23, __pyx_pybuffernd_winners.diminfo[1].strides) = __pyx_v_win_y;\n      |           ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~\nOrange/projection/_som.c:2887:7: note: ‘__pyx_v_win_y’ was declared here\n 2887 |   int __pyx_v_win_y;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c:3173:223: warning: ‘__pyx_v_win_x’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 3173 |           *__Pyx_BufPtrStrided2d(__pyx_t_5numpy_int16_t *, __pyx_pybuffernd_winners.rcbuffer->pybuffer.buf, __pyx_t_23, __pyx_pybuffernd_winners.diminfo[0].strides, __pyx_t_22, __pyx_pybuffernd_winners.diminfo[1].strides) = __pyx_v_win_x;\n      |           ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~\nOrange/projection/_som.c:2886:7: note: ‘__pyx_v_win_x’ was declared here\n 2886 |   int __pyx_v_win_x;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c: In function ‘__pyx_pw_6Orange_10projection_4_som_3update’:\nOrange/projection/_som.c:3357:7: warning: ‘__pyx_v_win_y’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 3357 |   int __pyx_v_win_y;\n      |       ^~~~~~~~~~~~~\nOrange/classification/_simple_tree.c: In function ‘gain_ratio_c’:\nOrange/classification/_simple_tree.c:63:9: warning: implicit declaration of function ‘qsort_r’; did you mean ‘qsort’? [-Wimplicit-function-declaration]\n   63 |         qsort_r(base, nel, size, compar, thunk)\n      |         ^~~~~~~\nOrange/classification/_simple_tree.c:147:9: note: in expansion of macro ‘QSORT_R_FUNC’\n  147 |         QSORT_R_FUNC(examples, size, sizeof(struct Example), (void*) &compar_attr, compar_examples);\n      |         ^~~~~~~~~~~~\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages/setuptools/command/easy_install.py:159: EasyInstallDeprecationWarning: easy_install command is deprecated. Use build and pip and other standards-based tools.\n  EasyInstallDeprecationWarning,\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34/.venv/lib/python3.7/site-packages/setuptools/command/install.py:37: SetuptoolsDeprecationWarning: setup.py install is deprecated. Use build and pip and other standards-based tools.\n  setuptools.SetuptoolsDeprecationWarning,\nwarning: no files found matching 'README*' under directory 'Orange'\nwarning: no files found matching 'LICENSE' under directory 'Orange'\nwarning: no files found matching '*.basket' under directory 'Orange/datasets'\nwarning: no files found matching '*.svg' under directory 'Orange/canvas'\nwarning: no files found matching '*.js' under directory 'Orange/widgets'\nwarning: no files found matching '*.css' under directory 'Orange/widgets'\nwarning: no files found matching '*.html' under directory 'Orange/widgets'\nwarning: no files found matching '*.svg' under directory 'distribute'\nwarning: no files found matching 'COPYING'\n",
    "new_commit_res_code": 0,
    "new_commit_res_stdout": "============================= test session starts ==============================\nplatform linux -- Python 3.7.9, pytest-7.4.4, pluggy-1.2.0\nrootdir: /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34\nplugins: anyio-3.7.1\ncollected 12 items\n\nr2e_tests/test_1.py ............                                         [100%]\n\n==================================== PASSES ====================================\n=========================== short test summary info ============================\nPASSED r2e_tests/test_1.py::TestGetUniqueNames::test_get_unique_names\nPASSED r2e_tests/test_1.py::TestGetUniqueNames::test_get_unique_names_domain\nPASSED r2e_tests/test_1.py::TestGetUniqueNames::test_get_unique_names_duplicated_proposals\nPASSED r2e_tests/test_1.py::TestGetUniqueNames::test_get_unique_names_from_duplicates\nPASSED r2e_tests/test_1.py::TestGetUniqueNames::test_get_unique_names_not_equal\nPASSED r2e_tests/test_1.py::TestGetUniqueNames::test_get_unique_names_with_domain\nPASSED r2e_tests/test_1.py::TestOneHot::test_dim\nPASSED r2e_tests/test_1.py::TestOneHot::test_dim_too_low\nPASSED r2e_tests/test_1.py::TestOneHot::test_dtype\nPASSED r2e_tests/test_1.py::TestOneHot::test_empty\nPASSED r2e_tests/test_1.py::TestOneHot::test_one_hot\nPASSED r2e_tests/test_1.py::TestSanitizedName::test_sanitized_name\n============================== 12 passed in 1.53s ==============================\n",
    "new_commit_res_stderr": "",
    "old_commit_res_code": 1,
    "old_commit_res_stdout": "============================= test session starts ==============================\nplatform linux -- Python 3.7.9, pytest-7.4.4, pluggy-1.2.0\nrootdir: /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8826e2e4a3e3a6c81b51f56115264a0141036c34\nplugins: anyio-3.7.1\ncollected 12 items\n\nr2e_tests/test_1.py ..F.........                                         [100%]\n\n=================================== FAILURES ===================================\n________ TestGetUniqueNames.test_get_unique_names_duplicated_proposals _________\n\nself = <r2e_tests.test_1.TestGetUniqueNames testMethod=test_get_unique_names_duplicated_proposals>\n\n    def test_get_unique_names_duplicated_proposals(self):\n        names = [\"foo\", \"bar\", \"baz\", \"baz (3)\"]\n    \n        self.assertEqual(\n            get_unique_names(names, [\"foo\", \"boo\", \"boo\"]),\n>           ['foo (1)', 'boo (1)', 'boo (2)']\n        )\nE       AssertionError: Lists differ: ['foo (1)', 'boo (1)', 'boo (1)'] != ['foo (1)', 'boo (1)', 'boo (2)']\nE       \nE       First differing element 2:\nE       'boo (1)'\nE       'boo (2)'\nE       \nE       - ['foo (1)', 'boo (1)', 'boo (1)']\nE       ?                              ^\nE       \nE       + ['foo (1)', 'boo (1)', 'boo (2)']\nE       ?                              ^\n\nr2e_tests/test_1.py:128: AssertionError\n==================================== PASSES ====================================\n=========================== short test summary info ============================\nPASSED r2e_tests/test_1.py::TestGetUniqueNames::test_get_unique_names\nPASSED r2e_tests/test_1.py::TestGetUniqueNames::test_get_unique_names_domain\nPASSED r2e_tests/test_1.py::TestGetUniqueNames::test_get_unique_names_from_duplicates\nPASSED r2e_tests/test_1.py::TestGetUniqueNames::test_get_unique_names_not_equal\nPASSED r2e_tests/test_1.py::TestGetUniqueNames::test_get_unique_names_with_domain\nPASSED r2e_tests/test_1.py::TestOneHot::test_dim\nPASSED r2e_tests/test_1.py::TestOneHot::test_dim_too_low\nPASSED r2e_tests/test_1.py::TestOneHot::test_dtype\nPASSED r2e_tests/test_1.py::TestOneHot::test_empty\nPASSED r2e_tests/test_1.py::TestOneHot::test_one_hot\nPASSED r2e_tests/test_1.py::TestSanitizedName::test_sanitized_name\nFAILED r2e_tests/test_1.py::TestGetUniqueNames::test_get_unique_names_duplicated_proposals\n========================= 1 failed, 11 passed in 1.66s =========================\n",
    "old_commit_res_stderr": ""
}
Orange/data/tests/test_util.py Orange/data/util.py
We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
[ISSUE]

**Title:** Duplicated Proposals Receive Identical Suffixes in `get_unique_names`

**Description:**
When multiple duplicate proposals are provided to the `get_unique_names` function, the suffixes appended to make the names unique are not incremented correctly. This results in duplicated names with identical suffixes instead of unique ones.

**Example Code:**
```python
names = ["foo", "bar", "baz", "baz (3)"]
proposed = ["foo", "boo", "boo"]

unique_names = get_unique_names(names, proposed)
print(unique_names)
```

**Expected Behavior:**
The function should return each duplicated proposal with an incremented suffix to ensure all names are unique. For the given example, the expected output is:
```
['foo (1)', 'boo (1)', 'boo (2)']
```

**Actual Behavior:**
Instead, the function returns duplicated proposals with the same suffix:
```
['foo (1)', 'boo (1)', 'boo (1)']
```

This issue makes it difficult to distinguish between the duplicated proposals, potentially leading to conflicts or errors in downstream processes that rely on unique names.

[/ISSUE]
--- END ISSUE ---

Below are some code segments, each from a relevant file. One or more of these files may contain bugs.

--- BEGIN FILE ---
```
### Orange/data/util.py
"""
Data-manipulation utilities.
"""
import re
from collections import Counter
from itertools import chain, count
from typing import Callable, Union, List, Type

import numpy as np
import bottleneck as bn
from scipy import sparse as sp

RE_FIND_INDEX = r"(^{})( \((\d{{1,}})\))?$"


def one_hot(
        values: Union[np.ndarray, List], dtype: Type = float, dim: int = None
) -> np.ndarray:
    """Return a one-hot transform of values

    Parameters
    ----------
    values : 1d array
        Integer values (hopefully 0-max).
    dtype
        dtype of result array
    dim
        Number of columns (attributes) in the one hot encoding. This parameter
        is used when we need fixed number of columns and values does not
        reflect that number correctly, e.g. not all values from the discrete
        variable are present in values parameter.

    Returns
    -------
    result
        2d array with ones in respective indicator columns.
    """
    dim_values = int(np.max(values) + 1 if len(values) > 0 else 0)
    if dim is None:
        dim = dim_values
    elif dim < dim_values:
        raise ValueError("dim must be greater than max(values)")
    return np.eye(dim, dtype=dtype)[np.asanyarray(values, dtype=int)]


# pylint: disable=redefined-builtin
def scale(values, min=0, max=1):
    """Return values scaled to [min, max]"""
    if len(values) == 0:
        return np.array([])
    minval = np.float_(bn.nanmin(values))
    ptp = bn.nanmax(values) - minval
    if ptp == 0:
        return np.clip(values, min, max)
    return (-minval + values) / ptp * (max - min) + min


class SharedComputeValue:
    """A base class that separates compute_value computation
    for different variables into shared and specific parts.

    Parameters
    ----------
    compute_shared: Callable[[Orange.data.Table], object]
        A callable that performs computation that is shared between
        multiple variables. Variables sharing computation need to set
        the same instance.
    variable: Orange.data.Variable
        The original variable on which this compute value is set. Optional.
    """

    def __init__(self, compute_shared, variable=None):
        self.compute_shared = compute_shared
        self.variable = variable

    def __call__(self, data, shared_data=None):
        """Fallback if common parts are not passed."""
        if shared_data is None:
            shared_data = self.compute_shared(data)
        return self.compute(data, shared_data)

    def compute(self, data, shared_data):
        """Given precomputed shared data, perform variable-specific
        part of computation and return new variable values.
        Subclasses need to implement this function."""
        raise NotImplementedError


def vstack(arrays):
    """vstack that supports sparse and dense arrays

    If all arrays are dense, result is dense. Otherwise,
    result is a sparse (csr) array.
    """
    if any(sp.issparse(arr) for arr in arrays):
        arrays = [sp.csr_matrix(arr) for arr in arrays]
        return sp.vstack(arrays)
    else:
        return np.vstack(arrays)


def hstack(arrays):
    """hstack that supports sparse and dense arrays

    If all arrays are dense, result is dense. Otherwise,
    result is a sparse (csc) array.
    """
    if any(sp.issparse(arr) for arr in arrays):
        arrays = [sp.csc_matrix(arr) for arr in arrays]
        return sp.hstack(arrays)
    else:
        return np.hstack(arrays)


def array_equal(a1, a2):
    """array_equal that supports sparse and dense arrays with missing values"""
    if a1.shape != a2.shape:
        return False

    if not (sp.issparse(a1) or sp.issparse(a2)):  # Both dense: just compare
        return np.allclose(a1, a2, equal_nan=True)

    v1 = np.vstack(sp.find(a1)).T
    v2 = np.vstack(sp.find(a2)).T
    if not (sp.issparse(a1) and sp.issparse(a2)):  # Any dense: order indices
        v1.sort(axis=0)
        v2.sort(axis=0)
    return np.allclose(v1, v2, equal_nan=True)


def assure_array_dense(a):
    if sp.issparse(a):
        a = a.toarray()
    return np.asarray(a)


def assure_array_sparse(a, sparse_class: Callable = sp.csc_matrix):
    if not sp.issparse(a):
        # since x can be a list, cast to np.array
        # since x can come from metas with string, cast to float
        a = np.asarray(a).astype(float)
    return sparse_class(a)


def assure_column_sparse(a):
    # if x of shape (n, ) is passed to csc_matrix constructor or
    # sparse matrix with shape (1, n) is passed,
    # the resulting matrix is of shape (1, n) and hence we
    # need to transpose it to make it a column
    if a.ndim == 1 or a.shape[0] == 1:
        # csr matrix becomes csc when transposed
        return assure_array_sparse(a, sparse_class=sp.csr_matrix).T
    else:
        return assure_array_sparse(a, sparse_class=sp.csc_matrix)


def assure_column_dense(a):
    # quick check and exit for the most common case
    if isinstance(a, np.ndarray) and len(a.shape) == 1:
        return a
    a = assure_array_dense(a)
    # column assignments must be (n, )
    return a.reshape(-1)


def get_indices(names, name):
    """
    Return list of indices which occur in a names list for a given name.
    :param names: list of strings
    :param name: str
    :return: list of indices
    """
    return [int(a.group(3) or 0) for x in filter(None, names)
            for a in re.finditer(RE_FIND_INDEX.format(re.escape(name)), x)]


def get_unique_names(names, proposed, equal_numbers=True):
    """
    Returns unique names for variables

    Proposed is a list of names (or a string with a single name). If any name
    already appears in `names`, the function appends an index in parentheses,
    which is one higher than the highest index at these variables. Also, if
    `names` contains any of the names with index in parentheses, this counts
    as an occurence of the name. For instance, if `names` does not contain
    `x` but it contains `x (3)`, `get_unique_names` will replace `x` with
    `x (4)`.

    If argument `names` is domain, the method observes all variables and metas.

    Function returns a string if `proposed` is a string, and a list if it's a
    list.

    The method is used in widgets like MDS, which adds two variables (`x` and
    `y`). It is desired that they have the same index. In case when
    equal_numbers=True, if `x`, `x (1)` and `x (2)` and `y` (but no other
    `y`'s already exist in the domain, MDS should append `x (3)` and `y (3)`,
    not `x (3)` and y (1)`.

    Args:
        names (Domain or list of str): used names
        proposed (str or list of str): proposed name
        equal_numbers (bool): Add same number to all proposed names

    Return:
        str or list of str
    """
    # prevent cyclic import: pylint: disable=import-outside-toplevel
    from Orange.data import Domain
    if isinstance(names, Domain):
        names = [var.name for var in chain(names.variables, names.metas)]
    if isinstance(proposed, str):
        return get_unique_names(names, [proposed])[0]
    indices = {name: get_indices(names, name) for name in proposed}
    indices = {name: max(ind) + 1 for name, ind in indices.items() if ind}
    if not (set(proposed) & set(names) or indices):
        return proposed
    if equal_numbers:
        max_index = max(indices.values())
        return [f"{name} ({max_index})" for name in proposed]
    else:
        return [f"{name} ({indices[name]})" if name in indices else name
                for name in proposed]


def get_unique_names_duplicates(proposed: list, return_duplicated=False) -> list:
    """
    Returns list of unique names. If a name is duplicated, the
    function appends the next available index in parentheses.

    For example, a proposed list of names `x`, `x` and `x (2)`
    results in `x (3)`, `x (4)`, `x (2)`.
    """
    indices = {name: count(max(get_indices(proposed, name), default=0) + 1)
               for name, cnt in Counter(proposed).items()
               if name and cnt > 1}
    new_names = [f"{name} ({next(indices[name])})" if name in indices else name
                 for name in proposed]
    if return_duplicated:
        return new_names, list(indices)
    return new_names


def get_unique_names_domain(attributes, class_vars=(), metas=()):
    """
    Return de-duplicated names for variables for attributes, class_vars
    and metas. If a name appears more than once, the function appends
    indices in parentheses.

    Args:
        attributes (list of str): proposed names for attributes
        class_vars (list of str): proposed names for class_vars
        metas (list of str): proposed names for metas

    Returns:
        (attributes, class_vars, metas): new names
        renamed: list of names renamed variables; names appear in order of
            appearance in original lists; every name appears only once
    """
    all_names = list(chain(attributes, class_vars, metas))
    unique_names = get_unique_names_duplicates(all_names)
    # don't be smart with negative indices: they won't work for empty lists
    attributes = unique_names[:len(attributes)]
    class_vars = unique_names[len(attributes):len(attributes) + len(class_vars)]
    metas = unique_names[len(attributes) + len(class_vars):]
    # use dict, not set, to keep the order
    renamed = list(dict.fromkeys(old
                                 for old, new in zip(all_names, unique_names)
                                 if new != old))
    return (attributes, class_vars, metas), renamed


def sanitized_name(name: str) -> str:
    """
    Replace non-alphanumeric characters and leading zero with `_`.

    Args:
        name (str): proposed name

    Returns:
        name (str): new name
    """
    sanitized = re.sub(r"\W", "_", name)
    if sanitized[0].isdigit():
        sanitized = "_" + sanitized
    return sanitized

```
--- END FILE ---

Please first localize the bug based on the issue statement, and then generate *SEARCH/REPLACE* edits to fix the issue.

Every *SEARCH/REPLACE* edit must use this format:
1. The file path
2. The start of search block: <<<<<<< SEARCH
3. A contiguous chunk of lines to search for in the existing source code
4. The dividing line: =======
5. The lines to replace into the source code
6. The end of the replace block: >>>>>>> REPLACE

Here is an example:

```python
### mathweb/flask/app.py
<<<<<<< SEARCH
from flask import Flask
=======
import math
from flask import Flask
>>>>>>> REPLACE
```
</solution>

Please note that the *SEARCH/REPLACE* edit REQUIRES PROPER INDENTATION. If you would like to add the line '        print(x)', you must fully write that out, with all those spaces before the code!
Wrap each *SEARCH/REPLACE* edit in a code block as shown in the example above. If you have multiple *SEARCH/REPLACE* edits, use a separate code block for each one.
[ISSUE]

**Title:** Duplicated Proposals Receive Identical Suffixes in `get_unique_names`

**Description:**
When multiple duplicate proposals are provided to the `get_unique_names` function, the suffixes appended to make the names unique are not incremented correctly. This results in duplicated names with identical suffixes instead of unique ones.

**Example Code:**
```python
names = ["foo", "bar", "baz", "baz (3)"]
proposed = ["foo", "boo", "boo"]

unique_names = get_unique_names(names, proposed)
print(unique_names)
```

**Expected Behavior:**
The function should return each duplicated proposal with an incremented suffix to ensure all names are unique. For the given example, the expected output is:
```
['foo (1)', 'boo (1)', 'boo (2)']
```

**Actual Behavior:**
Instead, the function returns duplicated proposals with the same suffix:
```
['foo (1)', 'boo (1)', 'boo (1)']
```

This issue makes it difficult to distinguish between the duplicated proposals, potentially leading to conflicts or errors in downstream processes that rely on unique names.

[/ISSUE]
{
    "TestGetUniqueNames.test_get_unique_names": "PASSED",
    "TestGetUniqueNames.test_get_unique_names_domain": "PASSED",
    "TestGetUniqueNames.test_get_unique_names_duplicated_proposals": "PASSED",
    "TestGetUniqueNames.test_get_unique_names_from_duplicates": "PASSED",
    "TestGetUniqueNames.test_get_unique_names_not_equal": "PASSED",
    "TestGetUniqueNames.test_get_unique_names_with_domain": "PASSED",
    "TestOneHot.test_dim": "PASSED",
    "TestOneHot.test_dim_too_low": "PASSED",
    "TestOneHot.test_dtype": "PASSED",
    "TestOneHot.test_empty": "PASSED",
    "TestOneHot.test_one_hot": "PASSED",
    "TestSanitizedName.test_sanitized_name": "PASSED"
}
diff --git a/Orange/data/util.py b/Orange/data/util.py
index 111f469bb..55e5fa512 100644
--- a/Orange/data/util.py
+++ b/Orange/data/util.py
@@ -213,8 +213,31 @@ def get_unique_names(names, proposed, equal_numbers=True):
         return get_unique_names(names, [proposed])[0]
     indices = {name: get_indices(names, name) for name in proposed}
     indices = {name: max(ind) + 1 for name, ind in indices.items() if ind}
+
+    duplicated_proposed = {name for name, count in Counter(proposed).items()
+                           if count > 1}
+    if duplicated_proposed:
+        # This could be merged with the code below, but it would make it slower
+        # because it can't be done within list comprehension
+        if equal_numbers:
+            max_index = max(indices.values(), default=1)
+            indices = {name: max_index
+                       for name in chain(indices, duplicated_proposed)}
+        else:
+            indices.update({name: 1
+                            for name in duplicated_proposed - set(indices)})
+        names = []
+        for name in proposed:
+            if name in indices:
+                names.append(f"{name} ({indices[name]})")
+                indices[name] += 1
+            else:
+                names.append(name)
+        return names
+
     if not (set(proposed) & set(names) or indices):
         return proposed
+
     if equal_numbers:
         max_index = max(indices.values())
         return [f"{name} ({max_index})" for name in proposed]
 

{"Orange/data/util.py": "\"\"\"\nData-manipulation utilities.\n\"\"\"\nimport re\nfrom collections import Counter\nfrom itertools import chain, count\nfrom typing import Callable, Union, List, Type\n\nimport numpy as np\nimport bottleneck as bn\nfrom scipy import sparse as sp\n\nRE_FIND_INDEX = r\"(^{})( \\((\\d{{1,}})\\))?$\"\n\n\ndef one_hot(\n        values: Union[np.ndarray, List], dtype: Type = float, dim: int = None\n) -> np.ndarray:\n    \"\"\"Return a one-hot transform of values\n\n    Parameters\n    ----------\n    values : 1d array\n        Integer values (hopefully 0-max).\n    dtype\n        dtype of result array\n    dim\n        Number of columns (attributes) in the one hot encoding. This parameter\n        is used when we need fixed number of columns and values does not\n        reflect that number correctly, e.g. not all values from the discrete\n        variable are present in values parameter.\n\n    Returns\n    -------\n    result\n        2d array with ones in respective indicator columns.\n    \"\"\"\n    dim_values = int(np.max(values) + 1 if len(values) > 0 else 0)\n    if dim is None:\n        dim = dim_values\n    elif dim < dim_values:\n        raise ValueError(\"dim must be greater than max(values)\")\n    return np.eye(dim, dtype=dtype)[np.asanyarray(values, dtype=int)]\n\n\n# pylint: disable=redefined-builtin\ndef scale(values, min=0, max=1):\n    \"\"\"Return values scaled to [min, max]\"\"\"\n    if len(values) == 0:\n        return np.array([])\n    minval = np.float_(bn.nanmin(values))\n    ptp = bn.nanmax(values) - minval\n    if ptp == 0:\n        return np.clip(values, min, max)\n    return (-minval + values) / ptp * (max - min) + min\n\n\nclass SharedComputeValue:\n    \"\"\"A base class that separates compute_value computation\n    for different variables into shared and specific parts.\n\n    Parameters\n    ----------\n    compute_shared: Callable[[Orange.data.Table], object]\n        A callable that performs computation that is shared between\n        multiple variables. Variables sharing computation need to set\n        the same instance.\n    variable: Orange.data.Variable\n        The original variable on which this compute value is set. Optional.\n    \"\"\"\n\n    def __init__(self, compute_shared, variable=None):\n        self.compute_shared = compute_shared\n        self.variable = variable\n\n    def __call__(self, data, shared_data=None):\n        \"\"\"Fallback if common parts are not passed.\"\"\"\n        if shared_data is None:\n            shared_data = self.compute_shared(data)\n        return self.compute(data, shared_data)\n\n    def compute(self, data, shared_data):\n        \"\"\"Given precomputed shared data, perform variable-specific\n        part of computation and return new variable values.\n        Subclasses need to implement this function.\"\"\"\n        raise NotImplementedError\n\n\ndef vstack(arrays):\n    \"\"\"vstack that supports sparse and dense arrays\n\n    If all arrays are dense, result is dense. Otherwise,\n    result is a sparse (csr) array.\n    \"\"\"\n    if any(sp.issparse(arr) for arr in arrays):\n        arrays = [sp.csr_matrix(arr) for arr in arrays]\n        return sp.vstack(arrays)\n    else:\n        return np.vstack(arrays)\n\n\ndef hstack(arrays):\n    \"\"\"hstack that supports sparse and dense arrays\n\n    If all arrays are dense, result is dense. Otherwise,\n    result is a sparse (csc) array.\n    \"\"\"\n    if any(sp.issparse(arr) for arr in arrays):\n        arrays = [sp.csc_matrix(arr) for arr in arrays]\n        return sp.hstack(arrays)\n    else:\n        return np.hstack(arrays)\n\n\ndef array_equal(a1, a2):\n    \"\"\"array_equal that supports sparse and dense arrays with missing values\"\"\"\n    if a1.shape != a2.shape:\n        return False\n\n    if not (sp.issparse(a1) or sp.issparse(a2)):  # Both dense: just compare\n        return np.allclose(a1, a2, equal_nan=True)\n\n    v1 = np.vstack(sp.find(a1)).T\n    v2 = np.vstack(sp.find(a2)).T\n    if not (sp.issparse(a1) and sp.issparse(a2)):  # Any dense: order indices\n        v1.sort(axis=0)\n        v2.sort(axis=0)\n    return np.allclose(v1, v2, equal_nan=True)\n\n\ndef assure_array_dense(a):\n    if sp.issparse(a):\n        a = a.toarray()\n    return np.asarray(a)\n\n\ndef assure_array_sparse(a, sparse_class: Callable = sp.csc_matrix):\n    if not sp.issparse(a):\n        # since x can be a list, cast to np.array\n        # since x can come from metas with string, cast to float\n        a = np.asarray(a).astype(float)\n    return sparse_class(a)\n\n\ndef assure_column_sparse(a):\n    # if x of shape (n, ) is passed to csc_matrix constructor or\n    # sparse matrix with shape (1, n) is passed,\n    # the resulting matrix is of shape (1, n) and hence we\n    # need to transpose it to make it a column\n    if a.ndim == 1 or a.shape[0] == 1:\n        # csr matrix becomes csc when transposed\n        return assure_array_sparse(a, sparse_class=sp.csr_matrix).T\n    else:\n        return assure_array_sparse(a, sparse_class=sp.csc_matrix)\n\n\ndef assure_column_dense(a):\n    # quick check and exit for the most common case\n    if isinstance(a, np.ndarray) and len(a.shape) == 1:\n        return a\n    a = assure_array_dense(a)\n    # column assignments must be (n, )\n    return a.reshape(-1)\n\n\ndef get_indices(names, name):\n    \"\"\"\n    Return list of indices which occur in a names list for a given name.\n    :param names: list of strings\n    :param name: str\n    :return: list of indices\n    \"\"\"\n    return [int(a.group(3) or 0) for x in filter(None, names)\n            for a in re.finditer(RE_FIND_INDEX.format(re.escape(name)), x)]\n\n\ndef get_unique_names(names, proposed, equal_numbers=True):\n    \"\"\"\n    Returns unique names for variables\n\n    Proposed is a list of names (or a string with a single name). If any name\n    already appears in `names`, the function appends an index in parentheses,\n    which is one higher than the highest index at these variables. Also, if\n    `names` contains any of the names with index in parentheses, this counts\n    as an occurence of the name. For instance, if `names` does not contain\n    `x` but it contains `x (3)`, `get_unique_names` will replace `x` with\n    `x (4)`.\n\n    If argument `names` is domain, the method observes all variables and metas.\n\n    Function returns a string if `proposed` is a string, and a list if it's a\n    list.\n\n    The method is used in widgets like MDS, which adds two variables (`x` and\n    `y`). It is desired that they have the same index. In case when\n    equal_numbers=True, if `x`, `x (1)` and `x (2)` and `y` (but no other\n    `y`'s already exist in the domain, MDS should append `x (3)` and `y (3)`,\n    not `x (3)` and y (1)`.\n\n    Args:\n        names (Domain or list of str): used names\n        proposed (str or list of str): proposed name\n        equal_numbers (bool): Add same number to all proposed names\n\n    Return:\n        str or list of str\n    \"\"\"\n    # prevent cyclic import: pylint: disable=import-outside-toplevel\n    from Orange.data import Domain\n    if isinstance(names, Domain):\n        names = [var.name for var in chain(names.variables, names.metas)]\n    if isinstance(proposed, str):\n        return get_unique_names(names, [proposed])[0]\n    indices = {name: get_indices(names, name) for name in proposed}\n    indices = {name: max(ind) + 1 for name, ind in indices.items() if ind}\n    if not (set(proposed) & set(names) or indices):\n        return proposed\n    if equal_numbers:\n        max_index = max(indices.values())\n        return [f\"{name} ({max_index})\" for name in proposed]\n    else:\n        return [f\"{name} ({indices[name]})\" if name in indices else name\n                for name in proposed]\n\n\ndef get_unique_names_duplicates(proposed: list, return_duplicated=False) -> list:\n    \"\"\"\n    Returns list of unique names. If a name is duplicated, the\n    function appends the next available index in parentheses.\n\n    For example, a proposed list of names `x`, `x` and `x (2)`\n    results in `x (3)`, `x (4)`, `x (2)`.\n    \"\"\"\n    indices = {name: count(max(get_indices(proposed, name), default=0) + 1)\n               for name, cnt in Counter(proposed).items()\n               if name and cnt > 1}\n    new_names = [f\"{name} ({next(indices[name])})\" if name in indices else name\n                 for name in proposed]\n    if return_duplicated:\n        return new_names, list(indices)\n    return new_names\n\n\ndef get_unique_names_domain(attributes, class_vars=(), metas=()):\n    \"\"\"\n    Return de-duplicated names for variables for attributes, class_vars\n    and metas. If a name appears more than once, the function appends\n    indices in parentheses.\n\n    Args:\n        attributes (list of str): proposed names for attributes\n        class_vars (list of str): proposed names for class_vars\n        metas (list of str): proposed names for metas\n\n    Returns:\n        (attributes, class_vars, metas): new names\n        renamed: list of names renamed variables; names appear in order of\n            appearance in original lists; every name appears only once\n    \"\"\"\n    all_names = list(chain(attributes, class_vars, metas))\n    unique_names = get_unique_names_duplicates(all_names)\n    # don't be smart with negative indices: they won't work for empty lists\n    attributes = unique_names[:len(attributes)]\n    class_vars = unique_names[len(attributes):len(attributes) + len(class_vars)]\n    metas = unique_names[len(attributes) + len(class_vars):]\n    # use dict, not set, to keep the order\n    renamed = list(dict.fromkeys(old\n                                 for old, new in zip(all_names, unique_names)\n                                 if new != old))\n    return (attributes, class_vars, metas), renamed\n\n\ndef sanitized_name(name: str) -> str:\n    \"\"\"\n    Replace non-alphanumeric characters and leading zero with `_`.\n\n    Args:\n        name (str): proposed name\n\n    Returns:\n        name (str): new name\n    \"\"\"\n    sanitized = re.sub(r\"\\W\", \"_\", name)\n    if sanitized[0].isdigit():\n        sanitized = \"_\" + sanitized\n    return sanitized\n"}
A user will ask you to solve a task. You should first draft your thinking process (inner monologue). Then, generate the solution.

Your response format must follow the template below:
<think>
Your thoughts or/and draft, like working through an exercise on scratch paper. Be as casual and as long as you want until you are confident to generate a correct solution.
</think>
<solution>
Final solution presented to the user.
</solution>
system
We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
[ISSUE]

**Title:** Duplicated Proposals Receive Identical Suffixes in `get_unique_names`

**Description:**
When multiple duplicate proposals are provided to the `get_unique_names` function, the suffixes appended to make the names unique are not incremented correctly. This results in duplicated names with identical suffixes instead of unique ones.

**Example Code:**
```python
names = ["foo", "bar", "baz", "baz (3)"]
proposed = ["foo", "boo", "boo"]

unique_names = get_unique_names(names, proposed)
print(unique_names)
```

**Expected Behavior:**
The function should return each duplicated proposal with an incremented suffix to ensure all names are unique. For the given example, the expected output is:
```
['foo (1)', 'boo (1)', 'boo (2)']
```

**Actual Behavior:**
Instead, the function returns duplicated proposals with the same suffix:
```
['foo (1)', 'boo (1)', 'boo (1)']
```

This issue makes it difficult to distinguish between the duplicated proposals, potentially leading to conflicts or errors in downstream processes that rely on unique names.

[/ISSUE]
--- END ISSUE ---

Below are some code segments, each from a relevant file. One or more of these files may contain bugs.

--- BEGIN FILE ---
```
### Orange/data/util.py
"""
Data-manipulation utilities.
"""
import re
from collections import Counter
from itertools import chain, count
from typing import Callable, Union, List, Type

import numpy as np
import bottleneck as bn
from scipy import sparse as sp

RE_FIND_INDEX = r"(^{})( \((\d{{1,}})\))?$"


def one_hot(
        values: Union[np.ndarray, List], dtype: Type = float, dim: int = None
) -> np.ndarray:
    """Return a one-hot transform of values

    Parameters
    ----------
    values : 1d array
        Integer values (hopefully 0-max).
    dtype
        dtype of result array
    dim
        Number of columns (attributes) in the one hot encoding. This parameter
        is used when we need fixed number of columns and values does not
        reflect that number correctly, e.g. not all values from the discrete
        variable are present in values parameter.

    Returns
    -------
    result
        2d array with ones in respective indicator columns.
    """
    dim_values = int(np.max(values) + 1 if len(values) > 0 else 0)
    if dim is None:
        dim = dim_values
    elif dim < dim_values:
        raise ValueError("dim must be greater than max(values)")
    return np.eye(dim, dtype=dtype)[np.asanyarray(values, dtype=int)]


# pylint: disable=redefined-builtin
def scale(values, min=0, max=1):
    """Return values scaled to [min, max]"""
    if len(values) == 0:
        return np.array([])
    minval = np.float_(bn.nanmin(values))
    ptp = bn.nanmax(values) - minval
    if ptp == 0:
        return np.clip(values, min, max)
    return (-minval + values) / ptp * (max - min) + min


class SharedComputeValue:
    """A base class that separates compute_value computation
    for different variables into shared and specific parts.

    Parameters
    ----------
    compute_shared: Callable[[Orange.data.Table], object]
        A callable that performs computation that is shared between
        multiple variables. Variables sharing computation need to set
        the same instance.
    variable: Orange.data.Variable
        The original variable on which this compute value is set. Optional.
    """

    def __init__(self, compute_shared, variable=None):
        self.compute_shared = compute_shared
        self.variable = variable

    def __call__(self, data, shared_data=None):
        """Fallback if common parts are not passed."""
        if shared_data is None:
            shared_data = self.compute_shared(data)
        return self.compute(data, shared_data)

    def compute(self, data, shared_data):
        """Given precomputed shared data, perform variable-specific
        part of computation and return new variable values.
        Subclasses need to implement this function."""
        raise NotImplementedError


def vstack(arrays):
    """vstack that supports sparse and dense arrays

    If all arrays are dense, result is dense. Otherwise,
    result is a sparse (csr) array.
    """
    if any(sp.issparse(arr) for arr in arrays):
        arrays = [sp.csr_matrix(arr) for arr in arrays]
        return sp.vstack(arrays)
    else:
        return np.vstack(arrays)


def hstack(arrays):
    """hstack that supports sparse and dense arrays

    If all arrays are dense, result is dense. Otherwise,
    result is a sparse (csc) array.
    """
    if any(sp.issparse(arr) for arr in arrays):
        arrays = [sp.csc_matrix(arr) for arr in arrays]
        return sp.hstack(arrays)
    else:
        return np.hstack(arrays)


def array_equal(a1, a2):
    """array_equal that supports sparse and dense arrays with missing values"""
    if a1.shape != a2.shape:
        return False

    if not (sp.issparse(a1) or sp.issparse(a2)):  # Both dense: just compare
        return np.allclose(a1, a2, equal_nan=True)

    v1 = np.vstack(sp.find(a1)).T
    v2 = np.vstack(sp.find(a2)).T
    if not (sp.issparse(a1) and sp.issparse(a2)):  # Any dense: order indices
        v1.sort(axis=0)
        v2.sort(axis=0)
    return np.allclose(v1, v2, equal_nan=True)


def assure_array_dense(a):
    if sp.issparse(a):
        a = a.toarray()
    return np.asarray(a)


def assure_array_sparse(a, sparse_class: Callable = sp.csc_matrix):
    if not sp.issparse(a):
        # since x can be a list, cast to np.array
        # since x can come from metas with string, cast to float
        a = np.asarray(a).astype(float)
    return sparse_class(a)


def assure_column_sparse(a):
    # if x of shape (n, ) is passed to csc_matrix constructor or
    # sparse matrix with shape (1, n) is passed,
    # the resulting matrix is of shape (1, n) and hence we
    # need to transpose it to make it a column
    if a.ndim == 1 or a.shape[0] == 1:
        # csr matrix becomes csc when transposed
        return assure_array_sparse(a, sparse_class=sp.csr_matrix).T
    else:
        return assure_array_sparse(a, sparse_class=sp.csc_matrix)


def assure_column_dense(a):
    # quick check and exit for the most common case
    if isinstance(a, np.ndarray) and len(a.shape) == 1:
        return a
    a = assure_array_dense(a)
    # column assignments must be (n, )
    return a.reshape(-1)


def get_indices(names, name):
    """
    Return list of indices which occur in a names list for a given name.
    :param names: list of strings
    :param name: str
    :return: list of indices
    """
    return [int(a.group(3) or 0) for x in filter(None, names)
            for a in re.finditer(RE_FIND_INDEX.format(re.escape(name)), x)]


def get_unique_names(names, proposed, equal_numbers=True):
    """
    Returns unique names for variables

    Proposed is a list of names (or a string with a single name). If any name
    already appears in `names`, the function appends an index in parentheses,
    which is one higher than the highest index at these variables. Also, if
    `names` contains any of the names with index in parentheses, this counts
    as an occurence of the name. For instance, if `names` does not contain
    `x` but it contains `x (3)`, `get_unique_names` will replace `x` with
    `x (4)`.

    If argument `names` is domain, the method observes all variables and metas.

    Function returns a string if `proposed` is a string, and a list if it's a
    list.

    The method is used in widgets like MDS, which adds two variables (`x` and
    `y`). It is desired that they have the same index. In case when
    equal_numbers=True, if `x`, `x (1)` and `x (2)` and `y` (but no other
    `y`'s already exist in the domain, MDS should append `x (3)` and `y (3)`,
    not `x (3)` and y (1)`.

    Args:
        names (Domain or list of str): used names
        proposed (str or list of str): proposed name
        equal_numbers (bool): Add same number to all proposed names

    Return:
        str or list of str
    """
    # prevent cyclic import: pylint: disable=import-outside-toplevel
    from Orange.data import Domain
    if isinstance(names, Domain):
        names = [var.name for var in chain(names.variables, names.metas)]
    if isinstance(proposed, str):
        return get_unique_names(names, [proposed])[0]
    indices = {name: get_indices(names, name) for name in proposed}
    indices = {name: max(ind) + 1 for name, ind in indices.items() if ind}
    if not (set(proposed) & set(names) or indices):
        return proposed
    if equal_numbers:
        max_index = max(indices.values())
        return [f"{name} ({max_index})" for name in proposed]
    else:
        return [f"{name} ({indices[name]})" if name in indices else name
                for name in proposed]


def get_unique_names_duplicates(proposed: list, return_duplicated=False) -> list:
    """
    Returns list of unique names. If a name is duplicated, the
    function appends the next available index in parentheses.

    For example, a proposed list of names `x`, `x` and `x (2)`
    results in `x (3)`, `x (4)`, `x (2)`.
    """
    indices = {name: count(max(get_indices(proposed, name), default=0) + 1)
               for name, cnt in Counter(proposed).items()
               if name and cnt > 1}
    new_names = [f"{name} ({next(indices[name])})" if name in indices else name
                 for name in proposed]
    if return_duplicated:
        return new_names, list(indices)
    return new_names


def get_unique_names_domain(attributes, class_vars=(), metas=()):
    """
    Return de-duplicated names for variables for attributes, class_vars
    and metas. If a name appears more than once, the function appends
    indices in parentheses.

    Args:
        attributes (list of str): proposed names for attributes
        class_vars (list of str): proposed names for class_vars
        metas (list of str): proposed names for metas

    Returns:
        (attributes, class_vars, metas): new names
        renamed: list of names renamed variables; names appear in order of
            appearance in original lists; every name appears only once
    """
    all_names = list(chain(attributes, class_vars, metas))
    unique_names = get_unique_names_duplicates(all_names)
    # don't be smart with negative indices: they won't work for empty lists
    attributes = unique_names[:len(attributes)]
    class_vars = unique_names[len(attributes):len(attributes) + len(class_vars)]
    metas = unique_names[len(attributes) + len(class_vars):]
    # use dict, not set, to keep the order
    renamed = list(dict.fromkeys(old
                                 for old, new in zip(all_names, unique_names)
                                 if new != old))
    return (attributes, class_vars, metas), renamed


def sanitized_name(name: str) -> str:
    """
    Replace non-alphanumeric characters and leading zero with `_`.

    Args:
        name (str): proposed name

    Returns:
        name (str): new name
    """
    sanitized = re.sub(r"\W", "_", name)
    if sanitized[0].isdigit():
        sanitized = "_" + sanitized
    return sanitized

```
--- END FILE ---

Please first localize the bug based on the issue statement, and then generate *SEARCH/REPLACE* edits to fix the issue.

Every *SEARCH/REPLACE* edit must use this format:
1. The file path
2. The start of search block: <<<<<<< SEARCH
3. A contiguous chunk of lines to search for in the existing source code
4. The dividing line: =======
5. The lines to replace into the source code
6. The end of the replace block: >>>>>>> REPLACE

Here is an example:

```python
### mathweb/flask/app.py
<<<<<<< SEARCH
from flask import Flask
=======
import math
from flask import Flask
>>>>>>> REPLACE
```
</solution>

Please note that the *SEARCH/REPLACE* edit REQUIRES PROPER INDENTATION. If you would like to add the line '        print(x)', you must fully write that out, with all those spaces before the code!
Wrap each *SEARCH/REPLACE* edit in a code block as shown in the example above. If you have multiple *SEARCH/REPLACE* edits, use a separate code block for each one.
user