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from distutils.version import LooseVersion
import unittest
import decimal


import numpy as np
import pandas as pd

from pyspark import pandas as ps
from pyspark.testing.pandasutils import ComparisonTestBase
from pyspark.testing.sqlutils import SQLTestUtils


class FrameCovMixin:
    @unittest.skipIf(
        LooseVersion(pd.__version__) >= LooseVersion("2.0.0"),
        "TODO(SPARK-43809): Enable DataFrameSlowTests.test_cov for pandas 2.0.0.",
    )
    def test_cov(self):
        # SPARK-36396: Implement DataFrame.cov

        # int
        pdf = pd.DataFrame([(1, 2), (0, 3), (2, 0), (1, 1)], columns=["a", "b"])
        psdf = ps.from_pandas(pdf)
        self.assert_eq(pdf.cov(), psdf.cov(), almost=True)
        self.assert_eq(pdf.cov(min_periods=4), psdf.cov(min_periods=4), almost=True)
        self.assert_eq(pdf.cov(min_periods=5), psdf.cov(min_periods=5))

        # ddof
        with self.assertRaisesRegex(TypeError, "ddof must be integer"):
            psdf.cov(ddof="ddof")
        for ddof in [-1, 0, 2]:
            self.assert_eq(pdf.cov(ddof=ddof), psdf.cov(ddof=ddof), almost=True)
            self.assert_eq(
                pdf.cov(min_periods=4, ddof=ddof), psdf.cov(min_periods=4, ddof=ddof), almost=True
            )
            self.assert_eq(pdf.cov(min_periods=5, ddof=ddof), psdf.cov(min_periods=5, ddof=ddof))

        # bool
        pdf = pd.DataFrame(
            {
                "a": [1, np.nan, 3, 4],
                "b": [True, False, False, True],
                "c": [True, True, False, True],
            }
        )
        psdf = ps.from_pandas(pdf)
        self.assert_eq(pdf.cov(), psdf.cov(), almost=True)
        self.assert_eq(pdf.cov(min_periods=4), psdf.cov(min_periods=4), almost=True)
        self.assert_eq(pdf.cov(min_periods=5), psdf.cov(min_periods=5))

        # extension dtype
        if LooseVersion(pd.__version__) >= LooseVersion("1.2"):
            numeric_dtypes = ["Int8", "Int16", "Int32", "Int64", "Float32", "Float64", "float"]
            boolean_dtypes = ["boolean", "bool"]
        else:
            numeric_dtypes = ["Int8", "Int16", "Int32", "Int64", "float"]
            boolean_dtypes = ["boolean", "bool"]

        sers = [pd.Series([1, 2, 3, None], dtype=dtype) for dtype in numeric_dtypes]
        sers += [pd.Series([True, False, True, None], dtype=dtype) for dtype in boolean_dtypes]
        sers.append(pd.Series([decimal.Decimal(1), decimal.Decimal(2), decimal.Decimal(3), None]))

        pdf = pd.concat(sers, axis=1)
        pdf.columns = [dtype for dtype in numeric_dtypes + boolean_dtypes] + ["decimal"]
        psdf = ps.from_pandas(pdf)

        if LooseVersion(pd.__version__) >= LooseVersion("1.2"):
            self.assert_eq(pdf.cov(), psdf.cov(), almost=True)
            self.assert_eq(pdf.cov(min_periods=3), psdf.cov(min_periods=3), almost=True)
            self.assert_eq(pdf.cov(min_periods=4), psdf.cov(min_periods=4))
        else:
            test_types = [
                "Int8",
                "Int16",
                "Int32",
                "Int64",
                "float",
                "boolean",
                "bool",
            ]
            expected = pd.DataFrame(
                data=[
                    [1.0, 1.0, 1.0, 1.0, 1.0, 0.0000000, 0.0000000],
                    [1.0, 1.0, 1.0, 1.0, 1.0, 0.0000000, 0.0000000],
                    [1.0, 1.0, 1.0, 1.0, 1.0, 0.0000000, 0.0000000],
                    [1.0, 1.0, 1.0, 1.0, 1.0, 0.0000000, 0.0000000],
                    [1.0, 1.0, 1.0, 1.0, 1.0, 0.0000000, 0.0000000],
                    [0.0, 0.0, 0.0, 0.0, 0.0, 0.3333333, 0.3333333],
                    [0.0, 0.0, 0.0, 0.0, 0.0, 0.3333333, 0.3333333],
                ],
                index=test_types,
                columns=test_types,
            )
            self.assert_eq(expected, psdf.cov(), almost=True)

        # string column
        pdf = pd.DataFrame(
            [(1, 2, "a", 1), (0, 3, "b", 1), (2, 0, "c", 9), (1, 1, "d", 1)],
            columns=["a", "b", "c", "d"],
        )
        psdf = ps.from_pandas(pdf)
        self.assert_eq(pdf.cov(), psdf.cov(), almost=True)
        self.assert_eq(pdf.cov(min_periods=4), psdf.cov(min_periods=4), almost=True)
        self.assert_eq(pdf.cov(min_periods=5), psdf.cov(min_periods=5))

        # nan
        np.random.seed(42)
        pdf = pd.DataFrame(np.random.randn(20, 3), columns=["a", "b", "c"])
        pdf.loc[pdf.index[:5], "a"] = np.nan
        pdf.loc[pdf.index[5:10], "b"] = np.nan
        psdf = ps.from_pandas(pdf)
        self.assert_eq(pdf.cov(min_periods=11), psdf.cov(min_periods=11), almost=True)
        self.assert_eq(pdf.cov(min_periods=10), psdf.cov(min_periods=10), almost=True)

        # return empty DataFrame
        pdf = pd.DataFrame([("1", "2"), ("0", "3"), ("2", "0"), ("1", "1")], columns=["a", "b"])
        psdf = ps.from_pandas(pdf)
        self.assert_eq(pdf.cov(), psdf.cov())


class FrameCovTests(FrameCovMixin, ComparisonTestBase, SQLTestUtils):
    pass


if __name__ == "__main__":
    from pyspark.pandas.tests.computation.test_cov import *  # noqa: F401

    try:
        import xmlrunner

        testRunner = xmlrunner.XMLTestRunner(output="target/test-reports", verbosity=2)
    except ImportError:
        testRunner = None
    unittest.main(testRunner=testRunner, verbosity=2)