import gzip
import os
import shutil
from bz2 import BZ2File
from importlib import resources
from io import BytesIO
from tempfile import NamedTemporaryFile

import numpy as np
import pytest
import scipy.sparse as sp

import sklearn
from sklearn.datasets import dump_svmlight_file, load_svmlight_file, load_svmlight_files
from sklearn.utils._testing import (
    assert_allclose,
    assert_array_almost_equal,
    assert_array_equal,
    create_memmap_backed_data,
)
from sklearn.utils.fixes import CSR_CONTAINERS

TEST_DATA_MODULE = "sklearn.datasets.tests.data"
datafile = "svmlight_classification.txt"
multifile = "svmlight_multilabel.txt"
invalidfile = "svmlight_invalid.txt"
invalidfile2 = "svmlight_invalid_order.txt"


def _svmlight_local_test_file_path(filename):
    return resources.files(TEST_DATA_MODULE) / filename


def _load_svmlight_local_test_file(filename, **kwargs):
    """
    Helper to load resource `filename` with `importlib.resources`
    """
    data_path = _svmlight_local_test_file_path(filename)
    with data_path.open("rb") as f:
        return load_svmlight_file(f, **kwargs)


def test_load_svmlight_file():
    X, y = _load_svmlight_local_test_file(datafile)

    # test X's shape
    assert X.indptr.shape[0] == 7
    assert X.shape[0] == 6
    assert X.shape[1] == 21
    assert y.shape[0] == 6

    # test X's non-zero values
    for i, j, val in (
        (0, 2, 2.5),
        (0, 10, -5.2),
        (0, 15, 1.5),
        (1, 5, 1.0),
        (1, 12, -3),
        (2, 20, 27),
    ):
        assert X[i, j] == val

    # tests X's zero values
    assert X[0, 3] == 0
    assert X[0, 5] == 0
    assert X[1, 8] == 0
    assert X[1, 16] == 0
    assert X[2, 18] == 0

    # test can change X's values
    X[0, 2] *= 2
    assert X[0, 2] == 5

    # test y
    assert_array_equal(y, [1, 2, 3, 4, 1, 2])


def test_load_svmlight_file_fd():
    # test loading from file descriptor

    # GH20081: testing equality between path-based and
    # fd-based load_svmlight_file

    data_path = resources.files(TEST_DATA_MODULE) / datafile
    data_path = str(data_path)
    X1, y1 = load_svmlight_file(data_path)

    fd = os.open(data_path, os.O_RDONLY)
    try:
        X2, y2 = load_svmlight_file(fd)
        assert_array_almost_equal(X1.data, X2.data)
        assert_array_almost_equal(y1, y2)
    finally:
        os.close(fd)


def test_load_svmlight_pathlib():
    # test loading from file descriptor
    data_path = _svmlight_local_test_file_path(datafile)
    X1, y1 = load_svmlight_file(str(data_path))
    X2, y2 = load_svmlight_file(data_path)

    assert_allclose(X1.data, X2.data)
    assert_allclose(y1, y2)


def test_load_svmlight_file_multilabel():
    X, y = _load_svmlight_local_test_file(multifile, multilabel=True)
    assert y == [(0, 1), (2,), (), (1, 2)]


def test_load_svmlight_files():
    data_path = _svmlight_local_test_file_path(datafile)
    X_train, y_train, X_test, y_test = load_svmlight_files(
        [str(data_path)] * 2, dtype=np.float32
    )
    assert_array_equal(X_train.toarray(), X_test.toarray())
    assert_array_almost_equal(y_train, y_test)
    assert X_train.dtype == np.float32
    assert X_test.dtype == np.float32

    X1, y1, X2, y2, X3, y3 = load_svmlight_files([str(data_path)] * 3, dtype=np.float64)
    assert X1.dtype == X2.dtype
    assert X2.dtype == X3.dtype
    assert X3.dtype == np.float64


def test_load_svmlight_file_n_features():
    X, y = _load_svmlight_local_test_file(datafile, n_features=22)

    # test X'shape
    assert X.indptr.shape[0] == 7
    assert X.shape[0] == 6
    assert X.shape[1] == 22

    # test X's non-zero values
    for i, j, val in ((0, 2, 2.5), (0, 10, -5.2), (1, 5, 1.0), (1, 12, -3)):
        assert X[i, j] == val

    # 21 features in file
    with pytest.raises(ValueError):
        _load_svmlight_local_test_file(datafile, n_features=20)


def test_load_compressed():
    X, y = _load_svmlight_local_test_file(datafile)

    with NamedTemporaryFile(prefix="sklearn-test", suffix=".gz") as tmp:
        tmp.close()  # necessary under windows
        with _svmlight_local_test_file_path(datafile).open("rb") as f:
            with gzip.open(tmp.name, "wb") as fh_out:
                shutil.copyfileobj(f, fh_out)
        Xgz, ygz = load_svmlight_file(tmp.name)
        # because we "close" it manually and write to it,
        # we need to remove it manually.
        os.remove(tmp.name)
    assert_array_almost_equal(X.toarray(), Xgz.toarray())
    assert_array_almost_equal(y, ygz)

    with NamedTemporaryFile(prefix="sklearn-test", suffix=".bz2") as tmp:
        tmp.close()  # necessary under windows
        with _svmlight_local_test_file_path(datafile).open("rb") as f:
            with BZ2File(tmp.name, "wb") as fh_out:
                shutil.copyfileobj(f, fh_out)
        Xbz, ybz = load_svmlight_file(tmp.name)
        # because we "close" it manually and write to it,
        # we need to remove it manually.
        os.remove(tmp.name)
    assert_array_almost_equal(X.toarray(), Xbz.toarray())
    assert_array_almost_equal(y, ybz)


def test_load_invalid_file():
    with pytest.raises(ValueError):
        _load_svmlight_local_test_file(invalidfile)


def test_load_invalid_order_file():
    with pytest.raises(ValueError):
        _load_svmlight_local_test_file(invalidfile2)


def test_load_zero_based():
    f = BytesIO(b"-1 4:1.\n1 0:1\n")
    with pytest.raises(ValueError):
        load_svmlight_file(f, zero_based=False)


def test_load_zero_based_auto():
    data1 = b"-1 1:1 2:2 3:3\n"
    data2 = b"-1 0:0 1:1\n"

    f1 = BytesIO(data1)
    X, y = load_svmlight_file(f1, zero_based="auto")
    assert X.shape == (1, 3)

    f1 = BytesIO(data1)
    f2 = BytesIO(data2)
    X1, y1, X2, y2 = load_svmlight_files([f1, f2], zero_based="auto")
    assert X1.shape == (1, 4)
    assert X2.shape == (1, 4)


def test_load_with_qid():
    # load svmfile with qid attribute
    data = b"""
    3 qid:1 1:0.53 2:0.12
    2 qid:1 1:0.13 2:0.1
    7 qid:2 1:0.87 2:0.12"""
    X, y = load_svmlight_file(BytesIO(data), query_id=False)
    assert_array_equal(y, [3, 2, 7])
    assert_array_equal(X.toarray(), [[0.53, 0.12], [0.13, 0.1], [0.87, 0.12]])
    res1 = load_svmlight_files([BytesIO(data)], query_id=True)
    res2 = load_svmlight_file(BytesIO(data), query_id=True)
    for X, y, qid in (res1, res2):
        assert_array_equal(y, [3, 2, 7])
        assert_array_equal(qid, [1, 1, 2])
        assert_array_equal(X.toarray(), [[0.53, 0.12], [0.13, 0.1], [0.87, 0.12]])


@pytest.mark.skip(
    "testing the overflow of 32 bit sparse indexing requires a large amount of memory"
)
def test_load_large_qid():
    """
    load large libsvm / svmlight file with qid attribute. Tests 64-bit query ID
    """
    data = b"\n".join(
        (
            "3 qid:{0} 1:0.53 2:0.12\n2 qid:{0} 1:0.13 2:0.1".format(i).encode()
            for i in range(1, 40 * 1000 * 1000)
        )
    )
    X, y, qid = load_svmlight_file(BytesIO(data), query_id=True)
    assert_array_equal(y[-4:], [3, 2, 3, 2])
    assert_array_equal(np.unique(qid), np.arange(1, 40 * 1000 * 1000))


def test_load_invalid_file2():
    with pytest.raises(ValueError):
        data_path = _svmlight_local_test_file_path(datafile)
        invalid_path = _svmlight_local_test_file_path(invalidfile)
        load_svmlight_files([str(data_path), str(invalid_path), str(data_path)])


def test_not_a_filename():
    # in python 3 integers are valid file opening arguments (taken as unix
    # file descriptors)
    with pytest.raises(TypeError):
        load_svmlight_file(0.42)


def test_invalid_filename():
    with pytest.raises(OSError):
        load_svmlight_file("trou pic nic douille")


@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
def test_dump(csr_container):
    X_sparse, y_dense = _load_svmlight_local_test_file(datafile)
    X_dense = X_sparse.toarray()
    y_sparse = csr_container(np.atleast_2d(y_dense))

    # slicing a csr_matrix can unsort its .indices, so test that we sort
    # those correctly
    X_sliced = X_sparse[np.arange(X_sparse.shape[0])]
    y_sliced = y_sparse[np.arange(y_sparse.shape[0])]

    for X in (X_sparse, X_dense, X_sliced):
        for y in (y_sparse, y_dense, y_sliced):
            for zero_based in (True, False):
                for dtype in [np.float32, np.float64, np.int32, np.int64]:
                    f = BytesIO()
                    # we need to pass a comment to get the version info in;
                    # LibSVM doesn't grok comments so they're not put in by
                    # default anymore.

                    if sp.issparse(y) and y.shape[0] == 1:
                        # make sure y's shape is: (n_samples, n_labels)
                        # when it is sparse
                        y = y.T

                    # Note: with dtype=np.int32 we are performing unsafe casts,
                    # where X.astype(dtype) overflows. The result is
                    # then platform dependent and X_dense.astype(dtype) may be
                    # different from X_sparse.astype(dtype).asarray().
                    X_input = X.astype(dtype)

                    dump_svmlight_file(
                        X_input, y, f, comment="test", zero_based=zero_based
                    )
                    f.seek(0)

                    comment = f.readline()
                    comment = str(comment, "utf-8")

                    assert "scikit-learn %s" % sklearn.__version__ in comment

                    comment = f.readline()
                    comment = str(comment, "utf-8")

                    assert ["one", "zero"][zero_based] + "-based" in comment

                    X2, y2 = load_svmlight_file(f, dtype=dtype, zero_based=zero_based)
                    assert X2.dtype == dtype
                    assert_array_equal(X2.sorted_indices().indices, X2.indices)

                    X2_dense = X2.toarray()
                    if sp.issparse(X_input):
                        X_input_dense = X_input.toarray()
                    else:
                        X_input_dense = X_input

                    if dtype == np.float32:
                        # allow a rounding error at the last decimal place
                        assert_array_almost_equal(X_input_dense, X2_dense, 4)
                        assert_array_almost_equal(
                            y_dense.astype(dtype, copy=False), y2, 4
                        )
                    else:
                        # allow a rounding error at the last decimal place
                        assert_array_almost_equal(X_input_dense, X2_dense, 15)
                        assert_array_almost_equal(
                            y_dense.astype(dtype, copy=False), y2, 15
                        )


@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
def test_dump_multilabel(csr_container):
    X = [[1, 0, 3, 0, 5], [0, 0, 0, 0, 0], [0, 5, 0, 1, 0]]
    y_dense = [[0, 1, 0], [1, 0, 1], [1, 1, 0]]
    y_sparse = csr_container(y_dense)
    for y in [y_dense, y_sparse]:
        f = BytesIO()
        dump_svmlight_file(X, y, f, multilabel=True)
        f.seek(0)
        # make sure it dumps multilabel correctly
        assert f.readline() == b"1 0:1 2:3 4:5\n"
        assert f.readline() == b"0,2 \n"
        assert f.readline() == b"0,1 1:5 3:1\n"


def test_dump_concise():
    one = 1
    two = 2.1
    three = 3.01
    exact = 1.000000000000001
    # loses the last decimal place
    almost = 1.0000000000000001
    X = [
        [one, two, three, exact, almost],
        [1e9, 2e18, 3e27, 0, 0],
        [0, 0, 0, 0, 0],
        [0, 0, 0, 0, 0],
        [0, 0, 0, 0, 0],
    ]
    y = [one, two, three, exact, almost]
    f = BytesIO()
    dump_svmlight_file(X, y, f)
    f.seek(0)
    # make sure it's using the most concise format possible
    assert f.readline() == b"1 0:1 1:2.1 2:3.01 3:1.000000000000001 4:1\n"
    assert f.readline() == b"2.1 0:1000000000 1:2e+18 2:3e+27\n"
    assert f.readline() == b"3.01 \n"
    assert f.readline() == b"1.000000000000001 \n"
    assert f.readline() == b"1 \n"
    f.seek(0)
    # make sure it's correct too :)
    X2, y2 = load_svmlight_file(f)
    assert_array_almost_equal(X, X2.toarray())
    assert_array_almost_equal(y, y2)


def test_dump_comment():
    X, y = _load_svmlight_local_test_file(datafile)
    X = X.toarray()

    f = BytesIO()
    ascii_comment = "This is a comment\nspanning multiple lines."
    dump_svmlight_file(X, y, f, comment=ascii_comment, zero_based=False)
    f.seek(0)

    X2, y2 = load_svmlight_file(f, zero_based=False)
    assert_array_almost_equal(X, X2.toarray())
    assert_array_almost_equal(y, y2)

    # XXX we have to update this to support Python 3.x
    utf8_comment = b"It is true that\n\xc2\xbd\xc2\xb2 = \xc2\xbc"
    f = BytesIO()
    with pytest.raises(UnicodeDecodeError):
        dump_svmlight_file(X, y, f, comment=utf8_comment)

    unicode_comment = utf8_comment.decode("utf-8")
    f = BytesIO()
    dump_svmlight_file(X, y, f, comment=unicode_comment, zero_based=False)
    f.seek(0)

    X2, y2 = load_svmlight_file(f, zero_based=False)
    assert_array_almost_equal(X, X2.toarray())
    assert_array_almost_equal(y, y2)

    f = BytesIO()
    with pytest.raises(ValueError):
        dump_svmlight_file(X, y, f, comment="I've got a \0.")


def test_dump_invalid():
    X, y = _load_svmlight_local_test_file(datafile)

    f = BytesIO()
    y2d = [y]
    with pytest.raises(ValueError):
        dump_svmlight_file(X, y2d, f)

    f = BytesIO()
    with pytest.raises(ValueError):
        dump_svmlight_file(X, y[:-1], f)


def test_dump_query_id():
    # test dumping a file with query_id
    X, y = _load_svmlight_local_test_file(datafile)
    X = X.toarray()
    query_id = np.arange(X.shape[0]) // 2
    f = BytesIO()
    dump_svmlight_file(X, y, f, query_id=query_id, zero_based=True)

    f.seek(0)
    X1, y1, query_id1 = load_svmlight_file(f, query_id=True, zero_based=True)
    assert_array_almost_equal(X, X1.toarray())
    assert_array_almost_equal(y, y1)
    assert_array_almost_equal(query_id, query_id1)


def test_load_with_long_qid():
    # load svmfile with longint qid attribute
    data = b"""
    1 qid:0 0:1 1:2 2:3
    0 qid:72048431380967004 0:1440446648 1:72048431380967004 2:236784985
    0 qid:-9223372036854775807 0:1440446648 1:72048431380967004 2:236784985
    3 qid:9223372036854775807  0:1440446648 1:72048431380967004 2:236784985"""
    X, y, qid = load_svmlight_file(BytesIO(data), query_id=True)

    true_X = [
        [1, 2, 3],
        [1440446648, 72048431380967004, 236784985],
        [1440446648, 72048431380967004, 236784985],
        [1440446648, 72048431380967004, 236784985],
    ]

    true_y = [1, 0, 0, 3]
    trueQID = [0, 72048431380967004, -9223372036854775807, 9223372036854775807]
    assert_array_equal(y, true_y)
    assert_array_equal(X.toarray(), true_X)
    assert_array_equal(qid, trueQID)

    f = BytesIO()
    dump_svmlight_file(X, y, f, query_id=qid, zero_based=True)
    f.seek(0)
    X, y, qid = load_svmlight_file(f, query_id=True, zero_based=True)
    assert_array_equal(y, true_y)
    assert_array_equal(X.toarray(), true_X)
    assert_array_equal(qid, trueQID)

    f.seek(0)
    X, y = load_svmlight_file(f, query_id=False, zero_based=True)
    assert_array_equal(y, true_y)
    assert_array_equal(X.toarray(), true_X)


@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
def test_load_zeros(csr_container):
    f = BytesIO()
    true_X = csr_container(np.zeros(shape=(3, 4)))
    true_y = np.array([0, 1, 0])
    dump_svmlight_file(true_X, true_y, f)

    for zero_based in ["auto", True, False]:
        f.seek(0)
        X, y = load_svmlight_file(f, n_features=4, zero_based=zero_based)
        assert_array_almost_equal(y, true_y)
        assert_array_almost_equal(X.toarray(), true_X.toarray())


@pytest.mark.parametrize("sparsity", [0, 0.1, 0.5, 0.99, 1])
@pytest.mark.parametrize("n_samples", [13, 101])
@pytest.mark.parametrize("n_features", [2, 7, 41])
@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
def test_load_with_offsets(sparsity, n_samples, n_features, csr_container):
    rng = np.random.RandomState(0)
    X = rng.uniform(low=0.0, high=1.0, size=(n_samples, n_features))
    if sparsity:
        X[X < sparsity] = 0.0
    X = csr_container(X)
    y = rng.randint(low=0, high=2, size=n_samples)

    f = BytesIO()
    dump_svmlight_file(X, y, f)
    f.seek(0)

    size = len(f.getvalue())

    # put some marks that are likely to happen anywhere in a row
    mark_0 = 0
    mark_1 = size // 3
    length_0 = mark_1 - mark_0
    mark_2 = 4 * size // 5
    length_1 = mark_2 - mark_1

    # load the original sparse matrix into 3 independent CSR matrices
    X_0, y_0 = load_svmlight_file(
        f, n_features=n_features, offset=mark_0, length=length_0
    )
    X_1, y_1 = load_svmlight_file(
        f, n_features=n_features, offset=mark_1, length=length_1
    )
    X_2, y_2 = load_svmlight_file(f, n_features=n_features, offset=mark_2)

    y_concat = np.concatenate([y_0, y_1, y_2])
    X_concat = sp.vstack([X_0, X_1, X_2])
    assert_array_almost_equal(y, y_concat)
    assert_array_almost_equal(X.toarray(), X_concat.toarray())


@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
def test_load_offset_exhaustive_splits(csr_container):
    rng = np.random.RandomState(0)
    X = np.array(
        [
            [0, 0, 0, 0, 0, 0],
            [1, 2, 3, 4, 0, 6],
            [1, 2, 3, 4, 0, 6],
            [0, 0, 0, 0, 0, 0],
            [1, 0, 3, 0, 0, 0],
            [0, 0, 0, 0, 0, 1],
            [1, 0, 0, 0, 0, 0],
        ]
    )
    X = csr_container(X)
    n_samples, n_features = X.shape
    y = rng.randint(low=0, high=2, size=n_samples)
    query_id = np.arange(n_samples) // 2

    f = BytesIO()
    dump_svmlight_file(X, y, f, query_id=query_id)
    f.seek(0)

    size = len(f.getvalue())

    # load the same data in 2 parts with all the possible byte offsets to
    # locate the split so has to test for particular boundary cases
    for mark in range(size):
        f.seek(0)
        X_0, y_0, q_0 = load_svmlight_file(
            f, n_features=n_features, query_id=True, offset=0, length=mark
        )
        X_1, y_1, q_1 = load_svmlight_file(
            f, n_features=n_features, query_id=True, offset=mark, length=-1
        )
        q_concat = np.concatenate([q_0, q_1])
        y_concat = np.concatenate([y_0, y_1])
        X_concat = sp.vstack([X_0, X_1])
        assert_array_almost_equal(y, y_concat)
        assert_array_equal(query_id, q_concat)
        assert_array_almost_equal(X.toarray(), X_concat.toarray())


def test_load_with_offsets_error():
    with pytest.raises(ValueError, match="n_features is required"):
        _load_svmlight_local_test_file(datafile, offset=3, length=3)


@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
def test_multilabel_y_explicit_zeros(tmp_path, csr_container):
    """
    Ensure that if y contains explicit zeros (i.e. elements of y.data equal to
    0) then those explicit zeros are not encoded.
    """
    save_path = str(tmp_path / "svm_explicit_zero")
    rng = np.random.RandomState(42)
    X = rng.randn(3, 5).astype(np.float64)
    indptr = np.array([0, 2, 3, 6])
    indices = np.array([0, 2, 2, 0, 1, 2])
    # The first and last element are explicit zeros.
    data = np.array([0, 1, 1, 1, 1, 0])
    y = csr_container((data, indices, indptr), shape=(3, 3))
    # y as a dense array would look like
    # [[0, 0, 1],
    #  [0, 0, 1],
    #  [1, 1, 0]]

    dump_svmlight_file(X, y, save_path, multilabel=True)

    _, y_load = load_svmlight_file(save_path, multilabel=True)
    y_true = [(2.0,), (2.0,), (0.0, 1.0)]
    assert y_load == y_true


def test_dump_read_only(tmp_path):
    """Ensure that there is no ValueError when dumping a read-only `X`.

    Non-regression test for:
    https://github.com/scikit-learn/scikit-learn/issues/28026
    """
    rng = np.random.RandomState(42)
    X = rng.randn(5, 2)
    y = rng.randn(5)

    # Convert to memmap-backed which are read-only
    X, y = create_memmap_backed_data([X, y])

    save_path = str(tmp_path / "svm_read_only")
    dump_svmlight_file(X, y, save_path)
