# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause

from itertools import product

import numpy as np
import pytest
from numpy.testing import assert_array_equal

from sklearn.datasets import load_iris
from sklearn.utils._seq_dataset import (
    ArrayDataset32,
    ArrayDataset64,
    CSRDataset32,
    CSRDataset64,
)
from sklearn.utils._testing import assert_allclose
from sklearn.utils.fixes import CSR_CONTAINERS

iris = load_iris()
X64 = iris.data.astype(np.float64)
y64 = iris.target.astype(np.float64)
sample_weight64 = np.arange(y64.size, dtype=np.float64)

X32 = iris.data.astype(np.float32)
y32 = iris.target.astype(np.float32)
sample_weight32 = np.arange(y32.size, dtype=np.float32)

floating = [np.float32, np.float64]


def assert_csr_equal_values(current, expected):
    current.eliminate_zeros()
    expected.eliminate_zeros()
    expected = expected.astype(current.dtype)
    assert current.shape[0] == expected.shape[0]
    assert current.shape[1] == expected.shape[1]
    assert_array_equal(current.data, expected.data)
    assert_array_equal(current.indices, expected.indices)
    assert_array_equal(current.indptr, expected.indptr)


def _make_dense_dataset(float_dtype):
    if float_dtype == np.float32:
        return ArrayDataset32(X32, y32, sample_weight32, seed=42)
    return ArrayDataset64(X64, y64, sample_weight64, seed=42)


def _make_sparse_dataset(csr_container, float_dtype):
    if float_dtype == np.float32:
        X, y, sample_weight, csr_dataset = X32, y32, sample_weight32, CSRDataset32
    else:
        X, y, sample_weight, csr_dataset = X64, y64, sample_weight64, CSRDataset64
    X = csr_container(X)
    return csr_dataset(X.data, X.indptr, X.indices, y, sample_weight, seed=42)


def _make_dense_datasets():
    return [_make_dense_dataset(float_dtype) for float_dtype in floating]


def _make_sparse_datasets():
    return [
        _make_sparse_dataset(csr_container, float_dtype)
        for csr_container, float_dtype in product(CSR_CONTAINERS, floating)
    ]


def _make_fused_types_datasets():
    all_datasets = _make_dense_datasets() + _make_sparse_datasets()
    # group dataset by array types to get a tuple (float32, float64)
    return (all_datasets[idx : idx + 2] for idx in range(0, len(all_datasets), 2))


@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
@pytest.mark.parametrize("dataset", _make_dense_datasets() + _make_sparse_datasets())
def test_seq_dataset_basic_iteration(dataset, csr_container):
    NUMBER_OF_RUNS = 5
    X_csr64 = csr_container(X64)
    for _ in range(NUMBER_OF_RUNS):
        # next sample
        xi_, yi, swi, idx = dataset._next_py()
        xi = csr_container(xi_, shape=(1, X64.shape[1]))

        assert_csr_equal_values(xi, X_csr64[[idx]])
        assert yi == y64[idx]
        assert swi == sample_weight64[idx]

        # random sample
        xi_, yi, swi, idx = dataset._random_py()
        xi = csr_container(xi_, shape=(1, X64.shape[1]))

        assert_csr_equal_values(xi, X_csr64[[idx]])
        assert yi == y64[idx]
        assert swi == sample_weight64[idx]


@pytest.mark.parametrize(
    "dense_dataset,sparse_dataset",
    [
        (
            _make_dense_dataset(float_dtype),
            _make_sparse_dataset(csr_container, float_dtype),
        )
        for float_dtype, csr_container in product(floating, CSR_CONTAINERS)
    ],
)
def test_seq_dataset_shuffle(dense_dataset, sparse_dataset):
    # not shuffled
    for i in range(5):
        _, _, _, idx1 = dense_dataset._next_py()
        _, _, _, idx2 = sparse_dataset._next_py()
        assert idx1 == i
        assert idx2 == i

    for i in [132, 50, 9, 18, 58]:
        _, _, _, idx1 = dense_dataset._random_py()
        _, _, _, idx2 = sparse_dataset._random_py()
        assert idx1 == i
        assert idx2 == i

    seed = 77
    dense_dataset._shuffle_py(seed)
    sparse_dataset._shuffle_py(seed)

    idx_next = [63, 91, 148, 87, 29]
    idx_shuffle = [137, 125, 56, 121, 127]
    for i, j in zip(idx_next, idx_shuffle):
        _, _, _, idx1 = dense_dataset._next_py()
        _, _, _, idx2 = sparse_dataset._next_py()
        assert idx1 == i
        assert idx2 == i

        _, _, _, idx1 = dense_dataset._random_py()
        _, _, _, idx2 = sparse_dataset._random_py()
        assert idx1 == j
        assert idx2 == j


@pytest.mark.parametrize("dataset_32,dataset_64", _make_fused_types_datasets())
def test_fused_types_consistency(dataset_32, dataset_64):
    NUMBER_OF_RUNS = 5
    for _ in range(NUMBER_OF_RUNS):
        # next sample
        (xi_data32, _, _), yi32, _, _ = dataset_32._next_py()
        (xi_data64, _, _), yi64, _, _ = dataset_64._next_py()

        assert xi_data32.dtype == np.float32
        assert xi_data64.dtype == np.float64

        assert_allclose(xi_data64, xi_data32, rtol=1e-5)
        assert_allclose(yi64, yi32, rtol=1e-5)


def test_buffer_dtype_mismatch_error():
    with pytest.raises(ValueError, match="Buffer dtype mismatch"):
        ArrayDataset64(X32, y32, sample_weight32, seed=42),

    with pytest.raises(ValueError, match="Buffer dtype mismatch"):
        ArrayDataset32(X64, y64, sample_weight64, seed=42),

    for csr_container in CSR_CONTAINERS:
        X_csr32 = csr_container(X32)
        X_csr64 = csr_container(X64)
        with pytest.raises(ValueError, match="Buffer dtype mismatch"):
            CSRDataset64(
                X_csr32.data,
                X_csr32.indptr,
                X_csr32.indices,
                y32,
                sample_weight32,
                seed=42,
            ),

        with pytest.raises(ValueError, match="Buffer dtype mismatch"):
            CSRDataset32(
                X_csr64.data,
                X_csr64.indptr,
                X_csr64.indices,
                y64,
                sample_weight64,
                seed=42,
            ),