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# -*- coding: utf-8 -*-
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import numpy as np
import pytest

import dpnp

ROUNDS = 30
ITERATIONS = 4

NNUMBERS = 2**26


@pytest.mark.parametrize(
    "function", [dpnp.random.beta, np.random.beta], ids=["dpnp", "numpy"]
)
def test_beta(benchmark, function):
    result = benchmark.pedantic(
        target=function,
        args=(
            4.0,
            5.0,
            NNUMBERS,
        ),
        rounds=ROUNDS,
        iterations=ITERATIONS,
    )


@pytest.mark.parametrize(
    "function",
    [dpnp.random.exponential, np.random.exponential],
    ids=["dpnp", "numpy"],
)
def test_exponential(benchmark, function):
    result = benchmark.pedantic(
        target=function,
        args=(
            4.0,
            NNUMBERS,
        ),
        rounds=ROUNDS,
        iterations=ITERATIONS,
    )


@pytest.mark.parametrize(
    "function", [dpnp.random.gamma, np.random.gamma], ids=["dpnp", "numpy"]
)
def test_gamma(benchmark, function):
    result = benchmark.pedantic(
        target=function,
        args=(
            2.0,
            4.0,
            NNUMBERS,
        ),
        rounds=ROUNDS,
        iterations=ITERATIONS,
    )


@pytest.mark.parametrize(
    "function", [dpnp.random.normal, np.random.normal], ids=["dpnp", "numpy"]
)
def test_normal(benchmark, function):
    result = benchmark.pedantic(
        target=function,
        args=(
            0.0,
            1.0,
            NNUMBERS,
        ),
        rounds=ROUNDS,
        iterations=ITERATIONS,
    )


@pytest.mark.parametrize(
    "function", [dpnp.random.uniform, np.random.uniform], ids=["dpnp", "numpy"]
)
def test_uniform(benchmark, function):
    result = benchmark.pedantic(
        target=function,
        args=(
            0.0,
            1.0,
            NNUMBERS,
        ),
        rounds=ROUNDS,
        iterations=ITERATIONS,
    )
