import numpy
import pytest

from cupy import testing
import cupyx.scipy.special  # NOQA


@testing.gpu
@testing.with_requires('scipy')
class TestGamma:

    @pytest.mark.parametrize('function', ['gamma', 'loggamma', 'rgamma'])
    @testing.for_all_dtypes(no_complex=True)
    @testing.numpy_cupy_allclose(atol=1e-5, scipy_name='scp')
    def test_arange(self, xp, scp, dtype, function):
        import scipy.special  # NOQA

        a = testing.shaped_arange((2, 3), xp, dtype)
        func = getattr(scp.special, function)
        return func(a)

    @pytest.mark.parametrize('function', ['gamma', 'loggamma', 'rgamma'])
    @testing.for_all_dtypes(no_bool=True)
    @testing.numpy_cupy_allclose(atol=1e-5, rtol=1e-5, scipy_name='scp')
    def test_linspace(self, xp, scp, dtype, function):
        import scipy.special  # NOQA

        a = numpy.linspace(-30, 30, 1000, dtype=dtype)
        if a.dtype.kind == 'c':
            a -= 1j * a
        a = xp.asarray(a)
        func = getattr(scp.special, function)
        return func(a)

    @pytest.mark.parametrize('function', ['gamma', 'loggamma', 'rgamma'])
    @testing.for_all_dtypes()
    @testing.numpy_cupy_allclose(atol=1e-2, rtol=1e-3, scipy_name='scp')
    def test_scalar(self, xp, scp, dtype, function):
        import scipy.special  # NOQA

        if xp.dtype(dtype).kind == 'c':
            val = dtype(1.5 + 1.0j)
        else:
            val = dtype(1.5)
        func = getattr(scp.special, function)
        return func(val)

    @pytest.mark.parametrize('function', ['gamma', 'loggamma', 'rgamma'])
    @testing.for_all_dtypes()
    @testing.numpy_cupy_allclose(atol=1e-2, rtol=1e-3, scipy_name='scp')
    def test_inf_and_nan(self, xp, scp, dtype, function):
        import scipy.special  # NOQA

        a = numpy.array([-numpy.inf, numpy.nan, numpy.inf]).astype(dtype)
        a = xp.asarray(a)
        func = getattr(scp.special, function)
        return func(a)

import numpy
import pytest

from cupy import testing
import cupyx.scipy.special  # NOQA


@testing.gpu
@testing.with_requires('scipy')
class TestGamma:

    @pytest.mark.parametrize('function', ['gamma', 'loggamma', 'rgamma'])
    @testing.for_all_dtypes(no_complex=True)
    @testing.numpy_cupy_allclose(atol=1e-5, scipy_name='scp')
    def test_arange(self, xp, scp, dtype, function):
        import scipy.special  # NOQA

        a = testing.shaped_arange((2, 3), xp, dtype)
        func = getattr(scp.special, function)
        return func(a)

    @pytest.mark.parametrize('function', ['gamma', 'loggamma', 'rgamma'])
    @testing.for_all_dtypes(no_bool=True)
    @testing.numpy_cupy_allclose(atol=1e-5, rtol=1e-5, scipy_name='scp')
    def test_linspace(self, xp, scp, dtype, function):
        import scipy.special  # NOQA

        a = numpy.linspace(-30, 30, 1000, dtype=dtype)
        if a.dtype.kind == 'c':
            a -= 1j * a
        a = xp.asarray(a)
        func = getattr(scp.special, function)
        return func(a)

    @pytest.mark.parametrize('function', ['gamma', 'loggamma', 'rgamma'])
    @testing.for_all_dtypes()
    @testing.numpy_cupy_allclose(atol=1e-2, rtol=1e-3, scipy_name='scp')
    def test_scalar(self, xp, scp, dtype, function):
        import scipy.special  # NOQA

        if xp.dtype(dtype).kind == 'c':
            val = dtype(1.5 + 1.0j)
        else:
            val = dtype(1.5)
        func = getattr(scp.special, function)
        return func(val)

    # skip on SciPy < 1.5 due to: https://github.com/scipy/scipy/issues/11315
    @pytest.mark.parametrize('function', ['gamma', 'loggamma', 'rgamma'])
    @testing.for_all_dtypes()
    @testing.numpy_cupy_allclose(atol=1e-2, rtol=1e-3, scipy_name='scp')
    @testing.with_requires('scipy>=1.5.0')
    def test_inf_and_nan(self, xp, scp, dtype, function):
        import scipy.special  # NOQA

        a = numpy.array([-numpy.inf, numpy.nan, numpy.inf]).astype(dtype)
        a = xp.asarray(a)
        func = getattr(scp.special, function)
        return func(a)

Add skip to test_inf_and_nan for SciPy<1.4