code: import contextlib
  import pickle
  import unittest
  import warnings
  
  import numpy
  import pytest
  try:
      import scipy.sparse
      scipy_available = True
  except ImportError:
      scipy_available = False
  
  import cupy
  from cupy.core import _accelerator
  from cupy import testing
  from cupyx.scipy import sparse
  
  
  def _make(xp, sp, dtype):
      data = xp.array([0, 1, 2, 3], dtype)
      indices = xp.array([0, 1, 3, 2], 'i')
      indptr = xp.array([0, 2, 3, 4], 'i')
      # 0, 1, 0, 0
      # 0, 0, 0, 2
      # 0, 0, 3, 0
      return sp.csr_matrix((data, indices, indptr), shape=(3, 4))
  
  
  def _make_complex(xp, sp, dtype):
      data = xp.array([0, 1, 2, 3], dtype)
      if dtype in [numpy.complex64, numpy.complex128]:
          data = data - 1j
      indices = xp.array([0, 1, 3, 2], 'i')
      indptr = xp.array([0, 2, 3, 4], 'i')
      # 0, 1 - 1j, 0, 0
      # 0, 0, 0, 2 - 1j
      # 0, 0, 3 - 1j, 0
      return sp.csr_matrix((data, indices, indptr), shape=(3, 4))
  
  
  def _make2(xp, sp, dtype):
      data = xp.array([1, 2, 3, 4], dtype)
      indices = xp.array([2, 1, 2, 2], 'i')
      indptr = xp.array([0, 1, 3, 4], 'i')
      # 0, 0, 1, 0
      # 0, 2, 3, 0
      # 0, 0, 4, 0
      return sp.csr_matrix((data, indices, indptr), shape=(3, 4))
  
  
  def _make3(xp, sp, dtype):
      data = xp.array([1, 2, 3, 4, 5], dtype)
      indices = xp.array([0, 2, 1, 0, 2], 'i')
      indptr = xp.array([0, 1, 3, 3, 5], 'i')
      # 1, 0, 0
      # 0, 3, 2
      # 0, 0, 0
      # 4, 0, 5
      return sp.csr_matrix((data, indices, indptr), shape=(4, 3))
  
  
  def _make4(xp, sp, dtype):
      data = xp.array([1, 2, 3, 4, 5, 6, 7, 8, 9], dtype)
      indices = xp.array([0, 2, 3, 0, 1, 3, 0, 1, 2], 'i')
      indptr = xp.array([0, 3, 6, 9], 'i')
      # 1, 0, 2, 3
      # 4, 5, 0, 6
      # 7, 8, 9, 0
      return sp.csr_matrix((data, indices, indptr), shape=(3, 4))
  
  
  def _make_unordered(xp, sp, dtype):
      data = xp.array([1, 2, 3, 4], dtype)
      indices = xp.array([1, 0, 1, 2], 'i')
      indptr = xp.array([0, 2, 3, 4], 'i')
      # 2, 1, 0, 0
      # 0, 3, 0, 0
      # 0, 0, 4, 0
      return sp.csr_matrix((data, indices, indptr), shape=(3, 4))
  
  
  def _make_duplicate(xp, sp, dtype):
      data = xp.array([0, 1, 3, 2, 4, 5], dtype)
      indices = xp.array([0, 0, 0, 2, 0, 2], 'i')
      indptr = xp.array([0, 3, 6, 6], 'i')
      # 4, 0, 0, 0
      # 4, 0, 7, 0
      # 0, 0, 0, 0
      return sp.csr_matrix((data, indices, indptr), shape=(3, 4))
  
  
  def _make_empty(xp, sp, dtype):
      data = xp.array([], dtype)
      indices = xp.array([], 'i')
      indptr = xp.array([0, 0, 0, 0], 'i')
      return sp.csr_matrix((data, indices, indptr), shape=(3, 4))
  
  
  def _make_square(xp, sp, dtype):
      data = xp.array([0, 1, 2, 3], dtype)
      indices = xp.array([0, 1, 0, 2], 'i')
      indptr = xp.array([0, 2, 3, 4], 'i')
      # 0, 1, 0
      # 2, 0, 0
      # 0, 0, 3
      return sp.csr_matrix((data, indices, indptr), shape=(3, 3))
  
  
  def _make_row(xp, sp, dtype):
      data = xp.array([1, 2, 3], dtype)
      indices = xp.array([0, 2, 3], 'i')
      indptr = xp.array([0, 3], 'i')
      # 1, 0, 2, 3
      return sp.csr_matrix((data, indices, indptr), shape=(1, 4))
  
  
  def _make_col(xp, sp, dtype):
      data = xp.array([1, 2], dtype)
      indices = xp.array([0, 0], 'i')
      indptr = xp.array([0, 1, 1, 2], 'i')
      # 1
      # 0
      # 2
      return sp.csr_matrix((data, indices, indptr), shape=(3, 1))
  
  
  def _make_shape(xp, sp, dtype):
      return sp.csr_matrix((3, 4), dtype=dtype)
  
  
  @testing.parameterize(*testing.product({
      'dtype': [numpy.float32, numpy.float64, numpy.complex64, numpy.complex128],
  }))
  class TestCsrMatrix(unittest.TestCase):
  
      def setUp(self):
          self.m = _make(cupy, sparse, self.dtype)
  
      def test_dtype(self):
          assert self.m.dtype == self.dtype
  
      def test_data(self):
          assert self.m.data.dtype == self.dtype
          testing.assert_array_equal(
              self.m.data, cupy.array([0, 1, 2, 3], self.dtype))
  
      def test_indices(self):
          assert self.m.indices.dtype == numpy.int32
          testing.assert_array_equal(
              self.m.indices, cupy.array([0, 1, 3, 2], self.dtype))
  
      def test_indptr(self):
          assert self.m.indptr.dtype == numpy.int32
          testing.assert_array_equal(
              self.m.indptr, cupy.array([0, 2, 3, 4], self.dtype))
  
      def test_init_copy(self):
          n = sparse.csr_matrix(self.m)
          assert n is not self.m
          cupy.testing.assert_array_equal(n.data, self.m.data)
          cupy.testing.assert_array_equal(n.indices, self.m.indices)
          cupy.testing.assert_array_equal(n.indptr, self.m.indptr)
          assert n.shape == self.m.shape
  
      def test_init_copy_other_sparse(self):
          n = sparse.csr_matrix(self.m.tocsc())
          cupy.testing.assert_array_equal(n.data, self.m.data)
          cupy.testing.assert_array_equal(n.indices, self.m.indices)
          cupy.testing.assert_array_equal(n.indptr, self.m.indptr)
          assert n.shape == self.m.shape
  
      @testing.with_requires('scipy')
      def test_init_copy_scipy_sparse(self):
          m = _make(numpy, scipy.sparse, self.dtype)
          n = sparse.csr_matrix(m)
          assert isinstance(n.data, cupy.ndarray)
          assert isinstance(n.indices, cupy.ndarray)
          assert isinstance(n.indptr, cupy.ndarray)
          cupy.testing.assert_array_equal(n.data, m.data)
          cupy.testing.assert_array_equal(n.indices, m.indices)
          cupy.testing.assert_array_equal(n.indptr, m.indptr)
          assert n.shape == m.shape
  
      @testing.with_requires('scipy')
      def test_init_copy_other_scipy_sparse(self):
          m = _make(numpy, scipy.sparse, self.dtype)
          n = sparse.csr_matrix(m.tocsc())
          assert isinstance(n.data, cupy.ndarray)
          assert isinstance(n.indices, cupy.ndarray)
          assert isinstance(n.indptr, cupy.ndarray)
          cupy.testing.assert_array_equal(n.data, m.data)
          cupy.testing.assert_array_equal(n.indices, m.indices)
          cupy.testing.assert_array_equal(n.indptr, m.indptr)
          assert n.shape == m.shape
  
      def test_init_dense(self):
          m = cupy.array([[0, 1, 0, 2],
                          [0, 0, 0, 0],
                          [0, 0, 3, 0]], dtype=self.dtype)
          n = sparse.csr_matrix(m)
          assert n.nnz == 3
          assert n.shape == (3, 4)
          cupy.testing.assert_array_equal(n.data, [1, 2, 3])
          cupy.testing.assert_array_equal(n.indices, [1, 3, 2])
          cupy.testing.assert_array_equal(n.indptr, [0, 2, 2, 3])
  
      def test_init_dense_empty(self):
          m = cupy.array([[0, 0, 0, 0],
                          [0, 0, 0, 0],
                          [0, 0, 0, 0]], dtype=self.dtype)
          n = sparse.csr_matrix(m)
          assert n.nnz == 0
          assert n.shape == (3, 4)
          cupy.testing.assert_array_equal(n.data, [])
          cupy.testing.assert_array_equal(n.indices, [])
          cupy.testing.assert_array_equal(n.indptr, [0, 0, 0, 0])
  
      def test_init_dense_one_dim(self):
          m = cupy.array([0, 1, 0, 2], dtype=self.dtype)
          n = sparse.csr_matrix(m)
          assert n.nnz == 2
          assert n.shape == (1, 4)
          cupy.testing.assert_array_equal(n.data, [1, 2])
          cupy.testing.assert_array_equal(n.indices, [1, 3])
          cupy.testing.assert_array_equal(n.indptr, [0, 2])
  
      def test_init_dense_zero_dim(self):
          m = cupy.array(1, dtype=self.dtype)
          n = sparse.csr_matrix(m)
          assert n.nnz == 1
          assert n.shape == (1, 1)
          cupy.testing.assert_array_equal(n.data, [1])
          cupy.testing.assert_array_equal(n.indices, [0])
          cupy.testing.assert_array_equal(n.indptr, [0, 1])
  
      def test_init_data_row_col(self):
          o = self.m.tocoo()
          n = sparse.csr_matrix((o.data, (o.row, o.col)))
          cupy.testing.assert_array_equal(n.data, self.m.data)
          cupy.testing.assert_array_equal(n.indices, self.m.indices)
          cupy.testing.assert_array_equal(n.indptr, self.m.indptr)
          assert n.shape == self.m.shape
  
      @testing.with_requires('scipy')
      def test_init_dense_invalid_ndim(self):
          for xp, sp in ((numpy, scipy.sparse), (cupy, sparse)):
              m = xp.zeros((1, 1, 1), dtype=self.dtype)
              with pytest.raises(TypeError):
                  sp.csr_matrix(m)
  
      def test_copy(self):
          n = self.m.copy()
          assert isinstance(n, sparse.csr_matrix)
          assert n is not self.m
          assert n.data is not self.m.data
          assert n.indices is not self.m.indices
          assert n.indptr is not self.m.indptr
          cupy.testing.assert_array_equal(n.data, self.m.data)
          cupy.testing.assert_array_equal(n.indices, self.m.indices)
          cupy.testing.assert_array_equal(n.indptr, self.m.indptr)
          assert n.shape == self.m.shape
  
      def test_shape(self):
          assert self.m.shape == (3, 4)
  
      def test_ndim(self):
          assert self.m.ndim == 2
  
      def test_nnz(self):
          assert self.m.nnz == 4
  
      def test_conj(self):
          n = _make_complex(cupy, sparse, self.dtype)
          cupy.testing.assert_array_equal(n.conj().data, n.data.conj())
  
      @testing.with_requires('scipy')
      def test_get(self):
          m = self.m.get()
          assert isinstance(m, scipy.sparse.csr_matrix)
          expect = [
              [0, 1, 0, 0],
              [0, 0, 0, 2],
              [0, 0, 3, 0]
          ]
          numpy.testing.assert_allclose(m.toarray(), expect)
  
      @testing.with_requires('scipy')
      def test_str(self):
          if numpy.dtype(self.dtype).kind == 'f':
              expect = '''  (0, 0)\t0.0
    (0, 1)\t1.0
    (1, 3)\t2.0
    (2, 2)\t3.0'''
          elif numpy.dtype(self.dtype).kind == 'c':
              expect = '''  (0, 0)\t0j
    (0, 1)\t(1+0j)
    (1, 3)\t(2+0j)
    (2, 2)\t(3+0j)'''
  
          assert str(self.m) == expect
  
      def test_toarray(self):
          m = self.m.toarray()
          expect = [
              [0, 1, 0, 0],
              [0, 0, 0, 2],
              [0, 0, 3, 0]
          ]
          assert m.flags.c_contiguous
          cupy.testing.assert_allclose(m, expect)
  
      def test_pickle_roundtrip(self):
          s = _make(cupy, sparse, self.dtype)
  
          s2 = pickle.loads(pickle.dumps(s))
          assert s._descr.descriptor != s2._descr.descriptor
          assert s.shape == s2.shape
          assert s.dtype == s2.dtype
          if scipy_available:
              assert (s.get() != s2.get()).count_nonzero() == 0
  
  
  @testing.parameterize(*testing.product({
      'dtype': [numpy.float32, numpy.float64, numpy.complex64, numpy.complex128],
  }))
  @testing.with_requires('scipy')
  class TestCsrMatrixInit(unittest.TestCase):
  
      def setUp(self):
          self.shape = (3, 4)
  
      def data(self, xp):
          return xp.array([1, 2, 3, 4], self.dtype)
  
      def indices(self, xp):
          return xp.array([0, 1, 3, 2], 'i')
  
      def indptr(self, xp):
          return xp.array([0, 2, 3, 4], 'i')
  
      @testing.numpy_cupy_equal(sp_name='sp')
      def test_shape_none(self, xp, sp):
          x = sp.csr_matrix(
              (self.data(xp), self.indices(xp), self.indptr(xp)), shape=None)
          assert x.shape == (3, 4)
  
      @testing.numpy_cupy_equal(sp_name='sp')
      def test_dtype(self, xp, sp):
          data = self.data(xp).real.astype('i')
          x = sp.csr_matrix(
              (data, self.indices(xp), self.indptr(xp)), dtype=self.dtype)
          assert x.dtype == self.dtype
  
      @testing.numpy_cupy_equal(sp_name='sp')
      def test_copy_true(self, xp, sp):
          data = self.data(xp)
          indices = self.indices(xp)
          indptr = self.indptr(xp)
          x = sp.csr_matrix((data, indices, indptr), copy=True)
  
          assert data is not x.data
          assert indices is not x.indices
          assert indptr is not x.indptr
  
      @testing.numpy_cupy_allclose(sp_name='sp')
      def test_init_with_shape(self, xp, sp):
          s = sp.csr_matrix(self.shape)
          assert s.shape == self.shape
          assert s.dtype == 'd'
          assert s.size == 0
          return s
  
      @testing.numpy_cupy_allclose(sp_name='sp')
      def test_init_with_shape_and_dtype(self, xp, sp):
          s = sp.csr_matrix(self.shape, dtype=self.dtype)
          assert s.shape == self.shape
          assert s.dtype == self.dtype
          assert s.size == 0
          return s
  
      @testing.numpy_cupy_allclose(sp_name='sp', atol=1e-5)
      def test_intlike_shape(self, xp, sp):
          s = sp.csr_matrix((self.data(xp), self.indices(xp), self.indptr(xp)),
                            shape=(xp.array(self.shape[0]),
                                   xp.int32(self.shape[1])))
          assert isinstance(s.shape[0], int)
          assert isinstance(s.shape[1], int)
          return s
  
      def test_shape_invalid(self):
          for xp, sp in ((numpy, scipy.sparse), (cupy, sparse)):
              with pytest.raises(ValueError):
                  sp.csr_matrix(
                      (self.data(xp), self.indices(xp), self.indptr(xp)),
                      shape=(2,))
  
      def test_data_invalid(self):
          for xp, sp in ((numpy, scipy.sparse), (cupy, sparse)):
              with pytest.raises(ValueError):
                  sp.csr_matrix(
                      ('invalid', self.indices(xp), self.indptr(xp)),
                      shape=self.shape)
  
      def test_data_invalid_ndim(self):
          for xp, sp in ((numpy, scipy.sparse), (cupy, sparse)):
              with pytest.raises(ValueError):
                  sp.csr_matrix(
                      (self.data(xp)[None], self.indices(xp), self.indptr(xp)),
                      shape=self.shape)
  
      def test_indices_invalid(self):
          for xp, sp in ((numpy, scipy.sparse), (cupy, sparse)):
              with pytest.raises(ValueError):
                  sp.csr_matrix(
                      (self.data(xp), 'invalid', self.indptr(xp)),
                      shape=self.shape)
  
      def test_indices_invalid_ndim(self):
          for xp, sp in ((numpy, scipy.sparse), (cupy, sparse)):
              with pytest.raises(ValueError):
                  sp.csr_matrix(
                      (self.data(xp), self.indices(xp)[None], self.indptr(xp)),
                      shape=self.shape)
  
      def test_indptr_invalid(self):
          for xp, sp in ((numpy, scipy.sparse), (cupy, sparse)):
              with pytest.raises(ValueError):
                  sp.csr_matrix(
                      (self.data(xp), self.indices(xp), 'invalid'),
                      shape=self.shape)
  
      def test_indptr_invalid_ndim(self):
          for xp, sp in ((numpy, scipy.sparse), (cupy, sparse)):
              with pytest.raises(ValueError):
                  sp.csr_matrix(
                      (self.data(xp), self.indices(xp), self.indptr(xp)[None]),
                      shape=self.shape)
  
      def test_data_indices_different_length(self):
          for xp, sp in ((numpy, scipy.sparse), (cupy, sparse)):
              data = xp.arange(5, dtype=self.dtype)
              with pytest.raises(ValueError):
                  sp.csr_matrix(
                      (data, self.indices(xp), self.indptr(xp)),
                      shape=self.shape)
  
      def test_indptr_invalid_length(self):
          for xp, sp in ((numpy, scipy.sparse), (cupy, sparse)):
              indptr = xp.array([0, 1], 'i')
              with pytest.raises(ValueError):
                  sp.csr_matrix(
                      (self.data(xp), self.indices(xp), indptr),
                      shape=self.shape)
  
      def test_unsupported_dtype(self):
          with self.assertRaises(ValueError):
              sparse.csr_matrix(
                  (self.data(cupy), self.indices(cupy), self.indptr(cupy)),
                  shape=self.shape, dtype='i')
  
      @testing.numpy_cupy_equal(sp_name='sp')
      def test_conj(self, xp, sp):
          n = _make_complex(xp, sp, self.dtype)
          cupy.testing.assert_array_equal(n.conj().data, n.data.conj())
  
  
  @testing.parameterize(*testing.product({
      'make_method': [
          '_make', '_make_unordered', '_make_empty', '_make_duplicate',
          '_make_shape'],
      'dtype': [numpy.float32, numpy.float64, numpy.complex64, numpy.complex128],
  }))
  @testing.with_requires('scipy')
  class TestCsrMatrixScipyComparison(unittest.TestCase):
  
      @property
      def make(self):
          return globals()[self.make_method]
  
      def test_len(self):
          for xp, sp in ((numpy, scipy.sparse), (cupy, sparse)):
              m = self.make(xp, sp, self.dtype)
              with pytest.raises(TypeError):
                  len(m)
  
      @testing.with_requires('scipy>=1.4.0')
      @testing.numpy_cupy_array_equal(sp_name='sp')
      def test_iter(self, xp, sp):
          m = self.make(xp, sp, self.dtype)
          rows = []
          for r in m:
              rows.append(r)
              assert isinstance(r, sp.spmatrix)
          assert len(rows) == 3
          return xp.concatenate([r.toarray() for r in rows])
  
      @testing.numpy_cupy_array_equal(sp_name='sp')
      def test_asfptype(self, xp, sp):
          m = self.make(xp, sp, self.dtype)
          return m.asfptype().toarray()
  
      @testing.numpy_cupy_allclose(sp_name='sp')
      def test_toarray(self, xp, sp):
          m = self.make(xp, sp, self.dtype)
          a = m.toarray()
          assert a.flags.c_contiguous
          return a
  
      @testing.numpy_cupy_allclose(sp_name='sp')
      def test_toarray_c_order(self, xp, sp):
          m = self.make(xp, sp, self.dtype)
          a = m.toarray(order='C')
          assert a.flags.c_contiguous
          return a
  
      @testing.numpy_cupy_allclose(sp_name='sp')
      def test_toarray_f_order(self, xp, sp):
          m = self.make(xp, sp, self.dtype)
          a = m.toarray(order='F')
          assert a.flags.f_contiguous
          return a
  
      @testing.with_requires('numpy>=1.19')
      def test_toarray_unknown_order(self):
          for xp, sp in ((numpy, scipy.sparse), (cupy, sparse)):
              m = self.make(xp, sp, self.dtype)
              with pytest.raises(ValueError):
                  m.toarray(order='#')
  
      @testing.numpy_cupy_allclose(sp_name='sp')
      def test_A(self, xp, sp):
          m = self.make(xp, sp, self.dtype)
          return m.A
  
      @testing.numpy_cupy_allclose(sp_name='sp')
      def test_tocoo(self, xp, sp):
          m = self.make(xp, sp, self.dtype)
          return m.tocoo()
  
      @testing.numpy_cupy_allclose(sp_name='sp')
      def test_tocoo_copy(self, xp, sp):
          m = self.make(xp, sp, self.dtype)
          n = m.tocoo(copy=True)
          assert m.data is not n.data
          return n
  
      @testing.numpy_cupy_allclose(sp_name='sp')
      def test_tocsc(self, xp, sp):
          m = self.make(xp, sp, self.dtype)
          return m.tocsc()
  
      @testing.numpy_cupy_allclose(sp_name='sp')
      def test_tocsc_copy(self, xp, sp):
          m = self.make(xp, sp, self.dtype)
          n = m.tocsc(copy=True)
          assert m.data is not n.data
          assert m.indices is not n.indices
          assert m.indptr is not n.indptr
          return n
  
      @testing.numpy_cupy_allclose(sp_name='sp')
      def test_tocsr(self, xp, sp):
          m = self.make(xp, sp, self.dtype)
          return m.tocsr()
  
      @testing.numpy_cupy_allclose(sp_name='sp')
      def test_tocsr_copy(self, xp, sp):
          m = self.make(xp, sp, self.dtype)
          n = m.tocsr(copy=True)
          assert m.data is not n.data
          assert m.indices is not n.indices
          assert m.indptr is not n.indptr
          return n
  
      # dot
      @testing.numpy_cupy_allclose(sp_name='sp')
      def test_dot_scalar(self, xp, sp):
          m = self.make(xp, sp, self.dtype)
          return m.dot(2.0)
  
      @testing.numpy_cupy_allclose(sp_name='sp')
      def test_dot_numpy_scalar(self, xp, sp):
          m = self.make(xp, sp, self.dtype)
          return m.dot(numpy.dtype(self.dtype).type(2.0)).toarray()
  
      @testing.numpy_cupy_allclose(sp_name='sp')
      def test_dot_csr(self, xp, sp):
          m = self.make(xp, sp, self.dtype)
          x = _make3(xp, sp, self.dtype)
          return m.dot(x)
  
      def test_dot_csr_invalid_shape(self):
          for xp, sp in ((numpy, scipy.sparse), (cupy, sparse)):
              m = self.make(xp, sp, self.dtype)
              x = sp.csr_matrix((5, 3), dtype=self.dtype)
              with pytest.raises(ValueError):
                  m.dot(x)
  
      @testing.numpy_cupy_allclose(sp_name='sp')
      def test_dot_csc(self, xp, sp):
          m = self.make(xp, sp, self.dtype)
          x = _make3(xp, sp, self.dtype).tocsc()
          return m.dot(x)
  
      @testing.numpy_cupy_allclose(sp_name='sp')
      def test_dot_sparse(self, xp, sp):
          m = self.make(xp, sp, self.dtype)
          x = _make3(xp, sp, self.dtype).tocoo()
          return m.dot(x)
  
      @testing.numpy_cupy_allclose(sp_name='sp')
      def test_dot_zero_dim(self, xp, sp):
          m = self.make(xp, sp, self.dtype)
          x = xp.array(2, dtype=self.dtype)
          return m.dot(x)
  
      @testing.numpy_cupy_allclose(sp_name='sp')
      def test_dot_dense_vector(self, xp, sp):
          m = self.make(xp, sp, self.dtype)
          x = xp.arange(4).astype(self.dtype)
          return m.dot(x)
  
      def test_dot_dense_vector_invalid_shape(self):
          for xp, sp in ((numpy, scipy.sparse), (cupy, sparse)):
              m = self.make(xp, sp, self.dtype)
              x = xp.arange(5).astype(self.dtype)
              with pytest.raises(ValueError):
                  m.dot(x)
  
      @testing.numpy_cupy_allclose(sp_name='sp', contiguous_check=False)
      def test_dot_dense_matrix(self, xp, sp):
          m = self.make(xp, sp, self.dtype)
          x = xp.arange(8).reshape(4, 2).astype(self.dtype)
          return m.dot(x)
  
      def test_dot_dense_matrix_invalid_shape(self):
          for xp, sp in ((numpy, scipy.sparse), (cupy, sparse)):
              m = self.make(xp, sp, self.dtype)
              x = xp.arange(10).reshape(5, 2).astype(self.dtype)
              with pytest.raises(ValueError):
                  m.dot(x)
  
      def test_dot_dense_ndim3(self):
          for xp, sp in ((numpy, scipy.sparse), (cupy, sparse)):
              m = self.make(xp, sp, self.dtype)
              x = xp.arange(24).reshape(4, 2, 3).astype(self.dtype)
              with pytest.raises(ValueError):
                  m.dot(x)
  
      def test_dot_unsupported(self):
          for xp, sp in ((numpy, scipy.sparse), (cupy, sparse)):
              m = self.make(xp, sp, self.dtype)
              with pytest.raises(TypeError):
                  m.dot(None)
  
      # __add__
      @testing.numpy_cupy_allclose(sp_name='sp')
      def test_add_zero(self, xp, sp):
          m = self.make(xp, sp, self.dtype)
          return m + 0
  
      def test_add_scalar(self):
          for xp, sp in ((numpy, scipy.sparse), (cupy, sparse)):
              m = self.make(xp, sp, self.dtype)
              with pytest.raises(NotImplementedError):
                  m + 1
  
      @testing.numpy_cupy_allclose(sp_name='sp')
      def test_add_csr(self, xp, sp):
          m = self.make(xp, sp, self.dtype)
          n = _make2(xp, sp, self.dtype)
          return m + n
  
      @testing.numpy_cupy_allclose(sp_name='sp')
      def test_add_coo(self, xp, sp):
          m = self.make(xp, sp, self.dtype)
          n = _make2(xp, sp, self.dtype).tocoo()
          return m + n
  
      @testing.numpy_cupy_allclose(sp_name='sp')
      def test_add_dense(self, xp, sp):
          m = self.make(xp, sp, self.dtype)
          n = xp.arange(12).reshape(3, 4)
          return m + n
  
      # __radd__
      @testing.numpy_cupy_allclose(sp_name='sp')
      def test_radd_zero(self, xp, sp):
          m = self.make(xp, sp, self.dtype)
          return (0 + m).toarray()
  
      def test_radd_scalar(self):
          for xp, sp in ((numpy, scipy.sparse), (cupy, sparse)):
              m = self.make(xp, sp, self.dtype)
              with pytest.raises(NotImplementedError):
                  1 + m
  
      @testing.numpy_cupy_allclose(sp_name='sp')
      def test_radd_dense(self, xp, sp):
          m = self.make(xp, sp, self.dtype)
          n = xp.arange(12).reshape(3, 4)
          return n + m
  
      # __sub__
      @testing.numpy_cupy_allclose(sp_name='sp')
      def test_sub_zero(self, xp, sp):
          m = self.make(xp, sp, self.dtype)
          return (m - 0).toarray()
  
      def test_sub_scalar(self):
          for xp, sp in ((numpy, scipy.sparse), (cupy, sparse)):
              m = self.make(xp, sp, self.dtype)
              with pytest.raises(NotImplementedError):
                  m - 1
  
      @testing.numpy_cupy_allclose(sp_name='sp')
      def test_sub_csr(self, xp, sp):
          m = self.make(xp, sp, self.dtype)
          n = _make2(xp, sp, self.dtype)
          return (m - n).toarray()
  
      @testing.numpy_cupy_allclose(sp_name='sp')
      def test_sub_coo(self, xp, sp):
          m = self.make(xp, sp, self.dtype)
          n = _make2(xp, sp, self.dtype).tocoo()
          return m - n
  
      @testing.numpy_cupy_allclose(sp_name='sp')
      def test_sub_dense(self, xp, sp):
          m = self.make(xp, sp, self.dtype)
          n = xp.arange(12).reshape(3, 4)
          return m - n
  
      # __rsub__
      @testing.numpy_cupy_allclose(sp_name='sp')
      def test_rsub_zero(self, xp, sp):
          m = self.make(xp, sp, self.dtype)
          return 0 - m
  
      def test_rsub_scalar(self):
          for xp, sp in ((numpy, scipy.sparse), (cupy, sparse)):
              m = self.make(xp, sp, self.dtype)
              with pytest.raises(NotImplementedError):
                  1 - m
  
      @testing.numpy_cupy_allclose(sp_name='sp')
      def test_rsub_dense(self, xp, sp):
          m = self.make(xp, sp, self.dtype)
          n = xp.arange(12).reshape(3, 4)
          return n - m
  
      # __mul__
      @testing.numpy_cupy_allclose(sp_name='sp')
      def test_mul_scalar(self, xp, sp):
          m = self.make(xp, sp, self.dtype)
          return m * 2.0
  
      @testing.numpy_cupy_allclose(sp_name='sp')
      def test_mul_numpy_scalar(self, xp, sp):
          m = self.make(xp, sp, self.dtype)
          return m * numpy.dtype(self.dtype).type(2.0)
  
      @testing.numpy_cupy_allclose(sp_name='sp')
      def test_mul_csr(self, xp, sp):
          m = self.make(xp, sp, self.dtype)
          x = _make3(xp, sp, self.dtype)
          return m * x
  
      def test_mul_csr_invalid_shape(self):
          for xp, sp in ((numpy, scipy.sparse), (cupy, sparse)):
              m = self.make(xp, sp, self.dtype)
              x = sp.csr_matrix((5, 3), dtype=self.dtype)
              with pytest.raises(ValueError):
                  m * x
  
      @testing.numpy_cupy_allclose(sp_name='sp')
      def test_mul_csc(self, xp, sp):
          m = self.make(xp, sp, self.dtype)
          x = _make3(xp, sp, self.dtype).tocsc()
          return m * x
  
      @testing.numpy_cupy_allclose(sp_name='sp')
      def test_mul_sparse(self, xp, sp):
          m = self.make(xp, sp, self.dtype)
          x = _make3(xp, sp, self.dtype).tocoo()
          return m * x
  
      @testing.numpy_cupy_allclose(sp_name='sp')
      def test_mul_zero_dim(self, xp, sp):
          m = self.make(xp, sp, self.dtype)
          x = xp.array(2, dtype=self.dtype)
          return m * x
  
      @testing.numpy_cupy_allclose(sp_name='sp')
      def test_mul_dense_vector(self, xp, sp):
          m = self.make(xp, sp, self.dtype)
          x = xp.arange(4).astype(self.dtype)
          return m * x
  
      def test_mul_dense_vector_invalid_shape(self):
          for xp, sp in ((numpy, scipy.sparse), (cupy, sparse)):
              m = self.make(xp, sp, self.dtype)
              x = xp.arange(5).astype(self.dtype)
              with pytest.raises(ValueError):
                  m * x
  
      @testing.numpy_cupy_allclose(sp_name='sp', contiguous_check=False)
      def test_mul_dense_matrix(self, xp, sp):
          m = self.make(xp, sp, self.dtype)
          x = xp.arange(8).reshape(4, 2).astype(self.dtype)
          return m * x
  
      def test_mul_dense_matrix_invalid_shape(self):
          for xp, sp in ((numpy, scipy.sparse), (cupy, sparse)):
              m = self.make(xp, sp, self.dtype)
              x = xp.arange(10).reshape(5, 2).astype(self.dtype)
              with pytest.raises(ValueError):
                  m * x
  
      def test_mul_dense_ndim3(self):
          for xp, sp in ((numpy, scipy.sparse), (cupy, sparse)):
              m = self.make(xp, sp, self.dtype)
              x = xp.arange(24).reshape(4, 2, 3).astype(self.dtype)
              with pytest.raises(ValueError):
                  m * x
  
      def test_mul_unsupported(self):
          for xp, sp in ((numpy, scipy.sparse), (cupy, sparse)):
              m = self.make(xp, sp, self.dtype)
              with pytest.raises(TypeError):
                  m * None
  
      # __rmul__
      @testing.numpy_cupy_allclose(sp_name='sp')
      def test_rmul_scalar(self, xp, sp):
          m = self.make(xp, sp, self.dtype)
          return 2.0 * m
  
      @testing.numpy_cupy_allclose(sp_name='sp')
      def test_rmul_numpy_scalar(self, xp, sp):
          m = self.make(xp, sp, self.dtype)
          return numpy.dtype(self.dtype).type(2.0) * m
  
      @testing.numpy_cupy_allclose(sp_name='sp')
      def test_rmul_csr(self, xp, sp):
          m = self.make(xp, sp, self.dtype)
          x = _make3(xp, sp, self.dtype)
          return x * m
  
      @testing.numpy_cupy_allclose(sp_name='sp', _check_sparse_format=False)
      def test_rmul_csc(self, xp, sp):
          m = self.make(xp, sp, self.dtype)
          x = _make3(xp, sp, self.dtype).tocsc()
          return x * m
  
      @testing.numpy_cupy_allclose(sp_name='sp')
      def test_rmul_sparse(self, xp, sp):
          m = self.make(xp, sp, self.dtype)
          x = _make3(xp, sp, self.dtype).tocoo()
          return x * m
  
      @testing.numpy_cupy_allclose(sp_name='sp')
      def test_rmul_zero_dim(self, xp, sp):
          m = self.make(xp, sp, self.dtype)
          x = xp.array(2, dtype=self.dtype)
          return x * m
  
      @testing.numpy_cupy_allclose(sp_name='sp', contiguous_check=False)
      def test_rmul_dense_matrix(self, xp, sp):
          m = self.make(xp, sp, self.dtype)
          x = xp.arange(12).reshape(4, 3).astype(self.dtype)
          return x * m
  
      def test_rmul_dense_ndim3(self):
          for xp, sp in ((numpy, scipy.sparse), (cupy, sparse)):
              m = self.make(xp, sp, self.dtype)
              x = xp.arange(24).reshape(4, 2, 3).astype(self.dtype)
              with pytest.raises(ValueError):
                  x * m
  
      def test_rmul_unsupported(self):
          for xp, sp in ((numpy, scipy.sparse), (cupy, sparse)):
              m = self.make(xp, sp, self.dtype)
              if m.nnz == 0:
                  # When there is no element, a SciPy's sparse matrix does
                  # not raise an error when it is multiplied with None.
                  continue
              with pytest.raises(TypeError):
                  None * m
  
      # Note: '@' operator is almost equivalent to '*' operator. Only test the
      # cases where '@' raises an exception and '*' does not.
      def test_matmul_scalar(self):
          for xp, sp in ((numpy, scipy.sparse), (cupy, sparse)):
              m = self.make(xp, sp, self.dtype)
              x = 2.0
              with pytest.raises(ValueError):
                  m @ x
              with pytest.raises(ValueError):
                  x @ m
  
      def test_matmul_numpy_scalar(self):
          for xp, sp in ((numpy, scipy.sparse), (cupy, sparse)):
              m = self.make(xp, sp, self.dtype)
              x = numpy.dtype(self.dtype).type(2.0)
              with pytest.raises(ValueError):
                  m @ x
              with pytest.raises(ValueError):
                  x @ m
  
      def test_matmul_scalar_like_array(self):
          for xp, sp in ((numpy, scipy.sparse), (cupy, sparse)):
              m = self.make(xp, sp, self.dtype)
              x = xp.array(2.0, self.dtype)
              with pytest.raises(ValueError):
                  m @ x
              with pytest.raises(ValueError):
                  x @ m
  
      @testing.numpy_cupy_equal(sp_name='sp')
      def test_has_canonical_format(self, xp, sp):
          m = self.make(xp, sp, self.dtype)
          return m.has_canonical_format
  
      @testing.numpy_cupy_allclose(sp_name='sp')
      def test_has_canonical_format2(self, xp, sp):
          # this test is adopted from SciPy's
          M = sp.csr_matrix((xp.array([2], dtype=self.dtype),
                             xp.array([0]), xp.array([0, 1])))
          assert M.has_canonical_format is True
          return M
  
      @testing.numpy_cupy_allclose(sp_name='sp')
      def test_has_canonical_format3(self, xp, sp):
          # this test is adopted from SciPy's
          indices = xp.array([0, 0])  # contains duplicate
          data = xp.array([1, 1], dtype=self.dtype)
          indptr = xp.array([0, 2])
  
          M = sp.csr_matrix((data, indices, indptr))
          assert M.has_canonical_format is False
  
          # set by deduplicating
          M.sum_duplicates()
          assert M.has_canonical_format is True
          assert 1 == len(M.indices)
          return M
  
      @testing.numpy_cupy_allclose(sp_name='sp')
      def test_has_canonical_format4(self, xp, sp):
          # this test is adopted from SciPy's
          indices = xp.array([0, 0])  # contains duplicate
          data = xp.array([1, 1], dtype=self.dtype)
          indptr = xp.array([0, 2])
  
          M = sp.csr_matrix((data, indices, indptr))
          # set manually (although underlyingly duplicated)
          M.has_canonical_format = True
          assert M.has_canonical_format
          assert 2 == len(M.indices)  # unaffected content
  
          # ensure deduplication bypassed when has_canonical_format == True
          M.sum_duplicates()
          assert 2 == len(M.indices)  # unaffected content
          return M
  
      @testing.with_requires('scipy>1.6.0')
      @testing.numpy_cupy_equal(sp_name='sp')
      def test_has_sorted_indices(self, xp, sp):
          m = self.make(xp, sp, self.dtype)
          return m.has_sorted_indices
  
      # TODO(asi1024): Remove test after the fixed version is released.
      # https://github.com/scipy/scipy/pull/13426
      @testing.with_requires('scipy<=1.6.0')
      @testing.numpy_cupy_equal(sp_name='sp')
      def test_has_sorted_indices_for_old_scipy(self, xp, sp):
          m = self.make(xp, sp, self.dtype)
          return bool(m.has_sorted_indices)
  
      @testing.numpy_cupy_allclose(sp_name='sp')
      def test_has_sorted_indices2(self, xp, sp):
          # this test is adopted from SciPy's
          sorted_inds = xp.array([0, 1])
          data = xp.array([1, 1], dtype=self.dtype)
          indptr = xp.array([0, 2])
          M = sp.csr_matrix((data, sorted_inds, indptr))
          assert M.has_sorted_indices
          return M
  
      @testing.numpy_cupy_allclose(sp_name='sp')
      def test_has_sorted_indices3(self, xp, sp):
          # this test is adopted from SciPy's
          sorted_inds = xp.array([0, 1])
          unsorted_inds = xp.array([1, 0])
          data = xp.array([1, 1], dtype=self.dtype)
          indptr = xp.array([0, 2])
          M = sp.csr_matrix((data, unsorted_inds, indptr))
          assert not M.has_sorted_indices
  
          # set by sorting
          M.sort_indices()
          assert M.has_sorted_indices
          assert (M.indices == sorted_inds).all()
          return M
  
      @testing.numpy_cupy_allclose(sp_name='sp')
      def test_has_sorted_indices4(self, xp, sp):
          # this test is adopted from SciPy's
          unsorted_inds = xp.array([1, 0])
          data = xp.array([1, 1], dtype=self.dtype)
          indptr = xp.array([0, 2])
          M = sp.csr_matrix((data, unsorted_inds, indptr))
  
          # set manually (although underlyingly unsorted)
          M.has_sorted_indices = True
          assert M.has_sorted_indices
          assert (M.indices == unsorted_inds).all()
  
          # ensure sort bypassed when has_sorted_indices == True
          M.sort_indices()
          assert (M.indices == unsorted_inds).all()
          return M
  
      @testing.numpy_cupy_allclose(sp_name='sp')
      def test_sort_indices(self, xp, sp):
          m = self.make(xp, sp, self.dtype)
          m.sort_indices()
          assert m.has_sorted_indices
          return m
  
      @testing.numpy_cupy_allclose(sp_name='sp')
      def test_sort_indices2(self, xp, sp):
          # this test is adopted from SciPy's
          data = xp.arange(5).astype(xp.float32)
          indices = xp.array([7, 2, 1, 5, 4])
          indptr = xp.array([0, 3, 5])
          asp = sp.csr_matrix((data, indices, indptr), shape=(2, 10))
          asp.sort_indices()
          assert (asp.indices == xp.array([1, 2, 7, 4, 5])).all()
          return asp.todense()
  
      @testing.numpy_cupy_allclose(sp_name='sp')
      def test_sorted_indices(self, xp, sp):
          m = self.make(xp, sp, self.dtype)
          m = m.sorted_indices()
          assert m.has_sorted_indices
          return m
  
      def test_sum_tuple_axis(self):
          for xp, sp in ((numpy, scipy.sparse), (cupy, sparse)):
              m = self.make(xp, sp, self.dtype)
              with pytest.raises(TypeError):
                  m.sum(axis=(0, 1))
  
      def test_sum_str_axis(self):
          for xp, sp in ((numpy, scipy.sparse), (cupy, sparse)):
              m = self.make(xp, sp, self.dtype)
              with pytest.raises(TypeError):
                  m.sum(axis='test')
  
      def test_sum_too_large_axis(self):
          for xp, sp in ((numpy, scipy.sparse), (cupy, sparse)):
              m = self.make(xp, sp, self.dtype)
              with pytest.raises(ValueError):
                  m.sum(axis=3)
  
      @testing.numpy_cupy_allclose(sp_name='sp')
      def test_sum_duplicates(self, xp, sp):
          m = self.make(xp, sp, self.dtype)
          m.sum_duplicates()
          assert m.has_canonical_format
          return m
  
      @testing.numpy_cupy_allclose(sp_name='sp')
      def test_transpose(self, xp, sp):
          m = self.make(xp, sp, self.dtype)
          return m.transpose()
  
      def test_transpose_axes_int(self):
          for xp, sp in ((numpy, scipy.sparse), (cupy, sparse)):
              m = self.make(xp, sp, self.dtype)
              with pytest.raises(ValueError):
                  m.transpose(axes=0)
  
      @testing.numpy_cupy_allclose(sp_name='sp')
      def test_eliminate_zeros(self, xp, sp):
          m = self.make(xp, sp, self.dtype)
          m.eliminate_zeros()
          return m
  
      @testing.numpy_cupy_equal(sp_name='sp')
      @unittest.skipIf(
          cupy.cuda.runtime.runtimeGetVersion() < 8000,
          'CUDA <8 cannot keep number of non-zero entries ')
      def test_eliminate_zeros_nnz(self, xp, sp):
          m = self.make(xp, sp, self.dtype)
          m.eliminate_zeros()
          return m.nnz
  
      # multiply
      @testing.numpy_cupy_allclose(sp_name='sp')
      def test_multiply_scalar(self, xp, sp):
          m = self.make(xp, sp, self.dtype)
          return m.multiply(2).toarray()
  
      @testing.numpy_cupy_allclose(sp_name='sp')
      def test_multiply_dense_row(self, xp, sp):
          m = self.make(xp, sp, self.dtype)
          x = xp.arange(4, dtype=self.dtype)
          return m.multiply(x).toarray()
  
      @testing.numpy_cupy_allclose(sp_name='sp')
      def test_multiply_dense_col(self, xp, sp):
          m = self.make(xp, sp, self.dtype)
          x = xp.arange(3, dtype=self.dtype).reshape(3, 1)
          return m.multiply(x).toarray()
  
      @testing.numpy_cupy_allclose(sp_name='sp')
      def test_multiply_dense_matrix(self, xp, sp):
          m = self.make(xp, sp, self.dtype)
          x = xp.arange(12, dtype=self.dtype).reshape(3, 4)
          return m.multiply(x).toarray()
  
      @testing.numpy_cupy_allclose(sp_name='sp')
      def test_multiply_csr_matrix(self, xp, sp):
          m = self.make(xp, sp, self.dtype)
          x = _make4(xp, sp, self.dtype)
          return m.multiply(x).toarray()
  
      @testing.numpy_cupy_allclose(sp_name='sp')
      def test_multiply_csr_row(self, xp, sp):
          m = self.make(xp, sp, self.dtype)
          x = _make_row(xp, sp, self.dtype)
          return m.multiply(x).toarray()
  
      @testing.numpy_cupy_allclose(sp_name='sp')
      def test_multiply_csr_col(self, xp, sp):
          m = self.make(xp, sp, self.dtype)
          x = _make_col(xp, sp, self.dtype)
          return m.multiply(x).toarray()
  
      def _make_scalar(self, dtype):
          if numpy.issubdtype(dtype, numpy.integer):
              return dtype(2)
          elif numpy.issubdtype(dtype, numpy.floating):
              return dtype(2.5)
          else:
              return dtype(2.5 - 1.5j)
  
      # divide
      @testing.for_dtypes('ifdFD')
      @testing.numpy_cupy_allclose(sp_name='sp')
      def test_divide_scalar(self, xp, sp, dtype):
          m = self.make(xp, sp, self.dtype)
          y = m / self._make_scalar(dtype)
          return y.toarray()
  
      @testing.for_dtypes('ifdFD')
      # type promotion rules are different for ()-shaped arrays
      @testing.numpy_cupy_allclose(sp_name='sp', type_check=False)
      def test_divide_scalarlike(self, xp, sp, dtype):
          m = self.make(xp, sp, self.dtype)
          y = m / xp.array(self._make_scalar(dtype))
          return y.toarray()
  
      @testing.numpy_cupy_allclose(sp_name='sp')
      def test_divide_dense_row(self, xp, sp):
          m = self.make(xp, sp, self.dtype)
          x = xp.arange(4, dtype=self.dtype)
          return m / x
  
      @testing.numpy_cupy_allclose(sp_name='sp')
      def test_divide_dense_col(self, xp, sp):
          m = self.make(xp, sp, self.dtype)
          x = xp.arange(3, dtype=self.dtype).reshape(3, 1)
          return m / x
  
      @testing.numpy_cupy_allclose(sp_name='sp')
      def test_divide_dense_matrix(self, xp, sp):
          m = self.make(xp, sp, self.dtype)
          x = xp.arange(12, dtype=self.dtype).reshape(3, 4)
          return m / x
  
      @testing.numpy_cupy_allclose(sp_name='sp')
      def test_divide_csr_matrix(self, xp, sp):
          m = self.make(xp, sp, self.dtype)
          x = _make4(xp, sp, self.dtype)
          return m / x
  
  
  @testing.parameterize(*testing.product({
      'dtype': [numpy.float32, numpy.float64, numpy.complex64, numpy.complex128],
  }))
  @testing.with_requires('scipy')
  class TestCsrMatrixPowScipyComparison(unittest.TestCase):
  
      @testing.numpy_cupy_allclose(sp_name='sp', _check_sparse_format=False)
      def test_pow_0(self, xp, sp):
          m = _make_square(xp, sp, self.dtype)
          return m ** 0
  
      @testing.numpy_cupy_allclose(sp_name='sp')
      def test_pow_1(self, xp, sp):
          m = _make_square(xp, sp, self.dtype)
          return m ** 1
  
      @testing.numpy_cupy_allclose(sp_name='sp')
      def test_pow_2(self, xp, sp):
          m = _make_square(xp, sp, self.dtype)
          return m ** 2
  
      @testing.numpy_cupy_allclose(sp_name='sp')
      def test_pow_3(self, xp, sp):
          m = _make_square(xp, sp, self.dtype)
          return m ** 3
          return m ** 3
  
      def test_pow_neg(self):
          for xp, sp in ((numpy, scipy.sparse), (cupy, sparse)):
              m = _make_square(xp, sp, self.dtype)
              with pytest.raises(ValueError):
                  m ** -1
  
      def test_pow_not_square(self):
          for xp, sp in ((numpy, scipy.sparse), (cupy, sparse)):
              m = _make(xp, sp, self.dtype)
              with pytest.raises(TypeError):
                  m ** 2
  
      def test_pow_float(self):
          for xp, sp in ((numpy, scipy.sparse), (cupy, sparse)):
              m = _make_square(xp, sp, self.dtype)
              with pytest.raises(ValueError):
                  m ** 1.5
  
      def test_pow_list(self):
          for xp, sp in ((numpy, scipy.sparse), (cupy, sparse)):
              m = _make_square(xp, sp, self.dtype)
              with pytest.raises(TypeError):
                  m ** []
  
  
  @testing.parameterize(*testing.product({
      'dtype': [numpy.float32, numpy.float64],
      'ret_dtype': [None, numpy.float32, numpy.float64],
      'axis': [None, 0, 1, -1, -2],
  }))
  @testing.with_requires('scipy')
  class TestCsrMatrixSum(unittest.TestCase):
  
      @testing.numpy_cupy_allclose(sp_name='sp')
      def test_sum(self, xp, sp):
          m = _make(xp, sp, self.dtype)
          return m.sum(axis=self.axis, dtype=self.ret_dtype)
  
      @testing.numpy_cupy_allclose(sp_name='sp')
      def test_sum_with_out(self, xp, sp):
          m = _make(xp, sp, self.dtype)
          if self.axis is None:
              shape = ()
          else:
              shape = list(m.shape)
              shape[self.axis] = 1
              shape = tuple(shape)
          out = xp.empty(shape, dtype=self.ret_dtype)
          if xp is numpy:
              # TODO(unno): numpy.matrix is used for scipy.sparse though
              # cupy.ndarray is used for cupyx.scipy.sparse.
              out = xp.asmatrix(out)
          return m.sum(axis=self.axis, dtype=self.ret_dtype, out=out)
  
      @testing.numpy_cupy_allclose(sp_name='sp')
      def test_mean(self, xp, sp):
          m = _make(xp, sp, self.dtype)
          return m.mean(axis=self.axis, dtype=self.ret_dtype)
  
      @testing.numpy_cupy_allclose(sp_name='sp')
      def test_mean_with_out(self, xp, sp):
          m = _make(xp, sp, self.dtype)
          if self.axis is None:
              shape = ()
          else:
              shape = list(m.shape)
              shape[self.axis] = 1
              shape = tuple(shape)
          out = xp.empty(shape, dtype=self.ret_dtype)
          if xp is numpy:
              # TODO(unno): numpy.matrix is used for scipy.sparse though
              # cupy.ndarray is used for cupyx.scipy.sparse.
              out = xp.asmatrix(out)
          return m.mean(axis=self.axis, dtype=self.ret_dtype, out=out)
  
  
  @testing.parameterize(*testing.product({
      'dtype': [numpy.float32, numpy.float64, numpy.complex64, numpy.complex128],
  }))
  @testing.with_requires('scipy')
  class TestCsrMatrixScipyCompressed(unittest.TestCase):
  
      @testing.numpy_cupy_equal(sp_name='sp')
      def test_get_shape(self, xp, sp):
          return _make(xp, sp, self.dtype).get_shape()
  
      @testing.numpy_cupy_equal(sp_name='sp')
      def test_getnnz(self, xp, sp):
          return _make(xp, sp, self.dtype).getnnz()
  
  
  @testing.parameterize(*testing.product({
      # TODO(takagi): Test dtypes
      'axis': [None, -2, -1, 0, 1],
      'dense': [False, True],  # means a sparse matrix but all elements filled
  }))
  @testing.with_requires('scipy>=0.19.0')
  class TestCsrMatrixScipyCompressedMinMax(unittest.TestCase):
      def _make_data_min(self, xp, sp, dense=False):
          dm_data = testing.shaped_random((10, 20), xp=xp, scale=1.0)
          if not dense:
              dm_data[abs(dm_data) < 0.95] = 0
          return sp.csr_matrix(xp.array(dm_data))
  
      def _make_data_max(self, xp, sp, dense=False):
          return -self._make_data_min(xp, sp, dense=dense)
  
      def _make_data_min_explicit(self, xp, sp, axis):
          dm_data = testing.shaped_random((10, 20), xp=xp, scale=1.0)
          if xp is cupy:
              dm_data[dm_data < 0.95] = 0
          else:
              # As SciPy sparse matrix does not have `explicit` parameter, we
              # make SciPy inputs such that SciPy's spmatrix.min(axis=axis)
              # returns the same value as CuPy's spmatrix.min(axis=axis,
              # explicit=True).
  
              # Put infinity instead of zeros so spmatrix.min(axis=axis) returns
              # the smallest numbers except for zero.
              dm_data[dm_data < 0.95] = numpy.inf
  
              if axis is None:
                  # If all elements in the array are set to infinity, we make it
                  # have at least a zero so SciPy's spmatrix.min(axis=None)
                  # returns zero.
                  if numpy.isinf(dm_data).all():
                      dm_data[0, 0] = 0
              else:
                  if axis < 0:
                      axis += 2
  
                  # If all elements in a row/column are set to infinity, we make
                  # it have at least a zero so spmatrix.min(axis=axis) returns
                  # zero for the row/column.
                  mask = numpy.zeros_like(dm_data, dtype=numpy.bool_)
                  if axis == 0:
                      rows = dm_data.argmin(axis=0)
                      cols = numpy.arange(20)
                  else:
                      rows = numpy.arange(10)
                      cols = dm_data.argmin(axis=1)
                  mask[rows, cols] = numpy.isinf(dm_data[rows, cols])
                  dm_data[mask] = 0
  
          return sp.csr_matrix(xp.array(dm_data))
  
      def _make_data_max_explicit(self, xp, sp, axis):
          return -self._make_data_min_explicit(xp, sp, axis=axis)
  
      @testing.numpy_cupy_array_equal(sp_name='sp')
      def test_min(self, xp, sp):
          data = self._make_data_min(xp, sp, dense=self.dense)
          return data.min(axis=self.axis)
  
      @testing.numpy_cupy_array_equal(sp_name='sp')
      def test_min_explicit(self, xp, sp):
          data = self._make_data_min_explicit(xp, sp, axis=self.axis)
          if xp is cupy:
              return data.min(axis=self.axis, explicit=True)
          else:
              return data.min(axis=self.axis)
  
      @testing.numpy_cupy_array_equal(sp_name='sp')
      def test_max(self, xp, sp):
          data = self._make_data_max(xp, sp, dense=self.dense)
          return data.max(axis=self.axis)
  
      @testing.numpy_cupy_array_equal(sp_name='sp')
      def test_max_explicit(self, xp, sp):
          data = self._make_data_max_explicit(xp, sp, axis=self.axis)
          if xp is cupy:
              return data.max(axis=self.axis, explicit=True)
          else:
              return data.max(axis=self.axis)
  
      @testing.numpy_cupy_array_equal(sp_name='sp')
      def test_argmin(self, xp, sp):
          # TODO(takagi) Fix axis=None
          if self.axis is None:
              pytest.skip()
          data = self._make_data_min(xp, sp, dense=self.dense)
          return data.argmin(axis=self.axis)
  
      @testing.numpy_cupy_array_equal(sp_name='sp')
      def test_argmax(self, xp, sp):
          # TODO(takagi) Fix axis=None
          if self.axis is None:
              pytest.skip()
          data = self._make_data_max(xp, sp, dense=self.dense)
          return data.argmax(axis=self.axis)
  
  
  @testing.parameterize(*testing.product({
      'dtype': [numpy.float32, numpy.float64, numpy.complex64, numpy.complex128],
  }))
  @testing.with_requires('scipy')
  class TestCsrMatrixData(unittest.TestCase):
  
      @testing.numpy_cupy_equal(sp_name='sp')
      def test_dtype(self, xp, sp):
          return _make(xp, sp, self.dtype).dtype
  
      @testing.numpy_cupy_allclose(sp_name='sp')
      def test_abs(self, xp, sp):
          m = _make(xp, sp, self.dtype)
          return abs(m)
  
      @testing.numpy_cupy_allclose(sp_name='sp')
      def test_neg(self, xp, sp):
          m = _make(xp, sp, self.dtype)
          return -m
  
      @testing.numpy_cupy_allclose(sp_name='sp')
      def test_astype(self, xp, sp):
          m = _make(xp, sp, self.dtype)
          if numpy.dtype(self.dtype).kind == 'c':
              t = 'D'
          else:
              t = 'd'
          return m.astype(t)
  
      @testing.numpy_cupy_equal(sp_name='sp')
      def test_count_nonzero(self, xp, sp):
          m = _make(xp, sp, self.dtype)
          return m.count_nonzero()
  
      @testing.numpy_cupy_allclose(sp_name='sp')
      def test_power(self, xp, sp):
          m = _make(xp, sp, self.dtype)
          return m.power(2)
  
      @testing.numpy_cupy_allclose(sp_name='sp')
      def test_power_with_dtype(self, xp, sp):
          m = _make(xp, sp, self.dtype)
          if numpy.dtype(self.dtype).kind == 'c':
              t = 'D'
          else:
              t = 'd'
          return m.power(2, t)
  
      @testing.numpy_cupy_allclose(sp_name='sp')
      def test_mean_axis_None(self, xp, sp):
          m = _make(xp, sp, self.dtype)
          return m.mean(axis=None)
  
      @testing.numpy_cupy_allclose(sp_name='sp')
      def test_mean_axis_0(self, xp, sp):
          m = _make(xp, sp, self.dtype)
          return m.mean(axis=0)
  
      @testing.numpy_cupy_allclose(sp_name='sp')
      def test_mean_axis_1(self, xp, sp):
          m = _make(xp, sp, self.dtype)
          return m.mean(axis=1)
  
      @testing.numpy_cupy_allclose(sp_name='sp')
      def test_mean_axis_negative_1(self, xp, sp):
          m = _make(xp, sp, self.dtype)
          return m.mean(axis=-1)
  
      @testing.numpy_cupy_allclose(sp_name='sp')
      def test_mean_axis_negative_2(self, xp, sp):
          m = _make(xp, sp, self.dtype)
          return m.mean(axis=-2)
  
  
  @testing.parameterize(*testing.product({
      'dtype': [numpy.float32, numpy.float64],
      'ufunc': [
          'arcsin', 'arcsinh', 'arctan', 'arctanh', 'ceil', 'deg2rad', 'expm1',
          'floor', 'log1p', 'rad2deg', 'rint', 'sign', 'sin', 'sinh', 'sqrt',
          'tan', 'tanh', 'trunc',
      ],
  }))
  @testing.with_requires('scipy')
  class TestUfunc(unittest.TestCase):
  
      @testing.numpy_cupy_allclose(sp_name='sp', atol=1e-5)
      def test_ufun(self, xp, sp):
          x = _make(xp, sp, self.dtype)
          x.data *= 0.1
          func = getattr(x, self.ufunc)
          complex_unsupported = {'ceil', 'deg2rad', 'floor', 'rad2deg', 'trunc'}
          if (numpy.dtype(self.dtype).kind == 'c' and
                  self.ufunc in complex_unsupported):
              with self.assertRaises(TypeError):
                  func()
              return numpy.array(0)
          else:
              return func()
  
  
  class TestIsspmatrixCsr(unittest.TestCase):
  
      def test_csr(self):
          x = sparse.csr_matrix(
              (cupy.array([], 'f'),
               cupy.array([], 'i'),
               cupy.array([0], 'i')),
              shape=(0, 0), dtype='f')
          assert sparse.isspmatrix_csr(x) is True
  
      def test_csc(self):
          x = sparse.csr_matrix(
              (cupy.array([], 'f'),
               cupy.array([], 'i'),
               cupy.array([0], 'i')),
              shape=(0, 0), dtype='f')
          assert sparse.isspmatrix_csc(x) is False
  
  
  @testing.parameterize(*testing.product({
      'dtype': [numpy.float32, numpy.float64, cupy.complex64, cupy.complex128],
  }))
  @testing.with_requires('scipy>=1.4.0')
  class TestCsrMatrixGetitem(unittest.TestCase):
  
      @testing.numpy_cupy_equal(sp_name='sp')
      def test_getitem_int_int(self, xp, sp):
          assert _make(xp, sp, self.dtype)[0, 1] == 1
  
      @testing.numpy_cupy_equal(sp_name='sp')
      def test_getitem_int_int_not_found(self, xp, sp):
          assert _make(xp, sp, self.dtype)[1, 1] == 0
  
      @testing.numpy_cupy_equal(sp_name='sp')
      def test_getitem_int_int_negative(self, xp, sp):
          assert _make(xp, sp, self.dtype)[-1, -2] == 3
  
      def test_getitem_int_int_too_small_row(self):
          for xp, sp in ((numpy, scipy.sparse), (cupy, sparse)):
              with pytest.raises(IndexError):
                  _make(xp, sp, self.dtype)[-4, 0]
  
      def test_getitem_int_int_too_large_row(self):
          for xp, sp in ((numpy, scipy.sparse), (cupy, sparse)):
              with pytest.raises(IndexError):
                  _make(xp, sp, self.dtype)[3, 0]
  
      def test_getitem_int_int_too_small_col(self):
          for xp, sp in ((numpy, scipy.sparse), (cupy, sparse)):
              with pytest.raises(IndexError):
                  _make(xp, sp, self.dtype)[0, -5]
  
      def test_getitem_int_int_too_large_col(self):
          for xp, sp in ((numpy, scipy.sparse), (cupy, sparse)):
              with pytest.raises(IndexError):
                  _make(xp, sp, self.dtype)[0, 4]
  
      @testing.numpy_cupy_allclose(sp_name='sp')
      def test_getitem_int(self, xp, sp):
          return _make(xp, sp, self.dtype)[1]
  
      @testing.numpy_cupy_allclose(sp_name='sp')
      def test_getitem_int_negative(self, xp, sp):
          return _make(xp, sp, self.dtype)[-1]
  
      def test_getitem_int_to_small(self):
          for xp, sp in ((numpy, scipy.sparse), (cupy, sparse)):
              with pytest.raises(IndexError):
                  _make(xp, sp, self.dtype)[-4]
  
      def test_getitem_int_to_large(self):
          for xp, sp in ((numpy, scipy.sparse), (cupy, sparse)):
              with pytest.raises(IndexError):
                  _make(xp, sp, self.dtype)[3]
  
      @testing.numpy_cupy_allclose(sp_name='sp')
      def test_getitem_int_none_slice(self, xp, sp):
          return _make(xp, sp, self.dtype)[1, :]
  
      @testing.numpy_cupy_allclose(sp_name='sp')
      def test_getitem_negative_int_none_slice(self, xp, sp):
          return _make(xp, sp, self.dtype)[-1, :]
  
      def test_getitem_int_too_small_none_slice(self):
          for xp, sp in ((numpy, scipy.sparse), (cupy, sparse)):
              with pytest.raises(IndexError):
                  _make(xp, sp, self.dtype)[-4, :]
  
      def test_getitem_int_too_large_none_slice(self):
          for xp, sp in ((numpy, scipy.sparse), (cupy, sparse)):
              with pytest.raises(IndexError):
                  _make(xp, sp, self.dtype)[3, :]
  
      @testing.numpy_cupy_allclose(sp_name='sp')
      def test_getitem_slice(self, xp, sp):
          return _make(xp, sp, self.dtype)[1:3]
  
      @testing.numpy_cupy_allclose(sp_name='sp')
      def test_getitem_slice_negative(self, xp, sp):
          return _make(xp, sp, self.dtype)[-2:-1]
  
      # SciPy prior to 1.4 has bugs where either an IndexError is raised or a
      # segfault occurs instead of returning an empty slice.
      @testing.numpy_cupy_allclose(sp_name='sp')
      def test_getitem_slice_start_larger_than_stop(self, xp, sp):
          return _make(xp, sp, self.dtype)[3:2]
  
      @testing.numpy_cupy_allclose(sp_name='sp')
      def test_getitem_ellipsis(self, xp, sp):
          return _make(xp, sp, self.dtype)[...]
  
      @testing.numpy_cupy_allclose(sp_name='sp')
      def test_getitem_int_ellipsis(self, xp, sp):
          return _make(xp, sp, self.dtype)[1, ...]
  
      @testing.numpy_cupy_allclose(sp_name='sp')
      def test_getitem_rowslice_all(self, xp, sp):
          # This test is adapted from Scipy
          return _make(xp, sp, self.dtype)[slice(None, None, None)]
  
      @testing.numpy_cupy_allclose(sp_name='sp')
      def test_getitem_rowslice_negative_stop(self, xp, sp):
          # This test is adapted from Scipy
          return _make(xp, sp, self.dtype)[slice(1, -2, 2)]
  
      def test_getrow(self):
  
          # This test is adapted from Scipy's CSR tests
          N = 10
          X = testing.shaped_random((N, N), cupy, seed=0)
          X[X > 0.7] = 0
          Xcsr = sparse.csr_matrix(X)
  
          for i in range(N):
              arr_row = X[i:i + 1, :]
              csr_row = Xcsr.getrow(i)
              assert sparse.isspmatrix_csr(csr_row)
              assert (arr_row == csr_row.toarray()).all()
  
      def test_getcol(self):
          # This test is adapted from Scipy's CSR tests
          N = 10
          X = testing.shaped_random((N, N), cupy, seed=0)
          X[X > 0.7] = 0
          Xcsr = sparse.csr_matrix(X)
  
          for i in range(N):
              arr_col = X[:, i:i + 1]
              csr_col = Xcsr.getcol(i)
  
              assert sparse.isspmatrix_csr(csr_col)
              assert (arr_col == csr_col.toarray()).all()
  
  
  @testing.parameterize(*testing.product({
      'dtype': [numpy.float32, numpy.float64, cupy.complex64, cupy.complex128],
  }))
  @testing.with_requires('scipy>=1.4.0')
  class TestCsrMatrixGetitem2(unittest.TestCase):
  
      @testing.numpy_cupy_allclose(sp_name='sp')
      def test_getitem_slice_start_too_small(self, xp, sp):
          return _make(xp, sp, self.dtype)[-4:None]
  
      @testing.numpy_cupy_allclose(sp_name='sp')
      def test_getitem_slice_start_too_large(self, xp, sp):
          return _make(xp, sp, self.dtype)[4:None]
  
      @testing.numpy_cupy_allclose(sp_name='sp')
      def test_getitem_slice_stop_too_small(self, xp, sp):
          return _make(xp, sp, self.dtype)[None:-4]
  
      @testing.numpy_cupy_allclose(sp_name='sp')
      def test_getitem_slice_stop_too_large(self, xp, sp):
          return _make(xp, sp, self.dtype)[None:4]
  
  
  # CUB SpMV works only when the matrix size is nonzero
  @testing.parameterize(*testing.product({
      'make_method': ['_make', '_make_unordered', '_make_duplicate'],
      'dtype': [numpy.float32, numpy.float64, cupy.complex64, cupy.complex128],
  }))
  @testing.with_requires('scipy')
  @testing.gpu
  @unittest.skipUnless(cupy.cuda.cub.available, 'The CUB routine is not enabled')
  class TestCubSpmv(unittest.TestCase):
  
      def setUp(self):
          self.old_accelerators = _accelerator.get_routine_accelerators()
          _accelerator.set_routine_accelerators(['cub'])
  
      def tearDown(self):
          _accelerator.set_routine_accelerators(self.old_accelerators)
  
      @property
      def make(self):
          return globals()[self.make_method]
  
      @testing.numpy_cupy_allclose(sp_name='sp')
      def test_mul_dense_vector(self, xp, sp):
          m = self.make(xp, sp, self.dtype)
          x = xp.arange(4).astype(self.dtype)
          if xp is numpy:
              return m * x
  
          # xp is cupy, first ensure we really use CUB
          func = 'cupyx.scipy.sparse.csr.cub.device_csrmv'
          with testing.AssertFunctionIsCalled(func):
              m * x
          # ...then perform the actual computation
          return m * x
  
  
  @testing.parameterize(*testing.product({
      'a_dtype': ['float32', 'float64', 'complex64', 'complex128'],
      'b_dtype': ['float32', 'float64', 'complex64', 'complex128'],
      'shape': [(4, 25), (10, 10), (25, 4)],
      'nz_rate': [0.1, 0.5],
      'opt': ['maximum', 'minimum'],
  }))
  @testing.with_requires('scipy')
  @testing.gpu
  class TestCsrMatrixMaximumMinimum(unittest.TestCase):
  
      def _make_array(self, shape, dtype, xp):
          dtype = numpy.dtype(dtype)
          if dtype.char in 'fF':
              real_dtype = 'float32'
          elif dtype.char in 'dD':
              real_dtype = 'float64'
          a = testing.shaped_random(shape, xp, dtype=real_dtype, scale=2)
          a = (a - 1) / self.nz_rate
          a[a > 1] = 0
          a[a < -1] = 0
          return a
  
      def _make_matrix(self, shape, dtype, xp):
          dtype = numpy.dtype(dtype)
          a = self._make_array(shape, dtype, xp)
          if dtype.char in 'FD':
              a = a + 1j * self._make_array(shape, dtype, xp)
          return a
  
      def _make_sp_matrix(self, dtype, xp, sp):
          return sp.csr_matrix(self._make_matrix(self.shape, dtype, xp))
  
      def _make_sp_matrix_row(self, dtype, xp, sp):
          shape = 1, self.shape[1]
          return sp.csr_matrix(self._make_matrix(shape, dtype, xp))
  
      def _make_sp_matrix_col(self, dtype, xp, sp):
          shape = self.shape[0], 1
          return sp.csr_matrix(self._make_matrix(shape, dtype, xp))
  
      def _make_sp_matrix_shape(self, shape, dtype, xp, sp):
          return sp.csr_matrix(self._make_matrix(shape, dtype, xp))
  
      @testing.numpy_cupy_array_equal(sp_name='sp')
      def test_sparse(self, xp, sp):
          a = self._make_sp_matrix(self.a_dtype, xp, sp)
          b = self._make_sp_matrix(self.b_dtype, xp, sp)
          return getattr(a, self.opt)(b)
  
      @testing.numpy_cupy_array_equal(sp_name='sp')
      def test_sparse_row(self, xp, sp):
          a = self._make_sp_matrix(self.a_dtype, xp, sp)
          b = self._make_sp_matrix_row(self.b_dtype, xp, sp)
          if xp == numpy:
              # SciPy does not support sparse broadcasting
              return getattr(a, self.opt)(b.toarray())
          else:
              return getattr(a, self.opt)(b).toarray()
  
      @testing.numpy_cupy_array_equal(sp_name='sp')
      def test_sparse_col(self, xp, sp):
          a = self._make_sp_matrix(self.a_dtype, xp, sp)
          b = self._make_sp_matrix_col(self.b_dtype, xp, sp)
          if xp == numpy:
              # SciPy does not support sparse broadcasting
              return getattr(a, self.opt)(b.toarray())
          else:
              return getattr(a, self.opt)(b).toarray()
  
      @testing.numpy_cupy_array_equal(sp_name='sp')
      def test_dense(self, xp, sp):
          a = self._make_sp_matrix(self.a_dtype, xp, sp)
          b = self._make_sp_matrix(self.b_dtype, xp, sp).toarray()
          return getattr(a, self.opt)(b)
  
      @testing.numpy_cupy_array_equal(sp_name='sp')
      def test_dense_row(self, xp, sp):
          a = self._make_sp_matrix(self.a_dtype, xp, sp)
          b = self._make_sp_matrix_row(self.b_dtype, xp, sp).toarray()
          return getattr(a, self.opt)(b)
  
      @testing.numpy_cupy_array_equal(sp_name='sp')
      def test_dense_col(self, xp, sp):
          a = self._make_sp_matrix(self.a_dtype, xp, sp)
          b = self._make_sp_matrix_col(self.b_dtype, xp, sp).toarray()
          return getattr(a, self.opt)(b)
  
      @testing.numpy_cupy_array_equal(sp_name='sp')
      def test_scalar_plus(self, xp, sp):
          a = self._make_sp_matrix(self.a_dtype, xp, sp)
          return getattr(a, self.opt)(0.5)
  
      @testing.numpy_cupy_array_equal(sp_name='sp')
      def test_scalar_minus(self, xp, sp):
          a = self._make_sp_matrix(self.a_dtype, xp, sp)
          return getattr(a, self.opt)(-0.5)
  
      @testing.numpy_cupy_array_equal(sp_name='sp')
      def test_scalar_zero(self, xp, sp):
          a = self._make_sp_matrix(self.a_dtype, xp, sp)
          return getattr(a, self.opt)(0)
  
      def test_ng_shape(self):
          xp, sp = cupy, sparse
          a = self._make_sp_matrix(self.a_dtype, xp, sp)
          for i, j in ((-1, 0), (1, 0), (0, -1), (0, 1)):
              shape = self.shape[0] + i, self.shape[1] + j
              b = self._make_sp_matrix_shape(shape, self.b_dtype, xp, sp)
              with self.assertRaises(ValueError):
                  getattr(a, self.opt)(b)
  
  
  @testing.parameterize(*testing.product({
      'a_dtype': ['float32', 'float64', 'complex64', 'complex128'],
      'b_dtype': ['float32', 'float64', 'complex64', 'complex128'],
      'shape': [(6, 15), (15, 6)],
      'opt': ['_eq_', '_ne_', '_lt_', '_gt_', '_le_', '_ge_'],
  }))
  @testing.with_requires('scipy>=1.2')
  @testing.gpu
  class TestCsrMatrixComparison(unittest.TestCase):
      nz_rate = 0.3
  
      def _make_array(self, shape, dtype, xp):
          dtype = numpy.dtype(dtype)
          if dtype.char in 'fF':
              real_dtype = 'float32'
          elif dtype.char in 'dD':
              real_dtype = 'float64'
          a = testing.shaped_random(shape, xp, dtype=real_dtype, scale=2)
          a = (a - 1) / self.nz_rate
          a[a > 1] = 0
          a[a < -1] = 0
          return a
  
      def _make_matrix(self, shape, dtype, xp):
          dtype = numpy.dtype(dtype)
          a = self._make_array(shape, dtype, xp)
          if dtype.char in 'FD':
              a = a + 1j * self._make_array(shape, dtype, xp)
          return a
  
      def _make_sp_matrix(self, dtype, xp, sp):
          return sp.csr_matrix(self._make_matrix(self.shape, dtype, xp))
  
      def _make_sp_matrix_row(self, dtype, xp, sp):
          shape = 1, self.shape[1]
          return sp.csr_matrix(self._make_matrix(shape, dtype, xp))
  
      def _make_sp_matrix_col(self, dtype, xp, sp):
          shape = self.shape[0], 1
          return sp.csr_matrix(self._make_matrix(shape, dtype, xp))
  
      def _make_sp_matrix_shape(self, shape, dtype, xp, sp):
          return sp.csr_matrix(self._make_matrix(shape, dtype, xp))
  
      def _compare(self, a, b):
          if self.opt == '_eq_':
              return a == b
          elif self.opt == '_ne_':
              return a != b
          elif self.opt == '_lt_':
              return a < b
          elif self.opt == '_gt_':
              return a > b
          elif self.opt == '_le_':
              return a <= b
          elif self.opt == '_ge_':
              return a >= b
  
      @contextlib.contextmanager
      def _ignore_efficiency_warning(self):
          with warnings.catch_warnings():
              warnings.simplefilter('ignore', sparse.SparseEfficiencyWarning)
              yield
  
      @contextlib.contextmanager
      def _assert_warns_efficiency(self, sp, scalar_rhs=None):
          if scalar_rhs is None and self._compare(0, 0):
              with testing.assert_warns(sp.SparseEfficiencyWarning):
                  yield
          elif scalar_rhs is not None and self._compare(0, scalar_rhs):
              if sp is sparse:  # cupy
                  # TODO(kataoka): Test it, too. But, it seems the current
                  # implementation does not depend on the scalar value.
                  with self._ignore_efficiency_warning():
                      yield
              else:  # scipy
                  with testing.assert_warns(sp.SparseEfficiencyWarning):
                      yield
          else:
              yield
  
      @testing.numpy_cupy_array_equal(sp_name='sp')
      def test_sparse(self, xp, sp):
          a = self._make_sp_matrix(self.a_dtype, xp, sp)
          b = self._make_sp_matrix(self.b_dtype, xp, sp)
          with self._assert_warns_efficiency(sp):
              return self._compare(a, b)
  
      @testing.numpy_cupy_array_equal(sp_name='sp')
      def test_sparse_row(self, xp, sp):
          a = self._make_sp_matrix(self.a_dtype, xp, sp)
          b = self._make_sp_matrix_row(self.b_dtype, xp, sp)
          if xp == numpy:
              # SciPy does not support sparse broadcasting
              return self._compare(a, b.toarray())
          else:
              with self._assert_warns_efficiency(sp):
                  return self._compare(a, b).toarray()
  
      @testing.numpy_cupy_array_equal(sp_name='sp')
      def test_sparse_col(self, xp, sp):
          a = self._make_sp_matrix(self.a_dtype, xp, sp)
          b = self._make_sp_matrix_col(self.b_dtype, xp, sp)
          if xp == numpy:
              # SciPy does not support sparse broadcasting
              return self._compare(a, b.toarray())
          else:
              with self._assert_warns_efficiency(sp):
                  return self._compare(a, b).toarray()
  
      @testing.numpy_cupy_array_equal(sp_name='sp')
      def test_dense(self, xp, sp):
          a = self._make_sp_matrix(self.a_dtype, xp, sp)
          b = self._make_sp_matrix(self.b_dtype, xp, sp).toarray()
          return self._compare(a, b)
  
      @testing.numpy_cupy_array_equal(sp_name='sp')
      def test_dense_row(self, xp, sp):
          a = self._make_sp_matrix(self.a_dtype, xp, sp)
          b = self._make_sp_matrix_row(self.b_dtype, xp, sp).toarray()
          return self._compare(a, b)
  
      @testing.numpy_cupy_array_equal(sp_name='sp')
      def test_dense_col(self, xp, sp):
          a = self._make_sp_matrix(self.a_dtype, xp, sp)
          b = self._make_sp_matrix_col(self.b_dtype, xp, sp).toarray()
          return self._compare(a, b)
  
      @testing.numpy_cupy_array_equal(sp_name='sp')
      def test_scalar_plus(self, xp, sp):
          a = self._make_sp_matrix(self.a_dtype, xp, sp)
          with self._assert_warns_efficiency(sp, 0.5):
              return self._compare(a, 0.5)
  
      @testing.numpy_cupy_array_equal(sp_name='sp')
      def test_scalar_minus(self, xp, sp):
          a = self._make_sp_matrix(self.a_dtype, xp, sp)
          with self._assert_warns_efficiency(sp, -0.5):
              return self._compare(a, -0.5)
  
      @testing.numpy_cupy_array_equal(sp_name='sp')
      def test_scalar_zero(self, xp, sp):
          if self.opt in ('_le_', '_ge_'):
              # <= and >= with 0 are not supported by SciPy
              pytest.skip()
          a = self._make_sp_matrix(self.a_dtype, xp, sp)
          with self._assert_warns_efficiency(sp, 0):
              return self._compare(a, 0)
  
      @testing.numpy_cupy_array_equal(sp_name='sp')
      def test_scalar_nan(self, xp, sp):
          a = self._make_sp_matrix(self.a_dtype, xp, sp)
          with self._assert_warns_efficiency(sp, numpy.nan):
              return self._compare(a, numpy.nan)
  
      def test_ng_shape(self):
          xp, sp = cupy, sparse
          a = self._make_sp_matrix(self.a_dtype, xp, sp)
          for i, j in ((-1, 0), (1, 0), (0, -1), (0, 1)):
              shape = self.shape[0] + i, self.shape[1] + j
              b = self._make_sp_matrix_shape(shape, self.b_dtype, xp, sp)
              with self.assertRaises(ValueError):
                  with self._ignore_efficiency_warning():
                      self._compare(a, b)
  
  
  @testing.parameterize(*testing.product({
      'shape': [(8, 5), (5, 5), (5, 8)],
  }))
  @testing.with_requires('scipy>=1.5.0')
  @testing.gpu
  class TestCsrMatrixDiagonal(unittest.TestCase):
      density = 0.5
  
      def _make_matrix(self, dtype):
          a = testing.shaped_random(self.shape, numpy, dtype=dtype)
          mask = testing.shaped_random(self.shape, numpy, dtype='f', scale=1.0)
          a[mask > self.density] = 0
          scipy_a = scipy.sparse.csr_matrix(a)
          cupyx_a = sparse.csr_matrix(cupy.array(a))
          return scipy_a, cupyx_a
  
      @testing.for_dtypes('fdFD')
      def test_diagonal(self, dtype):
          scipy_a, cupyx_a = self._make_matrix(dtype)
          m, n = self.shape
          for k in range(-m, n+1):
              scipy_diag = scipy_a.diagonal(k=k)
              cupyx_diag = cupyx_a.diagonal(k=k)
              testing.assert_allclose(scipy_diag, cupyx_diag)
  
      def _test_setdiag(self, scipy_a, cupyx_a, x, k):
          scipy_a = scipy_a.copy()
          cupyx_a = cupyx_a.copy()
          scipy_a.setdiag(x, k=k)
          cupyx_a.setdiag(cupy.array(x), k=k)
          testing.assert_allclose(scipy_a.data, cupyx_a.data)
          testing.assert_array_equal(scipy_a.indices, cupyx_a.indices)
          testing.assert_array_equal(scipy_a.indptr, cupyx_a.indptr)
  
      @testing.for_dtypes('fdFD')
      def test_setdiag(self, dtype):
          scipy_a, cupyx_a = self._make_matrix(dtype)
          m, n = self.shape
          for k in range(-m+1, n):
              m_st, n_st = max(0, -k), max(0, k)
              for d in (-1, 0, 1):
                  x_len = min(m - m_st, n - n_st) + d
                  if x_len <= 0:
                      continue
                  x = numpy.ones((x_len,), dtype=dtype)
                  self._test_setdiag(scipy_a, cupyx_a, x, k)
  
      @testing.for_dtypes('fdFD')
      def test_setdiag_scalar(self, dtype):
          scipy_a, cupyx_a = self._make_matrix(dtype)
          x = numpy.array(1.0, dtype=dtype)
          m, n = self.shape
          for k in range(-m+1, n):
              self._test_setdiag(scipy_a, cupyx_a, x, k)
  
      def test_setdiag_invalid(self):
          dtype = 'f'
          scipy_a, cupyx_a = self._make_matrix(dtype)
          x = numpy.array(1.0, dtype=dtype)
          m, n = self.shape
          for k in (-m, n):
              with self.assertRaises(ValueError):
                  scipy_a.setdiag(x, k=k)
              with self.assertRaises(ValueError):
                  cupyx_a.setdiag(x, k=k)
  
apis: numpy.zeros_like numpy.ones numpy.dtype numpy.isinf numpy.issubdtype numpy.arange numpy.array