# Path: dppy/utils.py
# def check_random_state(seed: object) -> np.random.RandomState:
# def inner1d(arr1, arr2=None, axis=0):
# def det_ST(array, S, T=None):
# def is_square(array):
# def is_symmetric(array):
# def is_projection(array, col_idx=None):
# def is_orthonormal_columns(array, col_idx=None):
# def is_equal_to_O_or_1(array, tol=1e-8):
# def is_in_01(array, tol=1e-8):
# def is_geq_0(array, tol=1e-8):
# def is_full_row_rank(array):
# def stable_filter(eigenvec, eigenval):
# def stable_invert_root(eigenvec, eigenval):
# def get_progress_bar(total=-1, disable=False, **kwargs):
#         def __init__(self, total=-1, disable=False, **kwargs):
#         def __enter__(self):
#         def __exit__(self, *args, **kwargs):
#         def set_postfix(self, **kwargs):
#         def update(self):
# def evaluate_L_diagonal(eval_L, X):
# def example_eval_L_linear(X, Y=None):
# def example_eval_L_polynomial(X, Y=None, p=2):
# def example_eval_L_min_kern(X, Y=None):
#     M = array[np.ix_(idx, idx)]
#     U = array[:, col_idx]
#     X = np.atleast_2d(X)
#         Y = np.atleast_2d(Y)
#     X = np.atleast_2d(X)
#         Y = X
#         Y = np.atleast_2d(Y)
#     class ProgBar(object):


import unittest
import numpy as np
import numpy.linalg as la
import numpy.random as rndm
import sys
from scipy.linalg import qr
from dppy import utils
                          (True, rndm.randn(rank, N)),
                          (False, rndm.randn(N + 1, N)),
                          (False, np.zeros((rank, N))),
                          (False, np.ones((rank, N)))]

        for idx, (flag, _input) in enumerate(list_of_inputs):
            with self.subTest(index=idx, is_full_row_rank=flag):

                if flag:
                    self.assertTrue(utils.is_full_row_rank(_input) is _input)
                else:
                    with self.assertRaises(ValueError) as context:
                        utils.is_full_row_rank(_input)

                    self.assertIn('not full row rank', str(context.exception))

    def test_evaluate_L_diagonal(self):
        """ checking np.diag(dpp.L) = utils.evaluate_L_diagonal(eval_L, X_data)
        """

        X = rndm.randn(100, 20)

        np.testing.assert_almost_equal(
          np.diag(utils.example_eval_L_linear(X)),
          utils.evaluate_L_diagonal(utils.example_eval_L_linear, X))

        X = rndm.rand(100, 1)

        np.testing.assert_almost_equal(
          np.diag(utils.example_eval_L_min_kern(X)),

# Path: dppy/utils.py
# def check_random_state(seed: object) -> np.random.RandomState:
# def inner1d(arr1, arr2=None, axis=0):
# def det_ST(array, S, T=None):
# def is_square(array):
# def is_symmetric(array):
# def is_projection(array, col_idx=None):
# def is_orthonormal_columns(array, col_idx=None):
# def is_equal_to_O_or_1(array, tol=1e-8):
# def is_in_01(array, tol=1e-8):
# def is_geq_0(array, tol=1e-8):
# def is_full_row_rank(array):
# def stable_filter(eigenvec, eigenval):
# def stable_invert_root(eigenvec, eigenval):
# def get_progress_bar(total=-1, disable=False, **kwargs):
#         def __init__(self, total=-1, disable=False, **kwargs):
#         def __enter__(self):
#         def __exit__(self, *args, **kwargs):
#         def set_postfix(self, **kwargs):
#         def update(self):
# def evaluate_L_diagonal(eval_L, X):
# def example_eval_L_linear(X, Y=None):
# def example_eval_L_polynomial(X, Y=None, p=2):
# def example_eval_L_min_kern(X, Y=None):
#     M = array[np.ix_(idx, idx)]
#     U = array[:, col_idx]
#     X = np.atleast_2d(X)
#         Y = np.atleast_2d(Y)
#     X = np.atleast_2d(X)
#         Y = X
#         Y = np.atleast_2d(Y)
#     class ProgBar(object):


# Path: tests/test_utils.py
import unittest
import numpy as np
import numpy.linalg as la
import numpy.random as rndm
import sys
from scipy.linalg import qr
from dppy import utils

                          (True, rndm.randn(rank, N)),
                          (False, rndm.randn(N + 1, N)),
                          (False, np.zeros((rank, N))),
                          (False, np.ones((rank, N)))]

        for idx, (flag, _input) in enumerate(list_of_inputs):
            with self.subTest(index=idx, is_full_row_rank=flag):

                if flag:
                    self.assertTrue(utils.is_full_row_rank(_input) is _input)
                else:
                    with self.assertRaises(ValueError) as context:
                        utils.is_full_row_rank(_input)

                    self.assertIn('not full row rank', str(context.exception))

    def test_evaluate_L_diagonal(self):
        """ checking np.diag(dpp.L) = utils.evaluate_L_diagonal(eval_L, X_data)
        """

        X = rndm.randn(100, 20)

        np.testing.assert_almost_equal(
          np.diag(utils.example_eval_L_linear(X)),
          utils.evaluate_L_diagonal(utils.example_eval_L_linear, X))

        X = rndm.rand(100, 1)

        np.testing.assert_almost_equal(
          np.diag(utils.example_eval_L_min_kern(X)),

          utils.evaluate_L_diagonal(utils.example_eval_L_min_kern, X))