import numpy as np
import six

from chainercv.utils.testing.assertions.assert_is_image import assert_is_image


def assert_is_semantic_segmentation_dataset(dataset, n_class, n_example=None):
    """Checks if a dataset satisfies semantic segmentation dataset APIs.

    This function checks if a given dataset satisfies semantic segmentation
    dataset dataset APIs or not.
    If the dataset does not satifiy the APIs, this function raises an
    :class:`AssertionError`.

    Args:
        dataset: A dataset to be checked.
        n_class (int): The number of classes including background.
        n_example (int): The number of examples to be checked.
            If this argument is specified, this function picks
            examples ramdomly and checks them. Otherwise,
            this function checks all examples.

    """

    assert len(dataset) > 0, 'The length of dataset must be greater than zero.'

    if n_example:
        for _ in six.moves.range(n_example):
            i = np.random.randint(0, len(dataset))
            _check_example(dataset[i], n_class)
    else:
        for i in six.moves.range(len(dataset)):
            _check_example(dataset[i], n_class)


def _check_example(example, n_class):
    assert len(example) >= 2, \
        'Each example must have at least two elements:' \
        'img and label.'

    img, label = example[:2]

    assert_is_image(img, color=True)

    assert isinstance(label, np.ndarray), \
        'label must be a numpy.ndarray.'
    assert label.dtype == np.int32, \
        'The type of label must be numpy.int32.'
    assert label.shape == img.shape[1:], \
        'The shape of label must be (H, W).'
    assert label.min() >= -1 and label.max() < n_class, \
        'The value of label must be in [-1, n_class - 1].'

import numpy as np
import six

from chainercv.utils.testing.assertions.assert_is_image import assert_is_image


def assert_is_semantic_segmentation_dataset(dataset, n_class, n_example=None):
    """Checks if a dataset satisfies semantic segmentation dataset APIs.

    This function checks if a given dataset satisfies semantic segmentation
    dataset APIs or not.
    If the dataset does not satifiy the APIs, this function raises an
    :class:`AssertionError`.

    Args:
        dataset: A dataset to be checked.
        n_class (int): The number of classes including background.
        n_example (int): The number of examples to be checked.
            If this argument is specified, this function picks
            examples ramdomly and checks them. Otherwise,
            this function checks all examples.

    """

    assert len(dataset) > 0, 'The length of dataset must be greater than zero.'

    if n_example:
        for _ in six.moves.range(n_example):
            i = np.random.randint(0, len(dataset))
            _check_example(dataset[i], n_class)
    else:
        for i in six.moves.range(len(dataset)):
            _check_example(dataset[i], n_class)


def _check_example(example, n_class):
    assert len(example) >= 2, \
        'Each example must have at least two elements:' \
        'img and label.'

    img, label = example[:2]

    assert_is_image(img, color=True)

    assert isinstance(label, np.ndarray), \
        'label must be a numpy.ndarray.'
    assert label.dtype == np.int32, \
        'The type of label must be numpy.int32.'
    assert label.shape == img.shape[1:], \
        'The shape of label must be (H, W).'
    assert label.min() >= -1 and label.max() < n_class, \
        'The value of label must be in [-1, n_class - 1].'

fix typo