import numbers
import array
from collections.abc import Iterable

import numpy as np


def make_blobs(n_samples=100, n_features=2, centers=None, cluster_std=1.0,
               center_box=(-10.0, 10.0), shuffle=True, random_state=None):
    """Generate isotropic Gaussian blobs for clustering.
    Read more in the :ref:`User Guide <sample_generators>`.
    Parameters
    ----------
    n_samples : int or array-like, optional (default=100)
        If int, it is the total number of points equally divided among
        clusters.
        If array-like, each element of the sequence indicates
        the number of samples per cluster.
    n_features : int, optional (default=2)
        The number of features for each sample.
    centers : int or array of shape [n_centers, n_features], optional
        (default=None)
        The number of centers to generate, or the fixed center locations.
        If n_samples is an int and centers is None, 3 centers are generated.
        If n_samples is array-like, centers must be
        either None or an array of length equal to the length of n_samples.
    cluster_std : float or sequence of floats, optional (default=1.0)
        The standard deviation of the clusters.
    center_box : pair of floats (min, max), optional (default=(-10.0, 10.0))
        The bounding box for each cluster center when centers are
        generated at random.
    shuffle : boolean, optional (default=True)
        Shuffle the samples.
    random_state : int, RandomState instance or None (default)
        Determines random number generation for dataset creation. Pass an int
        for reproducible output across multiple function calls.
        See :term:`Glossary <random_state>`.
    Returns
    -------
    X : array of shape [n_samples, n_features]
        The generated samples.
    y : array of shape [n_samples]
        The integer labels for cluster membership of each sample.
    Examples
    --------
    >>> from sklearn.datasets.samples_generator import make_blobs
    >>> X, y = make_blobs(n_samples=10, centers=3, n_features=2,
    ...                   random_state=0)
    >>> print(X.shape)
    (10, 2)
    >>> y
    array([0, 0, 1, 0, 2, 2, 2, 1, 1, 0])
    >>> X, y = make_blobs(n_samples=[3, 3, 4], centers=None, n_features=2,
    ...                   random_state=0)
    >>> print(X.shape)
    (10, 2)
    >>> y
    array([0, 1, 2, 0, 2, 2, 2, 1, 1, 0])
    See also
    --------
    make_classification: a more intricate variant
    """

    generator = np.random.RandomState(random_state)

    if isinstance(n_samples, numbers.Integral):
        # Set n_centers by looking at centers arg
        if centers is None:
            centers = 3

        if isinstance(centers, numbers.Integral):
            n_centers = centers
            centers = generator.uniform(center_box[0], center_box[1],
                                        size=(n_centers, n_features))

        else:
            # centers = check_array(centers)
            n_features = centers.shape[1]
            n_centers = centers.shape[0]

    else:
        # Set n_centers by looking at [n_samples] arg
        n_centers = len(n_samples)
        if centers is None:
            centers = generator.uniform(center_box[0], center_box[1],
                                        size=(n_centers, n_features))
        try:
            assert len(centers) == n_centers
        except TypeError:
            raise ValueError("Parameter `centers` must be array-like. "
                             "Got {!r} instead".format(centers))
        except AssertionError:
            raise ValueError("Length of `n_samples` not consistent"
                             " with number of centers. Got n_samples = {} "
                             "and centers = {}".format(n_samples, centers))
        else:
            centers = centers
            n_features = centers.shape[1]

    # stds: if cluster_std is given as list, it must be consistent
    # with the n_centers
    if (hasattr(cluster_std, "__len__") and len(cluster_std) != n_centers):
        raise ValueError("Length of `clusters_std` not consistent with "
                         "number of centers. Got centers = {} "
                         "and cluster_std = {}".format(centers, cluster_std))

    if isinstance(cluster_std, numbers.Real):
        cluster_std = np.full(len(centers), cluster_std)

    X = []
    y = []

    if isinstance(n_samples, Iterable):
        n_samples_per_center = n_samples
    else:
        n_samples_per_center = [int(n_samples // n_centers)] * n_centers

        for i in range(n_samples % n_centers):
            n_samples_per_center[i] += 1

    for i, (n, std) in enumerate(zip(n_samples_per_center, cluster_std)):
        X.append(generator.normal(loc=centers[i], scale=std,
                                  size=(n, n_features)))
        y += [i] * n

    X = np.concatenate(X)
    y = np.array(y)

    if shuffle:
        total_n_samples = np.sum(n_samples)
        indices = np.arange(total_n_samples)
        generator.shuffle(indices)
        X = X[indices]
        y = y[indices]

    return X, y

make_blobs(n_samples=10, centers=3, n_features=2, random_state=0)