import numpy as np
from numpy.linalg import qr
from ..kruskal import kruskal_to_tensor
from ..tucker import tucker_to_tensor


def check_random_state(seed):
    """Returns a valid RandomState

    Parameters
    ----------
    seed : None or instance of int or np.random.RandomState(), default is None

    Returns
    -------
    Valid instance np.random.RandomState

    Notes
    -----
    Inspired by the scikit-learn eponymous function
    """
    if seed is None or isinstance(seed, int):
        return np.random.RandomState(seed)

    elif isinstance(seed, np.random.RandomState):
        return seed

    raise ValueError('Seed should be None, int or np.random.RandomState')

def cp_tensor(shape, rank, full=False, random_state=None):
    """Generates a random CP tensor
    
    Parameters
    ----------
    shape : tuple
        shape of the tensor to generate
    rank : int
        rank of the CP decomposition
    full : bool, optional, default is False
        if True, a full tensor is returned
        otherwise, the decomposed tensor is returned
    random_state : `np.random.RandomState`
        
    Returns
    -------
    cp_tensor : ND-array or 2D-array list
        ND-array : full tensor if `full` is True
        2D-array list : list of factors otherwise
    """
    rns = check_random_state(random_state)
    factors = [rns.random_sample((s, rank)) for s in shape]
    if full:
        return kruskal_to_tensor(factors)
    else:
        return factors
    

def tucker_tensor(shape, rank, full=False, random_state=None):
    """Generates a random Tucker tensor
    
    Parameters
    ----------
    shape : tuple
        shape of the tensor to generate
    rank : int or int list
        rank of the Tucker decomposition
        if int, the same rank is used for each mode
        otherwise, dimension of each mode
    full : bool, optional, default is False
        if True, a full tensor is returned
        otherwise, the decomposed tensor is returned
    random_state : `np.random.RandomState`
        
    Returns
    -------
    tucker_tensor : ND-array or (ND-array, 2D-array list)
        ND-array : full tensor if `full` is True
        (ND-array, 2D-array list) : core tensor and list of factors otherwise
    """
    rns = check_random_state(random_state)

    if isinstance(rank, int):
        rank = [rank for _ in shape]

    for i, (s, r) in enumerate(zip(shape, rank)):
        if r > s:
            raise ValueError('The rank should be smaller than the tensor size, yet rank[{0}]={1} > shape[{0}]={2}.'.format(i, r, s))

    factors = []
    for (s, r) in zip(shape, rank):
        Q, _= qr(rns.random_sample((s, s)))
        factors.append(Q[:, :r])

    core = rns.random_sample(rank)
    if full:
        return tucker_to_tensor(core, factors)
    else:
        return core, factors