import unittest
import numpy
import theano.tensor as T
from theano.configparser import config, AddConfigVar, StrParam
import os, sys

AddConfigVar('unittests.rseed',
        "Seed to use for randomized unit tests. Special value 'random' means using a seed of None.",
        StrParam(666))

def fetch_seed(pseed=None):
    """
    Returns the seed to use for running the unit tests.
    If an explicit seed is given, it will be used for seeding numpy's rng.
    If not, it will use config.unittest.rseed (its default value is 666).
    If config.unittest.rseed is set to "random", it will seed the rng with None,
    which is equivalent to seeding with a random seed.

    Useful for seeding RandomState objects.
    >>> rng = numpy.random.RandomState(unittest_tools.fetch_seed())
    """
     
    seed = pseed or config.unittests.rseed
    if seed=='random':
      seed = None
    #backport
    #seed = None if seed=='random' else seed

    try:
        if seed:
          seed = int(seed)
        else:
          seed = None
        #backport
        #seed = int(seed) if seed else None
    except ValueError:
        print >> sys.stderr, 'Error: config.unittests.rseed contains '\
                'invalid seed, using None instead'
        seed = None

    return seed

def seed_rng(pseed=None):
    """
    Seeds numpy's random number generator with the value returned by fetch_seed.
    Usage: unittest_tools.seed_rng()
    """

    seed = fetch_seed(pseed)
    if pseed and pseed!=seed:
        print >> sys.stderr, 'Warning: using seed given by config.unittests.rseed=%i'\
                'instead of seed %i given as parameter' % (seed, pseed)
    numpy.random.seed(seed)
    return seed

def verify_grad(op, pt, n_tests=2, rng=None, *args, **kwargs):
    """
    Wrapper for tensor/basic.py:verify_grad
    Takes care of seeding the random number generator if None is given
    """
    if rng is None:
        seed_rng()
        rng = numpy.random
    T.verify_grad(op, pt, n_tests, rng, *args, **kwargs)

