import unittest
import unittest.mock
import random
import os
import time
import pickle
import warnings
from functools import partial
from math import log, exp, pi, fsum, sin, factorial
from test import support
from fractions import Fraction


class TestBasicOps:
    # Superclass with tests common to all generators.
    # Subclasses must arrange for self.gen to retrieve the Random instance
    # to be tested.

    def randomlist(self, n):
        """Helper function to make a list of random numbers"""
        return [self.gen.random() for i in range(n)]

    def test_autoseed(self):
        self.gen.seed()
        state1 = self.gen.getstate()
        time.sleep(0.1)
        self.gen.seed()      # different seeds at different times
        state2 = self.gen.getstate()
        self.assertNotEqual(state1, state2)

    def test_saverestore(self):
        N = 1000
        self.gen.seed()
        state = self.gen.getstate()
        randseq = self.randomlist(N)
        self.gen.setstate(state)    # should regenerate the same sequence
        self.assertEqual(randseq, self.randomlist(N))

    def test_seedargs(self):
        # Seed value with a negative hash.
        class MySeed(object):
            def __hash__(self):
                return -1729
        for arg in [None, 0, 0, 1, 1, -1, -1, 10**20, -(10**20),
                    3.14, 1+2j, 'a', tuple('abc'), MySeed()]:
            self.gen.seed(arg)
        for arg in [list(range(3)), dict(one=1)]:
            self.assertRaises(TypeError, self.gen.seed, arg)
        self.assertRaises(TypeError, self.gen.seed, 1, 2, 3, 4)
        self.assertRaises(TypeError, type(self.gen), [])

    @unittest.mock.patch('random._urandom') # os.urandom
    def test_seed_when_randomness_source_not_found(self, urandom_mock):
        # Random.seed() uses time.time() when an operating system specific
        # randomness source is not found. To test this on machines where it
        # exists, run the above test, test_seedargs(), again after mocking
        # os.urandom() so that it raises the exception expected when the
        # randomness source is not available.
        urandom_mock.side_effect = NotImplementedError
        self.test_seedargs()

    def test_shuffle(self):
        shuffle = self.gen.shuffle
        lst = []
        shuffle(lst)
        self.assertEqual(lst, [])
        lst = [37]
        shuffle(lst)
        self.assertEqual(lst, [37])
        seqs = [list(range(n)) for n in range(10)]
        shuffled_seqs = [list(range(n)) for n in range(10)]
        for shuffled_seq in shuffled_seqs:
            shuffle(shuffled_seq)
        for (seq, shuffled_seq) in zip(seqs, shuffled_seqs):
            self.assertEqual(len(seq), len(shuffled_seq))
            self.assertEqual(set(seq), set(shuffled_seq))
        # The above tests all would pass if the shuffle was a
        # no-op. The following non-deterministic test covers that.  It
        # asserts that the shuffled sequence of 1000 distinct elements
        # must be different from the original one. Although there is
        # mathematically a non-zero probability that this could
        # actually happen in a genuinely random shuffle, it is
        # completely negligible, given that the number of possible
        # permutations of 1000 objects is 1000! (factorial of 1000),
        # which is considerably larger than the number of atoms in the
        # universe...
        lst = list(range(1000))
        shuffled_lst = list(range(1000))
        shuffle(shuffled_lst)
        self.assertTrue(lst != shuffled_lst)
        shuffle(lst)
        self.assertTrue(lst != shuffled_lst)
        self.assertRaises(TypeError, shuffle, (1, 2, 3))

    def test_shuffle_random_argument(self):
        # Test random argument to shuffle.
        shuffle = self.gen.shuffle
        mock_random = unittest.mock.Mock(return_value=0.5)
        seq = bytearray(b'abcdefghijk')
        shuffle(seq, mock_random)
        mock_random.assert_called_with()

    def test_choice(self):
        choice = self.gen.choice
        with self.assertRaises(IndexError):
            choice([])
        self.assertEqual(choice([50]), 50)
        self.assertIn(choice([25, 75]), [25, 75])

    def test_sample(self):
        # For the entire allowable range of 0 <= k <= N, validate that
        # the sample is of the correct length and contains only unique items
        N = 100
        population = range(N)
        for k in range(N+1):
            s = self.gen.sample(population, k)
            self.assertEqual(len(s), k)
            uniq = set(s)
            self.assertEqual(len(uniq), k)
            self.assertTrue(uniq <= set(population))
        self.assertEqual(self.gen.sample([], 0), [])  # test edge case N==k==0
        # Exception raised if size of sample exceeds that of population
        self.assertRaises(ValueError, self.gen.sample, population, N+1)
        self.assertRaises(ValueError, self.gen.sample, [], -1)

    def test_sample_distribution(self):
        # For the entire allowable range of 0 <= k <= N, validate that
        # sample generates all possible permutations
        n = 5
        pop = range(n)
        trials = 10000  # large num prevents false negatives without slowing normal case
        for k in range(n):
            expected = factorial(n) // factorial(n-k)
            perms = {}
            for i in range(trials):
                perms[tuple(self.gen.sample(pop, k))] = None
                if len(perms) == expected:
                    break
            else:
                self.fail()

    def test_sample_inputs(self):
        # SF bug #801342 -- population can be any iterable defining __len__()
        self.gen.sample(set(range(20)), 2)
        self.gen.sample(range(20), 2)
        self.gen.sample(range(20), 2)
        self.gen.sample(str('abcdefghijklmnopqrst'), 2)
        self.gen.sample(tuple('abcdefghijklmnopqrst'), 2)

    def test_sample_on_dicts(self):
        self.assertRaises(TypeError, self.gen.sample, dict.fromkeys('abcdef'), 2)

    def test_choices(self):
        choices = self.gen.choices
        data = ['red', 'green', 'blue', 'yellow']
        str_data = 'abcd'
        range_data = range(4)
        set_data = set(range(4))

        # basic functionality
        for sample in [
            choices(data, k=5),
            choices(data, range(4), k=5),
            choices(k=5, population=data, weights=range(4)),
            choices(k=5, population=data, cum_weights=range(4)),
        ]:
            self.assertEqual(len(sample), 5)
            self.assertEqual(type(sample), list)
            self.assertTrue(set(sample) <= set(data))

        # test argument handling
        with self.assertRaises(TypeError):                               # missing arguments
            choices(2)

        self.assertEqual(choices(data, k=0), [])                         # k == 0
        self.assertEqual(choices(data, k=-1), [])                        # negative k behaves like ``[0] * -1``
        with self.assertRaises(TypeError):
            choices(data, k=2.5)                                         # k is a float

        self.assertTrue(set(choices(str_data, k=5)) <= set(str_data))    # population is a string sequence
        self.assertTrue(set(choices(range_data, k=5)) <= set(range_data))  # population is a range
        with self.assertRaises(TypeError):
            choices(set_data, k=2)                                       # population is not a sequence

        self.assertTrue(set(choices(data, None, k=5)) <= set(data))      # weights is None
        self.assertTrue(set(choices(data, weights=None, k=5)) <= set(data))
        with self.assertRaises(ValueError):
            choices(data, [1,2], k=5)                                    # len(weights) != len(population)
        with self.assertRaises(TypeError):
            choices(data, 10, k=5)                                       # non-iterable weights
        with self.assertRaises(TypeError):
            choices(data, [None]*4, k=5)                                 # non-numeric weights
        for weights in [
                [15, 10, 25, 30],                                                 # integer weights
                [15.1, 10.2, 25.2, 30.3],                                         # float weights
                [Fraction(1, 3), Fraction(2, 6), Fraction(3, 6), Fraction(4, 6)], # fractional weights
                [True, False, True, False]                                        # booleans (include / exclude)
        ]:
            self.assertTrue(set(choices(data, weights, k=5)) <= set(data))

        with self.assertRaises(ValueError):
            choices(data, cum_weights=[1,2], k=5)                        # len(weights) != len(population)
        with self.assertRaises(TypeError):
            choices(data, cum_weights=10, k=5)                           # non-iterable cum_weights
        with self.assertRaises(TypeError):
            choices(data, cum_weights=[None]*4, k=5)                     # non-numeric cum_weights
        with self.assertRaises(TypeError):
            choices(data, range(4), cum_weights=range(4), k=5)           # both weights and cum_weights
        for weights in [
                [15, 10, 25, 30],                                                 # integer cum_weights
                [15.1, 10.2, 25.2, 30.3],                                         # float cum_weights
                [Fraction(1, 3), Fraction(2, 6), Fraction(3, 6), Fraction(4, 6)], # fractional cum_weights
        ]:
            self.assertTrue(set(choices(data, cum_weights=weights, k=5)) <= set(data))

        # Test weight focused on a single element of the population
        self.assertEqual(choices('abcd', [1, 0, 0, 0]), ['a'])
        self.assertEqual(choices('abcd', [0, 1, 0, 0]), ['b'])
        self.assertEqual(choices('abcd', [0, 0, 1, 0]), ['c'])
        self.assertEqual(choices('abcd', [0, 0, 0, 1]), ['d'])

        # Test consistency with random.choice() for empty population
        with self.assertRaises(IndexError):
            choices([], k=1)
        with self.assertRaises(IndexError):
            choices([], weights=[], k=1)
        with self.assertRaises(IndexError):
            choices([], cum_weights=[], k=5)

    def test_choices_subnormal(self):
        # Subnormal weights would occasionally trigger an IndexError
        # in choices() when the value returned by random() was large
        # enough to make `random() * total` round up to the total.
        # See https://bugs.python.org/msg275594 for more detail.
        choices = self.gen.choices
        choices(population=[1, 2], weights=[1e-323, 1e-323], k=5000)

    def test_gauss(self):
        # Ensure that the seed() method initializes all the hidden state.  In
        # particular, through 2.2.1 it failed to reset a piece of state used
        # by (and only by) the .gauss() method.

        for seed in 1, 12, 123, 1234, 12345, 123456, 654321:
            self.gen.seed(seed)
            x1 = self.gen.random()
            y1 = self.gen.gauss(0, 1)

            self.gen.seed(seed)
            x2 = self.gen.random()
            y2 = self.gen.gauss(0, 1)

            self.assertEqual(x1, x2)
            self.assertEqual(y1, y2)

    def test_pickling(self):
        for proto in range(pickle.HIGHEST_PROTOCOL + 1):
            state = pickle.dumps(self.gen, proto)
            origseq = [self.gen.random() for i in range(10)]
            newgen = pickle.loads(state)
            restoredseq = [newgen.random() for i in range(10)]
            self.assertEqual(origseq, restoredseq)

    def test_bug_1727780(self):
        # verify that version-2-pickles can be loaded
        # fine, whether they are created on 32-bit or 64-bit
        # platforms, and that version-3-pickles load fine.
        files = [("randv2_32.pck", 780),
                 ("randv2_64.pck", 866),
                 ("randv3.pck", 343)]
        for file, value in files:
            with open(support.findfile(file),"rb") as f:
                r = pickle.load(f)
            self.assertEqual(int(r.random()*1000), value)

    def test_bug_9025(self):
        # Had problem with an uneven distribution in int(n*random())
        # Verify the fix by checking that distributions fall within expectations.
        n = 100000
        randrange = self.gen.randrange
        k = sum(randrange(6755399441055744) % 3 == 2 for i in range(n))
        self.assertTrue(0.30 < k/n < .37, (k/n))

try:
    random.SystemRandom().random()
except NotImplementedError:
    SystemRandom_available = False
else:
    SystemRandom_available = True

@unittest.skipUnless(SystemRandom_available, "random.SystemRandom not available")
class SystemRandom_TestBasicOps(TestBasicOps, unittest.TestCase):
    gen = random.SystemRandom()

    def test_autoseed(self):
        # Doesn't need to do anything except not fail
        self.gen.seed()

    def test_saverestore(self):
        self.assertRaises(NotImplementedError, self.gen.getstate)
        self.assertRaises(NotImplementedError, self.gen.setstate, None)

    def test_seedargs(self):
        # Doesn't need to do anything except not fail
        self.gen.seed(100)

    def test_gauss(self):
        self.gen.gauss_next = None
        self.gen.seed(100)
        self.assertEqual(self.gen.gauss_next, None)

    def test_pickling(self):
        for proto in range(pickle.HIGHEST_PROTOCOL + 1):
            self.assertRaises(NotImplementedError, pickle.dumps, self.gen, proto)

    def test_53_bits_per_float(self):
        # This should pass whenever a C double has 53 bit precision.
        span = 2 ** 53
        cum = 0
        for i in range(100):
            cum |= int(self.gen.random() * span)
        self.assertEqual(cum, span-1)

    def test_bigrand(self):
        # The randrange routine should build-up the required number of bits
        # in stages so that all bit positions are active.
        span = 2 ** 500
        cum = 0
        for i in range(100):
            r = self.gen.randrange(span)
            self.assertTrue(0 <= r < span)
            cum |= r
        self.assertEqual(cum, span-1)

    def test_bigrand_ranges(self):
        for i in [40,80, 160, 200, 211, 250, 375, 512, 550]:
            start = self.gen.randrange(2 ** (i-2))
            stop = self.gen.randrange(2 ** i)
            if stop <= start:
                continue
            self.assertTrue(start <= self.gen.randrange(start, stop) < stop)

    def test_rangelimits(self):
        for start, stop in [(-2,0), (-(2**60)-2,-(2**60)), (2**60,2**60+2)]:
            self.assertEqual(set(range(start,stop)),
                set([self.gen.randrange(start,stop) for i in range(100)]))