from __future__ import print_function

import ctypes
import ctypes.util
import os
import sys
import threading
import warnings

import numpy as np

import numba.unittest_support as unittest
from numba.compiler import compile_isolated, Flags
from numba import config, jit
from .support import TestCase, tag


# This CPython API function is a portable way to get the current thread id.
PyThread_get_thread_ident = ctypes.pythonapi.PyThread_get_thread_ident
PyThread_get_thread_ident.restype = ctypes.c_long
PyThread_get_thread_ident.argtypes = []

# A way of sleeping from nopython code
if os.name == 'nt':
    sleep = ctypes.windll.kernel32.Sleep
    sleep.argtypes = [ctypes.c_uint]
    sleep.restype = None
    sleep_factor = 1  # milliseconds
else:
    sleep = ctypes.CDLL(ctypes.util.find_library("c")).usleep
    sleep.argtypes = [ctypes.c_uint]
    sleep.restype = ctypes.c_int
    sleep_factor = 1000  # microseconds


def f(a, indices):
    # If run from one thread at a time, the function will always fill the
    # array with identical values.
    # If run from several threads at a time, the function will probably
    # fill the array with differing values.
    for idx in indices:
        # Let another thread run
        sleep(10 * sleep_factor)
        a[idx] = PyThread_get_thread_ident()

f_sig = "void(int64[:], intp[:])"

def lifted_f(a, indices):
    """
    Same as f(), but inside a lifted loop
    """
    object()   # Force object mode
    for idx in indices:
        # Let another thread run
        sleep(10 * sleep_factor)
        a[idx] = PyThread_get_thread_ident()

def object_f(a, indices):
    """
    Same as f(), but in object mode
    """
    for idx in indices:
        # Let another thread run
        sleep(10 * sleep_factor)
        object()   # Force object mode
        a[idx] = PyThread_get_thread_ident()


class TestGILRelease(TestCase):

    def make_test_array(self, n_members):
        return np.arange(n_members, dtype=np.int64)

    def run_in_threads(self, func, n_threads):
        # Run the function in parallel over an array and collect results.
        threads = []
        # Warm up compilation, since we don't want that to interfere with
        # the test proper.
        func(self.make_test_array(1), np.arange(1, dtype=np.intp))
        arr = self.make_test_array(50)
        for i in range(n_threads):
            # Ensure different threads write into the array in different
            # orders.
            indices = np.arange(arr.size, dtype=np.intp)
            np.random.shuffle(indices)
            t = threading.Thread(target=func, args=(arr, indices))
            threads.append(t)
        for t in threads:
            t.start()
        for t in threads:
            t.join()
        return arr

    def check_gil_held(self, func):
        arr = self.run_in_threads(func, n_threads=4)
        distinct = set(arr)
        self.assertEqual(len(distinct), 1, distinct)

    def check_gil_released(self, func):
        for n_threads in (4, 12, 32):
            # Try harder each time. On an empty machine 4 threads seems
            # sufficient, but in some contexts (e.g. Travis CI) we need more.
            arr = self.run_in_threads(func, n_threads)
            distinct = set(arr)
            try:
                self.assertGreater(len(distinct), 1, distinct)
            except AssertionError as e:
                failure = e
            else:
                return
        raise failure

    def test_gil_held(self):
        """
        Test the GIL is held by default, by checking serialized runs
        produce deterministic results.
        """
        cfunc = jit(f_sig, nopython=True)(f)
        self.check_gil_held(cfunc)

    @tag('important')
    def test_gil_released(self):
        """
        Test releasing the GIL, by checking parallel runs produce
        unpredictable results.
        """
        cfunc = jit(f_sig, nopython=True, nogil=True)(f)
        self.check_gil_released(cfunc)

    def test_gil_released_inside_lifted_loop(self):
        """
        Test the GIL can by released by a lifted loop even though the
        surrounding code uses object mode.
        """
        cfunc = jit(f_sig, nogil=True)(lifted_f)
        self.check_gil_released(cfunc)

    def test_gil_released_by_caller(self):
        """
        Releasing the GIL in the caller is sufficient to have it
        released in a callee.
        """
        compiled_f = jit(f_sig, nopython=True)(f)
        @jit(f_sig, nopython=True, nogil=True)
        def caller(a, i):
            compiled_f(a, i)
        self.check_gil_released(caller)

    def test_gil_released_by_caller_and_callee(self):
        """
        Same, but with both caller and callee asking to release the GIL.
        """
        compiled_f = jit(f_sig, nopython=True, nogil=True)(f)
        @jit(f_sig, nopython=True, nogil=True)
        def caller(a, i):
            compiled_f(a, i)
        self.check_gil_released(caller)

    def test_gil_ignored_by_callee(self):
        """
        When only the callee asks to release the GIL, it gets ignored.
        """
        compiled_f = jit(f_sig, nopython=True, nogil=True)(f)
        @jit(f_sig, nopython=True)
        def caller(a, i):
            compiled_f(a, i)
        self.check_gil_held(caller)

    def test_object_mode(self):
        """
        When the function is compiled in object mode, a warning is
        printed out.
        """
        with warnings.catch_warnings(record=True) as wlist:
            warnings.simplefilter('always', config.NumbaWarning)
            cfunc = jit(f_sig, nogil=True)(object_f)
        self.assertTrue(any(w.category is config.NumbaWarning
                            and "Code running in object mode won't allow parallel execution" in str(w.message)
                            for w in wlist), wlist)
        # Just check it doesn't crash.
        self.run_in_threads(cfunc, 2)


if __name__ == '__main__':
    unittest.main()
