import os
import mmap
import sys
import platform
import gc
import pickle
from time import sleep

from joblib.test.common import with_numpy, np
from joblib.test.common import setup_autokill
from joblib.test.common import teardown_autokill
from joblib.test.common import with_multiprocessing
from joblib.test.common import with_dev_shm
from joblib.testing import raises, parametrize, skipif
from joblib.backports import make_memmap
from joblib.parallel import Parallel, delayed

from joblib.pool import MemmappingPool
from joblib.executor import _TestingMemmappingExecutor
from joblib._memmapping_reducer import has_shareable_memory
from joblib._memmapping_reducer import ArrayMemmapReducer
from joblib._memmapping_reducer import reduce_memmap
from joblib._memmapping_reducer import _strided_from_memmap
from joblib._memmapping_reducer import _get_backing_memmap
from joblib._memmapping_reducer import _get_temp_dir
from joblib._memmapping_reducer import _WeakArrayKeyMap
import joblib._memmapping_reducer as jmr


def setup_module():
    setup_autokill(__name__, timeout=300)


def teardown_module():
    teardown_autokill(__name__)


def check_array(args):
    """Dummy helper function to be executed in subprocesses

    Check that the provided array has the expected values in the provided
    range.

    """
    data, position, expected = args
    np.testing.assert_array_equal(data[position], expected)


def inplace_double(args):
    """Dummy helper function to be executed in subprocesses


    Check that the input array has the right values in the provided range
    and perform an inplace modification to double the values in the range by
    two.

    """
    data, position, expected = args
    assert data[position] == expected
    data[position] *= 2
    np.testing.assert_array_equal(data[position], 2 * expected)


@with_numpy
@with_multiprocessing
def test_memmap_based_array_reducing(tmpdir):
    """Check that it is possible to reduce a memmap backed array"""
    assert_array_equal = np.testing.assert_array_equal
    filename = tmpdir.join('test.mmap').strpath

    # Create a file larger than what will be used by a
    buffer = np.memmap(filename, dtype=np.float64, shape=500, mode='w+')

    # Fill the original buffer with negative markers to detect over of
    # underflow in case of test failures
    buffer[:] = - 1.0 * np.arange(buffer.shape[0], dtype=buffer.dtype)
    buffer.flush()

    # Memmap a 2D fortran array on a offseted subsection of the previous
    # buffer
    a = np.memmap(filename, dtype=np.float64, shape=(3, 5, 4),
                  mode='r+', order='F', offset=4)
    a[:] = np.arange(60).reshape(a.shape)

    # Build various views that share the buffer with the original memmap

    # b is an memmap sliced view on an memmap instance
    b = a[1:-1, 2:-1, 2:4]

    # c and d are array views
    c = np.asarray(b)
    d = c.T

    # Array reducer with auto dumping disabled
    reducer = ArrayMemmapReducer(None, tmpdir.strpath, 'c')

    def reconstruct_array(x):
        cons, args = reducer(x)
        return cons(*args)

    def reconstruct_memmap(x):
        cons, args = reduce_memmap(x)
        return cons(*args)

    # Reconstruct original memmap
    a_reconstructed = reconstruct_memmap(a)
    assert has_shareable_memory(a_reconstructed)
    assert isinstance(a_reconstructed, np.memmap)
    assert_array_equal(a_reconstructed, a)

    # Reconstruct strided memmap view
    b_reconstructed = reconstruct_memmap(b)
    assert has_shareable_memory(b_reconstructed)
    assert_array_equal(b_reconstructed, b)

    # Reconstruct arrays views on memmap base
    c_reconstructed = reconstruct_array(c)
    assert not isinstance(c_reconstructed, np.memmap)
    assert has_shareable_memory(c_reconstructed)
    assert_array_equal(c_reconstructed, c)

    d_reconstructed = reconstruct_array(d)
    assert not isinstance(d_reconstructed, np.memmap)
    assert has_shareable_memory(d_reconstructed)
    assert_array_equal(d_reconstructed, d)

    # Test graceful degradation on fake memmap instances with in-memory
    # buffers
    a3 = a * 3
    assert not has_shareable_memory(a3)
    a3_reconstructed = reconstruct_memmap(a3)
    assert not has_shareable_memory(a3_reconstructed)
    assert not isinstance(a3_reconstructed, np.memmap)
    assert_array_equal(a3_reconstructed, a * 3)

    # Test graceful degradation on arrays derived from fake memmap instances
    b3 = np.asarray(a3)
    assert not has_shareable_memory(b3)

    b3_reconstructed = reconstruct_array(b3)
    assert isinstance(b3_reconstructed, np.ndarray)
    assert not has_shareable_memory(b3_reconstructed)
    assert_array_equal(b3_reconstructed, b3)


@with_numpy
@with_multiprocessing
def test_high_dimension_memmap_array_reducing(tmpdir):
    assert_array_equal = np.testing.assert_array_equal

    filename = tmpdir.join('test.mmap').strpath

    # Create a high dimensional memmap
    a = np.memmap(filename, dtype=np.float64, shape=(100, 15, 15, 3),
                  mode='w+')
    a[:] = np.arange(100 * 15 * 15 * 3).reshape(a.shape)

    # Create some slices/indices at various dimensions
    b = a[0:10]
    c = a[:, 5:10]
    d = a[:, :, :, 0]
    e = a[1:3:4]

    def reconstruct_memmap(x):
        cons, args = reduce_memmap(x)
        res = cons(*args)
        return res

    a_reconstructed = reconstruct_memmap(a)
    assert has_shareable_memory(a_reconstructed)
    assert isinstance(a_reconstructed, np.memmap)
    assert_array_equal(a_reconstructed, a)

    b_reconstructed = reconstruct_memmap(b)
    assert has_shareable_memory(b_reconstructed)
    assert_array_equal(b_reconstructed, b)

    c_reconstructed = reconstruct_memmap(c)
    assert has_shareable_memory(c_reconstructed)
    assert_array_equal(c_reconstructed, c)

    d_reconstructed = reconstruct_memmap(d)
    assert has_shareable_memory(d_reconstructed)
    assert_array_equal(d_reconstructed, d)

    e_reconstructed = reconstruct_memmap(e)
    assert has_shareable_memory(e_reconstructed)
    assert_array_equal(e_reconstructed, e)


@with_numpy
def test__strided_from_memmap(tmpdir):
    fname = tmpdir.join('test.mmap').strpath
    size = 5 * mmap.ALLOCATIONGRANULARITY
    offset = mmap.ALLOCATIONGRANULARITY + 1
    # This line creates the mmap file that is reused later
    memmap_obj = np.memmap(fname, mode='w+', shape=size + offset)
    # filename, dtype, mode, offset, order, shape, strides, total_buffer_len
    memmap_obj = _strided_from_memmap(fname, dtype='uint8', mode='r',
                                      offset=offset, order='C', shape=size,
                                      strides=None, total_buffer_len=None)
    assert isinstance(memmap_obj, np.memmap)
    assert memmap_obj.offset == offset
    memmap_backed_obj = _strided_from_memmap(fname, dtype='uint8', mode='r',
                                             offset=offset, order='C',
                                             shape=(size // 2,), strides=(2,),
                                             total_buffer_len=size)
    assert _get_backing_memmap(memmap_backed_obj).offset == offset


@with_numpy
@with_multiprocessing
@parametrize("factory", [MemmappingPool, _TestingMemmappingExecutor],
             ids=["multiprocessing", "loky"])
def test_pool_with_memmap(factory, tmpdir):
    """Check that subprocess can access and update shared memory memmap"""
    assert_array_equal = np.testing.assert_array_equal

    # Fork the subprocess before allocating the objects to be passed
    pool_temp_folder = tmpdir.mkdir('pool').strpath
    p = factory(10, max_nbytes=2, temp_folder=pool_temp_folder)
    try:
        filename = tmpdir.join('test.mmap').strpath
        a = np.memmap(filename, dtype=np.float32, shape=(3, 5), mode='w+')
        a.fill(1.0)

        p.map(inplace_double, [(a, (i, j), 1.0)
                               for i in range(a.shape[0])
                               for j in range(a.shape[1])])

        assert_array_equal(a, 2 * np.ones(a.shape))

        # Open a copy-on-write view on the previous data
        b = np.memmap(filename, dtype=np.float32, shape=(5, 3), mode='c')

        p.map(inplace_double, [(b, (i, j), 2.0)
                               for i in range(b.shape[0])
                               for j in range(b.shape[1])])

        # Passing memmap instances to the pool should not trigger the creation
        # of new files on the FS
        assert os.listdir(pool_temp_folder) == []

        # the original data is untouched
        assert_array_equal(a, 2 * np.ones(a.shape))
        assert_array_equal(b, 2 * np.ones(b.shape))

        # readonly maps can be read but not updated
        c = np.memmap(filename, dtype=np.float32, shape=(10,), mode='r',
                      offset=5 * 4)

        with raises(AssertionError):
            p.map(check_array, [(c, i, 3.0) for i in range(c.shape[0])])

        # depending on the version of numpy one can either get a RuntimeError
        # or a ValueError
        with raises((RuntimeError, ValueError)):
            p.map(inplace_double, [(c, i, 2.0) for i in range(c.shape[0])])
    finally:
        # Clean all filehandlers held by the pool
        p.terminate()
        del p


@with_numpy
@with_multiprocessing
@parametrize("factory", [MemmappingPool, _TestingMemmappingExecutor],
             ids=["multiprocessing", "loky"])
def test_pool_with_memmap_array_view(factory, tmpdir):
    """Check that subprocess can access and update shared memory array"""
    assert_array_equal = np.testing.assert_array_equal

    # Fork the subprocess before allocating the objects to be passed
    pool_temp_folder = tmpdir.mkdir('pool').strpath
    p = factory(10, max_nbytes=2, temp_folder=pool_temp_folder)
    try:

        filename = tmpdir.join('test.mmap').strpath
        a = np.memmap(filename, dtype=np.float32, shape=(3, 5), mode='w+')
        a.fill(1.0)

        # Create an ndarray view on the memmap instance
        a_view = np.asarray(a)
        assert not isinstance(a_view, np.memmap)
        assert has_shareable_memory(a_view)

        p.map(inplace_double, [(a_view, (i, j), 1.0)
                               for i in range(a.shape[0])
                               for j in range(a.shape[1])])

        # Both a and the a_view have been updated
        assert_array_equal(a, 2 * np.ones(a.shape))
        assert_array_equal(a_view, 2 * np.ones(a.shape))

        # Passing memmap array view to the pool should not trigger the
        # creation of new files on the FS
        assert os.listdir(pool_temp_folder) == []

    finally:
        p.terminate()
        del p


@with_numpy
@with_multiprocessing
@parametrize("factory", [MemmappingPool, _TestingMemmappingExecutor],
             ids=["multiprocessing", "loky"])
def test_memmapping_pool_for_large_arrays(factory, tmpdir):
    """Check that large arrays are not copied in memory"""

    # Check that the tempfolder is empty
    assert os.listdir(tmpdir.strpath) == []

    # Build an array reducers that automaticaly dump large array content
    # to filesystem backed memmap instances to avoid memory explosion
    p = factory(3, max_nbytes=40, temp_folder=tmpdir.strpath, verbose=2)
    try:
        # The temporary folder for the pool is not provisioned in advance
        assert os.listdir(tmpdir.strpath) == []
        assert not os.path.exists(p._temp_folder)

        small = np.ones(5, dtype=np.float32)
        assert small.nbytes == 20
        p.map(check_array, [(small, i, 1.0) for i in range(small.shape[0])])

        # Memory has been copied, the pool filesystem folder is unused
        assert os.listdir(tmpdir.strpath) == []

        # Try with a file larger than the memmap threshold of 40 bytes
        large = np.ones(100, dtype=np.float64)
        assert large.nbytes == 800
        p.map(check_array, [(large, i, 1.0) for i in range(large.shape[0])])

        # The data has been dumped in a temp folder for subprocess to share it
        # without per-child memory copies
        assert os.path.isdir(p._temp_folder)
        dumped_filenames = os.listdir(p._temp_folder)
        assert len(dumped_filenames) == 1

        # Check that memory mapping is not triggered for arrays with
        # dtype='object'
        objects = np.array(['abc'] * 100, dtype='object')
        results = p.map(has_shareable_memory, [objects])
        assert not results[0]