from queue import Queue
import threading

import pytest
import torch
from torch import nn

from torchgpipe.checkpoint import enable_checkpointing, enable_recomputing
from torchgpipe.microbatch import Batch
from torchgpipe.skip import pop, skippable, stash
from torchgpipe.skip.layout import SkipLayout
from torchgpipe.skip.tracker import SkipTracker, SkipTrackerThroughPotals, current_skip_tracker


def test_default_skip_tracker():
    q = Queue()

    def f():
        q.put(current_skip_tracker())

    t = threading.Thread(target=f)
    t.start()
    t.join()

    skip_tracker = q.get()

    assert type(skip_tracker) is SkipTracker
    assert type(skip_tracker) is not SkipTrackerThroughPotals


@pytest.mark.skipif(not torch.cuda.is_available(), reason='cuda required')
def test_default_skip_tracker_by_data_parallel():
    @skippable(stash=['foo'])
    class Stash(nn.Module):
        def forward(self, input):
            yield stash('foo', input)
            return input * 2

    @skippable(pop=['foo'])
    class Pop(nn.Module):
        def forward(self, input):
            foo = yield pop('foo')
            return foo

    model = nn.Sequential(Stash(), Pop())
    model = nn.DataParallel(model, device_ids=[0, 0], output_device=0)

    input = torch.rand(10, device=0)
    output = model(input)

    assert torch.allclose(output, input)


def test_reuse_portal():
    skip_layout = SkipLayout(num_partitions=2, skip_routes={(None, 'test'): (0, 1)})
    skip_tracker = SkipTrackerThroughPotals(skip_layout)

    batch = Batch(torch.tensor([1.0]))
    a = torch.tensor([2.0])
    b = torch.tensor([2.0])

    skip_tracker.save(batch, None, 'test', a)
    portal = skip_tracker.portals[(None, 'test')]

    skip_tracker.save(batch, None, 'test', b)
    assert portal is skip_tracker.portals[(None, 'test')]


def test_no_copy_no_portal():
    skip_layout = SkipLayout(num_partitions=2, skip_routes={
        (None, 'copy'): (0, 1),
        (None, 'not_copy'): (0, 0),
    })
    skip_tracker = SkipTrackerThroughPotals(skip_layout)

    batch = Batch(torch.tensor([1.0]))
    a = torch.tensor([2.0])
    b = torch.tensor([2.0])

    skip_tracker.save(batch, None, 'copy', a)
    skip_tracker.save(batch, None, 'not_copy', b)

    assert (None, 'copy') in skip_tracker.portals
    assert (None, 'copy') not in skip_tracker.tensors
    assert (None, 'not_copy') in skip_tracker.tensors
    assert (None, 'not_copy') not in skip_tracker.portals


def test_tensor_life_without_checkpointing():
    skip_layout = SkipLayout(num_partitions=2, skip_routes={(None, 'test'): (0, 1)})
    skip_tracker = SkipTrackerThroughPotals(skip_layout)

    batch = Batch(torch.tensor([1.0]))
    tensor = torch.tensor([2.0])

    skip_tracker.save(batch, None, 'test', tensor)
    assert skip_tracker.portals[(None, 'test')].tensor_life == 1

    skip_tracker.load(batch, None, 'test')
    assert skip_tracker.portals[(None, 'test')].tensor_life == 0


def test_tensor_life_with_checkpointing():
    skip_layout = SkipLayout(num_partitions=2, skip_routes={(None, 'test'): (0, 1)})
    skip_tracker = SkipTrackerThroughPotals(skip_layout)

    batch = Batch(torch.tensor([1.0]))
    tensor = torch.tensor([2.0])

    with enable_checkpointing():
        skip_tracker.save(batch, None, 'test', tensor)
    assert skip_tracker.portals[(None, 'test')].tensor_life == 2

    with enable_checkpointing():
        skip_tracker.load(batch, None, 'test')
    assert skip_tracker.portals[(None, 'test')].tensor_life == 1

    with enable_recomputing():
        skip_tracker.load(batch, None, 'test')
    assert skip_tracker.portals[(None, 'test')].tensor_life == 0

    with enable_recomputing():
        skip_tracker.save(batch, None, 'test', tensor)
    assert skip_tracker.portals[(None, 'test')].tensor_life == 0
