import numpy as np

from caffe2.python import workspace, memonger, core, model_helper, brew
from caffe2.proto import caffe2_pb2
import caffe2.python.hypothesis_test_util as hu
from future.utils import viewvalues
import hypothesis.strategies as st
from hypothesis import given, settings
import unittest


def has_blob(proto, needle):
    for op in proto.op:
        for inp in op.input:
            if inp == needle:
                return True
        for outp in op.output:
            if outp == needle:
                return True
    return False


def count_blobs(proto):
    blobs = set()
    for op in proto.op:
        blobs = blobs.union(set(op.input)).union(set(op.output))
    return len(blobs)


class MemongerTest(hu.HypothesisTestCase):
    @given(input_dim=st.integers(min_value=1, max_value=10),
           output_dim=st.integers(min_value=1, max_value=10),
           batch_size=st.integers(min_value=1, max_value=10),
           do=st.sampled_from(hu.device_options),
           algo=st.sampled_from(memonger.AssignmentAlgorithm))
    @settings(max_examples=5, deadline=None)
    def test_simple_memonger(self, input_dim, output_dim, batch_size, do, algo):
        m = model_helper.ModelHelper()
        fc1 = brew.fc(m, "data", "fc1", dim_in=input_dim, dim_out=output_dim)
        fc2 = brew.fc(m, fc1, "fc2", dim_in=output_dim, dim_out=output_dim)
        fc3 = brew.fc(m, fc2, "fc3", dim_in=output_dim, dim_out=output_dim)

        fc3.Relu([], fc3)\
           .Softmax([], "pred") \
           .LabelCrossEntropy(["label"], ["xent"]) \
           .AveragedLoss([], "loss")
        input_to_grad = m.AddGradientOperators(["loss"])
        m.net.Proto().device_option.CopyFrom(do)
        m.param_init_net.Proto().device_option.CopyFrom(do)
        static_blobs = \
            [o for op in m.param_init_net.Proto().op for o in op.output] + \
            ["data", "label", "loss", input_to_grad["fc1_w"]]

        optimization = memonger.optimize_interference(
            m.Proto(), static_blobs, algo=algo)
        data = np.random.randn(batch_size, input_dim).astype(np.float32)
        label = np.random.randint(
            low=0, high=output_dim, size=(batch_size,)).astype(np.int32)
        workspace.RunNetOnce(m.param_init_net)
        workspace.FeedBlob("data", data, device_option=do)
        workspace.FeedBlob("label", label, device_option=do)
        workspace.RunNetOnce(m.net)
        loss = workspace.FetchBlob("loss")
        grad = workspace.FetchBlob(str(input_to_grad["fc1_w"]))
        workspace.RunNetOnce(optimization.net)
        optimized_loss = workspace.FetchBlob("loss")
        optimized_grad = workspace.FetchBlob(str(input_to_grad["fc1_w"]))
        np.testing.assert_almost_equal(loss, optimized_loss)
        np.testing.assert_almost_equal(grad, optimized_grad)
        stats = memonger.compute_statistics(optimization.assignments)
        self.assertLess(stats.optimized_nbytes, stats.baseline_nbytes)

        # run with blob sizes
        blob_sizes = memonger.collect_blob_sizes(m.Proto())
        optimization1 = memonger.optimize_interference(
            m.Proto(), static_blobs, blob_sizes=blob_sizes, algo=algo)
        workspace.RunNetOnce(optimization1.net)
        optimized_loss = workspace.FetchBlob("loss")
        optimized_grad = workspace.FetchBlob(str(input_to_grad["fc1_w"]))
        np.testing.assert_almost_equal(loss, optimized_loss)
        np.testing.assert_almost_equal(grad, optimized_grad)
        stats = memonger.compute_statistics(optimization1.assignments)
        self.assertLessEqual(stats.optimized_nbytes, stats.baseline_nbytes)

    @given(input_dim=st.integers(min_value=1, max_value=10),
           output_dim=st.integers(min_value=1, max_value=10),
           batch_size=st.integers(min_value=1, max_value=10),
           do=st.sampled_from(hu.device_options))
    @settings(max_examples=5, deadline=None)
    def test_fast_memonger(self, input_dim, output_dim, batch_size, do):
        m = model_helper.ModelHelper()
        fc1 = brew.fc(m, "data", "fc1", dim_in=input_dim, dim_out=output_dim)
        fc2 = brew.fc(m, fc1, "fc2", dim_in=output_dim, dim_out=output_dim)
        fc3 = brew.fc(m, fc2, "fc3", dim_in=output_dim, dim_out=output_dim)

        fc3.Relu([], fc3)\
           .Softmax([], "pred") \
           .LabelCrossEntropy(["label"], ["xent"]) \
           .AveragedLoss([], "loss")
        input_to_grad = m.AddGradientOperators(["loss"])
        m.net.Proto().device_option.CopyFrom(do)
        m.param_init_net.Proto().device_option.CopyFrom(do)
        static_blobs = \
            [o for op in m.param_init_net.Proto().op for o in op.output] + \
            ["data", "label", "loss", input_to_grad["fc1_w"]]

        optimized_net = memonger.optimize_inference_fast(
            m.Proto(), static_blobs)
        data = np.random.randn(batch_size, input_dim).astype(np.float32)
        label = np.random.randint(
            low=0, high=output_dim, size=(batch_size,)).astype(np.int32)
        workspace.RunNetOnce(m.param_init_net)
        workspace.FeedBlob("data", data, device_option=do)
        workspace.FeedBlob("label", label, device_option=do)
        workspace.RunNetOnce(m.net)
        loss = workspace.FetchBlob("loss")
        grad = workspace.FetchBlob(str(input_to_grad["fc1_w"]))
        workspace.RunNetOnce(optimized_net)
        optimized_loss = workspace.FetchBlob("loss")
        optimized_grad = workspace.FetchBlob(str(input_to_grad["fc1_w"]))
        np.testing.assert_almost_equal(loss, optimized_loss)
        np.testing.assert_almost_equal(grad, optimized_grad)

        self.assertLess(count_blobs(optimized_net), count_blobs(m.Proto()))

    def test_fast_memonger_unique_outputs(self):
        m = model_helper.ModelHelper()
        fc = []
        for i in range(2):
            z = brew.fc(
                m, "data{}".format(i), "fc".format(i), dim_in=2, dim_out=2)
            fc.append(z)
        r = []
        # Trick is here to have same input appear twice in a same Sum
        for x in fc:
            for y in fc:
                r.append(brew.sum(m, [x, y], 1))
        concated = brew.concat(m, r, "concated")
        brew.relu(m, concated, "merged")

        static_blobs = \
            [o for op in m.param_init_net.Proto().op for o in op.output] + \
            ["merged"] + ["data{}".format(i) for i in range(len(fc))]

        optimized_net = memonger.optimize_inference_fast(
            m.Proto(), static_blobs)
        for op in optimized_net.op:
            self.assertEqual(len(op.output), len(set(op.output)), str(op))

    @given(input_dim=st.integers(min_value=1, max_value=4),
           output_dim=st.integers(min_value=1, max_value=4),
           batch_size=st.integers(min_value=1, max_value=4))
    def test_gradient_optim(self, input_dim, output_dim, batch_size):
        m = model_helper.ModelHelper()
        with core.NameScope("name_x"):
            fc1 = brew.fc(m, "data", "fc1", dim_in=input_dim, dim_out=output_dim)
            fc2 = brew.fc(m, fc1, "fc2", dim_in=output_dim, dim_out=output_dim)
            fc3 = brew.fc(m, fc2, "fc3", dim_in=output_dim, dim_out=output_dim)
            fc4 = brew.fc(m, fc3, "fc4", dim_in=output_dim, dim_out=output_dim)
            fc5 = brew.fc(m, fc4, "fc5", dim_in=output_dim, dim_out=output_dim)
            fc5.Relu([], fc5)\
               .Softmax([], "pred") \
               .LabelCrossEntropy(["label"], ["xent"]) \
               .AveragedLoss([], "loss")
        input_to_grad = m.AddGradientOperators(["name_x/loss"])

        blobs_before = count_blobs(m.net.Proto())
        optim_proto = memonger.share_grad_blobs(
            m.net,
            ["name_x/loss"],
            set(viewvalues(m.param_to_grad)),
            "name_x/",
            share_activations=False,
        )
        blobs_after = count_blobs(optim_proto)
        self.assertLess(blobs_after, blobs_before)

        optim_proto_wacts = memonger.share_grad_blobs(
            m.net,
            ["name_x/loss"],
            set(viewvalues(m.param_to_grad)),
            "name_x/",
            share_activations=True,
            dont_share_blobs=set([str(input_to_grad["name_x/fc1_w"])]),
        )
        blobs_wact_optim = count_blobs(optim_proto_wacts)
        self.assertLessEqual(blobs_wact_optim, blobs_after)

        # Check that the last activations are not shared
        self.assertTrue(has_blob(optim_proto, "name_x/fc5"))
        self.assertTrue(
            has_blob(optim_proto_wacts, "name_x/fc5"),
            "Dont remap final activation",
        )

        # Test networks produce exactly same gradients
        data = np.random.randn(batch_size, input_dim).astype(np.float32)
        label = np.random.randint(
            low=0, high=output_dim, size=(batch_size,)).astype(np.int32)
        workspace.RunNetOnce(m.param_init_net)
        workspace.FeedBlob("name_x/data", data)
        workspace.FeedBlob("name_x/label", label)
        workspace.RunNetOnce(m.net)
        loss = workspace.FetchBlob("name_x/loss")
        grad = workspace.FetchBlob(str(input_to_grad["name_x/fc1_w"]))
        workspace.RunNetOnce(optim_proto)
        optimized_loss = workspace.FetchBlob("name_x/loss")
        optimized_grad = workspace.FetchBlob(str(input_to_grad["name_x/fc1_w"]))
        np.testing.assert_almost_equal(loss, optimized_loss)
        np.testing.assert_almost_equal(grad, optimized_grad)

        workspace.FeedBlob(str(input_to_grad["name_x/fc1_w"]), np.array([0.0]))

        # Run with the forward optimization
        workspace.RunNetOnce(optim_proto_wacts)
        optimized_loss = workspace.FetchBlob("name_x/loss")
        optimized_grad = workspace.FetchBlob(str(input_to_grad["name_x/fc1_w"]))
        np.testing.assert_almost_equal(loss, optimized_loss)
        np.testing.assert_almost_equal(grad, optimized_grad)

    @unittest.skipIf(not workspace.has_gpu_support, "No gpu support.")
    def test_memonger_mix_cpu_gpu(self):
        '''
        Check that memonger does not make blobs cross CPU/GPU boundary
        '''
        m = model_helper.ModelHelper()
        with core.DeviceScope(core.DeviceOption(workspace.GpuDeviceType, 0)):
            fc1 = brew.fc(m, "data", "fc1", dim_in=2, dim_out=2)
            fc2 = brew.fc(m, fc1, "fc2", dim_in=2, dim_out=2)
            fc3 = brew.fc(m, fc2, "fc3", dim_in=2, dim_out=2)
            fc4 = brew.fc(m, fc3, "fc4", dim_in=2, dim_out=2)
            fc4_cpu = m.net.CopyGPUToCPU(fc4, "fc4_cpu")
        with core.DeviceScope(core.DeviceOption(caffe2_pb2.CPU, 0)):
            fc5_cpu = brew.fc(m, fc4_cpu, "fc5_cpu", dim_in=2, dim_out=2)
            fc6_cpu = brew.fc(m, fc5_cpu, "fc6_cpu", dim_in=2, dim_out=2)
            fc7_cpu = brew.fc(m, fc6_cpu, "fc7_cpu", dim_in=2, dim_out=2)
            fc7_cpu.Relu([], fc7_cpu) \
               .Softmax([], "pred") \
               .LabelCrossEntropy(["label"], ["xent"]) \
               .AveragedLoss([], "loss")
        m.AddGradientOperators(["loss"])

        blobs_before = count_blobs(m.net.Proto())
        optim_proto = memonger.share_grad_blobs(
            m.net,
            ["loss"],
            set(viewvalues(m.param_to_grad)),
            "",
            share_activations=True,
            dont_share_blobs=set(),
        )
        blobs_after = count_blobs(optim_proto)
        self.assertLess(blobs_after, blobs_before)

        # Create set of blobs on CPU side and GPU side and check they don't
        # overlap
        device_blobs = {caffe2_pb2.CPU: set(), workspace.GpuDeviceType: set()}
        for op in optim_proto.op:
            if op.type not in ['CopyCPUToGPU', "CopyGPUToCPU"]:
                dev = op.device_option.device_type
                for b in list(op.input) + list(op.output):
                    device_blobs[dev].add(b)

        device_crossers = device_blobs[caffe2_pb2.CPU].intersection(
            device_blobs[workspace.GpuDeviceType]
        )
        self.assertEquals(device_crossers, set())

    @given(input_dim=st.integers(min_value=4, max_value=4),
           output_dim=st.integers(min_value=4, max_value=4),
           batch_size=st.integers(min_value=4, max_value=4))
    @settings(deadline=1000)
    def test_gradient_optim_tree(self, input_dim, output_dim, batch_size):
        m = model_helper.ModelHelper()
        with core.NameScope("name_x"):
            fc1 = brew.fc(m, "data", "fc1", dim_in=input_dim, dim_out=output_dim)
            fc2 = brew.fc(m, fc1, "fc2", dim_in=output_dim, dim_out=output_dim)
            fc3 = brew.fc(m, fc2, "fc3", dim_in=output_dim, dim_out=output_dim)
            fc4 = brew.fc(m, fc3, "fc4", dim_in=output_dim, dim_out=output_dim)
            fc5 = brew.fc(m, fc4, "fc5", dim_in=output_dim, dim_out=output_dim)
            fc5.Relu([], fc5) \
               .Softmax([], "pred1") \
               .LabelCrossEntropy(["label"], ["xent1"]) \
               .AveragedLoss([], "loss1")
            fc6 = brew.fc(m, fc5, "fc6", dim_in=output_dim, dim_out=output_dim)
            fc6.Relu([], fc6) \
               .Softmax([], "pred2") \
               .LabelCrossEntropy(["label"], ["xent2"]) \
               .AveragedLoss([], "loss2")
        input_to_grad = m.AddGradientOperators(["name_x/loss1", "name_x/loss2"])

        blobs_before = count_blobs(m.net.Proto())
        optim_proto = memonger.share_grad_blobs(
            m.net,
            ["name_x/loss1", "name_x/loss2"],
            set(viewvalues(m.param_to_grad)),
            "name_x",  # "name_x//shared_gradinp_0_shared" if using "name_x/"
            share_activations=True,
            dont_share_blobs=set(['name_x/fc6', 'name_x/fc5',
                                   str(input_to_grad["name_x/fc1_w"])]),
        )
        blobs_after = count_blobs(optim_proto)
        self.assertLess(blobs_after, blobs_before)
        self.assertTrue(has_blob(optim_proto, "name_x/fc6"))

        # Test networks produce exactly same gradients
        data = np.random.randn(batch_size, input_dim).astype(np.float32)
        label = np.random.randint(
            low=0, high=output_dim, size=(batch_size,)).astype(np.int32)
        workspace.RunNetOnce(m.param_init_net)
        workspace.FeedBlob("name_x/data", data)
        workspace.FeedBlob("name_x/label", label)
        workspace.RunNetOnce(m.net)
        loss1 = workspace.FetchBlob("name_x/loss1")
        loss2 = workspace.FetchBlob("name_x/loss2")
        grad = workspace.FetchBlob(str(input_to_grad["name_x/fc1_w"]))
        workspace.FeedBlob(str(input_to_grad["name_x/fc1_w"]), np.array([0.0]))

        workspace.RunNetOnce(optim_proto)
        optimized_loss1 = workspace.FetchBlob("name_x/loss1")
        optimized_loss2 = workspace.FetchBlob("name_x/loss2")
        optimized_grad = workspace.FetchBlob(str(input_to_grad["name_x/fc1_w"]))
        np.testing.assert_almost_equal(loss1, optimized_loss1)
        np.testing.assert_almost_equal(loss2, optimized_loss2)
        np.testing.assert_almost_equal(grad, optimized_grad)

    @given(input_dim=st.integers(min_value=4, max_value=4),
           output_dim=st.integers(min_value=4, max_value=4),
           batch_size=st.integers(min_value=4, max_value=4))
    @settings(deadline=1000)
    def test_forward_optim_tree_daggy(self, input_dim, output_dim, batch_size):
        m = model_helper.ModelHelper()
        m.Proto().type = "dag"
        m.Proto().num_workers = 4

        with core.NameScope("name_x"):
            fc1 = brew.fc(m, "data", "fc1", dim_in=input_dim, dim_out=output_dim)
            fc2 = brew.fc(m, fc1, "fc2", dim_in=output_dim, dim_out=output_dim)

            fc3 = brew.fc(m, fc2, "fc3", dim_in=output_dim, dim_out=output_dim)
            fc4 = brew.fc(m, fc3, "fc4", dim_in=output_dim, dim_out=output_dim)
            fc5 = brew.fc(m, fc4, "fc5", dim_in=output_dim, dim_out=output_dim)

            # Branch
            fc3b = brew.fc(m, fc2, "fc3b", dim_in=output_dim, dim_out=output_dim)